Abstract: Logistic and logit functions play important roles in modern science, serving as foundational tools in various applications including artificial neural network (ANN). While there are functions that could produce distinct logistic and logit curves, no single, unified framework has been developed to generate both logistic and logit curves. We introduce a generalized logistic–logit function (CMG-GLLF) to fill this gap. CMG-GLLF provides four interpretable and trainable parameters that allow explicit control over: curve type and steepness, asymmetry, upper and lower limits of x- and y-axes. CMG-GLLF’s potential is explored in basic machine intelligence tasks. As a proof-of-concept on how this function can improve performance of deep learning, we propose a trainable input feature modulator (IFM) that consists in learning the parameters of the CMG-GLLF for each input layer node during backpropagation for multi-layer perceptron (MLP), which is a fundamental building block of many complex network architectures. Compared to various other learnable functions, across 3 different optimizers, CMG-GLLF allows superior MLP’s accuracy and stable training behavior on CIFAR-10 and CIFAR-100 image classification, but at the cost of increased computational time. Hence, we identified limitations to address in future studies, notably the need to derive an explicit mathematical expression for the logit phase, which could: (i) mitigate numerical instability in more complex architectures (e.g., CNNs) while reducing computational overhead, and (ii) enable a systematic evaluation of CMG as an activation function across all layers. Furthermore, CMG-GLLF adopted as data transformation function enhances the accuracy of affinity-graph-based neuron segmentation. CMG-GLLF combines in a unique framework the ability of logistic and logit function to modulate signals or variables, covering a full spectrum of attenuation or amplification transformations. CMG-GLLF is flexible and trainable, has potential to advance machine learning models, and can inspire further applications in other data analysis challenges in different domains of science.

Abstract: Chronic surgical mesh rejection is a complex pathological process characterized by persistent inflammation, foreign body reaction, and progressive extracellular matrix (ECM) remodeling, leading to impaired tissue integration. Although histopathological features are well described, the structural organization of the ECM at the mesh–tissue interface remains insufficiently understood. This study aimed to investigate multiscale morphological alterations associated with chronic mesh rejection, with emphasis on ECM disorganization and loss of structural coherence. Seven mesh–tissue complexes explanted due to clinically confirmed chronic rejection were analyzed. Histological evaluation (hematoxylin–eosin and Van Gieson staining) was combined with atomic force microscopy (AFM), including topographical imaging, directional analysis, coherence mapping, and roughness quantification according to ISO 25178-2. Histology revealed chronic inflammatory infiltrates, foreign body reaction, and disorganized collagen deposition. AFM analysis showed pronounced surface heterogeneity, fragmented fibrillar architecture, and absence of preferential orientation. Roughness parameters (increased Sa and Sq, elevated Sku, negative Ssk) indicated a structurally irregular surface dominated by depressions and isolated peaks. Directional and coherence analyses confirmed loss of organized fibrillar architecture. These findings suggest that chronic mesh rejection is associated with marked ECM disorganization and loss of structural coherence at the mesh–tissue interface, reflecting impaired tissue integration. Multiscale morphological analysis provides insight into the structural basis of mesh failure and highlights the importance of ECM organization in implant–tissue interactions.

Abstract: The aim of this study was to assess potential uplift in soil organic carbon (SOC) levels within different types of agricultural land. A total of 1,032 soil samples were collected from 43 fields across six farms during the same year. Fields were used for arable, temporary grass and permanent grass production. The study compared SOC levels (in g/kg, ratio to clay and ratio to nitrogen) between the field boundary and within field areas. The field boundary was classed as either open (boundary with fence and/or wall) or covered (with hedgerow and/or trees). From the fields sampled, 69% of within field samples and 88% of boundary samples were categorized as having ‘very good’ levels of SOC. On average, the SOC in g/kg and ratio to clay were higher for permanent grass and boundary field areas compared to temporary and within field areas, with no difference between open or covered boundary areas. Benchmarking fields against the field boundary area or based on SOC to clay ratio can be used by land managers to identify fields for potential SOC uplift.

Abstract: Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressive generation, and time-varying resources. This paper puts forward a process reward model (PRM)-aided two-stage decoupled acceleration (PRADA) framework, which is built on a fundamental change of perspective: instead of querying a PRM online, which cripples multi-user systems with prohibitive latency, we use the PRM solely as an offline teacher. Its reasoning-quality intuition is fully distilled into a lightweight policy that screen each step locally, without any context upload, while a Lagrangian scheduler at the server resolves resource contention through a threshold-structured policy. Across diverse reasoning benchmarks, PRADA retains the vast majority of the LLM's accuracy while substantially reducing end-to-end latency. The results further reveal threshold effects for both server parallel capacity and total bandwidth: performance saturates beyond critical resource levels, after which the system bottleneck shifts from queuing to computation or from communication to contention. These structural findings provide actionable guidance for joint provisioning of computation and communication resources without requiring per-benchmark tuning.

