Structure Of Form Research Articles

Algorithm and Tool Development for Creative Generation of Graphic Design of Folk Houses and Ancient Buildings Integrating Cultural and Creative Elements

This study presents an innovative approach called CNN-GA for graphic design for folk houses and ancient buildings, which integrates Convolutional Neural Networks (CNN) with Genetic Algorithms (GA) to foster the creation of culturally rich and aesthetically appealing graphic designs in architecture. Our research focuses on capturing the essence of folk houses and ancient buildings, deeply rooted in cultural heritage, and reimagining them through a modern computational lens. The CNN component of our model is trained on a diverse array of architectural imagery, enabling it to effectively recognize and categorize key elements such as motifs, textures, and structural forms inherent to various architectural styles. This neural network acts as an intelligent extractor of cultural and aesthetic features, providing a nuanced understanding of traditional architectural elements. The extracted features are then input into a GA, which embarks on an evolutionary process of design generation. This process iteratively combines and refines the architectural elements, fostering a creative exploration of design possibilities that maintain cultural integrity while introducing innovative interpretations. The synergy of CNN and GA in our CNN-GA framework allows for an automated yet insightful design process, yielding graphic designs that are not only architecturally sound but also resonate with the rich cultural narratives of folk houses and ancient buildings. This research holds significant potential in revolutionizing architectural graphic design, offering a novel tool for architects and designers to merge traditional aesthetics with contemporary design paradigms.

Design and optimization of the exhaust system for an aviation piston engine

Abstract To further enhance the performance of an aviation piston engine, an engine performance simulation analysis model was constructed by using GT-Power software to design improvements to its exhaust system. The impacts of different exhaust system schemes on the exhaust process, the in-cylinder combustion, and the heat release process at a cruising speed of 5500 RPM were analyzed. Based on the results, under cruising speed conditions, a new exhaust system was designed for the engine by referring to the structural form of a resonant exhaust system. The D-optimal Latin hypercube sampling method was used to obtain sample data for the experimental design of the structural parameters of the designed exhaust system. The response surface approximation models characterizing power and fuel consumption rate were established by using the least squares method. The significance of the impact of each structural parameter on engine performance was analyzed through a multifactor analysis of variance. The key structural parameters of the designed exhaust system were then subjected to multi-objective optimization. The results indicate that at 5500 RPM, the designed exhaust system can increase engine power by 2.88%; after optimization, engine power can be further increased by 3.15%, and the fuel consumption rate is reduced by 4.07%.

Multi-scale modeling: Finite element analysis of the thermophysical properties of carbon/carbon composites considering manufacturing defects and porosity at high temperature

Carbon/carbon (C/C) composites have strict requirements for their thermal properties in extreme environments, especially at high temperature, where their properties are crucial for long-term life. The structure of C/C composites is exceptionally complex and exhibits multi-scale characteristics, and their thermophysical properties are closely related to temperature. Therefore, exploring their thermal response and heat transfer mechanisms through experimental method is relatively costly. This paper constructs a multi-scale finite element model to investigate the influence of structure and manufacturing defects on performance, and analyzes the thermophysical properties of the composites at High-temperature. By constructing a micro-representative volume element (Micro-RVE) that includes fibers, matrix, and pores, and using a steady-state heat transfer analysis method, the influence of material phase distribution on performance is studied. At the same time, a Meso-RVE reflecting the layering form, needle punching effect and manufacturing defects is established, and the transient heat transfer analysis method is used to investigate the influence of the structural form on performance. Finite element analysis shows that, the simulation values of the three C/C composites of unidirectional fiber bundles, short-chopped fiber felts, and needle punched C/C composites show good consistency with the experimental results in the published literature. This paper uses numerical methods to calculate the thermophysical properties of C/C composites from room temperature to 900°C and explores their variation with temperature. The constructed multi-scale model provides an accurate and effective method for predicting the thermophysical properties of C/C composites and also provides new perspectives and insights for thermal-mechanical coupling analysis and structural design.

