A series of ten indanone derivatives was rationally designed, synthesized, and tested as potential monoamine oxidase (MAO) inhibitors. The indanones were substituted with the benzylidene moiety to maximize enzyme affinity and specificity. All these compounds were characterized structurally by spectral analysis and were biologically evaluated as inhibitors of the human MAO isoforms. Among the compounds synthesized, 2-(4-ethoxybenzylidene)-2,3-dihydro-1H-inden-1-one (IEB) was the most active (IC₅₀ = 0.010 µM) with more than 100-fold selectivity for MAO-B compared to MAO-A. Kinetic studies showed a reversible, competitive mode of inhibition. Molecular docking revealed strong interactions between IEB and key active-site residues of MAO-B, including Tyr398 and FAD, through π–π stacking and hydrogen bonding, consistent with the observed in vitro potency with a binding energy of -10.2 kcal/mol. ADMET analysis further supported the drug-like potential of IEB, indicating favourable absorption, non-toxicity, and good blood–brain barrier permeability. Molecular dynamics simulation further indicated that the IEB–MAO-B complex underwent conformational adjustment followed by relative stabilization during the 100.102 ns simulation, with persistent interactions involving Tyr60, Tyr326, Ile198 and Leu171. These results suggest that it is possible, through rational modification of the indanone scaffold, to obtain lead candidates of potential therapeutic use for the treatment of neurodegenerative disorders by selective inhibition of MAO-B.
Since they have negative impacts on human health, public safety, and the environment, utilizing conventional chemical additives to control the qualities of drilling mud presents serious issues. Therefore, there is a significant need for alternative multifunctional bio-enhancer drilling mud additives that can help optimize drilling fluid specifications and increase its efficacy with the least possible negative impacts on the environment and the safety of the drilling workers. At working pressure and temperature of 600 psi (4137 kPa) and 150°C (302°F), respectively, and varied amounts of cellulose and carboxymethylcellulose (CMC) derived from cocoa pod husk (CPH) as fluid loss control additives, the performance of the mud has been examined. On the created water-based drilling fluid, rheological measurements were made. The filtrate volume changed by -29.66% and -52.41%, respectively, for 2 g and 7 g of CPH CMC powder at 30 minutes. Filtration characteristics values show that CPH CMC concentrations can successfully reduce filtering rate and enhance mud cake properties in a drilling operation. Rheology and filtration control, wellbore stability and strengthening, cuttings suspension, and thermal properties are some of the drilling fluid characteristics that the CMC may easily alter.
Microgrids are increasingly regarded as a practical pathway for extending reliable electricity access and integrating renewable energy in sub-Saharan Africa. However, the volatility of solar and wind generation together with uncertain consumer demand makes the real day-ahead operating cost difficult to determine, and the historical data required for probabilistic scheduling are rarely available in weakly metered contexts such as Cameroon. This paper formulates a 24-hour robust day-ahead dispatch for a grid-connected microgrid comprising wind, photovoltaic, diesel and battery storage, in which source and load uncertainties are represented jointly within a single Bertsimas–Sim budgeted set. The inner worst-case problem is dualized into an exact deterministic equivalent, yielding a single stage convex quadratic program whose objective embeds diesel fuel and battery degradation terms and which is solved with quadratic programming in MATLAB. A customer type resolved load shifting matrix that conserves total daily energy is solved jointly with the dispatch to reduce peak demand. The framework is validated on field data from the Garoua-Boulaïfeeder. Accounting for joint source and load uncertainty raises the operating cost by 16.1% relative to the deterministic baseline, while demand-response load shifting lowers the cost by 6.21% and the peak demand by 10%. This 16.1% increase is a security premium for covering worst-case net demand, not an inevitable penalty. Sensitivity analyses show that cost rises monotonically with the forecast-error band and the uncertainty budget, saturating once the budget reaches Γ=24. An out-of-sample comparison confirms that the robust schedule attains near-stochastic average cost with the lowest cost dispersion using interval data alone.
