Background and Aim: An antimicrobial delivery in the form of surface-modified lectin of lipid nanoparticles was proposed to improve cellular accumulation. ArtinM, an active toll-like receptor 2 (TLR2) agonist lectin isolated from cempedak (Arthocarpus integrifolia) seeds, was selected to induce cellular engulfment of nanoparticles within infected host cells. Materials and Methods: Lipid nanoparticles were prepared using the emulsification technique before electrostatic adsorption of artinM. The formula comprising of rifampicin, soy phospholipid, and polysorbate 80 was optimized by Box-Behnken design to produce the desired particle size, entrapment efficiency, and drug loading. The optimum formula was characterized for morphology, in vitro release, and cellular transport. Results and Discussion: Soy phospholipid showed a profound effect on controlling drug loading and entrapment efficiency. Owing to its surface activity, polysorbate 80 contributed significantly to reduce particle size; however, a higher ratio to lipid concentration resulted in a decrease of rifampicin encapsulation. The adsorption of artinM on the surface of nanoparticles was accomplished by electrostatic binding at pH 4, where this process maintained the stability of encapsulated rifampicin. A high proportion of artinM adsorbed on the surface of the nanoparticles shown by haemagglutination assay, zeta potential measurement, and transmission electron microscopy imaging. Cellular uptake revealed by confocal microscopy showed the success in transporting Nile-red labelled nanoparticles across fibroblast cells. Conclusion: The delivery system of nanoparticles bearing artinM becomes a potential platform technology for antibiotic targeting in the treatment of life-threatening chronic diseases caused by intracellular infections.

The Business Model Canvas and value chain have significantly impacted how companies and entrepreneurs develop their business strategies. Both the Business Model Canvas and the value chain provide a simple, visual approach to effectively map out the key elements of a business. They have also been used as foundational models for ecosystem business models. Ecosystem business models are created by integrating the Business Model Canvas and the pie model, known as the Integrated Business Model. Another method for generating an ecosystem business model is integrating the value chain and the pie model, known as the service-dominated business model radar. Ecosystem business models with an organizational approach also provide a broad view of the roles of each organization, divided into five actors: governor, input provider, aggregator, enabler, and end user. In this research, the perspective of the ecosystem business model originates from an ecological analogy. The ecological concept consists of community ecology and ecosystem ecology. Community ecology relates to the types of actors that play roles in the ecosystem, while ecosystem ecology pertains to the value chain processes within the ecosystem. This research will discuss the advantages and limitations of the Business Model Canvas, the value chain, and organizations as foundational models for ecosystem business models, as well as opportunities to create new ecosystem business models based on the concepts of community ecology and ecosystem ecology. The new business model developed will be applied to electric vehicles in sustainable tourism areas.

Achieving the Sustainable Development Goals (SDGs), including the environmental goals (7, 12, 13, 14, and 15), requires analytical methods that capture long-term national trajectories. Existing studies have not widely used approaches that integrate similarity measurement, clustering, and forecasting. Aims: This study proposes a hybrid framework of similarity measurement, clustering, and forecasting for global SDG trajectories. By comparing cluster structures from historical SDG data with those generated using historical and forecasted trajectories, the study identifies how countries’ development patterns may shift over time. Methodology and results: The framework integrates time-series clustering and predictive modeling. Clustering utilizes both historical data from 2000 to 2025 and a combination of historical and forecasted values, employing DTW variants to measure similarities across 167 countries. K-Means, Agglomerative, and Spectral clustering algorithms are evaluated to identify the most coherent grouping. ARIMA, LSTM, GRU, and Prophet forecasting algorithms are assessed to determine the most accurate SDG score projections for 2026 to 2030. Results show that Soft DTW with K-Means produces the most coherent clusters, and ARIMA yields the lowest forecasting errors. The clustering reveals three groups representing different development pathways: strong SDG index but uneven environmental performance; strong environmental scores despite low SDG index performance; and high SDG performance with moderate environmental outcomes. These patterns highlight diverse sustainability trajectories and the multidimensional nature of global development progress. Conclusion, significance, and impact study: The study validates elastic similarity measures integrated with clustering and forecasting and provides a data-driven decision support framework to improve policy coherence and strengthen international cooperation.

Background: Construction workers are highly exposed to musculoskeletal disorders (MSDs) owing to awkward and repetitive postures. Traditional ergonomic tools lack real-time feedback, and motion-capture systems are impractical for field use. Smartphone-based inertial measurement units (IMUs) offer a cost-effective and portable alternative to traditional methods. Objective: This study aimed to develop and validate the Ergo Posture Analysis System (Ergo-PAS), an Android-based smartphone prototype designed to detect and alert users to high-risk postures in real-time. Method: The Ergo-PAS employed synchronized smartphones on the trunk, upper arm, and forearm to capture inertial data during trunk flexion, arm elevation, and repetitive forearm movements. Five participants performed controlled postures, which were validated using a Vicon motion capture system. Pearson's correlation, intraclass correlation coefficients (ICC), and linear regression were applied. Results: The Ergo-PAS demonstrated moderate-to-excellent validity and generally good-to-excellent reliability compared with the Vicon motion capture system. Pearson’s correlations ranged from 0.13–0.99 for trunk flexion, 0.50–0.95 for upper-arm elevation, and 0.57–0.94 for forearm movements, while ICC values ranged from 0.08–0.99. The performance varied across body segments. Exploratory linear regression calibration showed stable slopes across participants and positions but with joint-specific intercept differences, indicating the need for tailored calibration. Conclusion: This preliminary laboratory validation suggests that the Ergo-PAS can provide reasonably accurate and repeatable posture measurements using smartphone-based inertial sensors. The system shows promise as a low-cost platform for real-time ergonomic risk monitoring and providing feedback. Further research is required to evaluate the usability and effectiveness of these systems during dynamic work activities and in real-world construction environments.

Telemedicine has the potential to enhance both the accessibility and quality of healthcare services. Patients can access telemedicine through various modes, one of which is via applications. Evaluating the task–technology fit (TTF) of telemedicine applications is essential to ensure that they effectively meet user needs and, in particular, support physicians’ tasks in delivering remote healthcare services. However, no instrument has been specifically developed to assess the TTF of telemedicine applications from the physician’s perspective. This study, therefore, aimed to develop and validate a questionnaire-based instrument for evaluating the TTF of telemedicine applications from the physician’s perspective. A mixed-methods approach was employed, consisting of two phases. In the first phase, the instrument was developed through a literature review, interviews, and expert feedback. In the second phase, the instrument was validated through assessments of content validity, face validity, construct validity (convergent and discriminant), reliability (internal consistency), and predictive validity. Content and face validity were assessed by six experts and ten physicians, respectively. Construct validity, reliability, and predictive validity were evaluated using data from 185 physicians and analyzed with Partial Least Squares Structural Equation Modelling (PLS-SEM). The final TTF instrument, named Telemedicine Application Task–Technology Fit Questionnaire (TATTFQ), comprises 36 items across nine dimensions. The findings indicate that the developed TTF instrument has adequate relevance, reliability, and validity, making it suitable for evaluating the TTF of telemedicine applications from the physicians’ perspective. This instrument has strong potential for widespread use in evaluation and development programs aimed at optimizing telemedicine applications.