Search Results (1,129)

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons. Full article

Cassava is a critical staple crop for food security and rural livelihoods in Sub-Saharan Africa, yet high-resolution maps of its distribution remain scarce, particularly for smallholder systems. In this study, we generated a 10 m resolution cassava presence map for Uganda (CM24) by fine-tuning a Random Forest classifier on TESSERA foundation model embeddings derived from Sentinel-1 and Sentinel-2 time series. Using field survey data from the Copernicus4GEOGLAM campaign for training and validation, the model achieved excellent discriminative ability (validation AUC = 0.9532, test AUC = 0.9524). Visual validation against high-resolution satellite imagery confirmed good spatial agreement, capturing both large contiguous fields and small fragmented plots. Comparison with two existing global products (CassavaMap and SPAM2020) and two seasons of national survey data conducted by the Uganda Bureau of Statistics showed that CM24 produced national harvested area estimates that fell between the two survey totals, whereas CassavaMap and SPAM2020 systematically overestimated harvested area by factors of two to three. Our results demonstrate that foundation-model embeddings offer a robust and scalable approach for mapping cassava in heterogeneous smallholder landscapes. The resulting CM24 map provides a spatially explicit tool to support disease surveillance, agricultural monitoring, and food security planning in Uganda and beyond. Full article

Rice leaf diseases severely threaten global food security, and efficient on-site detection remains challenging for resource-constrained field inspection robots. This work introduces a lightweight visual detection framework designed for the real-time and accurate identification of rice leaf diseases on agricultural edge mobile platforms. A dataset of 4622 annotated images compiled from mobile-device acquisition and publicly available online sources, covering three representative disease categories, together with an independent public benchmark, was used for evaluation. The framework integrates three complementary modules: adaptive multi-scale feature extraction via a dynamic hybrid convolution backbone (C3k2-DICN), cross-scale parameter sharing in the detection head (CSDH) to reduce redundancy, and dual-path downsampling (ADown) to preserve disease-discriminative information during resolution compression. Compared to the YOLO11n baseline, the proposed approach reduced GFLOPs by 36.5% and parameter count by 34.6%, while achieving 88.42% mAP@0.5 and 45.82% mAP@0.5:0.95 on the compiled dataset and 91.71% mAP@0.5 on the public benchmark, indicating accuracy competitive with or superior to all evaluated comparison models. Deployed on an NVIDIA Jetson TX2 with TensorRT FP16 acceleration, the model ran in real time on-device, reaching 32.2 FPS for the TensorRT inference stage and 19.8 FPS for the full end-to-end pipeline including image pre- and post-processing. The framework offers a practical basis for lightweight on-device rice disease detection; closed-loop validation on a moving field robot is left to future work. Full article

Background/Objectives: Frailty and sarcopenia are common among hospitalized patients and are associated with poor clinical outcomes. Nutritional and exercise interventions are widely used to prevent muscle loss and functional decline; however, their independent and incremental effects remain unclear. This scoping review aimed to systematically map the characteristics and reported effects of these interventions during hospitalization. Methods: This scoping review followed the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. A comprehensive literature search was conducted in PubMed/MEDLINE, EMBASE, CENTRAL, and PEDro. Eligible studies included adult hospitalized patients receiving nutritional interventions, exercise interventions, or both. Interventions were categorized into four groups: no intervention, nutrition alone, exercise alone, and combined interventions. Data regarding study characteristics, intervention details, and clinical outcomes were extracted and descriptively summarized. Results: Thirty-three studies were included. Considerable heterogeneity was observed in patient populations, intervention characteristics, and outcome measures. Most studies evaluated configurations including an exercise component (exercise alone or combined nutrition–exercise), whereas studies isolating nutrition or providing direct head-to-head comparisons between combined and single-component configurations were limited. Intervention dose and reporting were highly variable across studies. Conclusions: Current evidence on the effects of nutritional and exercise interventions during hospitalization remains heterogeneous and limited. Future studies should adopt standardized intervention reporting and directly compare combined and single-component strategies to determine additive and synergistic effects in patients with frailty or sarcopenia. Full article

