Analysis Techniques Research Articles

Spatiotemporal analysis of regional and age differences in tuberculosis prevalence in mainland China.

Globally, tuberculosis is a leading cause of infectious disease deaths. China ranks third among the 30 high-burden countries for tuberculosis and accounts for approximately 7.4% of the cases reported worldwide. Since very few studies have investigated the age difference in tuberculosis prevalence in mainland China, therefore, the preliminary characterisation of age differences in tuberculosis patients is not well understood. The data of reported sputum smear-positive, tuberculosis and sputum smear-negative cases in 340 prefectures from mainland China were extracted from the China Information System for Disease Control and Prevention from January 2009 to December 2018. Multiple statistical analysis and GIS techniques were used to investigate the temporal trend and identify the spatial distribution of sputum smear-positive, tuberculosis and sputum smear-negative cases in the study area. The results showed that the incidence of sputum smear-positive and tuberculosis has dropped to a stable level, while sputum smear-negative exhibited a rising trend. Additionally, sputum smear-positive, tuberculosis and sputum smear-negative are still highly prevalent in northwestern and southwestern regions of China. Interestingly, the young adult group (20-50 age) and elder group (>50 age) are more susceptible to being infected with tuberculosis, while lower infection levels were recorded in the juvenile group (<20 age). The present study investigated the temporal-spatial distribution of sputum smear-positive, tuberculosis and sputum smear-negative cases in mainland China before the COVID-19 pandemic breakout, which would help the government agency establish an effective mechanism of tuberculosis prevention in high-risk periods and high-risk areas in the study region.

Unleashing the Potential of β -cyclodextrin Inclusion Complexes in Bitter Taste Abatement: Development, Optimization and Evaluation of Taste Masked Anti-emetic Chewing Gum of Promethazine Hydrochloride.

Motion sickness also known as kinetosis is a condition in which there exists a disagreement between visually perceived movement and the vestibular system's sense of movement. Nausea, vomiting, dizziness, fatigue, and headache are the most common symptoms of motion sickness. This study mainly focuses on the taste masking of Promethazine Hydrochloride (PMZ) by inclusion complexation method, its formulation development in the chewing gum form by using directly compressible gum base HIG® and its quality and performance testing. Different molar ratios (1:1, 1:2, 1:3 and 1:4) of PMZ-cyclodextrin complexes were prepared by using β-Cyclodextrin(β-CD) as a taste masking agent. These complexes were evaluated for FTIR, DSC, % Entrapment Efficiency, % drug yield, and taste evaluation by E-Tongue. The optimized ratio was further evaluated by sophisticated analytical techniques such as Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD). A central composite design (CCD) (3 ^2) was utilized to examine the effects of independent variables (amount of gum-X1 and amount of plasticizer-X2) on dependent variables (%CDRY1 and hardness Y2). The prepared gums were evaluated for drug content, organoleptic properties, in-vitro dissolution testing by fabricated disintegration apparatus, texture analysis, etc. The optimization statistics showed that on decreasing the amount of gum, in- vitro drug release increases and hardness decreases. The optimized batch MCG-2 of Promethazine MCG showed 92.34 ± 0.92% of drug release, whereas for marketed formulation (Phenergan®-25mg) drug release value was 86.19 ± 1.88%. Results provided evidence that PMZ MCGs could be a better alternative to conventional tablet formulations with improved drug release, palatability and texture.

Latest Developments in Direct and Non-Direct LC-MS Methods Based on Liquid Electron Ionization (LEI).

