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Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

The analytical framework proposed in this study aims to link the capital portfolio approach to sustaining human well-being, 2015 sustainable development goals and development action ARC-D concepts. Nepal case study is a “tribrid” power generation system that combines distributed solar, hydro and wind power generation capacities for the resilience of a community of around 500 people in a remote village with a total installed capacity of 28 kW. The second case study is about the solarization of 900 health centres in Chhattisgarh, India, with off-grid solar PV with a cumulative capacity of 3 MW.

Critical infrastructure at the community scale needs to be resilient to maintain community-level functionality in the face of adverse impacts. The present study provides two case study sites from Nepal and India to demonstrate various elements of resilience building for critical infrastructures, especially for the energy sector.

Granular technology and distributed generation in Nepal and India can act as critical infrastructure in providing on-demand electricity service to enhance community-level resilience along with future opportunities of scale up.

The analytical framework for evaluating community-scale resilience through critical infrastructure design and application of the framework using evidence based on case studies are the original contributions.

This paper evaluates the factors influencing the utilization of the Internet of Things (IoT) for sustainable facilities management (SFM) practices in Saudi Arabia.

A mixed approach, combining a literature review, pilot-testing and questionnaire survey, was adopted to evaluate the factors. Twenty-seven factors were identified and grouped into four groups: technical, business and organizational, operational and security and privacy. The questionnaire was distributed to 30 facilities managers and 30 IoT specialists, totaling 60 practitioners, to determine the effect index of each factor. The practitioners' consensus on the ranking of the factors was then determined.

The study identifies the top-ranking factors as: “Difficulty in ensuring data security and protection,” “Difficulty in ensuring data privacy and confidentiality” and “Limited awareness and understanding of IoT benefits and capabilities.” These factors highlight the challenges to successful IoT implementation in the FM sector. The FM sector could benefit from utilizing IoT while maintaining the security, privacy and effectiveness of building operations by successfully addressing these concerns. A high level of consensus on the ranking of the factors was observed between facilities managers and IoT specialists. This was substantiated by a Spearman’s rank correlation coefficient of 0.79.

This study enriches the literature by combining practical insights from facilities managers with technical expertise from IoT specialists on the factors impacting IoT implementation in the Saudi Arabian FM sector. Beyond academic contributions, it provides practical insights for industry professionals, fostering a culture of knowledge-sharing and guiding future research in this field.

This study aims to investigate the effect of speckle pattern on displacement measurements using different speckle diameters and coverage ratios.

In order to compare the coverage ratio and speckle diameter during the evaluation of the correlation of digital images (DIC) study, template speckle plates were produced on a computer numerical control (CNC) punch press with 600 punches per minute. After the speckle plates were manufactured, the speckled pattern was randomly painted on a plain white side through the manufactured template plates, and then tensile tests were performed under the same loading conditions for each sample to observe displacement variation via correlation parameters.

During the manufacturing of templates with thin plates, a punch diameter of less than 1.7 mm will cause tool failure; therefore, uniform speckle size can be assessed before operation. A higher coverage ratio resulted in more accurate and reliable results in displacement data. With smaller coverage, the facet size should be increased to achieve favorable results.

If thick template plates are selected, speckle painting cannot be done properly; therefore, template thickness shall also be assessed before operation.

For randomly distributed DIC templates, increasing coverage beyond 50% does not make sense due to difficulties in the production process in the punch press.

Evaluating DIC results via templates manufactured in a punch press with different speckle diameters and coverage ratios is a new topic in literature.

As climate change disproportionately affects vulnerable populations, ensuring thermal comfort for older adults is magnified in tropical senior living environments. This study explores the lived experiences of older adults' thermal comfort in senior living facilities in a tropical climate and how these experiences impact their overall well-being.

Employing Moustakas' transcendental phenomenology and the Modified Stevick-Colaizzi-Keen method, this study investigated older adults' thermal experiences through semi-structured interviews with 28 participants in six urban senior living facilities in Malaysia.

