Search Results (2,082)

The Proportional-Integral-Derivative (PID) controller has been recorded as the most practical, reliable, and easy to implement feedback control system since its invention in the second decade of the twentieth century. Its adequacy has led to its utilization in approximately 90% of all industrial applications. In the last decade, Proportional-Integral-Retarded (PIR) controller has been introduced as a derivative-free controller to avoid the problem of noise amplification by the derivative term present in the PID controller. Instead of employing the error’s derivative in the control law, PIR controller employs an intentional retarded (exponential) term to provide damping for oscillations in systems with time-delay and enhance their stability. This paper develops, analyzes, and assesses the PIR controller for the heat diffusion system. Stability boundary loci are used to define the stability regions of the PIR controller’s parameters. Compared to a published study which applied PID and fuzzy logic to control the heat diffusion system, the developed PIR controller provides higher speed response by reducing the settling time by 77.082% and 74.992% as compared to PID and fuzzy-PID control systems in response to a step input of +18 °C. It also reduces the integral square error by 54.15% in comparison with the PID control system. Finally, the peak deviation in PIR control system response to an +18 °C disturbance is less than that of the PID control system by 0.167 °C. Full article

Fused filament fabrication (FFF) has become an important additive manufacturing technique for producing functional polymer-composite components. The tensile performance of carbon fiber-reinforced polyethylene terephthalate glycol (PETG/CF) fabricated by FFF depends on multiple printing parameters. This study presents an integrated experimental and predictive framework for investigating the effects of extrusion temperature, printing speed, layer height, infill pattern, and infill density on the tensile strength of PETG/CF containing 15 wt.% carbon fiber. A mixed-level Taguchi L36 orthogonal array was employed, comprising 36 experimental runs with three independently printed specimens per run, resulting in 108 ASTM D638 Type V specimens. Analysis of variance showed that the printing speed had the largest contribution to tensile strength (20.51%), followed by layer height (18.29%). The highest tensile strength of 33.225 MPa was obtained using grid infill, 60% infill density, 270 °C extrusion temperature with 40 mm/s printing speed, and 0.3 mm layer height. An artificial neural network (ANN) was developed for the tensile-strength prediction, achieving R = 0.9801, R² = 0.9569, and MAPE = 1.52% for the overall dataset. Scanning electron microscopy qualitatively revealed bead-interface defects, fiber pullout, and localized void-like features. The proposed framework provides a systematic approach for evaluating process-parameter effects and predicting tensile strength within the investigated PETG/CF parameter domain. Full article

Providing a healthy environment in schools, particularly during a global pandemic, is crucial to saving occupants’ lives and reducing infection rates. This paper proposes a novel desk local exhaust ventilation (DLEV) system that uses a local exhaust diffuser integrated into a classroom desk. The performance of the system in providing a healthy and comfortable indoor thermal environment and reducing the risk of COVID-19 infection was assessed numerically. The assessment combined indoor thermal comfort indices and the bioaerosol dispersion behavior of airborne particles. The study was completed in a typical classroom layout, in which the results show that the DLEV system meets thermal comfort requirements by maintaining the gradients of vertical temperature within an acceptable range. The DLEV system increases the air motion in the breathing zone while keeping it within the recommended range of <0.25 m/s. The PMV and PPD indices are within recommended comfort levels for all but three occupants. Most notably, the DLEV system substantially reduces the concentration of bioaerosols, especially around the occupants’ head. This system works by capturing and removing the virus-rich aerosols exhaled by infected subjects before they disperse in the classroom. This lowers the risk of infection among healthy subjects. These findings confirm the effectiveness of the DLEV system in enhancing both thermal comfort and indoor air quality, making it suitable for environments where specific goals regarding occupants’ health and thermal management are required, such as in a classroom of healthy and infected subjects. Full article

