Investigating energy use is critical because it addresses the decreasing energy supply. The majority of global energy use is nonrenewable, with much of it coming from fossil fuels emitting greenhouse gases. As a result, energy consumption research is vital for understanding energy usage trends and developing methods to reduce energy use or employ renewable energy sources. This study investigates the impact of industry, service sectors, urbanization, exports, and inflation on energy consumption in a panel of 38 nations from 2019 to 2023. Based on the static panel approach, the key findings of the Pool model reveal that the industrial and service sectors have a positive and significant impact on energy consumption, emphasizing the vitality of these sectors as major energy users. The Fixed Effects model (FEM) suggests that the industrial and service sectors have a significant and negative impact on energy usage. Furthermore, the FE model reveals that urbanization and export significantly and negatively impact energy consumption. In the Pool model, inflation is associated positively with energy consumption. The dynamic panel approach additionally suggests that the industrial and service sectors significantly impact energy consumption in the investigated countries. Exports have a significant and negative impact on energy consumption. The CPI, a measure of inflation, significantly and positively impacts energy consumption. The findings of this study provide helpful policy recommendations for identifying the significant variables influencing world energy consumption. Policymakers in the examined countries must promote energy consumption efficiency initiatives and shift to renewable energy sources.

The incorporation of Azolla into soil was investigated in this study for its potential to enhance soil fertility by influencing key parameters, including organic carbon (Organic-C) content, total nitrogen (Total-N), and cation exchange capacity (CEC). This study was conducted in a controlled greenhouse environment using a Completely Randomized Design (CRD) with eight treatments and three replications. The primary objective was to evaluate the effects of Azolla on soil quality, particularly in improving organic matter content and nitrogen (N) retention, both of which are essential for sustainable agricultural management. The findings indicate that Azolla incorporation led to a 29% increase in soil Organic-C and a 21% increase in Total-N compared to control treatments (p < 0.05). Additionally, CEC was enhanced by 33.4%, demonstrating improved nutrient retention capacity. A strong positive correlation was observed between Organic-C content, soil pH, and CEC, suggesting that Azolla contributes to optimizing soil nutrient dynamics. These results highlight the capacity of Azolla to function as a biofertilizer, improving soil fertility and nitrogen cycling while reducing dependence on synthetic fertilizers. The potential of Azolla to serve as an eco-friendly amendment aligns with sustainable agricultural practices aimed at enhancing soil health and long-term productivity. The findings contribute to the growing body of research on biofertilizers, offering valuable insights for soil management strategies that prioritize environmental sustainability and resource efficiency.

The paper aims to evaluate the impact of green maritime logistics activities on operational performance in Iraqi oil ports affiliated with Basra Oil Company. The paper reviewed a set of previous studies related to the application of green indicators in maritime port logistics. Several problems were identified through a checklist distributed to selected employees in Iraqi oil ports. The study relied on a descriptive-analytical approach for a sample of 276 employees, where data were collected via a questionnaire and analyzed using (AMOS V.24 – JASP V.19) programs. The results showed a positive impact relationship and a statistically significant correlation between green maritime logistics activities and operational performance of 37%. The results also revealed that both safety and security codes and green environmental sustainability had strong positive impacts on operational performance, contributing 36% and 37% of the variance, respectively. While logistics coordination and integration and supporting maritime units showed moderate impacts of 29% and 27%, respectively. Information systems had a smaller impact on operational performance, at 3.4%. The most important conclusions that the paper comes up with are that incorporating green practices contributes to reducing pollution in Iraqi oil ports, and green maritime logistics boosts operational performance and fostering awareness.

Facial expression recognition (FER) remains a challenging task due to variations in facial features, occlusions, and imbalanced datasets, which often lead to misclassification of similar emotions. To address these challenges, this study proposes a hybrid Deep Autoencoder and AdaBoost model, leveraging deep feature extraction and ensemble learning to enhance classification robustness. The experimental evaluation on three benchmark datasets—MMAFEDB, AffectNet, and JAFFE—demonstrates outstanding performance, with the model achieving an AUC and Accuracy of 99.9% and 99.8% on large-scale datasets, while maintaining a strong performance of 94.9% AUC and 91.1% accuracy on smaller datasets. The confusion matrix analysis confirms the model's ability to accurately classify distinct emotions, with minor misclassifications occurring in expressions with overlapping features. These findings highlight the effectiveness of the proposed approach in improving FER accuracy, offering significant benefits for real-world applications such as human-computer interaction, emotion-aware systems, and psychological analysis, while also suggesting future enhancements through domain adaptation and refined feature extraction techniques.

This study aims to modify off-spec limestones using resin to enhance their mechanical properties. The modified limestones are intended for road construction. The mechanical properties are accessed through Aggregate Crushing Value (ACV), Aggregate Impact Value (AIV), Los Angeles (LA) abrasion, water absorption, and microstructure analyses. Then, the modified limestones are exposed to deterioration to resemble real-life altering conditions on highways. The exposure conditions consist of the direct immersion of water, 3% NaCl solution, and 1% HCl solution, and thermal stress cycles. This was followed by quality test analyses for comparisons and inferences. The samples were immersed in water, NaCl solution, and HCl solution for 63 days, with each cycle consisting of 3 days of immersion and four days of air drying. Nineteen thermal stress cycles were carried out, with 18 hours of immersion in NaCl solution and 6 hours of heating in an oven at 60 degrees. The findings show significant improvement in crushing value, impact value, and abrasion resistance of aggregates after surface treatment with resin. Microstructure analyses using SEM revealed that the treated limestone had a rougher surface texture, indicating enhanced bonding in concrete, facilitating chemical reactions, improving mechanical interlocking, and ultimately enhancing the overall performance and durability of the concrete structure.

