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The design and optimization of antimicrobial materials (polymers, biomolecules, or nanocomposites) can be significantly advanced by computational methodologies like molecular dynamics (MD), which provide insights into the interactions and stability of the antimicrobial agents within the polymer matrix, and machine learning (ML) or design of experiment (DOE), which predicts and optimizes antimicrobial efficacy and material properties. These innovations not only enhance the efficiency of developing antimicrobial polymers but also enable the creation of materials with tailored properties to meet specific application needs, ensuring safety and longevity in their usage. Therefore, this paper will present the computational methodologies employed in the synthesis and application of antimicrobial polymers, biomolecules, and nanocomposites. By leveraging advanced computational techniques such as MD, ML, or DOE, significant advancements in the design and optimization of antimicrobial materials are achieved. A comprehensive review on recent progress, together with highlights of the most relevant methodologies’ contributions to state-of-the-art materials science will be discussed, as well as future directions in the field will be foreseen. Finally, future possibilities and opportunities will be derived from the current state-of-the-art methodologies, providing perspectives on the potential evolution of polymer science and engineering of novel materials. Full article

COVID-19 has had unprecedented effects on construction markets globally due to supply chain disruptions and workforce restrictions, etc. However, construction industries in developing countries are more vulnerable to the obstacles associated with the pandemic. As such, the goal of this paper is to capture the impact of COVID-19 on construction projects’ performance in developing countries by taking the Pakistani construction sector into perspective. A multistep project-based research methodology was adopted, including a literature analysis and questionnaire survey with 20 construction professionals to assess factors affected by COVID-19; formulation of open-ended questions against each factor; collecting data using semi structured interviews; and presenting challenges and adopted mitigation strategies to examine the perceived impact. Results showed that the pandemic has caused a total of 13 factors to impact the project performance, which are grouped under four construction groups, including material and equipment, human resource, occupational health and safety, and financial and contracts. The factors impacted the most by the pandemic are cash flow management, supply chain disruptions, increased overheads, construction safety measures, material shortage, contractual issues, and construction workforce. This research contributes to the body of knowledge by providing a foundation for researchers to enhance investigating the impact of the COVID-19 in developing countries. Full article

The benchmarking of CO₂ emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO₂ emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. Full article

Preserving and restoring architectural monuments of symbolic national significance stand as an unequivocal priority for both local and state authorities. Indeed, with advancements in the technology, materials, and methods of work, there are some specific stages involved in the process of restoring historical buildings; however, maintaining their authentic form and impact poses a serious challenge, necessitating the development of innovative, non-standard technologies and the adaptation of a multifunctional methodology to achieve the desired outcome. The present study focuses on the restoration of the roof covering of the historical building of the “Dormition of the Mother of God” Cathedral in Varna, in terms of technological feasibility and cost-effectiveness. Proposed in this paper is a solution that strives to restore the magnificent roof in adherence to the heritage conservation principles by applying a genuine gold coating instead of retaining the previous dull goldish coating made from alkyd paint, yellow pigment, and gold dust. Through the implementation of descriptive geometry techniques and AutoCAD, followed by verification using Inventor, the study presents a feasible solution for accurately determining the surface area of the domes to be gilded. Full article

Second-hand housing transactions constitute a significant segment of the real estate market and are vital for its robust development. The dynamics of these transactions mirror the housing preferences of buyers, and their spatial and temporal analysis elucidates evolving market patterns and buyer behavior. This study introduces an innovative grid density clustering algorithm, dubbed the RScan algorithm, which integrates Bayesian optimization with grid density techniques. This composite methodology is employed to assess clustering outcomes, optimize hyperparameters, and facilitate detailed visualization and analysis of transaction activity across various regions. Focusing on Shenyang, a major urban center in Northeast China, the research spans from 2018 to 2023, exploring the second-hand housing transaction activity and its spatio-temporal attributes. The results reveal temporal fluctuations in transaction intensity across different Shenyang regions, although core areas of high activity remain constant. These regions display a heterogeneous pattern of irregularly stepped and clustered distributions, with a notable absence of uniformly high-activity zones. This study pioneers a novel methodological framework for investigating second-hand housing transactions, offering crucial insights for market development and policy formulation in Shenyang. Full article

