Generation Strategy Research Articles

Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing.

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.

Enhancing Energy Density in Aqueous Ammonium-Ion Supercapacitors via CuCo2S4@MoS2 Core@Shell Heterostructure Design.

Aqueous ammonium-ion supercapacitors (AASCs) are recognized for their rapid charge-discharge capability, long cycle life, and excellent power density. However, they still confront the challenges of low energy density. To address the above issue, this work proposes a novel strategy involving the establishment of CuCo2S4@MoS2 core@shell heterostructures to enhance the capacity of electrode material. The double electric layer energy storage mechanism of the MoS2 shell facilitates the storage and provision of a substantial ammonium source for NH4 + insertion into CuCo2S4, thereby enhancing the electrochemical performance of AASCs. The density functional theory (DFT) calculations demonstrate that the CuCo2S4@MoS2 core@shell heterostructures exhibit better affinity for NH4 + and improved conductivity. Furthermore, the internal electric field at the heterojunction accelerates NH4 + transfer, thereby enhancing the pseudocapacitive behavior of CuCo2S4. Owing to the abundant active sites and pronounced pseudo-capacitance, the CuCo2S4@MoS2 electrode achieves a specific capacity of 2045 C g-1 at 1 A g-1. With activated carbon (AC) as the negative electrode, the fabricated CuCo2S4@MoS2//AC AASC device attains a specific capacity of 591 C g-1 and an energy density of 83.23Wh kg-1. This work presents a promising new strategy for the next generation of AASCs.

Exploring How Female Scientists are Uplifted in Research Networks in STEM Fields in Public Universities in Uganda

Participation in research networks is inevitable for the female scientists in the Science Technology Engineering and Mathematics (STEM) fields because they provide a platform for collaborative activities. New research communities of women are created to support each other through mentoring, advancing shared research interests, co-organizing conferences with men, participating in the editorial and the publications. It was on this basis that this study explored how female scientists were uplifted by research networks in STEM fields in public universities in Uganda. This study was anchored on the existential phenomenology by Martin Heidegger in the interpretive world view. Eight female scientists in research networks in STEM fields in public universities in Uganda were interviewed and they were selected from the hard applied and hard pure disciplines as classified in Becher and Biglan’s typology of academic disciplines. Using thematic analysis, two themes such as research collaboration activities and capacity development emerged in this study. This qualitative study set out to explore how female scientists were uplifted in research networks in public universities in Uganda.The public universities included Makerere University, Mbarara University of Science and Technology, Gulu University and Busitema University. Both semi-structured interviews and document check data generation strategies were used in this study. In semi-structured interviews, a sample comprising eight female academics with not less than five years in research networks in public universities were selected, while documents such as the annual reports and strategic plans were used. This study concludes that research networks are uplifting the female scientists in STEM fields in public universities in Uganda because they were internationally exposed, networked, funded, research skills were strengthened, and progressed in career. This study concludes that it was important for female scientists to access physical and human resource in their research networks as this increased their research performance and outputs in STEM fields in public universities in Uganda. This study therefore recommends that, to uplift female scientists in research networks in STEM fields in public universities in Uganda, female scientists in the research networks and senior colleagues should act as ambassadors and mentors to young female scientists in STEM fields

Frontiers in CAR-T Cell Therapy: Current Applications and Future Directions

CAR-T cell therapy, an ascending star in the immunotherapy landscape, harnesses the patient's innate immune defenses to confront cancer and a spectrum of hematologic disorders, while also exhibiting promising avenues for application in other maladies. This comprehensive review delves into the fundamental mechanisms of CAR-T cell therapy, the evolution and design strategies of successive generations of CAR-T cells, as well as the advancements and clinical implementations achieved in treating a diverse array of diseases.

Collaboration in Contradiction: Self-Adaptive Synergistic ROS Generation and Scavenge Balancing Strategies Used for the Infected Wounds Treatment.

