Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 6 Artificial Intelligence for Energy Conversion

Many aspects of modern life are dependent on energy of various forms, which has already created strain on natural energy reserves and affected our environment adversely. Scientists and researchers are searching for alternative sources of energy that are sustainable, environment friendly, and renewable. However, any developmental work to invent a material or technique as a new source of energy involves a lengthy and complex experimental process to produce in scale. The last decade has seen remarkable progress in the field of Artificial Intelligence (AI) due to advancements of many new computer hardware, software’s, algorithms, technologies, and availability of a large amount of raw input data. We have started harnessing the power of AI to facilitate the process of discovering new materials as alternative energy sources and exploring the different advanced methodologies over the traditional approaches to utilize natural and eco-friendly sources for energy conversion. This book chapter will highlight some of the advancements in Machine Learning and Deep Learning techniques to explore new material resources and methodologies for energy conversion.
Petrography, mineralogy and geochemistry of the Ighrem diabase dyke (Anti-Atlas, Southern Morocco).

El dique de Ighrem puede considerarse como uno de los dos grandes diques doleriticos de la Provincia Magmatica del Atlantico Central que afloran en la region correspondiente a las montanas del Anti-Atlas (Sur de Marruecos). Su composicion quimica concuerda con la de las toleitas con cuarzo normativo y muestra, desde los bordes al centro, un aumento en el contenido de Si, Zr y Ce y una disminucion en el de Fe2O3 *, Ti O2 y Nb. Las composiciones globales de las rocas de este dique, junto con las de los piroxenos, confirman su caracter de toleitas continentales. Las ratios Zr/Nb = 12- 17 y (La/Ce)N ~ 1.29 son indicativas de un manto enriquecido semejante al de E-MORB. La cristalizacion fraccionada es el mecanismo principal para diferenciacion de estas rocas, cuya signatura geoquimica final es debida a la contribucion de contaminacion cortical y a las alteraciones hidrotermales secundarias
Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso

The popular Lasso approach for sparse estimation can be derived via marginalization of a joint density associated with a particular stochastic model. A different marginalization of the same probabilistic model leads to a different non-convex estimator where hyperparameters are optimized. Extending these arguments to problems where groups of variables have to be estimated, we study a computational scheme for sparse estimation that differs from the Group Lasso. Although the underlying optimization problem defining this estimator is non-convex, an initialization strategy based on a univariate Bayesian forward selection scheme is presented. This also allows us to define an effective non-convex estimator where only one scalar variable is involved in the optimization process. Theoretical arguments, independent of the correctness of the priors entering the sparse model, are included to clarify the advantages of this non-convex technique in comparison with other convex estimators. Numerical experiments are also used to compare the performance of these approaches.
Incentives for Information Production in Markets where Prices A ff ect Real Investment 1

We analyze the incentives for financial market traders to produce information about a firm’s investment opportunities, where such information may be noisily revealed in stock prices and thereby improve a firm’s resource allocation decisions. We show that incentives to produce costly information are sensitive to the ex ante likelihood with which an investment is undertaken by a firm. The more likely a firm is not to invest, the more likely it becomes that the traders’ information will have no speculative value. This generates an informational amplification effect of shocks to firm value. We show that information production by traders may exhibit strategic complementarities when projects would not be undertaken in the absence of positive news from the stock market. In these circumstances a small decline in a firm’s fundamentals can lead to a market breakdown where information production stops and there is a collapse in investment and firm value.
Regional Dynamics in a Decentralized Indonesia

This volume features contributions by over 40 writers with deep expertise on Indonesia. The book provides a timely, comprehensive and analytical assessment of the country's regional development dynamics in the post-decentralization environment. It explores historical, political and development patterns at the regional level; the relationship between decentralization and governance; local-level perspectives; migration, cities and connectivity; and the challenges confronting the peripheral regions of Aceh and Papua.
Preparing for N_f=2 simulations at small lattice spacings

We discuss some large effects of dynamical fermions. One is a cutoff effect, others concern the contribution of multi-pion states to correlation functions and are expected to survive the continuum limit. We then turn to the preparation for simulations at small lattice spacings which we are planning down to around a=0.04fm in order to understand the size of O(a^2)-effects of the standard O(a)-improved theory. The dependence of the lattice spacing on the bare coupling is determined through the Schr"odinger functional renormalized coupling.
Journal of Community Hospital Internal Medicine Perspectives Journal of Community Hospital Internal Medicine Perspectives

