This pilot research aims to investigate the effects of the use of artificial intelligence (AI) tools on students’ cognitive-metacognitive abilities. Freshman students of biology, chemistry, and physics education at a Malaysian university who enrolled via three pathways participated in the study. Students’ scores on yes (1) or no (0) structured surveys (on 11 research topics), high school/pre-university GPA, and test scores (on lower- and higher-order thinking science tests) were collected and analyzed. In many of the research topics, the mean scores of the surveys were high. Data analyses were carried out using t-tests, one-way ANOVA, and correlation and regression analyses. Comparisons of mean scores were carried out based on gender, pathway to university, and major areas of study. Several cases showed no statistically significant variations, most likely due to sample size limits or a lack of variability in students’ responses. However, many indicators showed that males, chemistry majors, and pathway 2 (pre-university foundation) students did better. Mean scores of many research topics on cognitive-metacognitive abilities were positively correlated with mean scores of the research topics on AI use. In all the regression models of the research topics, the predictor variables did not make significant contributions (p > 0.05). Qualitative examination of the data showed that the students were able to rate their cognitive-metacognitive abilities and AI use in some meaningful ways and distinct patterns. Given their strong academic background, students’ self-assessments of cognitive-metacognitive abilities and AI usage in STEM learning may reflect a reasonable level of awareness, though further qualitative validation is needed. A mean score of approximately 12 out of 25 on the topic of ‘the effects of AI in hindering STEM learning’ suggests neutrality or ambivalence about the negative consequences of AI while still recognizing its overall benefits, as shown by the high mean scores in the topic on ‘the benefit of AI in STEM learning.’ This suggests a nuanced perspective where students see AI as both a facilitator and a potential challenge in their education.

The subject matters are justified based on the challenges of minimising energy and CO2 utilisation in transportation and the contribution of electric vehicles to sustainable development. This work aims to establish the current trends in providing energy efficiency in electric vehicles through the use of new technologies in their design. The study employs theoretical approaches to analysing literature, comparing prior research findings, and assessing the most recent technological advancements in electric transportation. These study findings demonstrate that when new solar and wind energy sources are incorporated within the architecture of electric vehicle charging stations, the grid load and CO2 emissions are minimised. Implementing kinetic energy recovery systems and smart grids to enhance energy consumption makes running electric vehicles efficient. Blockchain technologies and innovative vehicle management systems, such as V2G, facilitate efficient charging and seamless integration of electric vehicles into energy systems, significantly improving power grid stability. The practical significance of these results lies in the possibility of using these technologies to develop infrastructure for electric vehicles, which will help reduce emissions and increase the environmental sustainability of the transport system.

Childbearing indices (CIs), i.e., age at first birth, age at last birth, the reproductive life span, and other related measures, are significant in understanding the fertility transition of a population. When direct estimates are missing, it is common practice in demography/ population studies to use indirect techniques to estimate the indicators. In this paper, an attempt has been made to estimate childbearing indices from only information on TFR, applying Horne et al. (1990) procedure and model fertility table (MFT) earlier constructed for Ethiopia by the researchers. Again, in this paper, fertility transition in Ethiopia and its regions was examined using estimated childbearing indices by inputting TFR data from DHS surveys for Ethiopia and its regions during 2000-2016. It was found that the application of MFT and Horne et al. (1990) procedure for estimating childbearing indices was found to be useful, and the derived CIs indices were found to be applicable in studying fertility transition. Childbearing transition was observed in most parts of Ethiopia during the 2000-2016 periods. Based on our findings, we recommend that policymakers strengthen family planning and reproductive health services, invest in women’s education and employment opportunities, and design special interventions for parts of the country lagging in fertility transition.

Kifilideen’s Geometric Matrix Progression Sequence of infinite and finite terms generates multiplication of tri- indexes or bi – indexes simultaneous equations of its components’ migration level value, migration step value, and first term. There is a need to develop a simple, short, effective, and standardized rule or model to tackle such a problem. This study develops Kifilideen’s Rule to solve the multiplication of bi-indexes, tri-indexes, and -indices simultaneous equations of two variables, three variables, and variables respectively. Elimination method, laws of indices, and cofactor and determinant of matrix were deployed in establishing, formulating, and modeling the Kifilideen’s Rule to solve multiplication of bi-indexes and tri-indexes simultaneous equations of two variables and three variables, respectively. The Kifilideen’s Rule was implemented in the form of a model in solving problems involving multiplication of bi-indexes and tri-indexes simultaneous equations of two variables and three variables, respectively. The general solution to solve the multiplication of index simultaneous equations of variables using Kifilideen’s rule was inaugurated. The Kifilideen’s Rule is in the form of a model to solve multiplication of bi-indexes and tri-indexes simultaneous equations of two variables and three variables, respectively, which was found to be effective, straightforward, reliable, interesting, easy, and intuitive to understand for students and beginners.

