Technological University Dublin

The following paper presents current thinking and research on fit indices for structural equation modelling. The paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers. As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting strategies of these indices and concludes with a discussion on the future of fit indices.

Abstract Since the publication of their inaugural paper on the topic in 2006, Braun and Clarke’s approach has arguably become one of the most thoroughly delineated methods of conducting thematic analysis (TA). However, confusion persists as to how to implement this specific approach to TA appropriately. The authors themselves have identified that many researchers who purport to adhere to this approach—and who reference their work as such—fail to adhere fully to the principles of ‘reflexive thematic analysis’ (RTA). Over the course of numerous publications, Braun and Clarke have elaborated significantly upon the constitution of RTA and attempted to clarify numerous misconceptions that they have found in the literature. This paper will offer a worked example of Braun and Clarke’s contemporary approach to reflexive thematic analysis with the aim of helping to dispel some of the confusion regarding the position of RTA among the numerous existing typologies of TA. While the data used in the worked example has been garnered from health and wellbeing education research and was examined to ascertain educators’ attitudes regarding such, the example offered of how to implement the RTA would be easily transferable to many other contexts and research topics.

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change. Current and future climate change is expected to impact human health, both indirectly and directly, through increasing temperatures. Climate change has already had an impact and is responsible for 37% of warm-season heat-related deaths between 1991 and 2018, with increases in mortality observed globally.

Purpose: We provide a standardized set of terminology, definitions, and thresholds of myopia and its main ocular complications. Methods: Critical review of current terminology and choice of myopia thresholds was done to ensure that the proposed standards are appropriate for clinical research purposes, relevant to the underlying biology of myopia, acceptable to researchers in the field, and useful for developing health policy. Results: We recommend that the many descriptive terms of myopia be consolidated into the following descriptive categories: myopia, secondary myopia, axial myopia, and refractive myopia. To provide a framework for research into myopia prevention, the condition of "pre-myopia" is defined. As a quantitative trait, we recommend that myopia be divided into myopia (i.e., all myopia), low myopia, and high myopia. The current consensus threshold value for myopia is a spherical equivalent refractive error ≤ -0.50 diopters (D), but this carries significant risks of classification bias. The current consensus threshold value for high myopia is a spherical equivalent refractive error ≤ -6.00 D. "Pathologic myopia" is proposed as the categorical term for the adverse, structural complications of myopia. A clinical classification is proposed to encompass the scope of such structural complications. Conclusions: Standardized definitions and consistent choice of thresholds are essential elements of evidence-based medicine. It is hoped that these proposals, or derivations from them, will facilitate rigorous, evidence-based approaches to the study and management of myopia.

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. INTERPRETATION: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. FUNDING: Australian Research Council and the Australian National Health and Medical Research Council.

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

Food packaging can be considered as a passive barrier that protects food from environmental factors such as ultraviolet light, oxygen, water vapour, pressure and heat. It also prolongs the shelf-life of food by protecting from chemical and microbiological contaminants and enables foods to be transported and stored safely. Active packaging (AP) provides the opportunity for interaction between the external environment and food, resulting in extended shelf-life of food. Chemoactive packaging has an impact on the chemical composition of the food product. The application of natural additive such as essential oils in active packaging can be used in the forms of films and coatings. It has been observed that, AP helps to maintain temperature, moisture level and microbial and quality control of the food. This review article provides an overview of the active packaging incorporated with essential oils, concerns and challenges in industry, and the effect of essential oil on the packaging microstructure, antioxidant and antimicrobial properties.

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models that lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested, coupled with several studies attempting to define the concept of explainability and its evaluation. This systematic review contributes to the body of knowledge by clustering all the scientific studies via a hierarchical system that classifies theories and notions related to the concept of explainability and the evaluation approaches for XAI methods. The structure of this hierarchy builds on top of an exhaustive analysis of existing taxonomies and peer-reviewed scientific material. Findings suggest that scholars have identified numerous notions and requirements that an explanation should meet in order to be easily understandable by end-users and to provide actionable information that can inform decision making. They have also suggested various approaches to assess to what degree machine-generated explanations meet these demands. Overall, these approaches can be clustered into human-centred evaluations and evaluations with more objective metrics. However, despite the vast body of knowledge developed around the concept of explainability, there is not a general consensus among scholars on how an explanation should be defined, and how its validity and reliability assessed. Eventually, this review concludes by critically discussing these gaps and limitations, and it defines future research directions with explainability as the starting component of any artificial intelligent system.

