Generation Ability Research Articles

Afterglow Performance of Phenylenevinylene-Based Semiconducting Polymer Nanoparticles Doped with Photosensitizers Containing Electron-Withdrawing Groups.

It is usually believed that doping with photosensitizers capable of generating singlet oxygen (1O2) plays a pivotal role in enhancing the afterglow performance of semiconducting polymer nanoparticles (SPNs). However, the effect of doping photosensitizer bearing electron-withdrawing groups has not been reported. Here we report the effect of doping with six photosensitizers possessing different electron-withdrawing groups on the afterglow performance of SPNs using poly[(9,9-di(2-ethylhexyl)-9H-fluo-rene-2,7-vinylene)-co-(1-methoxy-4-(2-ethylhexyloxy)-2,5-phenylenevinylene)] (PF-MEHPPV) as substrate. It was found that the afterglow performance of SPNs was significantly influenced by doping with photosensitizers bearing electron-withdrawing groups. For the doped photosensitizers with strong electron-withdrawing groups, the stronger the electron-withdrawing ability of the group, the worse of the afterglow performance of the SPN regardless of the 1O2 generation ability of the photosensitizer. When the doped photosensitizer exhibited weak or none electron-withdrawing effect, the 1O2 generation ability of the photosensitizer played a dominant role on the afterglow performance of the SPNs. This work deepens the understanding of the design and synthesis of SPNs with different afterglow properties.

Computational creativity of neural network Midjourney in a polymodal space

This article deals with the polymodal space in the field of computational creativity in neural networks. The object of research is a polymodal environment that integrates a series of heterogeneous codes to express a common idea, and the subject is the possibility of creating polymodal digital art using text and voice prompts in the generative network Midjourney. The aim of the study is to prove that computational creativity can be detected and described based on the results of iterations in the process of creating images, which in turn will allow us to talk about a complex polymodal system as a separate digital category of polymodality. We used the continuous sampling method when collecting linguistic units as they occur in the analysis process; contextual analysis for the systematic identification and description of the verbal and non-verbal contexts. It was necessary to conduct an experiment with the generative network Midjourney to identify patterns in the creation of a graphic space through text and voice data input, and then compare and contrast the results of iterations with the original image. The scientific novelty consists in the lack of research on the polymodal space in the context of neural networks and their generative ability. During the experiment, we obtained the following results: the term ‘polymodality’ in the context of the generative network Midjourney and its ‘digital art’ is due to the presence of three channels: verbal, visual and voice; tests have shown that the ability of the neural network to create images through prompt is at a high level, however, there are rough technical errors that do not allow users to fully approach the desired result when they generate an image; the summarization of the data allows us to talk about the presence of features of computational creativity in generative networks.

Research on Machine Learning Program Generation Algorithm Based on AORBCO

Abstract The design and development of machine learning programs require selecting appropriate data and algorithms, and coding and debugging based on specific task requirements and the programming experience of developers. However, the current knowledge structure in the field of machine learning is relatively complex, lacking systematic organization, and developers often face the problem of lack of experience when choosing algorithms and designing programs, resulting in a long development cycle and easy errors in machine learning programs. In response to the above issues, this article proposes and designs a machine learning program generation algorithm based on the AORBCO model. The program generation ability includes two sub abilities: algorithm decision-making ability and code generation ability. AD-EKG has been designed for algorithmic decision-making ability, allowing Ego to select appropriate machine learning algorithms based on datasets in massive data. This algorithm combines the characteristics of the AORBCO model's domain knowledge base, knowledge graph based recommendation algorithm, and collaborative filtering algorithm. By calculating the descriptive and structural information between the dataset and algorithm, the interaction probability between the dataset and algorithm is obtained, allowing Ego to make algorithmic decisions interaction probability based. Results of the experiment have shown that the AD-EKG algorithm can fully utilize structural and descriptive information to improve the accuracy of Ego algorithm decision-making. CodeT5-EKG has been designed for code generation capability, allowing Ego to automatically generate machine learning program code. This algorithm combines the CodeT5 generative model with the domain knowledge base of the AORBCO model, by adding auxiliary information extracted using DPR technology to the code generation task, and performing diversified fusion operations to improve code generation quality. The CodeT5-EKG algorithm combines the creativity and efficiency of generative models and DPR technology, and is an algorithm that can improve the quality of generated code while also having the advantages of generative models. The experiments have proved that the code generated by this algorithm has better quality compared to other generative models with the same number of parameters.