The rational utilization of ROS is key to treating infected wounds. Exogenous ROS can destroy bacterial structures, quickly kill bacteria, and inhibit secondary infections. However, excess ROS at the wound will cause a secondary inflammatory response. Acute infections exacerbate this damage by increasing endogenous ROS, complicating the maintenance of ROS homeostasis. Therefore, regulating the balance of ROS production and scavenging in wounds has emerged as a promising strategy for wound treatment. Conventional ROS balancing platforms are mostly based on the " all for one" strategy of functional superposition and lack self-adaptability and integration. To subvert this conventional strategy, this study proposes a "one for all" self-adaptive integrated photodynamic therapy (PDT)-antioxidant model to actively regulate the ROS balance. A gelatin-hyaluronic acid hydrogel embedded with Se-modified cerium dioxide nanoparticles (Gel-HA-Se@CeO2 NPs) is designed for treating infected wounds. The Se@CeO2 NPs serve both as nanoenzymes and photosensitizers(PS). As nanoenzymes, they exhibit catalase and superoxide dismutase activities, converting hydrogen peroxide and superoxide anions into oxygen. As a PS, it synergizes with oxygen under NIR irradiation to rapidly produce singlet oxygen. Additionally, Se modification enhances the PDT effects by disrupting bacterial antioxidant systems. In vitro and in vivo experiments revealed that the ROS balance platform polarizes M1-type macrophages to M2-type macrophages, altering the wound microenvironment from proinflammatory to prohealing. RNA sequencing revealed that this hydrogel accelerated the reconstruction of the vascular network of the wound by activating the PI3K/AKT pathway and increasing VEGF secretion.This strategy is believed to be beneficial not only for infected wounds but also for treating other conditions that involve the regulation of reactive oxygen species, such as tumors and bacterial infections.

Role of Morphology on Zinc Oxide Nanostructures for Efficient Photoelectrochemical Activity and Hydrogen Production.

Energy generation today heavily relies on the field of photocatalysis, with many conventional energy generation strategies now superseded by the conversion of solar energy into chemical or thermal energy for a variety of energy-related applications. Global warming has pointed to the urgent necessity of moving away from non-renewable energy sources, with a resulting emphasis on creating the best photocatalysts for effective solar conversion by investigating a variety of material systems and material combinations. The present study explores the influence of morphological changes on the photoelectrochemical activity of zinc oxide nanostructures by exploiting electrodeposition and capping agents to control the growth rates of different ZnO facets and obtain well-defined nanostructures and orientations. A zinc nitrate (Zn (NO3)2) bath was used to electrodeposit ZnO nanostructures on an indium tin oxide glass (ITO) substrate at 70 °C with an applied potential of -1.0 V. Ethylenediamine (EDA) or ammonium fluoride (NH4F) were added as capping agents to the zinc nitrate bath. Extensive evaluation and characterization of the photoelectrochemical (PEC) capabilities of the resulting morphology-controlled zinc oxide nanostructures confirmed that altering the ZnO morphology can have positive impacts on PEC properties.

High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area

ABSTRACT Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization

In large-scale many-objective optimization problems (LMaOPs), the performance of algorithms faces significant challenges as the number of objective functions and decision variables increases. The main challenges in addressing this type of problem are as follows: the large number of decision variables creates an enormous decision space that needs to be explored, leading to slow convergence; and the high-dimensional objective space presents difficulties in selecting dominant individuals within the population. To address this issue, this paper introduces an evolutionary algorithm based on population hierarchy to address LMaOPs. The algorithm employs different strategies for offspring generation at various population levels. Initially, the population is categorized into three levels by fitness value: poorly performing solutions with higher fitness (Ph), better solutions with lower fitness (Pl), and excellent individuals stored in the archive set (Pa). Subsequently, a hierarchical knowledge integration strategy (HKI) guides the evolution of individuals at different levels. Individuals in Pl generate offspring by integrating differential knowledge from Pa and Ph, while individuals in Ph generate offspring by learning prior knowledge from Pa. Finally, using a cluster-based environment selection strategy balances population diversity and convergence. Extensive experiments on LMaOPs with up to 10 objectives and 5000 decision variables validate the algorithm’s effectiveness, demonstrating superior performance.

Research on Management Strategies for Generation Z Employees from the Perspective of Two-Factor Theory

As Generation Z becomes a significant force in the workforce, corporate management faces new challenges. Generation Z employees exhibit strong individuality, autonomy, and a high reliance on digital technology, while also possessing a global perspective and a diverse cultural background. They place greater emphasis on autonomy, career development opportunities, psychological satisfaction, and social recognition in their work. Therefore, when motivating Generation Z employees, companies must not only meet their basic needs for compensation and working conditions (hygiene factors) but also stimulate their intrinsic motivation and work enthusiasm through providing career development opportunities, innovative tasks, and recognition mechanisms. This paper, based on Herzberg's Two-Factor Theory, explores strategies to effectively manage and motivate Generation Z employees by adjusting incentive mechanisms and optimizing team building to enhance corporate competitiveness and employee satisfaction.

