The development of learners’ competencies, particularly in the English language, remains an important concern within the educational system, especially in a context where learners continue to experience difficulties in achieving the expected level of language proficiency. In this regard, the relationship that learners establish with their teachers may positively or negatively influence their attitudes toward the learning process. Indeed, the quality of the learner–teacher relationship can shape learners’ classroom experiences, their willingness to participate, and their level of engagement in English language learning. Therefore, this article places particular emphasis on the learner–teacher relationship in order to examine the extent to which it influences learners’ involvement in learning English at the middle secondary school level. More specifically, the study seeks to explore learners’ perceptions of their English language teachers and to understand how teacher responsiveness, classroom interaction, and the relational climate may contribute to facilitating or constraining their engagement in the learning process. In doing so, the study highlights the importance of the relational and didactic dimensions of language teaching in creating a supportive environment conducive to English language learning.
This experimental work examines the thermal, thermohydraulic, and exergetic performance of a modified revised multi duct solar air heater (SAH) under real outdoor conditions. Four configurations were examined, including a smooth collector, ducts with corrugation, porous-lined ducts, and corrugated ducts equipped with inserted twisted tapes over the investigated Reynolds number (Re) interval (2000–10,000). The airflow was supplied by a DC fan powered directly by photovoltaic modules. Introducing internal flow-modifying elements resulted in a marked enhancement of the SAH heat transfer capability, as confirmed by the experimental observations. When compared to the smooth absorber, among the tested configurations, the twisted tape configuration delivered the highest useful heat gain of approximately 456 W under peak operating conditions, which is a significant improvement. At Re = 10,000, the Nusselt number (Nu) increased from 39.3 for the smooth collector to around 148.35 for the porous-lined ducts and roughly 108 for the twisted tape arrangement, showing the substantial impact of surface modification and flow disruption on heat transfer by convection. The overall thermo-hydraulic performance significantly improved even though the redesigned configurations lead to an increase in frictional losses; at higher Re, the thermohydraulic performance factor (THPF) reached a maximum value of about 1.651. A maximum exergy efficiency of about 10.45% was obtained from the exergy analysis conducted under peak operation conditions, but the exergy destruction was lower in comparison to smooth absorber. Also, from experimental data empirical correlations were calculated for friction factor (f) and Nu, and they demonstrated a high degree of agreement with the results that were observed. The findings indicate that passive flow modification techniques can significantly improve the SAH's thermal and exergetic performance for relevant renewable energy applications.
This study investigated the effects of reaction temperature and TiO₂ catalyst loading on the production yield and fuel properties of liquid fuel derived from remilling rubber latex waste through catalytic cracking. Experiments were conducted at temperatures of 300, 350, and 400°C using catalyst loadings of 33.7, 37.5, and 41.2 g for 60 minutes under a pressure of 70 psi. The liquid products were characterized in terms of yield, density, viscosity, flash point, calorific value, cetane number, and functional groups using Fourier Transform Infrared (FTIR) spectroscopy. The results demonstrated that both reaction temperature and catalyst loading significantly influenced product yield and fuel characteristics. The highest liquid fuel yield of 38.04% was obtained at 350°C with a catalyst loading of 41.2 g. The resulting fuel exhibited densities ranging from 0.8156 to 0.8628 g/mL, viscosities from 0.86 to 0.95 mm²/s, flash points between 30.6 and 36.2°C, and a maximum calorific value of 41.337 MJ/kg, which is comparable to that of commercial diesel fuel. FTIR analysis confirmed the predominance of aliphatic –CH₂– and –CH₃ functional groups, indicating the formation of alkane fractions through the cleavage of polyisoprene chains. The cetane number reached 51.1, suggesting favorable combustion quality and compliance with the typical range of commercial diesel fuels. These findings indicate that remilling rubber latex waste has considerable potential as a sustainable alternative energy resource for liquid fuel production.
