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Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1 ≤ C ≤ 2.0 and 0 ≤ α ≤ 4 , representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C = 2.0 and α = 0.0736 . The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R 2 , greater than 0.97 , indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations. [ABSTRACT FROM AUTHOR]

Typically, in high dimensional data sets, many covariates are not significantly associated with a response. Moreover, those covariates are highly correlated, leading to a multicollinearity problem. Hence, the model is sparse since the coefficient of most covariates are likely to be zero. The classical frequentist or likelihood-based variable selection via any criterion such as Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) or a stepwise subset selection becomes infeasible when the number of variables are large. An alternative solution is a Bayesian variable selection. In this study, we used a variable selection via a Bayesian variable selection and the least absolute shrinkage and selection operator (LASSO) method in the logistic regression model. Moreover, those methods were expanded to be applied to real datasets. [ABSTRACT FROM AUTHOR]

Genome Wide Association Studies (GWAS) are a type of experiment that aim to detect genetic variation that may be linked to a type of disease. GWAS typically contain many thousands of covariates, which makes variable selection an exceptionally computationally intensive process. In variable selection, one of the biggest challenges is the extremely large potential set of variants but a limited sample size. Hence, there are two problems: huge computational time burdens for analysing each dataset and another is the sparsity in the number of covariates associated to the response. In this study, we use variable selection via using Bayesian variable selection and LASSO method in logistic regression model. Moreover, the results are expanded for application in real dataset about cardiovascular disease. [ABSTRACT FROM AUTHOR]

As the main force and practice subject of rural revitalisation, farmers' health is intricately linked to agricultural production and the rural economy. This study utilizes open data from the 2015 China Nutrition and Health Survey and employs the Least Absolute Shrinkage and Selection Operator (LASSO) method to explore the factors influencing farmers' self-assessment of health. The findings reveal that education level, proactive nutrition knowledge seeking, healthy dietary preferences and habits, and the use of clean cooking fuel positively impact farmers' health self-assessment. Conversely, age, history of illness or injury, and participation in medical insurance negatively affect their self-assessment. Furthermore, factors influencing farmers' health self-assessment exhibit heterogeneity across regions. Our findings suggest that promoting health education, disseminating nutritional dietary knowledge, and enhancing rural household infrastructure play an important role in improving farmers' self-evaluation of health. Therefore, policymakers should design more targeted health interventions and infrastructure improvement plans based on farmers' self-assessment of health and the level of regional economic development. [ABSTRACT FROM AUTHOR]

Studies of the determinants of cross-border bank claims are based on the economic situations of the lending and borrowing countries--the traditional push/pull factors--but fail to take into account the situation of the international lending banks and the presence of their subsidiaries in emerging countries. They also fail to explain the huge decrease in cross-border bank flows after the 2008 global financial crisis. In this paper, we analyse the determinants of cross-border bank claims on a panel of 28 emerging countries and explicitly integrate banking determinants. Thus, we account for the financial situation of international lender banks and the existence of foreign locations in emerging countries as a potential pull stabilising factor. We show that the presence of foreign banks in emerging countries is clearly a factor of attraction for cross-border bank claims. It remains when we explicitly take into account the 2008 crisis but to a lower extent and in favour of interbank loans. This may be proof of support from the international parent banks to their affiliates. Last, the financial situation of international banks, notably their liquidity and ability to respect prudential rules, also plays a role in their financing strategies in emerging countries. [ABSTRACT FROM AUTHOR]

Modern statistical studies often encounter regression models with high dimensions in which the number of features p is greater than the sample size n. Although the theory of linear models is well–established for the traditional assumption p < n, making valid statistical inference in high dimensional cases is a considerable challenge. With recent advances in technologies, the problem appears in many biological, medical, social, industrial, and economic studies. As known, the LASSO method is a popular technique for variable selection/estimation in high dimensional sparse linear models. Here, we show that the prediction performance of the LASSO method can be improved by eliminating the structured noises through a mixed–integer programming approach. As a result of our analysis, a modified variable selection/estimation scheme is proposed for a high dimensional regression model which can be considered as an alternative of the LASSO method. Some numerical experiments are made on the classical riboflavin production and some simulated data sets to shed light on the practical performance of the suggested method. [ABSTRACT FROM AUTHOR]

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Purpose: The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing. Materials and methods: We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student's t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods. Results: The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity—from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification. Conclusion: The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification. [ABSTRACT FROM AUTHOR]

Background: The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients.Methods: We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model.Results: We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients.Conclusions: Our results may provide new insights into the prognostic evaluation of AEG. [ABSTRACT FROM AUTHOR]

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis. [ABSTRACT FROM AUTHOR]

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping. [ABSTRACT FROM AUTHOR]

