Conceptual aspects regarding the domicile and residence of the individual (part II)

The concept of domicile is to be researched in terms of both national and international regulations, because this term does not have the same meaning, or the scope of the term is much wider and should not be identified with the notion of ,,domicile” in the sense of the right to own a dwelling. The importance of establishing the domicile or temporary residence is determined by the legal effects it produces from the point of view of constitutional, civil, criminal, family law, etc. This study, divided into two parts, is an attempt to define the basic terms, identify situations for establishing the domicile in the case of minors, protected persons, rules and exceptions regulated both nationally and internationally.

Promoting TPR (Total Physical Response) Method in Teaching Vocabulary for EFL Beginners in Benin Secondary Schools

Vocabulary learning is one of the most important fields in teaching English or any foreign language. One could learn all of the grammatical rules and exceptions that the language contains, but what if our brain did not store the needful amount of words to complete and utter the message that a person want to transmit ? The present research work aimed at promoting the use of Total Physical Response (TPR) method, as a technique to introduce English language vocabulary to EFL beginners. An overall of 150 students and 26 EFL teachers were involved in this study. To achieve the objectives of the study, class observation was used as an instrument to gain insight into learners’ attitudes and reactions toward English language learning. Questionnaire was also used to collect reliable data. The results obtained revealed that teaching English vocabulary through Physical Response allows students to learn faster and easier, since children find support from the physical representation of their facilatator or their peers. Also the results may be a reference for other researchers who are interested in undertaking research on the learning or teaching of English vocabulary.

‘The exception to the rule’: exploring the exception and the exceptional in planning policy

This article explores exceptions to planning ‘rules’ as a specific form of discretion exercised by planners and decision makers. Theoretical and conceptual ideas on rules and exceptions to rules, drawing principally on administrative and political decision making, are used to examine the role of exceptions and exceptional circumstances in planning. This analysis addresses the interdependency between exceptions and ‘rules’, the circumstances in which planning decision makers are invited to consider exceptions to rules or exceptional circumstances, and the distinct forms of planning regulation created using exceptions. The conclusions call for systematic analysis of the role that exceptions play in different contexts and planning systems.

Can “Necessitous Men” Ever Be Politically Free?

This chapter begins with an account of necessity's story. It illustrates the moment of receding crisis in American life that produced Franklin Roosevelt's warning that “necessitous men are not free.” The chapter explains how necessity can produce dictatorship, because the people are willing to allow whatever it takes to solve their immediate needs. It looks into the theory that a president might suspend the constitutional order like a Roman dictator in order to post hoc political accountability. It also analyzes the misguided belief that constitutional systems can function in the so–called “states of exception,” which misconstrues the relation between rules and exceptions. The chapter explains “rule skepticism” that results from believing that if rules do not determine responses to new applications then rules cannot function as constraints.

Rules and Exceptions, in Law and Elsewhere

The structure of rule systems in different domains is addressed. A rule system is viewed as a mechanism delivering certain outputs (the system’s conclusions) given certain external inputs, and certain internal structures and processes. It is specified what it means for a rule system to consist of rules and exceptions rather than of flat rules. It is argued that there are three main exception structures: inhibitory input links, inhibitory rules, and prioritized conflicting rules. These structures can be found in many rule systems, including the law, and international law in particular. They make it so that rule systems provide defeasible outputs in response to incoming signals, namely outputs that may no longer be provided when a larger input set is given. It is argued that the current jurisprudential debate on rules and exceptions (as well as the debate on defeasibility) needs to be lifted beyond the law for clarity to be achieved on the existence, genesis, and function of systems of rules and exceptions.

How English as a Foreign Language (EFL) University Students Understand the Pronunciation of Adjectives Ending in -ed

This article looks at how EFL university students understand the pronunciation of adjectives that end in -ed. The goal is to determine the extent to which students are able to follow pronunciation rules and exceptions of -ed inflections when used as adjectives. The quantitative study presents data gathered from a 20-item multiple choice test that was taken by 61 students. Overall results reveal that students obtained low scores (M = 7.33, SD = 1.51, n = 61) and even lower scores (M = 2.00, SD = 2.33, n = 61 for non-governed-rule -ed endings) with regards to following rule-governed pronunciation patterns. On average, students were able to identify -ed endings pronounced /ɪd/ 8.2% of the times, followed by /d/ (7.31%) and /t/ (6.49%). Students scored lowest for non-standard pronunciation of participial adjectives (2.72%). The results reveal that students have not yet mastered the rules for the pronunciation of participial adjectives ending in -ed. The author proposes that changes be made to the curriculum and the design of the materials and that current classroom practices be assessed in order to help improve student pronunciation. Lastly, the author highlights the need to conduct further research focusing on pronunciation and auditory perception to attain this goal.

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

<p>Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing <i>d</i>-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.</p>

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

<p>Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing <i>d</i>-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.</p>