A smart city describes an urban setting which aims to effectively apply ICT technologies to help improve the well-being of its citizens and reduce the negative impacts of urbanization. The priority areas considered in the Global Smart City Index (SCI) by the Institute for Management Development’s (IMD) World Competitiveness Centre were key infrastructures and technologies in (1) health and safety, (2) mobility, (3) activities (e.g., recreational spaces), (4) opportunities (work and school), and (5) governance. A smart region is a term used to extend the concept of a smart city into both urban and rural settings to promote a sustainable planning approach at the regional level. A direction that must be considered is the adoption of a “Smart Region Mobility Framework” to effectively transform our urban and rural regional transportation networks. This research study focused on the development of the smart region mobility framework for an island region group in the Philippines. The smart region goal is to integrate intelligent transportation system (ITS) platforms such as advanced public transportation system (APTS), advanced traveler information system (ATIS), and advanced rural transportation system (ARTS) to the local public transportation route plans (LPTRP) of the region. The activities include the data collection, analysis, and evaluation of multimodal regional transportation networks and social services infrastructure. The transportation network modeling process follows the four-step transportation planning process of trip generation, trip distribution, modal-split analysis, and trip assignment. Based on the analysis of 6 provinces, 16 cities, and 114 municipalities included in the study, there are two cities identified as smart city candidates. One of the smart city candidates is designated as the smart city regional center. In the context of a smart region, the available social services (e.g., employment opportunities, education, and health services) in the designated smart cities can also be made accessible to connected cities/municipalities through ease of transportation and mobility services in the region. Lastly, the study presented the implementation of data flow architecture of the smart region mobility framework, and the regional traveler information system using mobile and web application services.

With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The application is able to process video files or live camera feed such as CCTV or surveillance cameras and generate reports indicating people detected per unit time, percentage of social distancing per unit time, detection and social distancing logs as well as color-coded bounding boxes to indicate if the detected people are following social distancing protocols.

Energy security is essential to ensure that energy is always available, affordable, and socially acceptable. To realize these aspects, this paper presents a design and business model for installing and operating a local energy community DC microgrid that allows individual households to power electrical loads for their homes and the whole community. Interested households can purchase solar PV units at a discounted price through a discount by the hosting local government unit (LGU). A percentage of the energy harnessed will be transferred through the microgrid to electrify public infrastructures such as streetlights, which covers the discount given. To optimize the setup so that both parties see the cost-saving potential of this new concept, a multi-objective linear programming (MOLP) model was developed to minimize the total cost by the participating households and the LGU. The optimization model was applied to a sample case study involving a community in the Philippines. Sensitivity analysis was also performed to identify the change in the objectives’ values when the case study’s inputs were changed. With the proper inputs to the model, the developed MOLP model successfully provided the optimal design and setup of the DC microgrid where both households and LGU have a lower lifetime electricity cost than relying on the main grid alone or the typical standalone solar energy setup.

Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness.