Schistosomiasis remains a major public health challenge in tropical countries, particularly in sub-Saharan Africa, where Bulinus globosus serves as the principal intermediate host of Schistosoma haematobium. The continued reliance on synthetic molluscicides has raised concerns regarding environmental safety, cost, and the potential development of resistance, highlighting the need for effective plant-based alternatives. This study evaluated the phytochemical composition and antischistosomal potential of Momordica charantia and Anthocleista djalonensis leaf extracts against Bulinus globosus. Fresh plant materials from both species were extracted with ethanol and subsequently subjected to qualitative phytochemical screening using established laboratory procedures. Adult Bulinus globosus snails were exposed to extract concentrations of 25, 50, 75, and 100 mg/L for 24, 48, and 72 hours, and mortality was recorded. Data were analysed using one-way analysis of variance, while lethal concentration values were estimated using probit analysis. Phytochemical screening reported alkaloids, flavonoids, saponins, and terpenoids in both plant extracts, together with tannins in the Anthocleista djalonensis sample. The reported qualitative observations also indicated differences between the plant species, although individual constituent concentrations and the presence of glycosides were not established by the qualitative results table. Both extracts exhibited concentration- and exposure time-dependent molluscicidal activity. At 100 mg/L after 72 hours, M. charantia produced 96.7% snail mortality, whereas A. djalonensis recorded 86.7%. The estimated 72-hour LC₅₀ values were 41.83 mg/L for M. charantia and 58.27 mg/L for A. djalonensis. Momordica charantia demonstrated stronger molluscicidal activity than Anthocleista djalonensis, although both plants exhibited considerable toxicity against Bulinus globosus. These preliminary observations suggest that phytochemical mixtures obtained from both plants could provide candidate botanical molluscicides for integrated schistosomiasis control, provided that efficacy, selectivity, and environmental safety are established through additional tests.

This study investigates the structural, electronic, and biological characteristics of an imidazole terpolymer derivative (DBF) as a potential antimicrobial and anticancer agent, integrating experimental investigations with computational analyses. Density functional theory (DFT) calculations at the B3LYP/aug-cc-pVDZ level indicated closely comparable optimized geometries across the investigated solvent environments, including dimethyl sulfoxide (DMSO), water, methanol, and ethanol. Fourier-transform infrared (FT-IR) spectroscopy was evaluated alongside calculated vibrational assignments, revealing both agreements and differences between experimental and theoretical frequencies. Frontier molecular orbital calculations indicated only slight solvent-dependent variation in the HOMO–LUMO energy gap, increasing in the following order: DBF_methanol (4.3930 eV) < DBF_water (4.3949 eV) < DBF_DMSO (4.3952 eV) < DBF_ethanol (4.3957 eV). Antimicrobial disc-diffusion assays showed increasing inhibition zones, ranging from 12 to 22 mm across the tested bacterial and fungal organisms and experimental treatment levels. Molecular docking predicted interactions between DBF and selected microbial proteins, as well as a human estrogen receptor associated with hormone-responsive breast cancer. The calculated docking score for DBF with fungal protein 1ACZ was more negative than that for fluconazole, whereas fluconazole showed a marginally more negative score against 1ZAP. The DBF ligand also exhibited predicted binding interactions with the bacterial targets and the estrogen-receptor structure 7MSA when compared with gentamicin and doxorubicin, respectively. Together, these results support continued investigation of DBF, while biological target engagement, inhibitory efficacy, anticancer selectivity, and the reliability of the predicted docking poses require further experimental confirmation.

The increasing burden of diabetes mellitus, accompanied by impaired insulin action and dysregulated glucose metabolism, presents a substantial global health challenge. This study investigates the electronic characteristics, predicted molecular interactions, and potential pharmacokinetic properties of ¹H-cyclopenta[c]furan-3(3aH)-one, 6,6a-dihydro-1-(1,3-dioxolan-2-yl)-, (3aR,1-trans,6a-cis) (CDD) and tetraacetyl-D-xylonic nitrile (TAN) as candidates for further investigation in diabetes-related research. Density functional theory calculations at the ωB97XD/6-311++G(2d,2p) level yielded optimised molecular geometries with selected bond lengths ranging from 1.093 to 1.539 Å. Frontier molecular orbital analysis indicated HOMO–LUMO energy gaps of 4.45 eV for CDD and 6.02 eV for TAN. Non-covalent interaction analysis indicated van der Waals interaction regions, particularly for TAN. Molecular docking against four selected protein structures (PDB IDs: 5EE7, 6JXR, 7SYD and 8HB0) yielded predicted binding affinities of −7.5 kcal/mol for 5EE7–CDD and −4.4 kcal/mol for 6JXR–TAN, representing the most and least favourable ligand–target scores reported in this comparison. The reference compound dasatinib exhibited more negative predicted binding energies, ranging from −7.1 to −9.6 kcal/mol. The docking poses contained reported hydrogen-bond and additional non-covalent contacts; these findings describe computationally predicted interactions rather than experimentally confirmed inhibition. ADMET predictions indicated that both compounds fulfilled Lipinski’s rule of five and the Ghose filter, although individual absorption, metabolic, and toxicity parameters differed. Taken together, the electronic-structure and docking results support further investigation of CDD and TAN, subject to validation of the docking protocol, target relevance, and experimentally determined biological activity.