Shallot farming in Indonesia has significant risks, primarily due to production variability and price instability. These risks deter farmers from adopting strategies involving higher risk tolerance levels. Risk aversion varies across individuals, leading to differences in decision-making processes. The study examines income risk levels in shallot farming. It explores farmers' behaviors in response to these risks in two distinct regions: the highlands of Karanganyar Regency, Central Java, and the lowlands of Bantul Regency, Daerah Istimewa Yogyakarta. A total of 200 shallot farmers were randomly selected for structured interviews to assess their risk behavior and the factors influencing it. The analysis reveals that shallot farming entails a high degree of income risk, and the highland areas exhibit a greater coefficient of variation (0.574) compared to the lowlands (0.544). Approximately 65% of highland farmers and 80% of lowland farmers were observed to be risk-averse concerning their shallot farming activities. Key factors influencing risk behavior include land size, household size, farming experience, age, frequency of crop failure, education, income, and farming location. Notably, farming experience, education, household size, and income positively impact risk behavior, increasing farmers' likelihood of adopting risk-taking strategies. The primary source of income risk was production variability, exacerbated by staggered planting schedules. This study highlights the importance of synchronizing planting schedules and strengthening farmer group networks to improve planning, marketing, input procurement, and knowledge exchange. The findings also provide a foundation for policymakers to design regulations that optimize planting times and mitigate income risks in shallot farming.

Solid oxide fuel cell/gas turbine (SOFC/GT) hybrid systems have been recognized as a promising solution in the pursuit of high-efficiency and low-emission power generation, offering electrical efficiencies exceeding 60% and notable fuel flexibility. However, the substantial amount of high-temperature exhaust gas (typically in the range of 700–800 K) released during operation has presented ongoing challenges in effective thermal energy recovery, thereby constraining further improvements in overall system efficiency. In recent years, various waste heat recovery technologies have been explored for their applicability to SOFC/GT systems. Among the most studied are the supercritical carbon dioxide (SCO₂) cycle, the transcritical carbon dioxide cycle (TRCC), the organic Rankine cycle (ORC), the Kalina cycle (KC), and the steam cycle (ST). In this review, the thermodynamic principles, performance metrics, and thermal integration compatibility associated with each technology were critically examined. In addition, a novel waste heat recovery configuration optimized for SOFC–GT hybrid systems was proposed and discussed. This approach was conceptually validated to enhance total system efficiency and to facilitate the development of advanced combined heat and power (CHP) systems. The results contribute to the broader efforts in clean energy system design and offer technical insights into the next generation of high-performance, low-emission power technologies.

This study investigated the activities surrounding crude oil and its impact on the economic performance of Nigeria. Therefore, some economic variables surrounding crude oil in Nigeria was analysed. Most multivariate economic variables suffer the problem of multicollinearity, though often not tested or sometimes ignored by researchers. The presence of multicollinearity among predictor variables often leads to bias estimate. In this study, explorative data analyses were conducted on the data of petroleum variables and gross domestic product and modelled using the Cobb-Douglas Production Function. Multicollinearity was detected in the full model and corrected. The results showed that Real Gross Domestic Product (RGDP) have a significant positive relationship with crude oil Revenue and petroleum to GDP in the full model. The crude oil consumption, and Petroleum to GDP significantly impact the RGDP in the reduced model. Based on the findings of this study, it is recommended that the government implement policies to preserve and manage the oil sector effectively to encourage international trade and increase revenue at the same time make petroleum products available for local use in line with sustainable development goals (SDGs) 7, to ensure that there is affordable, sustainable and modern energy for all by 2030.

Mobile Ad Hoc Networks (MANETs) plays an important role in various fields; however, this network unavoidably encounters difficulties at the network layer primarily owing to misbehavior or malicious nodes. Among the issues plaguing MANETs, the deliberate and accidental dropping of packets by intermediate nodes emerges as a noteworthy problem requiring attention. The work proposes a novel routing protocol that aims to mitigate the packet dropping problem in a thorough yet efficient manner by selecting only neighbors with proven stability and integrity during route discovery. The protocol devises a neighbor node election tactic reliant on residual status of energy and buffer so that it can compute stable route and avoid those neighbors in route which are having constrained energy and buffer. Additionally, it deploys counter-based authenticated acknowledgments and promiscuous monitoring to enable integrity in route and counter malicious packet drooping. Simulation results show the protocol's efficacy, consistently outperforming existing algorithms in packet delivery and energy efficiency. In conclusion, this work systematically addresses the complexities introduced packet dropping nodes in infrastructure-less networks.

The aim of this study is to evaluate the performance of a proposed model utilizing the Technology Acceptance Model (TAM) to forecast student perceptions of statistics education with advanced technology. A total of 379 undergraduate students from Malaysia’s East Coast region were recruited using a simple random sampling technique. This study incorporates six main constructs that are tested simultaneously, namely social influence, self-efficacy, perceived usefulness, perceived ease of use, attitude toward using, and behavioural intention. The Pooled Confirmatory Factor Analysis (PCFA) was employed to assess the factor loadings and fitness of the model being tested. Moreover, the Composite Reliability (CR) and Average Variance Extracted (AVE) were established to assess their reliability and validity. The results of the Confirmatory Factor Analysis (CFA) demonstrated that all six constructs achieved satisfactory levels of model fit, reliability, and validity. These findings confirm that the measurement model is statistically robust and that each construct is well-defined and appropriate for further analysis. Given their strong psychometric properties, these constructs provide a solid foundation for future research and should be considered for further investigation by examining the structural relationships among them, particularly in the context of technology adoption in statistics education.