The today competitive advantage of Ready-made garment industries depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industries have long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line system has attracted a number of researchers as one way for insightful analysis of system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to so many researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations has been demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on the production throughput of the trouser assembly line were studied. The increase of 28.63% of the production throughput was achieved for the best factors’ setting of the metamodel.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

As the necessity for safety and aesthetic of nightscape have arisen, the importance of nightscapes (i.e., nighttime landscape) planning has garnered the attention of mainstream consciousness. Therefore, this study is to suggest the guideline for nightscape planning using electroencephalography (EEG) technology and survey for recognizing the characteristics of a nightscape. Furthermore, we verified the EEG method as a tool for landscape evaluation. This study analyzed the change of relative alpha power and relative beta power and self-reporting of participants in order to investigate the correlation between EEG and fear according to twelve nightscape settings. Our findings indicated the corresponding measures of fear vary accordance with whether there was people or not, and the environmental settings (Built Nightscape Images; BNI vs Natural Nightscape Images; NNI). Based on our physiological EEG experiment, we provided a new analytic view of the nightscape. The approach we utilized enables a deeper understanding of emotional perception and fear among human subjects by identifying the physical environment which impacts how they experience nightscapes.

The COVID-19 pandemic has significantly impacted global health and economies, necessitating the development of effective vaccines against the novel virus. Understanding the structure and function of SARS-CoV-2 is crucial for rational vaccine design. The virus consists of several key proteins, including the spike protein, envelope protein, membrane protein, and nucleocapsid protein. The spike protein plays a crucial role in host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2) receptor on human cells. The replication of the virus within host cells is facilitated by various viral proteins, such as the RNA-dependent RNA polymerase (RdRp) and the main protease (Mpro). Rational vaccine design strategies for SARS-CoV-2 primarily focus on targeting the spike protein due to its role in host cell entry. However, developing vaccines against this protein is challenging due to its highly mutable nature and potential conformational changes. Alternative strategies involve using other viral proteins, such as the nucleocapsid protein, which is conserved and essential for viral replication. Considering T-cell responses in vaccine design is also vital as they play a vital role in controlling viral infections. Vaccines that elicit both antibody and T-cell responses are more likely to provide robust and durable immunity against SARS-CoV-2. Advancements in rational vaccine design for SARS-CoV-2 include mRNA-based vaccines, viral vector-based vaccines, and protein subunit vaccines. However, challenges remain in developing a universally effective vaccine, including the emergence of new SARS-CoV-2 variants and mutations that may affect the efficacy of existing vaccines. In conclusion, rational vaccine design for SARS-CoV-2 requires a comprehensive understanding of the virus's structure and function, targeting key viral proteins, and considering T-cell responses.

A novel approach to the remote-control system for the compact multi-crystal energy-dispersive spectrometer for x-ray emission spectroscopy (XES) applications has been developed. This new approach is based on asynchronous communication between software components and on reactive design principles. In this paper, we identify the challenges we have faced, our solution to them as well as the implementation and future development prospects. The main motivation of this work was the development of a new holistic communication protocol that can be implemented to control various hardware components.

Background: With the trend of intelligent display, the interface design of in-vehicle HUD is an expanding research field; Methods: To solve the subjectivity and uncertainty in the optimization of HUD interface design schemes, this paper proposed a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR. The entropy weight method was used to reduce the subjectivity the decision-maker's weighting and obtain the objective weight of each indicator; The VIKOR method was used to obtain the best ranking of alternative schemes, and then the optimal interface design scheme was selected; Results: The evaluation of in-vehicle HUD interface design schemes were taken as an example for verification and calculation. The results showed that this method considers the subjectivity and uncertainty of the decision-making process in the optimization of design scheme, which can effectively improve the objectivity and accuracy of the evaluation results, and provide a reference for designers to optimize interface design schemes.

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

In the study, a lipid mixture was developed and optimized using a broad range of excipients of different po-larities. Initially, the excipients were classified according to their behavior in water. Then binary mixtures were prepared using cetyl palmitate (Crodamol™ CP pharma) as the solid lipid. The miscibility between the excipients was evaluated using Raman chemical images obtained by classical least squares (CLS). Based on the results, the excipients Crodamol™ CP pharma (cetyl palmitate, hydrophobic), Super Refined™ DMI (di-methyl isosorbide; hydrophilic) and Super Refined™ Lauryl Lactate (medium polarity) were chosen to com-pose the lipid core. The ideal proportion of these excipients was determined using a Mixture Design and the standard deviation of image histograms as responses. The main goal was to develop a versatile lipid core for ‘brick-dust type of drugs’. After statistical evaluation of the DOE results, the final composition was deter-mined and drugs with different logP (0 to 10) and physiochemical characteristics were evaluated in the opti-mized mixture. The drugs butamben, tacrolimus, atorvastatin calcium and resveratrol presented a homoge-neous distribution in the optimized lipid core, indicating that the core has indeed the capability to be used in NLC formulations of such type of drugs.

Lung cancer, globally recognized as the leading cause of cancer-related mortality, presents a pressing need for innovative treatment strategies. Increasing evidence points towards the potential of monoamine oxidase (MAO) inhibitors in modulating cancer progression, primarily due to their role in regulating cellular metabolism and apoptotic pathways. This extraordinary study delves into the multifaceted therapeutic implications of MAO inhibitors in lung cancer management, elucidating their ability to pave the way for clinical advancement via novel targets and pathways. Capitalizing on recent advances in molecular biology, this research explores the molecular mechanisms underlying the anticancer actions of MAO inhibitors. By examining their impact on cell proliferation, migration, invasion, and apoptosis in lung cancer cell lines, we provide insight into the intricate signaling pathways and biochemical processes modulated by these compounds. The study also investigates the synergistic effects of MAO inhibitors in combination with established chemotherapeutic agents, elucidating their potential to enhance treatment efficacy and reduce cytotoxicity. Furthermore, we delve into the implications of MAO inhibitors on the tumor microenvironment, evaluating their influence on angiogenesis, immune regulation, and the epithelial-mesenchymal transition. This comprehensive analysis enables the identification of novel molecular targets, which hold promise for the development of advanced therapeutics tailored for specific lung cancer subtypes. To substantiate our findings, in this review, we thoroughly analyze the existing literature using state-of-the-art bioinformatics tools." in vivo orthotopic lung cancer models, and clinical patient cohorts. This robust and multidisciplinary approach permits the generation of clinical evidence supporting the integration of MAO inhibitors in lung cancer management. Conclusion: This ground-breaking study establishes the therapeutic potential of monoamine oxidase inhibitors in lung cancer management by unearthing novel pathways and targets for clinical advancement. The outcomes open doors for the development of innovative treatment strategies and inspire continued research efforts to combat the global burden of lung cancer.

This study presents the design and implementation of a PLC microprocessor adhering to the IEC-61131-3 standard, executed on a Cyclone-V FPGA using a DE10-NANO development board. Our microprocessor optimizes the central processing unit by streamlining the data path, achieving a remarkable simulated response time of approximately 60 ns, equivalent to three clock cycles at a 50MHz frequency for Boolean operations. To substantiate our approach, we conducted practical experiments utilizing a FESTO conveyor station, employing relays as actuators, and incorporating optical and inductive sensors. The results underscore the feasibility of our proposed approach and serve as practical validation of its efficacy. This work introduces a promising avenue for the development of cost-effective PLCs employing SoC FPGA variants. Additionally, a thorough comparison of execution times with other early reported architectures. Our microprocessor outperforms even well-established PLCs like the S7-312, with substantial reductions in execution times of 94.54% for floating-point operations, 71.42% to 93.33% for word operations, and up to 78.57% for bit operations.

In this study, the on-going research into the improvement of micro-gas turbine propulsion system performance and the suitability for application as propulsion systems for small tactical UAVs (<600 kg) is investigated. The study is focused around the concept of converting existing micro turbojet engines to turbofan with the use of a continuously variable gearbox, thus maintaining a single spool configuration and relative design simplicity. This is an effort to reduce the initial engine development cost, whilst improving propulsive performance. The BMT 120 KS micro turbojet engine is selected for performance evaluation of the conversion process using gas turbine performance software, GasTurb13. The preliminary design of a matched low-pressure compressor (LPC) for the proposed engine is then performed using meanline calculation methods. According to the analysis carried out, an improvement in the converted micro gas turbine engine performance in terms of thrust and specific fuel consumption is achieved. Furthermore, with the introduction of a CVT gearbox, fan speed operation may be adjusted independently of the core, allowing increased thrust generation or better fuel consumption. Therefore, enabling a wider gamut of operating conditions and enhances the performance and scope of tactical UAV.

As an implementation of inverse Radon transform, the Second-order Divided-difference Back Projection (SDBP) technique has been proposed in this paper, by which the accurate CT reconstruction can be theoretically realized. A computational model for CT reconstruction filter expressions has been derived on the basis of SDBP technique. The method reveals the correlation between the convolution kernel for data restoration and the filter expression. By substituting kernel functions into the computational model, the amplitude sequences of a variety of filters can be calculated. On the proposed filter construction method, the decomposition form of filters has been discovered, where a series of basic filters have been acquired. The properties of any filter depend on the decomposed basic filters. Basic filters can be used to compose filters in actual needs.