This paper focuses on volatility spillover effects and considers the issue of how to measure the connectedness of networks among financial firms. To assess the network connectedness of firms from different industries, we proposed a novel procedure and applied it to 20 leading financial institutions from four industries in China's stock markets. The results show that the total connectedness of the Chinese financial system was much higher during the stock market crisis between June 2015 and February 2016 than during stable periods of economic development. This analysis can be used to determine which firms play a dominant role in risk transmission throughout the entire system. It is suggested that the government should provide targeted regulatory policies to particular types of firms. [ABSTRACT FROM AUTHOR]

Complex networks with time-varying nodal parameters are of considerable interest and significance in many areas of science and engineering. Reconstructing networks with unknown but continuously bounded time-varying nodal parameters from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient and feasible, completely data-driven approach to predicting the structures of networks with unknown but continuously bounded time-varying nodal parameters in the presence or absence of noise. In particular, the reconstruction framework is implemented on several different kinds of artificial, two-layer and real complex networks composed of various parameter-varying nodal dynamics. Through numerical simulations, we demonstrate that, networks structures can be fully reconstructed with limited available information and presence or absence of noise, though systemic parameters are continuously time-varying. In addition, our method is also applicable to structure identification of multilayer networks as well as networks with constant nodal parameters. We expect our method to be useful in addressing issues of significantly current concern in the information era, natural networks, and large-scale multilayer networks. [ABSTRACT FROM AUTHOR]

Dynamic security assessment is widely used in dispatching operation systems, and calculation speed is one of its most important performance indices. In this study, an improved k-nearest neighbour (k-NN) method is proposed aiming to predict the stability indicators of power system, for example, critical clearing time. The method is much faster than the simulation and suitable for online analysis. Firstly, a simulation sample database is constructed based on historical online data and a logistic regression model with least absolute shrinkage and selection operator is trained to pick the stability features, which are chosen from static quantities like running state and active power of electric elements. While a new operation mode needs to be evaluated, a weighted k-NN is implemented to obtain the most familiar samples in the database using the chosen features; the final result will be determined comprehensively by the familiar samples. The validity of the proposed method is verified by simulation using online data of State Grid Corp of China and different key faults. It is proved that the method meets the requirements for speed and accuracy of online analysis system. [ABSTRACT FROM AUTHOR]

Considering the fact that faults may be revealed as undesired mutual effect of program predicates on each other, a new approach for localizing latent bugs, namely Hierarchy-Debug, is presented in this paper. To analyze the vertical effect of predicates on each other and on program termination status, the predicates are fitted into a logistic lasso model. To support scalability, a hierarchical clustering algorithm is applied to cluster the predicates according to their presence in different executions. Considering each cluster as a pseudo-predicate, a distinct lasso model is built for intermediate levels of the hierarchy. Then, we apply a majority voting technique to score the predicates according to their lasso coefficients at different levels of the hierarchy. The predicates with relatively higher scores are ranked as fault relevant predicates. To provide the context of failure, faulty sub-paths are identified as sequences of fault relevant predicates. The grouping effect of Hierarchy-Debug helps programmers to detect multiple bugs. Four case studies have been designed to evaluate the proposed approach on three well-known test suites, Space, Siemens, and Bash. The evaluations show that Hierarchy-Debug produces more precise results compared with prior fault localization techniques on the subject programs. [ABSTRACT FROM AUTHOR]

A survey on the economic and social conditions of households in the city of Modena was carried out in 2002 and in 2006 (two waves) by the CAPP (Centre for Analyses of Public Policies). In the first wave of 2002, each designated sampling unit (i.e., the family) had three units as reserves. If the first refused to be interviewed, the interviewer contacted the three reserves, one after the other, until obtaining either one respondent or four non-participant units. At the end of the survey four categories of units were distinguished: interviewees, refusals, noncontacts, and unused reserves. All units were matched with their corresponding record in the databases of the Ministry of Finance of 2002 and the Census of 2001. The resulting data set permitted the analysis of unit or total nonresponses. The distribution of fiscal income showed different shapes for the four categories, implying a selective participation of the families. The interviewees yielded a positive bias of about 600€, holding constant other factors. The selection of the significant factors affecting nonresponse was performed via backward elimination in a logit model and with the lasso method. Participation increased as fiscal income and age increased and by education level (secondary school and university degree), while it decreased among entrepreneurs, independent workers, managers, and medium-to-low skilled workers. [ABSTRACT FROM AUTHOR]

The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the Least Absolute Shrinkage and Selection Operator (LASSO) method to construct the stock network, compare the heterogeneity of bull and bear markets, and further use the Map Equation method to analyse the evolution of modules in the SSE A-shares market. Empirical results show that (1) the connected effect is more significant in bear markets than bull markets and gives rise to abnormal volatilities in the stock market; (2) a system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies. [ABSTRACT FROM AUTHOR]