The present study reports the design, synthesis, structural characterization, and in silico evaluation of heterocyclic Schiff bases investigated for their potential relevance to antituberculosis drug discovery. The docking calculations were reported against the structure identified as PDB 6PUV; however, this accession corresponds to a human lectin rather than a Mycobacterium tuberculosis protein, so the receptor identity requires verification before biological conclusions can be drawn. Three novel Schiff base derivatives were synthesized through the condensation of 2-phenylindole-3-carboxaldehyde with L-glutamine, L-histidine, and valine. The proposed structures of the synthesized compounds were investigated through Fourier-transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectroscopic analyses, together with the mass-spectrometric results described in the manuscript. Molecular docking studies were performed to estimate the binding affinities of the compounds toward the reported receptor structure, using the antituberculosis drug isoniazid as a comparator. All three Schiff bases yielded more negative predicted docking scores, ranging from −7.6 to −8.9 kcal/mol, than the −5.9 kcal/mol score reported for isoniazid; statistical significance was not established. The relative docking scores were interpreted in terms of the predicted hydrogen-bonding and hydrophobic contacts within the modeled binding region, without assuming experimentally confirmed stability or inhibition. Among the derivatives, 2-((2-phenyl-1H-indol-3-yl)methyleneamino)-3-methylbutanoic acid exhibited the most negative predicted docking score, making it a candidate for further investigation once the receptor identity and docking protocol have been verified. Overall, the synthetic and spectroscopic findings motivate further experimental and pharmacological investigations, but the docking calculations in their current form do not establish antituberculosis activity or inhibition of an M. tuberculosis protein.

This study evaluates a common experimental framework comprising deep learning, ensemble learning, and optimization-based feature selection techniques for spam email detection. To date, knowledge-based and machine learning-based methods have been used for spam detection. However, classical machine learning-based methods can be insufficient in representing and classifying complex spam content. In this study, experiments were conducted on the Enron and SpamAssassin datasets using CNN, GRU, XGBoost, and Random Forest algorithms, a genetic algorithm-based feature selection approach, and TF-IDF, Word2Vec, and embedding-layer representations. The contribution is the cross-comparison of established model, representation, and genetic-algorithm feature-selection combinations on two benchmark datasets rather than a new learning algorithm. Experimental results are presented using metrics such as AUC, F1 Score, and accuracy. The maximum F1-score and AUC value obtained in our study were 0.9997.

Asymmetric oscillations occur in numerous physical systems, in which the restoring force is often characterized by non-polynomial stiffness nonlinearities. In this study, an equivalent Helmholtz–Duffing oscillator was developed to derive approximate analytical solutions for the periodic response of asymmetric oscillators with non-polynomial stiffness nonlinearities under general initial conditions. The equivalent oscillator was formulated using quasi-static equilibrium principles, resulting in amplitude-dependent stiffness coefficients. The exact closed-form solution of the equivalent Helmholtz–Duffing oscillator was then employed to obtain an approximate analytical solution for the original asymmetric system. Numerical simulations demonstrate that the proposed approach yields highly accurate predictions, even under strongly nonlinear conditions, and significantly outperforms the conventional Taylor series expansion method in terms of accuracy.

This work presents a nonlinear dynamic analysis and delayed proportional–derivative (PD) control of piles subjected to prescribed lateral excitations. The model incorporates geometric nonlinearity, nonlinear Winkler-type soil reactions, and modal coupling within a Lagrangian framework. The nonlinear partial differential equation is examined through modal projection and through a separate semi-discretized finite-difference formulation integrated with a Runge–Kutta scheme. The reported numerical results exhibit approximately periodic, amplitude-modulated, dissipative, bursting-like, and irregular finite-time responses as the excitation frequency, load amplitude, and soil stiffness are varied. Harmonic excitation is treated as additive forcing, whereas constant-load responses are interpreted as transients rather than as evidence of sustained chaotic motion. The PDE simulations illustrate mixed oscillations, convergence toward a steady displacement under constant loading, and asymmetric bending distributions along the pile depth. A delayed PD controller is examined in modal space, and residual forced oscillations remain visible in the reported controlled response. The stability analysis concerns the unforced linearized modes and therefore does not establish nonlinear robustness or exact disturbance rejection. The contribution is an application-oriented study based on established modeling and control techniques. The three-oscillator approximation and the clamped–free PDE calculations employ different boundary idealizations; consequently, matched-model validation, physical calibration, and complete reproducible simulation details remain necessary before predictive engineering use.

