Mastery Goals for Black American Dementia Caregivers

About 5.8 million older American adults live with Alzheimer’s disease and related dementias; Black American older adults’ prevalence is more than twice that of non-Hispanic white older adults. The Black American dementia caregiving experience can be pictured within the Black Family Social-Ecological Context Model, which provides a conceptual basis for examining social determinants of health at individual, family, community, and societal levels with careful consideration for how the intersecting identities of race, gender, and class of Black American caregivers influence the multiple dimensions of their caregiving experiences. Family dynamics, community setting, and healthcare systems have a potentially bidirectional influence on these caregivers, which is informed by the larger historical reality of systemic racism and general disenfranchisement. This paper outlines how Stress Process and Perceived Control frameworks offer ways for Black American dementia caregivers to achieve a sense of mastery within the complicated and fraught ecology within which their caregiving occurs. We propose a research and development agenda to create a program for enhancing a sense of mastery among Black American dementia caregivers. Two concepts in particular, “constraints” and “efficacy expectations,” provide ways to develop a systematic approach to developing successful coping strategies for the constraints perceived by individuals as they undertake and function in the caregiving role. The recognition of the complexity of the caregiving ecosystem and intersectionality of caregivers’ experience emphasize the importance of individualization: each caregiver’s experience of this ecosystem– and therefore each Black American caregiver’s way to mastery within it– will be uniquely shaped and experienced.

Subsidiaries’ behavioural responses to volatile local contexts in emerging African markets: evidence from Nigeria

Purpose This paper aims to explore subsidiaries’ behavioural responses to volatile institutional pressures in the local context of the emerging Nigerian market. Design/methodology/approach The authors built on institutional and contingency theory to analyse previous literature on developed markets and apply it to African contexts. The authors used a context-specific volatile local context model to show how porous formal and strong informal institutions constitute international business (IB) as a contested terrain in the host country. The authors also used a qualitative methodology, involving multiple actors, to investigate this phenomenon in practice. Findings The findings indicated different types of institutional pressures shaping volatile local contexts, which together or separately impact subsidiaries, depending on their degree of exposure. Subsidiaries behaviourally respond to cope with these pressures through inclusive negotiations involving their home and host countries’ networks. Originality/value Previous research has imposed developed markets’ norms on emerging African markets, regardless of their volatility. As subsidiaries’ responses to local contexts in emerging African markets are poorly understood, the authors developed a volatile local context model, showing how IB becomes a contested terrain in host countries and the authors proposed a model that differentiates between informal institutions. The authors highlighted the impact of contextual pressures on subsidiaries, according to their levels of exposure to the local context. The authors concluded that committed alignment with a local context is necessary for presenting an effective contingent response to its volatilities.

Metrics for Assessing the Economic Impacts of Power Sector Climate and Clean Electricity Policies

Modeling tools are increasingly used to inform and evaluate proposed power sector climate and clean electricity policies such as renewable portfolio and clean electricity standards, carbon pricing, emissions caps, and tax incentives. However, claims about economic and environmental impacts often lack transparency and may be based on incomplete metrics that can obscure differences in policy design. This paper examines model-based metrics used to assess the economic efficiency impacts of prospective electric sector policies. The appropriateness of alternative metrics varies by context, model, audience, and application, depending on the prioritization of comprehensiveness, measurability, transparency, and credible precision. This paper provides guidance for the modeling community on calculating and communicating cost metrics and for consumers of model outputs on interpreting these economic indicators. Using an illustrative example of clean electricity standards in the U.S. power sector, model outputs highlight strengths and limitations of different cost metrics. Transformations of power systems with lower-carbon resources and zero-marginal-cost generation may entail shifts in when and where system costs are incurred, and given how these changes may not be appropriated reflected in metrics that were commonly reported in the past such as wholesale energy prices, showing a decomposition of system costs across standard reporting categories could be a more robust reporting practice. Ultimately, providing better metrics is only one element in a portfolio of transparency-related practices, and although it is insufficient by itself, such reporting can help to move dialogues in more productive directions and encourage better modeling practices.

Harmonic Classification with Enhancing Music Using Deep Learning Techniques

Automatic extraction of features from harmonic information of music audio is considered in this paper. Automatically obtaining of relevant information is necessary not just for analysis but also for the commercial issue such as music program of tutoring and generating of lead sheet. Two aspects of harmony are considered, chord and global key, facing the issue of the extraction problem by the algorithm of machine learning. Contribution here is to recognize chords in the music by the feature extraction method (voiced models) that performd better than manually one. The modelling carried out chord sequence, getting from frame-by-frame basis, which is known in recognition of the chord system. Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. Then, traditional classification is used to create the key classifier which is better than others or manually one. Datasets used to evaluate the proposed model show good achievement results compared with existing one.