GENERATION Z’S LINGOS ON TIKTOK: ANALYSIS OF EMERGING LINGUISTIC STRUCTURES

The internet is an environment where users display the prowess in language and all forms of semiotic resources for different forms of communication. The current study examines Generation Z’s use of language in order to highlight the emerging forms of language structures on TikTok, as a result of the innate ability of humans to create new forms, mostly in verbal communication. The research questions are: What are the forms of construction of Generation Z’s lingo on TikTok? What semantic peculiarities are Generation Z’s lingo on TikTok characterized by? The premise of this study is that, Generation Z’s creative use of language on TikTok, shows the innate ability of humans to create new forms, and adapt to the affordances of new platforms in using language for communication. Crystal’s (2005) propositions concerning Internet Linguistics— the Stylistic perspective is applied in this study. Fifty Tik Tok videos were downloaded from the TikTok platform, some of the videos had the same lingos, in which case only one of such videos was chosen. Seventeen videos were used, each video was played; the part of the video that had the Generation Z’s word(s) was screen shot and used as the data for the analysis. In all, seventeen images were used. Findings show that the Generation Z’s lingo is replete with: acronyms used as full words in speech, combination of words and image in speech, the use of neologisms, esoteric words, and laconic structures. Some words and phrases have new meanings in addition to their previous meanings; all these show a new variety of the English language. The study recommends that language users should deploy the affordance of new internet platforms to freely express themselves, in doing so, naturally, new varieties will spring up and widen the lexicon of the English language.

Information-extreme machine learning of an ophthalmic diagnostic system with a hierarchical class structure

The paper considers the method of hierarchical information-extreme machine learning for the system of ophthalmic diagnosis of eye pathology. Since the proposed method is developed within the framework of a functional approach to modeling the cognitive processes of natural intelligence, it, unlike neuro-like structures, acquires the properties of flexibility when retraining a diagnostic system and requires an order of magnitude fewer image samples. In addition, the decision rules based on the results of machine learning within the geometric approach in the form of a binary hierarchical structure of recognition classes ensure their practical invariance to the multidimensionality of both the space of diagnostic features and the alphabet of recognition classes. The modified Kullback-Leibler information measure, which is considered as a function of the accuracy of classification solutions, is chosen as a criterion for optimizing the parameters of the machine learning system for diagnosing eye pathologies. A hierarchical information-extreme machine learning algorithm for an ophthalmic diagnostic system for six eye pathologies was developed and programmatically implemented. Based on the results of functional diagnostics, it has been experimentally proved that the constructed decision rules are error-free according to the training matrices of recognition classes of each level of the constructed binary hierarchical structure.

Unraveling the Nanosheet Zeolite-Catalyzed Combustion of Aluminum Nanoparticles-Doped exo-Tetrahydrodicyclopentadiene (JP-10) Energetic Fuel.

Nanosheet MFI zeolites (Zeolite Socony Mobil, five) have grown in popularity in cracking catalysis considering their tunability in porous topologies, acidic sites, and sheet thickness, thus allowing them to selectively adsorb molecules of specific sizes, shapes, and polarities, resulting in improved cracking performance for a specific fuel. Five different MFI zeolites in the form of a mesoporous nanosheet structure with a controlled concentration of acidic sites denoted as NSMFI(y), where y is Si/Al ratio, have been synthesized. The effects of the relative acidity content of these NSMFI(y) samples on the zeolite-catalyzed combustion of aluminum nanoparticles (AlNPs)-aided exo-tetrahydrodicyclopentadiene (JP-10) mixed energetic fuel droplets levitated in an oxygen-argon atmosphere were investigated using time-resolved imaging (optical and thermal infrared) and spectroscopic techniques (UV-vis and FTIR). The addition of 1.0 wt % of NSMFI(y) zeolites to AlNPs-JP-10 fluid fuel results in critically reduced ignition delays (9 ± 2 ms), elevated ignition temperatures (2800 ± 170 K), and prolonged burning times (60 ± 10 ms) with an enhanced combustion efficiency. The NSMFI(y) zeolites, which possess high acidity and significant mesoporosity, play a crucial role in improving the combustion efficiency by effectively catalyzing the chemical activation of JP-10 and prolonging the burning of the igniting droplet. The NSMFI (60) variant with the highest acidic site content achieved a maximum combustion efficiency of 80 ± 6%. A comprehensive catalytic combustion mechanism has been elucidated based on the detected reactive intermediates such as hydroxyl radical (OH) and aluminum monoxide (AlO). These findings will help to critically advance the development of next-generation, sustainable, and innovative mixed nanofluid fuels.

