Terms Of Weight Research Articles

Evolutionary Algorithm-based Optimal Parametrization of Multi-objective Mixed-integer Linear Programming Scheduling Models

Modern manufacturing systems strive to optimize many different key performance indicators. Additionally, mathematical programming-based scheduling models enable the explicit considerations of constraints. However, due to the ever-increasing demand for customized products it is not guaranteed that all constraints can be met at the same time. An approach to circumvent this problem of infeasibility is to replace the constraints by soft-constraints, i.e., to penalize their violation in the objective function. The presence of multiple competing objectives and soft-constraints gives rise to multi-objective optimization problems, where a decision maker has to weigh and balance the different objectives and soft-constraints according to his/her preferences and priorities. Ideally the values of all objectives and soft-constraints should lie within the same order of magnitude, making it easy to weigh them against each other. However, for heterogeneous objectives and soft-constraints with different scales and units this might be challenging. This paper presents an evolutionary algorithm for the optimal parametrization of multi-objective mixed-integer linear programming-based scheduling models. The goal of the evolutionary algorithm is to compute weights for the different terms of the objective which lead to a balanced influence on the overall objective at the optimum. Furthermore, the algorithm is extended by introducing an a priori weighing of the objectives in the fitness function of the evolutionary algorithm. The method is demonstrated on a scheduling problem in which a given set of orders has to be allocated to machines within a manufacturing environment.

DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction.

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this context, we propose the unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) based on a novel iterative reconstruction framework to synergize the merits of deep learning and iterative reconstruction. DREAM-Net performs constraints using deep neural networks in the projection domain, residual space, and image domain simultaneously, which is different from the routine practice in deep iterative reconstruction frameworks. First, a projection inpainting module completes the missing views to fully explore the latent relationship between projection data and reconstructed images. Then, the residual awareness module attempts to estimate the accurate residual image after transforming the projection error into the image space. Finally, the image refinement module learns a non-standard regularizer to further fine-tune the intermediate image. There is no need to empirically adjust the weights of different terms in DREAM-Net because the hyper-parameters are embedded implicitly in network modules. Qualitative and quantitative results have demonstrated the promising performance of DREAM-Net in artifact removal and structural fidelity.

Noncovalent interactions of aromatic heterocycles: rotational spectroscopy and theoretical calculations of the thiazole-CF4 and thiazole-SF6 complexes.

The complexes of thiazole with CF4 and SF6 have been investigated by Fourier transform microwave spectroscopy and quantum chemical calculations. One rotational spectrum was observed for the thiazole-CF4 complex. Experiments and theoretical computations confirmed that the observed structure of thiazole-CF4 is primarily formed due to N⋯CCF4 interaction with the C atom of CF4 located in the plane of the thiazole ring. The rotational transitions of thiazole-CF4 exhibit A/E torsional splitting induced by the internal rotation of the -CF3 top. The potential barrier of the -CF3 internal rotation is 0.2411(1) kJ mol-1, consistent with the calculated value (∼0.3 kJ mol-1). For the thiazole-SF6 complex, one conformer with SF6 located above the thiazole ring is detected. The observed structure of thiazole-SF6 is mainly stabilized by van der Waals interactions. The energy decomposition analysis reveals that the electrostatics and dispersion are the dominant attractive contributions to the formation of thiazole-CF4 and thiazole-SF6 dimers, whereas the weight of the dispersion term becomes more significant in the thiazole-SF6 complex compared to that of the thiazole-CF4 complex.

Exponential stability for magnetic effected piezoelectric beams with time-varying delay and time-dependent weights

<p style='text-indent:20px;'>In this paper, we consider a system of magnetic effected piezoelectric beams with interior time-varying delay and time-dependent weights, in which the beam is clamped at the two side points subject to a single distributed state feedback controller with a time-varying delay. By combining the semigroup theory with Kato's variable norm technique, we obtain the existence and uniqueness of solution. By imposing appropriate assumptions on the time-varying delay term and time-dependent weights, we introduce suitable perturbed Lyapunov functional to obtain exponential stability estimates.</p>

Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text.

Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts to combat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best performance compared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features.

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Natural Language Processing, specifically text classification or text categorization, has become a trend in computer science. Commonly, text classification is used to categorize large amounts of data to allocate less time to retrieve information. Students, as well as research advisers and panelists, take extra effort and time in classifying research documents. To solve this problem, the researchers used state-of-the-art supervised term weighting schemes, namely: TF-MONO and SQRTF-MONO and its application to machine learning algorithms: K-Nearest Neighbor, Linear Support Vector, Naive Bayes Classifiers, creating a total of six classifier models to ascertain which of them performs optimally in classifying research documents while utilizing Optical Character Recognition for text extraction. The results showed that among all classification models trained, SQRTF-MONO and Linear SVC outperformed all other models with an F1 score of 0.94 both in the abstract and the background of the study datasets. In conclusion, the developed classification model and application prototype can be a tool to help researchers, advisers, and panelists to lessen the time spent in classifying research documents.

RELATIONSHIP BETWEEN THYROID DISEASE AND MOST COMMON PSYCHOLOGICAL PROBLEMS IN KSA: A SURVEY STUDY

Background:Mental disorders are known to merge with thyroid disease and some literature related some psychological changes to thyroid disease such as depression and anxiety. The current research aimed to determine the relationship between thyroid disease and most common psychological disorders in KSA. Methods:The current study aims to determine the relationship between thyroid disease and the most common psychological problems among population in KSA. The most suitable design is cross-sectional survey study. It was descriptive and correlational study. This enables the researcher to measure the effect and the outcome at a single point of time. This study design gives reliable results with short time and less effort. The study was conducted at (place). The participants were selected during October 2022. This study included adults aged 19 years or more via non-probability convenient sampling technique at a confidence level of 95% using Epi Info software equation. Results:Of 1038 participants included in the study, there were 180 participants only who suffered from thyroid disease. This gives a prevalence of (17.3%) thyroid disease among study participants. Thyroid disease was more prevalent among female participants (P<0.0001) and among more than 40 years age group (P<0.0001). Table 1 shows the distribution of thyroid disease according to gender and age groups. The duration since diagnosis was since more than 5 years (n= 78, 43.3%). The diagnosis was hypothyroidism among most of patients (n= 154, 85.6%). The vast majority of study participants of thyroid disease reported having personality changes after the diagnosis (n= 140, 77.8%). The most frequent change was disorder in weight in terms of increase and decrease (n= 69, 38.3%). And this was more prevalent among females (P= 0.035). Conclusion:Study showed that quarter of participants suffered from thyroid disease, mainly hypothyroidism. The most frequent personality change was weight disturbances and was more common among female participants. Other reported psychological changes were depression, anxiety and personality changes. Participants was told that these symptoms are related to their thyroid condition.

Fasulyenin Bakla Özelliklerine Gübre Dozlarının Etkisinin Araştırılması

The bean plant is a legume plant that binds free nitrogen of the air by the nodosity formed in the roots. For this reason, it was aimed to investigate the effect the pod of bean characteristics of bean by applying different doses of phosphorus and nitrogen on Göksun beans cultivar. In the study, 0, 3, 6, 9 kg da-1 phosphorus and 0, 4, 8, 12 kg da-1 nitrogen doses were applied to Göksun bean variety. The pod length (cm), pod width (mm), pod diameter (mm), number of pods per plant (edet), weight of a single pod (g), number of seeds per pod (pieces), weight of single seeds (g) of Göksun bean cultivar were investigated. In the research, it was noted that the effect of phosphorus doses only on the pod diameter, the effects of nitrogen doses on the pod length, pod diameter, pod width, single seed weight and seed number of pod were significant. The differences in number of pods in the plant, seed number of pods, pod length, pod weight, single seed weight in terms of phosphorus and nitrogen interaction were founded statistically significant. According to the results of the research, it was determined that 4 kg da-1 and above nitrogen dose applications made a positive contribution in terms of pod properties