Mass spectrometry (MS) enables precise identification and quantification of molecules, particularly when combined with chromatography. The advent of atmospheric pressure ionization (API) techniques allowed the efficient coupling of liquid chromatography with MS (LC-MS), extending analyses to nonvolatile and thermolabile compounds. API techniques present limitations such as low informative capacity and reproducibility of mass spectra, increasing instrument complexity and costs. Other challenges include analyzing poorly polar molecules and matrix effects (ME), which negatively impact quantitative analyses, necessitating extensive sample purification or using expensive labeled standards. These limitations prompted the exploration of alternative solutions, leading to the development of the Liquid Electron Ionization (LEI) interface. The system has demonstrated excellent robustness and reproducibility. LEI has been employed to analyze various compounds, including pesticides, drugs of abuse, phenols, polycyclic aromatic hydrocarbons (PAHs), phthalates, and many others. Its versatility has been validated with single quadrupole, triple quadrupole, and QToF detectors, operating in electron ionization (EI) or chemical ionization (CI) modes and with both reverse phase liquid chromatography (RPLC) and normal phase liquid chromatography (NPLC). LEI has also been successfully integrated with the Microfluidic Open Interface (MOI), Membrane Introduction Mass Spectrometry (MIMS), and Microfluidic Water-Assisted Trap Focusing (M-WATF), broadening its application scope and consistently demonstrating promising results in terms of sensitivity and identification power. The most recent advancement is the development of Extractive-Liquid Sampling Electron Ionization-Mass Spectrometry (E-LEI-MS), a surface sampling and real-time analysis technique based on the LEI concept. This review article offers a comprehensive and up-to-date picture of the potential of LEI.

Enhancing the Range and Reliability of the Spacer Layer Imaging Method

The spacer layer imaging method (SLIM) is widely used to measure the thickness of additive and lubricant films, in lubricant development and evaluation, and for fundamental research into elastohydrodynamic lubrication and tribofilm formation mechanisms. The film thickness measurement, as implemented on several popular tribometers, provides powerful, non-destructive in-situ mapping of film topography with nanometre-scale height sensitivity. However, the results can be highly sensitive to experimental procedure, machine condition, and image analysis, in some cases reporting unphysical film thickness trends. The prevailing image analysis techniques make it challenging to interrogate these errors, often hiding their multivariate nonlinear behaviour from the user by spatial averaging. Herein, several common ‘silent errors’ in the SLIM measurement, including colour matching to incorrect fringe orders, and colour drift due to the optical properties of the system or film itself, are discussed, with examples. A robust suite of novel a priori and a posteriori methods to address these issues, and to improve the accuracy and reliability of the measurement, are also presented, including a novel, computationally inexpensive circle-finding algorithm for automated image processing. In combination, these methods allow reliable mapping of films up to at least 800 nm in thickness, representing a significant milestone for the utility of SLIM applied to elastohydrodynamic contact.Graphical abstract

Time-frequency dependence between fintech and development of carbon neutrality under climate policy uncertainty in China: implications for the ocean carbon sink market

PurposeThis paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the ocean carbon sink market.Design/methodology/approachWe apply a novel wavelet analysis technique to investigate the time-frequency dependence between the CCPU index, the CSI (China Securities Index) Fintech Theme Index (CFTI) and the Carbon Neutral Concept Index (CNCI).FindingsThe empirical results show that CCPU and CFTI have a detrimental effect on CNCI in high-frequency bands. Furthermore, in low-frequency domains, the development of CFTI can effectively promote the realization of carbon neutrality.Practical implicationsOur findings show that information from the CCPU and CFTI can be utilized to forecast the movement of CNCI. Therefore, the government should strike a balance between fintech development and environmental regulation and, hence, promote the use of renewable energy to reduce carbon emissions, facilitating the orderly and regular development of the ocean carbon sink market.Originality/valueThe development of high-quality fintech and positive climate policy reforms are crucial for achieving carbon neutrality targets and promoting the growth of the marine carbon sink market.

Development of the Physical Literacy in Adults Scale (PLAS).

Physical literacy relates to an individual's capacity to live a physically active and healthy lifestyle across the lifespan. Our understanding of physical literacy as a lifelong journey is hindered by a lack of valid measurement for adult populations. In response, we created a measure of physical literacy applicable to adults. A sample of 1180 adults answered a series of existing questionnaires assessing aspects of physical literacy. Using exploratory and confirmatory factor analysis techniques, we reduced the items to a 23-item six factor model, which had good fit to the data in both the exploratory analysis (χ2 (130) = 254.770, p < 0.001; TLI = 0.965; RMSEA = 0.040 (95% CI: 0.031, 0.049)) and confirmatory analysis (χ2 ;(130) = 252.005, p < 0.001; TLI = 0.957; RMSEA = 0.046 (95% CI: 0.036, 0.056)). The resulting subjective measure, the Physical Literacy in Adults Scale (PLAS), is a single psychometric measure applicable to adults aged 18-75 years. The PLAS covers four domains (physical, psychological, social and cognitive) of physical literacy to provide a holistic physical literacy outcome. The PLAS represents an appropriate way to assess adult physical literacy, providing an important first step in understanding physical literacy as a lifelong journey.