Food security remains a critical concern for rural households in Ethiopia, particularly in drought-prone and land-degraded areas. This study aimed to evaluate the impacts of soil and water conservation (SWC) practices on household food security in Soqota Zuria Woreda, Ethiopia. A mixed-methods research approach with a cross-sectional survey design was employed. Data were collected from 208 sampled households using household surveys, focus group discussions, and key informant interviews. Food Consumption Score (FCS), Household Food Insecurity Access Scale (HFIAS), Multivariate Probit (MVP), and Propensity Score Matching (PSM) models were used for analysis. The findings revealed that soil bunds (50%) and tree planting (56%) were the most widely practiced SWC measures, while stone bunds (33%), compost application (40%), water harvesting (14%), and agroforestry (7%) had lower adoption rates. Food security analysis showed that only 15.48% of households were food secure, whereas 54.33% and 30.19% were categorized as borderline and poor food secure, respectively. Similarly, HFIAS results indicated that 49.04% and 19.71% of households experienced moderate and severe food insecurity, respectively. The MVP model showed that male-headed households were more likely to adopt physical SWC practices (Coef = 0.76, p < 0.05), while age negatively affected adoption. Education, livestock ownership, and extension services significantly increased adoption, with secondary education showing the highest positive effect (Coef = 1.14, p < 0.01). The PSM results indicated that agronomical SWC practices increased FCS by 16.61 points (18.18%), physical practices by 16.68 points (10.16%), and biological practices by 13.45 points (8.96%). The study concludes that SWC practices significantly improve household food security. Therefore, the Sekota Zuria Woreda Agriculture Office, the Regional Agriculture Bureau, and development partners should strengthen extension services and training, improve access to financial resources, and enhance soil and water conservation practices, with priority given to drought-prone communities to improve household food security and livelihoods.
Soil organic carbon (SOC) storage is fundamental for climate change mitigation and food security and more precise and detailed SOC stock data are required to accurately assess the main ecological functions on climate regulation. However, soil sampling for SOC stock assessment is labor-intensive when conducted according to international standards using the cylinder method for measuring bulk density (BD) and coarse fragment (CF) which are key components of SOC stock. This study evaluated the performance of soil auger as a soil sampling tool, as a simplified and less destructive method, to assess the BD and CF for SOC stock calculation across the two main soil types in Madagascar. Soil samples from ferrallitic and ferruginous open ecosystem areas were collected at five layers: 0-10 cm, 10-20 cm, 20-30 cm, 50-60 cm and 80-90 cm, using both soil auger and cylinder, with volumes of 100 cm3 and 502.65 cm3 respectively. The BD, CF and SOC stocks derived from each soil sampling method were assessed and compared by soil types and depth. Results showed that no significant differences in BD values were observed between auger and cylinder methods regardless of soil layers (p>0.06) in both soil types. The comparative analysis of CF values indicated that the auger method could serve as an alternative to the cylinder method only in ferruginous soil (p>0.13) due to lower CF values in this soil type. For SOC stocks, non-significant differences were observed between the two methods in ferruginous soil and for topsoil (0-20 cm) in ferrallitic soils highlighting the potential applicability of the simplified method in SOC stock assessment.
The mining industry generates enormous quantities of waste, particularly tailings estimated at 3.44 to 4 billion tonnes annually worldwide produced during the ore processing stage, which require integrated management strategies to reduce environmental risks while enabling sustainable resource utilization. This review focuses on copper mine tailings and provides a multidisciplinary perspective on their management, linking fundamental characterization to technological solutions. First, the mineralogical, chemical, and physical features of copper tailings are discussed as the foundation for understanding their behavior and valorization potential. Next, the review examines environmental behavior assessment through static tests, kinetic tests, and toxicity assessments, which are essential for predicting long-term stability. Tailings valorization is then addressed, highlighting pathways for the reprocessing of residual metals via flotation, bioleaching, and gravity separation, as well as reuse in construction materials. Finally, dewatering and water recovery techniques are reviewed as critical steps for reducing environmental hazards and supporting dry stacking or alternative reuse strategies. The paper concludes with perspectives on future research priorities, emphasizing cost-effective, energy-efficient technologies within a circular economic framework. By integrating these complementary approaches, this work provides a comprehensive reference for sustainable management of tailings with a focus on copper tailings and the advancement of environmentally responsible mining practices.
