Design Modeling Research Articles

Design and Nonlinear Modeling of a Modular Cable-Driven Soft Robotic Arm

Design and Nonlinear Modeling of a Modular Cable-Driven Soft Robotic Arm

Cyclic inelastic performance evaluation of locking bolt demountable shear connectors in steel-concrete composite structures

This study extends the research on the Locking Bolt Demountable Shear Connector (LBDSC), previously introduced and extensively analysed under monotonic loading through both experimental and numerical methods. The focus here shifts towards the inelastic cyclic performance of the LBDSC, a critical aspect for applications in seismic regions. The LBDSC, designed for use in composite floors or decks, such as in-situ solid slabs, profiled deck slabs, or precast slabs in buildings and bridges, features a grout-filled steel tube bolted to the top flange of steel sections. The design incorporates a unique 'locking bolt' technique to counteract the influence of construction tolerances on undesirable initial slip behaviour, significantly facilitating the deconstruction and reuse of steel sections without the need for recycling. Following the Eurocode 4 guidelines, a series of standard push out tests were conducted to explore the LBDSC's inelastic cyclic characteristics. The experimental results confirm the LBDSC's capability for high strength, high initial stiffness, and adequate slip capacity under cyclic loading, with ductile shear failure modes observed at the bolt shank and local concrete crushing above the steel flange. The cyclic loading tests reveal a larger zone of damaged concrete compared to monotonic loading conditions, yet the monotonic and cyclic force-slip curves closely align. Based on these findings, and supported by validated finite element analyses, this paper proposes theoretical monotonic and cyclic models for LBDSC under push out loading, aimed at accurately predicting the load-slip response. These models are intended to facilitate the efficient analyses, design and numerical modelling of composite structures incorporating LBDSCs, and to support the broader adoption of LBDSCs in earthquake-prone areas, promoting sustainable steel-concrete composite construction practices.

Tail-approach based design, synthesis, and molecular modeling of benzenesulfonamides carrying thiadiazole and urea moieties as novel carbonic anhydrase inhibitors.

We synthesized herein 16 compounds (SUT1-SUT16) as potential carbonic anhydrase (CA) inhibitors utilizing the tail-approach design. Based on this strategy, we connected benzenesulfonamide, the zinc-binding scaffold, to different urea moieties with the 1,3,4-thiadiazole ring as a linker. We obtained the target compounds by the reaction of 4-(5-amino-1,3,4-thiadiazol-2-yl)benzenesulfonamide with aryl isocyanates. Upon confirmation of their structures, the compounds were screened for their ability to inhibit the tumor-related human (h) isoforms human carbonic anhydrase (hCA) IX and XII, as well as the physiologically dominant hCA I and II. Most of the molecules demonstrated Ki values ≤ 10 nM with different selectivity profiles. The binding modes of SUT9, SUT10, and SUT5, the most effective inhibitors of hCA II, IX, and XII, respectively, were predicted by molecular docking. SUT16 (4-{5-[3-(naphthalen-1-yl)ureido]-1,3,4-thiadiazol-2-yl}benzenesulfonamide) was found to be the most selective inhibitor of the cancer-associated isoforms hCA IX and XII over the off-target isoforms, hCAI and II. The interaction dynamics and stability of SUT16 within hCA IX and XII were investigated by molecular dynamics simulations as well as dynophore analysis. Based on computational data, increased hydrophobic contacts and hydrogen bonds in the tail part of these molecules within hCA IX and XII were found as favorable interactions leading to effective inhibitors of cancer-related isoforms.

Implementasi Sistem Kalkulasi Berat Dan Iuran Sampah Berbasis Internet Of Things Pada Perumahan Bekasi Jaya Indah

Waste is a complex problem in developing countries like Indonesia, negatively impacting the environment and public health. Bekasi Jaya Indah Housing Estate, Bekasi City, faces the challenge of sub-optimal waste management. One of them is the gap between waste fees and the weight of waste generated, especially from houses with home industry activities. This research aims to improve monitoring efficiency by developing a tool based on firebase technology or user interface. This technology allows real-time access from various locations to monitor environmental conditions. This tool is not only a monitor of environmental conditions, but also a management tool to support better decision making in maintaining environmental stability and safety. The method used is the prototype method with the stages of communication, quick plan, quick design modeling, construction of prototype, and deployment delivery & feedback. This monitoring tool, it is expected that residents, officers, and administrators can easily monitor environmental conditions from various locations. User have the ability to take the necessary response actions quickly based on real-time information obtained from this monitoring tool.

