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Chip design with machine learning (ML) has been widely explored to achieve better designs, lower runtime costs, and no human-in-the-loop process. However, with tons of work, there is a lack of clear links between the ML algorithms and the target problems, causing a huge gap in understanding the potential and possibility of ML in future chip design. This paper comprehensively surveys existing studies in chip design with ML from an algorithm perspective. To achieve this goal, we first propose a novel and systematical taxonomy that divides target problems in chip design into three categories. Then, to solve the target problems with ML algorithms, we formulate the three categories as three ML problems correspondingly. Based on the taxonomy, we conduct a comprehensive survey in terms of target problems based on different ML algorithms. Finally, we conclude three key challenges for existing studies and highlight several insights for the future development of chip design with machine learning. By constructing a clear link between chip design problems and ML solutions, we hope the survey can shed light on the road to chip design intelligence from previous chip design automation.

This volume studies the role of natural resources in development and economic diversification. It brings together a variety of analytical perspectives, ranging from econometric analyses of economic growth to historical studies of successful development experiences in countries with abundant natural resources.

In this paper, we propose two fuzzy portfolio optimization models based on the Markowitz mean-variance approach. Uncertainty is an inherent property of the securities market, there turns of different types of securities can rarely be described statistically. Dealing with uncertainty, portfolio optimization theory began to move toward application of fuzzy mathematic. Besides presenting fuzzy models, this paper reveals the problem of reliability of the fuzzy model results. Solving this problem depends on the investor's attitude to the model results. The first model involves fuzzy numbers to extend statistical data, the model returns the portfolio expected return and variance as fuzzy numbers. In addition to the problem formulated in the first model, the second model works on improvement of the indicators reliability. Finally, we provide a numerical example to illustrate the work of the proposed methods, as well as to compare the methods with each other and with the classical mean-variance method.

This book contributes to the debate about the role of agriculture in poverty reduction by addressing three sets of questions:Does investing in agriculture enhance/harm overall economic growth, and if so, under what conditions? Do poor people tend to participate more/less in growth in agriculture than in growth in other sectors, and if so, when? If a focus on agriculture would tend to yield larger participation by the poor, but slower overall growth, which strategy would tend to have the largest payoff in terms of poverty reduction, and under which conditions?.

RESUMEN: El propósito del presente estudio fue diseñar, construir y validar el cuestionario de Adicción a Redes Sociales (ARS) mediante la aplicación del modelo de la Teoría de Respuesta al Ítem (TRI) para ítems politómicos de respuesta graduada. Inicialmente los ítems se diseñaron de acuerdo a los indicadores del DSM-IV para adicción a sustancias, adaptándolos al constructo estudiado, los cuales fueron evaluados en su validez de contenido sobre la base del criterio de jueces. La versión inicial de 31 ítems se aplicó a 380 estudiantes de diferentes universidades de la ciudad de Lima. Se analizó la estructura latente de los ítems aplicando el análisis factorial exploratorio a la matriz de correlaciones policóricas entre ítems. Los resultados indicaron que existen tres dimensiones que se analizaron de forma independiente. La estimación de los parámetros de los modelos se realizó con el método de máxima verosimilitud marginal. A partir de los resultados se excluyeron de la escala ocho ítems por presentar un comportamiento inadecuado. Los parámetros de localización se ubican en niveles medios y altos de la escala. Los parámetros de discriminación adoptaron valores moderados y altos. Las funciones de información de los ítems evidenciaron que las dimensiones son más precisas para discriminar a los individuos con niveles medios y altos del rasgo evaluado. Los resultados revelaron que la escala y sus componentes presentaron adecuadas propiedades psicométricas de validez y confiabilidad ABSTRACT: The purpose of this study was to design, construct, and validate The Social Network Addiction Questionnaire (SNA) by applying the Item Response Theory (ITR) for polyatomic graded response items. At first, items were designed according to the DSM-IV criteria for substance addiction and adapted to the construct under study that was evaluated in its content validity by the judges’ criterion. The initial version of 31 items was applied to 380 college students from different universities in the city of Lima. The latent structure of the items was analyzed using an exploratory factor analysis on the polychoric correlation matrix between all items. Results showed a factor structure of three dimensions that were analyzed independently. Parameter estimation of the models was conducted using the marginal maximum likelihood method. Based on the results, eight items were excluded due to its low item-total correlation. The localization parameters were between medium and high levels of the scale. The discrimination parameters adopted moderate and high values. The items information functions showed that the dimensions are more precise to discriminate individuals with medium and high levels of the evaluated trait. Results also showed adequate psychometric properties of validity and reliability of the scale and its components

