Design Approach Research Articles

The Urgency of Green Banking Regulations in Indonesia in the Implementation of Sustainable Development

This research highlights significant gaps in Indonesia's green banking regulations, particularly the lack of stringent sanctions and clear enforcement frameworks for sustainable finance principles. While the Financial Services Authority Regulation No. 51/POJK.03/2017 addresses environmental sustainability, it fails to impose effective penalties on banks that contribute to environmental degradation. As a result, green banking in Indonesia remains largely a voluntary initiative due to insufficient regulatory measures. This research employs a normative legal research design, utilizing both conceptual and statutory approaches. It advocates for the implementation of stricter penalties and enhanced oversight to ensure compliance with sustainability standards among financial institutions, thereby facilitating the transition to a green economy. Furthermore, the Sustainable Development Goals (SDGs) underscore the importance of indicators related to economic, social, and environmental sustainability, which green banking can help achieve. The proposed revised regulatory framework should include stringent sanctions for violations by both banks and customers, ensuring that financial institutions actively contribute to environmental protection.

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

AbstractReal-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Inverse-designed acoustic metasurfaces for broadband sound absorption

Acoustic metasurfaces are widely used for noise attenuation due to their outstanding acoustic performance and subwavelength characteristics. The paper introduces a topology-optimized inverse design approach for broadband sound-absorbing metasurfaces, aiming to achieve efficient sound absorption performance across a wide frequency spectrum. By integrating genetic algorithms with the finite element method and driven by objectives such as the absorption frequency range and absorption coefficient, we can transcend the limitations of manual design and fully exploit the design potential of structures. Furthermore, the study investigates the sound absorption mechanism and thermal-viscous dissipation through finite element simulations and experimental validations. The research findings reveal that the proposed broadband sound-absorbing metasurfaces demonstrate excellent sound absorption capabilities across the designated frequency range, boasting an average sound absorption coefficient exceeding 85%. These findings offer fresh perspectives and methodologies for advancing the research and practical application of broadband sound-absorbing metasurfaces.

Boosting Coercivity of 3D Printed Hard Magnets through Nano-Modification of the Powder Feedstock.

The demand for strong, compact permanent magnets essential for the energy transition drives innovation in magnet manufacturing. Additive manufacturing, particularly Powder Bed Fusion of metals using a laser beam (PBF-LB/M), offers potential for near-net-shaped Nd-Fe-B permanent magnets but often falls short compared to conventional methods. A less explored strategy to enhance these magnets is feedstock modification with nanoparticles. It is demonstrated that modifying a Nd-Fe-B-based feedstock with 1wt.%Ag nanoparticles boost the coercivity of the magnets to a record value of 935±6kAm-1 without further post-processing or heat treatments. Suitable volumetric energy densities for the PBF-LB/M process are determined using finite element simulations predicting melt pool behavior and part density. Microstructural analyses reveal finer grain sizes and more equiaxed nanocrystalline structures due to the modification. Atom probe tomography identifies three phases in the Ag-modified samples, with Ag forming nanophase regions with rare-earth elements near the amorphous Zr-Ti-B-rich intergranular phase, potentially decoupling the Nd2Fe14B primary phase. The study shows that superior magnetic properties primarily result from microstructure modification rather than part density. These findings highlight inventive material design approaches via feedstock surface modification to achieve superior magnetic performance in additively manufactured Nd-Fe-B magnets.

Molecular-Level Pore Tuning in 2D Conductive Metal-Organic Frameworks for Advanced Supercapacitor Performance.