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods

BackgroundIn randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual’s risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis.MethodsThree approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States’ participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran’s Q test was used to detect if ARR varied significantly by subgroup.ResultsThe proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0–26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1–24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4–39.4%). Cochran’s Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants.ConclusionsThe choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

High-resolution sequence stratigraphy research based on continuous wavelet transform and INPEFA -- A case study in the Kalashayi Formation, Tarim Basin, China

The Carboniferous Kalashayi Formation, situated in the Lungu-Sangtamu area within the Tarim Basin, exhibits protracted sedimentation periods, intricate sand-mud depositional sequences, and scant paleontological and core datasets. Precision in delineating high-resolution sequences using conventional core and logging data poses a considerable challenge. To enhance the stratigraphic accuracy of the Kalashayi Formation in the Tarim Basin and facilitate quantitative analysis, this study employs continuous wavelet transform on the gamma ray (GR) curves obtained from core wells within the study area. Subsequently, various sequence boundaries are discerned by integrating the resulting wavelet coefficient curves with time-frequency energy maps. Discrimination and subdivision of base-level cycle structures of varying orders are achieved through temporal trend analyses of integrated prediction error filter analysis (INPEFA) curves. Integration of drilling, logging, lithofacies, and core data enables the identification and subdivision of high-resolution sequence stratigraphy using wavelet analysis and INPEFA techniques. Ultimately, the Carboniferous Kalashayi Formation in the Lungu-Sangtamu region is classified into 2 long-term, 5 medium-term, and 14 short-term base-level cycles, establishing a meticulously delineated isochronous stratigraphic framework. This framework serves as a fundamental basis for subsequent discussions on reservoir prediction within the study area.

Ir-Fe bimetallic catalysts for selective glycerol hydrogenolysis

This work studied the effect of adding Fe to an Ir/γ-Al2O3 catalyst in different Fe/Ir molar ratios (0.5, 1.0, and 2.0) on the conversion and selectivity of the glycerol hydrogenolysis reaction. Additionally, the effect of the calcination of the solids was evaluated. The catalysts were prepared by incipient wetness impregnation of the Ir and Fe salts on the support and were characterized by textural analysis, XRD, TPR, XPS, and TEM-EDS techniques. The interaction between Ir and Fe was evidenced in the characterization of the catalysts. The addition of Fe to the Ir catalyst led to an increase in activity; however, this effect was better observed for the non-calcined catalyst series. The calcined catalyst series did not present a significant increase in glycerol conversion and selectivity to 1,2-PDO, probably due to the sintering of Ir occasioned by the catalyst's calcination, as observed in the characterization. For non-calcined bimetallic catalysts, the conversion increased from 7 to 21.5% for IrFe2.0/γ-Al2O3 catalyst, probably due to the synergic effect between both metals and the formation of Ir-Fe bimetallic particles, as evidenced in EDS mapping and XPS analysis. In the presence of this catalyst, selectivity to 1,2-PDO remained high (90.1%).

Pd-substitution impact in nickel–cobalt spinel oxide grown on Ni foam as very effectual electrocatalyst for green hydrogen production supported by DFT study

