The current emergency due to the worldwide spread of the COVID-19 caused by the new SARS-CoV-2 is a great concern for global public health. Already in the past, the outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle Eastern respiratory syndrome (MERS) in 2012 demonstrates the potential of coronaviruses to cross-species borders and further underlines the importance of identifying new-targeted drugs. An ideal antiviral agent should target essential proteins involved in the lifecycle of SARS-CoV. Currently, some HIV protease inhibitors (i.e., Lopinavir) are proposed for the treatment of COVID-19, although their effectiveness was not yet assessed. The main protease (Mpro) provides a highly validated pharmacological target for the discovery and design of inhibitors. We identified potent Mpro inhibitors employing computational techniques that entail the screening of a Marine Natural Product (MNP) library. MNP library was screened by hyphenated pharmacophore model, and molecular docking approaches. Molecular dynamics and re-docking further confirmed the results obtained by structure-based techniques and allowed to highlight some crucial aspects. Seventeen potential SARS-CoV-2 Mpro inhibitors have been identified among the natural substances of marine origin. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds could be bioactive is excellent.

Abnormal connections in brain networks of healthy people always bring the problems of cognitive impairments and degeneration of specific brain circuits, which may finally result in Alzheimer’s disease (AD). Exploring the development of the brain from normal controls (NC) to AD is an essential part of human research. Although connections changes have been found in the development, the connection mechanism that drives these changes remain incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncover the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a model named MINM from the perspective of topology-based mutual information to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiment results show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

During the recent decades, global warming by greenhouse gas evolution has attracted worldwide attention and evermore strict regulations thereon have become institutionalized as international policies. In the process, more environment-friendly power generation technologies have been developed utilizing fossil fuels with a view to timely commercialization. As one of such “clean coal” technology, Integrated Gasification Fuel Cell system is a promising power generation means and COS hydrolysis reactor is installed downstream of coal syngas to remove acidic gas constituents such as H2S and COS. The most significant design parameters affecting performance of the COS hydrolysis reactor were selected to be GHSV, (catalytic) reaction temperature, and length ratio and numerical modeling was performed considering heat and fluid flow transfer as well as chemical reaction kinetics. Effect of the selected design parameters on the variation of conversion rate and reactant gas mixture concentration were comprehensively investigated to predict performance of COS hydrolysis reactor. Stochastic modeling of reactor performance was finally performed using Monte Carlo simulation and linear regression fitting.

This paper models the factors explaining households members economic well-being in Burkina Faso, with a focus on the relative influence of gender inequality in literacy status. It does so, using data from the 2014 survey on household living conditions and a semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. The graphical results from the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending among households headed by literates is higher than that of their illiterate counterparts in 2009 and 2014. However, independently of the head of household’s literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. Using the poverty indices of Watts, Sen, Foster ( alpha= 1)) we found that poverty among households headed by literates is lower than that of their illiterates counterparts for both years, although overall poverty decreased nationally between 2009 and 2014. The results also show that although the gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness as female headed households have 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being also seems to exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

Investments in wind and solar power are driven by the aim to maximize the utilization of renewable energy (RE). This results in an increased concentration of wind farms at locations with higher average wind speeds and of solar panel installations at sites with higher average solar insolation. This is unfavourable for energy suppliers and for the overall economy when large power output fluctuations occur. Thus, when evaluating investment options for spatially distributed RE systems, it is necessary to model resource fluctuations and power output correlations between locations. In this paper, we propose a methodology for analyzing the spatial dependence, accurate modeling, and forecasting of wind power systems with special consideration to spatial dispersion of installation sites. We combine vine-copulas with the Kumaraswamy distribution to improve accuracy in forecasting wind power from spatially dispersed wind turbines and to model solar power generated at each location. We then integrate these methods to formulate an optimization model for allocating wind turbines and solar panels spatially, with an end goal of maximizing overall power generation while minimizing the variability in power output. A case study of wind and solar power systems in Central Ontario, Canada is also presented.

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.

For the Philippines, a country exposed to multiple natural hazards like severe wind, sustainable development includes resiliency. Severe wind hazard is brought by tropical cyclones in the Western Pacific, known as typhoons, that frequent the Philippines. Therefore, adequately evaluating the wind hazard and its impact is crucial for sustainable building design. Acknowledging the impacts of climate change on said hazards would require adaptation to its consequences which necessitate a deeper understanding on the changing behavior of typhoons in recent years. For this study, detailed wind information from the Japan Meteorological Agency from 1977-2021, the Holland-B parameter, and the radius of maximum wind speed for each typhoon, are determined for simulation of the regional cyclonic wind field. The analysis of the Holland-B parameters, which represent the steepness of the pressure gradient and tropical cyclone convection, suggest that the Holland-B parameters have been increasing since 2011. The regional wind fields caused by the typhoons also suggest an increasing trend in severe wind hazard. Seasonality for the location of severe wind hazard is also observed, with the Southern Philippines experiencing an increase (decrease) during the Northeast (Southwest) Monsoon season, and the Northern Philippines experiencing an increase (decrease) during the Southwest (Northeast) Monsoon season.

This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over five, ten, twenty and one hundred years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer-fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem.

This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.

Background: COVID-19 has a major impact on businesses, with employee absenteeism resulting in lost productivity and medical costs to employers as well as lost wages and medical costs to employees. The aim of this analysis was to assess the economic impact of workplace COVID-19 vaccination in the United States (US).Methods: A decision tree economic model, populated with US data for the working-age population, estimated the burden of COVID-19 in the workplace (symptomatic infections, long COVID cases, inpatient/outpatient care, absent days) with and without vaccination. Disease burden and impact of seasonal influenza vaccination was studied for context. Results: Without workplace vaccination, an average US business (with 10,000 employees), had an estimated 18,175 absent days from COVID-19, resulting in employer lost productivity costs of $ 5.08 million. Implementing COVID-19 workplace vaccination (at 70% US Healthy People target vaccination rate) prevented an estimated 3,132 absent days, saving employers $ 876,453 in lost productivity costs and $240,633 in medical costs. It also saved employees $ 182,196 in medical costs and $198,250 in lost wages . Seasonal influenza caused around 5,333 absent days, and seasonal influenza vaccination (at 70% coverage) prevented 1,675 absent days, saving employers $ 468,394 in lost productivity and saving employees $ 27,757 in medical costs. Conclusions: COVID-19 remains a burden to US businesses. Implementing COVID-19 workplace vaccination and increasing vaccination coverage rates for COVID-19 and seasonal influenza has the potential to prevent a significant number of absent days, while providing employers and employees with cost savings from averted disease and its consequences.

This research aims to review the educational and research operations of Mahasarakham University and propose development directions for the university to meet sustainable development criteria for ranking as a green university. This involves gathering data and analyzing the results of operations over the past 3 years, then using this analysis to synthesize lessons learned and develop guidelines for sustainable development in the coming years. The study found that the university's policies and strategies implemented following the annual performance evaluation criteria align with the green university assessment criteria for education and research. This has resulted in minor improvements in curriculum for sustainability. However, community service projects for sustainability have increased by 89.10%, while funding for sustainable research and academic publications related to sustainability has decreased. In summary, the projects under the university's development strategy adequately support and drive suitable development activities. Nevertheless, the university must continuously review its operations to adapt to changing contexts, budget constraints, and evolving competition and develop each year sustainably.

Background: Delis-Kaplan Executive Function System (D-KEFS) is a neuropsychological tool used in clinical practice to measure executive function in clinical and non-clinical populations, in individuals aged 8-89 years, and consists of nine subtests that can be administered either as a single system or separately. Aims: In the present study, were established norms for the Verbal Fluency Test as well as the Design Fluency Test of the D-KEFS in a Greek population aged 20 to 49 years, since there is no previous study conducted in Greece for these age clusters. Methods: The study sample consisted of 100 healthy adults (44.4% male and 55.6% female), aged 20 to 49 years (M=32.81 SD=11.04) with education from 12 to 18 years (M=14.73, SD= .91). First, Spearman and Pearson tests explored the associations of gender, age and education with D-KEFS subtests’ performance. Afterwards, we calculated normative data using raw scores and transformed to percentile scores. Results: The present study is the first attempt to calculate norms for the two fluency subtests of the D-KEFS in Greek adult population stratified by age and educational level. Conclusion: Future research will promote the use of D-KEFS, in order to introduce a widely used tool which measures all basic executive functions subtypes for Greek adult population. Given that the majority of neuropsychological tools are adapted for older adult population, the current study aims at filling the gap in neuropsychological assessment of young adults as well as those who have co existing neurological or psychiatric disorders.

Microwave-induced thermoacoustic tomography (MITAT) holds significant promise in biomedical applications. It creates images using ultrasonic sensors to detect thermoacoustic signals induced by microwaves. The key to generating thermoacoustic signals that accurately reflect the fact is to achieve sufficient and uniform microwave power absorption of the testing target, which is closely tied to the microwave illumination provided by the antenna. In this article, we introduce a novel design and implementation of an ultra-wideband water immersion antenna for an MITAT system. We analyze and compare the advantages of selecting water as the background medium. Simulations are conducted to analyze the ultra-wideband characteristics in impedance matching, axial ratio, and radiation pattern of the proposed antenna. The measured |S11| shows good agreement with the simulated results. We also simulate the microwave power absorption of tumor and brain tissue, and the uniform microwave power absorption and high contrast between the tumor and brain indicate the excellent performance of the proposed antenna in the MITAT system.

Leaky-wave antennas (LWAs) represent a recent class of radiating systems, first appeared in the second half of the past century. Initially treated with a certain dose of skepticism due to the apparent non-physical nature of supported wave solutions, LWAs have so far deeply evolved from the original configuration of slotted metallic lossy waveguide and adapted to a huge number of practical application fields, from microwave to millimeter waves and even up to the terahertz frequency range. Due to physics of relevant working principle, LWAs are often found in planar and low-profile configurations, being critically required in many cutting-edge applications, where device miniaturization, scaling and integration are required. Here we propose a review of some of the most recent and perspective array-fed 2-D LWAs, selecting two of most widespread categories in applications, Fabry–Perot Cavity Antennas (FPCAs) and Bull’s-Eye Antennas (BEAs), discussing and illustrating some advanced features recently proposed.

Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.

Brushless Direct Current Motors are significantly improved for using many electrical applications. The design investigation is increased year by year and most of the motor design researchers find out the superior efficiency of the BLDC motor. The traditional motor consumed more electrical power than BLDC motor. The design of BLDC motors that are compact, small, easy to control, save energy and light weight. Due to this fact, the design of BLDC motor should be analyzed to improve the motor characteristics performance. The research focuses on the design of BLDC motor performance using Taguchi Method and Response Surface Methodology (RSM) for Finite Element Method (FEM). Firstly, the Taguchi Method is applied to identify the optimization point of design parameters and consider using the reference model of actual BLDC electric fan motor with a rated power of 26Watts design. Based on the Taguchi Method to create the RSM is used to two-step optimization design performance using in FEM analysis. The performances of motor design proposed approach to provide the effeteness for motor design investigator. In this investigation aims the better structural performances in output power, torque, efficiency and torque ripple. The final design of proposed model achieved an energy efficiency is 10% higher than the reference design of prototype modes, the output power result is 10W higher this reference design. We can reduce the loss of the proposed motor design. Furthermore, the maximum torque can also increase the 0.032Nm of that design. The design simulation results were produced and studied by the FEM analysis of JMAG software. From the evaluation results, the configurations of reference model design are compared with the proposed design by using the three methods of motor design optimization showed the best characteristics for performance of motor investigation design.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

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.

Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

The α-synucleinopathies are a group of neurodegenerative diseases characterized by the deposition of α-synuclein aggregates (α-syn) in the brain. Currently, there is no suitable tracer to enable a definitive early diagnosis of these diseases. We reported candidates based on 4,4'-disarylbisthiazole (DABTA) scaffold with a high affinity towards α-syn and excellent selectivity over Aβ and tau fibrils. Based on prior in silico studies a focused library of 23 halide-containing and O-methylated DABTAs was prepared. The DABTAs were synthesized via a modified two-step Hantzsch thiazole synthesis, characterized, and used in competitive binding assays against [3H]PiB, and [3H]DCVJ. The DABTAs gave an overall chemical yield of 15 – 71%, with a calculated lipophilicity of 2.5 – 5.7. The ligands demonstrated an excellent affinity to α-syn with both [3H]PiB and [3H]DCVJ: Ki 0.1 – 4.9 nM and up to 20 – 3900-fold selectivity over Aβ and tau fibrils. It could be concluded that in-silico simulation is useful for the rational design of a new generation of DABTAs. Further investigation of the leads in the next step is encouraged: radiolabeling of the ligands with radioisotopes such as fluorine-18 or carbon-11 for in vivo, ex vivo and translational research and for further in vitro experiments on human-derived protein aggregates.

In crank presses, an important role is played by the presence of actuators with dwell, in which, with continuous movement of the input links, the output working link remains stationary at some part of the work. The use of a high-class structural group (fourth class) expands the functionality of the crank press, including it can ensure the link dwell of the working link on a given section of movement during the required period of time. As a result of the conducted research, four variants of actuators of the crank knee press were designed. Due to the rational choice of the positions of racks and crosshead guide, the best layout of the dimensions of the press mechanism has been achieved, changing the coordinates of the racks accordingly allows to design a mechanism with upper or lower dwells of the working links. When synthesizing (selecting) parameters such as: l_2 and h, it is possible to obtain a height of different duration. All this is shown on the basis of a numerical experiment based on kinematic analysis. In addition, the complex structure of the mechanism makes it possible to ensure the exact link dwell, which cannot be achieved due to the well-known knee presses based on the second-class mechanism [21].

A variety of factors ranging from ethnicity and occupants’ lifestyles to local climatic characteristics of any studied location and people’s age factors can affect thermal comfort assessment globally. Due to the detrimental impact of climate change, the building industry has started to implement energy efficiency schemes while considering the thermal comfort of vulnerable population both for the society and any sphere of economy. Although, considering human-based approaches have been neglected by other scholars in thermal comfort studies. This paper reviews energy effectiveness of state-of-the-art passive systems in providing neutral adaptive thermal comfort for elderly people, through exploring passive design strategies in four distinct climates namely, Canada, India, Abu Dhabi and the South-eastern Mediterranean basin. The aim of the study is to analyze the available data provided by the ASHRAE Global Thermal Comfort Database II version record 2.1. The main objective of the study is to develop an effective methodological framework for the on-going development of adaptive thermal comfort theory. To this extend, this study presents a comprehensive review on the assessment of energy effectiveness of passive design systems. To accomplish this, the impact of climate change factor in passive design systems was investigated. The meta-analysis method was adopted to determine the input variables for the statistical analysis. Cramer’s V and Fisher’s Exact tests were used to assess occupants’ thermal sensation votes (TSVs). The findings revealed that there are discrepancies detected between the in-situ field experiments and the data recorded in the ASHRAE Global Thermal Comfort Database II. According to the Köppen-Geiger climate classification, it was found from the study that the slightly high temperature fluctuations were identified in India where the weather is dry and savanna climate. The study findings contribute to the development of adaptive thermal comfort theory by reviewing the existing methodologies globally. Furthermore, a critical review on the significance of occupants’ age differences should be conducted in the identification of neutral adaptive thermal comfort.

PG16 is a broadly neutralizing antibody that binds to the gp120 subunit of the HIV-1 Env protein. The major interaction site is formed by the unusually long complementarity determining region (CDR) H3. The CDRH3 residue Tyr100H is known to represent a tyrosine sulfation site; however, this modification is not present in the experimental complex structure of PG16 with full-length HIV-1 Env. To investigate the role of sulfation for this complex, we modeled the sulfation of Tyr100H and compared the dynamics and energetics of the modified and unmodified complex by atomistic molecular dynamics simulations. Our results show that sulfation does not affect the overall conformation of CDRH3, but still enhances gp120 interactions both at the site of mutation and for the neighboring residues. This stabilization affects not only protein-protein contacts, but also the interactions between PG16 and the gp120 glycan shield. Further, we also investigated whether PG16-CDRH3 is a suitable template for the development of peptide mimetics. For a peptide spanning residues 93-105 of PG16 we obtained an experimental EC50 value of 3nM for the binding of gp120 to the peptide. This affinity can be enhanced by almost one order of magnitude by artificial disulfide bonding between residues 99 and 100F. In contrast, any truncation results in significantly lower affinity, suggesting that the entire peptide segment is involved in gp120 recognition. Their high affinity makes PG16-derived peptides useful building blocks for further optimization to obtain a potent inhibitor that efficiently blocks HIV-1 infection.

CO2 emissions resulting from the production of cement is a major issue, but can be limited by the partial substitution of cement by low-carbon-impact additions. The aim of this study was the formulation of a ternary binder based on ordinary Portland cement (OPC), ground granulated blast-furnace slag (GGBS) and flash-calcined sediment (FCS), a dredged waste which was valorized after applying a new heat treatment: flash calcination. The used materials were physically, chemically and mineralogically characterized. The composition of the formulations was optimized using mixture designs. Five formulations, one reference formulation RM (100% OPC), one binary formulation (50% OPC/50% GGBS), and three ternary formulations with a variable FCS rate (10%, 15%, 20%), were selected and characterized fresh and hardened. Results showed that the incorporation of FCS reduced the workability and increased the density. In addition, a decrease in the initial setting time and the heat of hydration peak were observed. In the hardened state, the formulation containing 10% FCS showed 90-day mechanical strengths superior to that of RM. The use of FCS in ternary binders could reduce the environmental impact by reducing greenhouse gas emissions.

Modelling errors, robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids excitation of unmodeled dynamics. The main purpose is to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges are compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely track the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision&robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics.

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.

The assessment of positive emotional states in animals has been advanced considerably through the use of judgement bias testing. JBT methods have now been reported in a range of species. Generally, these tests show good validity as ascertained through use of corroborating methods of affective state determination. However, published reports of judgement bias task findings can be counter-intuitive and show high inter-individual variability. It is proposed that these outcomes may arise as a result of inherent inter- and intra-individual differences as a result of biology. This review discusses the potential impact of sex and reproductive cycles, social status, genetics, early life experience and personality on judgement bias test outcomes. We also discuss some aspects of test design that may interact with these factors to further confound test interpretation. There is some evidence that a range of biological factors affect judgement bias test outcomes, but in many cases this evidence is limited and needs further characterisation to reproduce the findings and confirm directions of effect. It is our proposition that researchers should consider dedicated study on these factors and their impact on judgement biasing. This is needed to confirm effect and investigate mechanisms. Alternately, consideration and reporting of these factors in JBT studies through incorporation in statistical analyses will provide much needed additional data on their impact. These actions will enhance the validity and practical applicability of the JBT for welfare assessment.

SARS-CoV-2 is a novel betacoronavirus and the aetiological agent of the current COVID-19 outbreak that originated in Hubei Province, China. While polymerase chain reaction is the front-line tool for SARS-CoV-2 surveillance, application of amplification-free and culture-free methods for isolation of SARS-CoV-2 RNA, partnered with next-generation sequencing, would provide a useful tool for both surveillance and research of SARS-CoV-2. We here release into the public domain a set of bait capture hybridization probe sequences for enrichment of SARS-CoV-2 RNA from complex biological samples. These probe sequences have been designed using rigorous bioinformatics methods to provide sensitivity, accuracy, and minimal off-target hybridization. Probe design was based on existing, validated approaches for detecting antimicrobial resistance genes in complex samples and it is our hope that this SARS-CoV-2 bait capture platform, once validated by those with samples in hand, will be of aid in combating the current outbreak.

