The biomaterials polylactic acid (PLA), polycaprolactone (PCL), and hydroxyapatite (HA) were selected to fabricate composite filaments for 3D printing fused filament fabrication (FFF), which was used to fabricate a composite biomaterial for an interlocking nail for canine diaphyseal fractures instead of metal bioinert materials. Bioactive materials were used to increase biological activities and provide a high possibility for bone regeneration to eliminate the limitations of interlocking nails. HA was added to PLA and PCL granules in three ratios according to the percentage of HA: 0%, 5%, and 15% (PLA/PCL, PLA/PCL/5HA, and PLA/PCL/15HA, respectively), before the filaments were extruded. The test specimens were 3D-printed from the extruded composite filaments using an FFF printer. Then, a group of test specimens was coated by silk fibroin (SF) using the lyophilization technique to increase their biological properties. Mechanical, biological, and chemical characterizations were performed to investigate the properties of the composite biomaterials. The glass transition and melting temperatures of the copolymer were not influenced by the presence of HA in the PLA/PCL filaments. Meanwhile, the presence of HA in the PLA/PCL/15HA group resulted in the highest compressive strength (82.72 ± 1.76 MPa) and the lowest tensile strength (52.05 ± 2.44 MPa). HA provided higher bone cell proliferation, and higher values were observed in the SF coating group. Therefore, FFF 3D-printed filaments using composite materials with bioactive materials have a high potential for use in fabricating an interlocking nail for canine diaphyseal fractures.

The full-thickness articular cartilage defect (FTAC) is an abnormally severe grade of articular cartilage (AC) injury. An osteochondral autograft transfer (OAT) is the recommended treatment, but the increasing morbidity rate from osteochondral plug harvesting is a limitation. Thus, the 3D-printed bilayer’s bioactive-biomaterials scaffold is of major interest. Polylactic acid (PLA) and polycaprolactone (PCL) were blended with hydroxyapatite (HA) for the 3D-printed bone layer of the bilayer’s bioactive-biomaterials scaffold (B-BBBS). Meanwhile, the blended PLA/PCL filament was 3D printed and combined with a chitosan (CS)/silk firoin (SF) using a lyophilization technique to fabricate the AC layer of the bilayer’s bioactive-biomaterials scaffold (AC-BBBS). Material characterization and mechanical and biological tests were performed. The fabrication process consists of combining the 3D-printed structure (AC-BBBS and B-BBBS) and a lyophilized porous AC-BBBS. The morphology and printing abilities were investigated, and biological tests were performed. Finite element analysis (FEA) was performed to predict the maximum load that the bilayer’s bioactive-biomaterials scaffold (BBBS) could carry. The presence of HA and CS/SF in the PLA/PCL structure increased cell proliferation. The FEA predicted the load carrying capacity to be up to 663.2 N. All tests indicated that it is possible for BBBS to be used in tissue engineering for AC and bone regeneration in FTAC treatment.

Tourism is a dynamic industry that significantly contributes to the global economy, driven by the increasingly diverse preferences of tourists. Addressing these preferences requires sophisticated decision-making models capable of handling the uncertainty and subjectivity of human judgments. This study proposes sustainable models for effectively capturing and evaluating individual tourist preferences using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). These models leverage the strengths of the AHP to construct a flexible decision-making framework that adapts to diverse tourist preferences, offering personalized recommendations. In this study, three main criteria are considered: types of tourism, tourism facilities, and tourism areas. Tourists are encouraged to provide their preferences for these criteria and sub-criteria, enabling the AHP and Fuzzy AHP to recommend suitable destinations. An analysis was conducted with 30 respondents providing pairwise comparisons of the tourism criteria, which were then used to generate tourist attraction recommendations using both the AHP and Fuzzy AHP. The study assessed respondents’ satisfaction with the recommendations, finding that both methods were effective, with a slight preference for the Fuzzy AHP due to its ability to better capture individual preferences. The results underscore the potential of these models in sustainably enhancing decision support systems in the tourism industry, offering tailored recommendations that align more closely with tourist expectations.

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability.

This research is based on the purposes to find suitable hot compression conditions of composites materials, also to optimize mechanical and biological properties of the plate specimen. The type of composites materials under this investigation are bio-polymers (polylactic acid: PLA) and bio-ceramic (Hydroxyapatite: HA). In this research, the term “The design of the experiment (DOE)” was implemented to find the relation between forming factors including the ratio of HA ratio, molding temperature, and molding pressure. The result showed that the optimum conditions of the bending test were at 5%wt. of HA, 160 °C, 7 MPa of molding temperature and pressure respectively. Biodegradation rate of a specimen was 0.0175 g/day and the specimens acquired a good biocompatibility and they were non-toxic to living cells.

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model.