The promotion mechanism of different nitrogen doping types on the catalytic activity of activated carbon electro-Fenton cathode: Simultaneous promotion of H2O2 generation and phenol degradation ability

Doping with nitrogen atoms can improve the catalytic activity of activated carbon cathodes in electro-Fenton systems, but currently there is a lack of understanding of the catalytic mechanism, which limits the further development of high-performance activated carbon cathodes. Here, a multi-scale exploration was conducted using density functional theory and experimental methods to investigate the mechanism of different nitrogen doping types promoting the redox performance of activated carbon cathodes and the degradation of phenol. The density functional theory results indicate that the introduction of nitrogen atoms enhances the binding ability between carbon substrates and oxygen-containing substances, promotes the localization of surrounding electrons, and makes it easier for O2 to bind with protons and catalyze the hydrogenation reaction of *OOH. Due to its weak binding ability with oxygen-containing substances, AC is difficult to form H2O2, resulting in a tendency towards the 4e−ORR pathway. The binding energy between graphite-N carbon substrate and pyridine-N carbon substrate with *OOH is closer to the volcano top, so graphite n and pyridine n can better promote the selectivity of activated carbon for 2e-ORR. In addition, the calculation results also indicate that pyrrole-N and graphite-N are more capable of catalyzing the reaction energy barrier between ·OH and phenol. Finally, the simulation results were used to guide the modification of nitrogen doped activated carbon and experimental verification was carried out. The degradation results of phenol confirmed the efficient synergistic effect between different types of nitrogen doping, and the NAC-800 electrode exhibited efficient and stable characteristics. This work provides a guiding strategy for further developing stable and highly selective activated carbon cathode materials.

CodeS: Towards Building Open-source Language Models for Text-to-SQL

Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (LLMs), such as ChatGPT and GPT-4, which may have the limitations of unclear model architectures, data privacy risks, and expensive inference overheads. To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task. CodeS is a fully open-source language model, which achieves superior accuracy with much smaller parameter sizes. This paper studies the research challenges in building CodeS. To enhance the SQL generation abilities of CodeS, we adopt an incremental pre-training approach using a specifically curated SQL-centric corpus. Based on this, we address the challenges of schema linking and rapid domain adaptation through strategic prompt construction and a bi-directional data augmentation technique. We conduct comprehensive evaluations on multiple datasets, including the widely used Spider benchmark, the newly released BIRD benchmark, robustness-diagnostic benchmarks such as Spider-DK, Spider-Syn, Spider-Realistic, and Dr.Spider, as well as two real-world datasets created for financial and academic applications. The experimental results show that our CodeS achieves new SOTA accuracy and robustness on nearly all challenging text-to-SQL benchmarks.

On the Educational Significance of Historical Textbooks for Adolescents' Ideological Consciousness, and Their Impact on Sino-Japanese Relations — Taking the Changes in Narrations on the War of Resistance against Japan in Chinese and Japanese History Textbooks as an Example

Significant disparities exist in the narratives of the Sino-Japanese War (1931-1945) as presented in Chinese and Japanese history textbooks. Both countries, influenced by their unique political, economic, and cultural stances, have made diverse revisions to their history textbooks at different times. This paper compares narrative changes in the history textbooks of both countries, contending that textbook revisions aim to shape adolescents' ideological consciousness and historical perspectives, influencing their political socialization tendencies and behaviors. As Japan refashions its historical textbooks, it impedes the young generation's ability to glean insights from history, potentially cultivating a cohort characterized by unwarranted arrogance yet lacking a sense of responsibility. The ongoing discourse surrounding historical textbooks consistently rekindles poignant memories among the Chinese populace, instilling fears of Japan repeating the errors of militarism and consequently, fortifying the younger generation's recollection of a humiliating past. China and Japan, not only wielding significant influence on regional security in East Asia but also playing pivotal roles in global peace and stability, should embrace equitable and amicable exchanges. Such exchanges can foster increased mutual trust and reciprocal benefits, representing the appropriate path for China and Japan, nations separated by a narrow body of water with a longstanding history of friendship.