Corynebacterium glutamicum is prominent for the industrial production of secreted amino acids. Notably, it naturally accumulates the carotenoid pigment decaprenoxanthin in its membranes. Metabolic engineering enabled production of astaxanthin. Here, a bioprocess for astaxanthin production in lab-scale stirred bioreactors was established by a DoE-guided approach to optimize the basic process parameters pH, rDOS, aeration rate as well as inoculation cell density. The DoE-guided approach from 2 L scale cultivation revealed that the pH showed the strongest effect on the product formation. Subsequently, an optimum at pH 8, an aeration rate of 0.25 vvm, 30 % rDOS and an initial optical density of 1 was established that allowed production of 7.6±0.6mg L-1 astaxanthin in batch mode. These process conditions were successfully transferred to a fed-batch process resulting in a high cell density cultivation with up to 60 g CDW L-1 biomass and 64 mg L-1 astaxanthin and thus demonstrating an about 9-fold improvement compared to optimal batch conditions. Moreover, pH shifts experiments indicate that the cells can quickly adapt to a change from pH 6 to 8 and start producing astaxanthin showing the possibility of a biphasic bioprocesses for astaxanthin production.

(1) Background: Type-2 Diabetes (T2DM) is a long-term medical disorder characterized by Insulin deficiency and high blood glucose levels. Among other medication to cure T2DM, the review of the literature found that various Pyrimidine derivative act as an agonist for G-protein-coupled receptor 119 (GPR119) was proposed to control blood glucose levels by enhancing the function of pancreatic beta-cells and its mechanism of action with fewer adverse effects.; (2) Methods: In the present research work, in-silico investigations were carried out to investigate the potential of Pyrimidine analogue as an agonists to the protein target GPR119 receptor. We performed exhaustive molecular modelling and protein modelling methodologies such as homology mod-elling, molecular docking along with various drug designing tools such as 3D-QSAR and pharmacophore mapping to ascertain the design of better GPR119 agonists.; (3) Results: On the basis of in-depth computa-tional studies, we designed new pyrimidine moiety and analyzed them for GPR-119 receptor agonist and fur-ther explored the ADME properties. Designed compounds found to exhibit better predicted activities as com-pared to reference compound; (4) Conclusions: The current research on pyrimidine derivatives, using molec-ular docking, 3D-QSAR and Pharmacophore mapping demonstrated that the obtained computational model has significant properties and the designed molecules and Dataset from this model, produced antidiabetic compound against the target GPR119 i.e., compound 1S, 1Z and 1D with the docking score of −11.696, -9.314 and -8.721 respectively. The pharmacokinetics and drug-likeness studies revealed that these compounds may be the future candidate for the treatment of diabetes acting via GPR119 agonist mechanism.

The concept of Design Fatigue Factors (DFFs) was introduced for providing desired level of safety in structural fatigue design, often associated with damage calculated from S-N curves. Calculation of fatigue damage from S-N curves can be affected by multiple factors, e.g. types of weld class, corrosion condition, loading conditions, stress concentration on different geometries etc. Each of them can be subject to different level of uncertainties. This study intends to recalibrate the DFFs from a detailed reliability analysis by investigating the probabilistic models derived from the database of S-N curves that has been most frequently used in offshore wind industry. The results of such study indicate that the DFFs can be reduced substantially for the corrosive environmental fatigue models from current standards to the same level of target reliability.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

The construction industry is one of the most fatal industries, so it is important to pay more attention to safety solutions. Even though work-related accidents are known as major waste in construction projects, little attention has been paid so far to incorporating safety into the lean construction framework. In this research, lean construction theory is reviewed through the lens of safety. That being so, the identified challenges in previous research on improving safety in construction projects are categorized and those related to the concept of lean project delivery are introduced. Then, the principles of the lean construction framework are explained and the relevant changes for incorporating safety into the framework are introduced and discussed. It is expected that this hybrid model would further enrich the lean construction framework. The careful attention of project executives to this model may improve the safety situation in construction projects.

Methods for cost calculation of Laser-based Additive Manufacturing (LAM) have evolved over the last two decades. Starting from a rapid prototyping manufacturing method, cost calculation over time included more than single part production for small series or mixed batches. New machine developments nowadays aim at large scale production and introduce modular machine designs that give LAM factory operators more decision freedom on machine configurations and expansibility. To leverage this new possibilities, LAM costing methods must be adapted to calculate and visualize the economic consequences of different module configurations.In this work a modelling architecture of L-PBF machines using virtual modules is presented. Using principles of the industry 4.0 communication standard OPC UA, a virtual machine is set up, parametrized and connected to the AM digital process chain. Two different views on the cost information are used, optimized for different use cases.First, the product view allows detailed analysis of cost allocation to parts in mixed batches and the layered geometry of the part. Second, the machine view tracks utilization of all machine modules, divided into the states idle, active, off and maintenance. The theoretical Overall Equipment Effectiveness (OEE) is calculated on a module basis, enabling economic analysis and optimization of module composition as well as module improvements and new modular machine constellations. This allows focusing research and development on most valuable improvements. By calculating different scenarios like spare part production, mass production or rapid prototyping, the part portfolio to be manufactured can be considered.

In this study, the effect of extraction conditions on the total phenolic content of clove buds was investigated. Clove buds were extracted using a deep eutectic solvents like mixtures choline chloride: lactic acid in the 1:2 molar ratio. Additionally, the HPLC-DAD method was used to determine the main components of the clove extracts, including eugenol, eugenol acetate, and β-caryophyllene. The conditions used in the extraction with choline chloride: lactic acid (molar ratio 1:2) included extraction temperature (t = 40 – 80°C), water addition (5.6 – 40%), and extraction time (30 – 90 min). Determination of the phenolic compounds was done by the Folin-Ciocalteu method. The optimum operating conditions for the total phenolic content were identified at 77°C, 30 min, and a water addition of 40%. Based on the results, the highest amount of eugenol (307.26 ± 8,44 mg/g dry raw material) was determined in clove extracts which were extracted under condition of 60°C, 22.8%, and 30 min.

The research paper investigates the synthesis of metal matrix composites (MMCs) through the amalgamation of aluminum and silicon carbide particles, employing a two-step mixing methodology called the stir casting method. Metal matrix composites are materials that enhance the capabilities of a metallic matrix by incorporating another material. This leads to better characteristics such as tensile and shear moduli, fatigue and fracture properties, thermal expansion coefficient, and more. Aluminum-matrix composites are a class of materials that possess distinct characteristics that may be modified by altering factors such as the type of reinforcing material, the amount of reinforcement, and the fabrication technique employed to produce the composite. The concept aims to combine the desirable properties of metals and ceramics by incorporating durable refractory particles into a malleable metal structure. The fabrication process employs the stir casting technique, where reinforcing phases are mechanically stirred into a molten matrix to create cast composites with reinforcement volume percentages of up to 30%. The study utilizes Response Surface Methodology to enhance the mechanical properties of the composites through the optimization of manufacturing parameters, including stirring temperature, time, and speed. The study entails analyzing the correlation between different casting conditions and the resulting changes in hardness and tensile strength.

Endoscopy has made a significant and noteworthy contribution to the field of medical science and technology. Nevertheless, its potential remains constrained due to the limited availability of rigid or flexible endoscopes. This paper introduces a novel hydraulic actuator based on electrorheological fluid (ERF) as a pivotal advancement in bridging the existing gap within the realm of endoscopy. Following a comprehensive introduction that briefly outlines the electrorheological effect, the subsequent section is dedicated to the elucidation of the actuator’s development process. Challenges arise, particularly in terms of miniaturization and the realization of a hydraulically sealed system with integrated valve electrodes. An internal electrorheological valve system consisting of four valves that are controlled using a pulse-width modulated high voltage was suitable for position control of the antagonistic hydraulic actuators. High-precision stereolithography (SLA) printing has proven practical for manufacturing the actuator components. For functional testing, a test bench was set up in which the actuator follows a setpoint through a PI control loop. The control deviation ranged from 0.6 to 1 degree, with a response time between 6 and 8 seconds. The experiments have demonstrated that, through the use of ERF and integrated valve electrodes, a miniaturized functional actuator can be constructed.

Supercritical fluid extraction (SFE) is an innovative green technology for the extraction of phytochemicals from plants. Therefore, this study aimed to evaluate the application of SFE and to optimize the extraction condition of the Thai herbal formula, Kleeb Bua Daeng (KBD). A Box-Behnken design (BBD) with response surface methodology was used to determine the effect of extraction time, temperature, and pressure on response variables including extraction yield, total phenolic content (TPC), total flavonoid content (TFC), total carotenoid content (TCC) and total anthocyanin content (TAC) of KBD formula. The highest percentage extraction yield, TPC, TFC, and TCC of the extracts were 3.81%, 464.56 mg gallic acid equivalents/g extract, 217.19 mg quercetin equivalents/g extract, and 22.26 mg β-carotene equivalents/g extract, respectively. The results indicated that SFE is a suitable method of extraction for green recovery of some phytochemicals from the KBD formula.

Polymerase chain reaction (PCR) technique is one of the molecular methods in amplifying DNA for the detection of malaria. However, the limitations of PCR especially when used for routine clinical practice can hamper its sensitivity and specificity. With that, this study focuses on designing a microfluidic device that will mimic the function of a conventional genus-specific PCR based on 18S rRNA gene to detect malaria parasites (Plasmodium falciparum) at low grade parasitemia. The design was drawn and simulated using ANSYS 14.5 Computational Fluid Dynamics (CFD). The simulation shows that adding loops to the design increases its relative deviation but to a minimal extent as compared to having only a straight path design, which indicates that looping is acceptable in designing a microfluidic device to minimize chip length. Also, increasing the cross-sectional area of the fluid path decreases the efficiency of the design, thus, the design with a relatively smaller cross-sectional area is favored. And lastly, among the three materials utilized, the chip made of polypropylene is the most efficient with a relative deviation of 0.94 as compared to polycarbonate and polydimethylsiloxane which have relative deviations of 2.78 and 1.92, respectively.