Natural calamities observation, study and simulation has always been a prime concern for disaster management agencies. Billions of dollars are spent annually to explore geo-seismic movements especially earthquakes but it has always been a unique accident. The real-time study of seismic waves, ground motions, and earthquakes always needed a programmable mechanical structure capable of physically producing the identical geo-seismic motions with seismology domain definitions. A programmable multi-parametric five degrees of freedom electromechanical seismic wave events simulation platform to study and experiment seismic waves and earthquakes realization in the form of geo-mechanic ground motions is exhibited in this work. The proposed platform was programmed and interfaced through an IoT cloud-based Web application. The geo-mechanics was tested in the range of i) frequencies of extreme seismic waves from 0.1Hz to 178Hz; ii) terrestrial inclinations from -10.000° to 10.000°; iii) velocities of 1km/s to 25km/s iv) variable arrival times 1us to 3000ms; v) magnitudes M1.0 to M10.0 earthquake; vi) epi-central and hypo-central distances of 290+ and 350+ kilometers. Wadati and triangulation methods have been used for entire platform dynamics design and implementation as one of key contributions in this work. This platform is as an enabler for a variety of applications such as training self-balancing and calibrating seismic-resistant designs and structures in addition to studying and testing seismic detection devices as well as motion detection sensors. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.

Hybrid woven kenaf-carbon composite were fabricated in this study using epoxy resin as matrix. Effects of different fabric material namely weave designs (plain and satin) and fabric counts (5×5 and 6×6) on the properties of laminated woven kenaf polymer composite were evaluated. This study evaluates the mechanical and morphological properties of hybrid fabric kenaf-carbon from kenaf yarn of 500tex. Kenaf and carbon fabrics were used in this work, where vacuum infusion technique was selected to prepare the composite and epoxy resin was used as a matrix. The fibre weight content is 30% and four specimens were prepared for each samples and tested for their tensile, flexural, and impact strengths. The morphological properties of composites were analysed through the scanning electron microscope (SEM). The results revealed that plain woven fabric is favourable in terms of tensile and impact strengths compared to satin woven fabric. Meanwhile, 5×5 of fabric count gives better flexural modulus than composite fabricated with 6×6 fabric count. The morphologies of the fractured surface investigated by SEM demonstrated better adhesion properties and less fibre pull-out on plain woven fabric.

The recent COVID-19 pandemic has led to a reassessment of workplace necessities. It has resulted in the current shift from traditional workplace design to creating work experiences - a new par-adigm in workplace management. Hence, while some conventional workplace factors remain important to the experiences of workers and, thus, to their well-being, comfort and productivity, recent studies suggest other factors, not widely known, as important in creating the appropriate work experience. Our study explores these other factors in the workplace that contribute to, or may be very important to, workers’ comfort, well-being and productivity. These include working mode, facilities, sustainability practices and Diversity, Equity, and Inclusion (DEI) – cultural connection. A sample of 149 occupants across offices in New Zealand was conveniently collected for the study and analysed using descriptive and content analysis in SPSS vs 24. The results show that the occupants prefer to have private offices in the workplace and separate meeting rooms for better concentration on work. In terms of working mode, a combined model of remote working and in-office working is preferred. Furthermore, occupants prefer to have access control and fire safety systems essentially in the workplace and for their offices to be cleaned daily. For sustainable measures in the workplace, the respondents choose the following as practical and convenient: turning off computers outside work hours, using more LED light bulbs in the office, opening windows and doors when required and using a motion sensor lighting system. Interestingly, most of the respondents did not think having a cultural connection in their workplace was important. The study findings represent significant progress in identifying the needs of contemporary office workers. These findings assist facility managers and workplace designers in developing more proactive approaches to anticipated user issues in buildings.

Human beings are positively or negatively affected by their environment. This effect has been a topic of interest for environmental psychologists, who have strived over years to understand the environmental attributes, which would be favourable to people in physical, mental, and psychological terms. The present study focusses on biophilic design, one of the popular theories in the architectural literature aimed to create high quality and liveable environments. The positive effects of biophilic design have been investigated from different perspectives and thus it has become a well-accepted approach in the relevant architectural literature. Accordingly, the present study aimed to investigate to what extent biophilic design principles were put into practice. In the context thereof, contemporary university structures were analysed within the framework of biophilic design principles. The results were suggestive of the fact that although biophilic elements were encountered in the design of these structures, they were not meant to be biophilic, nevertheless, there was an approach based on human-nature interaction. Accordingly, the principles of biophilic design were equivalent to the required parameters of a good design and that architects considered nature as an important design input.

Photonic circuits find applications in biomedicine, manufacturing, quantum computing and communications. Photonic waveguides are crucial components, typically having cross-sections orders of magnitude inferior compared with other photonic components (e. g. optical fibers, light sources and photodetectors). Several light coupling methods exist, consisting of either on-plane (e. g. adiabatic and end-fire coupling) or off-plane methods (e. g. grating and vertical couplers). The grating coupler is a versatile light transference technique which can be tested at wafer level, not requiring specific fiber terminations or additional optical components, like lenses, polarizers or prisms. This study focuses on fully-etched grating couplers without bottom reflector, made from hydrogenated amorphous silicon (a-Si:H), deposited over a silica substrate. Different coupler designs were tested, of these we highlight two: superimposition of two lithographic masks with different periods and an offset between them to create a random distribution and a technique based on the quadratic refractive index variation along the device’s length. Results were obtained by 2D-FDTD simulation. The designed grating couplers achieve coupling efficiencies for the TE-like mode over -8 dB (mask overlap) and -3 dB (quadratic variation), at a wavelength of 1550 nm. The coupling scheme considers a 220 nm a-Si:H waveguide and an SMF-28 optical fiber.

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al, a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms.

Exploring the fundamental mechanisms of locomotion extends beyond mere simulation and modeling. It necessitates the utilization of physical test benches to validate hypotheses regarding real-world applications of locomotion. This study introduces cost-effective modular robotic platforms designed specifically for investigating the intricacies of locomotion and control strategies. Expanding upon our prior research in Electric-Pneumatic Actuation (EPA), we present the mechanical and electrical designs of the latest developments in the EPA robot series. These include EPA Jumper, a human-sized segmented monopod robot, and its extension EPA Walker, a human-sized bipedal robot. Both replicate the human weight and inertia distributions, featuring co-actuation through electrical motors and pneumatic artificial muscles. These low-cost modular platforms, with considerations for degrees of freedom and redundant actuation, 1) provide opportunities to study different locomotor subfunctions— stance, swing, and balance; 2) help investigate the role of actuation schemes in tasks such as hopping and walking; and 3) allow testing hypotheses regarding biological locomotors in real-world physical test benches.

This paper presents a customized adaptive cascaded deep learning (ACDL) model for the design and performance prediction of metasurface absorbers. A multi-resonant metasurface absorber structure is introduced, with 10 target-driven design parameters. The proposed deep learning model takes advantage of cascading several sub-deep neural network (DNN) layers with forward noise mitigation capability. The inherent appearance of sparse data is completely dealt with in this work by proposing a trained adaptive selection technique. On the basis of the findings, the prediction response is quite fast and accurate enough to retrieve the design parameters of the studied metasurface absorber and with two sized patches of 4000 and 7000 datasets. The training loss taken form the second DNN of our proposed model shows logarithmic mean squared errors of 0.039 and 0.033 when using Keras and the adaptive method, respectively, with a dataset split of 4000. On the contrary, for a dataset split of 7000, the errors are 0.049 with Keras and 0.045 with the adaptive method. On the other hand, the validation loss is evaluated using the mean square error method, which results in a loss with 4000 datasets split with the Keras method of 0.044, while it is 0.020 with the adaptive method. When extending the dataset to 7000, the validation loss with the keras splitting method is 0.0073, while it is improved, reaching 0.006 with the proposed adaptive method, and achieving a prediction accuracy of 94%. This proposed deep learning model can be deployed in the design process and synthesis of multi-resonant metasurface absorber structures. The proposed model shows its advantages of making the design process more efficient in sparse dataset handling, efficient approach in multi-resonance metasurface data pre-processing, less time consuming, and computationally valuable.

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at BS. The expression of the uplink achievable rate is derived and analyzed. Besides, genetic algorithm (GA) is used to optimize the phase shifts of RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS’s reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs, and reduce the demand for the number of BS antennas, which can significantly reduce the hardware costs at the BS.

Optical beam splitter is used for dividing an input optical beam into several separate beams with specific power ratio. Usually, conventional beam splitters have bulky dimensions and fixed dividing ratio which significantly limit the design of new miniaturized optical devices and integrated optical circuits. We propose and investigate a novel physical concept of a miniaturized planar optical splitter/coupler with a switching element in the form of a photonic molecule (PM) pair dispersing input optical fluxes along multiple ways with variable power proportions. The structural design of the proposed splitter is based on a silicon-on-insulator (SOI) platform and composed of high-quality resonators in the form of electromagnetically coupled submicron-sized microcylinders. The control on the power division ratio and the selection of optical beam directions is realized by tuning the photonic splitter structure to the corresponding resonance of PM supermode. Compared to known analogues, the proposed design is easy to fabricate, suitable for integration into a "System-on-a-chip" platform and can dynamically change the beam power division ratio by input wave-phase manipulation.

