This study was conducted to investigate the energy mobilisation preference and ionoregulation pattern of female tilapia, Oreochromis sp. living in different environments. Three different treatments of tilapia as physiology compromising model were compared; tilapia cultured in recirculating aquaculture system (RAS as Treatment I-RAS), tilapia cultured in open water cage (Treatment II-Cage) and tilapia transferred from cage and cultured in RAS (Treatment III-Compensation). Results revealed that tilapia from Treatment I and III mobilised lipid to support gonadogenesis, whilst Treatment II tilapia mobilised glycogen as primary energy for daily exercise activity and reserved protein for growth. The gills and kidney Na+/K+ ATPase (NKA) activities remained relatively stable to maintain homeostasis with a stable Na+ and K+ levels. As a remark, this study revealed that tilapia strategized their energy mobilisation preference in accessing glycogen as an easy energy to support exercise metabolism and protein somatogenesis in cage culture condition, while tilapia cultured in RAS mobilised lipid for gonadagenesis purposes.
It has been well established that pandemics affect mental health, yet few studies have been conducted in China regarding this issue following COVID-19's gradual decline and the recent H1N1 influenza outbreak. In response to this research gap, this investigation explores the risk factors linked to depression and anxiety symptoms among young adults in this specific setting. Data were collected via an online cross-sectional survey of 385 young adults living in Anyang city, Henan Province, China, between June 15 and July 21, 2023. Respondents were assessed for anxiety and depression symptoms using the GAD-7 and PHQ-9 scales. Additionally, to examine the factors that influenced the study, we utilized an ordered logit regression model. Results revealed depression and anxiety prevalence rates of 33.3% and 21.6%, respectively. Several factors were found to increase the likelihood of depression and anxiety among young adults, including gender, age, education status, marital status, and attitudes towards epidemics. Participants' concerns about pandemics and viruses had a significant negative impact relationship on depression levels. Women report moderate to severe anxiety more frequently than men. An evident correlation can be observed between the educational attainment level and the influence of depression and anxiety.
The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.
We experimentally demonstrated the generation of domain-wall dark pulse in an Erbium-doped fiber laser using the combination of a 10 cm graded index multimode fiber sandwiched by single mode fibers as artificial saturable absorber. The interaction of phase difference in grade index multimode fiber allowed the stable dual-wavelength oscillation in the cavity. The dual-wavelength centered at 1567.2 nm and 1569.4 nm produces the topological defect in temporal domain and achieved a dark pulse formation with repetition rate of 21.5 MHz. The highest average pulse energy is calculated as 769.6 pJ with pulse width of 5 ns. Throughout the operating pump power range, the average pulse energy and output power increase linearly, with R2 of 0.9999 and achieved the laser efficiency of 9.33%. From the measurement in frequency domain, the signal-to-noise ratio is measured as 49 dB. As compared to reported DW dark pulse works, the proposed structure only required a short length of multimode fiber, which allowed the domain-wall dark pulse to achieve higher pulse repetition rate. The venture of domain wall dark pulse is potentially to pave the foundation toward sustainable industrial growth.
In this article, an attribute control chart is proposed when the lifetime of a product follows a Weibull distribution in two-stage sampling, which is based on the number of failures from a truncated life test. The coefficients of the proposed double sampling attribute control chart and the test duration are determined so that the average run length when the process is in control is close to the target value. An overview is reported on how double sampling np control charts work. Tables reporting the out-of-control average run lengths are given for various shift parameters. A case study is given to illustrate the proposed control chart for industrial use. A comparison of two-stage and single-stage sampling of failure of products is discussed.
To analyze the public opinion related to the employment situation, a combined approach is proposed to study the valuable ideas from social media. Firstly, the popularity of public opinion was analyzed according to the time series from a statistical point of view. Secondly, the feature extraction was carried out on the public opinion information, and the thematic analysis of the employment environment was carried out based on the Latent Dirichlet Allocation model. Thirdly, the Bert model was used to analyze the sentiment classification and trend of the employment-related public opinion data. Finally, the employment public opinion texts in different regions were studied based on the spatial sequence popularity analysis, keyword difference analysis. A case study in China is conducted to verify the effectiveness of proposed combined approach. Results shown that the popularity of employment public opinion reached the highest level in March 2022. Public opinions towards employment situation are negative. There is a specific relationship between the popularity of employment public opinion in different provinces.
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37-73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23-2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation.
