Despite longstanding theoretical and practical interest in subjective well-being (SWB), its structural composition remains debated. Life satisfaction, along with positive and negative affect, is widely recognized as a core component of SWB; however, the nature of their interrelationships within a unified construct is still unclear. This study examined the factor structure of SWB using data from a sample of Ukrainian university students (N = 1111; age range = 18–26 years; 59.0% women). Participants completed the Ukrainian versions of the Satisfaction with Life Scale (SWLS) and the Scale of Positive and Negative Experiences (SPANE). Confirmatory factor analysis (CFA), bifactor CFA, exploratory structural equation modeling (ESEM), and bifactor ESEM were employed to evaluate competing theoretical models. Model selection was based on Akaike’s Information Criterion (AIC) weights, balancing model fit and parsimony. While four models demonstrated adequate fit, the bifactor ESEM model showed the best overall performance. This model accounted for cross-loadings and identified a strong general SWB factor along with three specific components. Measurement invariance across gender was confirmed at the configural, metric, and scalar levels. Findings support the bifactor ESEM as a comprehensive and robust framework for conceptualizing the multidimensional structure of subjective well-being in emerging adults.

This study developed an indicator system of instructional leadership by novice principals to facilitate the assessment of their leadership capabilities. This study employed a survey-based approach consisting of three studies to examine the reliability and validity of the proposed system and explored the perceived importance and actual performance of these indicators. The statistical techniques included expert review, item analysis, exploratory factor analysis, internal consistency reliability analysis, confirmatory factor analysis, cross-validation, measurement invariance analysis, and IPA. The proposed indicator system, consisting of 5 dimensions and 24 specific indicators, demonstrates good reliability and validity for assessing the overall, dimensional, and specific indicators of instructional leadership performance. Highly rated dimensions were enhancing school curriculum and instructional quality as well as creating supportive teaching and learning environments. Developing a vision for teaching and setting goals and tasks was also considered important; however, performance was relatively low.

Crime poses a significant challenge to the prosperity and growth of nations. Various factors, including poverty, deficiencies in the legal system, unstable economic conditions, and insufficient technological capabilities for crime analysis and detection, contribute to its emergence. This work proposes a novel framework for crime identification and detection utilizing generative and deep learning models. Initially, we extract latent features from an available dataset using a combination of generative model, Deep Learning (DL) and Machine Learning (ML), techniques, including a Variational Auto-Encoder (VAE), transformers, Convolutional Neural Networks (CNN), and Principal Component Analysis (PCA), followed by K-means clustering. We then evaluate the effectiveness of this clustering approach using renowned classifiers, such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Naive Bayes (NB). As a result, the framework, which utilizes VAE for feature extraction and combines it with RF as the classifier, achieves the highest accuracy of 0.9993. The strength of the proposed framework lies in its unsupervised learning approach, which attains significant information from data without relying on labeled datasets. This data-driven methodology adeptly leverages generative and deep learning models for feature extraction, subsequently employing these features for crime detection. Furthermore, we analyze individual attributes in latent spaces and apply classifiers, with the VAE demonstrating exciting performance, achieving an accuracy of 0.999.

Social networking sites have been frequently analyzed in psychological studies due to a great number of their users and the influence they have on people’s social life. Instagram is among the most popular image-based networking sites. The main aim of this research is to show problematic Instagram use (PIU) as a mediator in the relationship between self-esteem and life satisfaction. The study was carried out on a group of N = 912 people aged from 19 to 30. The following tools were used: Rosenberg Self-esteem Scale, Satisfaction with Family Life Scale, Problematic Instagram Use Questionnaire, and Satisfaction with Life Scale. It was found that there is a negative correlation between self-esteem and problematic Instagram use. The relation between PIU and life satisfaction was shown as well as the mediating impact of PIU on the relationship between selfesteem and life satisfaction after an average level of family satisfaction was taken into account.

The integration of Artificial Intelligence (AI) such as ChatGPT into teaching and learning has become a significant area of interest in higher education as it has significantly reshaped students writing practices. Studies suggest that it has the potential to dismantle language barriers and foster multilingualism and linguistic inclusivity in multilingual writing classrooms (Malik et al., 2023). However, while the use of AI has been proven to be a significant tool in enhancing students’ communicative competence (Hasanein and Sobaih, 2023), particularly for students from non-native English-speaking backgrounds, there are widespread concerns among the academic community that AI tools may negatively impact academic integrity, creativity, and critical thinking skills in academic writing (Neumann et al., 2023). To address these concerns, instead of discouraging the use of AI, we conducted a 12-week intervention academic writing course where we integrated ChatGPT in the course. The purpose of the study was to explore its use as a writing support tool as well as a strategy to foster students’ responsible use of the tool. At the end of the course, focus group interviews were carried out with students to explore their experiences with the use of ChatGPT as a writing support tool. The findings of the focus group interviews gave us pedagogical insights into how educators can leverage the affordances of AI in the teaching of academic writing and how AI can be used to enhance equality and level writing expectations for students who come from non- native English-speaking backgrounds.

