Renewable energy optimization in isolated microgrids: A Python-based tool for cost-effective solutions using genetic algorithms

A decoction-inspired genetic algorithm and PPO reinforcement learning for intelligent molecular discovery: Anti-colorectal cancer candidates from the tiao-pi AnChang formula.

In radiotherapy, medical image segmentation is performed to achieve a more structured view of the patient’s anatomical region. Automatic segmentation methods aim to eliminate the significant time investment required by the manual and semi-automatic segmentation processes that are most commonly used today. The present work describes an innovative seed-based automatic segmentation method for computed tomography (CT) images, known as LUNg Automatic Seeding and Segmentation (LUNAS). The study compares LUNAS with other segmentation algorithms from the Lung CT Segmentation Challenge (LCTSC), which are based on neural networks and multi-atlas approaches. The findings indicate that LUNAS achieved a Dice accuracy metric of 0.96 and 0.97 for the left and right lungs, respectively, matched the top-performing DL methods in the competition. Additionally, other state-of-the-art methods were evaluated for comparison, including one seed-based method similar to LUNAS, as well as a deep learning method. Using three other public thoracic CT image datasets, a detailed and fair analysis was performed to compare the algorithms indicating the effectiveness of LUNAS. LUNAS also has the ability to segment other regions, such as the trachea, bones, and skin. • LUNAS achieves state-of-the-art accuracy in lung CT segmentation, outper-forming traditional methods and matching deep learning approaches. • The method employs a novel automatic seed generation strategy combined with the Relaxed Oriented Image Foresting Transform (ROIFT). • LUNAS provides robust segmentation without requiring GPU acceleration, making it computationally efficient. • The method demonstrates superior adaptability, performing well on multiple publicly available thoracic CT datasets. • The methodology is adaptable to other anatomical structures such as trachea, bones, and skin.

In medical imaging, diffusion models have shown great potential for synthetic image generation. However, these approaches often lack interpretable correspondence between generated and real images and can create anatomically implausible structures or illusions. To address these limitations, we propose the Deformation-Recovery Diffusion Model (DRDM), a novel diffusion-based generative model that emphasizes morphological transformation through deformation fields rather than direct image synthesis. DRDM introduces a topology-preserving deformation field generation strategy, which randomly samples and integrates multi-scale Deformation Velocity Fields (DVFs). DRDM is trained to learn to recover unrealistic deformation components, thus restoring randomly deformed images to a realistic distribution. This formulation enables the generation of diverse yet anatomically plausible deformations that preserve structural integrity, thereby improving data augmentation and synthesis for downstream tasks such as few-shot learning and image registration. Experiments on cardiac Magnetic Resonance Imaging and pulmonary Computed Tomography show that DRDM is capable of creating diverse, large-scale deformations, while maintaining anatomical plausibility of deformation fields. Additional evaluations on 2D image segmentation and 3D image registration tasks indicate notable performance gains, underscoring DRDM's potential to enhance both image manipulation and generative modeling in medical imaging applications. The project page: https://jianqingzheng.github.io/def_diff_rec/.

ABSTRACT Persistent and stable radicals remain challenging to generate via photoinduction, despite their vast potential in magnetics and energy storage. Trisubstituted amines offer a promising platform for photoinduced radicals, but rapid charge recombination prevents the formation of nitrogen radical cations. Herein, we report a series of BN‐azepines featuring a negatively curved heptagon that enforces a twisted, rigid donor–acceptor architecture, enabling solution‐stable radicals via photoinduced charge separation. In the twisted heptagon backbone, boron captures an electron from nitrogen upon photoexcitation, yielding a charge‐separated singlet state, and the small singlet–triplet energy gap further facilitates the formation of a longer‐lived triplet charge‐separated state. This provides sufficient time for the boron‐localized electron to be lost, ultimately generating the nitrogen radical cation. Importantly, these BN‐azepines also exhibit anti‐Kasha emission and singlet oxygen sensitization. This work establishes a novel molecular design strategy for photoinduced radical generation and positions BN‐azepines as promising candidates for future optoelectronic applications.