The hydrothermal preparation of catalytic NiPdxCo2-xO4 (x ≦ 0.08) nano-electrocatalysts on Ni foam (NF) was successfully accomplished. The morphology and chemical composition of the catalysts are confirmed by advanced analytical techniques XRD, SEM, EDX, TEM, HR-TEM, and XPS. The NiPdxCo2-xO4 (x = 0.06) NF nano-electrocatalyst demonstrated remarkable performance in the HER, with an overpotential of 191.6 mV, a Tafel slope of 84.7 mV/dec, and extraordinary stability over 24 h using chronopotentiometry methods. The surface and electrochemical analyses demonstrated that the sample, doping with a 6.0% Pd2+ concentration, had appreciably enhanced performance in the HER. The improvement may be attributed to a much larger electrochemical surface area and fast charge transfer kinetics at the semiconductor and electrolyte interface. The influence of Pd dopants on the HER performance of CNs is studied with the help of density functional theory. It elucidates the way in which hydrogen and water molecules bind to the surface of PCN slabs. The theoretical results suggest that the incorporation of Pd dopants enhances the electrocatalytic process and boosts the HER activity as the concentration of Pd increases. The density of state spectra reveals their effect on spin-dependent electronic structure characteristics. Our findings point to a practical path for creating distinctive electrocatalysts that will serve as better electrodes for a variety of forthcoming hydrogen fuel cell applications.

Development, modelling and optimization of process parameters on the tensile strength of aluminum, reinforced with pumice and carbonated coal hybrid composites for brake disc application

This study focuses on optimizing double stir casting process parameters to enhance the tensile strength of hybrid composites comprising aluminum alloy, brown pumice, and coal ash, intended for brake disc applications. Analytical techniques including X-ray fluorescence, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy were employed to characterize the composite constituents. The Taguchi method was utilized for experimental design and optimization to determine the optimal weight compositions of brown pumice and coal ash, as well as stir casting parameters (stirrer speed, pouring temperature, and stirring duration). Regression analysis was employed to develop a predictive mathematical model for the tensile strength of the hybrid composites and to assess the significance of process parameters. The optimized composite achieved a predicted tensile strength of 186.81 MPa and an experimental strength of 190.67 MPa using 7.5 vol% brown pumice, 2.5 vol% coal ash, a pouring temperature of 700 °C, stirrer speed of 500 rpm, and stirring duration of 10 min. This represents a 52.23% improvement over the as-cast aluminum alloy’s tensile strength. Characterization results revealed that brown pumice and coal ash contain robust minerals (SiO2, Fe2O3, Al2O3) suitable for reinforcing metal matrices like aluminum, titanium, and magnesium. Thermogravimetric and differential thermal analyses demonstrated thermal stability up to 614.01 °C for the optimized composite, making it suitable for brake disc applications.

Validation and measurement uncertainty of analytical methods for various azo dye adulterants in Curcuma longa L.

Food adulteration involving the illegal addition of dyes to foodstuffs has become an alarming issue in recent years. This study developed and validated a high-performance liquid chromatography (HPLC)-DAD (diode array detector) method for the simultaneous determination of nine azo dyes (Butter Yellow, Sudan Orange G, Para Red, Sudan I, Sudan II, Sudan III, Sudan IV, Sudan Red 7B, and Scarlet 808). Moreover, a qualitative analysis method using liquid chromatography-tandem mass spectrometry was developed to more accurately identify peaks detected in HPLC-DAD. The calibration curve represented good linearity (r2≥0.9998) over the measured concentration range of 0.5-25mg/kg. limit of detection and limit of quantification were 0.01-0.04 and 0.04-0.12mg/kg, respectively. Accuracy and precision were 96.0-102.6 and 0.16-2.01 (relative standard deviation%), respectively. Additionally, the measurement uncertainty and HorRat value were estimated. Several Curcuma longa L. distributed in Korea were collected and monitored for azo dye contaminants. PRACTICAL APPLICATION: The proposed HPLC-DAD method represents a significant advancement in the field, offering a reliable means of quantifying azo dyes and identifying their presence even at trace levels in adulterated turmeric. This not only contributes to ensuring the safety and integrity of turmeric products but also establishes precedent for robust analytical techniques in addressing food safety challenges.