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics. Full article

Mobile RFID inventory in libraries must be planned and evaluated under noisy observations, configuration-dependent read regimes, and incomplete supervision. This paper presents an uncertainty-aware analytics framework for robot-assisted RFID inventory using the public RFID Location dataset. The framework has three phases. Phase 1 converts irregular list-encoded logs into atomic RFID events and quantifies how operating configuration changes read density and signal variability. Phase 2 performs map-constrained Bayesian shelf inference by synchronizing RFID reads with robot trajectory and antenna geometry and by fusing RSSI and carrier phase over feasible shelf candidates. Phase 3 translates posterior spread and non-convergence into proxy review workload and cost, enabling configuration comparison and certainty–throughput trade-off analysis when strict EPC-to-item linkage is unavailable. Across 688,073 aligned RFID observations, the pipeline produces 18,190 posterior tag estimates from five inventory runs. The empirical results show strong run dependence: the best run achieves a mean posterior spread of 0.906 m with a convergence rate of 0.553, whereas a degraded run reaches only 0.004 convergence with a mean spread above 2.1 m. Because EPC-to-item linkage is unavailable, these values are posterior concentration and workload indicators rather than ground-truthed localization-accuracy metrics. A saved phase-weight ablation further shows that adding phase information substantially sharpens posterior concentration relative to an RSSI-only baseline. Under the proxy workload model, autonomous-S1-P30 provides the most favorable balance among posterior certainty, scan effort, and implied review burden. Full article

Soil pH constitutes a key factor in the nutrient availability and initial growth of maize (Zea mays L.). Inadequate management of soil pH can lead to problems in plant growth, which may result in reduced food production yields and agricultural investment. To evaluate the effects of pH dynamics on seedling development in soils, not only was a correlational and quantitative study conducted, which included a completely randomised laboratory experiment design with three treatments (pH < 6, pH > 7, and pH 6–7), each with five replicates, but a systemic analysis using a causal map also described the impacts of pH on plant growth. The initial pH was measured every four days, as were the germination rate, electrical conductivity, and final biomass. The results show that in alkaline soil, seedling germination is reduced by 87%, whilst in acidic soil it is reduced by 80% in comparison to the neutral scenario. pH values are therefore shown to affect early development due to reduced nutrient availability. These results reveal the need for the consideration of measures that influence management practices for the promotion of uniform and sustainable growth to favour the early establishment of crops such as native maize. Full article

Cinematic representations of mental health operate at the intersection of science, culture and visual meaning, while medical academic discourse plays an important role in shaping how such representations are conceptualized. This study examines how the PubMed-indexed literature (2010–2025) engages with mental health in relation to narrative film and related screen media, combining bibliometric mapping with interpretive analysis. Through a structured PubMed query and VOSviewer co-occurrence analysis, this study identifies 5292 unique terms, of which 530 meet the minimum frequency threshold. Comparison between low- and high-frequency maps reveals a shift from lexical diversity to a consolidated biomedical core centered on classification, diagnosis and measurable affect. Six clusters are identified (neuro-affective, educational stigma, media–behavioral, neuropharmacological–technological, perceptual–emotional and pandemic-related), which together structure the field’s dominant semantic orientations. The findings indicate three main patterns: the predominance of standardized biomedical language, the limited visibility of intersectional categories (e.g., gender, race, identity) at the level of indexed metadata, and a gap between visual processes and narrative meaning. While individual studies often engage with cinematic complexity, this dimension is only partially reflected in the dominant lexical structure. Building on these results, a cluster-informed conceptual framework for film-based medical education is proposed, in which narrative film can support complementary forms of clinical, social and interpretive learning. This study contributes to the field of Medical Humanities by demonstrating that medical discourse not only reflects but also structures the visibility of mental health in relation to screen media, while highlighting the need for more integrated approaches that connect biomedical knowledge with narrative and cultural understanding. Full article

