The rapid advancement of environmental sequencing technologies, such as metagenomics, has significantly enhanced our ability to study microbial communities. The eubiotic composition of these communities is crucial for maintaining ecological functions and host health. Species diversity is only one facet of a healthy community's organization; together with abundance distributions and interaction structures, it shapes reproducible macroecological states, that is, joint statistical fingerprints that summarize whole-community behavior. Despite recent developments, a theoretical framework connecting empirical data with ecosystem modeling is still in its infancy, particularly in the context of disordered systems. Here, we present a novel framework that couples statistical physics tools for disordered systems with metagenomic data, explicitly linking diversity, interactions, and stability to define and compare these macroecological states. By employing the generalized Lotka-Volterra model with random interactions, we reveal two different emergent patterns of species interaction networks and species abundance distributions for healthy and diseased microbiomes. On the one hand, healthy microbiomes have similar community structures across individuals, characterized by strong species interactions and abundance diversity consistent with neutral stochastic fluctuations. On the other hand, diseased microbiomes show greater variability driven by deterministic factors, thus resulting in less ecologically stable and more divergent communities. Our findings suggest the potential of disordered system theory to characterize microbiomes and to capture the role of ecological interactions on stability and functioning.

Hydrogen peroxide (H2O2) is a sustainable oxidant with wide applications in energy and environmental technologies, yet its current anthraquinone-based synthesis is energy-intensive and environmentally hazardous. Electrochemical two-electron water oxidation (2e- WOR) offers a green alternative, but its efficiency is hampered by competition from the four-electron oxygen evolution reaction (OER). Here, we employ first-principles calculations and microkinetic simulations to unravel the catalytic behavior of carbon nitrides in the 2e- WOR. We show that pyridinic nitrogen has little impact on selectivity, whereas graphitic nitrogen substantially enhances 2e- WOR activity and selectivity in a concentration-dependent manner. Excessive nitrogen promotes the OER, while insufficient doping favors one-electron side reactions. Microkinetic analysis identifies an optimal graphitic nitrogen concentration of ∼5-10%, enabling near-unity faradaic efficiency and a low potential of 2.2 VRHE at 10 mA cm-2 for the 2e- WOR. These findings provide mechanistic insights and quantitative design principles for tailoring carbon-based catalysts, offering a pathway toward sustainable and scalable H2O2 electrosynthesis.

Perceptions of oyster farmers on adopting environmental monitoring technologies to mitigate ocean acidification: A case study in Bahía San Quintín, México

Leveraging environmental DNA monitoring technology to improve biodiversity detection and recognize a broader range of functional organisms in remote coral reef ecosystems: A case study from Mischief Reef

Fluorescent imine-linked covalent organic framework turn off sensor for sensitive Co2+ ions detection.

Best Practices for the Design and Evaluation of Bathing Spaces for Older Adults With Cognitive Impairment in Residential Care Settings: A Scoping Review.

What Constructs Affect Tourist Behaviour in the Purchasing Process: Analysis in Technological Innovation Environments

Field application of bioelectrochemical reduction technology for treating hexavalent chromium in groundwater.

The algorithm Can't see everything: Human capital as the hidden variable in environmental technology success.

Environmental sustainability and green technology development is an international need that has driven major developments in environmental technology especially on biodegradable items development and environmental surveillance. It is a critical review of how industrial innovation and policy frameworks can be amalgamated towards the promotion of sustainable environmental management practices. This paper examines and discusses the role of environmental awareness as a foundational element of encouraging the adoption of green technology through systematic study of the environmental education programs of secondary schools in the region of Owerri and Mbaise of Imo State Nigeria. The study is done using comparative case study methodology where data collected on 210 students in six schools are examined to determine the levels of environmental awareness, attitudes, and engagement in environmental activities. Results indicate that 83.7% of the respondents in Owerri and 71.2% in Mbaise exhibit the level of environmental concepts awareness, and the education exposure and pro-environmental behavior have significant correlations. According to the study, the gaps in the content delivery of environmental education are critical with half of students being satisfied with the existing curriculums. The assessment of the development trends in biodegradable materials suggests that there is significant advancement in polymer science, and the focus is on the principles of a circular economy and waste reduction plans. Monitoring systems regarding the environment have also changed considerably with Internet of Things integration and smart city projects, where real-time data can be collected and analyzed. It is observed that industrial innovation should also be seen as a key contributing factor to the development of green technologies, which is sustained by the policy frameworks that promote sustainable practices. The study shows that effective environmental education along with technological innovation and conducive policies have synergistic effects in promoting the environmental sustainability objectives.