Vehicle detection is an important perception task in Intelligent Transportation Systems (ITS) and Advanced Driver Assistance Systems (ADAS), and the performance under different illumination conditions still remains as an important challenge. The current deep-learning object detectors perform well under daylight conditions, while the detection behavior may be degraded in nighttime and low-light conditions due to low contrast and limited object visibility. This paper introduces a lightweight and reproducible framework for evaluating the pre-trained YOLO11s object detector in daylight and simulated night-time scenarios without any further retraining of the model. A typical road-scene frame from an actual daytime driving video was used to produce a corresponding simulated nighttime image with the same road geometry, vehicle locations, and camera viewpoint. The vehicle detection was preceded by enhancing the nighttime image using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction. Detection behavior was evaluated by the number of detected vehicles, confidence-score statistics, confidence-level distribution, confidence ranking, scatter visualization, confidence-weighted heatmaps, and exploratory Pearson correlation analysis. The experimental results showed that YOLO11s detected 10 vehicles during daylight conditions and 3 vehicles under simulated nighttime conditions, which is about 70% less detections under reduced illumination. This is a decrease, but the most visually prominent vehicle still had a high confidence score in both conditions, while a number of weaker detections, including a dark-colored vehicle, were lost in the simulated nighttime scene. The proposed framework provides a simple and reproducible way to study the effect of illumination on the detection behavior of pretrained YOLO11s and highlights the need for further validation on larger datasets and real nighttime driving scenes.
Distribution networks face increasing pressure from rising loads and renewable energy integration, making the simultaneous optimization of reconfiguration and distributed generation placement a critical engineering challenge, and conventional single-objective methods are insufficient for simultaneously addressing power losses, voltage profile degradation, and the structural constraints of radial networks. This paper proposes a hybrid framework that combines Ant Colony Optimization with the Truncated Newton Constrained method to jointly solve network reconfiguration alongside the optimal placement, sizing, and type selection of renewable distributed generation—both solar PV and wind—within a single unified model. The approach relies on a four-part weighted objective function covering active power loss, voltage quality, economic cost, and reactive power loss, while a spanning-tree constraint keeps the network radial and the exact DistFlow model handles power flow calculations. Real weather data from Baghdad was used to drive the renewable generation models, rather than relying on idealized standard conditions. Tested on the IEEE 33-bus and 69-bus benchmark systems, the hybrid ACO-TNC algorithm delivered results that surpassed what had been reported in prior studies, with the 69-bus case in particular going beyond the best figures previously documented in the literature for reconfiguration-only scenarios. It also consistently outperformed Particle Swarm Optimization across every metric tested, and did so more efficiently on the larger network. These results demonstrate the strong capability of this hybrid approach in solving the reconfiguration problem while simultaneously determining optimal DG locations and sizes for renewable energy sources—handling the whole task as one integrated problem rather than splitting it into separate steps, and doing so without any manual intervention in setting the generation unit parameters.
This study investigated a two-stage surface modification strategy for ZK60 magnesium alloy based on 3-aminopropyltriethoxysilane (APTES) pretreatment followed by electrospun polyvinylpyrrolidone (PVP) nanofibers containing ZnO and hydroxyapatite (HA) nanoparticles. Three PVP/ZnO/HA formulations were prepared to examine the effect of nanoparticle loading, and corresponding coatings with and without APTES pretreatment were compared. The coatings were characterized using Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX), and a cross-hatch adhesion test. Corrosion behavior was evaluated in simulated body fluid (SBF) using open-circuit potential (OCP) monitoring and potentiodynamic polarization. Antibacterial activity was screened against S. aureus and E. coli, while in vitro apatite-forming ability was evaluated after 14 days of SBF immersion. XRD analysis confirmed the presence of crystalline HA and ZnO phases in the electrospun coating. The APTES-pretreated specimen showed a lower coating-removal area (3.52%) than the electrospun-only specimen (8.05%). The best-performing APTES-pretreated specimen exhibited a corrosion rate of 3.003× 10-3 mm· year–1 compared with 0.488 mm· year–1 for uncoated ZK60, corresponding to a 99.38% reduction in the short-term electrochemical corrosion rate. The selected APTES-pretreated/electrospun specimen exhibited inhibition zones of 22 mm against S. aureus and 28 mm against E. coli. After SBF immersion, this specimen exhibited the highest Ca and P contents and a Ca/P atomic ratio of 1.63, indicating enhanced in vitro apatite-forming ability. Overall, the results indicate that APTES pretreatment combined with electrospun PVP/ZnO/HA nanofibers improves coating retention, suppresses the initial electrochemical corrosion activity of ZK60, and provides antibacterial and apatite-forming surface characteristics under the investigated in vitro conditions.