Conductive silicone interfaces are promising polymeric materials for wearable bioelectronic systems because they combine electrical continuity with elastomeric compliance, environmental durability, and moldability. In low-voltage wearable microcurrent interfaces, however, functional performance is governed not only by intrinsic material conductivity, but also by conductive network continuity, molded geometry, interfacial contact, and transient electrical response. In this study, we developed a spike-structured conductive silicone interface using a commercially available electrically conductive two-component silicone rubber and investigated its structure–electrical property relationships as a volume-resistive polymer interface. The interface consisted of a conductive silicone body with protrusions 7 mm in height and 3.6 mm in diameter, supported by a 1 mm base layer and electrically integrated through an Ag-paste-connected upper conduction region. Using a representative electrode-level resistance of 47.08 Ω, the geometry-derived apparent interfacial resistive response was estimated as 18.0 Ω·cm for the three-spike configuration and 24.0 Ω·cm for the four-spike configuration. The corresponding effective conductive areas were 0.305 cm² and 0.407 cm², respectively, giving analytical current-density amplification factors of 9.82 and 7.37 relative to a planar 3 cm² reference interface. Positional resistance mapping yielded an overall mean resistance of 47.80 ± 4.57 Ω, indicating acceptable electrical reproducibility across the structured conductive silicone interface. In addition, oscilloscope-based transient response analysis under a 5 V, 1 kHz square-wave input showed that the conductive silicone interface maintained the overall pulse waveform while showing a modest reduction in overshoot from 3.4 ± 0.1% to 2.7 ± 0.1%, with FFT traces used as qualitative waveform-monitoring displays. Formulation-dependent comparison further showed that increasing the silicone-rich fraction increased the measured resistance from 105 Ω to 145 Ω, whereas increasing conductive carbon loading reduced resistance but aggravated surface transfer. These results show that the conductive silicone interface functions not simply as a soft conductor, but as a volume-resistive, geometry-defined current-transfer medium whose behavior is governed by the coupled effects of conductive network formation, spike architecture, electrode-level resistance, and transient pulse response. This study provides a practical materials/interface design framework for spike-structured conductive silicone electrodes in wearable bioelectronic and electroceutical devices. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article

Contemporary iterations of avian phylogenies based on multiple genome sequence assemblies assign three major clades: Palaeognathae (mostly ratite birds), Galloanseres (land and waterfowl) and the largest group—Neoaves. The latter two are sister clades representing subdivisions of Neognathae, while Neoaves further subdivide into Columbaves (pigeons/doves/cuckoos/bustards, etc.), Mirandornithes (flamingos/grebes), Telluraves (“higher land birds”, including finches) and the newly recognized Elementaves (e.g., penguins/pelicans/hummingbirds/swifts/cranes/shorebirds). Molecular studies provide clade information, likely divergence timings and a framework from which gross genomic (chromosomal) changes may be mapped. In this review, we consider the patterns of chromosome change that have occurred throughout all avian clades thus far examined, citing studies from standard karyotyping through molecular cytogenetics to whole genome assemblies. Standard karyotyping led to the realization that most chromosomes (particularly the microchromosomes and dot chromosomes) could not be distinguished by classical means. Indeed, cross-species comparisons were difficult, even among the macrochromosomes, because of indistinct banding patterns. Based on fluorescence (or fluorescent) in situ hybridization (FISH), comparative genomics was thence progressed considerably by cross-species chromosome painting (Zoo-FISH) for the macrochromosomes and interspecific mapping of bacterial artificial chromosome (BAC) probes for the microchromosomes. A key finding was that the most studied species, the chicken, fortuitously, has a genomic organization somewhat akin to that of the ancestral karyotype and tends to be the standard from which all others are measured. A notable exception is the fusion of basal chromosome 4 with a smaller chromosome that convergently appears in some other Galliformes, at least one goose and one dove species. While some groups such as Falconiformes (falcons, etc.) and Psittaciformes (parrots, etc.) underwent extensive interchromosomal change, most, broadly speaking, retain a basic karyotype that differs little from bird to bird. Many, e.g., Passeriformes (finches, songbirds, etc.) and Columbiformes (pigeons, doves), do this despite multiple intrachromosomal rearrangements. The complete karyotype and fully established chromosome-level genome assembly of the chicken allow full integration of DNA sequence assembly with karyotype. They further permit cytogenetic studies to be performed using genome assemblies alone alongside cutting-edge long-read sequencing and optical mapping without the need for chromosome preparation. The classic ZW sex-determination system of birds is easily visible in most Neognathae species, but intrachromosomal change in the sex chromosomes is faster than in the autosomes; indeed, there are numerous examples of autosomal fusions and new sex chromosomes formed. Sex chromosomes aside, the classic avian karyotype represents a very successful mode of genome organization established before the emergence of the dinosaurs and perpetuated to this day in their only living descendants. Full article

