General Approach Research Articles

Deep Analogical Generative Design and Evaluation: Integration of Stable Diffusion and LoRA

Abstract The rapid evolution of generative design through artificial intelligence has opened new avenues for innovative product styling. Integrating this efficient generative technology with established professional theories presents a novel challenge in contemporary international design research. In response to this challenge, this paper introduces a pioneering and collaborative approach for the swift generation of automobile styling designs. The primary objective is to investigate an intelligent generation method that incorporates analogical reasoning and Stable Diffusion to support industrial designers in innovating product styling. This study scrutinizes traditional analogical reasoning design alongside the intelligent analogical reasoning design proposed herein, elucidating the distinctions through multidimensional comparisons using illustrative examples. The proposed methodological framework encompasses several key steps. Initially, a dataset comprising branded automobile images is meticulously constructed. Subsequently, an exclusive style model is trained leveraging Stable Diffusion techniques, coupled with advanced computer graphics and machine learning methodologies. Following this, design requirements are inputted, facilitating intelligent analogical reasoning design across multiple spatial dimensions to yield diverse and innovative automobile styling solutions. Finally, eye-tracking experiments are conducted to quantitatively compare the traditional analogical reasoning design approach with the Stable Diffusion-based analogical reasoning design method. The results substantiate that the latter effectively generates innovative and diversified automobile design solutions. This research contributes to enhancing the quality of automobile styling design, optimizing the design efficiency of enterprises, and catalyzing innovation in the automobile styling design process.

Modern Approaches in Domestic Psychology: Unity in Diversity

A brief result of the analysis of modern approaches undertaken within the framework of the collective monograph “Scientific approaches in modern domestic psychology” (M., 2023) is presented. 27 chapters (of different approaches) were divided into general psychological (developing within psychological science), general scientific approaches in psychology (interdisciplinary, resource, environmental, network, etc.), specific psychological in the field of psychology (personality psychology, social psychology, speech psychology, professional activities, etc.) and scientific approaches in applied psychology. Four options for comparisons are considered: the possibility of integration (V.A. Mazilov, V.A. Yanchuk) or comparison of scientific approaches (E.A. Sergienko, A.V. Yurevich). Based on the analysis, guidelines were proposed in an attempt to present the space of psychological science as differentiated by different approaches, and interconnected through the openness of borders and the flexibility of conceptual structures. The leading concepts, specifying concepts, methodological principles and leading methods of approaches are identified. all four groups demonstrate interconnections and complementarities in the understanding of nature, multidetermination of the psyche, and the ability to solve new and complex problems on their basis. Many approaches incorporate concepts and principles of other approaches in psychology. General scientific approaches have generated not only new concepts in psychology (interdisciplinarity, psychological resources), but also new methodological principles. I would like to emphasize that the analysis demonstrated a significant expansion of the methodological principles of psychological sciences, which are explicit not only in general psychological and general scientific, but also specific scientific approaches. The principles of interdisciplinarity, uncertainty, anticipation, subjectivity, evolution, multi-determination and self-development were justified, along with confirmation of the principles of systematicity, the unity of consciousness and activity, the unity of being and consciousness. But even the principles traditional for psychology are filled with new content, excluding determinism, which fetters a person by universal laws, and indeterminism, where a person is lost in infinite degrees of freedom. The author’s approach to the analysis of scientific approaches in psychology is an attempt to compare them and highlight trends in the development of methodology, which involves discussions and discussions, new solutions, new analysis possibilities, which is necessary for the development of our science and the implementation of the idea of the opponent circle.

Surface‐Functionalization of Oleate‐Capped Nano‐Emitters for Stable Dispersion in 3D‐Printable Polymers

AbstractTwo‐photon polymerization 3D‐printing is a well‐known technique for fabricating passive micro/nanoscale structures, such as microlenses and inversely designed polarization splitters. The integration of light emitting nanoparticle (NP) dopants, such as quantum dots (QDs) and rare‐earth doped nanoparticles (RENPs), into a polymer resist would enable 3D printing of active polymer micro‐photonic devices, including sensors, lasers, and solid‐state displays. Many NPs contain oleic acid ligands to prevent degradation, but oleate‐capped NPs (oc‐NPs) tend to agglomerate in nonpolar media despite the hydrophobicity of the ligand. This results in an uneven nanoparticle distribution and increased optical extinction. In this work, a general approach is proposed for dispersing oc‐NPs in commercial 3D printable polymers. Controlled growth of small carbon chains around the oc‐NPs is achieved by functionalizing them with methyl‐methacrylate monomers. The proposed approach is validated on RENPs (≈65 nm) and CdSe/ZnS quantum dots (≈12 nm) using commercial polymer resists (IP‐Dip and IP‐Visio). Dispersions of functionalized NPs (f‐NPs) have improved the NP density by an order of magnitude and are shown to be stable for several weeks with minimal impact on printing quality. The approach is generalizable to other oc‐NPs, enabling synthesis of functional resins for high‐quality polymer‐based optical and electronic devices.