Two-dimensional (2D) electrically conductive metal-organic frameworks (MOFs) have emerged as viable candidates for active electrode materials in supercapacitors due to their high electrical conductivity, high specific surface area, and intrinsic redox-active sites. Despite their promising electrochemical performance, their pseudocapacitive behavior via fast and reversible charge transfer reactions remains yet to be fully exploited. Here, we investigate the electrochemical energy storage mechanism of Cu3(HHTATP)2 (HHTATP = 2,3,6,7,10,11-hexahydroxy-1,5,9-triaminotriphenylene), a 2D conductive MOF featuring characteristic redox-active pendant aromatic amines. Cu3(HHTATP)2 exhibited pseudocapacitive charge storage with an average gravimetric capacitance of 340 ± 15 F g-1 at a discharge rate of 0.2 A g-1 and maintained a capacitance retention over 90% after 7000 galvanostatic cycles at 5 A g-1. The polar pendant amines not only enhanced capacitance via additional amine/imine redox activity but also reduced interfacial charge transfer resistance through modified electrode-electrolyte interactions. These results highlight the potential of molecular-level pore environment tuning as a strategic approach in materials design for energy storage applications.

A Future in the Past

Australia's History educators frequently face challenges regarding the value of post-school qualifications in history. Comments from Australian Government ministers have intensified claims that history degrees lack broad skills valued by employers. This study addresses the dearth of research examining the alignment between learning outcomes from history degrees and occupational skills. A comprehensive analysis was conducted on learning outcomes from 27 history degrees offered by Australian universities. These outcomes were mapped against the Australian Government's Skills Classification Core Competencies and Occupation Listings, employing a rigorous curriculum mapping methodology. The results identified 126 occupations that align with the skills and competencies developed through history degrees. This research not only challenges misconceptions about the employability of history graduates but also provides empirical evidence of the broad applicability of historical skills across various sectors. The findings reveal a significant concentration of history graduates in managerial and professional roles, indicating the high value placed on critical thinking, analytical, and communication skills developed by studying history. However, the study also highlights areas for improvement, particularly in digital engagement and numeracy skills. The research demonstrates that history degrees cultivate a range of transferable skills highly sought after in the modern job market, including advanced communication, critical analysis, research proficiency, and adaptability. These skills position history graduates to excel in diverse fields such as public policy, journalism, law, business, and education. Furthermore, the study underscores the importance of interdisciplinary approaches in curriculum design to enhance graduate employability. This research contributes valuable insights for curriculum development and career guidance in history education, demonstrating that history degrees lead to diverse and meaningful employment outcomes. It also provides a robust evidence base for advocating the value of historical studies in an increasingly skills-focused higher education landscape.

Immunoinformatics Design of a Multiepitope Vaccine (MEV) Targeting Streptococcus mutans: A Novel Computational Approach.

Dental caries, a persistent oral health challenge primarily linked to Streptococcus mutans, extends its implications beyond dental decay, affecting over 4 billion individuals globally. Despite its historical association with childhood, dental caries often persists into adulthood with prevalence rates ranging from 60 to 90% in children and 26 to 85% in adults. Currently, there is a dearth of multiepitope vaccines (MEVs) specifically designed to combat S. mutans. To address this gap, we employed an immunoinformatics approach for MEV design, identifying five promising vaccine candidates (PBP2X, PBP2b, MurG, ATP-F, and AGPAT) based on antigenicity and conservation using several tools including CELLO v.2.5, Vaxign, v2.0, ANTIGENpro, and AllerTop v2.0 tools. Subsequent identification of linear B-cell and T-cell epitopes by SVMTrip and NetCTL/NetMHC II tools, respectively, guided the construction of a MEV comprising 10 Cytotoxic T Lymphocyte (CTL) epitopes, 5 Helper T Lymphocyte (HTL) epitopes, and 5 linear B-cell epitopes, interconnected by suitable linkers. The resultant MEV demonstrated high antigenicity, solubility, and structural stability. In silico immune simulations showcased the MEV's potential to elicit robust humoral and cell-mediated immune responses. Molecular docking studies revealed strong interactions between the MEV construct and Toll-Like Receptors (TLRs) and Major Histocompatibility Complex (MHC) molecules. Remarkably, the MEV-TLR-4 complexes exhibited a low energy score, high binding affinity, and a low dissociation constant. The Molecular Dynamic (MD) simulation analysis suggested that MEV-TLR-4 complexes had the highest stability and minimal conformational changes indicating equilibrium within 40 nanosecond time frames. Comprehensive computational analyses strongly support the potential of the proposed MEV to combat dental caries and associated infections. The study's computational assays yielded promising results, but further validation through in vitro and in vivo experiments is needed to assess its efficacy and safety.

