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Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.

<jats:title>Abstract</jats:title><jats:p>Speaking truth ought to be normative in churches, and yet when it does, the foundations and structures of power are often shaken to the core. This paper explores the issues of identity and integrity in ecclesiology and is concerned with the ethical paradigms and moral frameworks that need to be in place if churches are to be places where honesty and truthfulness can be normative. Churches often fail as institutions because they presume they can conduct their affairs as organizations might. Churches become anger-averse, resisting the voices and experiences of victims, in order that the flow of power and its structures are unimpeded. At that point, churches become inherently committed to re-abusing victims and are unable to hear their pain and protests, which only leads to the perpetration of further abuse.</jats:p>

With the rise of the awareness of human rights, students with special educational needs (SEN) are receiving more attention. As many mainstream schools adopt inclusive education, society is focusing on teachers' professional awareness and attitudes toward inclusive education. This study examines the professional awareness and attitudes of post-90s early childhood teachers in Macau regarding inclusive education, aiming to improve policies and training programs. The research employs both qualitative and quantitative methods to explore teachers' views on inclusive education. The qualitative research consists of semi- structured interviews to understand teachers' readiness, attitudes, and the challenges they face in implementing inclusive education. The quantitative aspect uses a questionnaire adapted from the ""Survey on Equal Learning Opportunities for Students with Special Educational Needs under the Hong Kong Inclusive Education System,"" tailored for Macau. By combining interview findings and data, the study analyzes and summarizes the results. The results indicate that the professional awareness of inclusive education among post-90s early childhood teachers in Macau is at a relatively low to moderate level, and their willingness to engage in inclusive education is not high. Factors influencing teachers' willingness to implement inclusive education include a lack of professional knowledge (such as development history, relevant regulations, and guidance methods), insufficient resources, and unclear attitudes from schools toward the implementation of inclusive education. 隨著人權意識的掘起，特殊教育需要學生(SEN 學生)的學習受到關注， 澳門的融合教育逐漸受到關注，許多主流學校開始實施融合教育。就著學校教 育的轉型，教師對融合教育的知識和開展的態度受到社會所關注。因此，是次 研究以質量結合的方式進行調查，為廣泛地、深入地暸解澳門 90 後新生代幼兒 教師在融合教育方面的專業認知和態度，從而對澳門融合教育政策和教師課程 提出建議，促進融合教育的發展。 質性研究以半結構性的深度訪談暸解教師對實施融合教育的預備度和態 度，並探討當中的影響因素和教師在教學中的困擾與需求。量化的研究則參考 了《香港融合教育制度下有特殊教育需要學生的平等學習機會調查》的教師問 卷，向澳門 90 後新生代幼兒教師對融合教育認知和態度作調查。研究透過結合 訪談資料與數據，對研究問題作分析、討論與歸納，並撰寫出研究結果。 研究結果指出澳門 90 後新生代幼兒教師的融合教育專業認知處中等偏低的 水平，同時對融合教育開展的意願不高。而影響教師融合教育開展意願的因素 包括教師缺乏融合教育專業知識(如:發展歷史、相關法規和輔導方式)、資 源不足和學校對融合教育開展的態度不明確。

This research focuses on common misconceptions about the factors driving women to purchase footwear impulsively. Its primary objective is to explore how emotional and social triggers specifically influence women's purchasing decisions, contrasting with the traditionally rational consumer models.,An online questionnaire was administered to a sample of women, yielding 199 useable responses.,The findings reveal the key determinants of women's impulsive retail footwear purchases, which include self-regulation, hedonic motivations and the influence of the retail store environment. This research challenges the prevailing assumption that women's passion for shopping is driven solely by inherent characteristics and suggests that external factors substantially shape their impulsive buying behaviour. In summary, the stereotypical portrayal of women as compulsive retail footwear shoppers may result more from external stimuli and environmental factors rather than an intrinsic trait.,This study improves the existing knowledge of women’s impulsive buying behaviour by unveiling the determinants of women's impulsive footwear purchases and assessing whether prevailing stereotypes hold true.

This article sets a theoretical foundation to transformative mixed methods research that is rooted in the critical theory of Habermas and Honneth. This addresses Habermas’s knowledge-constitutive interests and communicative action for redressing societal pathologies, and Honneth’s work on (mis)recognition, (dis)respect, and social justice. In doing so, the article argues for broadening the scope and embrace of mixed methods research, to go beyond being empirical research only or largely, and to include theorisation, critical theoretical discourse and its analysis, and ideology critique, as legitimate methods for (transformative) mixed methods research. The article makes a case for these methods as constituting important research methods in themselves in the portfolio of mixed methods research, moving the boundaries of mixed methods research beyond solely empirical studies, and providing emancipatory lenses and consciousness-raising in recognising that transformation takes many forms.