This work aims to inhibit carbon steel surface as pipelines by adding carboxymethyl cellulose (CMC) with three concentrations (1, 3 and 5 g/L) in seawater environment at four temperatures in the range of 298 – 328 K. The investigation was done by recording Tafel plots and EIS curves to show that the lowest concentration is the best to protect the steel surface with efficiencies ranged between 95.68 – 89.36 % that decrease with increasing temperature due to the decomposition of CMC that begin after ≈ 308 K. An attempt to enhance the protection by CMC at higher temperature has been done by adding 0.1 g/L graphite and 1 g/L silica, the results of electrochemical measurements indicated the good role of graphite and silica through the sheet structure of graphite (Gr) that enhance the CMC by the formation of multi layers of (Gr/CMC/Gr/CMC) which theoretically studied through density functional theory (DFT) in addition to calculate some electronic parameters, while the silica nanoparticles (S) gave an important role to pre-treatment the steel surface to form a passive layer on surface and within CMC polymer. The inhibitive role of Gr. and S. is attributed to higher mobility and Zeta potential of two additives compared with that for CMC. From the data of EIS test, 1 g/L CMC recorded the highest value for resistance of solution (Rs) and resistance of charge transfer (Rct). Scanning electron microscopy with energy dispersive X-ray confirmed the adsorption of inhibitive materials by appearance O and C elements in inhibition by CMC and then increases with addition Gr., while decreases with addition Silica and appear Si element.
Electrospun nanofibrous membranes are a promising technique for wastewater treatment owing to their high porosity and surface properties. This manuscript presents a systematically designed investigation on the preparation and optimization of poly(vinylidene fluoride) (PVDF)/polyacrylonitrile (PAN) blended nanofibrous membranes for oil/water separation. Nanofibrous membranes were synthesized by electrospinning with PAN weight percentages of 0 wt%, 10 wt%, 25 wt%, and 50 wt%. The impact of PAN weight percentages on the morphology and separation performance of the membranes was investigated. The membranes were analyzed harnessing scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), and water contact angle (WCA) measurements. The results indicated that with an increase in PAN weight percentages, the diameter of the fibers decreased from 394 nm to 261 nm. Simultaneously, the pore sizes increased from 132 nm to 220 nm. Moreover, the contact angle was found to decrease from 150° to 130° with an increase in PAN weight percentages. FTIR and EDS confirmed the presence of PAN in the PVDF matrix. The performance evaluation of the membrane using crude oil- and kerosene-contaminated water revealed that the performance of the membrane was significantly affected by the ratio of PAN. Out of all the formulations, it was observed that the membrane containing 50 wt% PAN had the maximum flux, minimum fouling potential, maximum COD removal, and maximum separation efficiency, which were around 59% for crude oil and 85% for kerosene.
The healthcare industry has undergone substantial growth over the past few years. Despite the development of medical technology, the diagnosis of cardiac arrhythmia is still a significant challenge for modern diagnostic methods for correct and efficient classification of electrocardiogram (ECG) signals, especially in cases of class imbalance and the complexity of the arrhythmia. This study proposes a novel deep learning model where the class imbalance problem is addressed using the Synthetic Minority Oversampling Technique (SMOTE) and the effect of signal resolution on the classification is investigated using Cubic Spline (CS) interpolation and Lightweight Deep Neural Network (CSLWDNN). Both the MIT-BIH and the INCART datasets were preprocessed and resampled with CS interpolation with several control point configurations. The Spline-187 configuration performed best on MIT-BIH data, with an F1-score of 97.35% and the accuracy of 97.35%. The model performance for INCART dataset is an F1-score of 98.56% and an accuracy of 98.27% at 20 control points. Comparative experiments showed that CS interpolation has better class-wise recall and smoothness of the signal with various input representations. The outcomes indicate that SMOTE balancing coupled with spline-based preprocessing is a valuable scientific and practical approach to build reliable, scalable, and clinically applicable ECG classification systems.
