This paper explores the multifaceted impact of biofilms, bacterial communities enveloped in extracellular polymeric substance (EPS). Biofilms play a dual role, acting as environmental champions and health threats. They interact with heavy metals, either causing pollution or aiding environmental remediation. Beneficial biofilms generate electricity and repair concrete cracks, offering sustainable solutions. Conversely, they contribute to infections, posing health risks. The study emphasizes the need for in-depth research, utilizing bibliographic methods to analyze global biofilm research. By identifying keywords, core countries, and pivotal organizations, the paper informs future investigations, offering insights into biofilm research evolution and practical applications.

This paper explores the intricate world of microbial life, focusing on biofilm as a dynamic consortium of bacteria forming complex networks within a self-produced matrix of extracellular polymeric substances (EPS). Analogous to a bacterial house, these structures provide both shelter and support, playing a pivotal role in the microbial realm with dual effects on human and environmental contexts. Biofilm's significance extends beyond its microcosmic architecture, positively contributing to heavy metal removal, electricity generation through microbial fuel cells, and structural remediation, while also posing threats with detrimental formations implicated in conditions from lung to urinary tract infections. Control of biofilm becomes imperative to unlock its positive attributes and mitigate potential negatives, serving as a cornerstone for environmental remediation, sustainable energy, and infrastructure maintenance. Employing a comprehensive bibliographic approach, this study identifies keywords, countries, and organizations influencing biofilm research, proposing innovative avenues like machine learning for future exploration. The aim is not only to contribute to existing knowledge but to pioneer cutting-edge strategies for biofilm optimization and challenge mitigation, shaping its trajectory towards sustainable applications across diverse fields.

This study explores the pivotal biofilm life cycle, essential for comprehending intricate processes in biofilm formation and dispersal. With profound implications for medicine, environmental science, and industrial applications, the life cycle involves initial attachment, irreversible attachment, maturation, and dispersion. Employing bibliographic analysis, the paper unravels critical aspects, unveiling key keywords, prominent research countries/regions, and significant research organizations. This comprehensive approach provides insights into the current biofilm research landscape, identifying gaps for future exploration. Biofilm's influence extends to medical conditions, environmental ecosystems, and industrial challenges, accentuating the urgency of understanding its life cycle. The study also delves into global research distribution and influential organizations, guiding collaborative efforts. As a forward-looking initiative, it proposes future research opportunities, aiming to steer advancements and address challenges in biofilm science. Ultimately, this analysis contributes to the ongoing progress of biofilm research, fostering interdisciplinary collaboration and inspiring future initiatives.

This paper delves into the critical issue of concrete cracks, a common consequence of environmental factors and external forces, and their potential impact on the structural integrity of buildings. Recognizing the urgency of addressing this issue, the concept of self-healing concrete has emerged, aiming to revolutionize the construction industry by autonomously repairing cracks and minimizing the need for labor-intensive and time-consuming manual interventions. The paper employs a bibliographic analysis methodology to comprehensively explore the most significant technologies associated with self-healing concrete, identifying key research countries/regions and prominent research institutions in this field. Through an examination of scholarly articles, the analysis seeks to uncover key themes, technologies, and contributors within the self-healing concrete domain. Insights into the geographical distribution of research efforts and the roles of influential research institutions are provided, shedding light on collaborative networks and knowledge-sharing dynamics that drive innovation in this evolving field.

Heavy metal pollution poses a significant threat to global populations, particularly impacting human health. Developing countries, characterized by chemical engineering and mining industrial activities, often contribute to diverse heavy metal contaminants. Consumption of water contaminated with heavy metals, prevalent in such regions, can lead to severe health risks, including gastric cancer. Addressing the urgent issue of heavy metal pollution, this paper employs a bibliographic approach to explore the latest literature on heavy metal pollution. The study discusses recent research findings and potential methods for the treatment and mitigation of heavy metal pollution, emphasizing the pressing need for effective solutions to safeguard public health.

Biofilm serves as the residence for bacteria, wherein bacteria attach to surfaces and produce extracellular polymeric substances, including proteins, polysaccharides, and extracellular DNA. In recent years, biofilm reactors have gained increasing significance, playing a crucial role, for instance, in immobilizing heavy metals from waterways. This paper employs a bibliographic study to analyze the most important keywords and organizations in the field of biofilm reactors, identifying recent significant papers. Additionally, the paper discusses the opportunities for the future development of biofilm reactors. Through this study, a better understanding of key factors in biofilm reactor research is achieved, providing valuable guidance for future studies and applications in this field.

Biofilm matrix is akin to bacterial abodes, primarily composed of proteins, polysaccharides, extracellular DNA, and lipids, providing essential structural support to bacteria. While some biofilm matrix offers significant benefits, such as engineered biofilm reactors that assist in water pollutant removal, others pose risks to human health, potentially leading to conditions like lung infections. Realizing the potential of advantageous biofilm matrix and mitigating the harmful effects of detrimental ones necessitates a comprehensive and systematic exploration of this intricate domain. To facilitate this examination, our study leveraged a dataset of the 1000 most pivotal papers in the field and conducted an exhaustive bibliographic review. This paper serves as a valuable resource for understanding the current progress in biofilm matrix research and delving into vital avenues for future exploration in this dynamic field.

Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

The current epoch, known as the Anthropocene, is characterized by significant human impact on the global ecological system, including altered species survival and distribution patterns due to global warming resulting from industrialization. Accurately quantifying and assessing the extent of human impact on the environment is of utmost importance. To this end, four primary methods have been developed to measure human impact: (1) ecological footprint, (2) Human appropriation of net primary production (HANPP), (3) Planetary boundary, and (4) Living Planet Index. In this paper, we aim to provide a comprehensive understanding of these methods, including their strengths and weaknesses, to evaluate their efficacy in accurately assessing the impact of human activities on the global ecosystem. Furthermore, we explore the potential of utilizing novel machine learning modelling for future predictions in this area. By analyzing and comparing these methods, we can gain insight into the ways in which human activities impact the environment and use this knowledge to inform future policies and practices to mitigate negative effects.

Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.