Background: Severe malaria remains a major cause of morbidity and mortality, yet the conserved molecular signatures underlying complicated infections across Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum) are not well characterized. Identifying shared transcriptional biomarkers and host–parasite interaction networks is crucial for improving diagnosis and discovering new therapeutic targets. Methods: Public transcriptomic datasets (GSE55644, GSE59844, GSE34404) were analyzed using GEO2R to identify differentially expressed genes (DEGs). Volcano plots, Venn diagrams, and KEGG mapping were used to identify conserved DEGs. Principal Component Analysis (PCA) and Support Vector Machine (SVM) models were used to assess predictive performance. Host–parasite cross-species correlation analysis integrated parasite DEGs with host hub-genes. Functional enrichment and network module analysis were performed using Cytoscape v3.10.2 and GO/KEGG annotation tools. Results: A total of 3363 DEGs were identified in P. vivax (GSE55644) and only one DEG in P. falciparum (GSE59844) using adjusted p-values, though 772 DEGs emerged with unadjusted p-values. Cross-dataset comparison revealed 18 common DEGs, with eight upregulated genes—TIM9, NUF2, SRP68, HDAC1, GRP94, DHHC8, PPM9, and RPL27—showing robust predictive performance (AUC = 1.000; CA = 1.000) for distinguishing complicated from uncomplicated malaria in both species. Host analysis identified 1719 DEGs and six hub-genes (TNF, IL6, TLR4, CR1, CD40LG, ICAM1) linked to apoptosis, Toll-like receptor signaling, complement cascades, and cell adhesion. SVM validation predicted parasitemia levels with 75.5–84.0% accuracy. Cross-species correlation revealed strong positive interactions between parasite HDAC1/GRP94 and host IL6/TNF and negative correlations involving NUF2, TIM9, ICAM1, and CR1. Functional enrichment analysis highlighted ER stress, immune activation, and erythrocyte adhesion pathways, which together form three major host–parasite modules. Conclusion: These findings highlight conserved biomarkers and potential therapeutic candidates for future validation, demonstrating that combined DEG profiling and machine-learning approaches can provide a powerful framework for improving diagnostics and intervention strategies for severe malaria. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article

Background/Objectives: Diabetes mellitus (DM) is a critical metabolic condition with escalated blood glucose levels caused by insulin resistance, restricted insulin production, and the activity of alpha-amylase and alpha-glucosidase enzymes. Methods: This current work focuses on the synthesis and evaluation of novel Pyrimidine-derived pyrazole-based thiadiazole derivatives to target DM by inhibiting α-amylase and α-glucosidase. Results: The findings exhibited that, except for three compounds, all other synthesized derivatives inhibited α-amylase and α-glucosidase enzymes with IC₅₀ values ranging from 5.17 μM to 29.84 μM on α-amylase and 7.60 μM to 31.62 μM on α-glucosidase, in comparison to the standard drug Acarbose (α-amylase IC₅₀ = 8.25 ± 0.80 μM; α-glucosidase IC₅₀ = 10.75 ± 1.10 μM). Analogs 8g, 8k, and 8b displayed superior or comparable inhibitory activity compared to the reference drug Acarbose. The inhibition potential of the derivatives can be attributed to their stable contacts with crucial amino acid residues of targeted enzymes, as shown through molecular docking analysis. Moreover, DFT-calculated HOMO–LUMO parameters and electrostatic potential (ESP) maps were used to gain complementary insight into the electronic characteristics, charge distribution, and potential interaction behavior of the synthesized derivatives, which supported the molecular docking observations. Conclusions: Experimental outcomes and in silico support display that these derivatives serve as potential leads for anti-diabetic drug development. These potent pyrimidine-derived pyrazole-based thiadiazole derivatives were comparable to an existing diabetic mellitus inhibitor, specifying potential for further therapeutic development and optimization against diabetic mellitus. Full article