The production of conventional concrete has a significant impact on the environment, primarily due to the cement (C) component. To mitigate this impact, binders with reduced environmental footprints were used in concrete mixtures as a partial replacement for C, including powders of industrial byproducts. Waste marble powder (WMP) is one such alternative; however, its incorporation causes complicated interactions with cementitious materials, making the compressive strength (CS) prediction difficult. This study developed and evaluated four predictive models (multilinear regression (MLR), full quadratic regression (FQ), M5P tree, and artificial neural network (ANN)) using a dataset of 306 WMP-blended concrete mixes from 16 published studies. Statistical evaluation parameters (mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), scatter index (SI), and objective function (OBJ)) were used to assess the performance of models. The ANN model achieved superior performance on the testing subset (R2=0.96, RMSE=2.95 MPa), while 10-fold cross-validation confirmed its predictive robustness, yielding mean and RMSE values of 0.93 0.04 and 3.54 1.17 MPa, respectively. The SHAP (SHapley Additive exPlanations) method of sensitivity analysis illustrated that C and curing time (t) were the main factors affecting CS, while WMP had a negative context-dependent effect at higher levels of replacement. The results indicated that ANN models can successfully capture the nonlinear behavior of WMP-modified concrete and can be used as a practical tool for optimizing concrete mix designs for sustainability.
β-Lactam antibiotics constitute one of the most extensively prescribed classes of antimicrobial agents in human and veterinary medicine. However, their widespread use has raised growing concerns regarding antibiotic resistance, drug safety, and the presence of residual antibiotics in pharmaceutical products, biological fluids, foodstuffs, and environmental samples. As a result, the development of sensitive, selective, and rapid analytical methods for the determination of β-lactam antibiotics has become critically important. Among the available analytical techniques, fluorescence-based methods have gained increasing attention due to their high sensitivity, operational simplicity, low cost, and suitability for trace-level detection. This review presents a comprehensive overview of fluorescence-based approaches for the determination of representative β-lactam antibiotics, with particular emphasis on amoxicillin, cefixime, and cephalexin. Various fluorometric strategies including direct fluorescence, derivatization-based assays, fluorescence quenching and enhancement mechanisms, ratiometric sensing, and nanomaterial-assisted platforms such as quantum dots, carbon dots, metal–organic frameworks, and molecularly imprinted polymers are critically discussed. The analytical performance of reported methods is compared in terms of linear dynamic range, limits of detection, selectivity, and applicability to real samples. Overall, this review highlights the significant potential of fluorescence-based techniques for reliable β-lactam antibiotic analysis in pharmaceutical quality control, food safety monitoring, and environmental assessment.
Introduction and Objectives: Malaria remains a life-threatening medical emergency, particularly in Plasmodium falciparum infections, with 282 million cases and approximately 610,000 deaths recorded globally in 2024. Accurate laboratory diagnosis is critical, yet conventional methods including microscopy and rapid diagnostic tests are constrained by operator dependency, low sensitivity, and emerging HRP2/HRP3 gene deletions. This scoping review maps current and emerging malaria diagnostic technologies, evaluates their performance, and identifies implementation challenges and knowledge gaps. Methodology: Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, PubMed, Scopus, and Web of Science were searched for publications from 2000 to 2026. Eligible studies involved human malaria diagnosis, reported at least one diagnostic accuracy measure, and compared two or more methods. Findings were narratively synthesized to map diagnostic technologies, reported performance characteristics, implementation challenges, and future directions. Results: Twenty-seven studies were included. Microscopy performance was highly operator dependent with reduced sensitivity especially in low transmission settings. RDTs were rapid and affordable but showed variable diagnostic accuracies, particularly for low density infections. PCR was consistently reported among the most sensitive diagnostic methods across the included studies and frequently served as the reference standard in diagnostic evaluations. LAMP offered a field-feasible molecular alternative with simpler instrumentation. Emerging technologies including CRISPR-based assays, AI-assisted microscopy, and biosensor platforms demonstrated encouraging diagnostic performance with point-of-care potential. Conclusions: A clear transition toward sensitive, portable, and digitally integrated malaria diagnostics is evident. While PCR sets the sensitivity benchmark, operational constraints limit its field deployment. Scalable innovations such as LAMP, NGS, CRISPR assays, and AI-assisted microscopy are better positioned to support decentralized malaria diagnosis and elimination efforts. Large-scale field validation and integration into national programs remain critical priorities for emerging technologies.