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
Here we describe a molecular approach to assess conspecific identity that relies on the comparison of an evolved mutated transposable element sequence and its genomic insertion site in individuals from closely related species. This was explored with the IFP2 piggyBac transposon, originally discovered in Trichoplusia ni as a 2472 bp functional element, that was subsequently found as mutated elements in seven species within the Bactrocera dorsalis species complex. In a B. dorsalis [Hendel] strain collected in Kahuku, Hawaii, a degenerate 2420 bp piggyBac sequence (pBacBd-Kah) having ~ 94.5% sequence identity to IFP2 was isolated, and it was reasoned that common species, or strains within species, should share the same evolved element and its precise genomic insertion site. To test this assumption, PCR using primers to pBacBd-Kah and adjacent genomic sequences was used to isolate and compare homologous sequences in strains of four sibling species within the complex. Three of these taxa, B. papayae, B. philippinensis, and B. invadens, were previously synonymized with B. dorsalis, and found to share nearly identical pBacBd-Kah homologous elements (> 99% nucleotide identity) within the identical insertion site consistent with conspecific species. The fourth species tested, B. carambolae, considered to be a closely related yet independent species sympatric with B. dorsalis, also shared the pBacBd-Kah sequence and insertion site in one strain from Suriname, while another divergent pBacBd-Kah derivative, closer in identity to IFP2, was found in individuals from French Guiana, Bangladesh and Malaysia. This data, along with the absence of pBacBd-Kah in distantly related Bactrocera, indicates that mutated descendants of piggyBac, as well as other invasive mobile elements, could be reliable genomic markers for common species identity.
The production of oil palm (Elaeis guineensis) in Southeast Asia is vital to the economies of Indonesia and Malaysia. Both fertilisers and pesticides used in palm production can contain elevated concentrations of Trace Elements (TEs) which may accumulate in soils and leaf tissues of plants. We hypothesised that leaves from oil palms may be deficient in essential elements, while containing elevated concentrations of non-essential TEs commonly found in agrichemicals. Samples of plant materials (leaves and fruitlets) were collected from active and former plantations in Sumatra, Indonesia, and analysed for essential and non-essential elements. Indonesian palm oil samples were sourced in New Zealand and their elemental concentrations determined. Leaf materials from both active and abandoned production sites were deficient in N, K, S and Mo, while leaf materials from abandoned sites were deficient in P. These deficiencies may have been a contributing factor to the abandonment of production at these sites. Concentrations of non-essential elements were below or comparable to average plant concentrations and no evidence of contamination was found in plant tissues. Palm oil contained low concentrations of TEs, which did not pose any toxicity risks. However, Na and Al were present in concentrations of 1198 and 159 mg kg-1 respectively, which were higher than have been previously reported. Tropical oil palm production could benefit from the determination of bioaccumulation factors for fertiliser contaminants in E. guineensis, to limit the transfer of contaminants to plants and products if increased fertiliser applications were used to correct nutrient deficiencies.
The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO .
The fashion industry has a significant impact on the environment, and sustainable fashion consumption (SFC) has become a pressing concern. This study aimed to investigate the factors influencing sustainable fashion consumption behavior (SCB) among Chinese adults, specifically the role of values, attitudes, and norms in shaping such behavior, using the value-belief-norm framework. The study used an online cross-sectional survey design to collect data from 350 participants recruited through a convenience sampling method using social media platforms and email invitations, and the obtained data were analyzed using partial least squares structural equation modelling. The results of the study showed that biospheric (BV), altruistic (AV), and egoistic (EV) values significantly influenced the New ecological paradigm (EP), which, in turn, positively affected awareness of consequences (AC). Personal norms (PN) were positively influenced by EP, AC, and ascription of responsibility (AR). Social norms (SN) and trust in recycling (TR) were also found to positively influence sustainable fashion consumption intentions (SCI). Finally, the study found that SCI and TR were significant predictors of SCB, whereas the moderating effect of TR not statistically significant. The study's originality lies in its comprehensive investigation of the interplay between various factors (particularly using norms in two facets; PN and SN) in shaping SCB, using a structural equation modeling approach, and exploring the moderating effect of TR. The findings of this study suggest that interventions aimed at promoting SFC should focus on fostering values and beliefs that prioritize the environment, encouraging individuals to take responsibility for their actions, creating an environment in which SFC is normalized, and increasing TR.
Evaluation of genotypes to identify high-yielding and stable varieties is crucial for chilli production sustainability and food security. These analyses are essential, particularly when the breeding program aims to select lines with great adaptability and stability. Thirty chilli genotypes were evaluated for yield stability under four soilless planting systems viz; fertigation, HydroStock (commercial hydrogel), BioHydrogel (biodegradable hydrogel), and hydroponic to study the influence of genotype by environment interaction. The research used a split-plot randomized complete block design (RCBD) with two cropping cycles and five replications. The GGE biplot analysis was employed to assess the mean versus stability perspective in explaining the variation in genotypic and genotype-by-environment effects on the yield-related attributes for yield per plant, fruit number, fruit length, and width. Stability analysis denoted genotypes G26 and G30 as the most stable for yield per plant, while G16, G22, and G30 were stable for the number of fruits per plant. Among the four planting systems evaluated, HydroStock and BioHydrogel outperformed the others in yield per plant, demonstrating the highest level of informativeness or discrimination. These findings offer critical insights for future crop breeding programs and the optimization of agricultural practices.