Hardware masking is used to protect against side-channel attacks by splitting sensitive information into different parts, called hardware masking shares. Ideally, a side-channel attack would only work if all these parts were completely independent. But in real-world VLSI implementations, things are not perfect. Information from a hardware masking share can leak to another, making it possible for side-channel attacks to succeed without needing data from every hardware masking share. The theoretically supposed independence of these shares often does not hold up in practice. The effectiveness of hardware masking is reduced because of the parasitic impedance that stems from power delivery networks or the internal structure of the integrated circuit. When the coupling effect and noise spread among the hardware masking shares powered by the same power delivery network, side-channel attacks can be carried out with fewer measurements. To address this, we propose a new method of distributing on-chip voltage regulators to improve hardware masking security. The benefits of distributed on-chip voltage regulators are evident. Placing the regulators close to the load minimizes power loss due to resistive losses in the power delivery network. Localized regulation allows for more efficient adjustments to the varying power demands of different chip sections, improving overall power efficiency. Additionally, distributed regulators can quickly respond to power demand changes, maintaining stable voltage levels for high-performance circuits, leading to improved control over noise. We introduce a new DLDO voltage regulator that uses random clocking and randomizing limit cycle oscillations to enhance security. Our simulations show that with these distributed DLDO regulators, the t-test value can be as low as 2.019, and typically, a circuit with a t-test value below 4.5 is considered secure. Full article

Food wastes have a large number of functional ingredients that have potential for valorization. Melon peels are increasingly produced as waste in food industries in Thailand. This study aimed to optimize pectin extraction conditions from melon peel for its prebiotic potential. Optimization was conducted using a response surface methodology and Box–Behnken experimental design. An analysis of variance indicated a significant interaction between the extraction conditions on extraction yield and degree of esterification (DE). These include pH and solvent-to-sample ratio. The conditions for the extraction of pectin with low DE (LDP), medium DE (MDP) and high DE (HDP) were optimized. Pectin hydrolysate from LDP, MDP and HDP was prepared by enzymatic hydrolysis into LPEH, MPEH and HPEH, respectively. LDP, MDP, HDP, LPEH, MPEH and HPEH were compared for their efficiency in terms of the growth of three probiotic strains, namely Lactobacillus plantarum TISTR 877, Lactobacillus casei TISTR 390 and Enterococcus faecium TISTR 1027. Among the samples tested, HPEH showed the highest ability as a carbon source to promote the growth and prebiotic activity score for these three probiotic strains. This study suggests that melon peel waste from agro-industry can be a novel source for prebiotic production. Full article

Coastal areas are currently exposed to numerous hazards exacerbated by climate change, including erosion, flooding, storm surges, and other sea level rise phenomena. Mediterranean countries, in particular, are facing a constant shrinking of coastal areas. This region also hosts significant cultural heritage assets, including several UNESCO World Heritage Sites. The present research demonstrates a methodological approach to assess the current and future exposure of Mediterranean coastal areas and heritage assets to Sea Level Rise using open access data regarding elevation, vertical ground motion, and Sea Level Change factors (e.g., ice sheets, glaciers, etc.). The future projections regard 2050 and 2100 and are based on RCP scenarios 2.6, 4.5 and 8.5. The datasets used include Copernicus GLO-30 DSM, the European Ground Motion Service’s dataset on Vertical Ground Motion, the Sea Level Change Projections’ Regional Dataset by NASA, and a hybrid coastline dataset created for the present research purposes to assist in delineating the study area. The research results demonstrate that Greece, Italy, and France’s mainland and cultural heritage assets already face SLR-related hazards but are expected to be further exposed in the future, always taking into consideration the high level of uncertainty regarding SLR projections and RCP scenarios’ hypotheses. Full article

Chang-rae Lee, a contemporary Korean-American writer, is renowned in the literary world for his rich imagination, delicate emotional expression, unique transcultural perspective and idiosyncratic narrative technique. He is one of the representatives who succeeds in transcending the classical paradigm of ethnic literature. Cosmopolitanism stems from ancient Greek philosophy, further developing in the age of Enlightenment and thriving in the era of globalization. Taking close reading as the primary methodology and cosmopolitanism as the major theoretical framework, this research attempts to provide a multi-dimensional, interdisciplinary and in-depth interpretation of Lee’s A Gesture Life, and finds that Lee has expressed his ideal vision that rejects the essentialist paradigm of unchanging cultural identity and upholds cosmopolitanism which embraces cultural diversity and heterogeneity. Additionally, through the depiction of cosmopolitan community, Lee has expressed his expectation for peaceful coexistence, communal solidarity, and mutual assistance among various ethnicities, and he has visualized a picture that different ethnic groups engage in transcultural communication in a harmonious way. In conclusion, A Gesture Life has widened the boundaries of Korean-American literature, and the cosmopolitan vision in the text has contributed to the development and prosperity of American ethnic literature. Full article