The use of membrane separation and heterogeneous photocatalysis together appears to be an innovative way to remove long-lasting pollutants such as paracetamol (PCT) from water. To overcome the limited process by understanding hindering the practical deployment of solar-driven photocatalytic membrane reactors (PMRs), this study proposes a process-engineering-centered framework for a low-cost, integrated ceramic PMR. A laboratory-scale system was developed using inexpensive red clay ceramic membranes modified by immobilizing TiO2 (P25) for paracetamol degradation under natural sunlight and ultraviolet irradiation. Unlike previous studies mainly focused on material development, this work evaluates the coupled effects of TiO2 loading, membrane inclination, irradiation source, and transport phenomena on reactor performance. Systematic investigations of TiO2 loading (0–1 g/L), membrane inclination (0°–60°), and irradiation conditions revealed maximum paracetamol degradation efficiencies of 74.9% (UV) and 55.5% (sunlight) after 5.5 h on the retentate side. However, concentration polarization, fouling, and permeate flux decline limited overall removal efficiency. Reactor performance strongly depended on hydrodynamic orientation, with horizontal orientation (0° inclination) improving photon exposure and mass transfer. Reusability tests showed activity losses of 52% under UV and 33% under sunlight after five cycles, indicating the need for regeneration strategies. Kinetic analysis confirmed that degradation was controlled by photon flux, catalyst accessibility, and boundary-layer effects. Despite repeated operation, the ceramic support maintained its integrity. This study provides practical design principles for the development of potentially scalable, energy-efficient and cost-effective solar-driven photocatalytic ceramic membrane reactors for decentralized wastewater treatment.
Solid State transformers (SSTs) are pivotal for renewable energy integration and smart grids, yet their performance is critically limited by non-linear magnetic effects such as core losses, flux- density saturation, and magnetising-inductance drift, that conventional analytical methods and prior artificial neural network (ANN) studies have struggled to model simultaneously across the electrical, thermal and magnetic domains. This study develops and evaluates a unified multi-output ANN framework that predicts six SST performance indicators which are efficiency, power density, total harmonic distortion, temperature rise, core loss and magnetising inductance from six operating variables (switching frequency, duty cycle, load current, ambient temperature, input voltage and maximum flux density). The synthetic dataset was expanded to 2000 operating conditions generated from physically grounded equations, the modified Steinmetz core-loss model and a flux-density-dependent inductance roll-off model, with heteroscedastic noise added to emulate measurement variability and an explicit first order thermal-coupling term. A baseline model that faithfully reproduces the original two-hidden layer, Z-score normalised architecture on a 500-sample subset is reported alongside an improved model that combines a deeper feed-forward architecture, log-scale normalisation of skewed outputs, a physics-informed auxiliary input derived from the analytical Steinmetz equation, and oversampling of the high-flux-density on the loss-cliff region. On a held-out test set, the improved model achieves R2 = 0.991 (efficiency), 0.996 (power density), 0.990 (THD), 0.995 (temperature rise), 0.993 (core loss) and 0.995 (magnetising inductance), resolving the core-loss and inductance R2 inconsistencies. A single slice sweep around the operating median identifies a favourable magnetic-flux-density region ( ≈ 1.0 - 1.2T) in which efficiency remains above the exponent. An illustrative comparison with a nano-crystalline core option is included to broaden material scope. Because the dataset remains synthetic, all quantitative operating recommendations are explicitly framed ANN-predicted trends requiring experimental confirmation, and a concrete roadmap toward physics informed neural networks (PINNs) and laboratory validation is proposed.