Muscle fatigue caused by using staplers affects the surgical procedures of surgeons. Compared to manual staplers, electric staplers decrease the muscle fatigue by replacing repeated gripping and releasing with automatic model. Still powered by the battery, the electric staplers are confronted with new operation fatigue due to the extra weight of the battery. The weight and manual operation of the electric stapler can cause user's muscle fatigue. To tackle the unsettled fatigue issue, the study is to propose a new design method to offset the influence of extra weight of the battery attached to the electrical stapler by changing the opening and closing of the stapler end clamp to electric control. The purpose of this study is to verify whether the design of this electric stapler can offset the impact of weight. Experiments are set up to test the feasibility of the design on 12 male Chinese surgeons. The muscle fatigue is indicated by measuring the EMG (electromyography) signal, and the stapler's working effect is indicated by measuring the end movement of the stapler. The result shows the new-designed stapler reduce the operation fatigue, counteracting the impact of the battery weight.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

The COVID-19 pandemic has emphasized the crucial role of ventilation systems in mitigating cross-infections, especially in infectious disease hospitals. This study introduces a dual-design operation ventilation system that can operate under two sets of ventilation conditions for normal and epidemic times. A challenge is optimizing duct diameters for required airflow while maintaining hydraulic balance. We design a genetic algorithm with adaptive penalty factor, the velocity constraint and the improved crossover and mutation probability. The improved genetic algorithm is suitable for ventilation system networks, which can find a better air duct diameters combination to improve the hydraulic balance rate, and reduce the usage of air valves, resulting in efficient hydraulic balancing commissioning. Compared with the traditional genetic algorithm, it has a faster search speed and a better global search ability, which is effective for the optimal design of ventilation system networks.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design parameters to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number of studies demonstrates a significant interest in implementing multi-objective optimization methods to tackle building facade optimization problems. This study conducts a systematic review of optimization methods for building facade optimization (BFO). The optimization objectives and design variables are categorized based on their characteristics. The efficiency and effectiveness of optimization algorithms in addressing BFO problems are compared. Building optimization techniques and tools are showcased, along with their functions and limitations. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges. The limitations, challenges, and future potential of these methods are summarized and proposed.

One of the interactions that has been studied in recent decades under the ecohydrological approach is infiltration, which is difficult to determine because the existing methods for estimation depends on certain variables that are difficult to establish or identify, and in other cases, the implementation of instrumentation is expensive. Given this, saturated hydraulic conductivity has been the descriptive variable of the infiltration process by recent authors, being understood as the conduction of water that occurs through the soil and its composition through the vegetation cover. The present design generated an easily accessible portable instrumentation model for the monitoring and collection of infiltration data in situ for basins and ecosystems with different land uses, in an automated manner with the objective of determining the vegetation patterns necessary to sustain the infiltration function. The INDI-INECOL tension infiltrometer [1] was chosen as the reference instrument. It evaluates the saturated hydraulic conductivity based on a column of water that descends through the system and it is exchanged in the soil by the air between the pores. To automate data collection, a measurement system has been proposed through a SCADA system, which is defined as a control, action, decision, storage and data recording system. This system collects field data from a sensor VL053L0X connected to a master station, in our case an Arduino board UNO, connected through the serial port and by a graphical user interface programmed on Python, to display, storage and enrichment of the sensor data and finally to be exported in files suitable for Microsoft Excel.

As the global aging trend continues to intensify, there will inevitably be more complex and diverse aging problems in the future. There is no doubt that designers have the responsibility to explore the possibilities of the future and solve the problems that will be faced in the future. Based on the Future Cone, the Double Diamond Model and the IDEO Method Cards, this study proposes a new model to guide the design practice of future aging issues in the context of aging. With the aim of validating and refining the framework, an ageing designer workshop was held where participants were asked to imagine, explore and express ideas about future ageing issues. The workshop was used to refine the proposed model. More specifically, the model includes a future concept, a design guidance process based on the Double Diamond Model, and tools that can be applied at all stages of design, which can help designers generate ideas and solutions for future aging problems, and collectively lead society to a more desirable future. Moreover, this study also explores broader directions for the development of the model and provides a reference for continued research on this topic in the future.

In this work we successfully prepared a modified cobalt oxide (Co3O4) carbon paste electrode to detect Levofloxacin (LEV). By synthesizing Co3O4 nanoparticles through the chemical coprecipitation method, the electrochemical properties of the electrode and LEV were thoroughly investigated using CV, SWV, EIS, while material properties were scrutinized using ICP-OES, TEM, SEM, and XRD. The results showed that the prepared electrode displayed a better electrocatalytic response than the bare carbon paste electrode. After optimizing SWV, the electrode exhibited a wide linear working range from 1 to 85 μM at pH 5 of BRBS as the supporting electrolyte. The selectivity of the proposed method was satisfactory, with good repeatability and reproducibility, strongly suggesting a potential application for determining LEV in real samples, particularly in pharmaceutical formulations. The practicality of the approach was demonstrated through good recoveries, and the morphology of the materials was found to be closely related to other parameters, indicating that the developed method can provide a cost-effective, rapid, selective, and sensitive means for LEV monitoring. Overall, this project has made significant progress towards developing a reliable method for detecting LEV and has opened up new opportunities for future research in this field.

The study presents the synthesis and characterization of dual-layer sulfonated polyphenylenesulfone (SPPSu) nanocomposite hollow fiber nanofiltration membranes incorporating titanium dioxide (TiO2) nanoparticles using the phase inversion technique. The newly developed membranes were systematically characterized using advanced tools and methods to evaluate their properties and performance. The study focused on investigating the effects of TiO2 addition in the SPPSu inner layer on pure water permeability, and salt rejection. The nanocomposite hollow fiber nanofiltration membranes exhibited a significant enhancement in pure water permeability, with a three-fold increase compared to the pristine membranes, achieving a flux of 5.4 L/m2h.bar. The addition of TiO2 also led to an improvement in the mechanical properties of the membranes, as evidenced by the increase in the expected tensile strength from 2.4 to 3.9 MPa. To evaluate the salt rejection performance, rejection tests were conducted using a laboratory-scale filtration setup in recycle mode. Aqueous solutions containing different divalent ions, specifically magnesium sulfate (Mg2SO4) and sodium sulfate (Na2SO4) at a concentration of 500 ppm, were used as the feed solution. The modified dual-layer hollow fiber nanocomposite membranes exhibited a maximal rejection of 95% for Mg2SO4, demonstrating their effective separation capabilities for divalent ions. The findings of this study highlight the successful synthesis and characterization of dual-layer SPPSu nanocomposite hollow fiber nanofiltration membranes incorporating TiO2 nanoparticles. The incorporation of TiO2 resulted in improved pure water flux, mechanical strength, and salt rejection performance. These enhanced properties make the developed nanocomposite membranes promising candidates for applications in nanofiltration processes, particularly for the selective separation of divalent ions from aqueous solutions.

Synchronous homopolar machines (SHM) with excitation winding on the stator have attracted the attention of researchers for many decades due to their high reliability. They are used in a variety of applications such as aircraft and railway generators and traction motors. At the same time, the three-dimensional structure of their magnetic circuit makes the problem of their analysis and optimization challenging. This problem becomes even more complicated when considering an SHM with hybrid excitation, with ferrite magnets in the rotor slots. The article proposes a mathematical procedure for optimizing a synchronous homopolar generator with hybrid excitation based on the single-criterion Nelder-Mead method and two-dimensional finite element analysis (FEA). The use of the Nelder-Mead method compared to multicriteria methods and 2D FEA compared to 3D FEA makes possible to significantly reduce the computational burden. As a result of optimization, the power loss and torque ripples of the generator are significantly reduced. The article also compares the characteristics of synchronous homopolar generators with and without ferrite magnets.

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of those Random Hardware Failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, functional safety standards, like the ISO 26262 devoted to automotive applications, require embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting Control Flow Errors. Many Control Flow Checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on the their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called Model-Based Software Design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of Control Flow Checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; Secondly, by the implementation of the CFC algorithm in the application behavioral model is automatically translated. There is no need to modify the code generator. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller.

Design changes seem inevitable in engineering, procurement and construction EPC projects. Such changes create a need for a proactive approach to adjusting project scope, cost and time (the triple constraints) for efficiency and effectiveness in overall delivery. This study investigates the causes and implications of design changes in order to improve design change management practices. Data for the study was obtained through online interviews with New Zealand industry practitioners. Thematic analysis was used to collate the results into meaningful data. The study found that design changes were predominantly caused by clients' inadequate strategic planning, insufficient attention to design, EPC contractors' inadequate design ability, and on-site variations. There were three categories of such design changes: direct impact on the project, the reciprocal and complementary effect on stakeholders, and the far-reaching impact on the community. The study concludes by suggesting improvements, such as strengthening the integration of project teams to enhance design quality, strategic alignment of stakeholders at the planning stage, early contractor involvement (ECI) between the planning and design phases, and improving collaboration between design and construction teams. Further, a combination of high technical skills (e.g. design ability) and soft skills (can-do attitude, interpersonal skills, problem-solving skills, documentation skills, etc.) are needed to effect the desired improvement in design change management.

A 9.4 T/400 MHz superconducting magnet with a warm bore of 60 mm is being designed. The 400 MHz NMR magnet is composed of 9 coaxial coils to generate a central field of 9.4 T. The magnetic field homogeneity is better than 4 ppm for 50 mm diameter spherical volume (DSV) and better than 1 ppb for 5 mm DSV. Three types of NbTi strands were used to reduce the cost of the superconducting materials. A preload with 80 MPa was exerted on the superconducting coils and over-banding with stainless steel was adopted to release the stress from the electromagnetic forces. The magnet will be operated in persistent mode with superconducting joints to reduce the field decay. The pulse tube cryocooler with a cooling capacity of 1 W at 4.2 K can be used to reduce the liquid helium evaporation and vibration during operation. The magnet will be equipped with Bi2223/AgAu HTS current leads to reduce the heat losses. In this paper, the design of the 9.4 T NMR superconducting coils, electromagnetic field calculations, and stress analysis of the superconducting coils were presented.

The path towards decarbonization requires a progressive adaptation of all refrigeration systems, but only stationary ones have been intensely studied to improve their environmental performance. However, refrigerated transport is a vital piece of the cold chain, and it must be considered in the green transition. In this paper, we propose a model for a hybrid refrigerated van that includes photovoltaic panels and electric batteries to decrease total greenhouse gas emissions from the engine. Thermal, electrical, and battery sub-models are considered and integrated into the comprehensive hybrid solar-powered refrigerated van model. Different technologies are compared in economic terms, including Lithium and Lead-acid batteries and three different types of photovoltaic panels. The model was validated regarding van fuel consumption, showing a 4% deviation. Single and multiple delivery scenarios are considered to assess the energetic, economic, and environmental benefits. Monthly CO2,e emissions could be reduced to 20% compared to a standard refrigerated van. Despite the environmental benefits provided by this sustainable solution, the payback period is still too long (above 20 years) because of the necessary investment to adapt the vehicle and considering fuel and electricity prices currently.