Boundary-layer atmospheric stability, wind-shear variability, and thermodynamic forcing are all important factors for the initiation, intensification, and transport of dust storms. But there is limited knowledge of the quantitative evaluation of bulk-layer atmospheric stability and the relation between wind-driven dust dynamics and atmospheric stability in arid and semi-arid regions. In this research, a hybrid AI–meteorology framework, HyMet-Fusion, is presented that combines visual information derived from satellite observations with physics-based indicators of atmospheric stability to evaluate atmospheric stability during dust storm events over Iraq. The proposed framework is based on the use of deep features extracted from the satellite imagery through a frozen EfficientNetB0 backbone, combined with indicators derived from the ERA5 pressure level data for the atmosphere, such as the Bulk Richardson Number (Bulk Ri), the Wind Shear (WS) and the Dry Air Index (DAI). The two branches were merged using a late fusion (0.75 physics/0.25 image) and each hour was classified into three atmospheric stability conditions: Relatively Stable, Moderately Unstable and Unstable. The overall hourly accuracy using a Leave-One-Event-Out (LOEO) cross-validation scheme, where each dust event was used for independent testing and no dust event was used for training, was 72.4%, with 81.2% accuracy for the dominant stability state and 92.2% correct assessment of the unstable condition time for the severe dust events. Inaccuracies were mainly (66%) in the conservative direction (more instability). Unstable atmospheric conditions were also found to be associated with all severe dust storms and coincided with higher wind shear, lower Bulk Ri values and higher thermodynamic variability. Moderate and light dust events were primarily associated with transitional and relatively stable atmospheric conditions, and differed between the various regions, primarily in Kirkuk and Nasiriyah. Correlation analysis showed that wind shear had the highest correlation with atmospheric instability (r = 0.92), followed by DAI (r = 0.90) and Bulk Ri (r = −0.75). In addition, the wind shear also increased significantly from light to severe dust events at all stations investigated, showing that wind shear is a critical factor for turbulent mixing, vertical momentum exchange and dust uplift processes. The results suggest wind shear is the leading dynamics mechanism for bulk-layer instability in Iraqi dust storms. The findings highlight the complementary benefit of using physics-based atmospheric indicators embedded with deep learning satellite image analysis. The HyMet-Fusion system can be used as a transferable method for observing wind-driven instability of the atmosphere and related dust hazards, which could be employed in boundary-layer meteorology, air-quality forecasting, aviation safety and environmental risk assessment in arid and semi-arid areas. Full article

Background/Objectives: The identification of novel therapeutic targets is imperative to overcome the limitations of current anti-toxoplasmosis treatments. This study aims to investigate the potential of repurposing Pentamidine as an inhibitor against the HMG-Box domain-containing protein (TGARI_247020) in Toxoplasma gondii, a protein hypothesized to be essential for the parasite’s genomic stability. Methods: The study utilized a multi-layered in silico approach. First, the biological essentiality of the target gene was validated by analyzing CRISPR-Cas9-based phenomics data from the ToxoDB database. Second, the structural properties of the HMG-Box domain (ID: A0A139YAG1) were characterized using AlphaFold models. Finally, molecular docking simulations were conducted via the SwissDock server to evaluate the binding affinity and interaction dynamics between Pentamidine and the target protein. Results: Genomic analysis revealed a phenotype score of −1.2, confirming the indispensable role of the TGARI_247020 gene for parasite viability. Structural analysis identified a well-defined binding pocket within the HMG-Box domain. Molecular docking results demonstrated a high binding affinity for Pentamidine, yielding an optimal AC Score of −44.93, supported by a FullFitness value of −1134.13 kcal/mol. The interaction was primarily stabilized by a network of hydrogen bonds and favorable steric fits within the catalytic groove of the protein. Toxoplasmosis is widely classified as a neglected parasitic disease, posing persistent public health challenges and veterinary economic concerns globally. Traditional de novo drug discovery is often hindered by high costs and prolonged timelines, making drug repositioning (repurposing) a highly attractive and cost-effective strategy to identify novel therapeutics from established clinical agents over the past decade. Computer-Aided Drug Design (CADD), particularly Structure-Based Drug Design (SBDD), has provided a robust molecular framework to prioritize candidate drugs against essential parasitic targets. In apicomplexan parasites, high-mobility group box (HMGB) proteins, such as TgHMGB1a, serve as critical nuclear architectural factors that bind to distorted DNA structures and modulate genomic transcription, disrupting these essential DNA–protein interactions, representing a promising, yet under-explored, therapeutic target. The hypothesis for evaluating Pentamidine—an aromatic dicationic diamidine traditionally used in African trypanosomiasis—lies in its established ability to interact with nucleic acids and block critical molecular targets in other protozoa, providing a logical biochemical rationale for testing its potential as a structural inhibitor of the T. gondii HMG-box protein. Conclusions: Our findings provide preliminary in silico evidence that Pentamidine targets the HMG-Box protein, suggesting its potential for drug repurposing. However, due to established clinical limitations of Pentamidine (such as nephrotoxicity and poor blood–brain barrier permeability), further experimental in vitro and in vivo validation is strictly required to evaluate its therapeutic efficacy. Full article