Chronic Kidney Disease (CKD) affects medication dose safety due to altered pharmacokinetics. Current clinical decision support systems lack stage-specific personalization and explainability. Therefore, this study aimed to identify the appropriate medications and doses for chronic kidney disease using machine learning algorithms by comparing the methods. This study is divided into two phases. Phase 1 involves a literature review that compares four classification techniques for predicting drug prescriptions for individuals with kidney disease. After a screening and evidence evaluation process, 12 papers were selected for analysis. From the 12 studies, the types of analysis methods obtained were Decision Tree, K-Nearest Neighbour (kNN), Naive Bayes, and deep learning. Phase 2 was to test each method with data in the hospital to identify the accuracy. Novel framework development with explainable AI (SHAP), multi-stage classification (CKD Stages 1-5), and adverse event prediction module. After analysing the four methods, we concluded that they were suitable for testing using hospital data to assess their level of accuracy. However, a study comparing four classification algorithms showed that Decision Tree achieved 99% accuracy, kNN 75%, Naive Bayes 95%, and Deep Learning 96%. Meanwhile, based on AUC, Decision Tree achieved 0.99, kNN achieved 93%, Naive Bayes achieved 97%, and Deep Learning achieved 98%. Decision Tree achieved 99% accuracy (AUC=0.99). Novelty contributions: (1) SHAP-based feature importance identified cholesterol level, blood pressure, and Na-to-potassium ratio as top predictors; (2) Stage-specific recommendations reduced inappropriate prescriptions by 34%; (3) ADE prediction module achieved AUC=0.87 for nephrotoxicity risk. Our explainable, stage-aware framework provides clinically actionable drug recommendations with integrated safety monitoring, addressing critical gaps in CKD pharmacotherapy
Modern lifestyles reduce sunlight exposure and increase artificial light at night, potentially promoting vitamin D deficiency (VDD) and melatonin (MEL) suppression. The two hormones regulate immunity, erythropoiesis, and platelet (PLT) indices, but their combined hematological effects are not well characterized. We investigated the independent and combined effects of VDD and MEL deficiency on leukocyte profiles, erythrocyte indices, and PLT parameters in albino rats using two 12-week models (n = 8/group): continuous light (CL)-induced MEL suppression (Control, CL, VDD, and CL + VDD) and pinealectomy (Px)-induced MEL deficiency (Sham. Px, VDD, Px + VDD). Complete blood counts (CBCs) and inflammation-related ratios were assessed at study end. In the CL model, CL increased total white blood cells (WBCs) (p = 0.0135), whereas VDD increased the neutrophil to lymphocyte ratio (NLR) and Systemic Immune-Inflammation Index (SII) (p = 0.0021 and p = 0.0034, respectively). Notably, CL + VDD decreased SII (p < 0.0001), indicating non-additive interactions. In the Px model, Px increased granulocyte (GRA), NLR, and SII (p = 0.0136, p < 0.01, and p = 0.0011, respectively), with Px + VDD showing the highest elevations in WBC (p = 0.0482, SII p < 0.0001). Erythrocyte indices showed selective changes: The hematocrit (HCT) decreased after Px and Px + VDD compared to Sham (p < 0.0002 and p = 0.0429, respectively), and the mean corpuscular hemoglobin concentration (MCHC) decreased with VDD (p < 0.0001), consistent with partially distinct effects on red cell volume/turnover versus hemoglobinization. Overall, VDD favored a pro-inflammatory and hypochromic profile, while MEL deficiency, especially via Px, enhanced innate immune activation, together supporting early adaptive hematologic remodeling rather than overt pathology.
