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This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This paper seeks to explore the functions of Christmas mythemes for children’s consumption culture development. In addition, the purpose of this study is to provide an insight on the development of Central European Children into customers and how mythemes are associated with the wishing behaviour.

Levi-Strauss’ (1955) structural analysis was used to uncover the mythemes of the Christmas story for Austrian children. These mythemes then informed a thematic analysis of 283 Austrian children’s Christmas letters. Campbell’s (1970) functions of myths were used to reflect on the findings.

The Christmas mythemes uncovered were found to encourage materialism by linking self-enhancement (good acquirement) with self-transcendent (good behaviour) values. The role of myths to relieve the tension between the incongruent values of collective/other-oriented and materialistic values is expanded upon. Such sanctification of selfish good acquisition is aided by the mythemes related especially to the Christkind and baby Jesus. Instead, marketers should use Christmas mythemes which emphasise family and collective/other-centred values.

By first uncovering the “mythemes” related to Christmas, the authors contribute to the academic understanding of Christmas, going beyond origin or single myth understandings and acknowledging the multifaceted components of Christmas. The second contribution is in exploring mytheme’s representation in children’s Christmas letters and reflecting on their functions. This differs from previous literature because it looks at one of the main cultural vehicles for Christmas socialisation and its intersection with the mythemes that feed children’s consumption culture formation. Through the authors’ presentation of a conceptual framework that links mytheme functions with proximal processes using a socioecological viewpoint, the authors demonstrate the guidance of mythemes in children’s development. The third contribution is a reflection on the potential ethical implications for children’s formation of their consumer culture based on the functions of the mythemes. Furthermore, the authors add to the existing body of research by investigating a Central European context.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

The central idea of this research article is to examine the characteristics of Clairaut submersions from Lorentzian trans-Sasakian manifolds of type (α, β) and also, to enhance this geometrical analysis with some specific cases, namely Clairaut submersion from Lorentzian α-Sasakian manifold, Lorentzian β-Kenmotsu manifold and Lorentzian cosymplectic manifold. Furthermore, the authors discuss some results about Clairaut Lagrangian submersions whose total space is a Lorentzian trans-Sasakian manifolds of type (α, β). Finally, the authors furnished some examples based on this study.

This research discourse based on classifications of submersion, mainly Clairaut submersions, whose total manifolds is Lorentzian trans-Sasakian manifolds and its all classes like Lorentzian Sasakian, Lorenztian Kenmotsu and Lorentzian cosymplectic manifolds. In addition, the authors have explored some axioms of Clairaut Lorentzian submersions and illustrates our findings with some non-trivial examples.

The major finding of this study is to exhibit a necessary and sufficient condition for a submersions to be a Clairaut submersions and also find a condition for Clairaut Lagrangian submersions from Lorentzian trans-Sasakian manifolds.

The results and examples of the present manuscript are original. In addition, more general results with fair value and supportive examples are provided.

This study aims to examine the associations of social networks with the sense of community (SOC) construct and spatial colocation or having an office. The study site was an institute for health-care policy research formed in 2011 by bringing together scientists from more than 20 different university units. Only 30% of the scientists were had an office or physical presence at the institute. Therefore, the institute was an ideal site to examine whether SOC was correlated with different dimensions of network position – connectedness, reachability and brokerage – even when the authors account for the lack of spatial colocation for the off-site scientists.

A two-part (sociometric and workplace) internet survey instrument was administered in 2014 to the institute’s population of 411 individuals. The sociometric data were used to create an undirected interaction network and the following dependent variables (DVs) or network centralities: normalized degree to measure connectedness; average reciprocal distance to capture reachability; and normalized betweenness to proxy brokerage. Separate node-level network regressions were then run with random permutations (N = 10,000) and listwise deletion for each of the DVs with SOC and spatial colocation as the independent variables, and variables that controlled for gender, organizational affiliation and job category.

SOC and spatial colocation are both positively and significantly correlated with network connectedness and reachability. The results suggest that both SOC and spatial colocation have a larger impact on reachability than connectedness. However, neither SOC nor spatial colocation are significantly associated with network brokerage. Finally, the findings show that SOC and spatial colocation are more reliable predictors of network connectedness and reachability than are key individual- and unit-level control variables, specifically the individual’s sex, job category and organizational affiliation. The controls were not significantly associated with any of the three network centralities, namely, connectedness, reachability and brokerage.

