Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.
In this study, the author collected tweets about ChatGPT, an innovative AI chatbot, in the first month after its launch. A total of 233,914 English tweets were analyzed using the latent Dirichlet allocation (LDA) topic modeling algorithm to answer the question “what can ChatGPT do?”. The results revealed three general topics: news, technology, and reactions. The author also identified five functional domains: creative writing, essay writing, prompt writing, code writing, and answering questions. The analysis also found that ChatGPT has the potential to impact technologies and humans in both positive and negative ways. In conclusion, the author outlines four key issues that need to be addressed as a result of this AI advancement: the evolution of jobs, a new technological landscape, the quest for artificial general intelligence, and the progress-ethics conundrum.
Blockchain technology is an inchoate technology whose current popularity is peaking. Some of the most pervasive blockchain technology use cases exist for supply chains. Sustainable, and especially green, supply chains can benefit from blockchain technology, but there are also caveats. The sustainability and environmental management research and academic literature is only starting to investigate this emergent field. This paper seeks to help advance the discussion and motivate additional practice and research related to green supply chains and blockchain technology. This viewpoint paper provides insight into some of the main dimensions of blockchain technology, an overview of the use cases and issues, and some general research areas for further investigation.
Background: The WATCH-DM integer-based risk score (WATCH-DM(i)) was originally developed and validated to predict heart failure (HF) hospitalization risk in patients with type 2 diabetes mellitus (T2DM). However, its potential association with all-cause mortality in patients without HF remains unclear. Objectives: This study aimed to evaluate the prognostic utility of the WATCH-DM(i) score for all-cause mortality in a real-world outpatient cohort of T2DM patients without known HF. Design: This was a retrospective observational cohort study with national mortality registry linkage performed at a single center. Methods: We analyzed data from 449 adults with T2DM enrolled in a hospital-based cohort between 2016 and 2022. The WATCH-DM(i) score was calculated according to the original published integer-based model reported by Segar et al., using clinical and laboratory parameters. Patients were stratified into four risk groups based on their score. All-cause mortality data were obtained via national linkage. Cox regression models, Kaplan–Meier survival analysis, landmark analysis, and time-dependent receiver operating characteristic curves were used to assess mortality risk. Results: Over a median follow-up of 61 months, 39 patients (8.7%) died. Each 1-point increase in the WATCH-DM(i) score was associated with a 29% higher risk of all-cause mortality (HR: 1.287, 95% CI: 1.151–1.439, p < 0.001). Higher risk groups showed progressively greater mortality, especially after 24 months. The score demonstrated good discrimination for 5-year mortality (C-index: 0.751). Metformin use was independently associated with lower mortality risk (HR: 0.410, p = 0.019). Conclusion: The WATCH-DM(i) score is a robust prognostic marker of 5-year all-cause mortality in T2DM patients without HF and may serve as a practical tool for risk stratification in outpatient settings.
This study isolated five acid-producing strains (XQ1 and YZ1–YZ4) from naturally fermented pomace of Rosa roxburghii Tratt (RRT) in Guizhou’s karst region. Genetic and phenotypic analyses identified XQ1, YZ2, and YZ4 as Lactobacillus plantarum (L. plantarum), YZ3 as Weissella cibaria, and YZ1 as Bacillus licheniformis. A comparative evaluation with commercial strain AC revealed that XQ1, YZ2, and YZ4 exhibited superior acidification (reaching the stationary phase at 40 h) and tolerance to acidic conditions (pH 3.0), ethanol (6% v/v), bile salts (0.3%), and osmotic stress (6.5% NaCl), along with broad-spectrum antimicrobial activity against Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Shigella dysenteriae, and Pseudomonas aeruginosa. Their cell-free supernatants (CFSs) showed comparable superoxide dismutase activity and total antioxidant capacity (2.54–2.66 FeSO4·7H2O eq mmol/L) to AC (2.68), with DPPH radical scavenging exceeding 50%. YZ3 displayed weaker acid production, tolerance, and limited antimicrobial effects. Safety assessments confirmed non-hemolytic activity and antibiotic susceptibility. In conclusion, the L. plantarum strains XQ1, YZ2, and YZ4 demonstrated strong ensiling potential and remarkable probiotic properties, establishing them as promising indigenous microbial resources for the preservation of RRT pomace and other food products.
Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, combined with BEAST and Random Forest methods, to quantify and analyze the spatiotemporal evolution of forest resilience and its driving factors in Southwest China from 2000 to 2022. The results show the following: (1) Forest resilience exhibits a “high in the northwest and low in the southeast” spatial distribution, with a temporal pattern of “increase-decrease-increase.” The years 2010 and 2015 are key turning points. Trend shift analysis divides resilience into six types. (2) Although forest vegetation shows a clear greening trend, resilience does not necessarily increase with greening, and in some areas, an “increase in greening—decline in resilience” asynchronous pattern appears. (3) The annual average temperature, precipitation, and solar radiation are the main climate factors and their influence on resilience follows a nonlinear relationship. Higher temperatures and increased radiation may suppress resilience, while increased precipitation can enhance it. This study suggests incorporating the TAC indicator into ecological monitoring and early warning systems, along with applying trend classification results for region-specific management to improve the scientific basis and adaptability of forest governance under climate change.