Alexander A. Hernandez

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts.

In the original publication of our manuscript,1 we have identified several errors that require correction. We apologize for any inconvenience caused and appreci

In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.

In view of the imprecise calculation of sentimental polarity in User-Generated Content, a QCNSP model considering the modifier's sentimental polarity calculation is proposed. Based on the two dimensions of sentimental polarity direction and sentimental polarity intensity, the QCNSP model first determines the sentimental polarity direction of the user expression in the sentences from sentiment words, then by calculating the degree of the influence of adverbs and negation words on sentimental polarity, and according to the dependency syntax theory, it is to get the degree of deviation of sentiment tendency, thus to describe users’ sentiments in the comment User-Generated Content in more details. In order to eliminate the problem of excessively large and incomparable sentiment value changes caused by different users’ habits of language usage, the range of sentimental polarity values of sentiment phrases was normalized to [-5, 5]. The experimental results show that the sentimental polarity value calculated by the QCNSP model can reflect users’ sentiments more precisely, the value range is more reasonable, and the time efficiency is increased by about 20% -30% compared with the simple calculation method on sentiment words frequency.

The recommendation system makes recommendations based on the preferences of the users. These user preferences usually come from the user’s basic information, item rating, historical data, and so on. The "user cold start problem" happens when a new user cannot be appropriately suggested due to a lack of more detailed preference information. In many instances, the user cold start problem hinders the use of the recommendation system. Many researchers are currently trying to discover a solution to the user cold start problem. Unfortunately, there are two drawbacks in the current systematic reviews of how to deal with the user cold start problem. First, systematic reviews on how to deal with the user cold start problem are scarce or outdated. Second, existing reviews lack the distinction between the user cold start problem and the item cold start problem. Nevertheless, the solutions to the two problems differ. To address these problems, our study thorough review of all literature published by researchers from January 2016 to April 2023 about 8 years. Firstly, this study analyzes the literatures on approaches that addressed the user cold start problem during the past eight years and divides them into two categories: data-driven technology and approach-driven technology, and then describes and classifies each type of technology in detail. Secondly, this study also analyzes the main evaluation criteria currently used in these methods to provide a reference for researchers in related fields. Finally, this paper also points out the future research direction of this field.

Stress is everyone's concern in the academic life of students. However, stress elevates due to several factors, including academic demands, family, and social realities. More recently, due to the COVID-19 pandemic, several universities shifted to online modality, which may contribute to students' academic stress considering its complex requirements. To date, no study has been conducted to examine the perceived academic stress of students in Philippine higher education institutions. Thus, this paper aims to develop stress prediction models using machine learning through a classification approach based on student's perceived academic stress (PAS) scale survey responses. Results show that most students have high to very high perceived academic stress in both male and female respondents. Also, results show that decision tree (91%), naïve bayes (94%), support vector machine (95%), logistics regression (90%), and k-nearest neighbors (91%) have comparable classification performance. Furthermore, this study found that the difficulty of examination, unrealistic expectations from the teacher, examination time, excessive workload, and weakness of character contribute to the perceived academic stress of students. This study confirms the potential of machine learning to classify students' academic stress. This work presents some contributions and practical recommendations on academic stress experienced by students.

In the context of 5G communication, technologies such as the Internet of Things, big data, and supercomputers have been further developed, and artificial intelligence technology has also become one of the hot topics at present. In recent years, the image recognition technology of artificial intelligence has been pursued by most academic enthusiasts and has been rapidly developed. With the development of computer vision, a series of target detection algorithms have emerged in recent years. This paper classifies traffic signs based on the target detection algorithm of YOLOv5 and make use of the advantages of the accuracy and real-time performance of the YOLO algorithm. To restore the real traffic scene, different shooting angles and different shooting locations are prepared as data sets. Finally, to realize the detection and recognition of traffic signs and optimize the algorithm to improve the recognition rate.

In order to give full play to the advantages of advanced manufacturing technology and grasp the complexity of machining manufacturing system correctly, the mathematical models of process complexity and production organization in machining manufacturing system are established. Using MALTAB software graphical interface system, a program for calculating the complexity parameters of machining manufacturing system is developed. Through the man-machine interface, the parameters of layout complexity, man-machine complexity, operation complexity and decision complexity of different equipment are inputted into the program, and the corresponding mathematical models are calculated. At the same time, the corresponding curves are output in the system interface, and then the optimal scheme is obtained according to the actual manufacturing system of the parts.