Background : Acute kidney injury (AKI) is associated with signi ﬁ cant short-and long-term morbidity and mortality. In critically ill patients with sepsis, AKI tends to be more severe, more likely to require kidney replacement therapy (KRT), with less chance of recovery. Consequently, critically ill patients with sepsis-associated AKI (SA-AKI) have extended intensive care unit (ICU) stays and higher mortality rates. This study evaluated the predictive value of clinical and transthoracic echocardiographic (TTE) parameters for recovery from moderate-to-severe SA-AKI in critically ill patients. Methods : This single-center historical cohort study was conducted at a tertiary academic medical center. We analyzed the data of all adults (age ≥ 18 years) admitted to the ICU at Mayo Clinic, Rochester, MN, from June 1, 2018, to December 31, 2020. We included all patients who developed sepsis within the initial 24 h of their ICU stay. Results : We identi ﬁ ed 2919 eligible septic patients with available TTE, among which 1431 patients (49%) had moderate-to-severe SA-AKI. The mean age of the patients was 68 ± 15 years, and the male-to-female ratio was 1.3:1. The most common comorbidities were diabetes mellitus and chronic lung and kidney diseases. Clinical predictors associated with SA-AKI non-recovery were the presence of stage III AKI ( HR 1.5, 95% CI 1.0 e 2.1, p ¼ 0.03) and utilization of kidney replacement therapy (KRT) ( HR 6.8, 95% CI 3.6 e 12.4, p ¼ 0.01). On the other hand, higher TAPSE was the only TTE variable associated with SA-AKI recovery ( HR 1.1; 95% CI 1.08 e 1.15; p ¼ 0.01). Conclusion : Our data from a single-center provide new information on the clinical (AKI stage, utilization of KRT, BMI, and peak serum creatinine) and echocardiographic features (TAPSE) associated with improved recovery in SA-AKI. There is a de ﬁ nite knowledge gap in the current literature regarding optimizing recovery in moderate-to-severe SA-AKI. Larger, multi-center studies are required to con ﬁ rm these ﬁ ndings.
The relationship between performance in a dental school and performance on a clinical examination for licensure: a nine-year study.

BACKGROUND Licensure examinations in dentistry have become an increasing concern, owing to ethical issues in the use of patients, difficulties in seeing relationships between outcomes of licensure examinations and performance in educational programs, and questions on the reliability of "one-shot" clinical examinations. Using data from a nine-year period, the authors compared the results of clinical licensing tests and the academic class ranks of the candidates. METHODS The authors studied data for 835 dental school graduates of one school from 1994 through 2002. They compared the dental graduates' results from the North East Regional Board, or NERB, of Dental Examiners examinations with their class ranks. The authors used analysis of variance to analyze the differences among passing, failing and "no data" groups, kappa statistic and logistic regression for variation, and receiver operating characteristic, or ROC, curves for diagnostic utility. RESULTS The class rank of graduates who passed and failed NERB's restorative section of the examination did not differ. Differences for other sections of the examination were statistically significant but small. The variation in restorative and manikin exercises over time was highly significant. No consistency existed between these tests, and their ROC curves indicated no utility for diagnosing class rank. CONCLUSIONS The authors' analysis of nine years' data called into question the reliability and validity of initial licensure examinations based on certain of the one-time tests used by NERB. Future study should determine if the results generalize to other schools and clinical testing agencies. PRACTICE IMPLICATIONS If the results of this study can be generalized to all U.S. licensure examinations, basing licensing decisions on clinical licensure examination alone risks licensure decisions of low validity. Use of patients in examinations of questionable validity may be unethical because they may have been subjected to risk of irreversible damage without contribution to a valid decision-making process by the licensing authority.
A New Inverse Rayleigh Distribution: Properties and Application

—In this paper, a new generalization of inverse Rayleigh Distribution known as Alpha Power inverse Rayleigh distribution is proposed using Alpha Power Transformation. Some mathematical properties of the Alpha Power inverse Rayleigh distribution have been derived and the parameters are estimated using Maximum Likelihood estimation method. Two real life data sets have been incorporated to illustrate the applicability of the proposed distribution
Nostalgia, autobiographical memories and brand communication: a semiotic analysis

Based on a semiotic analysis, this paper contibutes to a better understanding of how to use nostalgia in brand communication. The study is based on 3 series of interviews with the same sample. First, 20 subjects were asked to discuss the products and brands connected with a pleasant moment in their lives as a way to grasp nostalgic associaion without mentioning it. Then, a year after, 16 out of the original 20 were interviewed again and shown the pictures of the nostalgic brands previously mentioned. They associated what came to mind when they saw these visuals. Finally, 13 of these participants discussed the memories they associated with 4 photographic forms representing four important stages in life. The study defined a classification of 4 profiles of nostalgic consumers ("kidult", traditional, transitional and transgenerational). It identified 4 possible nostalgia applications for brand communication that are related to the above moments and profiles.
Multiproduct batching and scheduling with buffered rework: The case of a car paint shop