Salmonellosis is the second most reported gastrointestinal disorder in the EU resulting from the consumption of Salmonella-contaminated foods. Symptoms include gastroenteritis, abdominal cramps, bloody diarrhoea, fever, myalgia, headache, nausea and vomiting. In 2018, Salmonella accounted for more than half of the numbers of foodborne outbreak illnesses reported in the EU. Salmonella contamination is mostly associated with produce such as poultry, cattle and their feeds but other products such as dried foods, infant formula, fruit and vegetable products and pets have become important. Efforts aimed at controlling Salmonella are being made. For example, legislation and measures put in place reduced the number of hospitalizations between 2014 and 2015. However, the number of hospitalizations started to increase in 2016. This calls for more stringent controls at the level of government and the private sector. Food handlers of “meat processing” and “Ready to Eat” foods play a crucial role in the spread of Salmonella. This review presents an updated overview of the global epidemiology, the relevance of official control, the disease associated with food handlers and the importance of food safety concerning salmonellosis.

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

We have investigated the influence of multimode fiber core (MMFC) diameters and lengths on the sensitivity of an SMS fiber based refractometer. We show that the MMFC diameter has significant influence on the refractive index (RI) sensitivity but the length does not. A refractometer with a lower MMFC diameter has a higher sensitivity. Experimental investigations achieved a maximum sensitivity of 1815 nm/ RIU (refractive index unit) for a refractive index range from 1.342 to 1.437 for a refractometer with a core diameter of 80 μm. The experimental results fit well with the numerical simulation results.

The rising world population and its corresponding energy demands pose a considerable burden on natural energy sources. The exploitation of fossil fuels at such an alarming rate blurs the goals of sustainable development and controlling global warming as pledged during the Paris Agreement. Due to the detrimental effects of exhausts from conventional diesel fuel on the environment, biodiesel has earned significant importance during the last decade. Biodiesel is produced from different feedstocks such as neem oil, palm oil, waste frying oil, vegetable oil, animal fat, microbial oil, etc. These feedstocks react with acidic, alkaline, enzymic, homogeneous, heterogeneous, and hybrid Deep Eutectic Solvents (DES) catalysts, along with monohydric alcohol via transesterification reaction. The flexibility in its feedstock and the type of catalysts used, production cost, biodegradable and renewable nature makes it a promising alternative fuel than conventional diesel. The selection of apt feedstock and catalyst is the challenging task and governing factor of economic biodiesel production. Green solvents such as DES have high thermal stability and low volatility and can address the economic and green production issues significantly as compared to conventional alkali and acid catalysts. This review bridges the gap between the selection of feedstock and optimal catalyst for the respective feedstock. The exploration of DES fills the gap by attributing to 3Rs (i.e., recyclability, recovery, and reusability). This review highlights the contemporary trends and prospects in the selection of the feedstocks, synthesis routes, and catalysts for the transesterification reactions for biodiesel production.

In the Internet of Medical Things (IoMT), the Internet of Things (IoT) is integrated with medical devices, enabling improved patient comfort, cost-effective medical solutions, quick hospital treatments, and even more personalized healthcare. The paper first provides the introduction of IoMTs and then introduces an architecture of IoMTs. Later, it provides the current operations of the healthcare system and discusses the mapping of these operations into the architectural diagram. Further, several emerging technologies such as Physically Unclonable Functions (PUF), Blockchain, Artificial Intelligence (AI), and Software-Defined Networking (SDN) are envisioned as important technologies to overcome several challenges in e-healthcare such as security, privacy, accuracy, and performance. Finally, we provide three case studies for IoMT based on – (1) PUF-based Authentication, (2) AI-enabled SDN Assisted e-healthcare, and (3) Blockchain Assisted Patient Centric System. The solutions presented in this paper may have a huge impact on the speed at which IoMT infrastructure can efficiently evolve with market evolution.

Biopolymer-based packaging materials have become of greater interest to the world due to their biodegradability, renewability, and biocompatibility. In recent years, numerous biopolymers-such as starch, chitosan, carrageenan, polylactic acid, etc.-have been investigated for their potential application in food packaging. Reinforcement agents such as nanofillers and active agents improve the properties of the biopolymers, making them suitable for active and intelligent packaging. Some of the packaging materials, e.g., cellulose, starch, polylactic acid, and polybutylene adipate terephthalate, are currently used in the packaging industry. The trend of using biopolymers in the packaging industry has increased immensely; therefore, many legislations have been approved by various organizations. This review article describes various challenges and possible solutions associated with food packaging materials. It covers a wide range of biopolymers used in food packaging and the limitations of using them in their pure form. Finally, a SWOT analysis is presented for biopolymers, and the future trends are discussed. Biopolymers are eco-friendly, biodegradable, nontoxic, renewable, and biocompatible alternatives to synthetic packaging materials. Research shows that biopolymer-based packaging materials are of great essence in combined form, and further studies are needed for them to be used as an alternative packaging material.