The use of cobalt-iron (VaCoFe) core is investigated as an alternative to silicon-iron (FeSi) on the design of interior permanent magnet synchronous motors (IPMSM). A spoke-type IPMSM geometry is optimized considering FeSi and VaCoFe cores for a torque range up to 40 N.m, providing a general comparative analysis between materials, considering the application of a 4-motor competition vehicle’s powertrain. A genetic optimization algorithm is applied over a hybrid analytical/finite-element model of the motor to provide sufficiently accurate electromagnetic and thermal results within a feasible time. VaCoFe can result in an estimated increase of up to 5 % in efficiency for the same torque, or up to 64 % torque increase for the same efficiency level. After optimization, and using a detailed time-dependent model, a potential 3.2 % increase in efficiency, a core weight reduction of 4.1 %, and a decrease of 9.6 % in the motor’s core volume was found for the VaCoFe at 20 Nm. In addition, for the same motor volume, the VaCoFe allows an increase of 51.9 % of torque with an increase of 1.1 % of efficiency, when compared with FeSi.

The aim of this review is to present the Quality by Design (QbD) model as a suitable methodology to perform research in the academic Costa Rican institutions that teach Pharmacy. Pubmed, Science Direct, and Google Scholar databases were screened for original and review papers, as well as short communications published not more than 10 years ago. Publications were screened by title and abstract. Relevant references were used to develop three important themes: The University’s Research Model in Costa Rica, QbD Model, and QbD as a Research Methodology for Industrial Pharmacy in the Academy. In this sense, the QbD model is a great methodology for carrying out research projects regarding Pharmaceutical Sciences but especially for Drug Development. Academic research based on this model enables training and developing practical, scientific, and leadership skills in pharmacy students. The generated knowledge can be shared in the classrooms, which represents an ideal environment to communicate their research results and to foster collaborative work between researchers, professors, and students. The participation of all these actors allows a high level of commitment to research work, which benefits the scientific advancement of the university and society. It is important to visualize the student body as potential key actors in the research process, encouraging in them the desire to become trained scientific researchers who want to pursue a career in the academy, giving continuity to it.

To realize structural vibration control,a two parameters H2 optimization design was proposed to optimize the tuning ratio and damping ratio for electromagnetic tuned mass damper (EMTMD). The control effect of this two parameters optimization design is better than that of classical tuned mass damper (TMD).For this two parameters optimization,the most important thing is that the inductance of the coil can be set very small and the external load resistance can be positive ,which can avoid the use of complex negative impedance circuit. If Ref.[6] were designed according to the H2 optimization of two parameters, the EMTMD can be used for multi-modal vibration control of structures without connecting negative inductance and negative resistance spontaneously.

Queretaro is currently one of the states with the highest growth rate in the automotive industry. In the last five years, the number of companies has increased, reaching more than 300 small and medium-sized companies in the sector. However, they show a high degree of ignorance and lack of strategic foresight, which translates into poor planning in the short, medium and long term and low competitiveness, leading many of them to failure. This paper presents the results of a study within a strategic foresight evaluation with the development of a Structural Equation Model that allows the analysis of foresight and strategic planning within the automotive SMEs in the state of Queretaro. The study analyzes the necessary indicators to be evaluated and establishes the relationship of dependence between the variables, which is necessary to create the constructs of the model. It is confirmed that the adjustment of the model used is adequate for the evaluation of SMEs. The contributions of the research were: A theoretical contribution related to strategic foresight within SMEs and the construction of the structural equation model to evaluate strategic foresight in automotive SMEs in the state of Queretaro.

The designs of two large arenas of different epochs are discussed, which are the most famous antique amphitheater the Roman Colosseum (Italy) and the modern Gazprom Arena stadium (St. Petersburg, Russia). 3D-computer models of the arenas have been built and evacuation of people has been simulated in the Sigma FS software (Russia). The arena designs are analyzed in terms of organizing pedestrian evacuation, a comparative analysis is made, and the specific characteristics affecting the evacuation time are established.

Influenza virus infection causes severe respiratory illness in people worldwide, disproportionately affecting infants. The immature respiratory tract coupled with the developing immune system is thought to synergistically play a role in the increased disease severity in younger age groups. Although vaccines remain the best solution for protecting this vulnerable population, no vaccines are available for those under 6 months, and for infants aged 6 months to 2 years, the vaccine elicits a dampened immune response. Dampened immune responses may be due to unique features of the infant immune system and a lack of pre-existing immunity. Unlike older children and adults, the infant immune system is Th2 skewed and has less antigen presenting cells and soluble immune factors. Paradoxically, we know that a person’s first infection with the influenza virus during infancy or childhood leads to the establishment of life-long immunity toward that particular virus strain. This is called influenza imprinting. To provide better protection against influenza virus infection and disease in infants, more research must be conducted to understand the imprinting event. We contend that by understanding influenza imprinting in the context of the infant immune system and the infant’s immature respiratory tract, we will be able to design more effective influenza vaccines for both infants and adults. Working through the lens of imprinting, using infant influenza animal models such as mice and ferrets, which have proven useful for infant immunity studies, we will gain a better understanding of imprinting and its implications regarding vaccine design. This review examines literature regarding infant immune development, current vaccine strategies, respiratory development, and the importance of researching the imprinting event in infant animal models to develop more effective and protective vaccines for young children.

This paper addresses a method to transmit streaming data via rolling shutter camera-based optical camera communications (OCC). Since the amount of data that can be contained within one frame is limited, and the continuity of received data cannot be guaranteed due to OCC environmental variations, we introduce the concept of dividing the streaming data into several fragmented sets that are transmitted sequentially. We propose a superframe to contain sequential packets of fragmented data and corresponding indexes, so that sequential packets for streaming data can be continuously collected. When redundant frame transmission is considered, any packet lost due to OCC environmental conditions can be recovered. Experimental results show that the proposed method can be successfully used to transmit streaming data, with the number of redundant frames required to acquire all data packets based on image resolution. In addition, we describe how to identify missing packets from a network point of view to reduce the number of redundant frames needed to acquire all the data. This paper presents baseline results of communication performance when sending streaming data via a rolling shutter-based OCC.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China and has subsequently spread worldwide. In the absence of any antiviral or immunomodulatory therapies, the disease is spreading at an alarming rate. 5 to 10% of recovered patients in Wuhan test positive again; this suggest that for controlling COVID-19, vaccines may be better option than drugs. A clinical trial to evaluate an anti-COVID-19 vaccine has started recently. However, its efficacy and potency have to be evaluated and validated. As an alternative, we are presenting a first-of-its-kind, designed multi-peptide subunit based epitope vaccine against COVID-19. The vaccine construct comprise an adjuvant, CTL, HTL, and B-cell epitopes joined by linkers. The vaccine is non-toxic, non-allergenic, thermostable and immunogenic with the capability to elicit a humoral and cell-mediated immune response. The findings are validated with high-end computation-based methods. This unique vaccine is made up of 33 highly antigenic epitopes from three proteins that have a prominent role in host receptor recognition, viral entry, and pathogenicity. We advocate this vaccine must be synthesized and tested urgently as public health priority.

Universitas Gadjah Mada (UGM) is implementing the concept of green campus by various means including through elements in its sustainable campus development plan. With regards to several factors related to Energy and Climate Change indicators set by UI GreenMetric, this research examined the existing condition of the area of UGM which belongs to academic zone in order to construct a proposal based on a building mass reconfiguration. This proposal was aimed to improve the implementation of energy-specific green campus concept within the Forestry cluster area as a sample case study. This selection was based on the average value of the Building Coverage Ratio (BCR) values in all of the clusters; thus, the scenario was relevant for generalization and could be replicated in other clusters in the university. The study was done in three stages of simulation, and was based on a series of digital simulation of sunlight hour and solar radiation run in Grasshopper using Lady Bug environmental analysis plug-in for a period of five summer months during the building office hours. Following the reconfiguration, results had shown a similar downtrend between the amount of sunlight hour and solar radiation in the area (up to 49% and 45% respectively on building envelopes, and up to 44% and 42% respectively on landscaping surfaces). This reduction indicated a potential of energy efficiency by applying selective building mass reconfiguration as a passive design strategy that goes hand in hand with the campus’s development policy to optimize the use of BCR for a greener UGM campus through its many undergoing and upcoming redevelopment projects.

The semi-enclosed estuary is very susceptible to changes in the physical and environmental characteristics of the inflow from the land and the coastal sea weather and so the continuous and comprehensive monitoring is necessary for managing the estuary. Nevertheless, the standard procedure or schematic framework has not been proposed appropriately to determine how many instruments are necessary and where they need to be deployed to detect critical changes. Therefore, the present work proposes a systematical strategy for the deployments of the monitoring array by using the combination of the graphical optimization with the objective mapping technique. In order to reflect the spatiotemporal characteristics of the bay, the representative variables and eigenvectors are determined by the Empirical Orthogonal Function (EOF), and the cosine angle among them are calculated for a design index of optimization. At the recommended locations, the sampled representative variables are interpolated to reconstruct their spatiotemporal distribution and compared with the distributions of the true values. Analysis confirms that the selected locations, even with a small number of points, can be used for on-site monitoring. Also, the present framework suggests how to determine installable regions for real-time monitoring stations, which reflect the global and local characteristics of the semi-enclosed estuary.

The potential of Thai industrial product design is still inferior to those of leading competitors in world market that give more importance on the design during their product development to increase their competitive edges on commercial scale. The product design is very important part for sustainable growth in this industry. Thus, this research aims at investigating footwear design strategies for Thai footwear industry to be excellence in world market. The research has been designed with the mixed method of both qualitative and quantitative study. The quantitative data were collected through semi-structure interview from 500 designers who presented their designs to join the award competition. The results revealed that the footwear design strategies consisted of 4 factors, i.e. 1) design, 2) market analysis, 3) innovation, and 4) information technology. This paper utilizes the method of Structural Equation Modeling (SEM) to establish a strategies model for competitive advantage in Thai footwear industry. The analysis results indicated that the footwear design strategies model could help make more effective policies and organization strategies for enterprises and designers to develop themselves to be excellence in world market.