The Optimisation of Ultrasound-Assisted Extraction for the Polyphenols Content and Antioxidant Activity on Sanguisorba officinalis L. Aerial Parts Using Response Surface Methodology

The aim of this study was to optimise ultrasound-assisted extraction (UAE) of the herb Sanguisorba officinalis L. in terms of the antioxidant activity (DPPH and FRAP method) and total polyphenol content (TPC). Optimisation was performed using the response surface methodology (RSM) with a third-degree (33) Central Composite Design (CCD) approach. The RSM was applied to obtain the optimal combination of (1) raw material content (2.25–7.5 g raw material/100 mL of solvent), (2) ethanol concentration (20–60% v/v), and (3) extraction time (1–15 min). The optimal conditions for the extraction of polyphenols and antioxidant potential were a raw material content of 7.5 g/100 mL of solvent (solid/solvent ratio 13.3 mL/g), an ethanol concentration of 47% v/v, and an extraction time of 10 min. At these optimal extraction parameters, the maximum extraction of polyphenols and antioxidant activity obtained experimentally was found to be very close to its predicted value and was 12.9 mmol Trolox/L (DPPH method), 19.4 mmol FeSO4/L (FRAP method), and 2.1 g GA/L (TPC). The mathematical model developed was found to fit with the experimental data on the antioxidant potential and polyphenol extraction. The n-octanol/water partition coefficient of the optimised extract was used to determine their lipophilicity. Our studies have shown that the optimised extract is highly hydrophilic (log P < 0). Optimal parameters can be used for the industrial extraction of the S. officinalis herb for the needs of, among others, the pharmaceutical or cosmetic industry.

The Influence of Prenatal Yoga Exercise on Mother Anxiety Pregnant

Anxiety during pregnancy not only affects the health of pregnant women but can also have an impact on the delivery process such as premature labor, prolonged labour, cesarean delivery, low birth weight. Based on WHO data, there are around 10% of pregnant women with psychological disorders and about 13% of postpartum women worldwide. This incidence is higher in developing countries in pregnant women, namely 15.6% and 19.8% in postpartum women. The purpose of this study is The Effect of Prenatal Yoga Exercise on the Anxiety of Pregnant Women at TPMB Mamah Suhaeriah in 2022. Research method This type of research uses the Pre-Experimental method with a one group pretest-posttest design approach. Sampling using total sampling with a total sample of 20 respondents. The research instrument used was an observation sheet. The data analysis used is the Wilcoxon Sign Rank Test statistical test. Research results based on the results of statistical tests using the Wilcoxon Sign Rank test, the p-value (0.000

Correction to: Optimizing Cold Spray Parameters for High Entropy Alloy Coatings Using Taguchi and Box–Behnken Design Approaches for Mechanically Alloyed Powder

Correction to: Optimizing Cold Spray Parameters for High Entropy Alloy Coatings Using Taguchi and Box–Behnken Design Approaches for Mechanically Alloyed Powder

A Human-Centered Shelter Design for People on the Move in the Al-Sahel Region

This article addresses the development of a human-centered shelter design tailored to meet the specific needs of refugees in the Al-Sahel Region. It focuses on five essential aspects of humanitarian-centered design. The goal is to create a livable unit that accommodates the three distinct phases of an emergency, transitional, and durable situation. We have adopted a non-linear design approach to develop the refugee shelter unit. We engage in discussions with team experts following each data collection phase. The conceptual design of the shelter unit is intended to align with the refugee settlement’s natural growth while maintaining a degree of control over its evolution. We have outlined a spatial configuration for a residential unit designed for three to six individuals and various patio options. Additionally, we have devised plans for an education and healthcare facility, all designed with the same structure to bring a more organized approach to the organic growth of the camp. The design proposal adopts a process-oriented approach, incorporating refugees indirectly in the design and construction of their shelters. While we do not assert that the framework of a ‘refugee camp’ can be sustainable, our goal is to show that its planning, in the absence of alternatives, should adhere to sustainability criteria.