Psychotherapy is one of the evidence-based clinical interventions for the treatment of depression in older adults with dementia. Randomized Controlled Trials are often the first methodological choice to gain evidence, yet they are not applicable to a wide range of humanistic psychotherapies. Amongst all, the efficacy of the Gestalt therapy (GT) is under-investigated. The purpose of this paper is to present a research protocol aiming to assess the effects of a GT-based intervention on people with dementia (PWD) and the indirect influence on their family carers. The study implements the Single-Case Experimental Design with Time-Series Analysis that will be carried out in Italy and Mexico. Ten people in each country, who received a diagnosis of dementia and present depressive symptoms, will be recruited. Eight or more GT sessions will be provided whose fidelity will be assessed by the GT Fidelity Scale. Quantitative outcome measures are foreseen for monitoring participants’ depression, anxiety, quality of life, carers’ burden, and the caregiving dyad mutuality, at baseline and follow-up. The advantages and limitations of the research design are considered. If GT will result effective in the treatment of depression in PWD, it could enrich the range of evidence-based interventions provided by healthcare services.

The objective of this study was to develop guidelines for rainwater harvesting system sizing of shopping centres in South Africa. Three generalized dimensionless relationships relating rainwater supply and demand to tank size, yield and reliability were developed based on 101 years long daily time step simulations of rainwater harvesting of 19 shopping centres located in four regions. Daily rainfalls were obtained from nearby rainfall stations and daily non-potable demands were based on the size of the retail area. The simulations revealed within-year storage behaviour with considerable variation of the yield specified as the number of days for which demand was met each year. The Weibull plotting position formula was applied on the time series of yields to obtain yield-reliability relationships. Simulation results of the hydrologically optimum systems were used to develop two of the generalized relationships and an additional one based on the dependence of the slope of the reliability – yield plots on the optimum yield was formulated to enable analysis of hydrologically non-optimal systems. Most of the relationships fitted best to the non-linear power law form with high correlation coefficients averaging 0.92 and ranging from 0.82 to 1.00. The application of the models for tank sizing and assessing system performance is demonstrated.

Arsenic is highly toxic, affecting millions of people in many regions of the world. That is why developing economic and efficient technologies is imperative to eliminate it. Sorption techniques are attractive as efficient and inexpensive sorbents can be used. Chitosan is an abundant, naturally occurring, biodegradable, low-cost biopolymer that can be combined with metal oxide to enhance its removability. This work aimed to synthesize a new chitosan-magnetite-based sorbent for arsenic removal. The synthesized sorbent does not present pores, and by FT-IR, functional groups of the chitosan and the presence of As(V) in the sorbent treated with arsenic were identified. The synthesized magnetite was characterized by XRD spectroscopy. Application of the Composite Central Design model showed that 0.22 g of the sorbent at pH 6.0 could remove 27.6% of As(V). Kinetic data, fitted with the pseudo-first and second-order models, indicated an ion exchange sorption and activation energy of 28.1-31.4 kJ mol-1. The isotherms were fitted with the Langmuir model, indicating a favourable monolayer adsorption with high affinity. The sorption energy, calculated by Dubinin-Radushkevich, 9.60-8.80 kJ mol-1, confirms a sorption mechanism mediated by ion exchange. The thermodynamic parameters of the process were ΔG°(-21.7/-19.7 kJ mol-1), ΔH°(16.7 kJ mol-1) and ΔS°(123.3 J mol-1 K-1).

Since its introduction almost 50 years ago, the concept of ‘flow’ has been descriptive of optimal experiences, also in relation to play. However, the explorative nature of play leads to some discrepancies between flow and the optimal experience of play. In this paper the differences between flow and play are explored, leading to proposing the state of ‘wonder’ (directed at exploration) as an alternative to ‘flow’. From this perspective, the study further explores how we may design toys that enable meaningful experiences of play, identifying opportunities in designing for toys as the enablers of immersed experiences of wonder.

Fashion industry is the second most polluting industry in the world representing a 2 trillion dollars and growing valuation (Pal, 2017). This dual context makes its challenges hard to address. From one side, fashion design education and practice systems have been perpetuating an industrial-focused approach which relies mostly in the economic improvement through fast cycles of product development (Pal, 2017). On the other side, fashion industry has also been closed to either multidisciplinary and transdisciplinary initiatives outside the scope of the artistic disciplines. Therefore, innovative approaches are needed to solve fashion industrial challenges. One of the most promising fields to tackle fashion current environment and technological problems is microbiology (Mazzoto et al., 2021). During the past 50 years, microbiology has played a vital role in solving human grand challenges in health, agriculture, food, and waste management sectors, and it also represents an opportunity for fashion industry as well. Microbiology biotechnological potential for the fashion industry relies mostly on the improvement of toxic waste bioremediation and the development of novel biomaterials and biomolecules. Moreover, the emergent field of synthetic biology is expanding the tools and approaches available, and they can already be seen in the development of engineered living materials that have functional properties (Mazzoto et al., 2021). Despite the urgent need for change, there is still a long way until a more sustainable fashion industry is achieved. Therefore, microbiological research and innovation need maturation to be able to scale-up and reach a global impact for tackling fashion industrial problems.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

The consistently positive Australian economic environment and stable population increase have led to a higher demand for new houses in recent years. Prefabrication is a promising method to help alleviate the issues related to housing shortage and affordability due to reduced material wastage, construction delays due to weather conditions, unexpected costs, shortage in labour and onsite risks. With the advancements in automation and manufacturing methods such as Design for Manufacturing and Assembly (DfMA), the quality and precision of prefabricated materials is tightly controlled, and the fabrication and assembly period are reduced. However, the full potential of prefabricated construction is yet to be realised in part due to most of developments being focused on its superstructure. A review of the current available options suitable for houses is necessary to understand the present state of the residential footing industry, which will help evaluate the necessary innovations for the growth of the Australian construction industry considering the local reactive soil conditions. This paper presents a summary of existing footing systems and potential prefabricated footing solutions for low-rise residential structures with one storey to two storeys. This paper also reviews the benefits and challenges of designing, manufacturing, transporting, handling and installing of prefabricated footings on site, which have great influence on the acceptance of these innovative footing systems.

Single-fluid electrospinning creates nanofibers from molten polymer solutions with active ingredients. The study utilized a combination of a fractional factorial design and a Box-Behnken design to examine crucial factors among a multitude of parameters and to optimize the electrospinning conditions that impact the fiber mats' morphology and entrapment efficiency of the Senna alata leaf extract. The findings indicated that the shellac content had the greatest impact on both fiber diameter and bead formation. The optimum electrospinning conditions were identified as a voltage of 24 kV, a solution feed rate of 0.8 mL/h, and a shellac-extract ratio of 38.5:3.8. These conditions produced nanosized fibers with a diameter of 306 nm, a low bead-to-fiber ratio of 0.29, and an extract entrapment efficiency of 96% within the fibers. The biphasic profile of optimized nanofibers was confirmed through an in vitro release study. This profile consisted of an initial burst release of 88% within the first hour, which was succeeded by a sustained release pattern surpassing 90% for the next 12 h, as predicted by zero-order release kinetics. The optimized nanofibers demonstrated antimicrobial efficacy against diverse pathogens, suggesting promising applications in wound dressings and protective textiles.

The Last Planner System (LPS) aims to enhance planning reliability by reducing variability in construction processes. While LPS applications have been explored in construction and detailed design, its application in architectural design remains underrepresented due to its abstract nature. This study addresses this gap by proposing an LPS framework tailored for architectural design, utilizing LPS metrics to assess planning reliability. Key issues hindering formal planning methods' implementation are identified, and relevant LPS principles are aligned with these challenges, culminating in a conceptual LPS model designed for architectural projects. Building upon the conceptual model, an implementation model is then developed and put into practice within an architectural design company in the United States, resulting in measured improvements in planning reliability and responsiveness. Additionally, it unveils hidden challenges associated with emerging tasks, guiding future design process enhancements. This study demonstrates how tracking design planning performance with LPS metrics can promote LPS adoption in architectural design, offering a benchmark for necessary interventions to achieve desired performance in architectural design.

The stick-free flight stability is an old-fashioned and non-progressive issue; nevertheless, it is still existent, and of significant importance to the design of aircraft whose control system is reversible. The problem’s existence necessitates a deep assessment of stick-free flight stability throughout the aircraft design. Up to now, this problem has been addressed using either analytical approaches, which are only related to the static stability evaluation, or performing flight tests. In this study, the problem is handled in its entirety, from static and dynamic flight stability assessment to design criteria with a comprehensive perspective. Moreover, it is also exhibited that contrary to what has been generally proposed in the literature, limiting the problem of stick-free flight stability through static stability assessment is far from the main challenge. As a brief scope, the derivation of the control surface dynamics, a stick-free trim algorithm, and assessment rationale of the stick-free static and dynamic flight stability using a simulation approach are proposed. As a consequence, the aim is to set a broad understanding for designers related to this phenomenon and add adjunct design criteria in the design optimization process by approaching it in terms of modeling, simulation, and flight test perspective.