Ethiopia’s construction sector is rapidly expanding and growing more technically and managerially complex, especially in public building projects, yet many small- and medium-sized domestic contractors still fail to meet cost, schedule, and quality targets. This study examines the stakeholder-rated factors relating to project performance, identifies key performance indicators, and evaluates current performance measurement practices in public building construction. A mixed-methods design combining qualitative and quantitative approaches was used. Data was collected through questionnaires and case studies and analyzed with the Relative Importance Index and SPSS (version 20). The study identified perceived performance constraints: delayed payments, rising material costs, exchange rate volatility, design changes, labor relations, availability of highly qualified personnel, climatic conditions, and project managers’ leadership effectiveness. These factors were rated in relation to construction project time, cost, quality, health and safety, client satisfaction, and productivity; the ratings do not establish their causal effects. Results show that contractors reported using formal, quarterly performance measurement systems and earned value analysis. The contribution is a Gondar-specific descriptive prioritization and conceptual framework, not a validated performance-improvement method.

Facial recognition systems often experience substantial performance degradation when essential facial regions are obscured by occlusions. These occlusions interfere with feature visibility and reduce the accuracy and robustness of conventional single-stage training processes. To address this challenge, this study proposes a multi-stage training framework designed to improve facial recognition robustness under occluded conditions. The framework consists of three progressive training phases: initial feature learning, occlusion-specific adaptation, and refinement and fine-tuning, with each phase guided by a specific loss configuration and optimization strategy. Targeted data augmentation techniques were incorporated to improve generalization under different occlusion conditions. Experiments were conducted using 12,000 facial images compiled from Labeled Faces in the Wild (LFW), CelebFaces Attributes Dataset (CelebA), and a MAFA masked-face dataset subset, with synthetic occlusions introduced for occlusion-focused training and evaluation. The proposed model achieved a final recognition accuracy of 89.7%, compared with 78.9% for the conventional single-stage CNN, representing a 10.8 percentage-point improvement. Convergence was approximately 25% faster than the baseline. Across face-mask, sunglasses, scarf, and combined-occlusion categories, the multi-stage model consistently achieved higher recognition accuracy than the single-stage model. Precision, recall, and F1-score were also reported for the multi-stage model, whereas the corresponding category-level baseline values were not retained in the available experimental record. These findings indicate that progressive training with occlusion-specific adaptation can improve facial recognition robustness under partially obscured conditions, although the additional computational requirements should be considered for resource-constrained deployment.

Environmental chemistry has entered a data-rich era in which analytical instruments, sensor networks, and remote-sensing platforms generate enormous volumes of complex information. Statistical methods have long been the foundation of environmental analysis, offering hypothesis testing, regression, variance analysis, and multivariate approaches for exploring patterns in pollutant distribution and chemical interactions. While these classical approaches remain essential for experimental design, calibration, and uncertainty assessment, the emergence of machine learning has provided new opportunities to address the high dimensionality, nonlinearity, and heterogeneity of modern environmental datasets. From random forests and support vector machines to gradient boosting and deep neural networks, these tools have been applied to problems such as pollutant source apportionment, non-target screening in high-resolution mass spectrometry, spectral deconvolution, sensor fusion, and water-quality forecasting. Their capacity to recognize complex patterns can deliver predictive accuracy that surpasses traditional methods in many study-specific comparisons, yet their adoption has introduced new concerns related to overfitting, data leakage, lack of interpretability, and poor reproducibility. In order to fully harness the benefits of machine learning in environmental chemistry, it is critical to embed domain knowledge, validate models using robust statistical frameworks, quantify uncertainty, and establish standardized practices for transparency and reproducibility. This review examines the evolution of statistical and machine learning tools in environmental chemistry, analyzes their applications and comparative strengths, highlights challenges and pitfalls, and proposes future directions that emphasize hybrid physics–ML models, interpretability, reproducibility, and ethical use. By integrating statistical rigor with computational innovation, environmental chemistry can better support discovery, decision-making, and sustainable management of pollutants.