Healing-Centered Restorative Justice

This concept paper maps out an approach to restorative justice that enriches current trauma-informed understandings and promotes healing and repair. The paper draws upon many sources, including qualitative research undertaken between 2021-23 in Israel/Palestine and New Zealand/Aotearoa, and an ongoing collaborative research project in the U.S. It also incorporates gathered wisdom on trauma and healing from leading experts across many disciplines. The paper begins by delineating different understandings of trauma, including personal, collective, historical, and structural forms. It then highlights three critical perspectives that offer useful insights into how the field of restorative justice can more effectively achieve its transformational potential: (1) Indigenous/collectivist perspectives; (2) positive psychology-related perspectives; and (3) transformative justice/abolitionist perspectives. The paper goes on to reframe these critical perspectives as core features of healing-centered restorative justice: first, a contextualized, multi-dimensional understanding of trauma; second, a strengths orientation toward human behavior; and third, a relational worldview grounded in interconnectedness, mutuality, and shared responsibility. By adopting this holistic and humanistic framework, restorative justice scholars and practitioners can develop effective and culturally sustaining conflict transformation processes that contribute to healing and repair at individual, interpersonal, and systemic levels of society.

β-Amyloids and Immune Responses Associated with Alzheimer's Disease.

Alzheimer's disease (AD) is associated with the accumulation of β-amyloids (Aβs) and the formation of Aβ plaques in the brain. Various structural forms and isoforms of Aβs that have variable propensities for oligomerization and toxicity and may differentially affect the development of AD have been identified. In addition, there is evidence that β-amyloids are engaged in complex interactions with the innate and adaptive immune systems, both of which may also play a role in the regulation of AD onset and progression. In this review, we discuss what is currently known about the intricate interplay between β-amyloids and the immune response to Aβs with a more in-depth focus on the possible roles of B cells in the pathogenesis of AD.

A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity

Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.

The Deepest Blue: Major Advances and Challenges in Deep Blue Emitting Quasi-2D and Nanocrystalline Perovskite LEDs.

In this review, the recent development of blue perovskite light-emitting diodes (PeLED) are summarized. On deep-blue (≤465nm) perovskite nanomaterials of different structural forms are mainly focused, including nanocrystals (NCs), quantum dots (QDs), nanoplatelets (NPLs), quasi-2D thin film, 3D bulk thin film, as well as lead-free perovskite nanomaterials. The current challenges are also examined in producing efficient deep-blue PeLED, such as material and spectral instability, imbalance charge transport, Joule heat impact, and poor optoelectronic performance. Several strategies are further discussed to overcome these challenges and achieve efficient deep-blue PeLED for next-generation display technology.

Research and Application Status of Soft Steel Dampers

Soft steel dampers have stable hysteresis characteristics and can effectively dissipate energy under repeated loads, providing good shock absorption effects for structures. It can be used for both seismic design of new buildings and seismic reinforcement of existing buildings. Suitable for various structural forms of buildings, including concrete structures, steel structures, and heavy-duty wooden structures. It is also applied in bridge structures, which can effectively reduce the vibration response of bridges under earthquake, wind vibration, etc., and improve the seismic performance and safety of bridges. Countries with rapid development in the application of structural control technology, such as the United States, Japan, Canada, New Zealand, and Mexico, are the main ones. Italy, France, Russia, Singapore, and other countries are also actively applying it in practical engineering. More than a hundred buildings abroad have adopted soft steel dampers.

Interaction of reinforcement, process, and form in Digital Fabrication with Concrete

Material, manufacturing process, and form are mutually dependent. In formwork-based concrete construction, the reinforcement must be positioned and fixed in the formwork, limiting material efficiency and freedom of form. In Digital Fabrication with Concrete (DFC), the formwork no longer limits the concrete forming process. Furthermore, the reinforcement no longer must be installed in advance, but can be placed before, during or after the concrete application. Therefore, the role of reinforcement and its interaction with processing must be fundamentally rethought in DFC. Furthermore, with reinforcement integration a concrete component expands from a contour-based shape into a structural form.The current paper proposes a new so-called RPF-framework expressing the interaction of reinforcement, process and form in DFC. The application of this framework is illustrated using current examples of DFC, whose structural forms are critically discussed. Finally, the need for a holistic approach to material, process and form in DFC is emphasised.

Benzimidazole-based small molecules as anticancer agents targeting telomeric G-quadruplex and inhibiting telomerase enzyme.