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform. Full article

Thin film printed electronics, such as flexible circuits and sensor sheets, require non-contact inspection to detect defects and edge degradation. The present paper presents a laser-enhanced machine vision framework for detecting and analyzing the edges of printed conductive tracks using Canny edge detection and Otsu thresholding. Using a coherent laser source, Otsu’s method enhances contrast at the ink–substrate interface, enabling robust segmentation of edge lines. Canny operator is applied to thresholded images to extract precise edge profiles. Multiple printed tracks are analyzed to calculate four lateral edge roughness values (Ra). As a result, the values are 40.43 µm, 40.09 µm, 50.26 µm and 40.94 µm. The results show that the suggested method can detect and qualify variations in edge parameters. Printed electronics are produced using an inline inspection and quality control system based on non-contact, high-resolution, and scalable technologies. Full article

In the digital era, the rapid proliferation of fake news poses critical challenges to information credibility and public trust, particularly in Arabic news ecosystems where linguistic complexity and limited annotated resources exacerbate detection difficulties. This study proposes a framework for Arabic fake news detection based on a weighted-distance ensemble learning method (WDELM). The WDELM framework combines posterior-probability estimates from six heterogeneous base classifiers: extreme gradient boosting (XGBoost), LightGBM (LGBM), random forest (RF), adaptive boosting (AdaBoost), gradient boosting (GB), and logistic regression (LR). The classifier outputs are integrated through a distance-aware adaptive weighting strategy based on cosine distance in the prediction space. Unlike conventional ensemble techniques, the proposed framework employs normalised distance-aware adaptive weighting to adapt classifier contributions while preserving the probabilistic interpretation of the final ensemble output. Experiments were conducted on an Arabic fake news dataset comprising 2538 manually annotated instances. The model was evaluated using multiple performance metrics, including precision, recall, F1-score, Cohen’s kappa, ROC-AUC, and accuracy, together with explainability analyses. Using stratified ten-fold cross-validation, the proposed WDELM achieved a mean accuracy of $92.120\pm 1.097\%$ and a mean ROC-AUC of $97.125\pm 0.686\%$. Analysis of the concatenated predictions generated across the ten outer-validation folds yielded an F1-score of 91.357% for the Fake class, an F1-score of 92.759% for the Real class, and a pooled macro-F1 score of 92.058%, indicating balanced classification performance across both classes. The results indicate that the proposed weighted-distance ensemble strategy provides improved empirical performance within the evaluated dataset and offers a transparent mechanism for combining heterogeneous classifiers. The framework is further assessed through statistical validation, error analysis, and explainability analysis, supporting its potential use as an auxiliary decision-support tool for Arabic fake news screening rather than as a fully automated replacement for professional fact-checking. Full article

Electric vehicles are emerging as a sustainable alternative to conventional transportation. However, route planning in Internet of Vehicles environments remains challenging because conventional routing algorithms based on travel distance or time do not adequately consider electric vehicle-specific constraints. Existing routing strategies often overlook the combined effects of battery energy, traffic congestion, and charging requirements, resulting in inefficient routing decisions. This paper proposes an adaptive Energy, Congestion, and Charging-Aware Routing (ECCAR) protocol that uses energy-feasibility verification, cost-based charging-station selection, and route re-optimization for electric vehicles in Internet of Vehicles environments. ECCAR integrates residual battery energy, traffic congestion, travel time, charging station availability, and charging delay into a unified routing decision framework. It first evaluates whether the remaining battery energy is sufficient to reach the destination. Otherwise, it identifies all reachable charging stations and selects the one that minimizes the routing cost rather than the nearest station. After charging, the route is recalculated using traffic and charging information obtained through V2V and V2I communications. Simulation results demonstrate that ECCAR reduces total energy consumption by up to 19.4%, travel time by up to 25.3%, and charging waiting time by up to 39.1% compared with existing routing schemes. These results demonstrate the benefits of integrating energy, traffic, and charging information for reliable electric vehicle routing in dynamic Internet of Vehicles environments. Full article

Background: This study evaluates transdermal delivery of tamoxifen (TXN) as an alternative to the oral route of administration in treating breast cancer, which is the leading cause of cancer-related death in women globally. Oral TXN, a Class II drug, is associated with first-pass metabolism and serious side effects, including secondary cancers. Objectives: To enhance transdermal delivery, lipid-based transethosomes (TRS) were formulated using three different 2⁴ factorial designs with various non-ionic surfactants, including Tween 20^®, Span 20^®, and Span 80^®. Methods: Optimized TRS formulations were incorporated into HPMC-based gels and characterized for morphology, drug content, pH, viscosity, spreadability, ex vivo skin penetration, and deposition. Additionally, cytotoxicity and stability were assessed. Results: All TXN-TRS gels were suitable for transdermal use; however, Span 20^®-based TRS gel demonstrated the highest skin penetration (40.3 ± 1.5 µg/cm²), representing a 127-fold enhancement rate compared with the non-ethosomal TXN gel. In line with the enhanced penetration profile, cellular studies on MCF-7 cells showed concentration-dependent cytotoxicity, reaching 91.24 ± 1.01% inhibition at 2% w/w after 72 h, with an IC₅₀ value of 0.85 ± 0.02% w/w. Stability testing showed all formulations were more stable under refrigeration than at dry room temperature storage, supporting their potential as preclinical transdermal tamoxifen delivery platforms. Conclusions: Span 20^®-based TXN transethosomal gel markedly enhanced skin penetration while maintaining potent cytotoxic activity, supporting its further preclinical evaluation as a promising transdermal alternative to oral tamoxifen. Full article