Integrating CDA with ideological rhetorical criticism in the investigation of Abe Cabinet’s discursive construction in “Indo-Pacific Strategy”

In recent years, the Indo-Pacific strategy and the Quad concept have been introduced and advocated by various countries. Since the second term of Japanese Prime Minister Shinzo Abe, the “Indo-Pacific Strategy” has been widely promoted. Assisted by corpus linguistics, this paper aims to explore the implicit ideology by providing insight into the context model from CDA perspective, and to decode the discursive construction by combining “ideographs” and “policy triggers” as Ideological Rhetorical Criticism instructed. The study shows these political texts are rich in forms of rhetorical strategies of “routinization”, “hegemony”, “uniformization” and “naturalization” used to construct a discursive system to distinguish “them” from “us”, so that to realize political aims.

Inaugural speech as a tool of forming speech portrait of the president

The paper deals with the inaugural speeches of Vladimir Putin (2018) and Donald Trump (2017) and is aimed at analyzing the role of verbal means in forming the speech portraits of political leaders. The article is of urgent interest as it demonstrates the speech portraits of country leaders in relation to national identity, mentality and socio-political course of the country. By means of comparative content analysis we looked for grammatical, lexical and stylistic elements peculiar to a specific linguistic persona while comparing the speeches of the presidents as well as we attempted to determine the specific national backgrounds of political discourse. Thus, each president’s inauguration context model is mostly characterized by a different set of linguistic means. The paper findings may be useful for researchers who deal with interdisciplinary studies, political and cognitive linguistics, political discourse and communication analysis.

A Service-Oriented Component-Based Framework for Dynamic Reconfiguration Modeling Targeting SystemC/TLM

To deal with the complex design issues of Dynamically Reconfigurable Systems-on-Chip (DRSoCs), it is extremely relevant to raise the abstraction level in which models are expressed. A high abstraction level allows great flexibility and reusability while bypassing low-level implementation details. In this context, model-driven engineering (MDE) provides support to build and transform precise and structured models for a particular purpose at different levels of abstraction. Indeed, high-level models are successively refined to low-level models until reaching the executable ones. Thus, this paper presents an MDE-based framework for DRSoCs design enabling the transformation of UML/MARTE specifications to SystemC/TLM implementation. To achieve a high degree of expressiveness for modeling dynamic reconfiguration, we use a suitable software engineering approach based on service-oriented component architecture. Since MARTE does not cover the common features of dynamic reconfiguration domain and service orientation concepts, new stereotypes are created by refinement to add missing capabilities to the profile. Likewise, SystemC does not provide native support for dynamic reconfiguration, thus leading us to adopt a design pattern based solution for DRSoCs implementation in compliance with standards. The proposed framework is validated through a reconfigurable active 3-way crossover case study in which we demonstrate the practicability of the approach by gradual model transformations with reduced implementation effort and significant design productivity gain.

Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.

Context dependency of nucleotide probabilities and variants in human DNA

Background: Genomic DNA has been shaped by mutational processes through evolution. The cellular machinery for error correction and repair has left its marks in the nucleotide composition along with structural and functional constraints. Therefore, the probability of observing a base in a certain position in the human genome is highly context-dependent. Mutations are also known to depend on the genomic context, but in previous work, the nucleotide distribution and the mutability have not been combined. Results: Here we use a context-dependent nucleotide model as the basis for a mutability model for the human genome. We first investigate simple models of nucleotides conditioned on sequence context and develop a bidirectional Markov model that depends on up to 14 nucleotides to each side. We show how the genome predictability varies across different types of genomic regions. Surprisingly, this model can predict a base from its context with an average of more than 50% accuracy. Inspired by DNA substitution models, we develop a model of mutability that estimates a mutation matrix (called the alpha matrix) on top of the nucleotide distribution. The advantage of this separation into two terms is that the alpha matrix can be estimated from a much smaller context than the nucleotide model, but the final model will still depend on the full context of the nucleotide model. With the bidirectional Markov model of order 14 and an alpha matrix dependent on just one base to each side, we obtain a model that compares well with a model of mutability that estimates mutation probabilities directly conditioned on three nucleotides to each side. For high-probability population variants, which are mainly CpG sites, the simple model fits better than our hybrid model, but for somatic variants it is opposite. Interestingly, the model is not very sensitive to the size of the context for the alpha matrix. Conclusions: Our study found strong context dependencies of nucleotides in the human genome. The best model can estimate the nucleotide probabilities depending on contexts up to 14 nucleotides to each side. Based on these models, a substitution model was constructed that separates into the context model and an alpha matrix dependent in a small context. These models fit variants very well.