Back and Forth from Urban Renewal: The Spatial Parameters of Affordable Housing in Two Cities

This study examines the impact of spatial parameters on urban well-being by comparing the urban design and housing policies of Roosevelt Island in New York City and Bat Yam in the Tel Aviv metropolitan area, Israel. Despite their distinct political and cultural contexts, these cities exhibit similar urban design approaches that integrate physical well-being parameters—such as density, building height, open spaces, and walkability—alongside social well-being parameters including age distribution, income levels, and ethnic diversity. The research traces the evolution of affordable housing through various historical phases, including Urban Renewal, Community Development, and Neoliberal Urbanism, and explores how different residential ownership structures have influenced demographic shifts, gentrification, and neighborhood transformation over the past decades. Using a mixed-methods approach that combines historical analysis with spatial and demographic data, this study highlights the contrasting impacts of Roosevelt Island’s affordable housing program and Bat Yam’s state-led urban renewal policies. The results illustrate how distinct housing models affect community resilience, social stability, and overall urban well-being. By uncovering parallel narratives and extracting valuable insights, this analysis offers lessons for other cities navigating similar market-led pressures and policy shifts. The findings underscore the importance of understanding the interplay between spatial design, housing policies, and ownership structures in shaping the long-term sustainability and quality of life in urban neighborhoods.

A lightweight model design approach for few-shot malicious traffic classification

Classifying malicious traffic, which can trace the lineage of attackers’ malicious families, is fundamental to safeguarding cybersecurity. However, the deep learning approaches currently employed require substantial volumes of data, conflicting with the challenges in acquiring and accurately labeling malicious traffic data. Additionally, edge network devices vulnerable to cyber-attacks often cannot meet the computational demands required to deploy deep learning models. The rapid mutation of malicious activities further underscores the need for models with strong generalization capabilities to adapt to evolving threats. This paper introduces an innovative few-shot malicious traffic classification method that is precise, lightweight, and exhibits enhanced generalization. By refining traditional transfer learning, the source model is segmented into public and private feature extractors for stepwise transfer, enhancing parameter alignment with specific target tasks. Neuron importance is then sorted based on the task of each feature extractor, enabling precise pruning to create an optimal lightweight model. An adversarial network guiding principle is adopted for retraining the public feature extractor parameters, thus strengthening the model’s generalization power. This method achieves an accuracy of over 97% on few-shot datasets with no more than 15 samples per class, has fewer than 50 K model parameters, and exhibits superior generalization compared to baseline methods.

How to drug a cloud? Targeting intrinsically disordered proteins.

Biologically active proteins/regions without stable structure (i.e., intrinsically disordered proteins and regions (IDPs and IDRs)) are commonly found in all proteomes. They have a unique functional repertoire that complements the functionalities of ordered proteins and domains. IDPs/IDRs are multifunctional promiscuous binders capable of folding at interaction with specific binding partners on a template- or context-dependent manner, many of which undergo liquid-liquid phase separation, leading to the formation of membrane-less organelles and biomolecular condensates. Many of them are frequently related to the pathogenesis of various human diseases. All this defines IDPs/IDRs as attractive targets for the development of novel drugs. However, their lack of unique structures, multifunctionality, binding promiscuity, and involvement in unusual modes of action preclude direct use of traditional structure-based drug design approaches for targeting IDPs/IDRs, and make disorder-based drug discovery for these "protein clouds" challenging. Despite all these complexities there is continuing progress in the design of small molecules affecting IDPs/IDRs. This article describes the major structural features of IDPs/IDRs and the peculiarities of the disorder-based functionality. It also discusses the roles of IDPs/IDRs in various pathologies, and shows why the approaches elaborated for finding drugs targeting ordered proteins cannot be directly used for the intrinsic disorder-based drug design, and introduces some novel methodologies suitable for these purposes. Finally, it emphasizes that regardless of their multifunctionality, binding promiscuity, lack of unique structures, and highly dynamic nature, "protein clouds" are principally druggable. Significance Statement Intrinsically disordered proteins and regions are highly abundant in nature, have multiple important biological functions, are commonly involved in the pathogenesis of a multitude of human diseases, and are therefore considered as very attractive drug targets. Although dealing with these unstructured multifunctional protein/regions is a challenging task, multiple innovative approaches have been designed to target them by small molecules.