In recent years, the increasing demand and rising prices of rare earth elements (REEs), along with their attendant supply risk (about 95% of these elements are supplied by China), have led the European Commission to consider REEs as critical raw materials. Developing and optimizing processes for recovering REEs from secondary sources such as NdFeB magnets is fundamental in this context. A novel method to recover REEs by leaching with citric acid and subsequently separating these elements using the solvent extraction method has been introduced. Therefore, this research investigates the leaching efficiency of REEs, Fe, and B from NdFeB magnets. A full factorial design, with 18 experimental setups, was conducted to optimize the citric acid concentration (1–3 mol/L), leaching time (1–3 h), and solid–liquid ratio (5–10%wt./vol.). All tests were carried out at room temperature and 150 rpm. Different optimizations (response surface methodology (RSM) and artificial neural network (ANN) analysis) are used to maximize the REEs’ leaching efficiency. RSM resulted in a maximum extraction yield of total rare earth elements (TREEs) of about 89% in the investigated experimental plan. This result is similar to that for ANN analysis (about 86%), but more accurate than that for RSM. In fact, for the ANN, an overall R-value higher than 0.99 was obtained. This result indicates that the developed ANN can be used as an accurate model for estimating the leaching efficiencies of REEs from NdFeB magnets. Full article

The gut microbiota represents a complex ecosystem of trillions of microorganisms residing in the human gastrointestinal tract, which is known to interact with the host physiology and regulate multiple functions. Alterations in gut microbial composition, diversity, and function are referred to as dysbiosis. Dysbiosis has been associated with a variety of chronic diseases, including Clostridioides difficile infections, but also cardiometabolic diseases, including obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM). The implication of gut microbiota dysbiosis in the pathogenesis of both obesity and T2DM has paved the way to implementing novel therapeutic approaches for metabolic diseases through gut microbial reconfiguration. These interventions include probiotics, prebiotics, and synbiotics, while a more innovative approach has been fecal microbiota transplantation (FMT). FMT is a procedure that delivers healthy human donor stool to another individual through the gastrointestinal tract, aiming to restore gut microbiota balance. Several studies have investigated this approach as a potential tool to mitigate the adverse metabolic effects of gut microbiota aberrations associated with obesity and T2DM. The aim of the present review was to critically summarize the existing evidence regarding the clinical applications of FMT in the management of obesity and T2DM and provide an update on the potential of this method to remodel the entire host microbiota, leading thus to weight loss and sustained metabolic benefits. Safety issues, long-term efficacy, limitations, and pitfalls associated with FMT studies are further discussed, emphasizing the need for further research and standardization in certain methodological aspects in order to optimize metabolic outcomes. Full article

The need for a cleaner and more efficient transportation sector emphasizes the development of new technologies aimed at the integrated reduction of pollutant emissions and increases in efficiency. Among these, promising technologies such as low-temperature combustion (LTC) systems operate in the field of the combustion physics, combining the attributes of both spark-ignited (SI) and compression-ignited (CI) engines. In particular, in a gasoline compression ignition (GCI) engine, gasoline is injected in closely spaced multiple pulses near the top dead center (TDC), creating a highly stratified charge which locally auto-ignites based on the thermodynamic conditions. In this work, a sectorial mesh of the combustion chamber was built. Initial and boundary conditions were set according to a one-dimensional model of the engine from a GT-suite platform. Then, a dedicated Matlab R2023b code was used to capture the effect of the pressure wave propagation on the shape of the fuel mass rate in closely spaced multiple injection events. Finally, a 3D-CFD code was validated comparing pressure trace, rate of heat release (RoHR) and emissions with experimental data provided by the test bench. The results highlight the robustness of the tabulated combustion model, which is able to capture the auto-ignition delay with a considerably low amount of computational time compared to common detailed kinetic solvers. Full article

Biosensors in the Terahertz (THz) region are attracting significant attention in the biomedical and chemical analysis fields owing to their potential for ultra-trace sensing of various solutions with high sensitivity. However, the development of compact, highly sensitive chips and methods for easy, rapid, and trace-amount measurements have been significantly hindered by the limited spatial resolution of THz waves and their strong absorption by water. In this study, we developed a nonlinear optical crystal (NLOC)-based compact THz sensor chip, and a near-field point THz source with a diameter of ~ϕ20 μm was locally generated via optical rectification. Here, only the single central meta-atom was excited. The reflective resonance responses highly depend on the array number and period of the meta-atom structures. The sensing performance was examined with several liquid biological samples, such as mineral water, DNA, and human blood. 1 μL of samples was directly dropped onto the meta-surface with an effective sensing area of 0.32 mm² (564 μm × 564 μm). Obvious resonance frequency shifts were clearly observed. This research holds significance in advancing liquid bio-sample sensing methodologies by facilitating easy, rapid, and trace-amount measurements and promoting the development of compact and highly sensitive THz sensors tailored for liquid biological samples. Full article

In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts, underscore the collective success of the models. The LSTM model stands out for its exceptional performance in consistently predicting completion time, personnel requirements, and estimated costs. Quantitative evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), corroborate the efficacy of the models. This study offers insights into the success observed, reflecting the potential for further refinement and continuous exploration to enhance the accuracy of predictive models in the ever-evolving landscape of project management. Full article

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building’s total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems. Full article