Supervised term-category feature weighting for improved text classification

Text classification is a central task in Natural Language Processing (NLP) that aims at categorizing text documents into predefined classes or categories. It requires appropriate features to describe the contents and meaning of text documents, and map them with their target categories. Existing text feature representations rely on a weighted representation of the document terms. Hence, choosing a suitable method for term weighting is of major importance and can help increase the effectiveness of the classification task. In this study, we provide a novel text classification framework for Category-based Feature Engineering titled CFE. It consists of a supervised weighting scheme defined based on a variant of the TF-ICF (Term Frequency-Inverse Category Frequency) model, embedded into three new lean classification approaches: (i) IterativeAdditive (flat), (ii) GradientDescentANN (1-layered), and (iii) FeedForwardANN (2-layered). The IterativeAdditive approach augments each document representation with a set of synthetic features inferred from TF-ICF category representations. It builds a term-category TF-ICF matrix using an iterative and additive algorithm that produces category vector representations and updates until reaching convergence. GradientDescentANN replaces the iterative additive process mentioned previously by computing the term-category matrix using a gradient descent ANN model. Training the ANN using the gradient descent algorithm allows updating the term-category matrix until reaching convergence. FeedForwardANN uses a feed-forward ANN model to transform document representations into the category vector space. The transformed document vectors are then compared with the target category vectors, and are associated with the most similar categories. We have implemented CFE including its three classification approaches, and we have conducted a large battery of tests to evaluate their performance. Experimental results on five benchmark datasets show that our lean approaches mostly improve text classification accuracy while requiring significantly less computation time compared with their deep model alternatives.

Term Weighting Based Indexing Class and Indexing Short Document for Indonesian Thesis Title Classification

Document classification nowadays is an easy thing to do because there are the latest methods to get maximum results. Document classification using the term weighting TF-IDF-ICF method has been widely studied. Documents used in this research generally use large documents. If the term weighting TF-IDF method is used in a short text document such as the Thesis Title, the document will not get a perfect score from the classification results. Because in the IDF will calculate the weight of words that always appear to be few, ICF will calculate the weight of words that often appear in the class to be few. While the word should have great weight to be the core of a short text document. Therefore, this study aims to conduct research on word weighting based on class indexation and short document indexation, namely TF-IDF-ICF-IDSF. This study uses a classification comparison Naïve Bayes and SVM. The dataset used is Thesis Title of Informatics Education student at Trunojoyo Madura University. The test results show that the classification results using the TF-IDF-ICF-IDSF term weighting method outperform other term weighting, namely getting 91% Precision, 93% Recall, 86% F1-Score, and 84% Accuracy on SVM.

USTW Vs. STW: A Comparative Analysis for Exam Question Classification based on Bloom’s Taxonomy

Bloom’s Taxonomy (BT) is widely used in educational institutions to produce high-quality exam papers to evaluate students’ knowledge at different cognitive levels. However, manual question labeling takes a long time, and not all evaluators are familiar with BT. The researchers worked to automate the exam question classification process based on BT as a solution. Enhancement in term weighting is one of the ways to increase classification accuracy while working with text data. However, all the past work on the term weighting in exam question classification focused on unsupervised term weighting (USTW) schemes. The supervised term weighting (STW) schemes showed effectiveness in text classification but were not addressed in past studies of exam question classification. As a result, this study focused on the effectiveness of STW in classifying exam questions using BT. Hence, this research performed a comparative analysis between the USTW schemes and STW for exam question classification. The STW schemes used in this study are TF-ICF, TF-IDF-ICF, and TF-IDF-ICSDF, whereas the USTW schemes used for comparison are TF-IDF, ETF-IDF, and TFPOS-IDF. This study used Support Vector Machines (SVM), Na¨ıve Bayes (NB), and Multilayer Perceptron (MLP) to train the model. Accuracy and F1 score were used in this study to evaluate the classification result. The experiment result showed that overall, the STW scheme TF-ICF outperformed all the other schemes, followed by the USTW scheme ETF-IDF. Both the ETF-IDF and TFPOS-IDF outperformed standard TFIDF. The outcome of this study indicates the future research direction where the combination of STW and USTW schemes may increase the Accuracy of BT-based exam question classification.