This study employs density functional theory (DFT) to investigate the fluorite-derived tetragonal hydride MgTiH4, highlighting its multifunctionality for energy-related applications. Structural optimization reveals lattice constants of a = b = 3.15 Å and c = 4.70 Å, with a unit cell volume of 46.59 Å3 and a high bulk modulus of 114.21 GPa, indicating mechanical robustness. Compared to its MgTiH4 and CaTiH4 analogs, SrTiH4 exhibits the highest phonon frequency (1355.19 cm-1). Superior thermodynamic stability (formation enthalpy: –3.35 eV) is seen in MgTiH4. Electronic band structure analysis reveals a narrow indirect band gap of 0.089 eV, suggesting semiconducting behavior with potential for low-light optoelectronics. Photocatalytic assessment indicates that the conduction band minimum of MgTiH4 lies at −1.837 V vs NHE and the valence band maximum at −1.748 V vs NHE. While the highly negative CBM provides a strong driving force for hydrogen evolution, the VBM is far below the water oxidation potential (+1.23 V), precluding spontaneous overall water splitting. The extremely small band gap further limits visible-light activation, suggesting these compounds are more suitable as hydrogen evolution-active materials rather than full water-splitting photocatalysts. Optical studies highlight strong IR absorption (up to 2.92 × 105 cm-1), high refractive index (7.81), and a pronounced dielectric constant (ε1 = 59.25), indicating excellent light-matter interaction. Gravimetric hydrogen storage capacity reaches 5.291 wt%, approaching DOE targets, while post-desorption studies confirm good structural reversibility and reduced bulk modulus (49.24 GPa), ensuring feasible hydrogen cycling. In conclusion, MgTiH4 outperforms CaTiH4 and SrTiH4 in multiple metrics, establishing it as a multifunctional candidate for future energy and photonic technologies.
Borate-based glasses modified with alkali oxides are of considerable interest due to their tunable structural and optical properties, which are important for optical and energy storage applications. In this work, glasses with a novel nominal composition of xLi2O-(75-x) B2O3—20Bi2O3—5V2O5, in which (x ¼ 5, 10, 15, 20, 25 mol %) were produced using the traditional melt-quenching technique to investigate the effect of substituting the glass former B2O3 with the modifier Li2O. The structural and optical properties were examined by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and UV—visible spectroscopy. The XRD analysis confirms the amorphous nature of the samples produced. The density increased from 2.955 to 3.499 g/cm3 with increasing Li2O content, while the molar volume decreased, indicating compaction of the borate network due to lithium ions' network-modifying role. Optical investigations demonstrated a shift of the absorption edge towards shorter wavelengths with increasing Li2O content. The optical band gap of lithium-modified borate glasses can be tuned by adjusting the lithium content. The indirect optical band gap values range from 2.13 to 2.52 eV, with a maximum at x ¼ 15 mol %, and the Urbach energy analysis indicated that this mixture exhibited a more homogeneous structure. FTIR analysis confirmed the presence of BO3 and BO4 structural units in the glass, and the addition of Li2O enhanced the asymmetric stretching vibrations of BO3. This study found that Li2O concentration significantly influences the structure, density, and optical properties of borate glasses, with x ¼ 15 mol % regarded as the best composition. Further investigations employing electrochemical methods, such as cyclic voltammetry (CV) and impedance spectroscopy, are recommended to evaluate their practical application and productivity.
Para-chlorophenols (PCPs) are toxic and persistent, chlorinated aromatics that are prevalent in industrial and petroleum-contaminated wastewater, which has serious environmental and health impacts because of their bioaccumulative and recalcitrant properties. The study aims to design and test chemically activated snail shell biochars (SSB) as an effective PCP remedial material, combining the molecular understanding of the material with the predictions of adsorption efficacy. To study both electronic, structural, and dynamic interactions of PCP with a series of biochar analogs (KOH@SSB, H2SO4@SSB, AMS@SSB, Mg@SSB, Zn@SSB), both Density Functional Theory (DFT) calculations and Monte Carlo (MC) simulations were used. Findings indicate that chemisorptive behavior is observed on all biochar surfaces, with Mg@SSB and Zn@SSB having the highest adsorption (Eads = -1.96 and -1.17 eV, respectively). The adsorption is confirmed to be stabilized by highly polarized closed-shell interactions, which are mostly non-covalent, plus partial covalent character in systems with metals, but these are confirmed through non-covalent interaction and QTAIM analyses. Collectively, this study provides a mechanistic understanding of PCP adsorption at the molecular level and demonstrates that chemically and metal-activated snail shell biochars offer sustainable, high-affinity platforms for the removal of chlorophenols from contaminated aqueous environments, supporting circular economy principles and guiding experimental adsorbent design.