The use of genetic programming (GP) in nonlinear system identification enables the automated search for mathematical models that are evolved by an evolutionary process using the principles of selection, crossover and mutation. Due to the stochastic element that is intrinsic to any evolutionary process, GP cannot guarantee the generation of similar or even equal models in each GP process execution; still, if there is a physical model underlying to the data that are analyzed, then GP is expected to find these structures and produce somehow similar results. In this paper we define a function for measuring the syntactic similarity of mathematical models represented as structure trees; using this similarity function we compare the results produced by GP techniques for a data set representing measurement data of a BMW Diesel engine.

This study proposes a novel method aimed at achieving more reliable parameter estimates for the Two-Sided Power Distribution (TSPD), particularly under small-sample conditions. The proposed approach enhances the flexibility and data sensitivity of the distribution by redefining it as a convex combination of two independent uniform components. A new estimation formula is introduced for the probability P r Y < X , which holds critical importance in fields such as system reliability and stress–strength modeling. Compared to classical theoretical expressions, this new formulation produces more accurate and stable results in small-sample settings. Simulation studies and real-data applications demonstrate that the proposed method reduces estimation error and yields values closer to empirical observations. Furthermore, the method provides a balanced and computationally feasible alternative to conventional techniques such as maximum likelihood estimation. The results reveal that the proposed approach offers a significant advantage for the reliable computation of key performance metrics such as reliability probabilities and related measures.

Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a single a priori hypothesis test. This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified. We show that the probability of finding at least one ‘significant' effect is high, even if all null hypotheses are true (e.g. 40% when starting with four predictors and their two-way interactions). This probability is close to theoretical expectations when the sample size (N) is large relative to the number of predictors including interactions (k). In contrast, type I error rates strongly exceed even those expectations when model simplification is applied to models that are over-fitted before simplification (low N/k ratio). The increase in false-positive results arises primarily from an overestimation of effect sizes among significant predictors, leading to upward-biased effect sizes that often cannot be reproduced in follow-up studies (‘the winner's curse'). Despite having their own problems, full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone. We favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results.

The possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information can be provided. The purpose of this research is to improve the strain-based methodology for Intelligent Tires to estimate all tire forces, based only on deformations measured in the contact patch. Firstly, through an indoor test rig data, an algorithm has been developed to pick out the relevant features of strain data and correlate them with tire parameters. This information of the tire contact patch is then transmitted to a fuzzy logic system to estimate the tire parameters. To evaluate the reliability of the proposed estimator, the well-known simulation software CarSim has been used to back up the estimation results. The software CarSim has been used to provide the vehicle parameters in complex maneuvers. Finally, the estimations have been checked with the simulation results. This approach has enabled the behaviour of the intelligent tire to be tested for different maneuvers and velocities, providing key information about the tire parameters directly from the only contact that exists between the vehicle and the road.

Drug trade-related violence has escalated dramatically in Mexico since 2007, and recent years have also witnessed large-scale efforts to combat trafficking, spearheaded by Mexico's conservative PAN party. This study examines the direct and spillover effects of Mexican policy toward the drug trade. Regression discontinuity estimates show that drug-related violence increases substantially after close elections of PAN mayors. Empirical evidence suggests that the violence reflects rival traffickers' attempts to usurp territories after crackdowns have weakened incumbent criminals. Moreover, the study uses a network model of trafficking routes to show that PAN victories divert drug traffic, increasing violence along alternative drug routes.