A structure-guided strategy to design Golgi apparatus-targeted type-I/II aggregation-induced emission photosensitizers for efficient photodynamic therapy

The Golgi apparatus (GA) is a vital target for anticancer therapy due to its sensitivity against reactive oxygen species (ROS)-induced oxidative stress that could lead to cell death. In this study, we designed a series of aggregation-induced emission (AIE)-based photosensitizers (TPAPyTZ, TPAPyTC, TPAPyTM, and TPAPyTI) carrying different ROS with selective GA-targeted ability. The in vitro study showed that TPAPyTZ and TPAPyTC displayed strong AIE characteristics, robust type-I/II ROS production capabilities, specific GA-targeted, high photostability, and high imaging quality. The cell-uptake of TPAPyTZ was found primarily through an energy-dependent caveolae/raft-mediated endocytosis pathway. Remarkably, TPAPyTZ induced GA-oxidative stress, leading to GA fragmentation, downregulation of GM130 expression, and activation of mitochondria caspase-related apoptosis during photodynamic therapy (PDT). In vivo experiments revealed that TPAPyTZ significantly inhibited tumor proliferation under lower-intensity white light irradiation with minimal side effects. Overall, our work presents a promising strategy for designing AIEgens for fluorescence imaging-guided PDT. Additionally, it enriched the collection of GA-targeted leads for the development of cancer theranostics capable of visualizing dynamic changes in the GA during cancer cell apoptosis, which could potentially enable early diagnosis applications in the future. Statement of significanceAIE luminogens (AIEgens) are potent phototheranostic agents that can exhibit strong fluorescence emission and enhance ROS production in the aggregate states. In this study, through the precise design of photosensitizers with four different electron-acceptors, we constructed a series of potent AIEgens (TPAPyTZ, TPAPyTC, TPAPyTM, and TPAPyTI) with strong fluorescence intensity and ROS generation capacity. Among them, TPAPyTZ with an extended π-conjugation displayed the strongest ROS generation ability and anti-tumor activity, resulting in an 88 % reduction in tumor weight. Our studies revealed that the enhanced activity of TPAPyTZ may be due to its unique Golgi apparatus (GA)-targeted ability, which causes GA oxidative stress followed by effective cancer cell apoptosis. This unique GA-targeted feature of TPAPyTZ remains rare in the reported AIEgens, which mainly target organelles such as lysosome, mitochondria, and cell membrane. The successful design of a GA-targeted and potent AIEgen could enrich the collection of GA-targeted luminogens, providing a lead theranostic for the further development of fluorescence imaging-guided PDT, and serving as a tool to explore the potential mechanism and discover new GA-specific drug targets.

LLM4VV: Developing LLM-driven testsuite for compiler validation

Large language models (LLMs) are a new and powerful tool for a wide span of applications involving natural language and demonstrate impressive code generation abilities. The goal of this work is to automatically generate tests and use these tests to validate and verify compiler implementations of a directive-based parallel programming paradigm, OpenACC. To do so, in this paper, we explore the capabilities of state-of-the-art LLMs, including open-source LLMs - Meta’s Codellama, Phind’s fine-tuned version of Codellama, Deepseek’s Deepseek Coder and closed-source LLMs - OpenAI’s GPT-3.5-Turbo and GPT-4-Turbo. We further fine-tune the open-source LLMs and GPT-3.5-Turbo using our own testsuite dataset along with using the OpenACC specification. We also explored these LLMs using various prompt engineering techniques that include code template, template with retrieval-augmented generation (RAG), one-shot example, one-shot with RAG, expressive prompt with code template and RAG. This paper highlights our findings from over 5000 tests generated via all the above mentioned methods. Our contributions include: (a) exploring the capabilities of the latest and relevant LLMs for code generation, (b) investigating fine-tuning and prompt methods, and (c) analyzing the outcome of LLMs generated tests including manually analysis of representative set of tests. We found the LLM Deepseek-Coder-33b-Instruct produced the most passing tests followed by GPT-4-Turbo.