Biological and computational exploration of a hydrogen-bonded charge transfer complex between 8-Hydroxyquinoline and Benzene-1,4-diol in different polar solvents: Synthesis and spectrophotometric analysis

The formation and characterization of a charge transfer complex (CTC) between 8-Hydroxyquinoline and Benzene-1,4-diol (Quinol) in various polar solvents. CTCs, marked by weak yet significant interactions, are pivotal in understanding electron transfer processes and molecular recognition phenomena. The choice of 8-Hydroxyquinoline (8HQ) as the donor and Quinol (Q) as the acceptor facilitates the investigation of their interaction, given their respective electron-rich and electron-poor properties. The spectroscopic analysis, including Fourier-transform infrared (FTIR) and UV-visible spectroscopy, reveals shifts in molecular vibrations and electronic transitions, indicating the formation of the CTC. Additionally, thermal analysis techniques, such as thermogravimetric analysis (TGA) and differential thermal analysis (DTA), provide insights into the thermal stability and decomposition behavior of the CTC. Powder X-ray diffraction (PXRD) analysis offers data on the crystalline structure and morphology of the complex. Furthermore, the manuscript explores the biological significance of the synthesized CTC, including its antioxidant, antibacterial, and antifungal activities. Conductivity measurements and computational studies, such as Density Functional Theory (DFT) and molecular docking, provide further insights into the electronic properties and structural characteristics of the CTC. Overall, the study contributes to advancing the understanding of CTCs and their applications, particularly in the context of novel donor-acceptor systems. The insights gained have implications in diverse fields, including materials science, biomedical engineering, and fundamental chemistry, paving the way for tailored molecular design and functional applications.

Exploring hydrogen storage safety research by bibliometric analysis

The application of hydrogen energy is affected by the safety of hydrogen storage system. To grasp the current status of research and application in the research field of hydrogen storage safety and explore its research development trend, data analysis techniques, such as co-occurrence, co-citation, and burst detection, were adopted to conduct bibliometric analysis of the relevant literature. The study results show that the research hotspots of hydrogen storage safety mainly concentrate on six aspects: the stability of metal hydride hydrogen storage materials, the study of thermal management and stress relief in metal hydride reactors, the safety analysis of hydrogen fuel cells and hydrogen leakage monitoring, the analysis of leakage and explosion risks in hydrogen refueling stations, the study of filling pressure and temperature distribution in hydrogen storage tanks, and the study of safety issues in underground hydrogen storage. Mg-based hydride stability and underground hydrogen storage risk analyses are current research frontiers in hydrogen storage safety.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Spatiotemporal analysis of multi-scale cell structure in spheroid culture reveals hypertrophic chondrocyte differentiation.

3D cell culture has emerged as a promising approach to replicate the complex behaviors of cells within living organisms. This study aims to analyze spatiotemporal behavior of the morphological characteristics of cell structure at multiscale in 3D scaffold-free spheroids using chondrogenic progenitor ATDC5 cells. Over a 14-day culture period, it exhibited cell hypertrophy in the spheroids regarding cellular and nuclear size as well as changes in morphology. Moreover, biological analysis indicated a signification up-regulation of normal chondrocyte as well as hypertrophic chondrocyte markers, suggesting early hypertrophic chondrocyte differentiation. Cell nuclei underwent changes in volume, sphericity, and distribution in spheroid over time, indicating alterations in chromatin organization. The ratio of chromatin condensation volume to cell nuclear volume decreased as the cell nuclei enlarged, potentially signifying changes in chromatin state during hypertrophic chondrocyte differentiation. Our image analysis techniques in this present study enabled detailed morphological measurement of cell structure at multi-scale, which can be applied to various 3D culture models for in-depth investigation.

Dream vacations of children: exploring key motivations and preferred experiences by drawing

ABSTRACT Children have an important place in the family travel market as they have a strong influence on holiday decisions and their satisfaction can directly affect their parents’ satisfaction and future behavioural intentions. To attract this market and provide child tourists with a satisfying holiday experience, it is important to better understand their motivations and preferences. The aim of this research is to identify children's motivations and preferred destinations and activities in family tourism context. In this qualitative study, where children's voices were taken into account by giving them the opportunity to express their dream family holiday experience ‘drawing’, one of the widely preferred child-friendly techniques to reveal children's perspectives, was used. A total of 103 literate children aged 6–17, residing in five different cities in four regions of Turkey, participated in the research. The data were analyzed using the visual content analysis technique in Maxqda software. The results show that children's family holiday motivations can be divided into four categories: family bonding, escaping and exploring new places, activities and play, rest and relaxation. Additionally, the activities that children seek in destinations include opportunities for group activities, activities related to nature, cultural experiences, and built environment experiences.