Abstract The metaverse refers to a digital environment that enables real-time user interaction through immersive technologies. Recent advancements in deep learning have particularly strengthened the capabilities of Natural Language Processing (NLP) and Large Language Models (LLMs). These developments have made human–computer interactions in the metaverse more natural and responsive, especially those involving users and non-Player Characters (NPCs). Many virtual platforms use LLMs-powered Application Programming Interfaces (APIs) to facilitate these interactions, but these often produce long, semantically irrelevant responses that weaken the user’s immersive experience. This study addresses this limitation by designing NLP systems capable of generating concise, context-aware, and task-oriented outputs for AI-powered NPCs. Unlike open-domain conversational agents, dialogue systems for metaverse-based NPCs operate under strict real-time and contextual constraints. NPC interactions require concise, task-oriented, and context-aware responses, as overly long or semantically irrelevant outputs can disrupt immersion and degrade user experience. Although recent advances in LLMs have improved dialogue generation, most existing studies focus on open-ended conversations or general-purpose question answering. This study addresses this gap by systematically investigating fine-tuning and Retrieval-Augmented Generation (RAG) strategies within a metaverse-focused dialogue domain. We propose a comparative evaluation of systems developed using fine-tuning and RAG techniques between decoder-only models (GPT-2, LLaMA, Qwen) and encoder-decoder models (mBART, mT5). The models trained on the dataset were evaluated using a combination of standard evaluation metrics and semantic-based criteria. All evaluation scores were normalized using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to ensure objective comparability between models. The findings indicate that RAG provides a more balanced performance, particularly when applied to encoder-decoder models such as mBART (~ 0.652) and mT5 (~ 0.555), even when trained on relatively small datasets. Additionally, this paper presents a speech-based interaction framework designed to enable personalized and real-time communication in metaverse environments. The proposed framework is structured as Speech-to-Text (STT) → LLMs → Text-to-Speech (TTS). This architecture improves interaction quality by enabling coherent and realistic speech-based communication.

Chirality plays a fundamental role in chemistry, biology, and materials science, where molecular activity and performance often depend on enantiomeric purity. However, most synthetic methods produce racemic mixtures, making efficient strategies for enantioseparation and chiral amplification essential. Among the available approaches, crystallization-based methods offer scalable and mechanistically versatile routes for controlling chirality in the solid state. This review summarizes recent advances in solid-state crystallization strategies for chiral resolution, chirality generation, and enantiomeric amplification. Emphasis is placed on the structural and thermodynamic factors governing the formation of racemic compounds, conglomerates, and solid solutions, and how these phase behaviors influence separation outcomes. Key methodologies including preferential crystallization, attrition-enhanced deracemization, and multicomponent crystallization via diastereomeric salts and chiral co-crystals are discussed to illustrate how rational control of intermolecular interactions and phase equilibria enables effective enantioseparation. Emerging insights into supramolecular symmetry breaking and confinement-driven chiral amplification are also highlighted, demonstrating that solid-state processes can generate or amplify chirality even from achiral precursors. By integrating representative examples with broader conceptual analysis, this review outlines key design principles governing chiral outcomes in crystalline systems and highlights expanding applications of chiral co-crystals in pharmaceuticals and as functional materials, providing a framework for future advances in crystallization-mediated chirality control.

Unlocking the potential of T cell engagers in solid tumors.

This phenomenological study investigated how Department of Education (DepEd) teachers in the Philippines experience and interpret their engagement in social media content creation alongside formal teaching responsibilities. In a context characterized by high digital participation and expanding creator economies, the study specifically analyzed motivations, identity construction, boundary-setting practices, monetization decisions, and instructional adaptations shaped by sustained online engagement. Five purposively selected public-school teachers from the elementary and secondary levels, each with a minimum of six months of active content creation experience, participated in in-depth, semi-structured interviews. Data were transcribed verbatim and analyzed using a systematic phenomenological procedure guided by Creswell’s six-step framework. Trustworthiness was established through credibility checks, audit trails, reflexive bracketing, and thematic validation. The study identifies eight main themes: (1) Passion Meets Platform: The Spark Behind the Screen; (2) Balancing Acts: Navigating Dual Roles with Grit; (3) Beyond the Classroom: Growth, Gains, and Gratitude; (4) Redefining the Teacher Identity: Empowerment Through Expression; (5) Ethics and Boundaries: Navigating Professionalism in the Digital Space; (6) From Stress to Self-Care: Content Creation as Emotional Outlet; (7) Monetization as Motivation: The Financial Frontier of Teaching; and (8) Inspiring by Example: Teachers as Digital Role Models. Results indicate that participants deliberately engineered their digital identities, applied platform analytics to refine communication strategies, and constructed public personas aligned with institutional expectations and audience demands. The most significant realization was that teachers can and should systematically study how content creators navigate digital infrastructures, shape public identities, and strategically deploy online tools for the benefit of their students. The study demonstrates that teacher influencers function as pedagogical practices beyond the classroom, a strategy for income generation, and an intentional redefinition of professional roles under conditions of constant online visibility and audience feedback.