Evaluation of Average Term Occurrences Weighting Technique for Arabic Textual Information Retrieval

Information retrieval of documents is an important process in the current time, and the vector space retrieval model uses a term weighting scheme as a basic method for matching queries with documents. Term frequency-Inverse document frequency is a widely used and famous term weighting scheme, and many studies proved its effectiveness in information retrieval. However, this term weighting scheme has some drawbacks like retrieving irrelevant documents, which sometimes reduces effectiveness. From this point, a new term weighting scheme called Term Frequency with Average Term Occurrence was proposed and experienced in the English language to minimize retrieving unnecessary documents. In this paper, an information retrieval system is built for the Arabic language, and Open-Source Arabic Corpora was used to complete experiments. Calculations were made using two schemes which are traditional Term frequency-inverse Document Frequency and proposed Term Frequency with Average Term Occurrence. After that, comparisons of results were made using evaluation measures. With all obtained queries, four case studies with two approaches (stop word removal and stemming) are implemented. In English experiments, stop word removal was applied with another discriminative approach, which calculates the centroid of documents. After the analysis of the results, it was found that the proposed scheme is applicable on Arabic text and applied approaches enhance IR effectiveness if they are both implemented. Furthermore, it was found that stop word removal has a favorable effect on both schemes which was also proved in English experiments.

Measuring unsteady drag of the flow around a sphere based on time series displacement measurements using physics-informed neural networks

Surface drag increases energy usage and emissions in numerous transport and engineering applications, with surface treatment being one technique aimed at reducing surface skin friction and hence, surface drag. One approach to investigate the performance of surface treatments is based on the measurement of the terminal velocities of settling spheres with and without surface treatment. This relatively affordable method is limited to the terminal velocity region, as the analysis of the data during the accelerating phase of the sphere requires the differentiation of the experimental data, which results in significant noise and drag measurements with high uncertainty. This paper compares two neural network approaches to determine the temporal derivatives of experimental data. Specifically, the ensemble averaged experimental data and physics information is used to define a model for a settling sphere, which is used to test the performance of neural networks trained on noisy displacement data using an approach based on the governing differential equation and a direct approach, with varying loss term weights, measurement error levels and temporal resolutions. The results show that the neural network trained directly on the experimental displacement data is able to reduce the uncertainty of the temporal derivatives to a level similar to that of the differential equation approach when initial conditions are included in the training. Finally, the two approaches are applied to experimental data to determine the unsteady drag. This paper demonstrates that neural networks can be used to determine the temporal derivatives of experimental data with relatively low uncertainty, particularly where the system physics is at least partially known.

Synthesis of Alkyd Resin Through Alcoholysis – Polyesterification Process from Used Cooking Oil Pretreatment Using Alkaline Activated Natural Bayah Zeolite

Waste cooking oil can be treated by an adsorption process using Bayah natural zeolite as raw material for alkyd resin synthesis. This study aimed to determine the optimum conditions for the equivalent ratio of OH:COOH and the temperature of the alkyd resin synthesis. The research was carried out by preparing used cooking oil and Bayah natural zeolite (BNZ), adsorption process, and alkyd resin synthesis through the alcoholysis-polyesterification process. The optimum addition of Bayah natural zeolite-activated NaOH 0.75 N in pretreatment waste cooking oil is 30% weight in terms of color, density, viscosity, and functional group characteristics of the resulting alkyd resin. The characteristics of the alkyd resin product at optimum conditions obtained light brown color with an OH: COOH equivalent ratio of 1.4, and the alcoholysis and esterification temperature at 190 oC with a density value is 1.112 g/cm3, FFA value is 0.1528%, viscosity value is 4808 cP. The ester functional group at wavelengths 1267 cm-1 and 1722 cm-1, the 1H NMR spectrum of the ester group at the peak of 3.5 ppm, and a functional group of 7.6 ppm.