Proposing a course schedule for architectural basic design studio guided by Bloom’s Revised Taxonomy

The article moves away from the traditional architectural basic design education methods and approaches it within the framework of the discipline of educational sciences and learning models. The multi-layered education structure transforms into a new learning field under the guidance of Bloom's Revised Taxonomy, which proposes a course schedule for the architectural basic design studio. Taxonomy allowed us to discover the reflections and effectiveness of the interdisciplinary structure of architecture and design in the educational process and to be instrumentalized and used in the context of basic design education. The course schedule proposal presents a flexible schedule outline with the example of an interdisciplinary studio schedule. The trial created within the scope of this study can be appropriately organized for different Ecoles, schools, and values. The paper is significant for its interdisciplinary nature and develops a new discourse using Bloom's Revised Taxonomy. Using Bloom's Revised Taxonomy as a method in the studio has led to a paradigm shift in methodology.

Effectiveness of Butterfly Pea ( Clitoria Ternatea ) Extract Against Streptococcus Mutans Bacterial Growth In Vitro

Dental caries is still a major problem in dentistry and is the most common infectious disease in children. Caries is a microbiological infectious disease that can damage the hard tissue of the teeth. This study aims to determine the effectiveness of butterfly pea flower extract ( Clitoria ternatea ) on the growth of Streptococcus mutans in vitro. This type of research is laboratory experimental research using a post test only control group design approach The samples used were butterfly pea flower extract ( Clitoria ternatea ) diluted with DMSO to a concentration of 100%, 50%, 25%, 12.5%, 3.125%, 6.125% and Streptococcus mutans ATCC 25175. This research was conducted using the MIC test dilution method and KBM to see the effectiveness of the antibacterial extract against the test bacteria. Analysis of research data using the one way ANOVA test (p <0.05) to see significant differences between study groups with the number of test bacteria, followed by a double comparison test with the Post Hoc Least Significance Different (LSD) method to determine the average difference between treatment groups. Antibacterial effectiveness test results showed that butterfly pea flower extract ( Clitoria ternatea ) was effective in inhibiting Streptococcus mutans bacteria (p<0.05), at a concentration of 25% was the minimum inhibitory level (MIC) and 50% concentration was the minimum killing rate (MBC). ). The conclusion of this study is that the extract of butterfly pea flower ( Clitoria ternatea ) is effective in inhibiting and killing Streptococcus mutans bacteria.

Designing orthopaedic seat cushion made of rattan using quality function deployment approach