We introduce MxDiffusion, a hybrid physics- and data-driven diffusion-based framework that enables the efficient and highly accurate generation of photonic structures from target optical properties. The improved accuracy is achieved through a two-stage generation strategy, in which the first diffusion model is explicitly trained with Maxwell's equation-based loss to embed physical insight directly into the inverse design process, while the second model maps the physically consistent intermediate representation to the final structural geometry with significantly higher fidelity than solely data-driven approaches. The performance of MxDiffusion is validated on two representative applications: gold pattern optimization for random spectral responses and a tunable bandpass filter design based on a phase change material. In both cases, the proposed framework consistently outperforms a conventional data-driven diffusion model benchmark, particularly for out-of-training distribution design targets and highly constrained resonance conditions. These results demonstrate the efficacy and superiority of MxDiffusion as a general physics-guided inverse design paradigm.

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations.

Ufmylation is a newly identified ubiquitin-like modification of histones and plays important roles in DNA-related processes. Dissecting histone ufmylation pathways necessitates the use of chemically defined proteins to assign their structural and functional consequences; however, the preparation of ufmylated histones has not yet been reported. Here, we report the chemical synthesis of ufmylated histones and their analogs through semisynthetic strategies integrating chemoenzymatic C-terminal hydrazinolysis of ubiquitin-fold modifier 1 (UFM1) and auxiliary-mediated formation of an isopeptide bond. The results indicated that the E1-mediated activation of UFM1 can be hijacked by nucleophilic reagents, forming the full-length UFM1 hydrazide that can be readily installed onto histones via auxiliary-mediated ligations. The synthetic histones enabled us to reveal that the two known UFM1-specific proteases 1 and 2 (UfSP1 and UfSP2) cannot efficiently cleave H4 ufmylation at Lys31 (H4K31UFM1) in the nucleosome context. Furthermore, cryo-electron microscopy (cryo-EM) analysis of the H4K31UFM1-nucleosome suggested that the steric hindrance of the nucleosome around the isopeptide bond might be one of the potential reasons for the weak activities of UfSPs. Collectively, we developed practical strategies for the efficient generation of ufmylated histones and exemplified their use in biochemical and structural studies related to histone ufmylation.

Urban agriculture has become increasingly significant in African cities as a strategy for food security, income generation, and sustainable urban development. This study employed geospatial techniques, anchored in the Sustainable Livelihoods Framework (SLF), to map and analyze market gardening activities and land use changes in Jos South Metropolis, Nigeria, over a ten-year period (2014–2024). The research addresses critical knowledge gaps regarding the spatio-temporal dynamics and sustainability challenges of urban agriculture in Jos Metropolis, where comprehensive data on arable farming activities had been lacking despite extensive crop cultivation. High-resolution satellite imagery with 15 cm spatial resolution was acquired from the National Space Research and Development Agency (NASRDA) for both time periods and processed using ArcGIS 10.8 software. Through systematic on-screen digitization, spatial analysis, and change-detection algorithms, the study quantified changes in agricultural land use, settlements, and water bodies. Extensive fieldwork involved systematic reconnaissance of Jos South Local Government Area (LGA) to identify active market gardening sites, establish ground control points, and conduct interviews with local farmers and agricultural extension agents. Results revealed that market gardens covered 9.59 km² in 2014, declining to 9.03 km² in 2024, representing a 5.84% reduction. Conversely, settlements expanded dramatically from 43.82 km² to 79.32 km², a 34.76% increase, while water bodies decreased marginally from 4.57 km² to 4.46 km², highlighting increasing pressure on water resources driven by urbanization. The study identified key constraints to sustainable urban agriculture including land tenure insecurity, with 63% of dry-season farmers being landless; limited access to irrigation water; pest and disease pressure; inadequate fertilizer supply; and increasing competition for land from urban development. Field surveys documented diverse cropping systems, with farmers cultivating temperate vegetables such as tomatoes, lettuce, cabbage, carrots, and Irish potatoes throughout the year using fadama lands along river channels and mine ponds. The intensive cropping systems documented included plots undergoing three cropping cycles per year, demonstrating both the productivity potential and sustainability challenges. The findings align with global evidence that urban agriculture persists in the face of urbanization, serving multiple livelihood, ecological, and social functions. The research demonstrates the capability of remote sensing and GIS in monitoring urban agricultural dynamics and provides baseline data for urban planning and agricultural policy formulation. The study recommends establishing agricultural zones in fadama areas, strengthening extension services, enacting secure land tenure policies, and fostering rural–urban production synergies to sustain this vital economic sector in the face of rapid urbanization.