Weighted unsupervised domain adaptation considering geometry features and engineering performance of 3D design data

The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-based engineering performance prediction models have been proposed to accelerate design optimization. However, they only guarantee predictions on training data and may be inaccurate when applied to new domain data. In particular, 3D design data have complex features, which means domains with various distributions exist. Thus, the utilization of deep learning has limitations due to the heavy data collection and training burdens. We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. The proposed method is the first to apply domain adaptation to deep learning-based engineering performance prediction to overcome the problem of insufficient industrial data. In addition, we present a source instance weighting method called the bi-weighting strategy to consider the geometry features and engineering performance of 3D design data and avoid negative transfer for accurate prediction. Domain-invariant features can be extracted through an adversarial training strategy by using hypothesis discrepancy, and a multi-output regression task can be performed with the extracted features to predict the engineering performance. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels. This mechanism can reduce the target risk for unlabeled target domains on the basis of weighted multi-source domain knowledge and can efficiently replace conventional finite element analysis.

Artificial Intelligence in Biomaterials: A Comprehensive Review

The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.

Application of Machine Learning- Based Sentiment Analysis in Packaging Design Style Prediction Modelling

Machine learning-based sentiment analysis plays a pivotal role in the innovative realm of packaging design style prediction modeling. By harnessing advanced algorithms, this approach analyzes consumer sentiments towards various packaging designs, extracting valuable insights into preferences and trends. The model utilizes machine learning techniques to identify patterns in historical data, allowing it to predict and recommend packaging design styles likely to resonate positively with target audiences. This research introduces an innovative approach to packaging design style prediction modeling by incorporating a machine learning-based sentiment analysis technique known as the Conditional Random n-gram Classifier Sentimental (CRn-gCS). Focused on enhancing the intersection of design aesthetics and consumer sentiments, this model employs advanced algorithms to analyze historical data and predict packaging design styles that resonate positively with target audiences. The CRn-gCS, as a key component, refines sentiment analysis by considering conditional relationships between n-grams, contributing to a nuanced understanding of consumer preferences. By leveraging this sophisticated model, designers and marketers can make informed decisions, ensuring that packaging not only aligns with aesthetic trends but also elicits positive emotional responses from consumers. This research contributes to the advancement of predictive modeling in packaging design, offering a comprehensive and data-driven approach to create visually appealing and emotionally resonant packaging.

Optimized Art Design Model With Statistical Model with Digital Media

Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media technology. The statistical technique comprises the estimation of the features in the art design model. Through the integration of WGO with the statistical model features related to the art design with the incorporation of digital media are evaluated. The statistical features in the art design are observed as the digital information such as geometric, GLCM and HUE are the essential features in the integrated WGO with statistical techniques. The estimated features are applied over the deep learning model with the LSTM network for the automated classification of art design that uses digital media for improvement. Simulation results demonstrated that the proposed WGO integrated statistical model achieves the HUE value ranges from 0 -360 which is effective for art design modeling. Also, the proposed model achieves a significant classification rate of 0.98 accuracy with a loss value of 0.2 which is ~9% less loss than the conventional techniques.

Pyrazoline Spiro-oxindole tethered 1,2,3-triazole hybrids: Design, synthesis, antimicrobial efficacy and molecular modelling studies.

Inspired from the important applications of spirocyclic compounds in medicinal chemistry, a new series of pyrazoline Spiro-oxindole tethered 1,2,3-triazole hybrids was reported via Cu(I)-catalyzed click reaction from isatin-pyrazoline linked terminal alkynes with in situ derived benzyl azides. Antimicrobial evaluation data showed that all hybrids exhibited promising efficacy towards the tested microbial strains. Antimicrobial screening as well as docking studies suggested that hybrid 6a was found to be most potent towards Aspergillus niger (MIC = 0.0122μmol/mL) and Escherichia coli (MIC = 0.0061μmol/mL). Molecular docking studies of 6a within the binding pockets of antibacterial and antifungal targets revealed good interactions with the binding energies of - 144.544kcal/mol and - 154.364kcal/mol against 1KZN (E. coli) and 3D3Z (A. niger), respectively. Further, MD simulations were performed to study the stability of the complexes formed at 300K. Based on the RMSD trajectories, it is evident that 3D3Z-6a complex exhibits minimal deviation, whereas the 1KZN-6a complex displayed little more deviation compared to the protein but, both are in acceptable range. Moreover, 3D3Z-6a and 1KZN-6a showed maximum number of hydrogen bonds at 50ns and 70ns, respectively, thereby complementing the stability of these complexes.