Acute myeloid leukemia (AML) is a hematological malignancy characterized by a limited number of durable therapeutic responses that are provided by standard chemotherapy and single-target FLT3 inhibitors, which are only partly attributable to molecular heterogeneity, acquired drug resistance and cooperative oncogenic signaling by FLT3, KIT, and dysregulated RUNX1. The study was designed using integrated systems pharmacology computational workflow to screen a library of natural multi-target phytochemical lead compounds that could target these three key proteins found in AML. The top hub genes, FLT3, KIT and RUNX1, driving AML leukemic proliferation and myeloid differentiation arrest, were confirmed by transcriptomic differential expression analysis, weighted gene co-expression network (WGCNA) and protein-protein interaction (PPI) network analysis of AML patient and healthy control RNA-seq data, with aberrant tyrosine kinase signaling pathways identified as the top pathways involved in poor clinical outcomes related to AML. Systematic physicochemical screening of a library of phytochemicals was performed to identify molecules that satisfy the Lipinski's Rule of Five, molecular weight, LogP and TPSA thresholds for predicted oral bioavailability. The molecular docking of nine high-affinity phytochemical hits with the resolved crystal structures of FLT3, KIT and RUNX1 proteins showed stable hydrogen bonding, pi–pi stacking, and hydrophobic contacts within the functional pocket of each protein with binding energies ranging from −7.9 to −9.1 kcal/mol. Our research data illustrate these phytochemicals have inherent polypharmacological activity to simultaneously inhibit hyperactive FLT3/KIT signaling and to restore RUNX1 transcription. This study provides a reproducible computational pipeline for validating the anti-leukemic efficacy in cells, and identifies low-toxic natural small molecules as therapeutic candidates with multi-target activity for high-risk AML.
The cumulative pervasiveness of herbicide-resistant weeds demands the improvement of novel herbicides with superior efficiency, selectivity and environmental protection. In this study, a series of substituted urea derivatives were computationally assessed as potential inhibitors of 4-hydroxyphenylpyruvate dioxygenase (HPPD) using a combined workflow comprising molecular docking, MM-GBSA binding free energy calculations, physicochemical and herbicide-likeness assessment, ADMET and toxicity prediction, molecular dynamics (MD) simulations and density functional theory (DFT) analysis. Molecular docking result recognized Compound 2 as the most promising candidate, displaying a superior binding affinity (MolDock score: -166.256kcal/mol) compared with the reference herbicide Mesotrione (-118.689kcal/mol) through favorable hydrogen bonding, hydrophobic, π- π interactions and van der Waals interactions. MM-GBSA calculations additionally confirmed the thermodynamic stability of the protein-ligand complex, with Compound 2 demonstrating the most favorable binding free energy among all the evaluated compounds. ADMET, toxicity and herbicide-likeness analyses demonstrated desirable agrochemical characteristics and a favorable predicted safety profile. Furthermore, 100ns MD simulations revealed that the Compound 2-HPPD complex exhibited greater structural stability, persistent intermolecular interactions and improved conformational integrity relative to Mesotrione. DFT analysis indicated enhanced electronic stability and appropriate chemical reactivity, supporting efficient molecular recognition within the HPPD active site. Cooperatively, the strong agreement among docking, MM-GBSA, MD, DFT and ADMET analyses identifies Compound 2 as the most promising lead in this series. These findings highlight substituted urea derivatives as valuable scaffolds for the rational design of next-generation HPPD inhibitors and provide a robust computational basis for future experimental validation and herbicide development.