Characterization of multifunctional β-cyclodextrin-coated Bi2O3 nanoparticles conjugated with curcumin for CT imaging-guided synergetic chemo-radiotherapy in breast cancer

Nanotechnology-based diagnostic, and therapeutic approaches revolutionized the field of cancer detection, and treatment, offering tremendous potential for cost-effective interventions in the early stages of disease. This research synthesized bismuth oxide (Bi2O3) nanoparticles (NPs) that were modified with polycyclodextrin (PCD), and functionalized with glucose (Glu) to load curcumin (CUR) for CT imaging and chemo-radiotherapy applications in Breast Cancer.The prepared Bi2O3@PCD-CUR-Glu NPs underwent comprehensive characterization, encompassing various aspects, including cell migration, cytotoxicity, cellular uptake, blood compatibility, reactive oxygen species (ROS) generation ability, real-time PCR analysis, in-vivo safety assessment, in-vivo anti-tumor efficacy, as well as in-vitro CT contrast and X-ray RT enhancement evaluation. CT scan was conducted before and after (1 and 3 h) intravenous injection of Bi2O3@PCD-CUR-Glu NPs. Through the use of coupled plasma optical emission spectrometry (ICP-OES) analysis, the final prepared nanoparticle distribution in the Bab/c mice was assessed.The spherical NPs that were ultimately synthesized and had a diameter of around 80 nm demonstrated exceptional toxicity towards the SKBr-3 breast cancer cell line. The cell viability was at its lowest level after 48 h of exposure to a radiation dose of 2 Gy at a concentration of 100 µg/mL. The combined treatment involving using Bi2O3@PCD-CUR-Glu NPs along with X-ray radiation showed a substantial increase in the generation of ROS, specifically a remarkable 420 % growth. Gene expression analysis indicated that the expression levels of P53, and BAX pro-apoptotic genes were significantly increased. The in-vitro CT imaging analysis conducted unequivocally demonstrated the notable superiority of NPs over Omnipaque in terms of X-ray absorption capacity, a staggering 1.52-fold increase at 80 kVp.The resultsdemonstrated that the targeted Bi2O3@PCD-CUR-Glu NPs could enhance the visibility of a small mice tumor that is detectable by computed tomography and made visible through X-ray attenuation. Results suggested that Bi2O3@PCD-CUR-Glu NPs, integrated with CT imaging and chemo-radiotherapy, have great potential as a versatile theranostic system for clinical application.

The Surface Confinement of FeO Assists in the Generation of Singlet Oxygen and High-Valent Metal-Oxo Species for Enhanced Fenton-Like Catalysis.

Transition metal compounds (TMCs) have long been potential candidate catalysts in persulfate-based advanced oxidation process (PS-AOPs) due to their Fenton-like catalyze ability for radical generation. However, the mechanism involved in TMCs-catalyzed nonradical PS-AOPs remains obscure. Herein, the growth of FeO on the Fe3O4/carbon precursor is regulated by restricted pyrolysis of MIL-88A template to activate peroxymonosulfate (PMS) for tetracycline (TC) removal. The higher FeO incorporation conferred a 2.6 times higher degradation performance than that catalyzed by Fe3O4 and also a higher interference resistance to anions or natural organic matter. Unexpectedly, the quenching experiment, probe method, and electron paramagnetic resonance quantitatively revealed that the FeO reassigned high nonradical species (1O2 and FeIV═O) generation to replace original radical system created by Fe3O4. Density functional theory calculation interpreted that PMS molecular on strongly-adsorbed (200) and (220) facets of FeO enjoyed unique polarized electronic reception for surface confinement effect, thus the retained peroxide bond energetically supported the production of 1O2 and FeIV═O. This work promotes the mechanism understanding of TMCs-induced surface-catalyzed persulfate activation and enables them better perform catalytic properties in wastewater treatment.