Design and Numerical Modeling of Terahertz Metasurface with Dual Functions of Sensing and Filtering.

This study proposes a dual-functional terahertz device based on the Dirac semimetal, serving as both a sensing element and a band-pass filter. The device's operating mode can switch between these two functions by utilizing the phase transition property of vanadium dioxide (VO2). When VO2 is in the insulating state, the device functions as a sensing element. The simulation results demonstrate an impressive refractive index sensitivity of 374.40 GHz/RIU (Refractive Index Unit). When VO2 is in the metallic state, the device functions as a band-pass filter, exhibiting a center frequency of 2.01 THz and a 3 dB fractional bandwidth of 0.91 THz. The integration of these dual functionalities within a single terahertz device enhances its utility in both sensing and filtering applications.

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.

Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us toward new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retro-biosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles.

Directly applying big language models for material and molecular design is not straightforward, particularly for real-scenario cases, where experimental validation accuracy is required. In this study, we propose a multimode descriptor design method for materials prediction and analysis, leveraging the advantages of the natural language processing literature model and density functional theory (DFT) calculations with the assistance of the genetic algorithm (GA). A case study on prediction of aqueous photocurrents of multisolvent engineered halide perovskite CH3NH3PbI3 is performed, and the following-up validation experiments are carried out to demonstrate the improved accuracy of the multimode descriptors (an unprecedented experimental validation accuracy of 87.5% via the GA is achieved) for predicting aqueous photocurrents of perovskite materials (c.f. only 50% experimental accuracy for other common machine learning models). The improved experimental accuracy of the descriptors is attributed to the successful deployment of a language model incorporating concise scientific information from >1 million articles into molecular descriptors in combination with DFT calculations. The subsequent machine learning analysis suggests the importance of cation···π and crystallization in molecule-modified halide perovskite materials representing ontological and conceptual understanding. Importantly, the genetic process affords an accurate "white-box" model to describe the perovskite stability (accuracy = 90.2% for the test data set and 92.3% for the train data set) with the mathematical equation , where F1 ∼ F5 atomic-level structural and chemical details such as cation···π interactions and highest occupied molecular orbital levels. This study offers a feasible descriptor design route to accurately predict complex material properties, leveraging both language models and density functional theories.

What goes wrong during phishing education? A probe into a game-based assessment with unfavorable results

Context:In recent years, there has been a significant surge in global Social Engineering (phishing) attacks. This upsurge has prompted governmental bodies, organizations, and educational institutions to formulate strategies aimed at mitigating this threat. Objective:The primary objective of this research is to create a game-based solution that educates participants about URLs and evaluates their understanding through multiple-choice questions. Methodology:To attain the aforementioned objectives, a multifaceted approach has been adopted in this study. Firstly, an extensive literature review was conducted to gain insights into the problem and prior research on the subject. This review was instrumental in comprehending the game development framework, developmental tools, and various design models for game design. A digital adaptation of the game has been created utilizing the CONSTRUCT 3 platform. Secondly, an empirical evaluation was executed, involving participants engaging with the game and their learning assessed through survey. A survey method was employed to further gauge participants’ knowledge and to solicit feedback on the game’s design. Results and Conclusion:The survey results indicate a lack of significant outcomes or dependencies on the dependent variable. fun to play, ease to play, and game-based learning did not significantly predict avoidance behavior while the intention to play and phishing knowledge were the significant positive predictors of avoidance behavior, with intention to play showing the biggest contribution in the models 2 and 3. Correspondingly, the negligible difference between the R2 value and △ R2 in models 2 and 3 also confirmed the small variance of model 2 (explained in the paper). Consequently, the research asserts that the assessment of the gaming method has not yielded success and underscores the necessity for enhancements and further evaluation.

