Institute for the Future

The blending process in raw material yards is essential for maintaining precise material proportions in downstream production, directly influencing product quality and energy efficiency in industries such as steel and coal processing. However, stringent operational constraints, including silo capacity limits, discharge rates, equipment movement delays, and a strict no-empty-silo requirement, result in a strongly coupled, high-dimensional combinatorial scheduling problem. In this paper, we develop a mixed-integer nonlinear programming (MINLP) model to capture the complex dynamics of silo weight and equipment operations. The primary scientific contribution of this work lies in the theoretical discovery of a structural decoupling property within the complex MINLP. We analytically prove that by fixing the replenishment sequence, the intractable global problem can be rigorously decomposed into two subproblems: a linear programming (LP) problem for silo-filling cart scheduling and a shortest-path problem solvable via dynamic programming (DP) for reclaimer scheduling. Leveraging this decomposition, a two-stage metaheuristic algorithm is proposed, combining greedy initialization with multi-round simulated annealing enhanced by local search. Experimental validation using real industrial data demonstrates that the proposed method consistently outperforms the greedy algorithm. Crucially, while the commercial solver Gurobi struggles to converge within a practical 1800 s time limit, our approach yields comparable solution quality in mere seconds. Furthermore, robustness analysis under a 20% demand surge confirms the algorithm’s adaptive capability, maintaining the silo weight stability through re-optimization. This research provides a robust, computationally efficient solution for the blending process in raw material yards.

Sustainable and scalable production of plant natural products remains a major challenge at the interface of synthetic biology, metabolic engineering, and drug discovery, often constrained by plant-membrane-bound cytochrome P450s. To overcome the bottleneck in P450-catalyzed methylenedioxy-bridge (MDB) formation, we designed a concise, modular Escherichia coli pathway to the alkaloid berberine that bypasses multiple native steps and enables high-level accumulation of the P450-dependent intermediate. We then implemented integrated engineering of CjCYP719A1, including expression optimization, structure-guided design, consensus mutagenesis, and machine learning, to obtain a variant with 8.9-fold higher activity and improved thermostability. This enabled a berberine titer of 353 mg/L in scaled-up preparations, the highest reported to date, demonstrating industrial potential. Furthermore, mechanistic studies reveal their improved catalytic activity and thermostability. Together, this work provides a practical framework for functional expression and systematic optimization of plant-derived P450s to enable efficient production of plant natural products in E. coli.

Circular agricultural systems (CAS) offer a promising pathway toward sustainable food production by closing nutrient loops, valorizing waste, and integrating multifunctional outputs. However, current techno-economic analysis (TEA) methodologies often fall short in capturing the complexity and benefit of these systems. This letter identifies key limitations in existing TEA approaches, including inconsistent system boundaries, inadequate valuation of coproducts and internal loops, lack of standardized indicators, and insufficient integration of environmental and social dimensions. To address these challenges, we propose an improved TEA framework tailored to CAS. The framework incorporates dynamic modeling, life cycle assessment (LCA), multicriteria decision analysis (MCDA), robust uncertainty analysis, and stakeholder engagement. By aligning economic feasibility with ecological sustainability and social value, this integrative approach enhances the relevance and applicability of TEA for circular agriculture, supporting informed decision-making and policy development.

We analyze the thermal and electrical characteristics of the metasurface composed of a coplanar interdigital array of the graphene microribbons (GMRs) connected by nanobridges (NBs). These nanobridges could be implemented using graphene nanoribbons (GNRs) or black-arsenic-phosphorus (b-AsP) nanostructures. When a bias voltage applied between neighboring GMRs, it induces electron and hole two-dimensional systems within the GMRs, leading to thermionic currents that flow through the connecting NB resulting in the self-heating effect. This self-heating effect increases the thermionic currents, creating an effective positive feedback loop between the carrier effective temperature and the injected currents, and the bias voltage. This mechanism may lead to thermal breakdown enabling threshold behavior of current–voltage characteristics and yielding an S-shaped response. The devices based on the GMR/GNR and GMR/AsP metasurface structures can serve as fast voltage-controlled current switches, sensors, thermal terahertz and infrared sources, and among other applications.

Ultrasmall-sized circularly polarized (CP) light-emitting devices are highly desired for a wide range of applications. However, most conventional approaches toward CP light-emitting diodes (CP-LEDs) have critical challenges in either device durability, polarization degree, or quantum efficiency to be addressed. Herein, we propose a (0001) InGaN-based CP-LED with a single-layer metasurface directly integrated on the light-emitting surface to convert unpolarized light into CP light. The metasurface is composed of GaN and achieves an efficient CP light exhibiting both a high circular polarization degree of 0.87 and a high transmission efficiency of around 35% for vertically extracted light. The CP-LED consists of inorganic materials that are highly stable under operating conditions, and these findings show that the novel, to the best of our knowledge, CP light source can overcome the conventional trade-off relationship between quantum efficiency and polarization degree.

This study examines undergraduate students’ expectations for learning scientific research, a topic often marked by challenges in engagement during academic training. Using a qualitative design grounded in a constructivist paradigm and a phenomenological–hermeneutic approach, we explored the expectations of students enrolled in an education program at a private university in Lima, Peru. Semi-structured interviews were conducted with ten participants using five open-ended prompts. Thematic analysis revealed six categories of expectations: (1) learning to search for scientific information; (2) conducting empirical research; (3) acquiring knowledge; (4) learning to write a thesis; (5) developing scientific writing skills; and (6) staying current with scientific advances. Students described these expectations as attainable and functional goals within their training context and consistently emphasized the teacher’s role as pivotal, serving as a model, guide, and provider of feedback. The findings suggest that aligning course design and instruction with these expectation categories may foster students’ interest in research and strengthen their engagement in scholarly practices.

This article presents a comprehensive analysis of contemporary socio-psychological approaches to the study of spiritual intelligence (SI), employing systematic-structural and axiological methodological frameworks. The paper examines the evolution of SI as a scientific construct from its emergence in the late 1990s to the present day, analyzing major theoretical models including those developed by Zohar and Marshall (2000), Emmons (2000), King and DeCicco (2009), Amram (2007), and Wigglesworth (2012). Through systematic analysis, the article identifies key structural components of SI across various conceptualizations, including critical existential thinking, personal meaning production, transcendental awareness, and conscious state expansion. The axiological dimension of SI is explored through its relationship to values, meaning-making, and ethical behavior. Empirical research findings demonstrating correlations between SI and mental health outcomes, resilience, life satisfaction, and professional effectiveness are synthesized. The article concludes with implications for psychological practice, education, and future research directions.