Navigable Genome Engineering: Stepwise Correlation for Precision-Guided Optimization of Microbial Cell Factory Phenotypes

Microbial cell factories, akin to “chips” in biomanufacturing, concentrate the most intricate scientific challenges, technical bottlenecks, and densest intellectual property. However, despite extensive efforts in rational engineering, the inherent complexity of biological systems and the limited knowledge of their underlying mechanisms still incur substantial trial-and-error costs. This Perspective seeks to explore the potential of a prior-knowledge-independent approach for optimizing microbial cell factory phenotypes. We discuss the feasibility of stepwise genotypic navigation in genome engineering and emphasize its ability to generate high-quality genotype–phenotype association data, thereby advancing AI-assisted genome modeling and further enabling precision-guided optimization.

The Roots of Rights—Special Issue: “Transformative Practices: Rights of Nature and the Good Life”

This special issue focuses on the social practices of Rights of Nature (RoN), specifically exploring the transformative competencies and skills involved. The research investigates both individual competencies, such as resilience, mindfulness, and creativity, and collective skills, like relationship building and sustainable forms of interaction with the social and the ecological environment. The central question is if RoN does include “best practice” examples of cultivating non-instrumental relationships with the self, the social other, and the natural other.

Mechanistic Insights into Photocatalytic WO₃ for Hydrogen Generation

Growing environmental concerns and the limitations of fossil fuel resources have recently led to increased focus on clean and renewable energy sources. Hydrogen (H₂) has gained importance as an alternative clean fuel with its potential to become the primary chemical energy carrier. Photocatalytic hydrogen generation offers a capable solution to the energy crisis and has gained significant attention as a renewable energy solution, offering independence from fossil fuels and zero carbon dioxide emissions. Tungsten oxide (WO₃) offers to be a promising photocatalyst for Hydrogen Evolution Reaction (HER) with its ability to tune the band gap, robust absorption in the visible spectrum range, steadiness in harsh reaction conditions, low cost, and reduced toxicity. Various synthetic methods can be employed to fabricate photocatalysts with diverse morphologies, sizes, and structures, all of which significantly influence their catalytic performance to varying extents. This review goals to explicitly highlight and discourse the main properties of WO₃ and its modifications for photocatalytic HER via different synthesis methods. Modification in WO₃ to its corresponding composites, heterojunctions are explicitly explained in this review.

A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.

Review on Vibration Control of Wafer Handling Robot

The wafer handling robot serves as the pivotal component of the wafer transfer system, wherein its operational speed and motion precision exert a direct influence on both the yield and productivity of wafer processing. With the semiconductor manufacturing process advancing towards nanoscale linewidths and heightened throughput, the time-varying stiffness characteristics of the flexible joints in wafer handling robots, along with the resultant end vibration issues, have emerged as critical challenges that constrain overall performance. A comprehensive understanding of the stiffness change mechanisms, coupled with enhancements in control methodologies, plays an indispensable role in the effective vibration control of wafer handling robots. To facilitate research in pertinent areas, this paper systematically reviews the cutting-edge methods for vibration suppression in variable stiffness flexible joint wafer handling robots, concentrating on the following core aspects: The impacts of diverse dynamic stiffness identification methodologies on the accuracy of stiffness identification are thoroughly examined; This paper also explores the potential of collaborative optimization strategies involving trajectory planning, control methodologies, and lightweight intelligent algorithms in enhancing real-time control. Furthermore, it evaluates the application scenarios and feasibility of passive vibration absorbers and semi-active adjustable dampers within the context of broadband vibration suppression technologies. In conclusion, this paper synthesizes and critically discusses the advantages and limitations inherent in various research findings, while also constructing a “model-control-vibration suppression” closed-loop optimization system aimed at facilitating ultra-precision vibration control of wafer handling robots under conditions of high dynamic operation. By elucidating the bottlenecks present in existing technologies alongside the trajectory for future interdisciplinary integration, this work provides theoretical support for the intelligent advancement of wafer handling robots and fosters the expedited and reliable development of wafer transfer systems.