Fundamental investigation of impact of water and TFA additions in peptide sub/supercritical fluid separations

The retention of three peptides was studied under analytical and overloaded conditions at different concentrations of trifluoroacetic acid (TFA) and water added to the co-solvent methanol (MeOH). Four columns with different stationary phase properties, i.e., silica, diol, 2-ethylpyridine and cyanopropyl (CN) columns, were evaluated in this investigation. The overall aim was to get a deeper understanding on how column chemistry as well as water and TFA in the co-solvent affect the analytical and overloaded elution profiles using multivariate design of experiments and adsorption measurements of co-solvent components. Multivariate experimental design modeling indicated that water had on average around five times higher effect on the retention than the addition of TFA. The results also showed that the retention increases with the addition of TFA and water to the co-solvent on all columns except the CN column, on which the retention decreased. When examining the effect of adding water to the co-solvent, evidence of a hydrophilic interaction liquid chromatography (HILIC)-like retention mechanism was found on the three other columns with more polar stationary phases. However, on the CN column water acted as an additive, decreasing the retention due to competition with the peptide for available adsorption surface. Adsorption isotherm measurements of the polar solvent MeOH showed that MeOH adsorbs much weaker on the CN column than on the other columns. Addition of TFA and water to the co-solvent substantially sharpened the elution profiles under both overloaded and analytical conditions. Adding a small amount of TFA (from 0 % to 0.05 %) to the co-solvent substantially improved the peak shape of the elution profiles, while further addition (from 0.05 % to 0.15 %) had only a minor effect on the elution profile shape. The reduced retention on the CN column could not be explained by TFA adsorption, which was very weak on all studied columns (retention factor, 0.05–0.15). One could therefore speculate that the ion-pairing complex formed between the peptide and TFA in the mobile phase, reduce the retention due to its reduced polarity. On the other columns displaying HILIC-like properties, the TFA probably just decreased the pH of the mobile phase, thereby promoting the partitioning of the peptide into the water-rich layer. Finally, peak deformation due to diluent–eluent mismatch was observed under overloaded conditions. This was most severe in the cases where MeOH adsorption to the stationary phase was strong and the peptides were only mildly retained. Adding 1,4-dioxan to the diluent resolved this issue.

Implementasi Sistem Informasi Manajemen Aset Kelompok Ternak Ikan Hias Sumedang Menggunakan Metode Prototype

This study develops and implements an Asset Management Information System for Ornamental Fish Breeding Groups in Sumedang using the prototype method. The objective is to streamline the management of asset data, inventory, and asset condition monitoring in an efficient and organized manner. The prototype method was selected because it promotes intensive interaction between developers and users, enabling quick identification and resolution of needs and issues. The development stages include communication, planning, design modeling, prototype creation, and system implementation. This system features the ability to record and monitor assets, track asset loans, and log asset maintenance, thereby generating accurate reports and minimizing data errors. It is expected that this information system will help ornamental fish breeding groups in Sumedang manage their assets more effectively, boost productivity, and support the sustainability of their business.

Optimizing TCAD Model and Temperature-Dependent Analysis of Pt/AlN Schottky Barrier Diodes for High-Power and High-Temperature Applications

This research presents a comprehensive investigation and optimization of the Pt/AlN Schottky Barrier diode (SBD) using technology computer-aided design (TCAD) modeling. The study explores the electrical characteristics of AlN SBDs with various metal contacts, including Aluminum (Al), Silver (Ag), Tungsten (W), Gold (Au), Nickel (Ni), and Platinum (Pt). Through the comparative analyses of different metal/AlN Schottky contacts, the Pt/AlN structure emerges as the most promising due to its superior barrier height and lower leakage current. At [Formula: see text]K, the diode demonstrates a barrier height of 2.72[Formula: see text]V, a nearly ideal leakage current of 0.046[Formula: see text]pA, and a breakdown voltage of 363[Formula: see text]V. The research extends to examining the temperature-dependent electrical behavior of Pt/AlN Schottky diodes, particularly for high-power and high-temperature applications. Analysis carried out across temperatures ranging from [Formula: see text]K to [Formula: see text]K reveals a trend of increasing ON resistance and consistently lower leakage current with rising temperature. Importantly, the study indicates that the impact of temperature on the barrier height and breakdown voltage of the diode is negligible, thus rendering it suitable for high-temperature operation. Leveraging the unique properties of AlN as an ultra-wide bandgap material within the III-V compound semiconductor family, this research provides valuable insights into the potential applications of Pt/AlN Schottky contact. The study highlights that the Pt/AlN Schottky contact is effective not only for high-power, high-temperature SBDs but also as superior metal/semiconductor gate contacts for field-effect transistors (FETs). Their suitability is attributed to their ability to handle high voltages, minimize reverse leakage current, and demonstrate improved thermal stability.