In order to improve the performance of the draft tube in hydraulic turbine, a multi–objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non–dominated sorting genetic algorithm (NSGA–II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA–II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

Lattice structures have emerged as promising materials for aerospace structures applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them at-tractive for use in anti-ice systems, where lightweight and heat exchanging are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using additive manufacturing Selective Laser Melting technique and subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells’ parameters setup. The finding described highlights the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for aerospace market. The paper also emphasizes the role of additive manufacturing in producing complex structures with pointwise tailored mechanical properties.

Target detection of defense technology is being rapidly upgraded with modern surveillance technologies. The latest techniques of surveillance are already being implemented for defense applications. Self-protection and hiding from opposing forces are the key principle for protection of special team in defense. Camouflage textiles aims to create confusing objects for target detection of military personnel. These textiles are applied for military protection such as clothing, weapon, vehicle and location hiding nets/tents, etc. The urgent need of camouflage textiles have been formulated with a technical solution and implementation of right camouflage materials for concealment of defense target signature against dry leaves, green leaves, tree bark-woodland combat background; water-marine combat background; sand-desertland combat background; stone-stoneland combat background; snow-snowland combat background; sky combat background and ice-iceland combat background, concrete-concreteland combat background (DGTWSICB) in ultraviolet-visible-infrared (UV-Vis-IR) spectrums. This hypothesis of optical & surveillance engineering has been coalesced for advancement of UV-Vis-IR-DGTWSICB camouflage textiles technology. The principle of camouflage engineering has been approached by broader spectrum probe in UV-Vis-IR rather than Vis ranges only. Furthermore, single formulation of camouflage textiles has been proposed for multidimensional CBs-DGTWSICB. Electromagnetic spectrum, reflection, electron energy, photonic signal and imaging mechanism in UV-Vis-IR have been presented for optical engineering of concealment, detection, recognition and identification of target signature against DGTWSICB. Spectrum relationship of camouflage materials and DGTWSICB materials have been illustrated and compared in UV-Vis-IR spectrums. Camouflage material design, method design & spectral design; adaptive camouflage; techniques for camouflage textile assessment for digital camera and hyperspectral camera imaging; image processing techniques and a hierarchical model have been demonstrated for augmentation of camouflage textiles in UV-Vis-IR illumination. Therefore, anticipated design of camouflage textiles may enhance high-performance innovation for modern surveillance of military protection related to digital camera, hyperspectral camera and radar. This hypothesis includes advance guideline for camouflage textiles design for CBs-DGTWSICB.

HIV treatment strategies against viral enzymes are continuously hampered by viral drug resistance. Recent findings show that viral substrate Gag contributes to HIV-1 Protease Inhibitor (PI) resistance, leading to demands for new strategies in HIV treatment where Gag is recognized as a drug target. To successfully target Gag, there is a need of in-depth understanding of the Gag polyprotein and the effects of Gag mutations. Here, we propose new strategies in designing novel Gag inhibitors against existing and novel emerging Gag mutations via a structural understanding of the Gag-Protease relationship in PI resistance. In this review, we discuss the role of both novel and previously reported mutations, revealing insights to how they aid in PI resistance, and how new Gag inhibitors can be designed.

In this paper, a new design of a helium-assisted hybrid drone is proposed for flight time enhancement. As is widely known, most of the drones with a VTOL (vertical take-off and landing) feature have a short operation time, limiting their capability to carry out sustainable operations for the given missions. Thus, with the clear goal of enhancing the flight time, this study aims to develop a hybrid drone system, where a helium balloon is used to provide a lifting force for this purpose. The proposed design for the hybrid drone has several benefits including easiness to manufacture and relatively small size when compared to other types of hybrid drones. Various analyses are conducted for the design of the hybrid drone system including the balloon shape and size, buoyant force, flight time, and connector design. Since stability and performance are one of the most important issues for the new design, the pole location analysis is conducted based on the control theory. This rigorous analysis provides that the proposed hybrid drone design is stable as well as robust against swinging motions. To validate the effectiveness of the proposed design and flight time enhancement, simulations were conducted and experimental results are also provided using the manufactured hybrid drone system. Through the real experiments, it is proved that the hybrid drone can increase the flight time more than 2.5 times while guaranteeing stable motions.

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at project level impact the emergence of systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analyzing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system, and to that end, mitigate the impacts of climate change. This paper demonstrates a workflow using drone-based photogrammetry, 3D modeling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modeled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Coaxial rotors can be found in multirotor vehicles for the added thrust compared to independent rotors while keeping similar area footprints but, performance losses should be considered. This experimental study analyzes the effects of varying motor throttle and propeller pitch values in motor-propeller systems with two to four coaxial rotors. The results show that in a two-rotor coaxial system, to lessen the adverse effects of a front rotor’s backwash and to operate at the maximum performance, only the back motor should be operated initially up to 75% duty cycle before using the front motor up to its 75% duty cycle. Additional thrust requirements should be generated from the back rotor and then from the front rotor up to their maximum duty cycles. In two, three, and four-rotor coaxial setups, total thrust output generated is 1.6, 2.1, and 2.5 times the thrust output at system thrust performance of 86%, 76%, and 66%, respectively of that of an isolated rotor. In a four-rotor coaxial setup, maximum system performance is achieved when the propeller pitch values are gradually increased from the first to the last rotor. The gradual increments in propeller pitch values also result in more uniform thrust sharing among rotors.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

The feeling of beauty is one of the positive human feelings. In architecture and design, it is associated with forms appropriate to their purpose. The usefulness of products to meet needs is also important. They are well received by users. The task of designers is to achieve perfection in their work. The rational design and use of artificial living environments are expressions of concern for natural resources (sustainable environment). These issues are addressed, i.e., by an interdisciplinary initiative of the New European Bauhaus. The aim of the paper is to expand knowledge about beauty and utility. They are important when choosing mass-produced goods. The article uses research methods: interpretive research, analyses, comparisons, logical argumentation, etc. Experimental qualitative research using a checklist was used. The results expand the knowledge on the selected topic and research scope of the research of the paper. The survey can be used to rationally evaluate architecture, interiors, and design facilities. It can be helpful for various aesthetic and functional strategies dedicated to different users. The paper demonstrates the role of beauty and functionality in the built environment. They are analyzed in the context of social and technical conditions, as well as contemporary sustainable design

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

In Europe we can see a change in the social structure in the last period. Average life expectancy has increased dramatically over the last 50 years. Because of the improved life situation and advanced level of health care, older people are slower. With advancing age, the likelihood of experiencing a variety of constraints such as visual impairment, reduced hearing or physical ability increases. In such a life stage tenants are often forced to leave their long-term living space because these homes can not serve "new" individual needs and the resulting personal protection goal. This transition from the privacy of their home to the new environment often appears to be a painful change. They will take their familiar and well-known surroundings, because their homes can not be adapted to serve new needs. It must be the policy’s role to create a new inclusive social space and the requirement for architects and designers to create new goals for the design of an adaptable environment. This is a comprehensive approach to the design of the outer and inner space that could serve people even if there is an unexpected situation and changes in movement and physiological limitations of older people. The contribution shows the results of the survey conducted in Germany and Slovakia. In the survey respondents expressed their opinion on what they considered important in creating an adaptive environment. Results are processed graphically with explanation. The results were mainly for designers and developers of the indoor environment. Based on the results of the questionnaire survey, studies of possible modifications in the interior of the flats were then prepared. The contribution yielded these results in three age groups of respondents; i. people aged 35, 50 and over 50.

To achieve the required mechanical properties in the heat treatment of steel, it is necessary to have an adequate cooling rate and to achieve the desired final temperature of the product. This should be achieved with one cooling unit for different product sizes. In order to provide high variability of the cooling system, different types of nozzles are used in modern cooling systems. Designers often use simplified, inaccurate correlations to predict the heat transfer coefficient, resulting in oversizing of the designed cooling or failure to provide the required cooling regime. This typically results in longer commissioning times and higher manufacturing costs of the new cooling system. Accurate information about the required cooling regime and the heat transfer coefficient of the designed cooling is critical. This paper presents a design approach based on laboratory measurements. Firstly, the way to find or validate the required cooling regime is presented. The paper then focuses on nozzle selection and presents laboratory measurements that provide accurate heat transfer coefficients as a function of position and surface temperature for different cooling configurations. Numerical simulations using the measured heat transfer coefficients allow the optimum design to be found for different product sizes.

The worldwide outbreak of a novel coronavirus, SARS-CoV-2 has caused a pandemic of respiratory disease. Due to this emergency, researchers around the globe have been investigating the evolution of the genome of SARS-CoV-2 in order to design vaccines. Here I examined the evolution of GC content of SARS-CoV-2 by comparing the genomes of the members of the group Betacoronavirus. The results of this investigation revealed a highly significant positive correlation between the GC contents of betacoronaviruses and their divergence from SARS-CoV-2. The betacoronaviruses that are distantly related to SARS-CoV-2 have much higher GC contents than the latter. Conversely, the closely related ones have low GC contents, which are only slightly higher than that of SARS-CoV-2. This suggests a systematic reduction in the GC content in the SARS-CoV-2 lineage over time. The declining trend in this lineage predicts a much-reduced GC content in the coronaviruses that will descend/evolve from SARS-CoV-2 in the future. Due to the three consecutive outbreaks (MERS-CoV, SARS-CoV and SARS-CoV-2) caused by the members of the SARS-CoV-2, the scientific community is emphasizing the need for universal vaccines that are effective across many strains including those, that will inevitably emerge in the near future. The reduction in GC contents implies an increase in the rate of GC→AT mutations than that the mutational changes in the reverse direction. Therefore, understanding the evolution of base composition and mutational patterns of SARS-CoV-2 could be useful in designing broad-spectrum vaccines that could identify and neutralize the present and future strains of this virus.