Housing Attribute Preferences in Bandung City

Each generation encounters distinct social, economic, cultural, and techno­logical contexts specific to their era. Moreover, their lifestyles vary, resulting in disparate housing preferences. This study analyzes five housing attribute preferences of Generation X, Generation Y, and Generation Z: Economic attribute, location and accessibility attribute, physical attribute, environment attribute, and social attribute. The research uses a stated preference approach. The study uses a mixed method and a stated preference approach while collecting data by online questionnaire. Three analytical techniques are employed in this research: correspondence analysis to identify differences in dwelling type preferences among generations, PCA and FA to identify the underlying housing attribute influencing housing preferences, and ANOVA to analyze housing attribute dimensions with significant differences among generations. The analysis result reveals that each generation tends to prefer Landed Housing. The housing attribute dimensions that are the main priority among generations are Utilities, Infrastructure, and Environmental Quality. Meanwhile, significant differences in housing preferences between Generation X, Generation Y, and Generation Z are found in the space comfort and social interaction dimensions.

Robust Nitrogen-Doped Microporous Carbon via Crown Ether-Functionalized Benzoxazine-Linked Porous Organic Polymers for Enhanced CO2 Adsorption and Supercapacitor Applications.

Nitrogen-doped carbon materials, characterized by abundant microporous and nitrogen functionalities, exhibit significant potential for carbon dioxide capture and supercapacitors. In this study, a class of porous organic polymer (POP) were successfully synthesized by linking Cr-TPA-4BZ-Br4 and tetraethynylpyrene (Py-T). The model benzoxazine monomers of Cr-TPA-4BZ and Cr-TPA-4BZ-Br4 were synthesized using the traditional three-step method [involving CH═N formation, reduction by NaBH4, and Mannich condensation]. Subsequently, the Sonogashira coupling reaction connected the Cr-TPA-4BZ-Br4 and Py-T monomers, forming Cr-TPA-4BZ-Py-POP. The successful synthesis of Cr-TPA-4BZ-Br4 and Cr-TPA-4BZ-Py-POP was confirmed through various analytical techniques. After verifying the successful synthesis of Cr-TPA-4BZ-Py-POP, carbonization and KOH activation procedures were conducted. These crucial steps led to the formation of poly(Cr-TPA-4BZ-Py-POP)-800, a carbon material with a structure akin to graphite. In practical applications, poly(Cr-TPA-4BZ-Py-POP)-800 exhibited a noteworthy CO2 adsorption capacity of 4.4 mmol/g, along with specific capacitance values of 397.2 and 159.2 F g-1 at 0.5 A g-1 (measured in a three-electrode cell) and 1 A g-1 (measured in a symmetric coin cell), respectively. These exceptional dual capabilities stem from the optimal ratio of heteroatom doping. The outstanding performance of poly(Cr-TPA-4BZ-Py-POP)-800 microporous carbon holds significant promise for addressing contemporary energy and environmental challenges, making substantial contributions to both sectors.

Characterization of RF-Sputtered ZnO Thin Film Coatings on Aluminum (Al6061): Microstructure, Wettability, Cavitation, and Corrosion Analysis

The study focuses on the characterization of Aluminum (Al-6061) coated with RF-sputtered Zinc Oxide (ZnO) thin film, aiming to understand the complex interactions between microstructure, wettability, cavitation resistance, and corrosion performance. The study involves the use of various analytical techniques such as Field Emission Scanning Electron Microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and water contact angle (WCA) measurements. The cavitation erosion testing has been conducted under various factors namely jet velocity (m/s), impingement angle (°), and stand-off distance (cm), respectively, and that too at various levels. Further, with the use of RSM, the study also contributes to the interplay in between factors and acquiring optimized results. Moreover, the findings of the study provide valuable insights into the effectiveness of the ZnO coating in enhancing corrosion resistance and reducing mass loss due to cavitation erosion. The study’s results offer significant implications for the engineering and design of protective coatings for Aluminum surfaces, with the potential to enhance durability and performance in various industrial applications.