A nitroreductase-responsive fluorescence turn-on photosensitizer for lysosomes imaging and photodynamic therapy

Nitroreductase (NTR) is a frequently used biomarker for the assessment of hypoxia level in tumors. As one of the main sources of enzymes, the dysfunction of lysosomes typically leads to various diseases. In this study, an NTR-triggered lysosome-targeting probe, M-TPE-P, was designed based on a tetraphenylethylene core. DFT calculation indicated that the probe possessed a narrow singlet–triplet energy gap (ΔEST), rendering it an efficient photosensitizer. The docking affinity of M-TPE-P to NTR revealed a strong structural match between them. Photophysical properties demonstrated that the probe exhibited high selectivity and sensitivity in a broad pH rang for detecting NTR with kcat/Km as 2.18 × 104 M−1 s−1. The detection limit was determined to be 53.6 ng/mL in 80 % PBS/DMSO solution. Cell imaging studies showed the probe could trace intracellular NTR behavior with green fluorescence. The colocalization analysis indicated its excellent lysosome-targeting specificity. In addition, the probe exhibited effective ROS generation ability and significant PDT effect after NIR irradiation, positioning it as a promising photosensitizer for cancer treatment.

Halogenated BOIMPYs and Their Efficiency in Photodynamic Therapy

BOIMPY (bis‐ (borondifluoride)‐8‐imidazodipyrromethene) photosensitizers were developed for imaging‐guided photodynamic therapy (PDT). The introduction of heavy atoms (Br and I) to the b‐positions of BOIMPY combined with the twisted structure of the molecule was the strategy to enhance the intersystem crossing process of the BOIMPYs and reduce intermolecular π−π interactions of BOIMPY core. To clarify the electronic features of BOIMPY derivatives, their optical properties were studied using UV‐vis absorption, fluorescence spectroscopy, electrochemistry, and density functional theory (DFT) computing. The halogenated BOIMPYs exhibited a high absorption coefficient with high singlet oxygen generation ability (Φ∆ = 0.46 and 0.94 for brominated and iodinated BOIMPY, respectively). More significantly, an in vitro investigation showed that all derivatives displayed vivid fluorescence in cancer cells and that the halogenated BOIMPYs increased the effectiveness of tumor inhibition upon exposure to 660 nm red LED light radiation. The half‐maximal inhibitory concentrations for the iodinated and brominated BOIMPYs were 2.14 µM and 14.78 µM, respectively. Consequently, iodinated BOIMPY has been shown to represent a new class of photosensitizers with potential use in imaging‐guided photodynamic therapy.

Molecular engineering to elevate reactive oxygen species generation for synergetic damage on lipid droplets and mitochondria

Photodynamic therapy (PDT), characterized by high treatment efficiency, absence of drug resistance, minimal trauma, and few side effects, has gradually emerged as a novel and alternative clinical approach compared to traditional surgical resection, chemotherapy and radiation. Whereas, considering the limited diffusion distance and short lifespan of reactive oxygen species (ROS), as well as the hypoxic tumor microenvironment, it is crucial to design photosensitizers (PSs) with suborganelle specific targeting ability and low-oxygen dependence for accurate and highly efficient photodynamic therapy. In this study, we have meticulously designed three PSs, namely CIH, CIBr, and CIPh, based on molecular engineering. Theoretical calculation demonstrate that the three compounds possess good molecular planarity with calculated S1-T1 energy gaps (ΔES1-T1) of 1.04 eV for CIH, 0.92 eV for CIBr, and 0.84 eV for CIPh respectively. Notably, CIPh showcases remarkable dual subcellular targeting capability towards lipid droplets (LDs) and mitochondria owing to the synergistic effect of lipophilicity derived from coumarin's inherent properties combined with electropositivity conferred by indole salt cations. Furthermore, CIPh demonstrates exclusive release of singlet oxygen (1O2)and highly efficient superoxide anion free radicals(O2⦁−) upon light irradiation supported by its smallest S1-T1 energy gap (ΔES1-T1 = 0.84 eV). This leads to compromised integrity of LDs along with mitochondrial membrane potential, resulting in profound apoptosis induction in HepG2 cells. This successful example of molecular engineering guided by density functional theory (DFT) provides valuable experience for the development of more effective PSs with superior dual targeting specificity. It also provides a new idea for the development of advanced PSs with efficient and accurate ROS generation ability towards fluorescence imaging-guided hypoxic tumor therapy.