We study a problem of scheduling products on the same facility, which is motivated by a car paint shop. Items of the same product are identical. Operations on the items are performed sequentially in batches, where each batch is a set of operations on the same product. Some of the produced items are of the required good quality and some items can be defective. Defectiveness of an item is determined by a given simulated function of its product, its preceding product, and the position of its operation in the batch. Defective items are kept in a buffer of a limited capacity, and they are then remanufactured at the same facility. A minimum waiting time exists for any defective item before its remanufacturing can commence. Each product has a sequence independent setup time which precedes its first operation or its operation following an operation of another product. A due date is given for each product such that all items of the same product have the same due date and the objective is to find a schedule which minimizes maximum lateness of product completion times with respect to their due dates. The problem is proved NP‐hard in the strong sense, and a heuristic Group Technology (GT) solution approach is suggested and analyzed. The results justify application of the GT approach to scheduling real car paint shops with buffered rework. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 458–471, 2014
It’s My Party, I’ll Endorse If I Want To:

This experimental study examines the impact of endorsements by party elites with a primary election for the U.S. Senate. This experiment used a pretest-posttest design to identify and measure an endorsement’s effect on participants’ evaluations of the endorsed primary candidate and the unendorsed primary candidate, as well as their evaluations of the endorser and their political party. Results showed that such endorsements impacted participants’ assessments of both the endorsed and unendorsed candidates, while the impact on perceptions of their political party approached a level of significance. Implications of these results are discussed, and recommendations for future research initiatives are presented.
A Hybrid Movie Recommender System and Rating Prediction Model

: In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and collaborative filtering exist and have been implemented for a long time, however recent studies show that some other approaches, especially using artificial neural networks, have promising improvements in this area of research. In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system, the users are divided into two main categories, namely average users, and non-average users. Then, various machine learning and deep learning methods are applied within these categories to achieve better results. Some methods such as decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This approach achieves better compared to the traditional methods.
Automated Knowledge Graph Completion for Natural Language Understanding: Known Paths and Future Directions

Knowledge Graphs (KGs) are large collections of structured data that can model real world knowledge and are important assets for the companies that employ them. KGs are usually constructed iteratively and often show a sparse structure. Also, as knowledge evolves, KGs must be updated and completed. Many automatic methods for KG Completion (KGC) have been proposed in the literature to reduce the costs associated with manual maintenance. Motivated by an industrial case study aiming to enrich a KG specifically designed for Natural Language Understanding tasks, this paper presents an overview of classical and modern deep learning completion methods. In particular, we delve into Large Language Models (LLMs), which are the most promising deep learning architectures. We show that their applications to KGC are affected by several shortcomings, namely they neglect the structure of KG and treat KGC as a classification problem. Such limitations, together with the brittleness of the LLMs themselves, stress the need to create KGC solutions at the interface between symbolic and neural approaches and lead to the way ahead for future research in intelligible corpus-based KGC.
Effects of dietary acetaminophen and vitamin C supplement on serum cortisol and tumor necrosis factor-alpha concentrations in pigs vaccinated with foot-and-mouth disease vaccine

: This study evaluated the effect of a combination of acetaminophen and vitamin C (CAV) on reducing serum cortisol and tumor necrosis factor-α (TNF-α ) concentrations in piglets vaccinated with foot-and-mouth disease (FMD) vaccine. Piglets were vaccinated with FMD vaccine and treated with CAV at concentrations of 0.0, 0.5, 1.0, and 2.0 kg/ton feed (P-CON, AD-1, AD-2, and AD-3, groups, respectively) for 5 days post-vaccination. Cortisol and TNF-α levels at 5 days post-treatment in the AD-1–3 groups were significantly lower than that in the P-CON group ( p < 0.05). There were no significant differences between AD-2 and AD-3 groups and non-vaccinated, non-CAV-treated piglets
Encyclopedia of Data Warehousing and Mining Second Edition