ANALISIS PERFORMA DAN KECEPATAN KOMPUTASI ALGORITMA K-MEANS DAN K-MEDOIDS PADA TEXT CLUSTERING

The large number of theses will certainly make it difficult to find categories on thesis topics that have been written by students at a university. One of the uses of the Text Mining method is being able to group thesis objects into the number of clusters formed by the clustering algorithm. This study aims to compare 2 clustering algorithms, namely the K-Means and K-Medoids algorithms to obtain an accurate evaluation of the performance and computational time in the case of thesis clustering, so that relevant topics can be grouped and have better clustering accuracy. The evaluation parameter used is the Davies Bouldin Index (DBI) which is one of the testing techniques on clustering results, with the distribution of training data and testing data using cross validation using a repetition parameter of 10 folds iteration. From the results of the study with the Term Weighting condition used is Term Occurrences and using the N-Grams value is 2, it can be concluded that the K-Means algorithm has a better DBI value of -0.426. Meanwhile, the range of DBI values owned by K-Medoids with the same conditions has a DBI value of -1,631. However, from the visualization results using t-SNE with the same supporting parameters, there are options that can be used, namely the number of clusters is 6, and the DBI value is -1.110. For testing the computational time in the clustering process of 50 thesis documents, the K-Means algorithm has an average time of 2.5 seconds while the K-Medoids algorithm has an average time of 261.5 seconds. The computer specifications used are Asus ZenBook UX425EA.312 with the processor used is 11th Gen Intel® Core™ i5-1135G7 @ 2.40GHz @ 2.40GHz, the graphics card is Intel® Iris® Xe Graphics, the RAM used is 8GB, with storage of 512GB SSD.

Evaluation of the association between precocious puberty and obesity

Objective: The marked variation in the timing of puberty despite similar living conditions shows that many factors are effective in determining the onset of puberty. Many factors, such as genetic and environmental factors, stress, socioeconomic status, metabolic rate, and body fat ratio, affect the age of puberty onset. Recent studies have shown that the age of puberty onset has shifted to earlier ages. The increasing prevalence of childhood obesity is deemed to contribute this change. Therefore, we planned the present study to determine the rates of obesity and overweight, to evaluate the association between obesity, bone age, and height age, and to examine the association between obesity and hormone profile.
 Materials and Methods: The medical records of 206 patients diagnosed with idiopathic precocious puberty who were being followed up at the Pediatric Endocrinology outpatient clinic of Sakarya Training and Research Hospital between October 2018 and October 2021 were retrospectively reviewed. Anthropometric data (age, gender, height, weight, body mass index, and puberty stage), bone age, height age, and weight age of the patients were recorded from the medical records. Patients were grouped as normal weight, overweight, and obese by body mass index (BMI).
 Results: Of the included patients, 97.6% (201 patients) were female and 2.4% (5 patients) were male. One hundred and thirty-one patients (63.6%) had normal weight, 40 (19.4%) were overweight and 35 (17%) had obesity. Significant differences were found between the groups (normal weight, overweight and obese) in terms of weight age, BMI percentile, height age, bone age, and bone age standard deviation score (SDS) (p =

Weight changes and the incidence of depressive symptom in the middle-aged and older adults: findings from the Chinese nationwide cohort study

