The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.

With the advancement of modernization, commoditization and grassroots governance have become important terms. Community governance not only promotes modern democracy but plays a key role in improving community governance capabilities and modernizing the governance system, which is receiving much attention. Despite the expanding number of articles on community governance, few evaluations investigate its evolution, tactics, and future goals. As a result, the particular goal of this study is to provide the findings of a thematic analysis of community governance research. Investigating the skills and procedures needed for practice-based community government. Data for this study were gathered through a thematic assessment of 66 papers published between 2018 and 2023. The pattern required by the researchers was provided by the ATLS.ti23 code used to record the review outcomes. This study proposes six central themes: 1) rural advancement, 2) community (social) capital, 3) public health and order governance, 4) governance technology, 5) sustainable development, and 6) governance model. The research results show that the research trend of community governance should focus on rural advancement, taking rural community governance as the starting point, the dilemma and adjustment of the governance model, community public health and order governance, and digital governance. It will yield new insights into new community governance standards and research trends.

In this review are developed insights from the current research work to develop the concept of functional materials. This is understood as real modified substrates for varied applications. So, functional and modified substrates focused on nanoarchitectures, microcapsules, and devices for new nanotechnologies highlighting life sciences applications were revised. In this context, different types of concepts to proofs of concepts of new materials are shown to develop desired functions. Thus, it was shown that varied chemicals, emitters, pharmacophores, and controlled nano-chemistry were used for the design of nanoplatforms to further increase the sizes of materials. In this regard, the prototyping of materials was discussed, affording how to afford the challenge in the design and fabrication of new materials. Thus, the concept of optical active materials and the generation of a targeted signal through the substrate were developed. Moreover, advanced concepts were introduced, such as the multimodal energy approach by tuning optical coupling from molecules to the nanoscale within complex matter composites. These approaches were based on the confinement of specific optical matter, considering molecular spectroscopics and nano-optics, from where the new concept nominated as metamaterials was generated. In this manner, fundamental and applied research by the design of hierarchical bottom-up materials, controlling molecules towards nanoplatforms and modified substrates, was proposed. Therefore, varied accurate length scales and dimensions were controlled. Finally, it showed proofs of concepts and applications of implantable, portable, and wearable devices from cutting-edge knowledge to the next generation of devices and miniaturized instrumentation.

Consumers’ interest in green consumption has increased rapidly in recent years with heightening concerns for environmental, social, and health risks. However, increased concerns and interest of consumers may not translate to their behavioral outcome which may be attributed to socio-economic and consumers’ internal stimuli. Furthermore, contextual differences in the marketplace may influence how consumers form their green attitudes and behavior. The purpose of this study is to assess the role of consumers’ intrinsic traits such as consumers’ personal values, their self-motivation for sustainable consumption (i.e., perceived consumer effectiveness), green skepticism, and environmental involvement in their green attitude and behavior, and to see if the country-specific contextual condition may influence consumers’ behavior. In addition, price sensitivity and environmental protection emotions are considered moderating constructs to explain the gap between green attitude and green behavior. Findings from this study provide insights into understanding Chinese and Singaporean consumers’ green behavior which is driven by their intrinsic traits and by extrinsic conditions. This understanding can help companies to develop effective green marketing communication strategies and to enhance consumer engagement in sustainable activities and consumption.

The digital era has brought immense attention to the tourism industry through the pervasive influence of social media. Social media content profoundly shapes travel aspirations among the Chinese Generation Z, mainly through short videos. This study aims to unravel the intricate dynamics between short videos and Gen Z’s travel preferences, shedding light on their motivations, environmental consciousness, and adoption of sustainable tourism practices. Three regression models were applied in this study to shed light on this correlation. The initial model examines factors influencing the general travel intentions of Chinese Gen Z. The subsequent model delves into determinants affecting the adoption of responsible tourism practices among Gen Z. Then, the last model identifies factors contributing to tourism-related environmental awareness among this population. Through empirical analysis conducted via a structured questionnaire administered to 506 Chinese Gen Z individuals, this study’s findings confirm that well-crafted short videos significantly impact the travel intentions of Chinese youth, thereby fostering responsible tourism practices and increasing environmental consciousness. This highlights the pivotal role of argumentation quality and source credibility in shaping Gen Z’s travel intentions, underscoring the importance of credibility in promoting responsible tourism practices and environmental awareness. Furthermore, this study analysis reveals that females exhibit greater susceptibility to the influence of short video content on travel decisions than males. In conclusion, this study emphasizes the critical role of integrating short video content into marketing strategies within the tourism sector, particularly in the Gen Z demographic.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.