One-dimensional shape memory polymer fibers (SMPFs) have obvious advantages in mechanical properties, dispersion properties and weavability. In this work, a method for fabricating semi-crystallization ethylene-vinyl acetate copolymer (EVA) fiber with two-way shape memory effect by melt spinning and ultraviolet (UV) curing was developed. Here, the effect of crosslink density on its performance was systematically analyzed by gel fraction measurement, tensile tests, DSC and TMA analysis. The results showed that the crosslink density and shape memory properties of EVA fiber could be facilely adjusted by controlling UV curing time. The resulting EVA fiber with cylindrical structure had a diameter of 247.13 ± 10.07 μm, and its mechanical strength and elongation at break were 64.46 MPa and 114.33%, respectively. The critical impact of the crosslink density and applied constant stress on the two-way shape memory effect were analyzed. Moreover, the single EVA fiber could lift more than 143 times its own weight and achieve 9% reversible actuation strain. The reversible actuation capability was significantly enhanced by a simple winding design of the single EVA fiber, which provided great potential applications in smart textiles, flexible actuators and artificial muscles.

The beautiful and stable posture is essential for ballet. Especially the pirouette which dancers balance themselves on their one leg is one of the most impressive postures. During the pirouette motion, loads from the rotation and the body weight are concentrated to the one leg. Proper ballet shoes can reduce the muscle burden and improve the stability. Four types of shoes; bare feet, running shoes, pointe shoes, and dance sneakers are analyzed. The motion capture system and SIMM are used for the analysis of muscle burden and stability during pirouette. The major 6 muscles in the leg (beceps femoris long head, beceps femoris short head, soleus, rectus femoris, gastrocnemius medialis, and gastrocnemius lateralis) are analyzed, and the effect of rotating velocity is considered. Dance sneakers are outstanding for improving the stability and lessening the muscle burdens in all conditions for beginners. That comes from the design feature of the divided shoe soles and protecting the ankle of the axis leg. With the results and analysis, the direction for design optimization of ballet shoes is suggested. Consequently, this research is about the verification of sports equipment using computer simulation with the musculoskeletal model from a scientific viewpoint of biomechanics.

This paper reports design of a 2 × 4 hybrid multimode interferometer-Mach-zehnder interferometer (MMI-MZI) configuration and compact thermo-optical switch consisting of slab waveguides on the silicon-on-insulator (SOI), platform. The device consisted of two identical MMIs as power splitters and couplers that are connected with two identical MMI-based phase shifters, and linear tapers at both ends of the MMIs to minimize the power coupling loss. A thin Al pad is used as a heating element, and a trench is created around this pad to prevent heat from spreading and to minimize loss. The calculated average thermo-optical switching power consumption, excess loss, and power imbalance are 1.4 mW, 0.9 dB, and 0.1 dB, respectively. The overall footprint of the device is 6 × 304 μm². The new heating method has advantages of compact size, ease of fabrication on SOI platform with the current CMOS technology, and offers low excess loss and power consumption as demanded by devices based on SOI technology. The device can act as two independent optical switches in one structure.

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism. Therefore, this research is proposing an Adaptive Content Management Learning System (ACMLS) model to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS model design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS model such as: caregivers and children with autism.

In the context of food packaging design, customization enhances the value of a product by meeting the needs of the consumer. Personalization is also linked to adaptation. This makes it possible to improve the properties of the packaging from various points of view: functional, aesthetic, economic and ecological. Currently the functional and formal properties of packaging are not investigated among themselves, however the study of both properties are the basis for creating a new concept of personalized and sustainable product. In accordance with this approach, the conceptual design procedure of packaging with personalized and adapted geometries based on the digitization of fresh food is proposed in this work. This study is based on the application of advanced technologies for the design and development of food packaging, in this case apples, in order to improve the quality of the packaging. The results obtained show that it is possible to use advanced technologies in the early stages of product design in order to obtain competitive products adapted to new emerging needs.

A key concern in an ageing society is citizens’ mobility. As populations age, disability impairments can affect active ageing, health-related wellbeing and quality of life. In this paper, we present the on-going research project SeriousGiggle—Game-based learning for triggering active ageing. Its goal is to assess the potential of game-based learning for active ageing and contribute to a sense of wellbeing and quality of life. It also seeks to improve the mobility of older adults by creating a set of journey plans with route guidance that are rated in terms of safety, community support, environment and age-friendliness. Drawn on our field work with 33 co-designers, 40 end users and 10 semi-structured interviews with Subject Matter Experts, we identify a set of necessary design requirements to an Age-friendly Playable City. This study recommends the use of gamification and playful techniques to engage the end-users to provide information about local traffic signs, pavement conditions, wayfinding and, therefore, help to create route guidance and walking assistance that are personalized to older adults’ context in terms of location, travel fitness, mobility impairments and motivations.

As an emerging building solution, cross-laminated timber (CLT) floors have been increasingly used in mass timber construction. The current vibration design of CLT floors is conservative due to the assumption of simple support conditions in the floor-to-wall connections. It is noted that end fixity occurs as a result of clamping action at the ends, arising from the gravity load applied by the structure above the floor and by the mechanical fasteners. In this paper, the semi-rigid floor-to-wall connections are treated as elastically restrained edges against rotations to account for the effect of partial constraint. A rotational end-fixity factor was first defined to reflect the relative bending stiffness between CLT floors and elastic restraints at the edges. Then, for the design of vibration serviceability of CLT floors as per the Canadian Standard for Engineering Design in Wood (CSA O86), restraint coefficients were defined and their analytical expressions were derived for natural frequencies and the mid-span deflection under a concentrated load, respectively. In particular, a simplified formula of the restraint coefficient for the fundamental frequency was developed to assist engineers in practical design. At last, by comparing with reported experimental data, the proposed design formula showed excellent agreement with test results. In the end, the proposed end fixity factor with their corresponding restraint coefficients is recommended as an effective mechanics-based approach to account for the effect of end support conditions of CLT floors.

Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent times due to their suitability to a wide variety of civil, military and societal missions. Development of an unmanned amphibious vehicle integrating the features of a multi-rotor UAV and a hovercraft is focus of the present study. Components and subsystems of the amphibious vehicle are developed with due consideration on aerodynamic, structural and environmental aspects. Finite element analysis (FEA) on static thrust conditions and skirt pressure are performed to evaluate the strength of structure. For diverse wind conditions and angles of attack (AOA), computational fluid dynamic (CFD) analysis is carried out to assess the effect of drag and suitable design modification is suggested. A prototype is built with a 7 kg payload capacity and successfully tested for stable operations in flight and water-borne modes. Internet of Things (IoT) based water quality measurement is performed in a typical lake and water quality is measured using pH, dissolved oxygen (DO), turbidity and electrical conductivity (EC) sensors. The developed vehicle is expected to meet functional requirements of disaster missions catering to the water quality monitoring of large water bodies.

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.

Aiming at the industrial scale development of a Scandium (Sc)-selective leaching process of Bauxite Residue (BR), a sufficiently numerous set of process design aspects has been investigated, by appropriate exploitation of available experimental data. The interpretation of experimental data for Sc leaching yield, with sulfuric acid as the leaching solvent, has shown significant impact from acid feed concentration, mixing residence time, liquid to solids ratio, and times of leachate re-usage onto fresh BR. The thin film diffusion model, as the fundamental theory for leaching, either with constant particle size for selective leaching, or with shrinking particle size for less-, or non-, selective leaching, interprets sufficiently well the relevant experimental data. In both cases, a concept for an unyielding core supplements the basic model. Especially for the selective leaching mild conditions, the simplest model version keeps step with the experiments, since both prove 1^st order kinetics, while especially for the extreme conditions including very low solvent excess, it is proposed a combined conversion rate model with diffusion and chemical reaction inside particles. The maximization of Sc recovery per unit of consumed solvent (i.e., specific recovery) emerged as highly critical for the process economics.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

A reliability-based criterion to estimate strength amplification factors for buildings with asymmetric yielding located within a seismic region presenting different soil conditions is proposed and applied. The approach involves the calculation of the mean annual rate of exceedance of structural demands of systems with different levels of asymmetric yielding. Two simplified mathematical expressions are developed considering different soil conditions of the valley of Mexico. The mathematical expressions depend on the ductility of the structural systems, their level of asymmetric yielding, their fundamental vibration period and the dominant period of the soil. In addition, the proposed expressions are compared with that recommended by the current Mexico City Building Code (MCBC). Since the expressions are developed with the help of simplified structural systems, the validity of such expressions is corroborated by comparing the expected ductility demand of multi-degree of freedom (MDOF) structural systems with respect to that of their equivalent simplified systems. Both structural representations are associated with a given annual rate of exceedance value of an engineering demand parameter. The expressions proposed in this study will be incorporated in the new version of the MCBC.

Manufacturing organizations shall recognize sustainability as a business occasion to capitalize on, rather than an undesirable pressing situation. Still, empirical evidence shows that this opportunity is hard to capture and communicate in global strategic decisions, through planning by tactical management, to daily operational activities. This paper systematically reviews the modeling challenges at the crossroad of value and sustainability decisions making, spotlighting methods and tools proposed in literature to link sustainability to customer value creation at strategic, tactical and operational level. While statistical results show that the topic of sustainability and value modeling is trending in literature, findings from content analysis reveal that recent attempts to promote a value-based view in the sustainability discussion remain at a strategic level, with most of the proposed indicators being suited for managerial decision-making. The lack of support at operational level points to the opportunity of cross-pollinating sustainability research with value-centered methodologies originating from the aerospace sector. The Value Driven Design framework is proposed as main hub from which to derive models supporting engineers and technology developers in the identification of win-win-win situations, where sustainable improvements are aligned with business advantages.