How Do Gender-Based Employment, Agricultural Machinery, and Fertilizers Influence Regional Agricultural Productivity? Panel Analyses for South and Southeast Asian Countries

The analysis delves into key strategies for enhancing agricultural productivity in Southeast Asia and South Asia. It underscores the vital role of mechanization, sustainable input practices, and gender-inclusive policies. Customized interventions in these realms hold promise for significantly amplifying agricultural performance in the region. Fertilizer and machinery productivity are pivotal factors that correlate strongly with overall agricultural productivity, as revealed by regression analyses. Notably, male employment in agriculture and agricultural machinery exhibits positive and substantial impacts on agricultural productivity, while female employment and fertilizer consumption indicators show significant yet negative associations. The study highlights systemic issues such as unequal resource access and differing gender roles in agriculture that may impede the immediate productivity gains from increased female labor force participation. Mechanization and efficient fertilizer utilization emerge as critical drivers of enhanced agricultural output, with consistent coefficients across models. Male employment consistently demonstrates a positive influence on productivity, emphasizing the significance of labor force engagement in agriculture. Moreover, the study underscores the imperative of judicious fertilizer management to avert environmental degradation and diminishing returns. The findings affirm the efficacy of the random effects model, supported by the Hausman test, which indicates congruence in results between fixed and random effects models. This methodological choice ensures robust and reliable conclusions regarding the relationships between male and female employment, machinery, fertilizer consumption, and agricultural productivity in South and Southeast Asia.

Cultural and Sex Differences in Emerging Adults: Identity Uncertainty, Psychological Symptoms and Adjustment at University

From the perspective of emerging adulthood, we investigated the role of culture and sex in associations between uncertainty and distress in identity development, psychological symptoms, and functioning at university among students in Canada, Spain, and Ecuador. The countries were categorized as individualistic or collectivistic according to Hofstede’s cultural dimensions. Participants included 661 students (median = 18 years, 76.6% female) in Canada (51.6%), Spain (16.2%), and Ecuador (32.2%). They completed the Identity Distress Scale, College Assessment of Psychological Problems Scale, and Student Adjustment to College Questionnaire with online surveys. Spanish students reported the greatest identity distress. Elevated academic adjustment was found for Ecuadorian students, who along with Spanish students exceeded those in Canada on social adjustment. Psychological symptoms mediated linkages between identity distress and academic and social adjustment for Canadian and Spanish women. Conversely, mediation was supported for the personal-emotional functioning of all students. Unexpected differences were found between males and females for identity distress and psychological difficulties among students in the individualistic countries. The findings underscore the need for the attention of researchers and counselors to potential variations in culture, sex, and other relevant personal and contextual factors and how they influence the identity development and well-being of university students worldwide.

Influence of Soil Damping and Aerodynamic Damping on the Dynamic Response of Monopile Wind Turbines under Earthquake and Wind Loads

Vibration damping is essential for predicting the responses of wind turbines, and contributions mainly come from structural, soil, and aerodynamic damping. In engineering design, it is difficult to precisely account for the individual contributions of each damping source. As a result, a simplified approach is commonly used, where a total damping factor is applied that combines the effects of structural, soil, aerodynamic, and other damping sources. However, the accuracy of this simplified approach in predicting the dynamic response of turbines has not been thoroughly evaluated. This study primarily focuses on the applicability of vibration-damping simplification methods, particularly in analyzing the dynamic response of turbines under earthquake and wind loads.

The Role of AI-Driven Synergy in Achieving Net Zero Buildings

Leveraging Drone Technology for Precision Agriculture: A Comprehensive Case Study in Sidi Bouzid, Tunisia

The integration of drone technology in precision agriculture offers promising solutions for enhancing crop monitoring, optimizing resource management, and improving sustainability. This study investigates the application of UAV-based remote sensing in Sidi Bouzid, Tunisia, focusing on olive tree cultivation in a semi-arid environment. REMO-M professional drones equipped with RGB and multispectral sensors were deployed to collect high-resolution imagery, enabling advanced geospatial analysis. A comprehensive methodology was implemented, including precise flight planning, image processing, GIS-based mapping, and NDVI assessments to evaluate vegetation health. The results demonstrate the significant contribution of UAV imagery in generating accurate land use classifications, detecting plant health variations, and optimizing water resource distribution. NDVI analysis revealed clear distinctions in vegetation vigor, highlighting areas affected by water stress and nutrient deficiencies. Compared to traditional monitoring methods, drone-based assessments provided high spatial resolution and real-time data, facilitating early detection of agronomic issues. These findings underscore the pivotal role of UAV technology in advancing precision agriculture, particularly in semi-arid regions where climate variability poses challenges to sustainable farming. The study provides a replicable framework for integrating drone-based monitoring into agricultural decision-making, offering strategies to improve productivity, water efficiency, and environmental resilience. The research contributes to the growing body of knowledge on agricultural technology adoption in Tunisia and similar contexts, supporting data-driven approaches to climate-smart agriculture.