In the proposed work, a novel architecture of a 10-channel hybrid time-wavelength division multiplexing/passive optical network (TWDM/PON) system is investigated for different data rates: low data rate (2.5 Gbps) and high data rate (10 Gbps) using different two-dimensional (2D) material-based photodetectors with a metal-2D material-metal (M-2DM-M) device structure. Integration with Silicon (Si) semiconductor makes the detectors highly compatible with complementary-metal-oxide-semiconductor (CMOS) technology. The performance of the system is measured in terms of eye diagrams obtained using OptSim Software. Graphene, transition metal dichalcogenides (TMDCs), and black phosphorus (BP) are the three materials used to enhance performance metrics of high-speed optical networks. Results reveal that, among the three materials, BP provides the best performance for a low-data-rate optical network at 2.5 Gbps with a high Quality factor (Q-factor) of 32 dB and low Bit error rate (BER) of 1 x 10-40. Whereas the worst results with a low Q-factor of 13.24 dB and high BER of 2.26 x 10-6 are attained by metal-TMDC-metal (M-TMDC-M) over the same length of fiber. Metal-Graphene-metal (M-G-M) gives intermediate results. For further analysis data rate is varied, and system is simulated for a high data rate at 10 Gbps over 10 km length of fiber. It is found that, for a high data rate, metal-graphene-metal (M-G-M) gives best result among others, with a maximum Q-factor of 28 dB and low Bit error rate (BER) of 1 x 10-38 is achieved using metal-graphene-metal (M-G-M) over 10 km length of fiber. Whereas the worst results with a low Q-factor of 12 dB and high BER of 5.46 x 10-5 are attained by metal-TMDC-metal (M-TMDC-M) over the same length of fiber. A metal-black phosphorus-metal (M-BP-M) photodetector yields intermediate results in a high-data-rate optical network. Jitter value and eye closure are other performance metrics obtained from eye diagram. It shows that the performance of system for low data rate and high data rate can be enhanced by proper selection of material. M-G-M shows best performance for high data rate, and M-BP-M shows best performance for low data rate. While M-TMDC-M poorly performs in both cases, because these materials show weak absorption in telecom operating wavelengths. Hence, TMDC is not suitable for telecom wavelengths.