Enhancing Neural Machine Translation Quality for Kannada–Tulu Language Pairs through Transformer Architecture: A Linguistic Feature Integration

The rise of intelligent systems demands good machine translation models that are less data hungry and more efficient, especially for low- and extremely-low-resource languages with few or no data available. By integrating a linguistic feature to enhance the quality of translation, we have developed a generic Neural Machine Translation (NMT) model for Kannada–Tulu language pairs. The NMT model uses Transformer architecture and a state-of-the-art model for translating text from Kannada to Tulu and learns based on the parallel data. Kannada and Tulu are both low-resource Dravidian languages, with Tulu recognised as an extremely-low-resource language. Dravidian languages are morphologically rich and are highly agglutinative in nature and there exist only a few NMT models for Kannada–Tulu language pairs. They exhibit poor translation scores as they fail to capture the linguistic features of the language. The proposed generic approach can benefit other low-resource Indic languages that have smaller parallel corpora for NMT tasks. Evaluation metrics like Bilingual Evaluation Understudy (BLEU), character-level F-score (chrF) and Word Error Rate (WER) are considered to obtain the improved translation scores for the linguistic-feature-embedded NMT model. These results hold promise for further experimentation with other low- and extremely-low-resource language pairs.

Foreign experience of administrative and legal regulation of discretionary powers in the branch of land relations

The article collects and analyzes approaches to understanding and conditions of application and implementation of discretionary powers of local self-government bodies in the sphere of land relations of foreign states. The realities of modern life require public authorities not only to timely and fully fulfill their tasks, but also to comply with high standards of integrity. The described tasks and goals are especially important in the implementation of discretionary rights and duties that can be carried out with a certain freedom of action or at all - at their own discretion. The scientific novelty of the study is that this study in the context of the application of administrative discretion of representative and executive bodies of local self-government is carried out for the first time and can be the basis for reviewing the content of national legislation on these issues and significantly influence the development of legal policy on these issues. The author analyzes the existing normative legal acts that are used by municipalities in different states to exercise their administrative functions at their own discretion on this issue. The study concerns both general approaches to understanding administrative discretion, which often takes place through the adoption of legislation on administrative procedure (their principles, legal certainty and purpose), and the options for their application to the sphere of land relations. The article describes the legal regulation of these phenomena in countries such as Italy, Spain, Germany, Austria, USA, France, Great Britain, Norway, Albania, Finland, Bulgaria, establishes patterns, trends and common features of legal regulation, speaks of successful practices, successful experience and prospects for its adoption into the national legal system. The author focuses on the trends in the development of administrative discretion and the importance of digitalization, openness, dialogue and public participation in resolving issues of land relations and concludes on the state of legal certainty of powers, which can be exercised at the discretion of municipalities only by describing the general requirements for the behavior of the latter, to a lesser or greater extent limits the freedom of their operation by law and always grants the right to interested persons to challenge decisions made that affect the sphere of their interests.

Bayesian updating of relationships between crack width and corrosion level in reinforced concrete based on a large set of experimental data

AbstractThe evaluation of rebar corrosion in reinforced concrete (RC) structures presents a significant challenge for structural engineers, with on‐site visual inspections and crack width measurements playing a key role in the preliminary assessment. A general approach is to use corrosion models to predict the corrosion level of existing structures, and subsequently update these predictions by means of on‐site data. In this process, empirical models are often applied that relate the observed damage, such as corrosion‐induced cracks, to the corrosion level. However, these models have large uncertainties and are often not applicable to conditions that deviate from the test setups used to derive the empirical relations. This research aims to expand the applicability of these practical, existing empirical models for on‐site corrosion assessment through Bayesian updating techniques. To this aim, a Bayesian framework is developed to update crack width–corrosion models. The Bayesian updating methodology is illustrated for a simple regression model, and for a more complex relation that accounts for additional variables. A novel online database (KUL‐edCCRC) is used that is published accompanying this paper. The obtained results illustrate that the updated models have better agreement with the experimental results, and it is found that the confidence intervals of the regression parameters decrease for an increasing number of observations, even for a small number of additional datapoints. The results hence prove the efficiency of the approach to integrate information from new observations with prior information to adjust the crack width–corrosion model for specific conditions or cases, resulting in a more relevant model as more information becomes available.