Telomeres, crucial for chromosomal integrity, have been related to aging and cancer formation, mainly through regulating G-quadruplex structures. G-quadruplexes are structural motifs that can arise as secondary structures of nucleic acids, especially in guanine-rich DNA and RNA regions. Targeting these structures by small compounds shows promise in the selective suppression of cell growth, opening up novel possibilities for anticancer treatment. A comprehensive investigation of the many structural forms of G-quadruplex ligands is required to create ground-breaking anticancer drugs. Recent research into using specific benzimidazole molecules in stabilizing telomeric DNA into G-quadruplex structures has highlighted their ability to influence oncogene expression and demonstrate antiproliferative characteristics against cancer cells. This review describes the benzimidazole derivative, designed to enhance the stability of the G-quadruplex structure DNA to suppress the activity of telomerase enzyme, exhibiting promising potential for anticancer therapy.

Flexural behavior of over-reinforced beam with ECC layer: Experimental and numerical simulation study

To optimize the brittle failure of reinforced conerete (RC) over-reinforeed beams and enhance their flexural performance, a novel structural form is proposed. To be specific, the Engineered Cementitious Composite (ECC) layer is installed on top of the RC over-reinforced beam (ERCOB). A total of six test beams are prepared, comprising one unreinforced beam and five reinforced beams. The variables comprised the depth of the ECC, reinforcement ratio, and whether the ECC is configured at the bottom. The test findings are subsequently compared with simulation outcomes to validate the model's precision. Next, the influence of various variables on ERCOB flexural performance, such as load-deflection response, bearing capacity, etc., is deeply analyzed. The research indicates that the ECC applied to both the top and bottom of the specimen exhibits enhanced bearing capacity and ductility. In comparison to CB-1, the maximum load and deflection ductility coefficient of EB-2 increased from 45.73 kN to 2.63–48.52 kN and 3.85, representing increases of 6.1 % and 29.6 %, respectively. It reveals that ECC layer improves the defects caused by excessive reinforcement of over-reinforced beams, and optimizes the tensile capacity of the steel bars, thus improving the bending capacity and ductility of the specimens. Finally, the prediction model of ERCOB flexural capacity is proposed to further verify the effectiveness of ERCOB. This study not only verifies the effectiveness of ECC reinforcement, but also helps to delay the failure process of structures, provide reference for future engineering application design.

Freedom of Speech di Ruang Virtual: Analisis Wacana Fairclough Pada Kanal Youtube Rocky Gerung Official

New media is a communication channel that brings about change by disseminating information through texts published by authors. Because text is a form of ideological practice, language, style, word choice, and grammatical structure, it is understood as a decision that has a certain ideological meaning in order to gain public support. This research aims to analyze Rocky Gerung's freedom of speech on the Rocky Gerung Official YouTube channel and how discourse practices take place on YouTube as a virtual space. This research was carried out to examine virtual media. Using a critical paradigm, this research is descriptive-qualitative using critical discourse analysis methods. Using Norman Fairclough's thinking, at the macro-level analysis, there are linguistic elements used that are philosophical, straightforward, and rich in meaning using metaphors. At the micro level, there is the use of special terminology in the world of politics, government, and philosophy. As well as using intertextual patterns, there is consistency between one sentence and another. At the meso level of analysis, researchers create relationships between characters or participants involved in the text in this research.

A Knowledge Graph-Based Consistency Detection Method for Network Security Policies

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method.

Application of computational techniques on non-covalent interactions, H-bond nature of monomeric and dimeric form of crystal structures, and topological insights of glycine glutaric acid

This study utilizes DFT to investigate and optimize the structure of Glycine Glutaric acid (GGA) crystal in both monomer and dimer forms, assessing its electronic and optical properties. Relaxed PES scanning identified potential conformers within the COOH and NH2 functional groups. FT-IR spectrum confirmed these groups and simulated spectra were correlated with the experimental data. The stable monomer was selected for detailed analysis of electronic charge transfer using MEP, FMOs, and UV–visible absorbance spectra. Non-covalent interactions, primarily O–H⋯O and N–H⋯O hydrogen bonds, were explored using optimized structures. Solvent effects, analyzed via the IEFPCM method, revealed heightened reactivity in the aqueous phase. Topological studies (AIM, LOL, ELF, and RDG) and Hirshfeld surface analysis were applied to understand inter and intramolecular contacts, with crystal packing dominated by O⋯H/H⋯O interactions contributing to 63.4 % efficiency. As per DFT prediction, the GGA exhibits strong NLO potential due to significantly higher polarizability and hyperpolarizability (⟨β⟩= 1.3618 × 10−30 e.s.u.) indicating promising nonlinear optical properties.