This study evaluates the petroleum generating potential of the Middle-Late Jurassic Sargelu and Naokelekan Formations across distinct tectonic zones in the Kurdistan Region, Iraq. Source rock quality is assessed through geochemical analyses of organic matter (OM) quantity, type, thermal maturity, and key factors influencing hydro-carbon generation. Rock-Eval pyrolysis was performed on outcrop samples from five sections located within the Imbricated and High Folded Zones of the Zagros Fold-Thrust Belt. Results demonstrate significant variability in total organic carbon (TOC) content, hydro-carbon generation potential (as reflected by S1, S2, and genetic potential values.), and kerogen types across these zones. The Sargelu and Naokelekan formations exhibit excellent source rock potential in the Banik and Gara sections, with TOC values up to 20 wt% and S2 values exceeding 80 g HC/gr rock. Kerogen types in these areas are predominantly oil-prone Type II and mixed Type II/III, with thermal maturity within the oil generation window (Tmax ~439°C in average). Conversely, samples from the Rania, Sargelu, and Chnaran sections demonstrate poor hydrocarbon potential, characterized by lower values TOC (
Cardiac hypertrophy alters vascular function and promotes myocardial damage via oxidative stress and inflammation, with calcium channels and the PI3K/AKT/eNOS signalling pathway being pivotal in vascular reactivity. Nonetheless, their precise role in acetylcholine (ACh)-mediated vascular responses during hypertrophy remains little elucidated. The study aims to investigate calcium channel functions and endothelial PI3K/AKT/eNOS signalling in modulating vascular reactivity in a rat model of cardiac hypertrophy. Aortic rings from control and ISO-treated rats were mounted in organ baths. Cumulative ACh dose–response curves (DRC) were obtained at baseline and after preincubation with Nω-nitro-L-arginine methyl ester (L-NAME), a nitric oxide synthase (NOS) blocker, PI3K inhibitor, AKT inhibitor, Mas receptor antagonist (Mas blocker), and amlodipine (L-type Ca²⁺ blocker). Potency (pD₂), maximal relaxation (Emax), and area under the curve (AUC) were compared. In control vessels, Mas receptor blockade, AKT inhibition, and L-NAME each reduced ACh-induced relaxation—confirming that PI3K/AKT/eNOS-dependent nitric oxide (NO) signalling drives vasodilation. Amlodipine slightly lowered relaxation parameters, suggesting that Ca²⁺ entry supports maximal NO production. After ISO treatment, these effects were reversed: Mas blockade and amlodipine enhanced relaxation, while AKT inhibition increased pD₂ and AUC without reducing Emax. L-NAME caused weaker inhibition, reflecting impaired NO signalling. PI3K inhibition had no effect in either condition, and overall AUC changes were minimal. Isoproterenol disrupts the balance between Ca²-dependent and phosphorylation-dependent activation of eNOS, reducing NO bioavailability and rewiring Mas/AKT signalling. Reducing pathological Ca²⁺ influx and dampening AKT activity reverse endothelial responsiveness; however, Mas signalling shifts from endothelial vasoprotective in physiology to maladaptive in hypertrophy. Consequently, the L-type Ca²⁺ channels, AKT, and the Mas axis appear to be reasonable targets for rebalancing endothelial sensitivity in hypertrophic conditions.
Nanostructured metal sulphides are gaining attention as potential materials for resistive chemical sensors due to their high electrical conductivity and chemical stability compared to metal oxides. Among these, tungsten disulfide (WS₂), a transition metal dichalcogenide (TMD), stands out because of its distinctive electrical, optical, and mechanical properties. In this study, WS₂ thin films were synthesized on glass substrates using the spray pyrolysis technique. Structural and electrical characterizations revealed a highly porous, granular morphology with nanoparticle agglomerations, as observed through FESEM. XRD analysis confirmed a well-crystallized hexagonal WS₂ phase with an average crystallite size of 35.81 nm. The films exhibited a resistivity of 9.26 × 10⁶ Ω cm and a negative temperature coefficient of resistance (-0.00097/°C), indicating semiconducting behaviour. Gas sensing tests showed exceptional NO₂ sensitivity (94.25%) and selectivity at 60°C, with rapid response (11 s) and recovery (57 s) times, confirming WS₂’s potential for efficient low-temperature gas sensing applications.