Real-Time Reconfigurable Radio Frequency Arbitrary-Waveform Generation via Temporal Pulse Shaping with a DPMZM and Multi-Tone Inputs

Benefitting from a large bandwidth and compact configuration, a time-domain pulse-shaping (TPS) system provides possibilities for generating broadband radio frequency (RF) arbitrary waveforms based on the Fourier transform relationship between the input–output waveform pair. However, limited by the relatively low sampling rate and bit resolution of an electronic arbitrary-waveform generator (EAWG), the diversity and fidelity of the output waveform as well as its reconfiguration rate are constrained. To remove the EAWG’s limitation and realize dynamic real-time reconfiguration of RF waveforms, we propose and demonstrate a novel approach of RF arbitrary-waveform generation based on an improved TPS system with an integrated dual parallel Mach–Zehnder modulator (DPMZM) and multi-tone inputs. By appropriately adjusting the DC bias voltages of DPMZM and the power values, as well as the center frequencies of the multi-tone inputs, any desired RF arbitrary waveform can be generated and reconfigured in real time. Proof-of-concept experiments on the generation of different user-defined waveforms with a sampling rate up to 27 GSa/s have been successfully carried out. Furthermore, the impact of modulation modes and higher-order dispersion on waveform fidelity is also discussed in detail.

What uses of digital technology support and enhance the learning and the engagement of diverse learners in high school classes?

Digital technologies may play an important role in helping students from all walks of life to overcome barriers and benefit from teaching and learning. However, due to a lack of knowledge or skills related to educational technologies, K-12 teachers struggle to harness the full potential of digitalisation to support student learning and engagement, causing further marginalization and exclusion that put diverse group of learners at risk of being left behind in education (Chevalère et al., 2021). In response to these research gaps, the aim of this paper is to explore which high school uses of digital technology cater for learners’ needs and preferences for the sake of a more digitally inclusive environment that supports engagement and equitable learning opportunities for all students (Stenman & Pettersson, 2020). We adopted a qualitative approach to attend to the richness and depth in various contexts. We have conducted individual semi-structured interviews with 17 high school teachers, aiming to obtain in-depth qualitative data through open-ended questions. Then, a general inductive data analysis approach was followed to generate preliminary results on the research objectives, which are presented in this study.

Catalytic Impedance Spectroscopy: Concept and Application on CO2 Methanation.

Complex modeling of periodic excitation combined with time-resolved product detection is used to describe the complex response function of a catalytic system. We describe this concept of catalytic impedance spectroscopy (CIS) and the underlying general experimental approach and a concrete setup. The feasibility of CIS is experimentally demonstrated along the catalytic CO2 methanation reaction. The measurements confirm the theoretically anticipated rate-determining step of HCO* → CH* on Ni catalysts. Limitations and prospects of CIS to unravel reaction mechanisms are discussed.

Co-pyrolysis of plastic waste and macroalgae Ulva lactuca, a sustainable valorization approach towards the production of bio-oil and biochar

To address the pressing demand for sustainable energy in light of environmental challenges, this study explored the synergetic effects of the co-pyrolysis of polyethylene terephthalate (PET) and Ulva lactuca macroalgae to yield bio-oil and biochar. The objective is to enhance the bio-oil quality for wider usability and to combat marine pollution. By employing co-pyrolysis, notable progress has been achieved in bio-oil yield and quality, particularly in hydrocarbon content, through the integration of PET. The highest bio-oil yield of 37.91 % was achieved under optimal conditions at 500 °C with a feedstock mixture consisting of 40 % U. lactuca and 60 % PET. Under these conditions, the bio-oil exhibited a significant increase in hydrocarbon content, reaching 57.16 %, which is essential for improving its energy potential. Biochar quality was also enhanced, with the biochar from a 70 % U. lactuca and 30 % PET blend showing a BET surface area of 20.18 m2/g, compared to the initial 1.38 m2/g of raw U. lactuca, indicating improved surface properties. This study presents a sustainable energy generation and environmental preservation approach, underscoring the potential of synergistically utilizing marine resources and plastic waste.

