The tensile and flexural properties of composite materials containing Babassu fiber (Attalea Speciosa) as a novel natural fiber in a polyester resin matrix have been thoroughly investigated. These composite materials were created using fibers that were removed using retting and hand techniques. In order to evaluate their tensile and flexural qualities, these composites have undergone extensive testing. They have also been compared to well-known composites including vakka, sisal, bamboo, and banana, all of which were produced under uniform laboratory circumstances. The composites have been produced with a maximum fiber volume fraction of 0.30 for tensile and flexural evaluations. It has been noted that the tensile attributes enhance in correlation with the volume fraction of Babassu fiber present within the composite and exceed those exhibited by sisal and banana composites. Additionally, the flexural strength of the Babassu fiber composite has been determined to surpass that of vakka, sisal, bamboo, and banana composites. Furthermore, the density of Babassu fiber has been quantified at 587.85 kg/m3, which is significantly lower than that of sisal (1450 kg/m3), bamboo (910 kg/m3), and banana fibers (1350 kg/m3); following chemical treatment, the density of Babassu fibers is noted to decrease to 454.88 kg/m3. This characteristic is crucial as it facilitates the development of low-density natural composites suitable for lightweight applications.
Low public awareness regarding the prohibition of waste disposal into water bodies in Lampenai Village has led to a long-term accumulation of riverine debris that flows into the sea, causing persistent ecosystem degradation. This study evaluates the effectiveness of physical intervention through the installation of trash barriers at strategic locations as a pollution mitigation strategy. A quantitative observational method was employed, involving direct on-site monitoring and variable measurement without experimental manipulation. The research analyzes the correlation between the trash barrier's capture efficiency and ecological conditions, integrated with average daily waste generation data based on population statistics. The results demonstrate a significant waste capture performance across four monitoring stations: Station 1 (123.2%), Station 2 (111.4%), Station 3 (108.1%), and Station 4 (103.8%). These percentages, exceeding 100%, indicate that the actual captured waste load surpasses the estimated daily generation per capita, suggesting the presence of existing accumulated debris within the river flow.
The increasing adoption of solar photovoltaic (PV) systems highlights the need for intelligent monitoring to maintain efficiency and reliability. Conventional monitoring methods are often passive, lacking real-time analytics and automated fault detection, which can lead to undetected performance issues and higher maintenance costs. This paper develops an IoT-based solar energy monitoring system with a web interface for real-time performance evaluation and predictive fault detection. The system continuously measures voltage, current, power, and temperature using an Arduino Uno and transmits data via an ESP32 to the ThingSpeak cloud. A Random Forest model, trained in Google Colab and deployed on a Raspberry Pi through a Python Flask API, enables real-time classification of normal operation, partial shading, open circuit, and overheating faults. Data and fault status are visualized on a custom HTML dashboard with automated alert notifications. Experimental results demonstrate that the integrated system achieves an overall classification accuracy of 100%, effectively distinguishing between four distinct operational states: Normal, Partial Shading, Open Circuit, and Overheating under varying environmental conditions. The validation also confirms a low-latency alert mechanism, with Telegram notifications delivered within 1-3 seconds of fault detection. The system enhances PV reliability and efficiency by enabling proactive maintenance, minimizing downtime, and supporting sustainable energy management through the integration of IoT hardware and machine learning.
The increasing sophistication of cyberattacks requires intrusion detection systems that can model nonlinear and complex network-traffic patterns beyond the capabilities of conventional machine-learning designs. This paper proposes a Hybrid Quantum-Inspired Cyber Threat Detection (Hybrid QCTD) framework that combines amplitude-based quantum feature encoding, a variational quantum circuit (VQC), and a lightweight classical classifier. Evaluated on the CICIDS2017 dataset, the proposed model achieved an accuracy of 0.9550, an F1-score of 0.8779, a precision of 0.8108, a recall of 0.9571, and an ROC-AUC of 0.9931. Random Forest and XGBoost achieved higher accuracy and F1-scores, whereas the Hybrid QCTD model maintained strong detection performance and stable training convergence. A prediction-level example further illustrates that the hybrid model may produce different decisions from the classical baselines for complex network flows; however, such examples should be interpreted together with the aggregate test metrics. These findings demonstrate the feasibility of entanglement-based feature fusion for intrusion detection and motivate further evaluation on noisy quantum hardware and resource-constrained, real-time cybersecurity systems.