Structural forms of denominative verbs and deverbalization tendencies in the southern sub-dialects

Structural forms of denominative verbs and deverbalization tendencies in the southern sub-dialects

Research advances on CaMKs-mediated neurodevelopmental injury.

Calcium/calmodulin-dependent protein kinases (CaMKs) are important proteins in the calcium signaling cascade response pathway, which can broadly regulate biological functions in vivo. Multifunctional CaMKs play key roles in neural development, including neuronal circuit building, synaptic plasticity establishment, and neurotrophic factor secretion. Currently, four familial proteins, calcium/calmodulin-dependent protein kinase I (CaMKI), calcium/calmodulin-dependent protein kinase II (CaMKII), eukaryotic elongation factor 2 kinase (eEF2K, popularly known as CaMKIII) and calcium/calmodulin-dependent protein kinase IV (CaMKIV), are thought to have been the most extensively studied during neurodevelopment. Although their spatial structures are extremely similar, as well as the initial starting point of activation, both require the activation of calcium and calmodulin (CaM) complexes to be involved in the process, and the phosphorylation sites and modes of each member are different. Furthermore, due to the high structural similarity of CaMKs, their members may play synergistic roles in the regulation of neural development, but different CaMKs also have their own means of regulating neural development. In this review, we first describe the visualized protein structural forms of CaMKI, CaMKII, eEF2K and CaMKIV, and then describe the functions of each kinase in neurodevelopment. After that, we focus on four main mechanisms of neurodevelopmental damage caused by CaMKs: CaMKI/ERK/CREB pathway inhibition leading to dendritic spine structural damage; Ca2+/CaM/CaMKII through induction of mitochondrial kinetic disorders leading to neurodevelopmental damage; CaMKIII/eEF2 hyperphosphorylation affects the establishment of synaptic plasticity; and CaMKIV/JNK/NF-κB through induction of an inflammatory response leading to neurodevelopmental damage. In conclusion, we briefly discuss the pathophysiological significance of aberrant CaMK family expression in neurodevelopmental disorders, as well as the protective effects of conventional CaMKII and CaMKIII antagonists against neurodevelopmental injury.

The preparation and axial compressive properties of 3D-printed polymer lattice-reinforced cementitious composite columns

Research on the mechanical properties of 3D-printed lattice-reinforced cementitious materials is becoming increasingly widespread. The bending and compression performances of such materials have been studied from the perspectives of the lattice preparation materials, structural forms, gradient design, and cement-based material types. The extant research has primarily focused on the bending performance of lattice-reinforced specimens, while little research has been conducted on the compressive properties. In addition, the reinforcing effect of the lattice on cementitious materials under lateral constraints has not been considered. This article investigates the compressive performance of cement mortar reinforced by 3D-printed spiral lattices or skin structures with different volume reinforcement ratios (different grid constraint layers). Multi Jet Fusion (MJF) technology was used to print four types of spiral lattices, and six specimens, including a control group, were prepared. The experiment considered the performance differences of polymer-reinforced mortars with different volume reinforcement ratios, bonding performance, and skin reinforcement. The load, displacement, stress, strain and other data of the specimen were measured, and combined with the full field strain measured by DIC technology, the strength, energy absorption performance, ductility, elastic modulus, constraint effect, deformation mechanism, and failure mode of the specimen were comprehensively analyzed. The experimental results indicate that 3D-printed spiral or skin lattices can improve the ductility and energy absorption capacity of cementitious materials. However, the elastic modulus of the raw materials used to reinforce the structure is lower than that of cementitious materials, which will reduce the compressive strength and elastic modulus. In addition, the lateral deformation constraint effect of the lattice or skin structure on the cement mortar under axial compression is not significant. All cementitious specimens were found to exhibit the shear failure of the inclined section under axial compression. This article reveals that 3D printed spiral lattices can improve the deformation ability of cementitious composite materials under compression, providing new ideas for the ideal replacement of stirrups and the development of building energy conservation and emission reduction.