BackgroundPrevious studies, predominantly in Western individuals, have reported weight gain or weight loss are related to the increased depressive symptoms at all ages, but no study of depressive symptoms has examined its relation to actual (not just self-reported) weight changes in the middle-aged and older adults. Evidence of the relationship in older Asian individuals remains sparse. The study aimed to examine the relationship between weight changes and incidence of depressive symptoms in a nationally representative sample of community-dwelling older Asians.MethodData were obtained from the China Health and Retirement Longitudinal Study (CHARLS), which included 17,284 adults aged 45 years. Participants were followed every two years using a face-to-face, computer-aided personal interview (CAPI) and structured questionnaire. We excluded participants with no follow-up data. The numbers of individuals who completed both the baseline and follow-up surveys were 3118 for the short-term (two years from 2011–2013) and the long-term (four years from 2011–2015). Additionally, to associate weight change with subsequent depressive symptoms, we also included 2472 participants without depressive symptom in 2013 and observed the incidence of depressive symptom in 2015 (subsequent term from 2013–2015). Finally, weight changes were classified as loss > -3%, stable-3–3%, gain3-6%, gain6-9%, and gain > 9%. Multivariable-adjusted cox regression in the study were used to assess the hazard ratios (HRs) of each weight change category.ResultsThe incidence of depressive symptoms was 20.72% in the 2011–2013, 27.04% in the 2011–2015, and 23.02% in 2013–2015. Weight loss > 3% for all intervals was associated with higher depressive symptoms than stable weight during the 2011–2013 [1.305(1.031,1.651)] among the total populations. When stratified by sex, the results in males and females were different from those in the total population [females:1.389(0.997, 1.935); males:1.263(0.902, 1.767)]. Weight loss > 3% for intervals was associated with higher depressive symptoms than stable weight during the 2013–2015[1.643(1.140, 2.368)] among the males and its effect was also stronger for the total in 2011–2013. Moreover, there was no significant association between weight gain and incident depressive symptom, and no significant interaction effect in terms of the sex*weight changes.ConclusionOur findings could inform health promotion interventions to body-weight management aimed at improving the health of the middle-aged and older adults, particularly in the total people with short-term weight loss and males with subsequent term weight loss.

ControlVAE: Tuning, Analytical Properties, and Performance Analysis.

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) controller, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point) that is effective in avoiding posterior collapse and learning disentangled representations. While prior work developed alternative techniques for controlling the KL divergence, we show that our PI controller has better stability properties and thus better convergence, thereby producing better disentangled representations from finite training data. In order to improve the ELBO of ControlVAE over that of the regular VAE, we provide a simplified theoretical analysis to inform the choice of set point for the KL-divergence of ControlVAE. We evaluate the proposed method on three tasks: image generation, language modeling, and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, our method can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, it can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation.

IBC-Ox22 Greater Adherence to Mediterranean Diet leads to longer term weight loss

Abstract We were able to obtain dietary data of 134 weight loss surgery patients with an average follow up of five years with annual scoring of the 9 point Mediterranean Diet. The closer adherence to the MD showed more weight loss than those who had not. Background The Mediterranean Diet has been associated with decreased all cause mortality for those who adhere strongly to it, as well as decreased risk of heart disease, and cancer. The diet is rich in polyphenols with fruits, vegetables, legumes, fish, mono-unsaturated fats, whole grains, while minimizing dairy, meat and poultry, and some moderate alcohol consumption. Methods Pre operatively all bariatric patients patients were counseled about the Mediterranean diet, how to score the diet, and adherence to the diet was reemphasized at their follow ups. Annually they were asked to fill out their scores. We looked at other diet plans that some of the patients reported (low carbohydrate, weight watchers) and the results were not significant. Results Of the 134 patients there were 65 which had scores of 5 points or more and showed an absolute reduction in weight of 54% total weight loss on average, vs those of 1-3 who had 32% total weight loss (total of 60 patients). The remaining 9 patients who escorted 4 points had a wide spread of weight loss that could not be calculated (these patients predominantly followed a self-described low-carbohydrate diet). There was no difference in weight loss regarding the type of surgery that the patient had (Lap-Band (N=74) vs Gastric Sleeve (N=50) vs Gastric bypass (N=10). Nor was there any difference in weight loss when measuring physical activity. Using the 9 point scale of the Mediterranean Diet those who had greater than 5 of the 9 points on the MD had greater weight loss over five years than those who were 0-3. Those who scored 4 points were indeterminate. Conclusions The Mediterranean Diet scale provides a quick and easy guide, providing targets for patients (example, less than 4 ounces per day of poultry or meat is a point, more is 0 points). The emphasis on a post operative diet for health, as opposed to weight loss, inadvertently provided a good model for post operative weight loss.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant.

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12kcal/mol to ~ 2.4kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.