This study examines the chemical characteristics, microstructural behaviour and strength evolution of a low-carbon blended binder incorporating silica fume (15%), ground granulated blast-furnace slag (20%), coal-washery rejects (15%) and ordinary Portland cement (50%). Coal-washery rejects were processed to a particle size below 75 μm and utilized as a supplementary component within the binder system. Elemental characterization using SEM-EDS confirmed the presence of oxygen (44.6%), silicon (13.3%), calcium (10.4%), carbon (8.9%), iron (6.0%), aluminum (5.1%), potassium (3.4%), phosphorus (3.3%), magnesium (1.7%), titanium (0.6%) and sodium (0.6%), indicating a mineralogical composition compatible with cementitious reactions. Microstructural assessment revealed the development of a dense hydration matrix dominated by calcium-silicate-hydrate (C-S-H) gel and uniformly distributed reaction products. SEM-EDS mapping demonstrated effective interaction among silica fume, GGBS, and coal-washery rejects, with silica fume enhancing secondary C-S-H formation and GGBS contributing to sustained hydration and matrix densification. Progressive refinement of gel phases and Si-Ca co-located regions was observed with curing, supporting continuous strength development. Compressive strength increased steadily from 24.8 MPa at 3 days to 34.2 MPa at 7 days, 48.9 MPa at 28 days, and 58.3 MPa at 90 days, reflecting effective hydration and microstructural evolution. Thermal and phase analyses further confirmed partial consumption of portlandite and increased amorphous gel formation, consistent with strong pozzolanic and latent hydraulic activity. From an environmental perspective, replacing 50% of ordinary Portland cement with industrial byproducts substantially reduces clinker demand and associated CO2 emissions while enabling beneficial utilization of coal-washery waste. The results demonstrate that coal-washery rejects can be effectively integrated into silica fume and GGBS-based blended binders, providing a technically robust and environmentally sustainable pathway for low-carbon cementitious material development.
This research investigates the transformation of starch-based membranes when combined with sodium salts, focusing on their physical and structural changes. Membranes were fabricated using a solution casting method, incorporating starch, sodium hydrogen sulfate, distilled water, and glycerin. The pristine membrane exhibited a clear and even surface, reflecting a homogeneous polymer network. However, introducing sodium hydrogen sulfate at different levels led to noticeable alterations in the membrane's appearance, such as increased cloudiness and structural irregularities. Advanced imaging techniques, including high-resolution photography, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), were employed to analyze the material. These methods revealed the development of both large-scale and fine-scale structures, as well as the even dispersion of sodium ions throughout the polymer network. The interaction between the salt ions and the polymer chains disrupted the balance between crystalline and amorphous regions, promoting a shift toward greater amorphous character. This change affected the membrane's transparency and light-diffusing capabilities. The results offer valuable insights into how varying salt concentrations can alter the physical and structural attributes of starch-based membranes, potentially expanding their utility in various material science applications.
In this paper, artificial neural networks (ANNs) are used to predict the nonlinear deflection of beams subjected to concentrated loads at different sections and under various limit boundaries. The research focuses on developing ANN models capable of accurately predicting large deflections of beams. A comprehensive dataset, generated through semi-analytical methods, finite element simulations, and experimental results, was used to train and validate the models. The input variables included excitation force, contribution coefficient, and linear and nonlinear terms of rigidity. The performance of the ANN models was evaluated by comparing predicted deflections with known data available in the literature, showing high accuracy and reliability. The results demonstrate the potential of ANN-based approaches for efficient and accurate deflection prediction in structural engineering applications, offering a valuable tool for designing and analyzing beams in complex loading scenarios.
This study presents an approach to optimize the design of hydraulic fracture treatments in the low-permeability area of the Lower Miocene sandstone reservoir in the Cuu Long Basin of southern Vietnam. Six major design variables influenced the fracture length, which may enhance oil production when the fracture length was maximized. Thus, the fracture length was analyzed using the central composite face design based on statistical modeling, which is an experimental design for response surface methodology. The results showed that a maximum fracture length of 828 ft was obtained at an injection rate of 30 bpm with an injection time of 79 minutes. The slurry concentration at the end of the job was 8.6 ppg, the leak-off coefficient was 0.0023 ft/min0.5, and the fracture height migration to the pay zone height ratio was 1.065.