Lignocellulose biomass (LCB) is a plant biomass mainly composed of cellulose, hemicellulose, and lignin. Cellulose is the most abundant biodegradable polymer on Earth and is very useful for various applications. However, the complex structure of lignocellulosic biomass, in which cellulose is tightly bound to hemicellulose and lignin, poses significant challenges in the isolation of cellulose from LCB components. This necessitates efficient pretreatment techniques to enable cellulose separation. Conventional pretreatment methods are not sustainable because they involve the use of harsh chemicals, elevated temperatures, high energy consumption, low productivity, and the formation of fermentation inhibitors which can increase the cost of biomass processing with negative environmental impacts. Green pretreatment methods have been proposed to address these issues using milder and more sustainable approaches. These green methods are evaluated in terms of their delignification efficiency, reduced solvent requirement, environmental impact, economic viability, and relevance to recent advancements in sustainable pretreatment technologies. This review provides overview of the application of green and environmentally friendly methods in the isolation of cellulose from various lignocellulosic biomass, as well as the potential of cellulose as a key feedstock in the bioeconomy by showcasing cellulose versatility in various industrial applications.

BACKGROUND: and mortality across various regions of the world. METHODS: exposure was calculated. FINDINGS: exposure during the study period. INTERPRETATION: was associated with increased risk of mortality. Urgent action is needed to reduce health risks from the increasing wildfires. FUNDING: Australian Research Council, Australian National Health & Medical Research Council.

In 2018, European Union adopted a European strategy for plastics in a circular economy as a part of their action plan for a circular economy. Sustainability is the underlying motivation behind the plastics strategy with a goal of addressing how plastics are designed, used and recycled in the EU. One of the strategies outlined is that by 2030, all plastic packaging placed on the EU market is either reusable or can be recycled in a cost-effective manner. A large portion of food packaging is multi-layer plastic that is not recyclable in a cost-effective manner. Given the difficulties associated with recycling today’s complex food packaging, what impacts will the European Union’s strategies for plastics in a circular economy have on food safety? This article explores what is being done and what can be done to mitigate the risks to food safety while adhering to the EU’s plastic strategy. It has been observed that the plastic plays a vital role in maintaining food safety, extending shelf-life and minimising food waste. However, it is currently not possible to recycle multi-layer plastic packaging which is widely used throughout the food industry, and there are currently no viable alternatives offering the same level of protection. Unless possible substitutes to multi-layer plastics offering the same level of food protection can be developed then there will be detrimental effects on food quality, safety and shelf-life, which will lead to increased food waste, additional food costs and a reduction in the variety and availability of certain foods.

This paper presents an investigation on a single-mode-multimode-single-mode fiber structure. A one-way guided-mode propagation analysis for the circular symmetry waveguide is employed to model the light propagation and the approximated formulations are derived and evaluated concerning the accuracy. Phase conjunction of the multimode interference within the fiber structure is revealed. A simple way to predict and analyze the spectral response of the structure is presented through the space to wavelength mapping with the derived approximated formulations. The prediction of spectral response is verified numerically and experimentally.

Purpose This research leverages an integrated framework that uses the technology acceptance model (TAM) and the theory of planned behaviour (TPB) to analyse the main drivers of users' intention to use food delivery apps. The purpose of this paper is to investigate the consumer's willingness to adopt online food delivery (OFD) using the models' constructs and extend them to consider food choices, convenience, trust and the effect of the perceived risks related to the coronavirus disease 2019 (COVID-19) pandemic as contextual factors. Design/methodology/approach The study adopts the partial least squares approach to structural equation modelling (PLS-SEM) to examine the data. The final sample consists of 425 people in Italy. Findings The authors have found that combining the TAM and the TPB provides a valid and significant model that can be used to understand OFD users' behavioural intentions. Moreover, the results show that subjective norms have a stronger effect on behavioural intentions than the personal attitude and that trustworthiness and the perception of risks related to COVID-19 have different effects. Accordingly, the authors derive several theoretical and managerial implications from these results. Originality/value This research contributes to the current debate on consumer behaviour in the OFD context. Only a few studies have integrated the TAM and TPB models in this context. This paper sheds light on the factors useful in predicting people's choice to buy food via OFD. Furthermore, it highlights the key role of some contextual factors and subjective norms over more technical ones.

Organic photosensitizers possessing efficient intersystem crossing (ISC) and forming long-living triplet excited states, play a crucial role in a number of applications. A common approach in the design of such dyes relies on the introduction of heavy atoms (e.g. transition metals or halogens) into the structure, which promote ISC via spin-orbit coupling interaction. In recent years, alternative methods to enhance ISC have been actively studied. Among those, the generation of triplet excited states through photoinduced electron transfer (PET) in heavy-atom-free molecules has attracted particular attention because it allows for the development of photosensitizers with programmed triplet state and fluorescence quantum yields. Due to their synthetic accessibility and tunability of optical properties, boron dipyrromethenes (BODIPYs) are so far the most perspective class of photosensitizers operating via this mechanism. This article reviews recently reported heavy-atom-free BODIPY donor-acceptor dyads and dimers which produce long-living triplet excited states and generate singlet oxygen. Structural factors which affect PET and concomitant triplet state formation in these molecules are discussed and the reported data on triplet state yields and singlet oxygen generation quantum yields in various solvents are summarized. Finally, examples of recent applications of these systems are highlighted.