The use of epoxy-based composites has increased significantly due to their wide range of applications across various industries. However, these composites often require enhanced mechanical properties tailored to specific applications through the incorporation of different types of reinforcements. In this study, the effect of natural fibers on the mechanical properties (flexural strength, tensile strength, and impact energy) of composites based on two types of epoxy resins (LR202 and Sikadur®-52 LP) was investigated using the hand lay-up method. Different reinforcement fibers (fiberglass, cotton, and jute) were incorporated at various percentages. The results indicate that, among composites reinforced with a single type of fiber, the highest mechanical properties were obtained with 15% fiberglass. However, increasing the percentage of fiberglass in hybrid epoxy composites does not always lead to improved mechanical performance. The highest maximum flexural strength was achieved in a hybrid composite reinforced with 3% cotton, 7% jute, and 5% fiberglass, while the highest impact energy was observed in composites containing 5% cotton, 3% jute, and 7% fiberglass. In addition, reducing the fiberglass content while increasing the proportion of natural fibers such as jute and cotton resulted in a significant enhancement in the impact energy and flexural strength of the composites.
Diesel thermal power plants are an essential link to ensure energy security in the countries of the Sahel, despite the development of renewable energies. In Burkina Faso, these plants contribute greatly to electricity production. However, their operation is influenced by a particularly restrictive environment characterized by ambient temperatures up to 49 °C, humidity variability greater than 70% and high exposure to dust. These conditions accelerate equipment degradation, reduce reliability and increase unexpected failures. This study proposes a contextualized predictive maintenance tool. Indeed, it is based on a Contextualized Global Risk Index (GPRI) combining a climate risk indicator, a probability of failure estimated by Random Forest and a vulnerability indicator based on the history of failures. The tool allows you to classify generators into four levels of criticality (low, moderate, high and critical) and to associate them with appropriate maintenance actions. Simulations show that such an approach could reduce unexpected failures by 20–30% while significantly improving operational reliability. The proposed methodology is therefore based on a hybrid approach combining statistical reliability models and machine learning techniques. Indeed, Weibull-AFT (Accelerated Failure Time) – Random Forest is therefore used to move from a fixed calendar preventive maintenance to a contextualized predictive maintenance, better suited to the environmental contexts of Burkina Faso and the Sahel countries. The results show a strong heterogeneity in the performances of the studied groups. For example, the Diesel Generator Set (G4) and Diesel Generator Set (G6) groups have the best reliability performance, while Diesel Generator Set (G3) appears to be the most vulnerable to degradation.
Future studies offer many solutions to reduce problems from internal combustion engines; one is the use of blended fuels containing alcohols. This research aimed to investigate the effect of blending Iraqi regular gasoline with heavy alcohols, such as 1-butanol and 1-octanol, by conducting experiments on a 4-cylinder spark-ignition (SI) engine. The first goal is to prepare a blended gasoline consisting of 88% regular gasoline, 6% 1-butanol, and 6% 1-octanol by mass, and to use it for thermal analysis of engine operation. This work fills a gap by experimentally evaluating a mixed mid- and long-chain alcohol blend (1-butanol/1-octanol) with gasoline, an area with limited prior data. Results show that blended regular gasoline (B06O06) significantly affects engine performance. B06O06, compared to base fuel, showed higher brake-specific fuel consumption (BSFC), with an 8% increase at low load and a 13% increase at high load on average. Brake thermal efficiency (BTE) decreased by 3% at low load and 5% at high load. The noise of B06O06 was moderate at both high and low loads. Overall sound pressure level (OASPL) decreased 8% at 1500 rpm, increased 2% at 2100 rpm, and remained unchanged compared to regular gasoline. At high load, the distribution matched that of low load but decreased 1.5% as engine speed increased. Carbon monoxide (CO) emissions dropped by about 34% at low load and 11.5% at high load, with an average reduction across engine speeds. Nitrogen oxide (NOx) emissions decreased with both load and speed: at low load, NOx dropped by 30% at high engine speeds and by 20% at low engine speeds. At high load, reductions were 20% at high engine speeds and 25% at low engine speeds. Carbon dioxide (CO2) emissions and unburned hydrocarbons (HC) did not increase significantly, remaining nearly constant compared to regular gasoline. Previous studies show that the 1-butanol/1-octanol blend offers a better balance between gasoline solubility and high-octane number. It also reduces hydrocarbon emissions, making it a promising alternative fuel that reduces environmental impact.