Four primary themes emerged: fabric and function; atmospheric conditions and living dynamics; thermal dynamics and environmental comfort; temperature tensions of stress, sound, and sensitivity. Our findings underscore the importance of considering the multisensory and multi-faceted nature of thermal comfort for older adults, considering sensory aspects, early life experiences, cultural practices, and personal preferences, particularly in tropical climates.

As one of the first to explore the thermal comfort of older adults in senior-friendly accommodations in a tropical climate, the findings provide a comprehensive understanding of older adults' diverse thermal comfort needs and offer practical recommendations for environments that support healthy aging. By integrating insights from hospitality, gerontology, and environmental studies, this research contributes to the promotion of public health and aligns with global objectives to improve the well-being of the aging population.

This paper explores the relevant fire code requirements and outlines the development of an evaluation tool based on these codes to evaluate fire safety measures in dining properties.

Existing literature was examined to identify the combustible materials, fire causes and factors making these properties prone to fire incidents. An evaluation method, based on code regulations, for ensuring fire safety in dining properties was then developed and tested on a specific dining facility to validate its practicality.

Forty requirements, grouped into seven categories, were identified for ensuring fire safety in dining properties. The case study exposed multiple violations of fire safety, leading to corrective measures for enhancing the fire safety status of the building.

This study introduced a methodical approach for raising awareness, among property managers of dining properties, about fire incidences and their consequences. It presents an evaluation tool for assessing the compliance level with fire codes and standards.

Dining properties are facilities that offer both dine-in and take-out food services. Given the increasing number of fire incidents in dining properties worldwide, there is a substantial demand for a process to audit the adherence to fire safety codes in these properties. This study presents a systematic approach to increase public knowledge of fire events and their effects in dining properties.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

This paper proposes selecting a construction project portfolio in the context of a public institution, which makes it possible to assess quantitative and qualitative criteria, thereby meeting the needs of the institution and the existing constraints.

The research design follows a framework using technique for order preference by similarity to ideal solution (TOPSIS) associated with integer linear programming.

The method involves a flow of assessments allowing criteria and weights to be elicited where outcomes are based on the experts' intra-criteria assessment of alternatives and decision-makers' inter-criteria assessment. This is of utmost interest to public organizations, where selections must result in benefits and lower costs, integrating the experts' technical and management perspectives.

Public institutions are characterized by having limited financial and personnel resources for project development despite having a high demand for requests not associated with profits, making it essential to have a framework that enables using multiple criteria to better evaluate the benefits related to these decisions.

The main contributions of this article are: (1) the proposition of a framework for selecting construction project portfolios considering the organization's strategic needs; (2) identifying quantitative and qualitative assessment criteria for project selection; (3) integrating TOPSIS with an optimization process for selecting the construction project portfolios and (4) providing a structured decision process for selecting the portfolio that best represents the interests of the institution within its limited resources and personnel.

There is a gap in understanding with regards to the discrimination and prejudice experienced by gay entrepreneurs. To address this, an intersectional perspective is adopted to facilitate a better understanding of how lesbian and gay entrepreneurs may experience heterosexism.

This qualitative study uses semi-structured interviews to explore the experiences of 14 lesbian and gay entrepreneurs as they navigate homophobia and heterosexism.

The study contributes novel insights to the field of entrepreneurship, extending the study of lesbian and gay entrepreneurs to include gender and a fine-grained analysis of the experience of heterosexism. Its inclusion of an intersectional perspective of the lesbian-female entrepreneur expands the emerging body of literature examining intersectional identities of minority entrepreneurs.

The authors provide a more nuanced understanding of the impact of heterosexism on LGBT+ entrepreneurial activities. This is facilitated by the authors' adoption of an intersectional perspective which shows how the different axes of identity influenced gender identity performance in relation to the model of perceived neutrality in LGBT+ entrepreneurship. The authors also make an original contribution to minority stress literature through the authors' exploration of one facet of minority entrepreneurship, namely the impact of heterosexism on LGBT+ entrepreneurial activities.