180 specimens of Clarias batrachus (6.59-6.72cm and 3.10-6.96g) reared upon six experimental diets comprising 25% CP (T1), 30% CP (T2), 35% CP (T3), 40% CP (T4), 45% CP (T5), and 50% CP (T6), were used to study the concentrations (µg/g) of twelve metals (Fe, Cu, Zn, Cr, Co, Mn, Na, K, Ca, Mg, Cd and Pb) in whole carcasses with relation to body size and condition factor by using atomic absorption spectrophotometer. Fe, Cu, Zn, and Mg were found to be highest in T4 (p<0.05). Cr, Mn, Na, K, and Ca remained constant (p>0.05) among different treated groups. Cd and Pb were not quantified as their concentrations are below detection. Regression analyses represented significant correlation for Cu in T1 and T3; Co, Mn, and Na in T1; while Ca and Mg in T4, with the BW showing a positive impact on metal concentrations. Total length showed significant relationships for Cu and Na in T1, Zn in T3, and Co in T4. Condition factor represented correlations for Zn in T1, Co in T4 and T5, and for K in T1. Overall findings showed fish reared in T4 contained high nutritional values with respect to essential metals (Fe, Cu, Zn, and Mg), as these minerals are essential to our everyday lives.

Analysis of digestive enzyme concentration has key importance in understanding and obtaining valuable information about the digestive physiology of fish. In the present study, we evaluated and compared the amylase, lipase, and protease concentrations relative to gut morphometric parameters: relative gut length (RGL), relative gut mass (RGM), and condition factor in the snakehead Channa marulius. A total of 60 specimens of Channa marulius were collected from Head Marala, River Chenab. Samples were weighed to get the body weight and the total length. Then excised each specimen and removed its complete gut and measured the total gut length and weight. The concentration of digestive enzymes (amylase, lipase and protease) was determined. The protease concentration (0.946***), lipase (0.960***), and amylase (0.974***) were observed to be highly significantly (P<0.001) correlated with an increase in gut length in both pre-adult and adult groups. The log weight with log gut length (0.996***), (0.975***), lipase (0.979***), and amylase (0.982***) were observed to be highly significantly (P<0.001) correlated with an increase in gut length in both pre-adult and adult groups. Gut length (GL) versus condition factor relative to the concentration of digestive enzyme indicated a significant correlation (P<0.05) and condition factor relative to amylase concentration was observed highly significant (P<0.01; r=0.783) in the Pre-adult group while in adult-group condition factor regression parameters observed highly significant (P<0.05) relative to gut length and with the concentration of protease, lipase, and amylase. Information from the present study provides valuable data to understand the digestive physiology of pre-adult and adult groups of wild carnivore fishes.

This paper aims to assess the key factors that control groundwater quality using multivariate statistical methods such as Principal Component Analysis (PCA) and Cluster Analysis (CA) in Kombolcha town, Northern Ethiopia. For this purpose, 18 groundwater samples were collected from boreholes during the months of May and November 2017, representing the dry and wet seasons, respectively. The PCA results showed two components in the dry season and three components in the wet season. PCAs 1 to 3 explain variances of 45.32, 20.29, and 14.15%, respectively, in the wet season. Meanwhile, PCAs 1 and 2 represent 45.50 and 35.55% of the variance, respectively in the dry season. Moreover, based on the similarities in water quality characteristics, the Q-mode cluster analysis divided the water samples from the wells into three (3) and four (4) distinct homogenous clusters in the dry and wet seasons, respectively. The mean values of EC, TDS, major ions, NO₃⁻, NO₂⁻, PO₄³⁻, and F⁻ follow the order: Cluster 1 > Cluster 2 > Cluster 3 in the dry season, and Cluster 1 > Cluster 2 > Cluster 3 > Cluster 4 in the wet season. A Piper diagram classified the groundwater in the area into Ca-HCO3-SO4, Ca-Na-HCO3-SO4, and Ca-Na-SO4-HCO3 water types in the dry season and Ca-HCO3-SO4 and Ca-Mg-HCO3-SO4 water types in the wet season, indicating the dominant hydrochemical facies. In conclusion, the PCA and CA results revealed that the groundwater quality in the research area is predominantly controlled by anthropogenic processes (liquid wastes released from industries and municipal sewage), with a secondary contribution from geogenic sources.