Kurangnya Minat Baca Anak Generasi Alpha Di Era Perkembangan Teknologi

The alpha generation is the children that the millenial generation has left behind. This article was written by Mark Mc Crindle and published in the Business Insider magazine (Christina Sterbenz, 2015). The Alpha Generation (2011–2025) is the most advanced generation in terms of digital technology and the least advanced generation in comparison to the previous generation. Every day, almost 2.5 million children of the Alpha Generation are born into the world. The generation most familiar with the internet over the long term is Gen A. Mc Crindler also asserts that the Alpha generation is not averse to technology, somewhat socially adept, somewhat creative, and somewhat independent. Alpha generators require quick and somewhat chaotic processes. By enticing them with a technology, they become socially alienated. This article aims to understand the challenges faced by the Alpha Generation in the Industrial Revolution. For many people, literacy is a familiar language. Literasi is a concept with a complex, dynamic, and ever-changing definition that is defined by several methods with extreme caution. According Keywords: Literacy, Technology, Development, and Generation Alpha. to an online Merriam-Webster dictionary, the words "literature" and "letter" come from the Latin and English languages, respectively. Literasi is quality, or the ability to read and write with huruf/aksara that is included in the text. Technology is described as a concept that has to do with the type of application and knowledge about how it might improve human ability to identify and modify objects that are around. At this point in time, technological advancement is experiencing relatively slow advancement. The idea of technological advancement is to advance technology from one era to the next, making it more advanced or better. The ability of the Alpha generation to communicate (read, write, study, and sketch) has somewhat declined as a result of the rapid advancement of technology. Many Alpha children nowadays are highly dependent on their gadgets (games, YouTube videos, and other apps). However, due to technological developments, alpha generation children are lagging behind in literature, which is why they are interested in reading at an age when they should be able to read and understand the content of what they read.

Interlayer anions modulated ZnAl-layered double hydroxides for enhanced photocatalytic CO2 reduction

Photocatalytic CO2 reduction has drawn widespread attention across all sectors of society. Layered double hydroxides (LDHs) have emerged as promising photocatalysts, renowned for their adjustable bandgaps and reaction sites. As a typical anionic layered material, LDHs exhibit intricate interactions between interlayer anions and brucite-like metal layers, greatly influencing their inherent physicochemical properties. In this work, we synthesized ZnAl-LDH samples intercalated with four distinct anions: carbonate (CO32–), chloridion (Cl–), nitrate (NO3–), and dodecyl sulfate (DS–), denoted as LDH-CO3, LDH-Cl, LDH-NO3, and LDH-DS, respectively. These LDHs samples have comparable crystallinity and morphology but differ in their interlayer spacing. XRD results reveal interlayer spacings of 0.77 nm for LDH-CO3, 0.78 nm for LDH-Cl, 0.88 nm for LDH-NO3, and 1.24 nm for LDH-DS, between adjacent brucite-like ZnAl hydroxide layers. Remarkably, LDH-NO3 demonstrates the highest photocatalytic CO2 reduction performance, with CO as the primary reduction product. Specifically, the average CO production rate of LDH-NO3 are 3.80 μmol g–1 h–1, surpassing that of LDH-CO3, LDH-Cl, and LDH-DS by 1.9, 2.1, and 2.5 times, respectively. This superior performance can be attributed to the intermediate interlayer spacing of LDH-NO3, which facilitates strong light absorption and a robust local built-in electric field. Theses properties enhance charge generation and separation abilities, leading to efficient photocatalytic reactions. The obtained results highlight the critical role of controlling small molecules within the interlayer, alongside defects, in the design and fabrication of layered-structure photocatalysts. This study provides valuable insights into optimizing the performance of LDH-based photocatalysts for CO2 reduction and potentially other photocatalytic applications.