Breast cancer (BC) is increasingly prevalent in Ghana, particularly among younger women, and is often characterized by aggressive molecular subtypes such as triple-negative BC. This study models the stages and phases of BC severity in Ghana using tumor characteristics and demographic data from 558 patients aged 13–97 years. The accelerated phase was found to occur between follow-up times t = 4 and t = 32 months, with the highest peak occurring at t = 19 months. This was followed by the delayed phase (t = 32 to t = 70 months), marked by a staggered mortality trend. In the latent phase, a four-month delay was observed after death from other causes began at approximately age 57, reaching a maximum at age 75. The three phases were further divided into five zones, and analysis revealed two distinct stages of disease severity (SDSs): SDS1 (0–58 months), encompassing the accelerated and delayed phases, and SDS2 (>58 months), where deaths from causes other than BC dominated. An empirical model was formulated for these stages, with a proportionality factor that remained consistent in SDS1 but deviated in SDS2 due to competing risks. This factor enables the prediction of BC mortality and survival rates and could serve as a basis for international comparisons of disease prevalence and severity.
Climate change is expected to exert significant pressure on food systems in Africa with implications for future food inflation. However, limited studies have been done to spatially project how climate change will affect food inflation in Africa. Hence, this study sought to provide scenario-based projections of food inflation in Africa up to 2037 and identifies countries at higher risk under changing climatic and socioeconomic conditions. The study utilized secondary data from the World Development Indicators, World Bank, and Climate Change Knowledge Portal. Exponential smoothing forecasting and forest-based and boosted classification and regression analysis were employed to forecast future trends and assess relative importance of climate and socioeconomic drivers on food inflation across 54 African countries by 2037. The findings indicate that food inflation is projected to increase across several African countries from 2023 to 2037 under the historical trend-continuation scenario. Among climatic variables, precipitation emerged as the most important predictor of projected food inflation (40%), followed by temperature (32%) and carbon dioxide emissions (28%). Combining climate with socioeconomic drivers, population growth (28%) and precipitation (17%) were found to be most important predictors of food inflation within the model framework. Spatial projections showed that Ethiopia, Somalia, Nigeria, Burkina Faso and Kenya are likely to experience the highest rises in food inflation by 2037. Governments, AU, UN, and development agencies such as FAO should strengthen climate-resilient agricultural systems, improve water management systems; promote domestic food production; and have policies in place for population and trade to increase food security and mitigate future food inflation risks in Africa.
Glaucoma is characterized by optic nerve degeneration and an increase in intraocular pressure, potentially leading to irreversible blindness. The severe impact of this disease can be prevented with early detection. There are no symptoms in early glaucoma, so early detection is essential to avoid vision loss. A computer-aided diagnosis system has been presented that uses deep learning and machine learning for the early detection of glaucoma with high accuracy. In this paper, an efficient system is proposed for diagnosing and classifying glaucoma disease. Accordingly, the proposed work contains four stages. First, gamma correction enhances image contrast as a preprocessing step. Second, the features are extracted by ResNet152 for local features and Swin Transformer for global features. Third, attention-based feature fusion is used to strengthen feature representation. Finally, the diagnosis stage is implemented using a bagging classifier to improve generalizability and reduce overfitting, thereby enabling more stable predictions. The proposed system has been evaluated on seven public datasets, utilizing both binary (normal vs. glaucoma) and multiclass (normal, early, and advanced glaucoma) classification. Binary classification is performed on the ACRIMA, LAG, Drishti-GS1, RIME-ONE, ORIGA, and sjchoi86-HRF datasets, achieving accuracies of 99%, 9%, 93%, 92%, 84%, and 90%, respectively. Multiclass classification is performed on the HVD dataset, where the proposed system achieves 90% accuracy. To ensure transparency and reliability in decision-making, explainable artificial intelligence (XAI) techniques are used to provide insights into feature importance and model predictions. Experimental results demonstrate high classification performance, indicating the effectiveness of the suggested strategy in assisting ophthalmologists with interpretable and trustworthy glaucoma diagnosis.