Background: Scientific discipline of artificial intelligence (AI) studies the computational understanding of intelligent behaviour as well as the development of artifacts that display this behaviour. Very limited information regarding artificial intelligence is included in the current syllabus of dental undergraduate students and hence, their perception about AI remains unclear. Aim: was to determine the attitudes and perceptions on artificial intelligence in dentistry among undergraduate dental students in South India. Materials and Methods: Four clusters based on geographic locations were formed from a list of southern colleges, with two schools selected from each via convenience sampling. A self-administered pre-validated 15-item questionnaire was mailed to 1174 undergraduate students to reach a sample size of 385. Data obtained was subjected to statistical analysis using SPSS v 25.0, IBM. Chi-square tests were used to study any association between the variables. A p-value of p < 0.05 was considered statistically significant. Results: A total sample size of 385 was attained. 12.7% were males and 87.3% were female participants. The mean attitude and perception score was 57.3. There were significant differences in the year of study with mean scores pertaining to AI’s diagnostic, treatment, prognosis and forensic dentistry applications (p<0.05). Conclusions: Majority of students strongly agreed that AI cannot replace dentists in future, but with mean scores significantly higher among final year students (X̄ = 4.36 Vs 4.05). Most of the students desired that AI applications be a part of undergraduate and postgraduate training (X̄ = 4.14 and 4.2 respectively).

Education is a complex process beyond the simple transmission of knowledge in the classroom. Students learn from their teachers and are influenced by various factors including their family environment, past experiences, and cultural traditions. In particular, the didactics of geography in secondary school face the challenge of connecting curricular content with the reality and experience of students. The present work focuses on analyzing the educational field, specifically in the context of geography didactics at Tulucești Secondary School. The main objective of this research is to understand how the teaching and learning process of geography has developed in the school during the mentioned period. A mixed methodology combining qualitative and quantitative data collection techniques was used. In-depth interviews were conducted with teachers to explore perceptions, experiences, and practices related to pedagogy and geographic education. In addition, questionnaires were administered to a representative sample of teachers to collect quantitative data about geography.

Inula britannica L. (IBL) is an important medicinal plant traditionally used in the treatments of back pain in Iranian folk medicine. The aim of this study was to evaluate the analgesic effects of Inula britannica L. flower essential oil (IBLEO) and one of its major constituents, Patuletin (Pn), in male mice. In this study, we used pain assessment tests including acetic acid-induced writhing, tail flick, formalin, and glutamate-induced paw licking (GPL). For understanding the supposed analgesic mechanisms of IBLEO, opioid and L-Arginine/NO/cGMP/ KATP pathways were examined. A rotarod test was performed to evaluate any possible effect of IBLEO on the motor activity of mice. In the tail flick, writhing, GPL, and formalin tests, a dose of 100 mg/kg of IBLEO showed significant analgesic effects (p < 0.05). Administration of selective opioid antagonists (Naltrindole, Nor-binaltorphimine, and Naloxonazine) attenuated the antinociceptive effect of IBLEO (p < 0.001). Patuletin showed strong analgesic effects in all tests (p < 0.01). The results of this study showed that IBLEO and Patuletin have analgesic effects. The modulation of glutamatergic systems by opioid receptors could be involved, at least in part, in these effects. Our data also suggest the activation of the L-Arginine/NO/cGMP/KATP pathway in IBLEO antinociceptive effects.