When choosing a cryptographic block cipher, several factors must be considered, including security, performance, implementation feasibility, and application environment. However, such choices should often be made with professional expertise. The paper shows a domain-specific conversational recommendation system, which consists of a lightweight Large Language Model (Llama 3.2-1B) fine-tuned using Quantized Low-Rank Adaptation (QLoRA) and Retrieval-Augmented Generation (RAG) based on a structured knowledge base of block-cipher properties. During inference, the user query (with requirements) is embedded using all-MiniLM-L6-v2 and used to retrieve the most relevant cipher entries from a FAISS index, which is built offline by using the structured knowledge base. To reduce hallucinations and ensure accurate recommendations, the retrieved metadata of the cipher is injected into the prompt to ground the model's reasoning. The proposed system was evaluated on a custom dataset of 10,000 queries (80/20 split) by using the same fine-tuned LLM without retrieval augmentation and the content-based recommender. The results of the proposed system were 92.4% (top-1 accuracy), 90.1% (macro-F1), 123ms (latency), and 87.5% (robustness to prompt paraphrasing). These findings show that the lightweight LLMs, when used together with a structured knowledge retrieval, can provide credible and real-time cryptographic guidance.
CV SP Alumunium is a small and medium-sized aluminum casting industry that produces aluminum-based frying pans using a make-to-order system. Based on data from the period of March–June 2025, the company was unable to fulfill the entire demand of 323,336 units due to a capacity imbalance between work stations. The casting work station, finishing 1-Grinding, and finishing 2-Turning produced capacity requirement values that exceeded the capacity available. This research aims to determine the best improvement alternative to increase production capacity so that the demand for super frying pans and aluminum frying pans can be optimally fulfilled using the Theory of Constraints approach. Bottleneck identification was carried out by comparing the capacity available and capacity requirement at each work station based on standard time obtained through 30 stopwatch observations. Evaluation was conducted on three capacity improvement alternatives, namely the addition of labor and machines, overtime, and subcontracting. The research results show that the finishing 2-Turning work station is the most dominant constraint with the highest bottleneck frequency and the highest CR/CA ratio of 166.02% in April. The best recommended alternative is the combination of overtime and subcontracting with a total margin of Rp3,767,282,311. The combined alternative generated a higher margin compared to the overtime alternative of Rp3,638,566,758 and the subcontracting alternative of Rp3,070,417,980. The combined alternative was able to fulfill the entire demand during the bottleneck period without leaving a capacity deficit that would cause a loss of margin.
This paper presents the results of a study on the relationship between firing angle and range when firing 12.7mm and 14.5mm heavy rifles under the same weather conditions. Relevant information includes aiming angle, firing angle, bullet trajectory height, range, and the lethal kinetic energy of the projectile. Using specialized software to calculate the external launch algorithm, the authors calculated the range for the 12.7mm and 14.5mm rifles. The research results can be used as a reference in weapon design, optical sight design … especially in the case of heavy rifles.
Aviation safety improvement remains a continuous engineering challenge because modern aircraft are sensor-dense, software-intensive, and operationally interconnected, while health and operations data often remain fragmented across onboard, maintenance, dispatch, and ground systems. This fragmentation can delay weak-signal recognition and coordinated response. This paper proposes AI-SAFE FlightNet, a Safety-Aware, Intelligent, Federated, Explainable Flight Automation Network that integrates aircraft data intelligence, onboard edge AI, subsystem digital twins, phase-aware predictive analytics, evidence-grounded coordination, ground operations support, federated fleet learning, and governance by design. The framework supports pilots, engineers, dispatchers, and maintenance controllers without replacing certified human authority or certified aviation systems. A formal model combines sensor, flight-phase, environmental, and maintenance-history variables to estimate risk trajectories, prioritize alerts, and generate auditable response packages. The proposed evaluation uses public engine-degradation and trajectory datasets, synthetic subsystem telemetry, simulated maintenance records, and digital-twin experiments. Expected contributions are earlier warning, improved maintenance readiness, faster aircraft-to-ground coordination, privacy-preserving fleet learning, explainable recommendations, and measurable operational risk reduction. The objective is improved resilience and decision support, not zero risk or unrestricted autonomous control.