The use of sugarcane molasses (SCM) as a sustainable stabilizing agent for geomaterials, particularly granular subbase soils, has recently attracted growing attention as an alternative to traditional stabilization methods employing cement, lime, or bitumen, which are often associated with high costs and environmental concerns. This study presents a mechanistic–experimental evaluation of SCM for stabilizing granular subbase materials at dosages of 2.5%, 5%, 7.5%, and 10% by weight of dry granular material. A comprehensive testing program was conducted, including compaction characteristics, California Bearing Ratio (CBR), resilient modulus (${\mathrm{M}}_{\mathrm{r}}$), permanent deformation under repeated loading, optical microscopy, and FTIR spectroscopy. In addition, multilayer elastic analysis using KENLAYER was performed to assess pavement structural performance in terms of critical strains and service life. The results showed that SCM significantly improved subbase performance within an optimum dosage range. The mixture containing 5% sugarcane molasses (5SCM) exhibited the highest overall performance, increasing CBR from approximately 26% to 34% and reducing accumulated permanent strain by approximately 45% compared with the control mixture. Optical microscopy and FTIR analyses supported the proposed stabilization mechanism, indicating improved particle contact at moderate SCM contents, whereas excessive SCM contents adversely affected performance due to lubrication and excessive particle-coating effects. Mechanistic analysis demonstrated that 5SCM improved pavement durability, increasing allowable load repetitions from 9.54 × 10⁵ to 1.14 × 10⁶ and extending pavement service life by approximately 20%. A durability–cost assessment further identified 5SCM as the most efficient dosage from both engineering and economic perspectives. Water immersion assessment indicated that SCM stabilization is suitable for pavement structures with effective drainage, whereas its application in continuously saturated or flood-prone environments is not recommended. Overall, SCM demonstrates strong potential as an environmentally sustainable stabilizer for granular subbase materials, with an optimum dosage of approximately 5% for enhancing both material performance and pavement durability. Full article

High penetration of distributed energy resources (DERs) is reshaping radial distribution networks, yet weather-dependent generation, variable demand, and feeder-level surplus–deficit imbalance can compromise voltage quality and coordinated operation. Existing planning approaches often optimize feeders independently and therefore provide limited insight into how local DER portfolios should support inter-feeder energy exchange under time-varying conditions. This study proposes a weather-driven two-stage Geese V-Formation Algorithm (GVFA) framework for planning DER integration and feeder coordination in a practical five-feeder 11 kV Tajeeyaat/North Baghdad system, with complementary validation on a five-instance IEEE 33-bus benchmark cluster. Stage 1 optimizes the siting and sizing of photovoltaic units, wind turbines, battery energy storage systems, capacitor banks, and feeder-specific auxiliary resources using backward/forward-sweep load flow. Stage 2 uses hourly surplus–deficit profiles to select tie-switch configurations and exchange capacities for feeder-to-feeder energy sharing. The framework is evaluated through convergence analysis, optimizer comparison, N-1 contingencies, and seasonal load-growth tests. For the practical system, 24 h aggregate losses decreased from 11,258.1571 to 3367.8481 kWh-eq, corresponding to a 70.0853% reduction. The minimum-voltage range improved from 0.9497–0.9898 to 0.9897–0.9997 p.u., while grid-import reduction reached 94.0270%. For the IEEE-33 cluster, 24 h aggregate losses decreased from 31,746.4712 to 6381.1403 kWh-eq, corresponding to a 79.8997% reduction. The minimum-voltage range improved from 0.8268–0.8632 to 0.9465–0.9683 p.u., while grid-import reduction reached 84.4596%. The framework provides a planning-oriented, sensor-ready decision-support basis for DER siting, voltage-support assessment, grid-import reduction, and candidate inter-feeder exchange corridors. Full article