Abstract PbS quantum dots (QDs) are promising materials for short‐wave infrared (SWIR) photodetectors due to their tunable bandgap and broad spectral absorption. This study explores the impact of trioctylphosphine (TOP)‐mediated surface reconstruction on PbS QDs, revealing that the TOP treatment enhances QD surface morphology, reduces trap states, and improves QD stacking behavior in solid films. Notably, a diode‐type photodetector based on TOP‐treated QDs exhibits a significantly enhanced specific detectivity ( D *) of 2.07 × 10 11 Jones at 1290 nm, which is 50 times higher than that of devices using conventional QDs. It also shows a marked reduction in dark current density to 237 nA cm −2 at −0.5 V. Furthermore, the TOP‐QD photodetector demonstrates improved storage stability under non‐encapsulated conditions, underscoring the effectiveness of TOP‐mediated surface reconstruction in enhancing both performance and reliability. This work offers valuable insights into the surface engineering of PbS QDs and presents a pathway for the development of high‐performance, solution‐processed SWIR imaging systems and environmental monitoring technologies.

Ubiquitous digitalization is one of the key global trends shaping the transformation of modern pedagogical reality. The digital educational environment is fundamentally changing the content, forms, and methods of teaching, leading to a rethinking of the professional role of teachers and increased demands on their metacompetence profile. The relevance of this study stems from the need to prepare teachers capable of functioning effectively in a highly technological environment, characterized by uncertainty and continuous change. The purpose of this article is to analyze the impact of the digital educational environment on the development of future teachers' metacompetence and to identify the pedagogical conditions that facilitate its development. The methodological framework is based on the principles of competency-based and activity-based approaches, as well as an analysis of modern digital educational practices. The study utilizes theoretical analysis, a comparative pedagogical review, questionnaires, and elements of pedagogical modeling. The findings demonstrate that the digital educational environment facilitates the development of key components of metacompetence: reflexivity, critical thinking, digital agility, self-organization, and metacognition. Pedagogical conditions that enhance this process were identified: the integration of digital tools into educational trajectories, the creation of self-education situations, project-based activities, digital collaboration, and continuous feedback. The practical significance of the study lies in the possibility of using the proposed model for developing metacompetence in the design of educational programs, modules, and digital educational environments at universities.

Aquatic plants are key contributors to oxygen production and ecosystem stability. This study quantifies oxygen generation capacity of Hydrilla, Vallisneria, and Potamogeton under varying concentrations of potassium bicarbonate (KHCO3) using a dual-limb apparatus to measure oxygen output via water displacement. The experiment was complemented by gas chromatography-thermal conductivity detector (GC-TCD) analysis and numerical simulations to validate the results. An in silico diffusion model was developed to simulate oxygen release dynamics assuming uniform oxygen generation across plant surfaces and steady-state mass transport through the surrounding medium. The findings indicate that KHCO3 significantly enhances photosynthetic activity and oxygen production, with Hydrilla exhibiting the highest oxygen generation rate, followed by Potamogeton and Vallisneria. The optimal concentration of KHCO3 was determined to be 5 mg/mL, beyond which oxygen production declined due to osmotic stress and ionic imbalances. GC-TCD analysis confirmed oxygen (∼90%) as the primary gas produced, while simulated results closely aligned with the experimental data, reinforcing the robustness of the in silico analysis. This study highlights the role of bicarbonate ions in enhancing carbon availability for aquatic photosynthesis, thereby optimizing oxygen generation rate. The experimental methodology coupled with a numerical framework based on spatial diffusion model, as discussed in this endeavor, is novel in estimating oxygen generation rate from whole-plant in a closed system, enabling reproducible scaling for state-of-the-art environmental technologies. The insights gained from this in silico endeavor are expected to have broad implications for wastewater treatment (enhancing aerobic biodegradation), aquaculture (maintaining high dissolved oxygen), and carbon capture (biomass-based CO2 sequestration). Future research could focus on the exploration of long-term physiological effects of KHCO3 supplementation on oxygen generation and improvisation of modeling framework to incorporate biological feedback mechanisms into the underlying analysis.