The textile and garment industry has a long history of being a catalyst for product and technological innovations, and it is a prioritized sector in Ethiopia. This study employs a Sequential Mixed-Methods design, incorporating quantitative data analysis using R4.4.0 software and qualitative data analysis through Thematic coding. This integrative approach enables triangulation and enriches findings. The objective of this study is to examine the extent to which formal Vocational Skills Development (VSD) programs contribute to Company Growth and Transformation in Ethiopia’s Textile and Garment industries. Key findings indicate that total pre-employment VSD programs are strongly and positively associated with significant advancements in product and technological innovations within the Ethiopian textile and garment industries. Specifically, pre-employment VSD programs show a positive correlation with export-oriented industries, contributing to overall industrial growth and transformation. Furthermore, in-employment VSD programs significantly enhance the likelihood of achieving higher sales volume growth. Based on the results, the study recommends that companies should prioritize investing in pre-employment and in-employment VSD programs for company growth and transformation. Government initiatives are crucial in creating Technical and Vocational Education and Training (TVET) industry linkage policies and encouraging companies to invest in VSD programs to produce export-quality products.

The photovoltaic devices had gone through many phases or stages of modification since their evolution in 1839. From mundane crystalline silicon (first generation) to Graphene and its derivatives (fourth generation), the photovoltaic devices are of great importance for generating electricity, storage devices, and several other applications. The reduction in the cost of production of electricity via solar cells (photovoltaic) has been greatly impacting the global economy to the extent that countries like the US are projected to generate 58% of their total electric generation by 2024 from solar energy. With an increase in global energy consumption, solar energy remains the only energy source that could power the globe because the solar energy absorbed by the earth from the sun is the same as the energy from all the non-renewable energy sources (coal, oil, natural gas, and uranium) as reported by the World Counts 2024. Hence, PV has a significant potential to play a prominent part in all significant future energy problems. In fact, the photovoltaic devices help in reducing unemployment by creating jobs, trade balance between countries, and easing the individual cost of living, thereby reducing the poverty rate in countries of the world. In conclusion, the cost of solar power reduces every year, and it might be cheaper than fossil fuels, depending on the sun and weather conditions at the site of installation. Currently, solar energy is the cheapest in the United States, with generation costing about 0.70 USD per watt. In this review paper, the impact of photovoltaic (PV) devices (solar energy) on the global economy is examined, which objectively enlightens on the economic benefits and job creation opportunities, by analyzing reports from journals and other publications from international energy agencies.

Channa marulius is a valuable food fish, known as “Sol”. In Pakistan, C. marulius plays an important role in meeting the protein requirement of people in their daily diet. Due to overfishing, destruction of habitat, and water pollution, the population of C. marulius is declining rapidly. Therefore, for the conservation of C. marulius, we evaluated its genetic diversity by collecting specimens from five major rivers (Chenab, Indus, Ravi, Jhelum, and Sutlej) of Punjab, Pakistan using random amplified polymorphic DNA (RAPD) molecular markers. 75 specimens of C. marulius were collected from five rivers and evaluated its genetic diversity by using five RAPD DNA markers OPA-02, OPA-05, OPA-07, OPA-11, and OPA-16. Results showed 106 polymorphic bands out of a total of 154 bands and detected five primers associated with genetic diversity. The highest genetic diversity was observed in River Jhelum with 28/35 (80%) polymorphic bands. In others, it is recorded as 26 (78.78%) in Ravi, 20 (74.07%) in Sutlej, 24 (71.44%) in Indus and lowest in River Chenab with 08 (28.58%) polymorphic bands. Nei’s genetic diversity (h) observed in Jhelum (0.782) and Chenab (0.323) reflect high genetic diversity in Jhelum and lowest genetic diversity in Chenab. UPGMA dendrogram showed that the population of C. marulius in Sutlej and Ravi has close genetic connectivity and genetic similarity and similarly the population of C. marulius in Indus and Chenab also has close genetic connectivity and genetic similarity. The present study reports that the lowest genetic diversity in the Chenab River is a matter of serious concern for fisheries resource managers to take immediate steps for C. marulius conservation.