Strategy and Advancement in Hybrid Hydrogel and Their Applications: Recent Progress and Trends

Advancements in polymers have made significant contribution in diverse application‐oriented fields. The multidisciplinary application of polymers generates a range of strategies, which is applicable in a wide range of biomedicines. Polymeric compounds such as hydrogels have been explored globally as a potent biomaterial that can furnish essential attributes due to their three‐dimensional (3D) soft and highly hydrophilic polymeric network. These hydrogels have been used extensively in various applications due to their biocompatible, biodegradable, and biosorption nature. However, they have some limitations, owing to their soft nature, low mechanical properties, and unsatisfactory degradation profile. Therefore, there is a need to develop novel, stronger, and more durable hybrid hydrogels with enhanced biocompatible and multifunctional properties. The current review gives a broad spectrum of hybrid hydrogels with its importance and significance. Further, the review highlights the strategies of generation of hybrid hydrogels reinforced with nanomaterials, biomaterials, and nanobiocomposites along with their advantages and disadvantages. The review also features various applications of these hybrid hydrogels in the field of biomedicines, photocatalysis, agriculture, and food industry over the last 5 years. This review will give a comprehensive overview to researchers working in the field of hybrid hydrogels development.

Rustlantis: Randomized Differential Testing of the Rust Compiler

Compilers are at the core of all computer architecture. Their middle-end and back-end are full of subtle code that is easy to get wrong. At the same time, the consequences of compiler bugs can be severe. Therefore, it is important that we develop techniques to increase our confidence in compiler correctness, and to help find the bugs that inevitably happen. One promising such technique that has successfully found many compiler bugs in the past is randomized differential testing , a fuzzing approach whereby the same program is executed with different compilers or different compiler settings to detect any unexpected differences in behavior. We present Rustlantis , the first fuzzer for the Rust programming language that is able to find new correctness bugs in the official Rust compiler. To avoid having to deal with Rust’s strict type and borrow checker, Rustlantis directly generates MIR, the central IR of the Rust compiler for optimizations. The program generation strategy of Rustlantis is a combination of statically tracking the state of the program, obscuring the program state for the compiler, and decoy blocks to lead optimizations astray. This has allowed us to identify 22 previously unknown bugs in the Rust compiler, most of which have been fixed.

The Role of Generative Learning Strategy in Bridging the Gap between High and Low Achievers in Genetics Studies in Sefwi-Wiawso Municipality, Ghana

The study sought to investigate the use of the Generative Learning Strategy (GLS) in bridging the performance gap between high- and low-achieving students in genetics in Ghana's Western North region's capital, Sefwi-Wiawso Municipality. The study adopted an embedded research design involving the quasi-experimental pretest/posttest groups with a sample size of 106 SHS 3 Biology students. Two intact classes were randomly selected, treated as one group and exposed to the same treatment conditions. The Genetic Concepts Test and a semi-structured interview guide collected the quantitative and qualitative data, respectively. The GCT's internal consistency was 0.784, indicating a preferable internal consistency. The quantitative data was analyzed using the SPSS version 26, by employing descriptive and inferential statistics. The study found no significant performance difference between high and low achievers after using the GLS. The approach improved the lower achievers’ performance. The interview results revealed that Senior High School Biology students perceived that the Generative Learning Strategy improved the understanding, motivation, retention and self-directed learning of genetic concepts, thereby enhancing their learning outcomes. The study recommended that SHS Biology teachers employ the Generative Learning Strategy in teaching genetic concepts to bridge the performance gap between high and low achievers.

Retraction Note: Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

Retraction Note: Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

Li-Ga Alloy-Contained Hybrid Solid Electrolyte Interphase Induced by In Situ Polymerization for High-Performance Lithium Metal Batteries.