Estimation of biomass and organic carbon content in mangrove plants presents a realistic measure to determine the carbon sequestration capacity of individual plants, species and communities. Allometric models are widely used for the purpose and considered as a sufficiently reliable tool in tree biomass estimation. Although diameter at breast height (dbh) is the most commonly used as the quantifiable variable to develop allometric models to estimate mangrove plant biomass, it requires accessing each mangrove plants in a sample area to measure the dbh, a cumbersome task, in particularly, in a muddy and waterlogged remote location. Present study indicated the importance of the tree height over the dbh as a reliably quantifiable variable in estimating biomass and TOC (total organic carbon) of Bruguiera gymnorrhiza (L.) Lamk. and Lumnitzera racemose Willd., abundant species in mangrove ecosystems. Sample trees of both B. gymnorrhiza, and L. racemosa were selected to represent the range of dbh of the respective species in the mangrove area at Kadolkele in Negombo estuary, Sri Lanka. Dry weights (biomass) were determined for the plant components based on wet to dry weight ratio and TOC content of biomass was calculated using previously published data for the same locality. A significant relationship (p< 0.001) was observed between tree height and dbh of both species. Allometric models were developed for biomass/TOC using with tree height of B. gymnorrhiza and L. racemosa. The result of the study provides a reliable method of estimation of biomass/TOC from the measure of tree height, an easily accessed and measurable from distance instead of dbh, the measure difficult to make in mangroves due to inaccessibility.

Wireless power transfer (WPT) and rapid charging system is important issue for many applications, particularly on mobile and biomedical devices. To perform high efficiency, first the system tracks maximum power transfer by sweeping the resonant frequency from feedback sensing. The transmitter can use the best resonant frequency for wireless power transfer, and the receiver device charges a battery as fast. This can correct the frequency drift in manufacture, to promote the transferring efficiency. A voltage detection circuit of the receiver device is detecting charging state of the battery. Transmitter device checks whether a high-frequency transmission circuit transmits a fully-charged signal. The transmitter device shuts down the power and enters standby mode as the battery is fully charged. By maximum power tracking approach, higher wireless transmission efficiency can be achieved. The prototyping chips of transmitter and receiver had been implemented using TSMC 0.25um HV process. Experiments shown the efficient of WPT can achieve about 70%.

ABSTRACT\nThe Ichthyofaunal Assemblage in Gio/Kporgho Creek, Rivers State, Niger Delta, Nigeria was investigated from the months of April 2012 to December 2013. The fishes were collected with various fishing gears namely: traps, hook and lines, and nets of various mesh sizes. The upper, middle and lower courses of the creek as well as seasonality were accorded cognizance in the fish collection processes. Fish species identification was done with standard keys provided by Reed et al, (1979), Tobor and Ajayi (1978), Schneider (1990), Sikoki & Francis (2007). The survey identified 47 fish species belonging to 32 genera of 23 families. The families Lutjanidae, Clupeidae and Carangidae had the highest species composition of 4 species each, while the families, Cynoglossidae, Betrachoidae, Belonidae, Elopidae, Eleotridae, Periophthalmidae, Gerreidae, Symbrachidae, Achanthuridae, Monodactylidae, and Cyprinodontidae had the least species composition of 1 species each. The fish species which occurred in the creek were evident that the creek is of brachish water system. The checklist of 47 fish species in the area that the creek will be rich in fish fauna if the pollution status is remedied and monitored effectively. Some of the fish species found on the checklist are among those mentioned in previous studies as economically important species which exist in the Nigerian Coastal Zones. This is also pointer to the fact that the Gio/Kporgho Creek is of much economic importance.

Abstract: Tinfa is a small catchment situated South of Nekemte town in Western part of Ethiopia. Variably thick weathered mantle cover basalt bedrock in the area. Well-developed columnar joints that are affected by sub-horizontal sets of discontinuities are abundant on the basalt bedrock. The area receives high amount of rainfall that reaches 2100mm annually. Much of this influx infiltrates into the thick accumulation of in-situ residua. The thick weathered profile generally serves as a huge reservoir that intercepts vast portion of rainfall and releases it at slower pace to the underlying discontinuous bedrock. There are three boreholes (yields vary from 6 to 27l/s) tapping this aquifer which supply water to Nekemte town. Water samples from these boreholes were analyzed to investigate the chemical composition and physical parameters to check if it meets the WHO guidelines for potable water. It was found that pH and TDS vary over narrow ranges of 6.9 to7.5 and 156 to 196 mg/l, respectively. The analytical results revealed that concentration of cations such as Ca2+ range from 21.4 to 32.9mg/l, Mg2+ from 13.5 to 17.3 mg/l, K+ from 2.1 to 2.8mg/l and Na+ from 12.5to19.5mg/l. Anion concentrations vary over a narrow ranges: HCO3- from 163.4 to 190.6mg/l, Cl- from 1 to 2mg/l, NO3- from 3.25 to 7.5mg/l and SO42- from 1.1 to 2mg/l. The mean cationic concentration follows the order Ca2+>Na+>Mg2+>K+ while anions follow HCO3->NO3->Cl->SO42- order indicating that Ca2+ and HCO3- are dominant ionic species in the catchment. Our findings revealed that none of the physical and chemical parameters exceeded the maximum limit suggested by WHO and Ethiopian guidelines for potable water.