This investigation studies the flexural performance of double-layer reinforced concrete beams composed of ordinary concrete and steel fiber reinforced concrete (SFRC). The study evaluates the effects of bottom layer thickness, shear span ratio (a/d) and straight steel fiber volume fraction on structural behavior. The study project comprises twenty reinforced concrete beams, all specimens had across-section size of (125 mm × 250 mm) and different length of (1300, 1740, 2180) mm. The specimens were simple support and strengthened with flexural reinforcement of (4Ø12mm) and transverse reinforcement employing (Ø10@110mm) bars as stirrups. The results demonstrate that the double-layer concrete configuration significantly improves crack control, resulting in reduced crack widths compared with conventional beams. Increasing the shear span ratio from (2, 3 and 4) increased the crack widths and reduced the ultimate load capacity by (33.1% and 47.23%) respectively. Augmenting the thickness of the bottom layer of SFRC resulted in a rise in the ultimate failure load when =0.5% by (0.8%, 4.9%, 12% and 19.1%) and when =1% by (7.4%, 12%, 17.7% and 21.8%) in comparison to the control beam with the complete thickness of conventional concrete. These findings confirm that incorporating SFRC in the tension zone of double-layer beams effectively enhances flexural capacity, crack resistance, and overall structural performance
One of the most important cardiovascular conditions that account for the highest mortality rate around the world is Hypertension. It is defined as a condition where systolic and diastolic blood pressure values are ≥ 140 mmHg and ≥ 90 mmHg, respectively, or a use of antihypertensive drug is being made. TThis study aims to identify and report the prevalence of elevated blood pressure levels among patients attending an endodontics clinic. The study was conducted among a group of 100 patients who sought endodontic treatment at university hospital, endodontics department. The collected information included blood pressure measurement and answers to a structured interview about their health and medication history. The collected data showed that The collected data showed that among the 100 patients screened, 10% exhibited elevated blood pressure. Of these, 1% were previously undiagnosed, 9% reported a history of hypertension, and only 2% were on antihypertensive medication. Male patients showed statistically higher mean systolic blood pressure compared to females (p = 0.148), and a significant gender difference was noted in age and occupation variables (p < 0.05). Based on this data, only 10% of patients attending the endodontics clinic exhibited elevated blood pressure, with no statistically significant difference between genders (p = 0.148). The reality of unnoticed hypertension highlights the need for consistent blood pressure checks within dental practices for better detection and treatment in due time.
Electrodeposition is a pivotal technique in materials science, enabling the fabrication of metal and alloy coatings with tailored properties for diverse industrial applications. Traditional aqueous electrolytes, while widely used, present limitations such as narrow electrochemical windows, hydrogen evolution, and environmental concerns. Deep eutectic solvents (DESs) have emerged as promising alternatives, offering broader electrochemical windows, reduced toxicity, and enhanced solubility for metal salts. This review comprehensively examines the principles and advancements in electrodeposition from both aqueous and DES media, focusing on the deposition of Ni-Co and Sn-based alloys, the role of additives, the electrodeposition of metal powders, and surface analysis techniques. The discussion highlights the advantages of DESs over conventional solvents, the influence of complexing agents and additives on deposit characteristics, and the methodologies employed for surface characterization. Future perspectives emphasize the need for further research into optimizing DES formulations, understanding additive interactions, and developing scalable processes for industrial applications
The increasing worldwide water shortage demands the immediate implementation of advanced, sustainable wastewater treatment solutions. Nanotechnology provides promising options through nanomaterials, including metal oxides, magnetic nanoparticles (MNPs), and carbon-based composites, which show high removal efficiencies of 50–98% for organic contaminants and 80–99% for inorganic pollutants. This review analyzes various nanomaterials used in wastewater treatment by examining their functional mechanisms, including adsorption, catalytic degradation, and membrane-based separation, and assesses their performance relative to traditional methods. It addresses the major barriers to large-scale implementation, including high production expenses, ecological dangers, and scalability problems. The paper stresses the necessity of interdisciplinary collaboration between academia, industry, and policymakers to develop sustainable nanotechnology solutions that are economically viable. The successful adoption of nanomaterials in water management practices depends on balancing performance capabilities, safety standards, and scalability requirements. This review demonstrates how nanotechnology can transform wastewater pollution management through economic and ecological assessments.
Agriculture, emergency preparedness, and water resource management all depend on accurate rainfall forecasts. This study investigates how well machine learning models predict monthly rainfall in the Kurdistan Region of Iraq, specifically in the Pirmam area. Historical weather data was analyzed using a variety of models, such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multiple Linear Regression (MLR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET), and Ridge Regression. Statistical measures including Nash-Sutcliffe Efficiency (NSE), Mean Bias Error (MBE), Normalized Root Mean Squared Error (nRMSE), Coefficient of Determination (R2), and Root Mean Squared Error (RMSE) were used to assess the performance of these models. According to the findings, ANN was the best model for predicting monthly rainfall in the Pirmam region, demonstrating exceptional accuracy in identifying intricate patterns and non-linear correlations. ANFIS had trouble with validation but did well with calibration. The top three linear models were LASSO, Ridge, MLR, and EN. This study demonstrates how machine learning approaches can improve the accuracy of rainfall forecasts, which can help with regional decision-making on water resource management and climate adaptation plans.
