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1 – 10 of 11Orla Dolan, Joanne O’Halloran, Micheal O’Cuill, Atiqa Rafiq, Jennifer Edgeworth, Michael Hogan and Agnes Shiel
Dementia is a complex, progressively degenerative condition. It results in loss of cognitive and functional capabilities, along with a significant increase in the level of…
Abstract
Purpose
Dementia is a complex, progressively degenerative condition. It results in loss of cognitive and functional capabilities, along with a significant increase in the level of dependency. A reduction in the use of pharmacological interventions correlates with an increased in good quality non-pharmacological interventions in dementia care. The purpose of this study is to examine the impact of 14-session face-to-face cognitive stimulation therapy (CST) and Sonas group interventions on individuals living with dementia with moderate cognitive impairment, from pre-intervention to post-intervention in terms of their cognition, communication, neuropsychiatric symptoms, activities of daily living and quality of life.
Design/methodology/approach
A pilot single blind prospective controlled trial evaluated two group intervention approaches, cognitive stimulation therapy (CST) and Sonas, with 28 participants with moderate dementia. Pseudorandomisation and single blinding were implemented. CST has a solid evidence base. Sonas is a widely used multi-sensory intervention in Ireland with an emerging evidence base. Participants were recruited from a mental health service. Participants who had a formal diagnosis of dementia, moderate cognitive impairment and some ability to communicate and understand communication were included.
Findings
Results supported CST to a greater extent than Sonas. The CST group showed significant changes in cognition (p = 0.032) and communication (p = 0.006). Both groups had significant changes in carer quality of life (CST, p = 0.019; Sonas, p = 0.035). Results support the recommendations for a future definitive trial.
Research limitations/implications
Rehabilitation potential of individuals living with moderate dementia was demonstrated. This study suggests that group interventions like these impact on the trajectory of dementia.
Practical implications
Rehabilitation interventions impact on the trajectory of dementia. CST and Sonas have no impact on activities of daily living. Future studies with larger sample sizes, 16 weeks intervention period and control groups are required.
Social implications
This pilot study supports CST over Sonas interventions for individuals living with moderate dementia. Multiple outcome measures demonstrated trends towards significance for both interventions. Future definitive trials may detect a significant effect of both interventions.
Originality/value
A dementia diagnosis is devastating and generally creates negative perceptions and associations (Alvira, 2014). In contrast, the outcomes of this study are positive. This study provides evidence that occupational therapist intervention can impact on the trajectory of the condition with people with dementia demonstrating that they do have rehabilitation potential by responding to treatment and improving and maintaining their abilities as they progress through the condition.
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Assad Mehmood, Kashif Zia, Arshad Muhammad and Dinesh Kumar Saini
Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental…
Abstract
Purpose
Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.
Design/methodology/approach
In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.
Findings
For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.
Originality/value
This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.
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Joseph F. Hair Jr. and Luiz Paulo Fávero
This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.
Abstract
Purpose
This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.
Design/methodology/approach
The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.
Findings
From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.
Originality/value
Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.
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Daragh O'Leary, Justin Doran and Bernadette Power
This paper analyses how firm births and deaths are influenced by previous firm births and deaths in related and unrelated sectors. Competition and multiplier effects are used as…
Abstract
Purpose
This paper analyses how firm births and deaths are influenced by previous firm births and deaths in related and unrelated sectors. Competition and multiplier effects are used as the theoretical lens for this analysis.
Design/methodology/approach
This paper uses 2008–2016 Irish business demography data pertaining to 568 NACE 4-digit sectors within 20 NACE 1-digit industries across 34 Irish county and sub-county regions within 8 NUTS3 regions. A three-stage least squares (3SLS) estimation is used to analyse the impact of past firm deaths (births) on future firm births (deaths). The effect of relatedness on firm interrelationships is explicitly modelled and captured.
Findings
Findings indicate that the multiplier effect operates mostly through related sectors, while the competition effect operates mostly through unrelated sectors.
Research limitations/implications
This paper's findings show that firm interrelationships are significantly influenced by the degree of relatedness between firms. The raw data used to calculate firm birth and death rates in this analysis are count data. Each new firm is measured the same as another regardless of differing features like size. Some research has shown that smaller firms have a greater propensity to create entrepreneurs (Parker, 2009). Thus, it is possible that the death of differently sized firms may contribute differently to multiplier effects where births induce further births. Future research could seek to examine this.
Practical implications
These findings have implications for policy initiatives concerned with increasing entrepreneurship. Some express concerns that public investment into entrepreneurship can lead to “crowding out” effects (Cumming and Johan, 2019), meaning that public investment into entrepreneurship could displace or reduce private investment into entrepreneurship (Audretsch and Fiedler, 2023; Zikou et al., 2017). This study’s findings indicate that using public investment to increase firm births could increase future firm births in related and unrelated sectors. However, more negative “crowding out” effects may also occur in unrelated sectors, meaning that public investment which stimulates firm births in a certain sector could induce firm deaths and crowd out entrepreneurship in unrelated sectors.
Originality/value
This paper is the first in the literature to explicitly account for the role of relatedness in firm interrelationships.
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Noura AlNuaimi, Mohammad Mehedy Masud, Mohamed Adel Serhani and Nazar Zaki
Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time…
Abstract
Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.
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