Synthetic azo dyes are the largest class of industrial colorants having widespread application in textile, food, cosmetic, and pharmaceutical industries. Moreover, they are persistent and toxic, threatening aquatic environments as well as human health. Disperse red 1 (DR1), a mono-azo dye belonging to the disperse dye group and widely used in polyester dyeing, cosmetics, and other applications, is of particular concern due to its mutagenic potential and resistance to conventional treatment processes. The present study investigated an integrated DR1 removal strategy using thermotolerant Bacillus licheniformis Tol1 as well as its EPS-stabilized biogenic silver nanoparticles (AgNPs). With a maximum tolerable concentration of 0.5 g L⁻¹, B. licheniformis Tol1 showed a maximum decolorization of 70.86% (0.2 g L⁻¹, 55 °C). However, response surface methodology (RSM) based on the Box–Behnken design showed an actual decolorization efficiency of 73.13%. The artificial neural network (ANN) model predicted an accuracy of R² = 0.9933, confirming the robustness and reliability of the experimental findings. To enhance dye removal efficiency, Tol1 EPS-stabilized AgNPs were synthesized via a green method and characterized using UV-Vis, SEM-EDAX, TEM, AFM, FTIR, DLS and zeta potential. Characterization of AgNP confirmed the formation of spherical stable AgNPs with an average size of 19.99 ± 0.38 nm, indicating polydisperse colloids nature with moderate electrostatic stability. A sunlight/H₂O₂-assisted process (photocatalytic experiments) demonstrated DR1 decolorization (80.72 ± 1.72% within 5 h under sunlight) following pseudo-first-order kinetics (k = 0.271 h⁻¹). Furthermore, FTIR analysis confirmed the degradation of the chemical structure of DR1 through the disappearance of the characteristic azo (–N=N–) bond, indicating cleavage of the dye molecule. Overall, the present study provides a dual biological–nanotechnological approach for DR1 decolorization using single bacteria as well as its polysaccharide-stabilized AgNP, a sustainable eco-friendly future approach. However, further studies on complete mineralization, transformation products, toxicity evaluation, detailed catalyst reusability, and silver (Ag) leaching are needed to facilitate the practical implementation for wastewater treatment. Full article

Drug delivery systems based on nanoparticles have emerged as promising approaches for overcoming the blood–brain barrier (BBB), a major obstacle to treating disorders of the central nervous system (CNS). There are several reasons why conventional therapies fail, including poor brain penetration, rapid drug clearance, and nonspecific distribution. This review critically evaluates recent advances in nanoparticle-mediated BBB targeting, focusing particularly on in vivo findings. As part of this review, lipid-based, polymeric, metallic, dendrimeric, exosome-inspired, and magnetic nanoparticles are discussed in conjunction with their transport mechanisms. The review compares their therapeutic efficacy, biodistribution, targeting ability, and safety across a variety of neurological conditions. Additionally, emerging technologies are discussed, including biomimetic nanoparticles, stimuli-responsive systems, artificial intelligence, and personalized nanomedicine. Additionally, this review critically discusses the major barriers to clinical translation, including biosafety, manufacturing, and regulatory challenges. As a result, this review provides an updated perspective on current progress and future prospects for developing effective brain-targeted nanomedicine. Full article

The lack of freshwater in the world requires a paradigm shift from linear water consumption to resilient and low-energy desalination technologies. Although reverse osmosis (RO) is the standard in the industry, its usefulness is essentially constrained by concentration polarization (CP) and non-useful hydraulic pressure losses. This paper applies a high-fidelity computational model in ANSYS Fluent 2022 R1 to conduct a comparative parametric evaluation of hexagonal and sinusoidal feed-spacer geometries relative to a baseline grid configuration. The solute concentration gradients at the fluid–membrane interface were solved using a 3D species transport model, which was optimized using one-micron near-wall inflation layers. The hexagonal configuration produced the lowest maximum membrane-surface salt mass fraction, decreasing it from 0.1127 kg/kg for the baseline grid to 0.0429 kg/kg, corresponding to a 61.9% reduction. Although the hexagonal design required an inlet pressure of 205.7 Pa, it produced a more favorable normalized mass-transfer–friction trade-off than the sinusoidal configuration (447.8 Pa), with a System Performance Index (η) of 2.53. These results demonstrate comparative micro-scale improvements in concentration polarization control and hydraulic performance under the simulated conditions. Experimental testing and system-level modeling are required before conclusions can be drawn regarding full-module energy consumption, photovoltaic integration, long-term fouling behavior, or economic feasibility. This study is consistent with the emerging Concepts and design for sustainability, whereby a circular and energy-efficient water economy is facilitated through an innovative mechanical design. Full article