Process simulation represents an important tool for plant design and optimisation, either applied to well established or to newly developed processes. Suitable thermodynamic packages should be selected in order to properly describe the behaviour of reactors and unit operations and to precisely define phase equilibria. Moreover, a detailed and representative kinetic scheme should be available to predict correctly the dependence of the process on its main variables. This review points out some models and methods for kinetic analysis specifically applied to the simulation of catalytic processes, as a basis for process design and optimisation. Attention is paid also to microkinetic modelling and to the methods based on first principles, to elucidate mechanisms and calculate thermodynamic and kinetic parameters. Different case histories support the discussion. At first, we have selected two basic examples from the industrial chemistry practice, e.g. ammonia and methanol synthesis, which may be described through a relatively simple reaction pathway. Then, a more complex reaction network is deeply discussed to define the conversion of bioethanol into syngas/hydrogen or into building blocks, such as ethylene.

This study evaluated the presence of Human Papillomavirus (HPV) and Bovine Leukemia Virus (BLV) DNA in breast cancer (BC) samples from women in Northeastern Brazil. We analyzed 186 formalin-fixed, paraffin-embedded tissue block samples using PCR and qPCR to detect HPV and BLV DNA. BLV DNA was not detected in any BC patients. However, HPV DNA was found in 46.2\% of the BC samples: 44.6\% were positive for HPV16, 3.5\% for HPV31, and 2\% were coinfected with HPV16 and HPV31. Additionally, we found an association between HPV-positive BC and Ki-67 expression (P=0.03, OR=1.85; CI=1.02-3.34), as well as between HPV31-positive BC and mitotic index (P=0.047). Although BLV was not detected in BC patients from Northeastern Brazil, the association between HPV and cell proliferation markers warrants further investigation, as HPV may contribute to the aggressiveness of BC in these patients.

The convergence of digital twin technology with additive manufacturing (AM) represents a transformative advancement in the fields of design, simulation, and production. Digital twins—virtual replicas of physical assets—enable real-time monitoring, simulation, and optimization of manufacturing processes by mirroring their physical counterparts. When integrated with additive manufacturing, which allows for the creation of complex geometries and rapid prototyping, digital twins enhance the ability to design, test, and refine products with unprecedented precision and efficiency. This integration facilitates iterative design improvements, predictive maintenance, and real-time process adjustments, leading to more accurate simulations and optimized production workflows. By leveraging data from digital twins, manufacturers can anticipate issues, reduce material waste, and shorten development cycles. This abstract explores how the fusion of digital twin technology with AM not only refines traditional manufacturing paradigms but also paves the way for more innovative and adaptive manufacturing practices.

Cancer is a devastating disease with female breast cancer becoming the leading cause of global cancer morbidity. Herein we report the design and synthesis of a sorafenib analog 1A and its base-promoted arranged compound 1A2. Their in vitro cytotoxicity and potencies in EFGR inhibition study revealed that compound 1A displayed good antiproliferative activity in MDA-MB-231 (breast) cells with an IC50 value of 16.18±1.42 μM. Crystallization 1A2 affored 1A2a as a solvate with dichloromethane which was explicitly confirmed by the single crystal X-ray diffraction analysis.

Access to nasopharyngeal swabs for sampling remain a bottleneck in some regions for COVID-19 testing. This study develops a distributed manufacturing solution using only an open source manufacturing tool chain consisting of two types of open source 3-D printing and batch UV curing, and provides a parametric fully free design of a nasopharyngeal swab. The swab was designed using parametric OpenSCAD in two components (a head with engineered break point and various handles), which has several advantages: i) minimizing print time on relatively slow SLA printers, ii) enabling the use of smaller print volume open source SLA printers, iii) reducing the amount of relatively expensive UV resin, and iv) enabling production of handle on more accessible material extrusion 3-D printers. A modular open source UV LED box was designed, fabricated for $45 and tested for batch curing. Swabs can be fabricated for $0.06-$0.12/swab. The results of the mechanical validation tests showed that the swabs could withstand greater forces than would be expected in normal clinical use. The swabs were also able to absorb a significant amounts of synthetic mucus materials and passed abrasion and handling tests. The results show the open source swab are promising candidates for clinical trials.

This study investigated riverbank protection strategies along the dynamic Jamuna River in Bangladesh, a braided river prone to erosion and sedimentation. It reviews past countermeasures and emphasizes the effectiveness of groyne-type structures in redirecting flow and preventing erosion. Notably, the left bank exhibited greater stability than the right bank, emphasizing the need for effective groyne installations. A systematic methodology utilizing numerical modeling using International River Interface Cooperation (iRIC Nays2DH) ensured accuracy in assessing morphological impacts. This study presents novel countermeasures incorporating groyne installations in Zone 1 (from Singrabari to Meghai) and Zone 2 (from Pachthakuri to Subhagacga), accompanied by simulations for various flood conditions. Zone 2, identified as highly erosion-vulnerable, showed the greatest susceptibility. Simulations (Cases_0 1, 2, and 3) revealed groynes in Zone 1 alone ineffective, while Zone 2 installations successfully diverted oblique flow, reducing erosion risk. Although Case_3's combined approach was effective, it was uneconomical for Zone 1. Case_2, focusing on Zone 2 groynes, emerged as the optimal solution, redirecting flow and shaping sandbars to mitigate Jamuna River's riverbank erosion. It provides a practical and economical solution. Further research is recommended for groyne length and spacing variations. This study enhances Jamuna River protection, emphasizing groyne-type structures' importance and promoting a holistic understanding of morphological dynamics for future river management and effective countermeasures.

This research article aims to present implementation and evaluation of the HPPT design based on using multi-parallel pumps switching for yielding performance with reference to potential change of daily irradiance. After that, the curve fitting of the hydraulic performance scenarios is tested to compare the hydraulic energy growth behaviors with reference to exponential and power growth models.

Mathematical models of non-small cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of NSCLC. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

Açaí, Euterpe oleracea Mart. is a native plant from the Amazonian and is rich in several phyto-chemicals with anti-tumor activities. The aim was to analyze the effects of açaí seed oil on colo-rectal adenocarcinoma (ADC) cells. In vitro analyses were performed on CACO-2, HCT-116, and HT-29 cell lines. The strains were treated with açaí seed oil for 24, 48, and 72 h, and cell viability, death, and morphology were analyzed. Molecular docking was performed to evaluate the inter-action between the major compounds in açaí seed oil and Annexin A2. The viability assay showed the cytotoxic effect of the oil in colorectal adenocarcinoma cells. Acai seed oil induced increased apoptosis in CACO-2 and HCT-116 cells and interfered with the cell cycle. Western blotting showed increased expression of LC3-B, suggestive of autophagy, and annexin A2, an apoptosis regulatory protein. Molecular docking confirmed the interaction of major fatty acids with annexin A2, suggesting a role of açaí seed oil in modulating annexin A2 expression in these cancer cell lines. Our results suggest the antitumor potential of açaí seed oil in colorectal adeno-carcinoma cells and contribute to the development of an active drug from a known natural product.

Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered. In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents. Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell’s invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+ BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.

In this work, the deoxygenation of organic liquid products (OLP) obtained by thermal catalytic cracking of palm oil at 450 °C, 1.0 atmosphere, with 10% (wt.) Na₂CO₃ as catalyst, in multistage countercurrent absorber columns using supercritical carbon dioxide (SC-CO₂) as solvent, with Aspen-HYSYS process simulator was systematically investigated. In a previous study, the thermodynamic data basis and EOS modeling necessary to simulate the deoxygenation of OLP has been presented [Molecules 2021, 26, 4382. https://doi.org/10.3390/molecules26144382]. This work address a new flowsheet, consisting of 0₃ absorber columns, 10 expansions valves, 10 flash drums, 08 heat exchanges, 01 pressure pump, and 0₂ make-up of CO₂, aiming to improve the deacidification of OLP. The simulation was performed at 333 K, 140 bar, and (S/F) = 17; 350 K, 140 bar, and (S/F) = 38; 333 K, 140 bar, and (S/F) = 25. The simulation shows that 81.49% of OLP could be recovered and the concentrations of hydrocarbons in the extracts of absorber-01 and absorber-02 were 96.95 and 92.78% (wt.) in solvent-free basis, while the bottom stream of absorber-03 was enriched in oxygenates compounds with concentrations up to 32.66% (wt.) in solvent-free basis, showing that organic liquid products (OLP) was deacidified and SC-CO₂ was able to deacidify OLP and to obtain fractions with lower olefins content. The best deacidifying conditions was obtained at 333 K, 140 bar, and (S/F) = 17.

Slot-die coatings are advantageous when used for coating large-area flexible devices; in particular, the coating width can be controlled, and simultaneous multi-layer coatings can be processed. Till date, the effects of ink widening and coating gap on the coating thickness have only been considered in a few studies. To this end, we developed two mathematical models to accurately estimate the coating width and thickness considering these two effects. We used root mean square deviation (RMSD) to experimentally verify the developed method. The coating width was seen to increase and the coating thickness was seen to decrease when the coating gap was increased. Experimental results showed that the estimation performances of the coating width and thickness models were as high as 98.46 % and 95.8 %, respectively. We believe that the developed models can be useful for determining the coating conditions according to the ink properties to coat a functional layer with user-defined widths and thicknesses in both lab- and industrial-scale roll-to-roll slot-die coating processes.

First of all the paper presents a solid solution of piezoelectric ceramic that was synthesized according to the general formula Pb(1-x)Srx(Ti0.48Zr0.52)(1-y)NbyO3 with x = 0.05 and y = 0.02, using wet ceramic processing technology and using an oxide mix as prime material. The effects of dopants (Sr2+ and Nb5+) on phase constitution, on microstructure and on the dielectric and piezoelectric properties were determinate. The Zr/Ti ratio was chosen near the morphotropic phase boundary of the PZT system in studied composition. The XRD data revealed that the PZT doped composition had tetragonal perovskite structure. Secondly the paper presents the design of the novel piezo-motor based on a surface wave which translates the linear extension of different piezoelectric segments of a piezoelectric cylinder into a rotational bending movement. This rotational bending of the piezoelectric cylinder is then transformed into a continuous rotation of the rotor through a calculated contact. The design of the motor takes advantage of the high piezoelectric constants of the developed material in an optimal way in order to increase the energetic efficiency. A brief mathematical model of electromechanical answer of piezoelectric materials is presented as it was used in the modeling of the material during the numerical simulations.