Background: Carbon accounting has moved from a specialist scientific discipline into a mainstream requirement of corporate governance, financial regulation, and ecosystem management. The literature measuring it, however, remains divided between two largely non-communicating traditions: administrative and disclosure-based accounting on one hand, and physically measured, sensor-based accounting on the other. Objective: This study systematically reviews the governance and standards literature on carbon accounting and synthesises reported measurement-accuracy metrics from primary studies spanning forest, soil, marine, agricultural, built-environment, and corporate domains, with the aim of characterising the degree of alignment — or misalignment — between these two traditions and their implications for energy-efficiency-linked policy. Methods: A systematic literature review with quantitative evidence synthesis was conducted following the PRISMA 2020 framework. Searches in Scopus and Web of Science used the Boolean string, restricted to English-language peer-reviewed sources from 2011 to June 2026. After deduplication and screening, 79 unique records were retained: 35 governance and standards sources synthesised narratively, and 55 quantitative primary studies (60 statistical records) pooled descriptively by ecosystem and sector domain. Formal inverse-variance-weighted meta-analysis was not performed due to outcome-measure heterogeneity; accuracy statistics (R2, RMSE, rRMSE) are reported as domain-level ranges. A five-criterion quality appraisal was applied to all quantitative records, and a sensitivity analysis examined the effect of excluding China-based studies on domain-level accuracy estimates. Results: Model accuracy varied sharply by domain. Engineered and semi-controlled systems — UAV-LiDAR forest biomass inversion (R2 = 0.93-0.95) and residential building carbon-intensity modelling (R2 = 0.91) — consistently outperformed open-ecosystem reconciliation models, where regional forest-carbon inversions fell as low as R2 = 0.147. Sensitivity analysis showed that excluding China-based forest studies reduced the domain upper bound from R2 = 0.95 to R2 = 0.558. No primary study in the marine and blue-carbon subset reported a comparable accuracy statistic, despite well-quantified carbon-stock densities (mangrove density = 937 t/ha; Liu et al., 2024). Narrative synthesis identified persistent reliance on estimate-based reporting, unresolved tension between attributional and consequential accounting logics, and a documented concentration of unverified exposure in Scope 3 emissions. Conclusions: Administrative carbon-accounting frameworks and physically measured, sensor-based accounting are developing along separate trajectories with materially different accuracy standards. Carbon claims tied to built-environment retrofits and grid-connected systems rest on firmer empirical ground than those derived from open-ecosystem reconciliation models. The marine and blue-carbon domain — where accuracy benchmarks are absent and credit issuance is expanding — represents the most consequential governance gap identified in this review. Regulatory extension of mandatory disclosure into ecosystem-based carbon domains should be sequenced against demonstrated measurement readiness.
Solar energy is a promising resource for development in Indonesia. Indonesia's solar energy potential is estimated to generate over 200 GW of electricity, given the efficiency of existing solar module technology. However, utilization in solar power plants remains below 100 MW. With solar energy potential spread evenly across Indonesia, the largest potential is found in West Kalimantan Province (20 GW), followed by South Sumatra in second place (17 GW), and East Kalimantan in third place (13 GW). Therefore, accelerating the transition from coal or fossil fuels to renewable energy sources, particularly solar energy, is highly feasible in various regions across Indonesia. One method for cooling solar panels that has been implemented is a water-cooling system. This is what underlies the author to conduct research on the effect of water cooling on the performance of 3 X 100 WP solar panels. The results showed that the maximum efficiency of the solar cell was achieved on Saturday before cooling was carried out, with a figure of 28.34%. Conversely, the lowest efficiency occurred on Tuesday after the cooling process, which was 90%. The implemented air-based cooling system was able to reduce the temperature of the solar panel. The highest average temperature measured without temperature was 54.6°C on Saturday, while the lowest average temperature after temperature was recorded was 46.88°C on Tuesday. The maximum average voltage recorded was 54.22 Volts without cooling, while the lowest average voltage was 42.33 Volts after cooling was applied. For the highest average current recorded was 3.93 Amperes before cooling, while after cooling, the average was only 3.25 Amperes. The maximum average power without cooling reached 175.02 Watts, while the lowest average power after cooling was 146.43 Watts.