Clinical trials often involve new substances characterized or developed for a specific pathology or class of pathologies. At other times, a clinical trial is observational; in other words, a known sub-stance has a range of effects on a given cohort of subjects. The types of trials are different and complex, and often, young clinical novice and the inexperienced researchers may struggle to evaluate how to proceed correctly. In this paper, we aim to create a mini-guide for beginners in clinical trials and provide elements for designing a clinical trial. Our intention is to highlight where to find the materials and how to adapt, even in the absence of sponsors, so that even the most inexperienced and under-resourced researcher can gain an understqanding of the complexi-ty of the process andaddress it appropriately. We do not claim to be exhaustive or cover all ele-ments, but rather, we aim to create a checklist. With this approach, starting discussioms about the experimental phases of the drug and concluding with the clinical trial, we aim to assist those facing this challenging "world."

Turbo-roundabouts are one of the most frequently recommended road junction design options when it comes to increasing traffic capacity and traffic safety. This is in particular true of suburban areas, with dominant traffic flow on the main road and moderate traffic flow on the side road. Other intersections handling local traffic and considerably constricting the availability of space are usually located in the vicinity of such intersections. Another factor contributing to less availability of land for construction in these locations is the presence of surrounding residential housing. Then choosing the suitable turbo-roundabout type becomes the main issue. The article presents a case study for the selection of a type of turbo roundabout under the conditions of considerably constricted land availability, based on analyses of provision of swepth path for the chosen design vehicles. The article considers the typical Egg type turbo roundabouts, "flattened" turbo roundabouts and ellipse based roundabouts, with unconventional geometries of the truck apron and central island. For each of the analysed roundabout types setting out methods were described in detail, which enable designers to quickly select a type of roundabout suitable for specific constrained site conditions.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble docking-based compound screening. Specifically, we investigated which pocket structure should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand binding pocket on the PPARγ side of the PPFP is common to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to PPFP for 97 drug or drug-like compounds with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ, registered in the DrugBank database. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics-Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

Synanthropic flora (weeds, spontaneously developed plants) are plants that accompany humans, appearing as a result of anthropopressure as well as after its cessation. The potential of synanthropic flora in shaping green areas usually brings many measurable benefits that depend on land management in cities. The study aimed to determine the preferences and attitudes of adults towards synanthropic flora in settlement units. The article presents survey results from 447 city residents. The obtained results were statistically analyzed using the k-means clustering method to identify segments of respondents with similar attitudes and preferences toward synanthropic flora. Mann-Whitney U tests were employed to determine statistical differences in the demographic variables among the analyzed clusters of respondents. Additionally, multiple regression analysis was performed to identify the opinions and attitudes towards synanthropic flora, that may influence their greater acceptance in the vicinity of residential areas. The results obtained in multiple regression allow for the statement that the respondent’s evaluation of the level of support for the introduction of more synanthropic vegetation into urban greenery (Yw) grows with the increase of acceptance of synanthropic plants occurrence in surroundings. Respondents’ opinions on the subject of the presence of synanthropic plants were studied using such variables concerning: synanthropic vegetation found in turfgrasses has a positive impact on the visual attractiveness of a site (X1), synanthropic plant communities present themselves better than others (X2), presence of synanthropic vegetation in an estate improves the quality of life in that location (X3), introduction of synanthropic flora into estate arrangements is a positive (X4), support for the use of synanthropic flora for educational purposes - as elements of playgrounds or in the form of environmental workshops (X5). Residents’ acceptance of synanthropic flora in green areas is at a relatively high level.

Lumbar support exoskeletons with active and passive actuators are currently the cutting-edge technology for preventing back injuries in workers while lifting heavy objects. However, many challenges still exist in both types of exoskeletons, including rigid actuators, risks of human-robot interaction, high battery consumption, bulky design, and limited assistance. In this paper, the design of a compact, lightweight energy storage device combined with rotary series elastic (ES-RSEA) is proposed for use in a lumbar support exoskeleton to increase the level of assistance and exploit the human bioenergy during the two stages of the lifting task. ES takes the responsibility to store and release passive mechanical energy while RSEA provides excellent compliance and prevents injury from the human body's undesired movement. The experimental tests on the spiral spring showed excellent linear characteristics (above 99%) with an actual spring stiffness of 9.96 Nm/rad. The results demonstrate that ES-RSEA can provide maximum torque assistance in the ascent phase with 66.6 Nm while generating nearly 21 Nm of spring torque during descent without turning on the DC motor. Ultimately, the proposed design can maximize the energy storage of human energy, exploit the biomechanics of lifting tasks, and reduce the burden on human effort to perform lifting tasks.

Background: A new access technique was developed to reduce postoperative adverse events after the extraction of impacted maxillary wisdom teeth. Hence, this study aimed to assess the occurrence of adverse events after extraction of maxillary impacted wisdom teeth using traditional or modified access. Methods: Two different surgical incision designs were used for bilateral wisdom tooth extractions in 30 patients. The traditional incision was performed distal to the second molar in the center of the tuberosity, followed by a buccal releasing incision. After tooth extraction the wound was secured by sutures. The new technique consists of an oblique incision from the distal palatal aspect of the tuberosity towards the buccal aspect of the second molar. After tooth extraction, cyanoacrylate glue was used on the wound. Results: Lower pain was reported by patients at the site treated with the new technique (p&lt;0.01). Edema, postsurgical bleeding, and hematoma were similar in both groups. The surgical time was shorter for the new technique (p&lt;0.01). Conclusions: The new technique applied for the extraction of impacted maxillary wisdom teeth reduced postsurgical pain and the duration of surgery.

Macrolide antibiotics, effective antimicrobial agents, are intensively used in human and veterinary medicine as well as in agriculture and therefore are found all over the world as environmental pollutants, harming sensitive eco-community organisms and provoking the selection of resistant forms. A novel azithromycin derivative was synthesized and as a rationally designed hapten conjugate ensured group immunorecognition of 6 major macrolide representatives (105-41%), erythromycin, erythromycin ethylsuccinate, clarithromycin, roxithromycin, azithromycin and dirithromycin in competitive immunoassay based on anti-clarithromycin antibodies. The heterologous hapten-based ELISA format simultaneously contributed to a 5-fold increase in sensitivity (ERY IC50 = 0.04 ng/mL). However, for the detection of trace macrolides in environmental samples, an underexploited in immunoassay field strategy was proposed in the present study to significantly improve the detectability of analytes. Unlike most approaches, it does not require special enhancers/amplifiers or additional concentration/extraction procedures, but only involves a larger volume of test samples. Gradual volume increase of samples (from 0.025 to 10 mL) analyzed in direct competitive ELISA, immunobeads, and immunofiltration assay formats based on the same reagents resulted in a significant improvement (more than 50-fold) in assay sensitivity and detection limit up to 5 and 1 pg/mL, respectively. The suitability of the test for detecting macrolide contamination of natural water was confirmed by recovery of macrolides from spiked blank samples (71.7-141.3%). A series of natural water samples from Lake Onega and its influents near Petrozavodsk were analyzed during a 2022-2023 using both the developed immunoassay and HPLC-MS/MS and revealed no macrolide antibiotic contamination.

Historically, the lack of comprehensive and real-time data is the main challenge in city sustainability assessments. However, with the increase in the availability of crowdsourced data over the last decade, a rich and real-time data source provides detailed images of urban systems. This has opened up new opportunities for practitioners, including geographers, social researchers, and data scientists, to gather and utilize social media data as a core source for studying cities and assessing their sustainability. This paper proposes an assessment model for the sustainability of cities based on crowdsourced data and the principles of General System Theory. The model aims to utilize crowdsourced data for the analysis and decision-making processes to enhance the sustainability and resilience of smart future cities.

This study presents structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performances to a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM is that it can be realized as a bearing-less drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Also, the deformation of a generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For the design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software Optistruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the opti-mized 10MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator) while that of an optimized conventional spoke arm-type generator is obtained as 325.6 tons.

Ice covering on overhead transmission lines would cause damage to transmission system and long-term power outage. Among various de-icing devices, modular multilevel converter (MMC) based DC de-icer (MMC-DDI) is recognized as a promising solution due to its excellent technical performance. Its principle feasibility has been well studied, but few literature discuss its economy or hardware optimization, thus the designed MMC-DDI for high voltage transmission lines is usually too large and too expensive for engineering applications. To fill this gap, this paper presents a quantitative analysis on the converter characteristics of MMC-DDI, and calculates the minimal converter rating and its influencing factors. It reals that, for a given de-icing requirement, the converter rating varies greatly with its AC-side voltage. Then an optimization configuration is proposed to reduce the converter rating and improve its economy. The proposed configuration is verified in a MMC-DDI for a 500kV transmission line as a case study. The result shows, in the case of outputting same de-icing characteristics, the optimized converter rating is reduced from 151 MVA to 68 MVA, and total cost of MMC-DDI is reduced by 48%. This analysis and conclusion are conductive to the optimized design of multilevel DC de-icer, then to its engineering application.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. The preceding methodology, AEC Industry 6.0, has substantially improved the efficacy and sustainability of construction by using new technology, digitalization, and environmentally conscious methods. Extensive discussions have occurred on the various benefits of AEC Industry 5.0. These merits include heightened stakeholder involvement, increased efficiency via automation and robots, decision-making processes centered around data, and the optimization of resources. Nevertheless, this study also examines the obstacles linked to the complete adoption of AEC Industry 6.0. These challenges require proficient professionals who can effectively utilize contemporary technologies, manage diverse stakeholder technological competencies, establish interoperable systems and standards, and reinforce cybersecurity measures. This study examines the viability of implementing concepts from Industry 6.0 to create intelligent, sustainable, and environmentally friendly structures. The objective is to showcase the potentially revolutionary impact that these principles may have on the construction industry. This study explores the possible applications of AEC Industry 6.0 within the Architecture, Engineering, and Construction (AEC) sector, explicitly highlighting the anticipated industry-wide consequences of the forthcoming paradigm shift. This paper comprehensively examines big data analytics, the Internet of Things (IoT), and collaborative robots as they pertain to the AEC Industry 6.0. This research investigates the interplay between organizational dynamics, human actors, and robotic systems to provide light on the opportunities and obstacles associated with integrating AEC Industry 6.0 concepts into the construction of buildings.

