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This paper explores access to palliative care services by people with learning disabilities. It is based on a study of specialist palliative care professionals in London, involving 32 semi‐structured interviews and 543 postal questionnaires. We focus on one aspect of the findings, related to the current use of specialist palliative care services by people with learning disabilities. The results indicate that such services are under‐used by this group. We discuss possible reasons for low referral rates, including late diagnosis and lack of understanding among both learning disability services and palliative care services about each other's roles. We highlight the importance of collaboration, and the need for further staff training.

This study presents a hybrid meta-modeling algorithm for optimum carbon dioxide (CO₂) emission estimation. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). Different FLR models are considered to cover the latest algorithms and viewpoints. ANN with different training algorithms and transfer functions is also applied to data sets. The proposed hybrid algorithms uses analysis of variance (ANOVA), and mean absolute percentage error (MAPE) to select between ANN, FLR or conventional regression for future CO₂ emission estimation. The intelligent algorithm of this study is then applied to estimate CO₂ emission in seven countries including India, Canada, Brazil, France, Japan, United Kingdom and United States. Different models are selected as preferred model for annual CO₂ emission estimation in these countries. The preferred model for India, Brazil, United Kingdom and United States is selected as FLR whereas the preferred model for CO₂ emission estimation in Japan, Canada and France is ANN. This shows how adopting the proposed hybrid algorithm could help in selecting the preferred model between FLR, ANN and CR in order to cover possible noise, complexity and ambiguity. This is the first study that utilizes a hybrid algorithm based on ANN, FLR and CR for accurate and optimum long term CO₂ emission estimation.

Construction materials comprise a major part of the total construction cost. Given the importance of bitumen as a fundamental material in construction projects, it is imperative to have an accurate forecast of its consumption in the planning and material sourcing phases on the project. This study aims to introduce a flexible genetic algorithm-fuzzy regression approach for forecasting the future bitumen consumption.

In the proposed approach, the parameter tuning process is performed on all parameters of genetic algorithm (GA), and the finest coefficients with minimum errors are identified. Moreover, the fuzzy regression (FR) model is used for estimation. Analysis of variance (ANOVA) is used for selecting among GA, FR or conventional regression (CR). To show the applicability of the proposed approach, Iran’s bitumen consumption data in the period of 1991-2006 are used as a case study.

Production, import, export, road construction and price are considered as the input data used in the present study. It was concluded that, among all the forecasting methods used in this study, GA was the best method for estimating.

The proposed approach outperforms the conventional forecasting methods for the case of bitumen which is a fundamental economic ingredient in road construction projects. This approach is flexible, in terms of amount and uncertainty of the input data, and can be easily adapted for forecasting other materials and in different construction projects. It can have important implications for the managers and policy makers in the construction market where accurate estimation of the raw material demand is crucial.

This is the first in this field introducing a flexible GA-FR approach for improving bitumen consumption estimation in the construction literature. The proposed approach’s significance has two folds. Firstly, it is completely flexible. Secondly, it uses CRs as an alternative approach for estimation because of its dynamic structure.

The purpose of this paper is to report the effect of sputtering time on the electrical and physical properties of ZrO_x. ZrO_x (measured thickness is ranging from 20.5 to 51.3 nm) thin films as gate oxide materials are formed by metal deposition at different sputtering time and thermal oxidation techniques.

Zirconium is deposited on silicon substrate at three different sputtering time; 30‐, 60‐ and 120‐s continued with an oxidation process conducted at 500°C for 15 min to form ZrO_x thin films. High‐resolution X‐ray diffraction (HR‐XRD), Fourier transform infrared (FTIR) spectroscopy and electrical characterizations were used to examine the properties of the thin film.

A broad ZrO_x peak lies in between 26° and 31° from HR‐XRD is presumed as the effect of small thickness of ZrO_x and or the ZrO_x is still partially crystalline. FTIR spectroscopy results suggested that besides ZrO_x, SiO_x interfacial layer (IL) has also formed in all of the investigated samples. As the sputtering time increases, hysteresis between the forward and reverse bias of capacitance‐voltage curve has reduced. The lowest leakage current density and the highest oxide breakdown voltage have been demonstrated by 60‐s sputtered sample. These may be attributed to a lower effective oxide charge and interface trap density. The extracted dielectric constant (κ) of these oxides is ranging from 9.4 to 18, in which the κ value increases with the increase in sputtering time.

ZrO_x thin film which was fabricated by sputtering method at different sputtering time and thermal oxidation techniques showed distinctive electrical results. SiO_x IL formed in the samples.

This study examines the impact of trade openness, human capital, public expenditure and institutional performance on unemployment in various income groups of Organization of Islamic Cooperation (OIC) countries.

Traditional panel data methodologies neglect the issue of cross-sectional dependence and provide ambiguous outcomes. A novel approach, “dynamic common correlated effects (DCCE)”, is utilized in this study to tackle with aforementioned issue. Pooled mean group (PMG) estimation is also applied to verify the robustness of the findings.

The long-run estimates show that trade openness has a significant and negative relationship with the unemployment rate in overall and lower-income OIC economies and a positive correlation with unemployment in higher-income OIC countries. Public expenditure is negatively and significantly correlated with unemployment in higher-income and overall OIC economies. Moreover, human capital reduces unemployment in higher-income and overall OIC countries while increases unemployment in lower-income OIC economies.

The research tends to endorse the argument for continuous trade openness policy along with efficient use of public expenditure and improved institutional performance to reduce unemployment in OIC countries.

The DCCE approach in this research considers heterogeneity and cross-sectional dependence between cross-sectional units and thus gives robust outcomes.

In Japan, since an outbreak of mass food poisoning in 2000, consumer interest in food safety and security has increased, focusing on activities such as Chisan-Chishō (Local Production, Local Consumption), Slow Food, and LOHAS. Activities related to food safety and security in Japan have a strong local character, are moving toward industrialization, are not bound by tradition, and can be said to be activities in pursuit of alternative forms of consumption and development. In Japan, most supporters of Slow Food, LOHAS, and Chisan-Chishō have been women. In societies where production is important, consumption has been entrusted to women. Therefore, activities related to food safety and security are tied to social reform with women in central roles. Receiving social recognition, these activities develop business opportunities, move toward globalized industrialization, and, in a gendered society centered on men, become activities with men in central positions. Gender in the area of food does not allow women to take part in production and distribution and is moving to exclude women. To secure women's position in food, it is necessary to industrialize according to women's ways such as maintaining the viewpoint of living nature, mutual support, collective leadership, and networking.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring.

Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics.

The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research.

The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring.

This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.