Roselle (Hibiscus sabdariffa) is a species of Hibiscus native, it may be known as carcade, which has a medical benefit as reducing blood pressure for hypertensive patients. Ficus carica is an Asian species of flowering plants in the mulberry family, known as the common fig. Fig color may vary between cultivars due to various concentrations of anthocyanins, with cyanidin-3-O-rutinoside having particularly high content. The objective of the present study was to evaluate the in vitro genotoxicity for both plant extracts. \nResults: No significant difference in the induction of micronuclei was observed between cultures treated with the Hibiscus or Ficus, and the negative control, demonstrating the absence of genotoxic activity at the tested concentration. The same results were obtained with the chromosomal aberration assay on the two extracts; therefore the two extracts have not any significant effect of chromosomal aberrations, induced with tested concentrations. \nIn conclusion, the present work showed that the used concentrations of the two tested extracts (Hibiscus and Ficus), were considered as a save extracts in using with human, beside their medicinal activities.

To understand the parameters which can be used as quick criteria for drought tolerance screening of large population, the present investigation has been performed to evaluate thirty-eight upland rice varieties against five drought levels (0, -2, -4, -6, and -8 bars) at germination and early seedling growth stage of plant development. Data were analyzed statistically for germination and growth parameters under experimental conditions. Experiment units were arranged factorial completely randomized design with four replications. Mean comparison showed that the highest value for most of parameters was recorded for varieties JA HAU and IR 5533-15-1-1. Upland rice varieties of JA HAU and IR 5533-15-1-1 showed greater drought tolerance during germination and early seedling growth stages. Results of variance analysis made clear that different osmotic potential had significant effect on all parameters. There was a significant two-way interaction (P<0.01) (variety × PEG) for studied germination and seedling growth stages. In conclusion, using germination and growth parameters at seedling growth stage can be used as selectable parameters to discriminate between tolerance and sensitive varieties under drought stress in breeding programs. The confined seedlings environmental of laboratory experiment would appear to be suitable for screening large population under drought stress.

Present investigation conveys numerical computations for energy flow by mixed convection under the impact of magnetic field through ferrofluid saturating porous medium contained in trapezoidal enclosure having triangular notched heater at bottom wall when top wall of enclosure is moving with constant speed. The side and top walls of the container are assumed cold where bottom wall is insulated and triangular notched heater supplies uniform heat to the fluid. Equations governing the flow are first exposed to penalty scheme to eliminate pressure term from momentum equation and subsequently Galerkin weighted residual method is applied to compute the numerical solutions for velocity and temperature profiles.

The supply chain of every enterprise comprises a highly distributed environment, in which complex processes evolve in a network of companies and this distributed environment creates a lot of fluctuation and uncertainty in the supply chain. Demand forecasting is the downstream part of the supply chain. Accurate forecasting of the future demand of the product will eliminate the uncertainty and makes the supply chain stable. Therefore, demand forecasting is very critical for any organization to make the correct decisions and to achieve the benefits in this regularly changing business scenario. The objective of this work is to study the basics of Artificial Neural Network (ANN) and its application in supply chain management and develop an ANN model which will predict the future demand with high accuracy as compared to the conventional forecasting methods. To demonstrate the effectiveness of the present study, demand forecasting issue was investigated on a gear manufacturing company as a real-world case study. Three ANN models with TANSIGMOID, LINEAR and LOGSIGMOID transfer function has been developed using MATLAB software for forecasting the demand. A comparative analysis of different ANN models and various traditional forecasting methods like exponential smoothing, moving average and weighted moving average method has been done on the basis of the results obtained from the various forecasting models.

The present work reports low-cost, green synthesis of pure and Cu doped ZnO nanoparticles using: 10%, 30%, 40% & 50% (v/v) of Azadirachta indica (Neem) leaf extract and its antibacterial application on the drug resistant bacteria. Zinc acetate and copper nitrate were used to synthesize the nanoparticles. Thermal gravimetric analysis results showed that the synthesized materials were stable above 400⁰C. The XRD analysis revealed that all the synthesized particles have hexagonal wurtzite structure, phase pure, with size ranging from 16.07 to 23.74 nm. The SEM analysis indicated the spherical shape morphology of the particles and the increase in grain size as the concentration of the capping agent increases. The FT-IR study confirmed the interaction between the capping agents and ZnO. The antibacterial activity studies of the synthesized materials against Staphylococcus aureus, Bacillus subtilis and Escherichia coli have better performance with the reference to the selected standards gentamicin and ciprofloxacin.