Background: Currently, limited number of therapeutic options are available to treat Diabetes mellitus, and to find a potential candidate, laboratory work takes time and also needs animal studies. So, the aim of this study was to determine the efficacy of some natural phytocompounds with the aid of molecular docking, bioinformatics and in silico drug design approaches. Method: Two proteins (Human CYP3A4 linked to metformin and Human dipeptidyl peptidase-IV) were selected and molecular docking studies were conducted using Pymol, AutoDock Vina, PyRx, and Discovery Studio. Different important pharmacokinetic parameters like ADME and toxicity data were obtained from online databases SwissADME and pkCSM program. Results: It was found that human dipeptidyl peptidase-IV (PDB ID: 4A5S) has exhibited a maximal affinity of -9.7 Kcal/mol for bryonolic acid and hesperidin, but only -6.7 kcal/mol for metformin hydrochloride. Similarly, the highest affinity of hesperidin for human CYP3A4 bound to metformin (PDB ID: 5G5J) is -10.7 Kcal/mol compared to metformin hydrochlor (-6.3 Kcal/mol). Besides, all the compounds have been documented outstanding ADMET profile, and accepted by drug-likeness or Lipinski rule. Conclusions: The present study suggested that these compounds can be further investigated in vitro and in vivo to establish them as lead compounds against Diabetes mellitus..

The article presents a hybrid method for calculating the chemical composition of steel with the required hardness after cooling from the austenitizing temperature. Artificial neural networks and genetic algorithms were used to develop the model. Based on 550 diagrams of continuous cooling transformation (CCT) of structural steels available in the literature, a dataset of experimental data was created. Artificial neural networks were used to develop a hardness model describing the relationship between the chemical composition of the steel, the austenitizing temperature, and the hardness of the steel after cooling. A genetic algorithm was used to identify the chemical composition of the steel with the required hardness. The value of the objective function was calculated using the neural network model. The developed method for identifying the chemical composition was implemented in a computer application. Examples of calculations of mass concentrations of steel elements with the required hardness after cooling from the austenitizing temperature are presented. The model proposed in this study can be a valuable tool to support chemical composition design by reducing the number of experiments and minimizing research costs.

Machining titanium alloy (Ti6Al4V) used in orthopedic implants by conventional metal cutting processes is challenging due to excessive cutting forces, low surface integrity and tool wear. To overcome these difficulties and for ensuring high-quality products, various industries employ wire electrical discharge machining (WEDM) for precise machining of intricate shapes in titanium alloy. The objective is to make WEDM machining parameters as efficient as possible for machining the bio-compatible alloy Ti6Al4V using box-behnken design (BBD) and Non-dominated Sorting Genetic Algorithm II (NSGA II). A quadratic mathematical model is created to represent the productivity and the quality factor (MRR and surface roughness) in terms of varying input parameters, such as pulse active (Ton) time, pulse inactive (Toff) time, peak amplitude (A) current and applied servo (V) voltage. The established regression models and related prediction plots provide a reliable approach for predicting how the process variables affect the two responses viz MRR and SR. The effects of four process variables on both the responses were examined, and the findings revealed that the pulse duration and voltage has a major influence on the rate at which material is removed (MRR) whereas pulse duration influence quality (SR).  The trade-off between MRR and SR, when significant process factors are included emphasizes the need for a reliable multi-objective optimization method. The intelligent metaheuristic optimization method named non-dominated sorting genetic algorithm II (NSGA II) is utilized to provide pareto optimum solutions in order to achieve high material removal rate (MRR) and low surface roughness (SR).

The terahertz (THz) frequency range, often overlooked in the past, is now gaining recognition for its unique properties and diverse applications. THz waves, positioned between microwave and infrared frequencies, offer safety, broad bandwidth, and low energy consumption. This review highlights the burgeoning importance of THz technology in communication systems, spectroscopy, biomedical imaging, and various industries. It emphasizes the significance of novel antenna designs in enhancing THz system performance and functionality, enabling rapid communication, data transmission, spectroscopic analysis, and sensor capabilities.

In recent years, exterior-rotor brushless DC motors have become increasingly popular in robotics applications due to their compact shape and high torque density. However, these motors were originally used for continuous operation in drones. For applications such as exoskeletons, prostheses, or legged robots, short bursts of high power are often required. Unfortunately, vendors do not typically provide data on the motors' performance under these conditions. This paper presents experimental data on the torque-speed relationship, efficiency, and thermal responses of one of the most widely used outrunner-type brushless motors across its full operating range, including high-power, short-duration operation. The results of this study can inform the selection and design of actuators for a range of robotics applications, particularly those that require high power output for brief periods of time.

Sustainability is a concept shrouded in abstraction. While we have definitions in existence, it is often difficult to explain the concept itself. The current definition of ‘sustainable development’ was given by the Brundtland Commission’s report in 1987. The Earth Summit at Rio in 1992 gave us Agenda 21, an action plan to achieve sustainable development. Now in the 21^st century, philosophers, academicians, and researchers across the globe are paving the way for a new understanding of the term ‘sustainability’, its contextual nature, and its relation to humans, politics, and ecology. This article investigates the origins of the term ‘sustainability’, its derivatives, and the concept of sustainable development. A semantical analysis is carried out to understand the differences between ‘sustainability’ and ‘sustainable development’. Next, the development of the three pillars of sustainability and the application of these concepts in the field of architecture and design is also investigated.

Patriarchy culture is formed indirectly through a concept of a man as a head of family or the holder of the highest position. It leads to viewpoint that catcalling is assumed normally. Although catcalling seems like a compliment, but it is quite different with the exact compliment. The basic difference from them both is a compliment comes to sincerely from its giver, meanwhile catcalling is aimed to harass indirectly to women. This study aims to give an information and solution about the catcalling expansion in public areas especially by women community. The use of method to finish this study is qualitative design from literature sources for the last four years about problems of catcalling. The result of this study is giving information about catcalling by providing data and looking for solution related to catcalling phenomenon.

The prevalence of algorithms and computational tools in the modern-day has intersected with nearly every field. Generative design, specifically those using genetic algorithms, is an increasingly effective, yet cost efficient way to generate architectural designs in modern engineering. Thus, we adopt a genetic algorithm model in pursuit of maximizing the durability of a structure when it is stressed while minimizing the material cost. After the model is formulated, the algorithm is able to approximate with high accuracy the load a small-scale structure is able to bear, as well as iterate upon its designs to maximize a fitness function.

In this paper, we revisit the hydrodynamics supporting the design and development of the RiverCat class of catamaran ferries operating in Sydney Harbor since 1991. More advanced software is used here. This software accounts for the hydrodynamics of the transom demisterns which experience partial or full ventilation, depending on the vessel speed. This ventilation gives rise to the hydrostatic drag, which adds to the total drag of the vessel. The presence of the transom also creates a hollow in the water. This hollow causes an effective hydrodynamic lengthening of the vessel, which leads to a reduction in the wave resistance. Hence a detailed analysis is required in order to optimize the size of the transom. It is demonstrated that the drag of the vessel and the wave generation can be predicted with good accuracy. Finally, the software is also used to optimize the vessel further by means of affine transformations of the hull geometry.

The design, facilities and conditions inside a classroom play a significant role in the teaching and learning experience for both students and lecturers. Prior studies of primary schoolchildren indicate three design principles affecting student learning, namely: naturalness, individualisation and stimulation. The current study extends these investigations to Higher Education through a survey of undergraduate students and university lecturers aimed at determining the most critical factors in undergraduate classroom design. One-to-one interviews were conducted with students and lecturers (n. 31) at the University of Nottingham, Malaysia Campus. Interviewees were encouraged to express their opinions, comments, concerns and suggestions through open-ended questions. The interviews were recorded and then transcribed and coded using NVivo10. Results show a strong desire among lecturers and students for improved classroom equipment, greater flexibility in classroom arrangement, more attractive decoration and for the addition of natural elements to the classroom environment. Of the three design principles, individualisation and naturalness emerged most strongly from the interviews and appear to be more important factors for undergraduates than stimulation. These findings could make a novel and significant contribution to the physical aspects of classroom design in Higher Education settings. Educational institutions are increasingly employing non-traditional classroom designs, which are expected to provide for more flexible, collaborative, and active learning and teaching experiences. Taking into consideration the environmental psychology of teaching and learning, several of the reported design attributes can serve as benchmarks for upgrading current classroom design and facilities in the future, as institutions look to upgrade their physical infrastructures to meet the changing demands of teachers and learners arising from technological innovations and shifts in our understanding of the methods and purposes of Higher Education.