PDF | Purpose Whilst the majority of academic studies have focused on the for-profit business-to-consumer type of sharing economy, the community-based... | Find, read and cite all the research you need on ResearchGate

Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.

<jats:p>Antibiotic pollution poses a serious environmental concern worldwide, posing risks to ecosystems and human well-being. Transforming waste activated sludge into adsorbents for antibiotic removal aligns with the concept of utilizing waste to treat waste. However, the adsorption efficiency of these adsorbents is currently limited. This study identified KOH modification as the most effective method for enhancing tetracycline (TC) adsorption by sludge biochar through a comparative analysis of acid, alkali, and oxidant modifications. The adsorption characteristics of TC upon unmodified sludge biochar (BC) as well as KOH-modified sludge biochar (BC-KOH) were investigated in terms of equilibrium, kinetics, and thermodynamics. BC-KOH exhibited higher porosity, greater specific surface area, and increased abundance of oxygen-based functional groups compared to BC. The TC adsorption on BC-KOH conformed the Elovich and Langmuir models, with a maximum adsorption capacity of 243.3 mg/g at 298 K. The adsorption mechanisms included ion exchange, hydrogen bonding, pore filling, and electrostatic adsorption, as well as π-π interactions. Interference with TC adsorption on BC-KOH was observed with HCO3−, PO43−, Ca2+, and Mg2+, whereas Cl−, NO3−, and SO42− ions exhibited minimal impact on the adsorption process. Following three cycles of utilization, there was a slight 5.94% reduction in the equilibrium adsorption capacity, yet the adsorption capacity remained 4.5 times greater than that of unmodified sludge BC, underscoring its significant potential for practical applications. This research provided new insights to the production and application of sludge biochar for treating antibiotic-contaminated wastewater.</jats:p>

Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This paper presents an empirical study that evaluates four existing deep learning models—VGG16, DenseNet, ResNet50, and GoogLeNet—utilizing the Facial Expression Recognition 2013 (FER2013) dataset. The dataset contains seven distinct emotional expressions: angry, disgust, fear, happy, neutral, sad, and surprise. Each model underwent rigorous assessment based on metrics including test accuracy, training duration, and weight file size to test their effectiveness in FER tasks. ResNet50 emerged as the top performer with a test accuracy of 69.46%, leveraging its residual learning architecture to effectively address challenges inherent in training deep neural networks. Conversely, GoogLeNet exhibited the lowest test accuracy among the models, suggesting potential architectural constraints in FER applications. VGG16, while competitive in accuracy, demonstrated lengthier training times and a larger weight file size (512MB), highlighting the inherent balance between model complexity and computational efficiency.

The objective is to assess whether the extent to which employee resilience and organizational culture would be significantly related to and statistically predict the three facets of employee work engagement. Resilience was measured by four facets (Determination, Endurance, Adaptability, and Recuperability); and Organization Culture was measured for three types (Bureaucratic, Innovative, and Supportive). The dependent measures were the three facets of Work Engagement (Cognitive, Emotional, and Physical). This research by questionnaire was conducted in 2023. The questionnaires completed by 316 full-time workers revealed that all four facets of employee resilience had significant positive correlations with all three types of work engagement. Also, all three facets of work engagement were significantly higher in Innovative and Supportive cultures compared to Bureaucratic cultures. The regression analyses performed showed that the resilience factors of Determination and Adaptability were strong positive predictors of all three facets of work engagement. Furthermore, Innovative culture had additional positive effects on all three facets of work engagement; while Supportive culture had an additional positive effect on Emotional Work Engagement. The implications of the results for management are also discussed in this paper.

<jats:title>Abstract</jats:title><jats:p>This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k‐Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1‐score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.</jats:p>

<jats:p>In this study, sixteen Sprague Dawley (SD) female rats and eight SD male rats were co-housed to mate. Pregnant SD female rats were fed with a control diet or an MA diet. Breast milk, maternal ileum, and intestinal samples of the offspring were collected at the day of birth and ten days afterwards. The results showed that the impact of MA was more obvious on the microbiota of mature milk (p = 0.066) than on that of colostrum. In addition, MA additive did not significantly affect maternal ileal microbiota, but affected offsprings’ colonic microbiota significantly ten days after birth (p = 0.035). From the day of giving birth to ten days afterwards, in addition to the increase in microbial richness and diversity, at genus level, the dominant bacteria of breastmilk changed from Pseudomonas veronii to Bacillus and Lactococcus. Different from breastmilk microbiota, ten days after giving birth, the maternal ileal microbiota and the offsprings’ intestinal microbiota were dominated by Lactobacillus. Instead of ileal microbiota, offsprings’ colonic microbiota is a key action site of maternal MA additive. Therefore, the current findings have significant implications for the development of maternal feed aimed at modulating the intestinal microbiota of offspring, ultimately leading to improved health outcomes for both mothers and their offspring.</jats:p>

<jats:p>To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM10, PM2.5, NO2, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau.</jats:p>