Vehicular Ad Hoc Networks (VANETs) are widely vulnerable to several kinds which threaten the reliability and safety of the network. Traditional diagnosis strategies sometimes concentrate on unique kinds of misbehavior and fail to consider the complicated, complex, simultaneous attacks aspect. Here, we offer a new multi-task learning architecture integrated with sensor fusion methods for diagnosing and concurrently grouping several misbehaviors in VANET areas. Through combining heterogeneous data sources-such as network parameters, behavioral features, and positional info—our model efficiently obtains interrelated features of different kinds of attacks, like denial of service, message forgery, and false location reporting. The multi-task learning framework enables knowledge sharing across tasks, developing whole diagnosis accuracy and strength. Broad simulations performed under different traffic densities show that the presented strategy performs better than conventional unique-task models in the two binary and multi-class classification scenarios. The outcomes highlight the ability of an architecture to develop security in VANETs through presenting a general and effective misbehavior diagnosis system.
The rapid evolution of cyberattacks in corporate networks requires intelligent and adaptive defence systems that do not rely solely on conventional rule-based intrusion-detection frameworks. This work presents ZT-GNN-MARL, a comprehensive zero-trust cyber-defence framework that combines threat detection using Graph Neural Networks (GNNs) with Multi-Agent Reinforcement Learning (MARL) for dynamic policy enforcement. Network traffic is represented as a temporal interaction graph, enabling the GNN to learn latent structural behaviours and detect malicious DDoS activity with high fidelity. The MARL engine autonomously makes dynamic zero-trust access-control decisions, including micro-segmentation, host quarantine, and step-up authentication, using risk scores derived from GNN predictions. The proposed system is evaluated on the CIC-IDS-2017 dataset and achieves an overall accuracy of 0.998, perfect recall for malicious flows, and superior performance compared with the Random Forest and MLP baselines. Additional analyses using ROC curves, confusion matrices, latent-space projections, and reinforcement-learning reward convergence demonstrate the robustness, adaptability, and generalization capability of the framework. The findings indicate that ZT-GNN-MARL is an effective and scalable solution for implementing zero-trust architectures in practical network environments.
Cloud-IoT deployments increasingly require fine-grained access control that can respond to rapidly changing operational contexts, including suspicious access behavior, abnormal time or location, and device trust. This paper proposes a risk-adaptive Ciphertext-Policy Attribute-Based Encryption (CP-ABE) framework in an edge-cloud architecture, where an edge gateway computes a risk score and automatically strengthens or relaxes the encryption policy in real time. In low-risk states, data are encrypted under a baseline policy based on role and department; high-risk contexts trigger stricter, short-lived constraints, including location-zone verification, multi-factor authentication (MFA), and a 15-minute access window. The prototype was evaluated using a topology of 10 IoT nodes, an edge gateway, and cloud storage under brute-force, stolen-credential, and replay attacks. The results show that all 15 attacks were mitigated, corresponding to 100% adaptive defense accuracy, while maintaining practical overhead: 12.1 ms average encryption latency, 21.6 ms average decryption latency, and 59.0625 KB of cloud storage usage.
This study evaluated and compared four predictive models - Linear Regression, Decision Tree, Random Forest, and Support Vector Regression (SVR) - in estimating engineering students' final grades in Differential Equations. Academic grade records of 646 students from the Technological University of the Philippines enrolled in Mathematics in the Modern World (MMW), Calculus 1, Calculus 2, and Differential Equations were used. All models were evaluated using 10-fold cross-validation with R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) as performance metrics. Results showed that all models produced near-zero or negative R² values, with Linear Regression performing best (R² = -0.011, RMSE = 2.255, MAE = 1.699). Machine learning models did not outperform the linear baseline, suggesting that prerequisite mathematics grades alone are insufficient predictors of Differential Equation performance. The findings recommend the inclusion of richer predictor variables in future predictive models.