The aim of the present study was to monitor implant stability after sinus floor elevation with two biomaterials during the first 6 months of healing by a resonance frequency analysis (RFA), and how physico-chemical properties affect the implant stability quotient (ISQ) at the placement and healing sites. Bilateral maxillary sinus augmentation was performed in 10 patients in a split-mouth design using a bobine HA (BBM) as a control and porcine HA (PBM). Six months after sinus lifting, 60 implants were placed in the posterior maxilla. The ISQ was recorded on the day of surgery from RFA at T1 (baseline), T2 (3 months), and T3 (6 months). Statistically significant differences were found in the ISQ values during the evaluation period. The ISQ (baseline) was 63.8±2.97 for BBM and 62.6±2.11 for PBM. The ISQ (T2) was ~ 73.5±4.21 and 67±4.99, respectively. The ISQ (T3) was ~ 74.65±2.93 and 72.9±2.63, respectively. All the used HAs provide osseointegration and statistical increases in the ISQ at baseline, T2 and T3 (follow-up), respectively. The BBM, sintered at high temperature with high crystallinity and low porosity, presented higher stability, which demonstrates that variations in the physico-chemical properties of a bone substitute material clearly influence implant stability.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from 34.5% and 30.9% of the state where standard surveys predicted the highest likelihood of occurrence of the respective vectors. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs, of the convenience collections, frequently were associated with adjacency to at least one SDM or errors in geocoding algorithms that failed to correctly locate convenience samples. These geocoding errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for vector survey data used in spatial models.

The oil and gas sector has recognised structural integrity assessment of ageing platform for prospective life extension as a rising concern, particularly in encountering the randomness of the harsh ocean environments. This condition leads to uncertainty in wave-in-deck load estimates and a high load level being imposed on offshore structures. This emphasises the necessity of enhanced reliability, as failure might result in inaccessibility because of the uncertainties related to long-distance services, such as accuracy of predictions of loads and responses. Even though the established guidelines present a fundamental assessment, additionally, comprehensive rules are required. This paper performed a reliability analysis incorporating practical approaches that can more accurately represent time-dependent structural deterioration. The following two procedures have been adopted by a majority of significant oil and gas operators to monitor the safety and integrity of these structures: a) Ultimate Strength Assessment (USA) method and b) Reliability Design Assessment (ReDA) method. A comparison of these two reliability approaches was performed on selected ageing jacket structures in the region of the Malaysian sea. The comparative findings, namely, reserve strength ratio (RSR) at various years of the return period (RP) and ratio value for risk of failure regarding the probability of failure (POF), provided a check and balance in strengthening confidence in the results. The findings showed that the structural components might safely survive either using the USA and ReDA method in such conditions, as the reliability indexes were determined to be satisfactory compared to allowable values from ISO 19902 design specifications. Therefore, these evaluations were determined to control the risk level of the structure during the remaining of its lifetime and undertake cost-effective inspections or mitigation strategies when necessary.

Ucuùba fat is fat obtained from a plant found in South America, mainly in Amazonian Brazil. Due to its biocompatibility and bioactivity, the Ucuùba fat was used for production of ketoconazole-loaded nanostructured lipid carriers (NLC) in view of an application for the treatment of onychomycosis and other persistent fungal infections. The development and optimization of the Ucuùba fat based NLC were performed using a Box-Behnken design of experiment. The independent variables were surfactant concentration (% w/v), liquid lipids concentration (% w/v), solid lipids concentration (% w/v), while the outputs of interest were particle size, polydispersity index (PDI) and drug encapsulation efficiency (EE). The Ucuùba fat based NLC were produced and the process optimized determining a predictive mathematical model. Applying the model, two formulations with the pre-required particle size, i.e., 30 and 85 nm, were produced for further evaluation. The optimized formulations were characterized and showed a particle size in agreement to the predicted value, i.e. 33.6 nm and 74.6 nm, respectively. The optimized formulations were also characterized using multiple techniques in order to investigate the solid state of drug and excipients (DSC and XRD), particle morphology (TEM) and interactions between the formulation components (FTIR). Furthermore, particle size and surface charge of the formulations was studied during a one-month stability study and did not evidence any significative modification during storage.

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

This paper describes the early-stage prototyping process developed to probe into the socio-cultural dimensions of the Co-Drive project, which is a new service concept for traveling and socializing by car between a real driver on a semi-automated car and a remote passenger connected via virtual reality from home. By focusing on the intertwining journeys of the two actors of the experience, the driver and the remote passenger, three key interactions have been isolated and addressed as separate prototyping interventions in the wild. Such a seamful prototyping strategy enabled the users to experiment the whole service although the technology was not yet completely developed, and more specifically it allowed them to accommodate and appropriate each part of the experience into their everyday activities, revealing initial evidences of the potential social impact drawn by a future deployment of the service.

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

This paper outlines the evolution of a learning technology interface from a Beta version to launch with educators and its alignment to pedagogical theory, designed to maximise collaboration between teacher and student. It explores how the “co-creation” of curricula via textual sources that underpin teaching and learning can be enhanced through interactive e-books and outlines the challenges involved in this praxis. With a number of stakeholders involved in the live use of the platform (including teachers, students, HE educators, course instructors, curriculum designers and software developers), iterative feedback loops informed real-time design improvements to both the interface and the-ory. This collaboration utilised a design-based research (DBR) approach (Wang and Hannafin, 2005), which is outlined here as explanation of how the tool developed and how iterative feedback helped to reconsider the challenges educators face in implementing pedagogical innovation across differing contexts. We propose how speculative design (Lukens and DiSalvo, 2012) and Activity Theory (Engeström, 1987) workshops can methodologically enhance the planning and evaluation phases inherent to DBR approaches, which involve students to prepare for educators’ innovation. Finally, we conclude with how educators can utilise these design principles using stratified approaches to help prepare organisational approaches for innovation.

With this article we would like to clarify the often-disregarded fact by virtue of which the European Missionaries in Asia acted as catalysts of a kind of nuanced acculturation named Accommodatio (adaptation). To a great extent they became harbingers of Culture and Science more than Faith itself to the dismay of many, including the Roman Church. Such cultural and scientific transference was actually two-pronged, for simultaneously they presented in Europe unique findings related to Language, e.g. the Chinese Characters (considered to be the sole natural language), Geography, Cosmology and even Governance. We would try to prove that such procedure contributed positively to the modern scientific notions of sustainability and to provide the kind of accoutrements that model the modern world as we know it.

The creation of nano-zero-valent iron (nZVI) by implementing plant extracts is an environmentally friendly process. High antioxidant capacity and phenol content indicated the possibility of oak-nZVI synthesis using oak leaf extract as a stable material with minimal agglomeration. Simultaneously removal of Cd and phosphates and Ni and phosphates was optimized by a statistically designed experiment with a definitive screening design. In terms of significance, 4 input parameters on process productivity were monitored: initial metal concentration (1 - 9 mgL-1), initial ion concentration (1- 9 mgL-1), pH value (2 - 10) and oak-nZVI dosage (2 - 16 ml). Phosphate removal efficiency, in the presence of cadmium, was the most influenced by oak-nZVI dose and cadmium concentration, while pH gave three statistically significant interactions with oak-nZVI dosage, cadmium concentration, and phosphate concentration. Phosphate removal efficiency, in the presence of Ni, is the most influenced by nickel concentration, phosphate concentration, pH, two-factor interaction between nZVI dose and Ni concentration, and quadratic phosphate interaction. The process optimization yielded the highest simultaneous removal efficiency of 98.99% and 87.30% for cadmium and phosphate ions, respectively. Also, the highest simultaneous removal efficiency of nickel and phosphates ion was 93.44% for 96.75%, respectively. The optimization process fits into the confidence intervals, confirming the assumption that the adopted regression model describes the process well. This work demonstrated an enormous potential and prosperous application for the Cd(II), Ni(II), and phosphate removal from water matrices.

Ombrian curves, i.e. curves linking rainfall intensity to return period and time-scale, are well-established engineering tools, crucial to the design against storm waters and floods. Whereas at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modelling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modelling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure except for a spatially varying scale parameter which is itself modelled by a spatial smoothing model for the 24 h average rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13 700 km2 water district of Greece characterized by varying topography and hydrometeorological properties.

Directed evolution methods are becoming increasingly popular, as they are extremely powerful toward developing new biomolecules with altered/novel activities, e.g., proteins with new catalytic functions or substrate specificities, and nucleic acids that recognize an intended target. Especially useful are systems that have incorporated continuous evolution, where the protein to be evolved undergoes continuous mutagenesis to evolve a desired trait with little to no input from the researcher once the system is started. However, continuous evolution methods can be challenging to implement in the lab and daunting for researchers to invest time and resources. Our intent is to provide basic information and helpful suggestions that we have gained from our experience with bacterial phage-assisted continuous evolution (PACE). Specifically, we review factors to consider before adopting PACE for a given evolution scheme, different types of selection circuits that can be utilized with particular focus on the PACE-B1H selection system, what optimization of a PACE selection circuit may look like using directed evolution of ME47 as a case study, and additional techniques that may be incorporated into a PACE experiment. With this information, researchers will be better equipped to determine if PACE is a valid strategy to use to evolve their proteins and how to set up a valid selection circuit.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding .

Like other ICT communities, sustainability in software engineering is a major research and development concerns. Current research focusses on eliciting the meanings of sustainability and proposing approaches for its engineering and integration into the mainstream software development lifecycle. However, few concrete guidelines that software designers can apply effectively are available and applicable. Such guidelines are needed for the elicitation of sustainability requirements and testing software against these guidelines. This paper introduces a sustainability design catalogue to assist software developers and managers in eliciting sustainability requirements, and then in measuring and testing software sustainability. The paper reviews the current research on sustainability in software engineering which is the grounds for the development of the catalogue. Four different case studies were analyzed using the Karlskrona manifesto on sustainability design. The output from this research paper is a software sustainability design catalogue through which a pilot framework is proposed that includes a set of sustainability goals, concepts and methods. The integration of sustainability for/in software systems requires a concrete framework that exemplifies how to apply and quantify sustainability. The paper demonstrates how the proposed software sustainability design catalogue provides a step towards this direction through a series of guidelines.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

Ferulic acid esters have been suggested as a group of natural chemicals with sunscreen function. The study aimed to utilize an environment-friendly enzymatic method to produce octyl ferulate by esterification of ferulic acid with octanol. The Box-Behnken design with response surface methodology (RSM) was adopted to evaluate the effects of synthesis variables, including reaction temperature (70–90 °C), enzyme amount (1000–2000 PLU) and stir speed (50–150 rpm), on the molar conversion of octyl ferulate. According to the joint test, both the reaction temperature and enzyme amount had great impacts on the molar conversion. RSM-developed second-order polynomial equation further showed great ability on data-fitting. Based on ridge max analysis, the optimum parameters for the biocatalyzed reaction were: 72 h reaction time, 92.2 °C reaction temperature, 1831 PLU enzyme amount and 92.4 rpm stir speed, respectively. Finally, the molar conversion of octyl ferulate under optimum condition was verified to be 93.2 ± 1.5%. In conclusion, high yield of octyl ferulate synthesized by commercial immobilized lipase under elevated temperature conditions has been suggested, which our findings could broaden the utilization of the lipase and provide a biocatalytic approach, instead of the chemical method, for ferulic acid ester synthesis.

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

Autonomous and cooperative vehicle systems represent a key priority in the automotive realm. Networked driving simulation can be utilized as a safe, cost-effective experimental replica of real traffic environments in order to support and accelerate the development of such systems. In networked driving simulation, different independent systems collaborate to achieve a common task: multi-driver traffic scenario simulation. Yet distinct system complexity levels are necessary to fulfill the requirements of various application scenarios, such as development of vehicle systems, analysis of driving behavior, and training of drivers. With myriad alternatives of available systems and components, developers of networked driving simulation are typically confronted with high design complexity. There are no systematic approaches to date for the design of networked driving simulation in accordance with the specific requirements of the concerned application scenarios. This paper presents a novel design method for networked driving simulation. The method consists mainly of a procedure model that is accompanied by a configuration software. The procedure model includes the necessary phases for the systematic design of application-oriented platforms for networked driving simulation. The configuration software embeds supportive decision-making processes that enable developers to apply the design method and easily create different system models. The design method was validated by generating system models and developing platforms of networked driving simulation for three different application scenarios.

The manuscript presents a novel approach to designing and fabricating a stretchable patch antenna designed for strain sensing and wireless communication of sensing data at the same time. The challenge lies in combining flexible and stretchable textile materials with different physical mor-phologies, which can hinder adhesion among multiple layers when stacked up, resisting the overall stretchability of the antenna. The proposed antenna design overcomes this challenge by incorpo-rating a lattice hinge pattern in the non-stretchable conductive e-textile, transforming it into a stretchable structure. The innovative design includes the longitudinal cuts inserted in both the patch and the ground plane of the antenna, allowing it to stretch along in the perpendicular direction. Implementing the lattice hinge pattern over the conductive layers of the proposed patch antenna in combination with a 2 mm thick Polydimethylsiloxane (PDMS) substrate achieves a maximum of 25% stretchability compared to its counterpart antenna without lattice hinge design. The stretchable textile antenna resonates around a frequency of 2.45 GHz and exhibits a linear resonant frequency shift when strained up to 25%. This characteristic makes it suitable for use as a strain sensor. Ad-ditionally, the lattice hinge design enhances the conformability and flexibility of the antenna compared to a solid patch antenna. The realized antenna gains in the E and H-plane were measured as 2.21 dBi and 2.34 dBi, respectively. Overall, the presented design offers a simple and effective solution for fabricating a stretchable textile patch antenna for normal use or as a sensing element, opening up possibilities for applications in communication and sensing fields.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

The development of electric aircrafts is becoming an important technology for achieving the goals set by the European Commission for the reduction of gases emissions by 2050 in the aeronautical transportation system. However, there is a gap between the values of specific power in commercial electric machines and the ones required for aeronautical applications. Therefore, the search for alternative materials and non-conventional designs is mandatory. One emergent solution is using superconducting machines and systems to overcome the current limits of conventional electrical machines. This work reviews the new hybrid and all-electric aircraft tendencies, complementing it with recent research on the design and development of high specific power superconducting machines. This includes the main topologies for hybrid and all-electric aircraft, with an overview of the ongoing worldwide projects of these types of aircrafts, systematizing the main characteristics of their propulsion systems. It also includes the research on superconducting machines for the purpose of high specific power, considering the impact on the redesign of aircraft systems in the electrical, cooling, and fuel source sense.

Wind power is one of the most important sources of renewable energy available today. A large part of the wind energy cost is due to the cost of maintaining wind power equipment. When a wind turbine component fails to function, it might need to be replaced under the less than ideal circumstances. This is known as corrective maintenance. To minimize unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimal preventive maintenance algorithm is designed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance which allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine and the optimal maintenance plans are computed for various initial conditions. The modeling results showed clearly the benefit of PM planning compared to pure CM strategy (about 30% lower maintenance cost).

In contemporary society, an increasing number of individuals are identified as the "Indoor Generation". Prolonged periods spent indoors can have detrimental effects on our overall well-being. Consequently, the examination of biophilic indoor environments and their impact on occupant well-being and comfort has emerged as a significant area of study. Besides the construction of new biophilic buildings, numerous existing building stocks retain substantial social, economic, and environmental value. The need for stock renovation is considerable, revitalizing existing structures rather than embarking on new constructions. Swift feedback is crucial during the initial stages of renovation design, fostering consensus among all stakeholders. A comprehensive understanding of proposed renovation plans contributes to improved indoor environment design. Initially, generative adversarial networks (GANs) produce interior renovation drawings based on user preferences and designer styles. Subsequently, the implementation of Mixed Reality (MR) and Diminished Reality (DR) provides users with an immersive perspective, transforming 2D drawings into interactive MR experiences, and facilitating the presentation of interior renovation proposals. This paper outlines the development of a real-time system for architectural designers that seamlessly integrates MR, DR, and GANs results, with the aim of enhancing feedback efficiency during the renovation design process, enabling stakeholders to evaluate and understand renovation proposals more comprehensively. Furthermore, we incorporate several full-reference image quality assessment (FR-IQA) methods to evaluate the quality of the GANs-generated images. The evaluation results indicate that the majority of images fall within the moderate range of quality.

Silybum marianum (L.) Gaertn. (aka milk thistle) constitute the almost exclusive source of silymarin, a mixture of different flavonolignans, and is thus considered as a unique model for their extraction. The present research deals with ultrasound-assisted extraction (UAE) of S. marianum flavonolignans and their quantification using LC system. The optimal conditions for UAE were: aqueous EtOH 54.5% (v/v) as solvent, applying an ultrasound frequency of 36.6 kHz during an extraction time of 60 min at 45°C with a liquid to solid ratio of 25:1 ml/g DW. Following optimization, the extraction method was validated according to international standards of the association of analytical communities (AOAC) in order to ensure its precision and accuracy for the quantitation of the individual silymarin components. The efficiency of UAE was compared with maceration protocol of the same duration. The optimized and validated conditions allowed highest extraction yields of flavonolignans in comparison to maceration. The antioxidant capacity of the extracts was confirmed by the CUPRAC assays and inhibition of advanced glycation end products. The skin anti-aging action was also confirmed toward the strong in vitro inhibition capacity of the obtained extract against collagenase and elastase enzymes. The procedure presented here allows a green efficient extraction and quantification of the main flavonolignans from the fruits of S. marianum with attractive antioxidant and anti-aging activities for future cosmetic applications.

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. 3D pharmacophore models are powerful computational tools in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently ‘reaction-prone’. The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic ‘hydrophobic ↔ polar’ mutations serve as a potential breakthrough.

Although solar photovoltaic (PV) system costs have declined, capital cost remains a barrier to widespread adoption. Do-it-yourself (DIY) system designs can decrease costs by about 50% by reducing labor costs, but if not attached to a building structure demand ground penetration for conventional footings. This is not technically and economically feasible at all sites. To overcome these challenges, this study details systems designed to eliminate drilling holes and pouring concrete. Two different designs for both fixed tilt and variable tilt PV racking are investigated to assess their structural integrity, constructability, and economic cost when fabricated with a ballast-supported foundation. The ballast-supported foundations are analyzed for eight such systems by proposing two separate ballast designs: one for single line of post systems, and one for double line of post systems built on a 4-kW basis. The results of the analysis found that both designs are slightly more expensive than typical in-ground concrete systems by 25%, but the overall DIY systems costs remain economically advantageous. Sensitivity analyses are conducted to show how modifications to the dimensions including clearances and distances between gabions influence the weight of the system and thus change the economic value of the design, so users can trade dimensional freedom for cost-savings, and vice versa. Overall, all the wood-based PV racking system designs provide users with cost-effective and easy DIY alternatives to conventional metal racking, and the novel ballast systems presented provide more versatility for PV systems installations.