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Encyclopedia of data warehousing and mining / John Wang, editor. -2nd ed. p. cm. Includes bibliographical references and index. Summary: "This set offers thorough examination of the issues of importance in the rapidly changing field of data warehousing and mining"-Provided by publisher. All work contributed to this encyclopedia set is new, previously-unpublished material. The views expressed in this encyclopedia set are those of the authors, but not necessarily of the publisher. If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating the library's complimentary electronic access to this publication. INTRODUCTION The high dimensionality of data poses a challenge to learning tasks such as classification. In the presence of many irrelevant features, classification algorithms tend to overfit training data (Guyon & Elisseeff, 2003). Many features can be removed without performance deterioration, and feature selection is one effective means to remove irrelevant features (Liu & Yu, 2005). Feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models. Usually a feature is relevant due to two reasons: (1) it is strongly correlated with the target concept; or (2) it forms a feature subset with other features and the subset is strongly correlated with the target concept. Optimal feature selection requires an exponentially large search space (O(2 n), where n is the number of features) (Almual-lim & Dietterich, 1994). Researchers often resort to various approximations to determine relevant features, and in many existing feature selection algorithms, feature relevance is determined by correlation between individual features and the class (Hall, 2000; Yu & Liu, 2003). However, a single feature can be considered irrelevant based on its correlation with the class; but when combined with other features, it can become very relevant. Unintentional removal of these features can result in the loss of useful information and thus may cause poor classification performance, which is studied as attribute interaction in (Jakulin & Bratko, 2003). Therefore, it is desirable to consider the effect of feature interaction in feature selection.
Investigating the Fair Treatment of Suppliers and its Trust-Fostering Role and Performance Benefits

An increasing number of companies have begun to make efforts to treat their suppliers fairly as a part of wider corporate social responsibility (CSR) initiatives. Few studies, however, have investigated the performance implications of such efforts for buying firms. This paper uses both organisational climate theory and social exchange theory to investigate (1) if buying firms' efforts in the form of a code of conduct for its procurement practitioners pay off, and (2) its mechanisms from the perspectives of procurement practitioners. We use a multi-method approach, combining analysis of survey data complemented by results from a behavioural experiment. First, survey data were gathered from 327 Korean manufacturing companies and analysed using structural equation modelling. Second, the findings were complemented by a behavioural experiment involving 120 subjects. The results support the positive performance implications of fair supplier treatment in the form of codes of conduct for procurement practitioners. The paper also offers insights into how such efforts benefit buying firms, which is due to the trust in the buyer-supplier relationship fostered by the resulting ethical behaviours of procurement practitioners.
ATG9A supports Chlamydia trachomatis infection via autophagy-independent mechanisms

ABSTRACT Chlamydia trachomatis infection can be regulated by autophagy-related (ATG) genes. Here, we found that the depletion of ATG9A, one of the core ATG genes, in HeLa cells suppressed C. trachomatis growth in the inclusion. The growth was restored by re-expressing ATG9A or an ATG9A mutant impairing lipid scramblase activity in ATG9A-knockout (KO) cells. Moreover, the depletion of lipid transfer proteins ATG2A/B, responsible for isolation membrane expansion together with ATG9A, did not significantly alter the growth, suggesting that the non-autophagic function of ATG9A supports C. trachomatis infection. ATG9A-KO cells showed no infection-induced redistribution of the Golgi from the perinuclear region to inclusion, which was restored by re-expressing the mutant but not the ATG9A mutant lacking an N-terminal adapter protein-binding domain. Re-expression of the N-terminal deletion mutant in ATG9A-KO cells did not rescue C. trachomatis growth, suggesting the importance of this domain for its growth. Although ATG9A-KO cells showed enhanced TBK1 activation, interferon (IFN)-β was not significantly increased, excluding the possibility that upregulation of stimulator of IFN genes (STING) signaling suppressed bacterial growth. Taken together, these findings suggest that the proper trafficking, rather than the isolation membrane expansion function, of ATG9A assists C. trachomatis growth in the inclusion. IMPORTANCE ATG9A is an autophagy-related gene that functions during the isolation membrane expansion process to form autophagosomes, but it also has other functions independent of autophagy. In this study, we employed ATG9A-deficient HeLa cells and found that the absence of ATG9A negatively impacted proliferation of Chlamydia trachomatis in inclusions. Furthermore, rescue experiments using ATG9A mutants revealed that this action was mediated not by its autophagic function but by its binding ability to clathrin adapter proteins. These findings suggest that the proper trafficking of ATG9A assists C. trachomatis growth in the inclusion. ATG9A is an autophagy-related gene that functions during the isolation membrane expansion process to form autophagosomes, but it also has other functions independent of autophagy. In this study, we employed ATG9A-deficient HeLa cells and found that the absence of ATG9A negatively impacted proliferation of Chlamydia trachomatis in inclusions. Furthermore, rescue experiments using ATG9A mutants revealed that this action was mediated not by its autophagic function but by its binding ability to clathrin adapter proteins. These findings suggest that the proper trafficking of ATG9A assists C. trachomatis growth in the inclusion.