Construction of quasi-solid-state lithium metal batteries (LMBs) by in situ polymerization is considered a key strategy for the next generation of energy storage systems with high specific energy and safety. Poly(1,3-dioxolane) (PDOL)-based electrolytes have attracted wide attention among researchers, benefiting from the low cost and high ionic conductivity. However, interfacial deterioration and uncontrollable growth of lithium dendrites easily appeared in LMBs due to the high reactivity of lithium metal, resulting in the failure of LMBs. In this work, a strategy is developed of using Ga(OTF)3 as the initiator to obtain a PDOL-based gel electrolyte (GaPD). In addition, a hybrid stable solid electrolyte interphase (SEI) of lithium fluoride/Li2O/Li-Ga alloys is observed on the surface of lithium metal. Combined with density functional theory calculations, the hybrid SEI shows high affinity toward Li+, indicating that a uniform deposition of Li+ could be achieved. Therefore, the Li/GaPD/Li cell operates stably for 1600 h at room temperature. In addition, the LiFePO4/GaPD/Li cell retains a capacity retention rate of 90.2% over 200 cycles at 1 C. This work provides a reference for the practical application of in situ polymerization technology in high-performance and safe LMBs.

Habitat requirements of Luronium natans (L.) Raf. in northern Belgium

We investigated the environmental range of Luronium natans populations in lower Belgium (Flanders) with particular focus on key factors influencing its population size and the interplay of nutrient availability and physical disturbances in determining its abundance. Soil calcium content and total phosphorus in the water layer were the only physical-chemical variables related to L. natans abundance. Physical water body and vegetation characteristics influenced population dynamics and incidence of reproductive strategies. The species has a ruderal character, and occurs early in vegetation succession or depends on regular physical disturbance events for prolonged presence. Presenting limited competitive abilities, especially in terms of light availability, the species is hindered by taller-growing vegetation. Large populations are typically associated with well-lit conditions. Its flexibility to switch between vegetative and generative reproduction strategies may be associated with nutrient and light conditions. The need for long-term monitoring to consider population dynamics and evaluate the effectiveness of conservation efforts is emphasized. Overall, our study provides further insight into the relation between abiotic conditions, population dynamics and conservation management and extends the basis for sustainable management of the remaining L. natans populations in Flanders and similar regions of Atlantic Europe.

Establishment and Characterization of a Stable Producer Cell Line Generation Platform for the Manufacturing of Clinical-Grade Lentiviral Vectors.

Background/Objectives: To date, nearly 300 lentiviral-based gene therapy clinical trials have been conducted, with eight therapies receiving regulatory approval for commercialization. These advances, along with the increased number of advanced-phase clinical trials, have prompted contract development and manufacturing organizations (CDMOs) to develop innovative strategies to address the growing demand for large-scale batches of lentiviral vectors (LVVs). Consequently, manufacturers have focused on optimizing processes under good manufacturing practices (GMPs) to improve cost-efficiency, increase process robustness, and ensure regulatory compliance. Nowadays, the LVV production process mainly relies on the transient transfection of four plasmids encoding for the lentiviral helper genes and the transgene. While this method is efficient at small scales and has also proven to be scalable, the industry is exploring alternative processes due to the high cost of GMP reagents, and the batch-to-batch variability predominantly attributed to the transfection step. Methods: Here, we report the development and implementation of a reliable and clinical-grade envisioned platform based on the generation of stable producer cell lines (SCLs) from an initial well-characterized lentiviral packaging cell line (PCL). Results: This platform enables the production of VSV-G-pseudotyped LVVs through a fully transfection-free manufacturing process. Our data demonstrate that the developed platform will facilitate successful technological transfer to large-scale LVV production for clinical application. Conclusions: With this simple and robust stable cell line generation strategy, we address key concerns associated with the costs and reproducibility of current manufacturing processes.