The Effects of an Online Paragraph Text-Writing Intervention for Students with Dyslexia

ABSTRACT Students with dyslexia often struggle with developing text-writing skills at the paragraph level. However, the literature is filled with examples of how explicit instruction paired with time-based feedback can be used to improve such skills for students with a variety of disabilities. Examples from the literature have demonstrated positive effects with intervention being implemented face-to-face, but recently there is an interest in exploring the usability and effectiveness of online interventions. In this study, we examined the effects of a well-researched, traditionally face-to-face, intervention for developing text-writing skills in an online setting with three fourth-grade students with dyslexia. We used a multiple-baseline across participants design, visual analysis, and multilevel modeling to conduct our investigation. Data regarding students’ perceptions of participating in the intervention were also collected. Two students demonstrated growth in their writing, increasing both their writing accuracy and their paragraph-writing skills measured through the paragraph rubric. One of those two participants’ changes was modest and only occurred toward the end of intervention. The third participant demonstrated no changes across any dependent measures. All students reported enjoying participating in the intervention despite experiencing a history of writing difficulties.

Exploring the horizon of AI development: Navigating constraints of chips and power in the technological landscape

With the proliferation of AI technology, machine learning has emerged as a cornerstone of AI systems, facilitating pattern recognition and decision-making through robust data analysis. This encompasses various learning paradigms such as supervised, unsupervised, and reinforcement learning, all of which are indispensable for the advancement of artificial intelligence. Nevertheless, the development of AI necessitates substantial computational resources, with specialized chips serving as the linchpin, particularly in demanding tasks such as deep learning. Dedicated chip development, exemplified by GPUs and TPUs, plays a pivotal role in enhancing the performance of AI systems, notwithstanding challenges related to costs and market monopolies. Moreover, AI systems require significant power support, especially during the training of large-scale models. To address these challenges, this paper reviews the existing literature on modeling techniques aimed at enhancing the efficiency of machine learning and reducing energy consumption. This review encompasses optimal algorithm design, hardware optimization, and spatial modeling. Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.

Barbiturate-sulfonate hybrids as potent cholinesterase inhibitors: design, synthesis and molecular modeling studies.

Aim: Design and synthesis of a series of 5-benzylidene(thio)barbiturates 3a-r.Methodology: Evaluation of the inhibitory activity of the new chemical entities on acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) using Donepezil as the standard reference.Results & Conclusion: Compound 3r emerged as the most potent AChE inhibitor (IC50=9.12μM), while compound 3q exhibited the highest inhibitory activity against BChE (IC50=19.43μM). Toxicological bioassays confirmed the absence of cytotoxicity for the most potent compounds at the tested doses. Molecular docking analysis demonstrated that the tested derivatives effectively bind to the active sites of both enzymes. Overall, this study sheds light on the potential of barbiturate-sulfonate conjugates as promising drug candidates.

Chinese dragon modeling based on Openscad

Nowadays, 3D modeling technology has become a vital link between modern technology and traditional culture. The question of how to replicate and innovate traditional cultural elements using advanced computer graphics (CG) technology has become a topic worthy of exploration. Therefore, this paper aims to discuss how to construct a dragon model rich in Chinese traditional characteristics through the powerful 3D modeling tool OpenSCAD, to demonstrate the potential of integrating modern modeling technology with Chinese traditional cultural elements. The paper first introduces the basic features of OpenSCAD and its potential applications in constructing complex 3D models, followed by a detailed discussion on the design concept, modeling process, and technical details of the Chinese dragon model, including the dragon's posture, detail depiction, and color coordination. Ultimately, a digitally rendered Chinese dragon with profound traditional aesthetic sensibilities was successfully created. The experimental evaluation results garnered widespread recognition from evaluators, particularly in terms of appearance.