BackgroundExtracorporeal circulation (ECC) is increasingly supported by biomaterials engineered to minimize chemical leaching, and many biomedical manufacturers have now removed or substantially reduced phthalate plasticizers particularly di(2-ethylhexyl) phthalate (DEHP) from tubing and circuit components. Nevertheless, emerging evidence highlights a parallel and underexplored concern: the generation of microplastic particles, especially during manual cutting and customization of circuit segments in the operating room. While screen and depth filters used in CPB and ECMO circuits efficiently remove particles above defined size thresholds, the fate of smaller microplastics potentially capable of transiting through current filtration systems remains largely unknown. This raises new biocompatibility questions in an area where evidence is sparse and risk assessment remains incomplete.Materials and methodsA structured narrative review was conducted using PubMed and Web of Science, supplemented by manual reference screening. Search terms included "phthalates," "DEHP-free circuits," "microplastics," "extracorporeal circulation," "cardiopulmonary bypass," "ECMO," "biomaterials," and "particle shedding." Studies published between 1989 and 2025 addressing chemical migration, particulate release, biomaterial-blood interactions, or clinical exposure were eligible. After screening titles, abstracts, and full texts, 15 relevant studies were identified and included for qualitative synthesis.ResultsAcross included studies, phthalate reduction strategies such as the adoption of DEHP-free materials (e.g., TOTM-based tubing) demonstrably decreased chemical migration during ECC. However, the shift toward alternative materials does not preclude the mechanical generation of microplastic particles. Evidence from biomaterial studies suggests that shear stress, handling, and intraoperative cutting may contribute to particulate release. Unlike phthalates, microplastic contamination has not been systematically quantified in ECC settings, and current filters are optimized for macro- and microdebris but not necessarily for sub-micron or small-microplastic fractions. This gap contrasts with the well-characterized exposure profiles of phthalates, leaving the particulate dimension of ECC biocompatibility largely uncharted. Preliminary mechanistic data indicate that microplastics may act as carriers for residual plasticizers or other adsorbed molecules, potentially amplifying biological interactions.ConclusionsAlthough phthalate exposure has decreased with industry-wide adoption of alternative plasticizers, microplastic release particularly from manual circuit preparation represents an emerging and insufficiently characterized risk in ECC. Existing filtration technologies may not capture the smallest particles, and no standardized monitoring or exposure-reduction protocols currently address this issue. These findings underscore critical knowledge gaps and highlight the need for targeted research on microplastic generation, trans-filter passage, biological effects, and mitigation strategies. Open questions regarding particulate contamination challenge the current definition of biocompatibility and call for a broadened translational framework to ensure safer extracorporeal technologies.

Macrocyclic natural products, including macrolactones, macrolactams, macrocyclic (depsi)peptides, and macrocyclic cyclophanes, occupy a chemical space that does not overlap significantly with that of traditional low molecular weight and sp2-carbon rich pharmaceuticals. Traditionally, total synthesis toward macrocyclic natural products has been typically based on installation of backbone stereogenic centers at early- to mid-stage and closure of the macrocyclic backbone at late stage. However, these synthesis strategies suffer from multiple concession steps, making them less attractive in terms of step-economy. In this review, we provide an overview of late-stage chirality generation strategies in macrocyclic natural product synthesis, embodying stereoselective functional group and/or skeletal transformations that take advantage of macrocyclic conformational constraints. Expanding our repertoire of transformations amenable to late-stage chirality generation as well as advancing controllability over the conformational property of macrocycles will facilitate future developments in the total synthesis of macrocyclic natural products.

Energy management strategy for multisource power generation during hypersonic vehicle mode transition based on improved deep deterministic policy gradient

Artificial neural network-assisted thermokinetic modeling of sugarcane bagasse fast pyrolysis for enhanced bio-oil production.

Development strategy for sustainable energy generation: synergistic effects and ash fusion control in rattan biomass – brown coal co-combustion

Tumor in-situ Self-Assembling Gold Nanorods for Photothermally Enhanced Radiotherapy Enabled by Multilevel Radiosensitization Mechanisms.

Optimized adsorption energy of intermediates on bimetallic NiCoSe nanoflowers for synergistic urea electrooxidation and hydrogen evolution.