Competitive Data-Structure Dynamization

Data-structure dynamization is a general approach for making static data structures dynamic. It is used extensively in geometric settings and in the guise of so-called merge (or compaction) policies in big-data databases such as LevelDB and Google Bigtable. Previous theoretical work is based on worst-case analyses for uniform inputs—insertions of one item at a time and non-varying read rate. In practice, merge policies must not only handle batch insertions and varying read/write ratios, they can take advantage of such non-uniformity to reduce cost on a per-input basis. To model this, we initiate the study of data-structure dynamization through the lens of competitive analysis via two new online set-cover problems. For each, the input is a sequence of disjoint sets of weighted items. The sets are revealed one at a time. The algorithm must respond to each with a set cover that covers all items revealed so far. It obtains the cover incrementally from the previous cover by adding one or more sets and optionally removing existing sets. For each new set the algorithm incurs build cost equal to the weight of the items in the set. In the first problem the objective is to minimize total build cost plus total query cost , where the algorithm incurs a query cost at each time \(t\) equal to the current cover size. In the second problem, the objective is to minimize the build cost while keeping the query cost from exceeding \(k\) (a given parameter) at any time. We give deterministic online algorithms for both variants, with competitive ratios of \(\Theta(\log^{*}n)\) and \(k\) , respectively. The latter ratio is optimal for the second variant.

Stereoselective Total Synthesis of Rhodocoranes I and J.

We report the stereoselective total synthesis of rhodocoranes I and J in 10 steps and 16.4% overall yield from (S)-limonene. The synthesis was accomplished through the convergent assembly of a highly substituted chiral cyclopentanone and a lithiated furanyl silyl ketene acetal. The requisite cyclopentanone framework was strategically constructed from the chiral pool, (S)-limonene, through a sequence of steps that included a hydroboration/oxidation, ozonolysis, aldol condensation, reduction, and palladium-catalyzed diastereoselective allylic transposition. This study provides a general approach to the synthesis of the rhodocorane family, known for their antibacterial, antifungal, and cytotoxic properties.

Effectiveness Of Visual Feedback Balance Training On Equiboard Versus Traditional Balance Training In Post Stroke Patients- A Comparative Study

Background: A Stroke is classically characterized as a neurological deficit attributed to an acute focal injury of the central nervous system by a vascular cause and is a major cause of disability and death worldwide.(1) Balance problems are thought to be common after stroke and they have been implicated in the poor recovery of activities of daily living and mobility and increased risk of falls.(2) Postural sway for patients with hemiplegia can be twice that of their age matched peers. Many balance training are used but No general physiotherapy approach has proven to be superior for promoting balance recovery from stroke.(3)Novel biofeedback devices based on Virtual reality (VR)technology are now being used for neuromotor rehabilitation. Traditional balance training is based on the automatic repetition of specific movements. These methods can become repetitive and thus reduce the motivation of the patient and adherence to treatment. So the need of my study is to compare the effectiveness of visual feedback balance training on equiboard and traditional balance training in post stroke patients. Aims and objectives-To study and compare the effectiveness of visual feedback balance training on equiboard versus traditional balance training in post stroke patients. Methodology- 30 patients with stroke were equally divided into two groups. patients in group A received the conventional physiotherapy while the patients in group B received visual feedback balance training on equiboard. All the subjects were treated for 1 month, 5 days a week. BBS-Berg Balance Scale and step test was used as the outcome measure at the beginning and end of study. Results-Results showed significant improvement within both the groups for both the outcome measure(p,0.05), while there was no significant difference between two groups in BBS(p>0.5), but there was significant difference in the step test of experimental group(p<0.5) Conclusion-It can be concluded that visual feedback balance training is more effective than the traditional balance training alone in stroke patients.

The Efficiency Aspect of Taxation in Pakistan: A Computable General Equilibrium Approach

The Efficiency Aspect of Taxation in Pakistan: A Computable General Equilibrium Approach

Electric-Field Catalysis on Carbon Nanotubes in Electromicrofluidic Reactors: Monoterpene Cyclizations.