The objective of this study was to examine the relationships among environmental and health values, ecological worldview, perception of consequences, the ascription of responsibility, and personal norms in the context of the value-belief-norm (VBN) model and how compatibility influences the intentions and behaviors of Chinese youth regarding the use of hydroponic farming technology. The study employed a survey questionnaire to collect data from the target population. The sample size was determined through a power analysis to ensure sufficient statistical power for the analysis. A total of 727 potential respondents' responses were analyzed using SmartPLS (4.0) to perform structural equation modeling. The results confirmed that environmental, emotional, and health values significantly associated with individuals' ecological worldviews. There was an interconnection between ecological worldview, awareness of consequences, and ascription of responsibility, and all three significantly influenced personal norms. The key determinants of the intentions and behaviors to adopt hydroponic farming technology are personal norms and technology compatibility. Therefore, to promote and motivate the interest and intention to use hydroponics among unemployed youth, government agencies, and related companies should focus on providing technology-related and pro-environmental information and training. This is expected to increase the acceptance and awareness of hydroponics among this group, thus increasing the adoption rate of hydroponics.
Polymeric based composites have gained considerable attention as potential candidates for advanced radiation shielding applications due to their unique combination of high-density, radiation attenuation properties and improved mechanical strength. This study focuses on the comprehensive characterisation of polymeric based composites for radiation shielding applications. The objective of this study was to evaluate the physical, mechanical and microstructural properties of tungsten carbide-based epoxy resin and tungsten carbide cobalt-based epoxy resin for its efficiency in shielding against gamma-rays ranging from 0.6 up to 1.33 MeV. Polymeric composites with different weight percentages of epoxy resin (40 wt%, 35 wt%, 30 wt%, 25 wt%, 20 wt%, 15 wt% and 10 wt%) were fabricated, investigated and compared to conventional lead shield. The attenuation of the composites was performed using NaI (Tl) gamma-ray spectrometer to investigate the linear and mass attenuation coefficients, half value layer, and mean free path. High filler loadings into epoxy resin matrix (90% filler/10% epoxy) exhibited excellent gamma shielding properties. Mechanical properties, such as hardness were examined to assess the structural integrity and durability of the composites under various conditions. The fabricated composites showed a good resistance, the maximum hardness was attributed to composites with small thickness. The high loading of fillers in the epoxy matrix improved the microhardness of the composites. The distribution of the filler powder within the epoxy matrix was investigated using FESEM/EDX. The results revealed the successful incorporation of tungsten carbide and cobalt particles into the polymer matrix, leading to increased composite density and enhanced radiation attenuation. The unique combination of high-density, radiation attenuation, and improved mechanical properties positions polymeric based composites as promising candidates for radiation protection field.
It is well known in the field of materials science that a substance's longevity is significantly influenced by its environment. Everything begins with the initial contact on a material's surface. This influence will then deteriorate and have an extended negative impact on the strength of the material. In this study, the effect of natural weathering in tropical climates on magnetorheological elastomer (MRE) was investigated through microstructural evaluation to understand the aging behavior of the environmentally exposed MRE. To understand and elucidate the process, MREs made of silicone rubber and 70 wt% micron-sized carbonyl iron particles were prepared and exposed to the natural weathering of a tropical climate for 90 days. The MRE samples were then mechanically tensile tested, which revealed that Young's modulus increased, while elongation at break decreased. Surface degradation due to weathering was suspected to be the primary cause of this condition. Using scanning electron microscopy (SEM), the degradation of MRE was investigated as a function of morphological evidence. Upon examination through SEM, it was noted that the weathering effects on the morphology of the exposed samples showed distinct characteristics on the degraded surfaces of the MRE, including numerous microvoids, cavities, and microcracks. While these features were not prominent for the MRE itself, they bear resemblance to the effects observed in similar materials like rubber and elastomer. An atomic force microscope (AFM) is used to investigate the surface topography and local degradation conditions. This observation revealed a distinctive degradation characteristic of the MRE in connection to natural weathering in tropical climates. The surface damage of the MRE samples became severe and inhomogeneous during the environmental aging process, and degradation began from the exposed MRE surface, causing the mechanical characteristics of the MRE to significantly change.
The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.