Mutation‐Guided Metamorphic Testing of Optimality in AI Planning

ABSTRACTAutonomous systems such as space‐ or underwater‐exploration robots or elderly people assistance robots often include an artificial intelligence (AI) planner as a component. Starting from the initial state of a system, an AI planner automatically generates sequential plans to reach final states that satisfy user‐specified goals. Generating plans having a minimum number of intermediate steps or taking the least time to execute is usually strongly desired, as these plans exhibit minimal costs. Unfortunately, testing if an AI planner generates optimal plans is almost impossible because the expected cost of these plans is usually unknown. Based on mutation adequacy test suite selection, this article proposes a novel metamorphic testing framework for detecting the lack of optimality in AI planners. The general idea is to perform a systematic but non‐exhaustive state space exploration from the initial state and to select mutant‐adequate states to instantiate new planning tasks as follow‐up test cases. We then check a metamorphic relation between the automatically generated solutions of the AI planner for these new test cases and the cost of the initial plan. We implemented this metamorphic testing framework in a tool called MorphinPlan. Our experimental evaluation shows that MorphinPlan can detect non‐optimal behaviour in both mutated AI planners and off‐the‐shelf, configurable planners. It also shows that our proposed mutation adequacy test selection strategy outperforms three alternative test generation and selection strategies, including both random state selection and random walks through the state space in terms of mutation scores.

CRISPR/Cas-mediated “one to more” lighting-up nucleic acid detection using aggregation-induced emission luminogens

CRISPR diagnostics are effective but suffer from low signal transduction efficiency, limited sensitivity, and poor stability due to their reliance on the trans-cleavage of single-stranded nucleic acid fluorescent reporters. Here, we present CrisprAIE, which integrates CRISPR/Cas reactions with “one to more” aggregation-induced emission luminogen (AIEgen) lighting-up fluorescence generated by the trans-cleavage of Cas proteins to AIEgen-incorporated double-stranded DNA labeled with single-stranded nucleic acid linkers and Black Hole Quencher groups at both ends (Q-dsDNA/AIEgens-Q). CrisprAIE demonstrates superior performance in the clinical nucleic acid detection of norovirus and SARS-CoV-2 regardless of amplification. Moreover, the diagnostic potential of CrisprAIE is further enhanced by integrating it with spherical nucleic acid-modified AIEgens (SNA/AIEgens) and a portable cellphone-based readout device. The improved CrisprAIE system, utilizing Q-dsDNA/AIEgen-Q and SNA/AIEgen reporters, exhibits approximately 80- and 270-fold improvements in sensitivity, respectively, compared to conventional CRISPR-based diagnostics. We believe CrisprAIE can be readily extended as a universal signal generation strategy to significantly enhance the detection efficiency of almost all existing CRISPR-based diagnostics.

Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies

Hydrothermal gasification is an import-ant way to utilize biomass resources, and accurate prediction of the biomass hydrothermal gasification process is of great significance for the formulation and optimization of process parameters and equipment. Data is a key factor in machine learning, but due to the high experimental costs associated with hydrothermal gasification processes, acquiring a large amount of experimental data for machine learning modeling is a major challenge. To address this issue, this study proposes a data generation and screening strategy based on generative adversarial networks (GAN). The data generation and screening strategy primarily rely on GAN networks trained with real data as data generators and random forest models trained with real data as data screeners. High-quality synthetic data is selected through screening criteria to augment the dataset. Four machine learning models are used to model the biomass hydrothermal gasification process based on synthetic data to validate this strategy. The results show that, compared to the original data, modeling with synthetic data leads to a significant increase in the evaluation metrics for predicting H2, CH4, and CO2, especially during the testing phase. This indicates the rationality of the proposed data generation and screening strategy.

Mechanistic insight into piezo-catalytic H2 evolution on BiOCl loaded with AuPt alloy cocatalysts

The use of piezo-catalysis for H2 production from water at ambient conditions has recently gained significant attention, yet the importance of cocatalyst engineering has been largely overlooked. In this study, AuPt alloy nanoparticles were used as cocatalysts on the piezoelectric material BiOCl for H2 evolution driven by sonication. The Au:Pt ratio of 3:1 significantly enhanced the reaction, resulting in a doubling of H2 production compared to Au alone and a tripling compared to Pt alone. Experiments and simulations showed that AuPt alloys promote H* adsorption and facilitate the separation of piezoelectric carriers. Additionally, the cocatalysts enhance the piezo-catalytic reaction by increasing the stress and deformation of the BiOCl microplates. Unlike the photocatalytic process, the piezo-catalytic H2 evolution on BiOCl succeeded due to the suppression of BiOCl reduction by the periodically accumulated piezo-electrons and holes. This research highlights the critical role of cocatalyst engineering in optimizing piezo-catalytic H2 production and presents a promising strategy for efficient H2 generation.