Carbon capture and storage (CCS) is promoted as a promising decarbonization strategy. It is argued to be a potential solution for Egypt’s greenhouse gas emissions, which have substantially grown over the past three decades. A widely recognized CCS method is carbon geological storage in petroleum reservoirs. However, there is a remarkable lack of research on CO2 conditioning and transport in oil fields. This research aims to assess the storage of a pure source of CO2 emitted from a gas process plant in Obaiyed oilfield in Egypt. To do so, this paper performed a systematic review of the available surface facilities and well assets at Obaiyed and made a cost-benefit analysis. The findings showed that assuming 3333 barrels/day condensate recovery from injection, the project would yield an internal rate of return of 100%, a net present value (NPV) of $403 million, and a payback period of 1 year. A sensitivity analysis found that the NPV increases sharply with an increase in oil prices and the adoption of carbon pricing. Trading carbon storage credits in the Egyptian carbon market is found to be capable of entirely financing the project, even without any condensate recovered. Since most of the research in this area focuses on subsurface investigation, the novelty of this paper comes from its interdisciplinary approach by combining surface and well analysis and financial estimation, reflecting the very local context in Egypt.
Oil Spill Dispersant (OSD) is a product that can remove oil layers on the water surface due to the presence of a mixture of surfactants (surface active agents) and solvents that can break down oil molecules into small droplets that can lower the surface tension between oil and water, allowing them to disperse naturally in the water. This study aims to formulate an oil spill dispersant from a combination of methyl ester sulfonate (MES) and the natural surfactant soybean lecithin (SL). OSD was formulated with 13 variations: the SL/MES substrate ratio (1/3; 1/1; 3/1), the lecithin concentration in diethylene glycol ethyl ether (dEGEE) (2%, 4%, 6%), and the MES concentration in distilled water (4%, 8%, 12%). Characterization of the resulting soybean lecithin surfactant was carried out by functional group analysis using a Fourier Transform Infrared (FTIR) spectrophotometer and hydrophilic-lipophilic balance (HLB) value analysis. The best OSD was obtained at a variation of the substrate ratio (SL/MES) = 3/1 with lecithin and MES concentrations to the solvent of 6% and 4%, with a density value of 1.0022 g/mL, viscosity of 2.438 cP, pH 5.9, and surface tension of 29.465 dyne/cm.
The abundant plastic and banana stem waste is a serious environmental problem, but it has the potential to be used as a composite material for structural applications such as electric motorcycle bodies. This study aims to analyze the effect of variations in the composition of PET plastic waste and banana stem natural fibers on the impact strength of epoxy resin-based composites, and to compare the performance of composites fabricated using two different methods, namely hand lay-up and compression molding. Three variations of the PET to SPP ratio were used in this study, namely 10%: 30%, 20%: 20%, and 30%: 10%, with an epoxy resin fraction of 60%. The specimens were tested using the Charpy Impact Test method with ISO-179 standards. The results showed that the compression molding method produced higher impact strength values than hand lay-up. The highest value was recorded in the composition of 30% SPP and 10% PET with the compression molding method, which was 4.9 kJ/m². Conversely, the lowest value of 1.77 kJ/m² was obtained in the same composition with the hand lay-up method. Based on the research results, the composition of 30% SPP and 10% PET fabricated using the compression molding method is recommended as an alternative material for electric motorbike bodies that is environmentally friendly and has adequate mechanical performance.
Building image segmentation is a critical task in urban planning, disaster management, and environmental monitoring. Traditional segmentation methods using either satellite or drone imagery face challenges such as resolution limitations and vegetation occlusion. In this study, a novel approach integrating multi-source remote sensing data-combining drone imagery captured at multiple heights (150m, 200m, 250m, 300m) with multi-view perspectives combined with satellite imagery to enhance building segmentation accuracy. A key challenge in segmentation is the interference of vegetation and shadows, which can obscure building boundaries. To address this, advanced vegetation removal techniques have been incorporated to refine the extracted structures. By leveraging the complementary advantages of drone and satellite imagery, this approach improves segmentation robustness and reliability. Our method achieves a segmentation accuracy of 96%, significantly outperforming conventional techniques. This approach has been evaluated against existing segmentation methods, demonstrating its effectiveness in extracting high-precision building footprints, even in complex environments. The findings highlight the potential of integrating multi-source data and vegetation suppression strategies to enhance automated urban feature mapping.