Pembuatan Website REST API Ensiklopedia Tumbuhan dengan Kombinasi Framework Spring Boot dan MyBatis Generator

This research focuses on the development of the Plant Encyclopedia REST API, a website application designed using Java, HTML, Spring Boot, MyBatis Generator, and Swagger technologies, with PostgreSQL as the database used to store plant data from the API. This study highlights the declining abilities of the younger generation, especially in mathematics, literacy, and science. Statistical data from PISA test scores from 2003 to 2022 on 15-year-olds worldwide show a peak decline in 2022. The applied research methods include needs analysis, problem identification, system design, application design, implementation, and system testing. This research aims to overcome the lack of knowledge about plants that can have a negative impact on the ecosystem and provide various other benefits. The result of this research is a plant encyclopedia website application that can be accessed by various users to find information related to plants and a swagger website for administrators to manage plant data.

Promoting selected core thinking skills using math stations rotation

ABSTRACT The study aimed to investigate the impact of teaching mathematics using the station rotation model on secondary students' core thinking skills, specifically in analysing, generating, and evaluating information. A total of 120 Egyptian female tenth-graders were randomly selected and divided into two groups: an experimental group (60 students) studied the unit of similarity and triangle proportionality theorems through the station rotation model, while the control group (60 students) learned the same unit using the traditional method. A teacher's guide and student worksheets were prepared for the unit, incorporating the station rotation model. A core thinking test was developed to assess students' abilities in analysis, generation, and evaluation. The results revealed a statistically significant effect of using the station rotation model in developing secondary students' core thinking skills. Based on the findings, the study provided guidelines for the effective implementation of the station rotation model in teaching mathematics.

VCAFusion: An infrared and visible image fusion network with visual perception and cross-scale attention

Infrared and visible image fusion methods aim to combine salient target instances and abundant texture details into fused images. However, due to the interference of harsh conditions, such as dense smoke, fog, and intense light, it is feasible for external interference information to be integrated into the fused image, which seriously affects the image quality. To this end, we propose an infrared and visible image fusion network with visual perception and cross-scale attention modules, termed VCAFusion, which integrates critical information from source images in harsh conditions more efficiently. Specifically, considering that the human eye can identify key information under adverse conditions, we design a visual perception module (VPM), guiding information integration from the perspective of human visual perception. In addition, we propose a cross-scale attention module (CSAM) based on shifted window cross-attention, which aims to capture the long-distance correlation between adjacent scale features, providing more accurate image information for image restoration. Experiments with a variety of datasets reveal that the VCAFusion can adaptively retain image information and further improve image generation ability in harsh conditions.

Conical porous hollow g-C3N4 charge separation with high efficiency for enhancing visible-light photocatalytic activity

The performance of the photocatalysts is influenced by factors such as electron generation ability, charge separation rate, and active sites distribution. In this study, needle-shaped precursors were prepared through low-temperature supramolecular self-assembly, and conical porous hollow carbon nitride (PHCN) was obtained after high-temperature pyrolysis. The PHCN not only possesses a thin-walled porous structure but also contains nitrogen defects embedded in its structure, which synergistically enhance the efficiency of photogenerated charge separation and provide more reactive sites. The DFT calculations showed that nitrogen defects and Pt-loading synergistically optimize the intermediate reaction process of graphitic carbon nitride (g-C3N4). PHCN had excellent photocatalytic degradation of tetracycline hydrochloride (TCH) and Rhodamine B (Rh B). The photocatalytic hydrogen evolution rate of PHCN was 20.0 times higher than that of bulk g-C3N4 (BCN). This study elucidated the relationship between crystallization rate and precursor morphology, as well as the influence of their physicochemical properties on photocatalytic performance. It also lays the foundation for constructing materials with nitrogen defects and special morphology through supramolecular self-assembly.

COLLEGE ENQUIRY CHAT-BOT SYSTEM

Nowadays, many people are using smartphone with many new applications i.e. technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. As there are many online Artificial Intelligence (AI) systems or chat bots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond the queries of students on college related issues. A chat bot has information stored in its database to identify the sentences and making a decision itself as response to answer a given question. The college enquiry chat bot will be built using algorithm that analyses queries and understand user’s message Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandora bots as the interpreter. This paper introduces approach made for improving the efficiency of the chatbot or artificial conversational entity used in various commercial and banking sector. Humanizing is to improving the response generation ability of the chatbot. Key Words: Artificial Intelligence, Database. Intelligence Machine.