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Synthesis, structural, spectroscopic (NMR, FT-IR and UV-Vis), NLO, in silico (ADMET and molecular docking) and DFT investigations of a flavonol derivative 2-(4-chlorophenyl)-7-fluoro-3-hydroxy-4H-chromen-4-one
No full text<p>The geometric, spectroscopic and electronic features of the newly synthesized 3-Hydroxy-2-(4-chlorophenyl)-7fluoro-4H-chromen-4-one have been analyzed using a combination of single crystal XRD, FT-IR, NMR shifts, and UV-Vis. spectroscopic methods both experimentally and theoretically. The molecule has crystallized in orthorhombic space group Pca21 and it has stabilized with C-H & sdot;& sdot;& sdot;O, O-H & sdot;& sdot;& sdot;O intra-molecular and O-H & sdot;& sdot;& sdot;O intermolecular interactions. The geometric parameters (bond lengths and bond angles), vibrational wavenumbers, 1H and 13C NMR chemical shifts, UV-Vis. electronic transitions, the HOMO (highest occupied molecular orbital) and the LUMO (lowest unoccupied molecular orbital) analyses, NLO (non-linear optical properties) and the MEP (molecular electrostatic potential) surface have been computed by using the DFT/B3LYP quantum chemical method with 6-311++G(d,p) level of theory to compare with the experimental findings. Absorption bands of the molecule have computed around 300 and 360 nm, while observed at 244, 312 and 344 nm in the experimental UV-Vis. spectrum. Moreover, the mean polarizability value of the compound has been calculated 6.47 times greater than urea. The objective of the in silico investigation was to ascertain the biological effect profile of the title compound. The ADMETlab 2.0 web service has been employed to analyze the physicochemical, medicinal chemistry, metabolism, distribution, absorption, excretion and toxicity features of the compound. Its protein kinase CK2 inhibitory activity for the target macromolecule 5M4U has been investigated by means of a molecular docking study to investigate the anti-cancer activity of our flavonol derivative compound.</p>
Selective synthesis of ZnO nanorods on graphene for solar cell applications
No full text<p>In this study, we achieved the selective growth of spatially ordered ZnO nanorods (NRs) in large scale with varying diameters and lengths on graphene pre-coated glass surfaces, resulting in the manufacture of a thirdgeneration core-shell Cu2ZnSnS4(CZTS) solar cell. Using a combination of hydrothermal technique and nanosphere lithography, ZnO NR arrays with different diameters and lengths were synthesized on single and bilayer graphene grown on copper foils and transferred to glass substrates via the PMMA-assisted transfer technique. The Langmuir-Blodgett method facilitated the transfer of polystyrene nanospheres as a single layer onto the ZnO seed layer. It was found that the morphology of the ZnO seed layer, crucial for NR synthesis on graphene, significantly influenced the quality of the mono-layer nanosphere mask. Adjustable nanospheres via O2 plasma treatment were used to synthesize spatially ordered ZnO NR arrays. To reduce defect density at the core (ZnO NR) and shell (CdS/CZTS) interfaces, ZnO NRs were coated with a thin TiO2 layer before applying the CZTS absorber layer. The monophase kesterite CZTS absorber layer was successfully applied to ZnO NRs synthesized on graphene through thermal evaporation of polycrystalline CZTS powder. Prototype solar cells (Glass/Graphene/ZnO-NRs/TiO2/ CdS/CZTS/Ag) were constructed to demonstrate the application of selectively grown spatially ordered ZnO NRs on graphene layers in a core-shell architecture. A second solar cell, using pristine ZnO NRs, was also fabricated to compare their performances. For the TiO2-passivized ZnO NR solar cell, Voc, Jsc, FF, and efficiency were 0.38 V, 20 mA/cm2, 26 %, and 1.9 %, respectively, compared to 0.32 V, 11 mA/cm2, 24 %, and 0.84 % for the pristine ZnO NRs based cell, highlighting the significant performance improvement due to TiO2 passivation.</p>
Involution-based HarmonyNet: An efficient hyperspectral imaging model for automatic detection of neonatal health status
No full text<p>Background and Objective: Neonatal health is critical for early infant care, where accurate and timely diagnoses are essential for effective intervention. Traditional methods such as physical exams and laboratory tests may lack the precision required for early detection. Hyperspectral imaging (HSI) provides non-invasive, detailed analysis across multiple wavelengths, making it a promising tool for neonatal diagnostics. This study introduces HarmonyNet, an involution-based HSI model designed to improve the accuracy and efficiency of classifying neonatal health conditions. Methods: Data from 220 neonates were collected at the Neonatal Intensive Care Unit of Sel & ccedil;uk University, comprising 110 healthy infants and 110 diagnosed with conditions such as respiratory distress syndrome (RDS), pneumothorax (PTX), and coarctation of the aorta (AORT). The HarmonyNet model incorporates involution kernels and residual blocks to enhance feature extraction. The model's performance was evaluated using metrics such as overall