The control over the movement of electrons during chemical reactions with oriented external electric fields (OEEFs) has been predicted to offer a general approach to catalysis. Recently, we suggested that many problems to realize electric-field catalysis in practice under scalable bulk conditions could possibly be solved on multiwalled carbon nanotubes in electromicrofluidic reactors. Here, we selected monoterpene cyclizations to assess the scope of our system in organic synthesis. We report that electric-field catalysis can function by stabilizing both anionic and cationic transition states, depending on the orientation of the applied field. Moreover, electric-field catalysis can promote reactions which are barely accessible by general Brønsted and Lewis acids and field-free anion-π and cation-π interactions, and drive chemoselectivity toward intrinsically disfavored products without the need for pyrene interfacers attached to the substrate to prolong binding to the carbon nanotubes. Finally, interfacing with chiral organocatalysts is explored and evidence against contributions from redox chemistry is provided.

Novel Pseudo-Two-Dimensional 19F NMR Spectroscopy for Rapid Simultaneous Detection of Amines in Complex Mixture.

Rapid detection of amines in complex mixtures presents a significant challenge. Here, we introduce a novel nuclear magnetic resonance (NMR) method for amine detection utilizing a probe with two fluorine atoms in distinct chemical environments. Upon interaction with an amine, the probe generates two atomic resonance peaks, which are used to create coordinates, revealing fluorine chemical shifts on the 19F NMR spectroscopy. This innovative approach allows for the clear distinction of amine signals in a two-dimensional plane. This method has been effectively employed in analyzing amines in pharmaceuticals and amino acids in Ophiopogon japonicus and dry white wine, providing a robust and general approach for amine analysis.

Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment.

We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict as a proxy target variable. We conduct a simulation study to investigate several aspects including the presence or absence of confounding and colliding effects and the merits of locally centering the treatment and/or the response. Our study incorporates both existing and new implementations of random forests. The results indicate that locally centering both the response and treatment variables is generally the best strategy, and both general approaches are viable. Additionally, we provide an illustration using data from the 1987 National Medical Expenditure Survey.

The Identification of Network Intrusions with Generative Artificial Intelligence Approach for Cybersecurity

Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Generative AI in network intrusion detection, with the goal of developing a proactive and adaptable cyber protection mechanism. In this work the use of Generative AI use for identifying network intrusions, using the NSL-KDD dataset. The work begins with meticulous data preprocessing using handling missing values, encoding, feature scaling, and feature extraction to enhance the dataset and then focuses on using Generative AI for intrusion detection. In this work many models have been used like SVM, AE, DAE, and GANs, and in this GANs has the best accuracy with 91.12% compared with other models.

An analysis and solution of ill-conditioning in physics-informed neural networks

Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PDEs). However, ensuring stable training and obtaining accurate results remain challenging in many scenarios, often attributed to the ill-conditioning of PINNs. Despite this, a deeper analysis is still lacking, which hampers progress and application of PINNs in complex engineering problems. Drawing inspiration from the ill-conditioning analysis in traditional numerical methods, we establish a strong connection between the ill-conditioning of PINNs and the Jacobian matrix of the PDE system. Specifically, for any given PDE system, we construct a controlled system that allows for the adjustment of the Jacobian matrix's condition number while retaining the same solution as the original system. Our numerical experiments show that as the condition number of the Jacobian matrix decreases, PINNs exhibit faster convergence and higher accuracy. Building upon this principle and the extension of controlled systems, we propose a general approach to mitigate the ill-conditioning in PINNs, leading to successful simulations of three-dimensional flow around the M6 wing at a Reynolds number of 5,000. To the best of our knowledge, this is the first time that PINNs have successfully simulated such complex systems, offering a promising new technique for addressing industrial complexity problems. Our findings also provide valuable insights to guide the future development of PINNs.

Optimized Method for the Synthesis of Alkyne-Modified 2'-Deoxynucleoside Triphosphates.

A general approach is presented for synthesizing alkyne-modified nucleoside triphosphates via the Sonogashira cross-coupling reaction of unprotected halogenated 2'-deoxynucleoside, followed by monophosphorylation and the reaction of the corresponding phosphoromorpholidate with tributylammonium pyrophosphate. A highly efficient approach for the milligram-scale synthesis of base-modified nucleoside triphosphates with an amino acid-like side chain was developed. The present chemical method outweighs the other reported methods of a base-modified nucleoside triphosphates synthesis in terms of it being a protection-free strategy, the shortening of reaction steps, and increased yields (about 70%). The resulting 8-alkynylated dATP was tested as a substrate for DNA polymerases in a primer extension reaction.