The production of butanol, acetone and ethanol by Clostridium acetobutylicum is a biphasic fer-mentation process. In the first phase the carbohydrate substrate is metabolized to acetic and bu-tyric acid, in the following second phase the product spectrum is shifted towards the economi-cally interesting solvents. Here we present a cascade of six continuous stirred tank reactors (CCSTR), which allows performing the time dependent metabolic phases of an ace-tone-butanol-ethanol (ABE) batch fermentation in a spatial domain. Experimental data of steady states under four operating conditions - with variations of the pH in the first bioreactor between 4.3 and 5.6 as well as the total dilution rate between 0.042 1/h and 0.092 1/h - were used to optimize and validate a corresponding mathematical model. Beyond a residence time distribution representation and substrate, biomass and product kinetics this model also includes the differen-tiation of cells between the metabolic states. Model simulations predict a final butanol product concentration of 8.2 g/L and a butanol productivity of 0.75 g/(L h) in the CCSTR operated at a pH in bioreactor 1 of 4.3 and D = 0.092 1/h, while 31 % of the cells are differentiated to the solventogenic state. Aiming at an enrichment of solvent-producing cells, a feedback loop was introduced into the cascade - sending cells from a later state of the process (bioreactor 4) back to an early stage of the process (bioreactor 2). In agreement with the experimental observations, the model accurately predicted an increase of butanol formation rate in bioreactor stages 2 and 3, resulting in an overall butanol productivity of 0.76 g/(L h) for the feedback loop cascade. The here presented CCSTR and the validated model will serve to investigate further ABE fermentation strategies for a controlled metabolic switch.

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. This paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 co-crystallized with COVID-19 spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure-activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. In parallel, at all stages, authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 co-crystallized with COVID-19 spike protein. Results: Carnosine emerged as the best known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the co-crystallized protein structure, carnosine bind with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 co-crystallized with COVID-19 spike protein and hence could offer potential mitigating effect against current COVID-19 pandemic.

Brine-dependent recovery process has seen much global research efforts in the past two decades because of their benefits over other oil recovery methods. The process involves the tweaking of the ionic composition and strength of the injected water to improve oil production. In recent years, several studies ranging from laboratory coreflood experiments by many researchers to field trials by several companies admit to the potential of recovering additional oil in sandstone and carbonate reservoirs. Sandstone and carbonate rocks are composed of completely different minerals, with varying degree of complexity and heterogeneity, but wettability alteration has been widely considered as the consequence rather than the cause of brine-dependent recovery. However, there is no consensus on the cause as several mechanisms have been proposed to relate the wettability changes to the improved recovery. This review paper aims to provide a state-of-the-art development in published research and various efforts of the industry. This review outlines an overview of laboratory and field observations, descriptions of underlying mechanisms and their validity, the complexity of the oil-brine-rock interactions, modelling works, and comparison between sandstone and carbonate rocks. The provided information is intended to provide the reader with up-to-date information, point to relevant studies for those who are new and those implementing either laboratory- or field-scale projects to speed up the process of further investigations in this research area. Overall, the outcome of this review would potentially be of immense benefit to the oil industry.

The defining characteristic of Alzheimer's disease (AD) is the aberrant deposition of pathogenic proteins, particularly a massive build-up of amyloid-β peptides, specifically in the brain. Amy-loid-β aggregation is neurotoxic and ultimately results in dysfunction of the nervous system. The present investigation aims to predict potential amyloid-β fibrils disaggregating molecules from plant sources through molecular modeling techniques, this appears to be a very promising and appealing treatment philosophy. Here, 500 flavonoids from various plants were considered, ini-tially undergoing ADME studies to screen molecules that could cross the blood-brain barrier. Later, potential Amyloid-β-disaggregating molecules were identified by molecular docking and dynamics (MD) simulation studies. Five molecules, prenylmethoxy flavonol (-7.3 kcal×mol-1), isopentenyl flavonol (-7.3 kcal×mol-1), 7,3'-Dihydroxyflavone (-7.2 kcal×mol-1), 7-Hydroxy-5-methyl-4'-methoxyflavone (-7.2 kcal×mol-1), 8-hydroxy-7-methoxyflavone (-7 kcal×mol-1) showed higher binding affinity score against Alzheimer's Aβ (1-42) fibrils, these are very close to the standard drug (Donepezil) (-7.90 kcal×mol-1). Further, the MD simulation studies established the intermolecular interaction and stability of the five selected ligands-Amyloid-β oligomer protein complexes. These results suggest that the chosen five flavonoid molecules could be used as possible agents to disaggregate amyloid-β fibrils; however, more in vitro and in vivo investigations are required to verify the therapeutic effectiveness.

Introduction – Elections are a cornerstone of democratic societies, enabling citizens to express their preferences and shape the direction of their governments. Economic conditions, political dynamics, and social factors often play pivotal roles in influencing voter behavior and determining electoral outcomes, making elections a key area of study in understanding governance and policy change. Objectives – This study examines how key economic factors like inflation, unemployment, and poverty influence election results in Ghana. It also looks at how these economic elements shape voter choices and predicts the possible outcomes of the 2024 general elections using past data. Methodology – The study used historical economic and political data from 1992 to 2023, applying statistical models like ARIMA (Auto-Regressive Integrated Moving Average) to forecast future election outcomes. Economic indicators such as inflation and unemployment were included to study their relationship with voter behavior. Findings - The results show that economic factors significantly impact voter turnout and party vote share. High inflation and unemployment rates tend to reduce voter support for incumbent parties. The model also predicts possible outcomes for the 2024 election based on these economic trends. Conclusion and Recommendation – The study concludes that improving economic conditions, such as reducing unemployment, could positively influence electoral outcomes for incumbents. It is recommended that policymakers focus on stabilizing the economy to enhance electoral success.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Cavitation resulting from underwater explosions in compressible multiphase or multicomp-onent flows presents significant challenges due to the dynamic nature of shock-cavitation-structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock-cavitation-structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces; free surface and a rigid surface. The simulations are conducted using the Unstructured Compressible Navier-Stokes (UCNS3D) finite volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilising the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing.

The stochastic modelling of chemical physical processes is essentially based on a coarse-grained formulation of fluctuations, usually described by means of Wiener processes. By taking the paradigmatic example of chemical reaction kinetics, we propose a simple representation of microscopic stochastic dynamics grounded on the use of distributional derivatives of counting processes that account for elementary reactive events. This approach is consistent with the statistical analysis based on the Chemical Master Equation, and provides the formal setting for the existing algorithmic approaches (Gillespie algorithm). Some practical advantages of this formulation are addressed, and several other examples are discussed in connection with cluster formation, velocity fluctuations in liquid systems, and quantum transitions (radiative interactions) at the molecular level.

The grinding process by friction at the micro-scale in a mill with stones is considered a variable combination of contacts: with two-body (the asperities of lower millstone in direct contact with the asperities of upper millstone) and the three-body (micro-particles of ground seeds trapped between the asperities of lower and the upper stones of the mill). Three elements are described: (1) the mechanic contact of the asperities of the lower and upper millstone, to predict pressures on asperities, by modeling; (2) tests on a millstone sample covered with grinding particles, and (3) tests on a wafer sample formed by the millstones with the grinding particles between them. The paper highlights the combined effects of the micro-scale friction by individual measurements and to sum these effects an analytical model was used, and to validate the model, several experiments were performed. A suitable grind by friction assumes the grain’s movement and the interaction between the seeds and solid surfaces and is highlighted through theoretical and experimental studies. Topography analysis of the surface of the millstones revealed the model of microscopic frictional force. Endpoint measurements (the traces of the surface topography evolution), enable model verification in the grinding process.

Despite air pollution being a leading cause of health issues in developing nations, public awareness and understanding of local air quality remains notably low. The present study assesses the perception, attitude, and environmental knowledge of local air pollution among adult urban residents (n=870) in a city with leading air pollution rates among cities of emerging economies: Astana, Kazakhstan. Structural equation modeling (SEM) was employed to investigate the causal relationship between perceived air quality, environmental literacy, and willingness to pay (WTP) for environmental protection. Findings indicate over 40% of residents neither consider the city being highly polluted nor recognize the association between air quality and adverse health outcomes, correlated with a generally low level of environmental literacy. The age, education, and health status of the participants significantly affected (p<.001) their level of environmental knowledge and awareness. The SEM analysis indicates that knowledge is the major determinant in improving public awareness and perception of local air pollution (path value=0.626). Moreover, a close association between WTP and environmental attitude was also evident (path value=0.533). The findings of the present study may provide valuable insights for healthcare professionals, environmental researchers, and governmental institutions for implementing more effective public interventions to protect local air quality.

Groundwater modeling is a useful tool for assessing sustainability in water resources planning. However, groundwater models are difficult to construct in regions with limited data availability. We illustrated how remote sensing data can be used leverage limited in situ data to build and calibrate a regional groundwater model in the Goulbi Maradi alluvial aquifer in Southern Niger in Western Africa. We used data from the NASA Gravity Recovery and Climate Experiment (GRACE) satellite mission to estimate recharge rates, the primary source of water to the aquifer. Additionally, we incorporated groundwater storage changes obtained from GRACE data from 2009 to 2021 to establish an overall water budget from which we could back-calculate groundwater withdrawals from pumping in the region. This approach allowed us to calibrate the model and then convert it to a predictive tool to analyze the impact of various assumptions about future recharge and groundwater extraction patterns associated with the development of groundwater infrastruction in the region. The results indicate that the Goulbi Maradi alluvial aquifer is sustainable, even an increase of groundwater extraction up to 28%.