Although smallholder farmers in sub-Saharan Africa are adopting various climate adaptation strategies to cope with increasing climate variability, rigorous evidence on the effects of these strategies on household resilience remains limited. This study provides quantitative evidence on how different bundles of adaptation practices are associated with farm households' resilience to climate change in the Borkena watershed, Northeastern Ethiopia. Using cross-sectional survey data from 385 sample farm households, a multidimensional resilience index based on the FAO Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool was constructed. To address potential selection bias and strengthen causal inference, the Inverse Probability Weighted Regression Adjustment (IPWRA) estimator was applied. The results show that all adaptation strategy bundles are positively associated with resilience compared to non-adopters. Integrated strategies showed the greatest estimated gains: the fully integrated bundle of crop varieties and management, livelihood diversification, and soil and water management (C+L+S) was associated with a 19.1% higher resilience index, while crop varieties and management combined with soil and water management (C+S) was associated with an 18.7% higher resilience index. By contrast, the combination of crop varieties and management with livelihood diversification (C+L) generated a smaller estimated gain of 12.5%, suggesting possible trade-offs in resource allocation. Adoption of improved crop varieties and management (C) alone was associated with a 17.7% higher resilience index. These findings highlight that resilience is associated not merely with the fact of adoption but with the synergistic integration of practices that simultaneously strengthen agronomic, ecological, and socioeconomic capacities. Policy and development programs should prioritize bundled, context-specific strategies rather than single interventions, particularly those aligning crop varieties and management with soil and water management, to enhance climate resilience in smallholder farming systems.
High penetration of photovoltaic and wind generation exposes low-inertia microgrids to frequency excursions, while vehicle-to-grid (V2G) support is constrained by owner participation, converter limits, and communication quality. This study evaluates an integrated framework combining a fractional tilted-derivative–tilted-integral (TD–TI) controller, offline wave search algorithm (WSA) tuning, diesel-generator regulation, and bounded active-power support from aggregated plug-in electric vehicles. WSA minimizes the integral of time-weighted absolute frequency error (ITAE), and the optimized parameters remain fixed throughout the robustness tests. MATLAB/Simulink studies cover step-load, multi-step and random load changes, renewable intermittency, voluntary PEV participation, V2G command delay and interruption, and ±20%/±40% parameter uncertainty. Under the step-load test, the complete TD–TI + WSA + V2G configuration achieves a maximum frequency deviation of 0.130 Hz, a settling time of 10.4 s, and ITAE of 0.410, representing reductions of 62.9%, 62.2%, and 59.0%, respectively, relative to PID. Under multi-step and random loading, the corresponding reductions reach 81.0%, 72.7%, and 65.0%. Renewable intermittency is regulated with a 0.005 Hz peak deviation, 4.8 s settling time, and ITAE of 0.290. Complete PEV opt-out retains regulation through the independent TD–TI/diesel path, whereas V2G participation improves the transient response. Severe communication impairment progressively degrades performance, while the tested one-at-a-time parameter perturbations remain bounded. Thus, the contribution lies in the coordinated integration and validation framework—not a new TD–TI topology, and V2G serves as a fast supplementary resource rather than a prerequisite for closed-loop regulation.
In this paper, we introduce a new unit interval probability model, called the transmuted unit moment exponential (TUME) distribution, to provide a flexible framework for modeling bounded data arising in reliability and lifetime analysis. The TUME model extends the baseline unit moment exponential distribution by incorporating a transmutation parameter, which significantly enhances its ability to capture varying shapes, including different levels of skewness and tail behavior. We derive several important statistical properties of the new distribution, including closed-form expressions for the moments, moment generating function, incomplete moments, mean residual life function, stochastic ordering, order statistics, and entropy. Parameter estimation is performed using the maximum likelihood method, and the finite-sample performance of the estimators is evaluated through an extensive Monte Carlo simulation study based on bias, mean relative error, and mean squared error. The results demonstrate that the proposed model provides greater flexibility than the baseline distribution, with improved capability to accommodate diverse data patterns on the unit interval. Applications to real-life datasets show that the TUME distribution yields a better fit compared to existing competing models, as supported by standard goodness-of-fit criteria. These findings highlight the usefulness of the TUME model as an effective alternative for analyzing bounded lifetime data.