This study proposes a robust control strategy to improve the stability and reliability of grid-following inverters with LCL filters, particularly under varying disturbances and instability conditions. A detailed survey of existing control strategies is presented to identify their limitations and highlight the advantages of Internal Model-Based Control (IMC). The analytical representation of IMC has been examined by demonstrating its inherent robustness against system uncertainties and external disturbances. The research focuses on a control method for grid-following inverters operating under challenging conditions such as grid disturbances, nonlinearities, and parameter variations. The effect of these factors on inverter performance is analyzed, and corresponding mitigation strategies such as advanced filtering and adaptive control mechanisms are discussed. A simulation framework is developed to assess the effectiveness of the proposed IMC-based control approach under various grid conditions. The results confirm that IMC enhances system stability, reduces harmonic distortion, and improves dynamic response. Moreover, the outcomes highlight the potential of IMC as a robust and adaptive control solution by providing valuable evaluations for advancing inverter technologies in weak grid environments and optimizing filter designs to achieve improved power quality.

Digital literacy competence of a teacher is currently becoming a prerequisite in successful teaching and learning in the rapidly evolving digitalized world. The paper involves the discussion of how the digital literacy competence of teachers could be used to enhance learning outcomes in classrooms in the 21st century by improving pedagogical practices. The research used a mixed-method design and examined the relationship between teacher digital skills, attitudes towards technologies and technological pedagogical knowledge (TPACK), institutional support, and student engagement. Structured questionnaires and semi-structured interviews were used to sample 300 teachers in the secondary and university levels. Quantitative data were analyzed with descriptive statistics, t-tests, regression, and ANOVA, qualitative data was analyzed with thematic analysis. It was determined that institutional support and continuous professional development are close predictors of the technology integration of the teachers and digitally skilled teachers encourage more learners to be motivated and cooperative particularly in cases where technology is integrated with pedagogical and content knowledge. The paper points out the necessity of setting up sustainable online learning environments within learning institutions. The study concludes that along the technical competency, the development of critical thinking, creative ability, and flexibility to changing technological environments enhance digital literacy of teachers.

This interpretivist study examines the perspectives of digital native prospective teachers towards AI-assisted lesson planning and classroom management in teacher training colleges in Punjab. Basing on the grounded theory model by O'Donoghue, this paper investigates how the prospective teachers understand, experience as well as integrate the topic of artificial intelligence into their teacher training process. The study fills a gap arising from the lack of research on how Digital Native teacher candidates learn to use AI technologies to design structures by focusing on instruction and classroom management within the Pakistani educational system. Twenty-four prospective teachers in four colleges of teacher education in Punjab were interviewed through semi-structured interviews based on the theoretical sampling and constant comparative analysis following the interpretivist paradigm provided by O'Donoghue. The study included data collection on the experience of the participants referring to their work with AI tools, potential benefits and challenges, and the development of their perspectives on the technology-enhanced pedagogy. Early results indicate multidimensional views of AI integration with some fears of pedagogical authenticity as well as the benefits of AI in improving instructional effectiveness. They showed a range of AI literacy, and desire training about the ethics of AI use. The research could help reveal the conceptualization of the role of AI in the educational process as the digital native teachers and serve as a reference on the development of the teacher education curriculum in the changing technological environment in Pakistan.