This study proposes the use of a protection casing string as a liner in low- and medium-pressure zones of directional wells to reduce drilling costs, minimize non-productive time (NPT), and enhance operational safety. Two directional wells were redesigned using Halliburton Landmark software to analyze casing stresses, while Microsoft Excel was employed to model costs. The conventional well design was modified to replace the traditional 9⅝″ production casing with a liner system, supported by single-stage cementing and mechanical hangers. This improved design reduced casing pipe length, eliminated the need for additional wellhead components, and avoided cold cutting operations. Pipe costs decreased by 50–60%, cement costs by 30–50%, and NPT related to the relevant operations was reduced by approximately 90%. Overall, the approach lowered well construction costs by $190,000 to $200,000 per well. Using the protection string as a liner is a technically viable, cost-effective, and safer alternative for directional wells, particularly in depleted reservoirs. This method improves drilling efficiency, reduces operational risks, and offers significant economic benefits in multi-well development scenarios.
Renewable Energy Sources (RES) are widely used as the primary energy source in modern civilization. The utilization of clean energy, such as solar energy, is efficiently utilized for generating pure power generation. Solar Photo Voltaic (PV) modules produce low output voltage, and it is essential to regulate and improve it to attain optimal power. The inherent intermittency of solar energy and fluctuations in irradiance pose challenges in maintaining a stable and regulated DC output. To meet load requirements, an insolar PV system with a cascaded boost converter is integrated, in which high voltage gain with a lower value of duty ratio is attained. This paper presents a PV power conversion system utilizing a Class Topper Optimized (CTO) Proportional-Integral (PI) Controller. A closed-loop control mechanism is implemented, where the CTO-PI controller efficiently regulates the output of the designed converter by minimizing the error between the reference voltages. The proposed approach is implemented in MATLAB/Simulink. From the simulation results, the improved converter attains an efficiency of 95.19% with a settling time of 0.1s. The system's capability is validated to maintain consistent output voltage despite fluctuations in solar irradiance and load conditions, making it highly suitable for load applications.
Floating solar photovoltaic (FPV) systems are an emerging renewable energy innovation that addresses land-use constraints while contributing to water-energy-environment synergies. This systematic review consolidates global FPV research between 2010 and 2025, following PRISMA guidelines, and evaluates the technology from technical, environmental, economic, and policy perspectives. FPV systems show consistent efficiency improvements of 7-18% compared with land-based PV, primarily due to water-based cooling effects, with reservoir-based deployment currently dominant and offshore FPV emerging as a future frontier. Environmental outcomes are dual in nature: FPV reduces evaporation and algal growth but raises unresolved questions regarding aquatic biodiversity and long-term ecological impacts. Economic studies indicate improving competitiveness, with capital costs only 5-15% higher than ground-mounted PV, offset by land savings and higher yields. Social and policy research highlights the importance of governance frameworks, incentives, and water-use regulations in scaling adoption. The review identifies critical gaps in standardized design, ecological monitoring, and offshore durability, while underscoring FPV’s multifunctional potential in hybrid hydropower integration and ecological restoration. Overall, FPV represents a promising, multidisciplinary pathway to support renewable energy transitions in Asia and globally.
This study aims to analyze quantitatively the relationship between vehicle volume and air pollution level in Makassar, a city that has experienced a significant increase in traffic density due to rapid urbanization. The main issue addressed in this research is the extent to which vehicle intensity contributes to the rising air pollution concentration in developing tropical urban areas. This study employs a correlational quantitative design with a descriptive-analytical approach. Primary data were obtained through direct measurements of vehicle volume and air pollutant concentrations (CO, NOx, and SO₂) on several main roads, while secondary data were obtained from relevant institutions. Data collection techniques included automatic vehicle counters, surveillance cameras, and portable air quality measuring devices. Data analysis includes descriptive statistical tests, Peason’s correlation test, and multiple linear regression with a significance level of 0.05. The study’s result reveals a strong positive relationship between vehicle volume and air level pollution (r > 0, 9; R² > 90%). Increases in vehicle volume significantly raise the concentration of CO, NOx, and SO₂, while meteorological factors act as moderating variables. This study affirms that traffic management and sustainable transport policies can reduce pollution levels effectively. The findings contribute theoretically to the development of urban environmental management studies and offer empirical foundation for the formulation of environmentally friendly transportation policies in developing cities like Makassar.