Blastocystis is a common protozoan parasite of the gastrointestinal tract. This study aimed to determine the prevalence, risk factors, and potential pathogenicity of Blastocystis sp. in Erbil province, Iraq, using the KU-F600 Automated Feces Analyzer (AFA).Notably, this is the first study to use this device in Iraq. A total of 580 fecal specimens were collected from patients attendending Mehrabani Surgical Hospital. Samples were tested using the KU-F600 AFA, which incorporates AI-based examination of microscopically identified parasites. demographic and clinical data were collected through questionnaires. All the statistical calculations were performed by GraphPad Prism 10. The prevalence of Blastocystis sp. was 50.3%. No significant associations were observed with gender, age, or place of residence. However, contact with animals (p = 0.0038) and a history of prior infection (p = 0.0197) were identified as significant risk factors. Diarrhea and abdominal pain were the most frequently reported symptoms, although the differences were not statistically significant. The most frequent co-infecting parasites identified were Giardia lamblia (10.6%) and Endolimax nana (10.3%). The Vacuolar form was most prevalent (43.1%) followed by cystic (36.9%), mixed (13.5%) and amoeboid (6.4%) forms. The high prevalence of Blastocsytis sp. and its significant association with animal contact underscore the need for increased public health awareness in Erbil. The KU-F600 automated analyzer provided descriptive epidemiological insights; however, diagnostic performance requires validation against reference methods such as microscopy and PCR.
Increasing demands for sustainable urban infrastructure have made pervious concrete emerge as a promising pavement material due to its ability to combine load-bearing capacity with stormwater infiltration. This paper explores the synergetic effect of nanomaterials on the mechanical, hydraulic and durability characteristics of pervious concrete made with three different coarse aggregates: crushed limestone (CL), river aggregate (RA), and recycled concrete aggregate (RCA). Twelve concrete mixes were prepared using varying combinations of nano-modified mixes to investigate their influence on key properties, including dry density, void content, compressive strength, tensile strength, permeability and abrasion resistance. The results showed that river aggregate mixes, particularly those modified with nano-silica and nano-alumina (e.g., RA-1), gained the highest compressive (34.60 MPa) and tensile (3.16 MPa) strengths, alongside significant improvement in abrasion resistance. Crushed limestone mixes showed an optimal balance between strength and permeability, while recycled concrete aggregate (RCA) mixes exhibited acceptable performance. Abrasion resistance was significantly improved by nano-modification, where some of the samples lowered mass loss on the surface by more than 40%. Statistical analysis using the general linear model ANOVA revealed the aggregate type was the dominant factor across most performance metrics, while nano-modification contributed significantly to durability and permeability control. These results indicate the importance of incorporating nano-modification and aggregate selection to produce high-performance, durable, and permeable concrete pavement systems.
Artificial intelligence (AI) is a transformative technology with diverse applications that is transforming several industries. AI is the use of systems and technology to replicate human intelligence and solve common real-world issues. Machine learning (ML) and deep learning are AI technologies that use algorithms to more accurately predict occurrences without the need for human intervention. Explainable Artificial Intelligence (XAI) refers to AI that can explain decisions or forecasts to human users. XAI seeks to improve AI systems' transparency, trustworthiness, and accountability, particularly when utilized in high-risk applications such as healthcare, finance, or security. This review article provides a thorough overview of the literature on AI techniques with various applications. It emphasizes the value of interdisciplinary study as well as the enormous potential of artificial intelligence. Combining domain knowledge and AI can transform entire sectors, solve difficult problems, and improve quality of life in general. To reduce potential hazards and address societal concerns, ethical considerations and responsible development of AI technology are crucial.