Local perceptions provide important insights into how forest conditions, degradation pressures, and conservation strategies are experienced by forest-dependent populations, but they remain insufficiently documented in relation to community and sacred forests in northern Benin. This study examined local perceptions of forest conservation status, perceived drivers of forest degradation, and locally preferred pathways for sustainable forest management across seven municipalities in the Borgou and Alibori departments of northern Benin. A convergent mixed-methods design was adopted, combining a structured household survey of 382 respondents with qualitative focus group discussions. The study relied primarily on perception-based information and did not include direct quantitative measurements of forest ecological condition. Survey data were analysed using descriptive statistics and generalized linear models, while qualitative information was thematically analysed and integrated during interpretation. Respondents predominantly perceived forests as open and reported a generally degraded conservation status. Agriculture was identified as the main perceived driver of forest degradation (50.2%), followed by logging (30.31%), while charcoal production, cash-crop expansion, wildfires, and climate-related pressures were also reported. Perceptions varied according to key socio-demographic and spatial characteristics, whereas no statistically significant differences were detected among socio-professional groups. Qualitative findings further highlighted the importance of customary institutions, local awareness, and community participation in conservation. Religious and community leaders particularly emphasized awareness raising and community-based reforestation as locally acceptable management strategies. These findings demonstrate that local perceptions can complement, rather than replace, ecological assessments by revealing socially experienced patterns of forest change and locally legitimate management priorities. Integrating these perspectives with ecological monitoring and landscape-level planning could strengthen the design and social legitimacy of sustainable forest management interventions in northern Benin.
In semi-arid regions, soil quality evaluation is challenging due to spatial heterogeneity and sparse field data. This research developed a digital soil mapping (DSM) framework for Vertisols in the Blue Nile region, Sudan (38150 km2), using spectral indices derived from Landsat, topographic attributes, and drought indices. A Soil Quality Index (SQI) was derived from 14 physicochemical properties using PCA-based indicator selection and non-linear scoring functions. Three models were compared: (1) SCORPAN-based Random Forest, (2) Ordinary Kriging, and (3) a weighted ensemble (One Out All Out). SQI ranged from 0.27 to 0.95 (mean 0.56 ± 0.18), positively correlated with vegetation indices (NDVI: r = 0.76; EVI: r = 0.77, NPP: r = 0.83) and negatively associated with drought indices (DI: r = -0.68; SPEI: r = -0.71, and LST: r = -0.75). The most accurate was the One Out All Out (R2 = 0.91, Kappa = 0.96), followed by SCORPAN (R2 = 0.82), for SQI predicted. SQI was controlled by the drought index, SPEI, land surface temperature, and potential evapotranspiration. The drought indices, the indices drought index, SPEI, land surface temperature, and potential evapotranspiration were the most important factors controlling soil quality degradation. Climate projections (CMIP6, SSP5-8.5, 2024-2100) indicate that 34% of high-quality soils may be threatened by increased aridity. This framework provides a reliable baseline for soil quality monitoring in data-limited semi-arid regions.