This exploratory study used social network analysis and node-level network regressions to examine the associations from SOC and spatial colocation to dimensions of network position. SOC is positively and significantly associated with network connectedness and reachability, suggesting that SOC is an important consideration when individuals are disadvantaged from the absence of spatial colocation. The findings have implications for work in the context of the COVID-19 pandemic as they imply that interventions based on the SOC construct could potentially lessen the negative effects of remote work on workplace social networks due to factors such as the reduction of social contacts.

Approaches to solving sustainability problems require a specific problem-solving mode, encompassing the complexity, fuzziness and interdisciplinary nature of the problem. This paper aims to promote a complex systems’ view of addressing sustainability problems, in particular through the tool of network science, and provides an outline of an interdisciplinary training workshop.

The topic of the workshop is the analysis of the Sustainable Development Goals (SDGs) as a political action plan. The authors are interested in the synergies and trade-offs between the goals, which are investigated through the structure of the underlying network. The authors use a teaching approach aligned with sustainable education and transformative learning.

Methodologies from network science are experienced as valuable tools to familiarise students with complexity and to handle the proposed case study.

To the best of the authors’ knowledge, this is the first work which uses network terminology and approaches to teach sustainability problems. This work highlights the potential of network science in sustainability education and contributes to accessible material.

This paper aims to examine the relationship between psychiatry and occupational therapy in Ireland through a case study of the development of the occupational therapy department in St. Patrick’s Hospital, Dublin, from 1935 to 1969. Patronage by psychiatrists was an important factor in the professionalisation of occupational therapy internationally.

Documentary sources and oral history interviews were analysed to conduct an instrumental case study of occupational therapy at St. Patrick’s Hospital from 1935 to 1969.

The research identified key individuals associated with the development of occupational therapy at St. Patrick’s Hospital, including psychiatrist Norman Moore, occupational therapy worker Olga Gale, occupational therapist Margaret Sinclair, and social therapist Irene Violet Grey. Occupational therapy was considered by the hospital authorities to be “an important part in the treatment of all types of psychiatric illness” (Board Meeting Minutes, 1956). It aimed to develop patient’s self-esteem and facilitate social participation. To achieve these objectives, patients engaged in activities such as dances, arts and crafts, and social activities.

This study has highlighted the contributions of key individuals, identified the links between occupational therapy and psychiatry, and provided an insight into the development of the profession in Ireland prior to the establishment of occupational therapy education in 1963. Occupational therapy practice at St. Patrick’s Hospital from 1935 to 1969 was congruent with the prevailing philosophy of occupational therapy internationally, which involved treatment through activities to enhance participation in society.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.

Alcohol, tobacco, marijuana and other drugs use typically increases in prevalence and frequency during middle and late adolescence. School health instruction often focusses on providing facts and rarely provides tools for addressing the psychosocial risk factors needed to prevent substance use. The purpose of this paper is to report about the effectiveness of a prevention programme delivered in US high school health classes. The intervention augments typical instruction by providing teachers with activities that can be infused in their daily teaching.

In total, 26 schools were randomly assigned to receive the intervention or serve as controls. Pupils were pretested near the beginning of the school year, posttest near the end of the school year and administered a final test near the beginning of the following school year. Teachers in treatment schools were provided with activities designed to target psychosocial variables known to mediate substance use onset and self-initiated cessation. These include normative beliefs, intentionality, lifestyle incongruence, beliefs about consequences of use, peer pressure resistance skills, decision-making skills, goal setting skills and stress management skills.

Hierarchical modelling analytic strategies revealed the intervention to have definable positive impacts on alcohol and cigarette use. Moreover, the intervention had strongest effects on alcohol and cigarette use among pupils who were identified at pretest as being lower-than-average risk.

This research provides support for providing teachers with a strategy for preventing alcohol, tobacco and other drugs that can be used in a flexible manner to augment the instruction they are already mandated to provide.