accuracy, precision, recall, and area under the curve (AUC). Ablation studies were conducted to optimize hyperparameters and network architecture. Results: HarmonyNet achieved an AUC of 98.99%, with overall accuracy, precision and recall rates of 90.91%, outperforming existing convolution-based models. Its low parameter count and computational efficiency proved particularly advantageous in low-data scenarios. Ablation studies further demonstrated the importance of involution layers and residual blocks in improving classification accuracy. Conclusions: HarmonyNet represents a significant advancement in neonatal diagnostics, offering high accuracy with computational efficiency. Its non-invasive nature can contribute to improved health outcomes and more efficient medical interventions. Future research should focus on expanding the dataset and exploring the model's potential in multi-class classification tasks.</p>
Modules and abelian groups with a restricted domain of projectivity
No full text<p>In this paper, we study the modules whose projectivity domains are contained in the class of all pure-split modules and call them p-impecunious modules. Every quotient of a p-impecunious R-module is p-impecunious if and only if R is right pure semisimple. An abelian group A is p-impecunious if all p-components of A are nonzero and B-p(A)not equal 0 for some prime p where B-p(A) is the basic subgroup of the p-component T-p(A). The converse is true if A is torsion. We characterize Dedekind domains over which there are injective p-impecunious modules.</p>
RFID-enabled ML-assisted microwave liquid sensor design for complex dielectric characterization of water-methanol mixture
No full text<p>In this study, an RFID tag inspired microwave sensor design is proposed for the dielectric parameter characterization of the water-methanol binary mixture through the RFID tag operating principle with RSSI magnitude and phase output values based on the input variables of operating frequency, RFID reader power strength and sample location in machine learning assisted manner. The proposed microwave sensor design operates at ETSI frequencies of UHF band reserved for RFID. In the characterization of the water-methanol binary mixture by processing the RSSI data received from the RFID reader with machine learning Gaussian Process Regression, the mixing ratios of the liquid components and real and imaginary parts of the complex dielectric constant of the mixture can be conveniently obtained. For the machine learning study, eleven mixtures with 10 % differences, three different power levels, four different frequencies, and four different locations have been combined and carried out with a total of 528 data obtained. Three different machine learning algorithms have been developed using the same input data for three different characterization outputs. R2 values of Gaussian Process Regression method have been obtained as 0.99, 0.99 and 0.98 for the volumetric mixing ratios, real part, and imaginary part of the dielectric constant, respectively.</p>
Investigating the Effectiveness of Problem Based Learning on Academic Achievement in EFL Classroom: A Meta-Analysis
No full text<p>Problem based learning (PBL) has great potential to enhance learning outcomes and this potential has been investigated and proved in different disciplines by many meta-analysis studies. However, there are not any meta-analysis studies aiming to investigate the effectiveness of problem based learning in English as a Foreign Language (EFL) classrooms which is an important gap that needs to be filled. Therefore, this meta-analysis study aimed to investigate the overall effect size of PBL on achievement in EFL classrooms and to examine the possible moderator variables that might be effective on this overall effect size. Along with this aim, the studies investigating the effect of PBL on academic achievement in EFL classrooms are included and analyses were carried out with 41 data (extracted from 36 unique studies). Investigation of publication bias using various methods showed that there is no publication bias. This study showed that the effect size of PBL is 1.067 indicating a large effect which means that PBL is highly effective to enhance the language achievement of students in EFL classrooms. Moderator analyses showed that language skill is a real moderator on the effect size of PBL on EFL success while the study group, treatment duration, location of study, learning environment, and document type are not real moderators.</p>
Improving the power factor of spark plasma sintered Bi 0,5 Sb 1,5 Te 3 via TiC dispersion