The genus Drimia (syn. Urginea) comprises bulbous plants belonging to the family Asparagaceae (formerly Hyacinthaceae), widely recognized for their medicinal and toxic properties. Several Drimia species are indigenous to South Africa, where they have been traditionally employed for centuries to treat a range of ailments, including dropsy, respiratory infections, bone and joint disorders, skin diseases, epilepsy, and cancer. The bulbs are particularly valued for treating colds, pneumonia, coughs, fever, and headaches. This review aims to comprehensively summarize and critically evaluate the ethnomedicinal uses, phytochemical composition, pharmacological activities, and toxicological aspects of Drimia species, elucidating their therapeutic potential and highlighting existing knowledge gaps. A systematic search of scientific databases, including ScienceDirect, Scopus, PubMed, Web of Science, and Google Scholar, was conducted. Literature published up to 2025 was analyzed, emphasizing studies between 2017 and 2025 to assess recent trends in phytochemical and pharmacological investigations. Cardiac glycosides were identified as the principal bioactive constituents across Drimia species, accompanied by phenolic compounds, phytosterols, and other secondary metabolites. Reported pharmacological effects include antiviral, antibacterial, antioxidant, anti-inflammatory, immunomodulatory, and anticancer activities. Despite extensive biological evaluation, only 4 out of 464 retrieved publications addressed toxicity and safety, indicating a substantial research gap in toxicological characterization. Analytical Drimia represents a pharmaceutically valuable yet toxic genus whose dual nature demands cautious investigation. Further studies are warranted to isolate novel therapeutic compounds, define safety thresholds, and establish standardized preparations that balance efficacy with toxicity for safe ethnopharmacological application.
Agricultural production forecasting in Sub-Saharan Africa faces persistent challenges: data quality constraints, structural breaks from conflict and policy shifts, and methodological fragmentation. This study develops and validates a methodological framework specifically designed for data-constrained environments. Using national agricultural production data (1961-2024) for five Southern African countries and six crops, we evaluate three dimensions: (1) rigorous quality criteria (minimum 20 observations, maximum 3-year gaps) for series inclusion; (2) integration of Bai-Perron structural break detection with historical contextualization; and (3) comparative assessment using Diebold-Mariano tests. Of 26 potential series, 19 met quality criteria (MAPE ≤ 25%), yielding a mean MAPE of 12.5% (range: 1.6-24.5%). Models incorporating structural breaks achieved MAPE reductions of 17-23% compared to specifications assuming parameter stability. XGBoost outperformed ARIMA for volatile crops (maize), while ARIMA performed better for stable crops (beans, cassava), confirmed by Diebold-Mariano tests (p < 0.05 for 4 approved series). Projections reveal heterogeneous patterns: Malawi’s soybean production is projected to decline (-7.2%), while Angola’s soybean production is projected to remain relatively stable (-0.0%). These patterns coincide with documented structural conditions, though causality is not directly tested. These findings indicate that in data-constrained environments, forecast reliability depends as much on data quality control and structural break integration as on model sophistication. The framework offers a replicable methodology for agricultural forecasting under data constraints, with implications for food security planning across developing regions.
A series of novel pyrido[2,3-b]pyrazine-based imine derivatives (2a–2c) were synthesized by condensation of 7-bromopyrido[2,3-b]pyrazine-2,3(1H,4H)-dione with aliphatic primary amines and characterized using nuclear magnetic resonance (NMR) spectroscopy and high-resolution mass spectrometry (HRMS). The antibacterial activity of the synthesized compounds was evaluated against selected bacterial strains using disc diffusion and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. The results demonstrated that all compounds exhibit antibacterial activity, with compound 2b showing the highest efficacy.Molecular docking studies targeting the ATP-binding domain of DNA gyrase B revealed favorable binding interactions, supporting a potential mechanism of action based on enzyme inhibition. In addition, in silico drug-likeness and ADMET predictions indicated that the compounds possess suitable pharmacokinetic properties, including high intestinal absorption, compliance with Lipinski’s rule of five, and low predicted toxicity. Density Functional Theory (DFT) calculations further revealed that all compounds share similar electronic properties, with moderate HOMO–LUMO energy gaps and balanced reactivity.Overall, the results highlight the potential of pyrido[2,3-b]pyrazine-based imine derivatives as promising antibacterial agents. Among them, compound 2b emerges as a lead candidate due to its optimal balance between biological activity, binding affinity, and pharmacokinetic properties.