Using ergonomic orthopaedic chairs can address spinal issues, particularly HNP, due to their flexible and elastic properties. By achieving optimal ergonomic design, various positive aspects can be achieved, such as increasing work quality productivity and reducing the cost of injuries or accidents. This research demonstrates diverse product design approaches to enhance posture health ergonomics, address specific musculoskeletal disorder issues, and consider local needs and available materials. Rattan is a tropical vine widely used in the furniture industry and predominantly found in wooded areas such as Indonesia. This research combines market research, morphology, ergonomic study, concept selection, QFD, HoQ, stiffness test, and prototype review. The QFD method was chosen in this study due to its excellence in connecting consumer desires with the product development process. Data of respondents consist of 51 % of respondents were male, 49 % were female, 88 % were aged between 18 and 34 years old, and 10 % were aged between 35 and 54 years old. The approach of 6 stages of product design as the primary framework for developing our product such as: planning, concept development (concept screening, product morphology, concept selection), system-level design, detailed design, testing, refinement and launching. Based on the calculations using the HOQ, it is identified that the primary focus in designing the rattan-based orthopaedic seating, lies in the orthopaedic design that provides maximum comfort for the spine. The comparison results indicate that the stiffness level of rattan weaving is nearly like latex, with a similarity percentage of 89 %. Data analysis using the QFD method indicates that the primary priority aspect is the orthopaedic design, followed by the selection of quality materials, cost-effectiveness, versatility, and appealing design aspects. Rattan's superior air circulation provides comfort by preventing excessive heat accumulation, excellence in strength and durability, ensuring ease of lightweight modelling, and providing more sustainable material.

Socioeconomic Determinants of Household Access to HIV/AIDS Healthcare in Kibra Sub-County, Nairobi County, Kenya

This study examined the impact of social and economic factors on the uptake of HIV/AIDS care in Kibra Sub County, Nairobi. The research was grounded in Behavioral theory and focused on how household income, literacy levels, and access to health information influenced the adoption of HIV/AIDS treatment. A descriptive survey design approach was employed, involving a sample of 365 households chosen through simple random and stratified sampling techniques from target population of 61,690 households, as outlined by Krejcie and Morgan's formula. Data collection was carried out using key informant interviews and self-administered questionnaires. Descriptive techniques, such as frequency and median analysis, as well as inferential techniques, such as bivariate correlation analysis, chi-square testing, and binomial logistic regression, were used. The results were displayed using tables, graphs, and percentages. Thematic analysis was also used to examine qualitative data in order to find new topics. The analysis revealed a strong positive relationship between access to health information and the likelihood of seeking treatment. Households with higher income and literacy levels were more likely to pursue HIV/AIDS care. Quantitative results showed that access to HIV-related information increased the likelihood of treatment uptake by 6.666 times. Additionally, increased household income and higher literacy levels were linked to a 7.434-fold and 3.554-fold rise, respectively, in the probability of seeking care. The study concluded that socio-economic factors, particularly income, literacy, and information access, significantly influenced healthcare-seeking behaviors in Kibra. To enhance access to HIV/AIDS treatment, it is recommended that policymakers, NGOs, and other stakeholders implement targeted interventions, including income-generating programs and comprehensive health education, to address the socio-economic barriers that hinder healthcare uptake.

Designing Hand Orthoses: Advances and Challenges in Material Extrusion

The intricate structure of human hands requires personalized orthotic treatments, especially with the growing aging population’s demand for accessible care. While traditional orthoses are effective, they face challenges of cost, customization time, and accessibility. Additive manufacturing, particularly material extrusion (MEX) techniques, can effectively address challenges in orthotic device production by enabling automated, complex, and cost-effective solutions. This work aims to provide engineers with a comprehensive set of design considerations for developing hand orthoses using MEX technology, focusing on applying design for additive manufacturing principles, to enhance rehabilitation outcomes. This objective is achieved by establishing design requirements for hand orthoses, reviewing design choices and methodologies across conventional and state-of-the-art MEX-based devices, and proposing an innovative approach to orthotic design. Hand orthosis design requirements were gathered through workshops with occupational therapists and categorized into engineer-, medical-, and patient-specific needs. A review of 3D-printed hand orthoses using MEX analyzes various design approaches, providing insights into existing solutions. The study introduces a modular design concept aimed at improving rehabilitation by enhancing customizability and functionality. It highlights the potential of MEX for creating personalized, cost-effective orthoses and offers recommendations for future research, to optimize designs and improve patient outcomes.

A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer's disease (AD) and Parkinson's disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.