No full text<p>Integrating ceramic particles into the main thermoelectric material is a novel approach to enhance thermoelectric properties. In this work, we fabricated highly dense p-type Bi0,5Sb1,5Te3 thermoelectric materials dispersed with x wt% TiC particles (x = 0, 0.4, 0.6, 0.8) using the melting-solidification followed by spark plasma sintering (SPS) methods. The crystal structures were analyzed using X-ray diffraction (XRD), and surface and cross-sectional microstructures, as well as the presence of TiC particles in the matrix, were systematically investigated through scanning electron microscopy (SEM). The incorporation of TiC into the Bi0,5Sb1,5Te3 alloy resulted in a notable enhancement of electrical conductivity, exceeding 10 % at all range temperatures across all samples. The rise in weighted mobility and grain boundary coefficients are the main factors responsible for this enhancement. Nevertheless, the Seebeck coefficient measurements indicate a reduction in all samples as a result of the n-type semiconductor characteristics of TiC, where the carrier concentration is mostly governed by electrons. As a result, the power factor has improved by around 5 %. Additionally, the Vickers hardness of the Bi0,5Sb1,5Te3/TiC composites exhibited improvement exceeding 15 % compared to the base matrix.</p>
Development of electrochemical 3-MCPD sensor based on molecularly imprinted polymer coating on metal organic framework modified gold electrode
No full text<p>3-Chloropropane-1,2-diol (3-MCPD) is formed during the food processing and defined as a potential carcinogen. For this reason practical, effective and economical detection of this substance is crucial. In this work, Fe-MIL-88metal organic framework (MOF) included electrochemical molecularly imprinted polymer (MIP) based sensor was designed for 3-MCPD detection. Gold screen printed electrodes (AuSPE) were modified with MOF structure and then a MIP layer was formed on the electrode surface via electropolymerization where aniline was used as a monomer and 3-MCPD as a template molecule. In order to improve the response of the prepared sensor, various experimental parameters such as MOF amount, MIP layer thickness, template molecule: monomer ratio, extraction time and rebinding time were optimized. Then, as an analytical characteristic parameter, linear ranges were detected for 3-MCPD in the range of 0.05 mu M to 0.5 mu M with a limit of detection (LOD) value of 0.43 mu M as well as in the range of 0.05 and 0.5 mu M with LOD value of 0.01 mu M. The specificity of the developed electrochemical sensor was investigated in the presence of interferent reagents and then developed sensor was adapted for 3-MCPD determination in vegetable oil and soy sauce. Lastly the real sample analysis was also validated with gas chromatograpy-mass spectrometry technique which is accepted as gold standard method for 3-MCPD analysis.</p>
Deep learning based colorectal cancer detection in medical images: A comprehensive analysis of datasets, methods, and future directions
No full text<p>This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and 9 publicly accessible medical image datasets used for training and validation. Various convolutional neural network architectures—including ResNet (40 implementations), VGG (18 implementations), and emerging transformer-based models (12 implementations)—for classification, object detection, and segmentation tasks are systematically categorized and evaluated. The investigation encompasses hyperparameter optimization techniques utilized to enhance model performance, with particular focus on genetic algorithms and particle swarm optimization approaches. The role of explainable AI methods in medical diagnosis interpretation is analyzed through visualization techniques such as Grad-CAM and SHAP. Technical limitations, including dataset scarcity, computational constraints, and standardization challenges, are identified through trend analysis. Research gaps in current methodologies are highlighted through comparative assessment of performance metrics across different architectural implementations. Potential future research directions, including multimodal learning and federated learning approaches, are proposed based on publication trend analysis. This review serves as a comprehensive reference for researchers in medical image analysis and clinical practitioners implementing AI-based colorectal cancer detection systems.</p>
Efficient synthesis of phosphorus-promoted and alkali-modified ZSM-5 catalyst for catalytic dehydration of lactic acid to acrylic acid
No full text<p>This study presents a novel one-pot synthesis method for phosphorus-enhanced ZSM-5 zeolite, followed by postsynthesis alkali treatment. The resulting catalyst is designed for the sustainable production of acrylic acid (AA) from lactic acid (LA). Adding phosphorus as a promoter during synthesis significantly improved the acid-base properties of the zeolite. Additionally, the alkali treatment contributed to the overall optimization of the catalyst's performance. Comprehensive analytical techniques, including XRD, BET, FT-IR, TGA, XPS, SEM, ICP-MS, DRIFT spectra, NH3, and CO2-TPD, were employed to elucidate the structural and acid-base properties of the ZSM-5/P-Na catalyst. Finally, an experimental design was developed to optimize the important operational variables in the LA dehydration reaction. The optimized ZSM-5/P-Na catalyst demonstrated excellent performance, achieving 83 % AA selectivity and 98 % LA conversion with a long catalytic lifetime of 50 h. This research demonstrates a promising approach for developing efficient catalysts for sustainable AA production.</p>