Purpose Grounded in dynamic capabilities theory and institutional theory, this study investigates how AI capabilities enhance organisational resilience and sustainable competitiveness in the logistics sector. Moreover, it explores the role of government institutional support and government regulatory intervention in shaping these relationships. Design/methodology/approach Data collected from a sample of 296 logistics managers in China were analysed using the PLS-SEM technique to empirically test the proposed hypotheses. Findings AI capabilities play a pivotal role in fostering both resilience and competitiveness, with resilience serving as a crucial mechanism in this process. Additionally, supportive policies strengthen these effects, while excessive intervention can weaken the positive relationship between AI and resilience. Research limitations/implications This study highlights the importance of government support in fostering AI-driven competitiveness, suggesting that further research is needed on these influences within the AI landscape. Practical implications The findings guide logistics managers in leveraging AI capabilities to strengthen resilience and achieve competitiveness. They also show that government support amplifies these benefits, whereas overly restrictive regulations can weaken them. Originality/value The findings significantly enrich the academic discourse by underscoring the critical relevance of dynamic capabilities theory and institutional theory within the distinct institutional and technological environments prevalent in the logistics firms. This research highlights the crucial role that AI capabilities and proactive governmental frameworks play in promoting resilience and ensuring sustainable competitiveness. It offers valuable insights for managers and policymakers aiming to navigate the complexities of this evolving industry.

Phase composition of kaolin (kaolinite clays, Middle Timan, Russia) has been studied. A method for synthesizing zeolites from low-grade aluminosilicate raw material — kaolinite clay (Middle Timan, Russia) — is adapted. The resulting zeolite sorbents are analyzed by a range of modern analytical methods, and their porous structure parameters are determined. We have found that hydrothermal treatment in an alkaline solution resulted in the formation of two types of zeolites: LTA and FAU (faujasite), both with a high degree of crystallinity. The sorption capacity for Sr2+ ions is 180 mg/g, exceeding the values for known micro- and mesoporous sorbents. We have shown that the morphological and structural properties of the zeolite influence its sorption capacity. The resulting zeolites represent an effective and affordable material for use in environmental technologies.

Assembly lines are critical to modern manufacturing, facilitating efficient and consistent large-scale production. Nonetheless, traditional assembly lines struggle with challenges such as downtime, operational inefficiencies, and quality control. Integrating artificial intelligence (AI) and machine learning (ML) offers transformative solutions to these longstanding issues, enhancing not only productivity and quality but also sustainability across various sectors. This study provides a comprehensive review of recent advancements in the application of AI and ML to assembly line operations. It categorizes the existing literature and analyzes the various models and algorithms used to optimize operational efficiency. This review makes a distinctive contribution by integrating AI/ML applications with manufacturing principles driven by sustainability. It introduces longitudinal analysis concerning algorithmic evolution from 2015 to 2025 and provides a novel approaches–challenges matrix that maps real industrial problems to specific AI/ML techniques. The review further links available datasets to their corresponding industrial sectors, allowing researchers to choose the contextually appropriate data source for optimizing assembly lines. By offering both a theoretical foundation and practical insights, this study aims to support researchers and contribute to the broader adoption and continued development of ML technologies in smart assembly line environments.

In the current context of global digitalization of education, the formation of national and regional educational identities that integrate technological, cultural, and value-based dimensions has become a critical issue. The relevance of this study is determined by the growing need to align digital educational transformation with cultural heritage preservation and social cohesion within educational communities. This challenge is particularly significant for the European educational space as well as for Ukraine, which is pursuing a strategic course toward digital integration while maintaining its cultural identity. The purpose of the study is to identify key trends and mechanisms involved in the formation of educational identity under conditions of digital transformation, with the object of analysis being national and regional education systems that implement culturally oriented development strategies. The methodological framework is based on logical-analytical, comparative-institutional, and descriptive statistical approaches applied to international educational and statistical datasets. The results demonstrate a substantial expansion of digital integration in education alongside the strengthening of the cultural dimension of identity. The proportion of educational institutions implementing intercultural digital modules increased from 34 to 63 percent, while the educational autonomy index rose from 0.57 to 0.74. In countries with high levels of digital readiness, more than ninety percent of students participate in programs aimed at cultural self-realization, whereas in Ukraine digital initiatives primarily emphasize the development of national and cultural self-identification. Based on comparative trend analysis of aggregated international data, parallel developments were observed between the expansion of digital education initiatives and the reinforcement of cultural integration indicators, reflecting a descriptive alignment between technological development and socio-cultural cohesion without implying inferential statistical relationships. The practical significance of the study lies in the application of its findings to educational policy design, program modernization, and the integration of digital competencies with civic and cultural awareness.