Search results

The Internet of Things-based digital twin (IoT-DT) technologies offer a transformative approach to building retrofitting for reducing operational carbon (ROC) emissions. However, a notable gap exists between the potential and adoption of the two emerging technologies, further exacerbated by the nascent state of research in this domain. This research aims to establish the best practices that innovatively strengthen the identified enablers to decisively tackle challenges, ensuring the efficient implementation of IoT-DT for ROC emissions in buildings.

This study adopted a mixed-method approach. Questionnaire data from 220 multidiscipline professionals were analysed via structural equation modelling analysis, while interview data obtained from 18 stakeholders were analysed using thematic content analysis. The findings were triangulated for cohesive interpretation.

After the analysis of questionnaire data, a structural model was established, depicting the critical challenges (inadequate data security, limited technical expertise and scalability issues) and key enablers (robust data security measures, skill development and government incentives) of implementing IoT-DT for ROC. Sequentially, analysis of in-depth interview data revealed the IoT-based DT best practices (safeguarding data, upskilling and incentivization). Upon triangulating the questionnaire and interview findings, this study explicitly highlights the potential of the established best practices to strategically strengthen enablers, thereby mitigating challenges and ensuring the successful implementation of IoT-based DT for ROC emissions in buildings.

This study provides practical guidance for stakeholders to effectively implement IoT-DT in ROC in buildings and contributes significantly to climate change mitigation.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

Malnutrition has a significant effect on the onset and progression of infective pathology. The malnutrition status in COVID-19 cases are not understood well. Prognostic Nutritional Index (PNI) is a new and detailed assessment of nutrition and inflammation cases. This study aims to investigate the effect of PNI on mortality in COVID-19 patients.

In total, 334 patients (males, 142; females, 192; 64.5 ± 12.3 years of age) with COVID-19 bronchopneumonia were enrolled in this investigation. Cases were divided into two groups with respect to survival (Group 1: survivor patients, Group 2: non-survivor patients). Demographic and laboratory variables of COVID-19 cases were recorded. Laboratory parameters were calculated from blood samples taken following hospital admission. PNI was calculated according to this formula: PNI = 5 * Lymphocyte count (10⁹/L) + Albumin value (g/L).

When the patients were assessed with respect to laboratory values, leukocytes, neutrophils, CRP, ferritin, creatinine and D-Dimer parameters were significantly lower in Group 1 patients than Group 2 patients. Nevertheless, serum potassium value, lymphocyte count, calcium and albumin values were significantly higher in Group 1 cases than in Group 2 cases. PNI value was significantly lower in Group 2 cases than in Group 1 cases (39.4 ± 3.7 vs 53.1 ± 4.6).

In this retrospective study of COVID-19 cases, it can be suggested that PNI may be a significant risk factor for mortality. In conclusion of this research, high-risk patients with COVID-19 can be determined early, and suitable medical therapy can be begun in the early duration.

Recent developments in the psychometric assessment of youth psychopathic traits suggest that the inclusion of a conduct disorder (CD) factor to the traditional three factors of the psychopathy construct may improve the incremental validity of these measures. The purpose of the current study is to examine whether the addition of a CD factor incrementally improves the ability of the Youth Psychopathic Traits Inventory Short version (YPI-S) to predict criminal recidivism.

A longitudinal quantitative research design was used with a sample detained male youth (N = 214, M_age = 16.4 years, SD_age = 1.3 years).

Results using the area under the curve analysis suggest that the inclusion of a CD factor slightly improves the capacity of the YPI-S to predict one-year general criminal recidivism, but it does not significantly increase its capacity to predict violent criminal recidivism. Results also indicate that a CD scale outperforms the YPI-S, even with an additional CD factor included, in terms of predicting one-year general and violent recidivism.

Self-reported youth psychopathic trait measures, even those that include a CD factor as a fourth factor, should be used with caution when the aim is to predict youth criminal recidivism.

To the best of the authors’ knowledge, this is the first study using a self-reported youth psychopathic traits measure with a CD factor to examine youth criminal recidivism.

This paper aims first to describe the most prevalent teachers’ and students’ behaviors in synchronous online classes in emergency remote teaching; second, to discern behavior profiles and third, to investigate what features explain the observed behaviors.

An adapted COPUS observation protocol was employed to observe 292 online classes from 146 higher education teachers.

The most prevalent behaviors were: Presenting for teachers and Receiving for students, followed by Teachers Guiding and Students Talking to Class. Furthermore, cluster analysis showed two groups: Traditional and Interactive. The variables that better explained belonging to the Interactive lecture group were disciplinary area – social sciences and humanities –and teaching in technical institutions.

In a context where higher education institutions intend to project the lessons learned into post-pandemic learning experiences, this study provides observational evidence to realize the full potential expected from online and blended teaching and learning.

Despite the prevalence of synchronous online lectures during COVID-19, there is a paucity of observational studies on the actual behaviors that occurred in this context. Most research has been based on surveys and interviews. This study addresses this gap.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

Numerous design methods are available to facilitate digital innovation processes in user interface design. Nonetheless, little guidance exists on their appropriate selection within the design process based on specific situations. Consequently, design novices with limited design knowledge face challenges when determining suitable methods. Thus, this paper aims to support design novices by guiding the situational selection of design methods.

Our research approach includes two phases: i) we adopted a taxonomy development method to identify dimensions of design methods by reviewing 292 potential design methods and interviewing 15 experts; ii) we conducted focus groups with 25 design novices and applied fuzzy-set qualitative comparative analysis to describe the relations between the taxonomy's dimensions.

We developed a novel taxonomy that presents a comprehensive overview of design conditions and their associated design methods in innovation processes. Thus, the taxonomy enables design novices to navigate the complexities of design methods needed to design digital innovation. We also identify configurations of these conditions that support the situational selections of design methods in digital innovation processes of user interface design.

The study’s contribution to the literature lies in the identification of both similarities and differences among design methods, as well as the investigation of sufficient condition configurations within the digital innovation processes of user interface design. The taxonomy helps design novices to navigate the design space by providing an overview of design conditions and the associations between methods and these conditions. By using the developed taxonomy, design novices can narrow down their options when selecting design methods for their specific situations.