Abstract: Background/Objectives: Oritavancin therapy for complex infections remains challenging due to the lack of well-established dosing regimens. The objective of this work was to apply PK/PD modeling and Monte Carlo simulation considering different PK/PD targets to identify multiple dosing regimens that may ensure effective concentrations of oritavancin for the treatment of long-term infections. Methods: Plasma concentration–time profiles were simulated for different regimens (single dose of 1200 mg, 1200 mg followed by 800 mg every 7 days, 1200 mg followed by 800 mg every 10 days, 1200 mg q7d, 1200 mg q10d, 1200 mg every 14 days, 1200 mg every 21 days, and 1200 mg followed by 1200 mg on day 8, then 1200 mg q14d), and the probability of target attainment (PTA), indicative of treatment success, was estimated. Results: All dosing regimens provided probabilities of target attainment of 100% up to MICs of 0.5 mg/L when AUC_0-24/MIC and C_max/MIC were applied. Considering AUC_0-72/MIC, the regimens would be adequate up to MIC of 0.125 mg/L. For fC_min > MIC, all except 1200 mg q21d resulted adequate for MIC of 0.125 mg/L, and 1200 mg day 1 + 800 mg q7d, and 1200 mg q10d may be useful to treat infections due to bacteria with MIC of 0.25 mg/L. Conclusions: More studies involving patients with complex infections are needed to better stablish the relationships among plasma concentrations, MIC values, and clinical outcomes. fC_min > MIC should be investigated as a potential PK/PD target for the treatment of these infections with oritavancin.

Abstract: Effective asset management is critical for university sustainability, yet the mechanisms linking governance and digitalization to asset performance remain unclear, particularly in developing-country higher education. This study investigates how governance and digitalization influence sustainable asset performance in Indonesian public universities, focusing on the mediating role of partnership capabilities and the moderating roles of organizational readiness and environmental dynamism. Survey data were collected from 113 staff involved in asset management, governance, and partnerships across three Indonesian universities. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships. The results show that partnership capabilities fully mediate the effects of both governance and digitalization on sustainable asset performance. Neither governance nor digitalization exhibits significant direct effects. Organizational readiness moderates the governance-partnership capabilities relationship, while environmental dynamism does not significantly moderate the digitalization-partnership capabilities link. These findings extend dynamic capabilities theory to public university asset management and suggest that universities should prioritize building partnership capabilities, align digitalization investments with collaborative needs, and assess organizational readiness before implementing governance reforms.

Abstract: Background: Antimicrobial resistance, driven by inappropriate and overuse of antibiotics, is a major public health threat. Diarrhea and respiratory illness are the leading causes of pediatric healthcare visits in low- and middle-income countries like Bangladesh. Despite clear WHO guidelines recommending limited use of antibiotics for these conditions, misuse is widespread. Methods: We interviewed caregivers of 3025 under-5 children via cellphones to assess common illnesses, associated care-seeking practices, and antibiotic use for diarrhea and respiratory illnesses experienced by their children in prior 14 days. Results: Among the participants, 116 (8.83%) reported diarrheal disease and 570 (18.84%) experienced respiratory illness during the preceding 2-week recall period. Among the children with diarrhea, 52.59% received antibiotics, and 73.77% obtained them over-the-counter from pharmacies. Among those with respiratory illness, 26.32% received antibiotics, with 58% procured them from local drugstores without a prescription from a registered physician. For diarrhea, azithromycin and metronidazole were the commonly used antibiotics; while for respiratory illness cefixime and azithromycin were frequently used. Notably, 68% of the diarrheal children either sought care from local drugstores, self-medicated, or did not receive any formal treatment. Conventional practice, long wait times at healthcare facilities, distance, and poverty were the main reasons for not seeking care from a registered healthcare provider. Conclusion: Understanding community-level antibiotic use and care-seeking behavior is essential to strengthen antibiotic stewardship and child health programs. Our findings highlight the urgent need for community awareness and enforcement of regulations restricting the over-the-counter sale of antibiotics to curb irrational and excessive antibiotic use.

Abstract: This paper presents a fully automated pipeline for converting monolingual, illustrated PDF storybooks into multilingual, AI-narrated interactive digital publications. The system was developed to disseminate 53 children's storybooks—originally produced in English by the Houston Education Attaché Office of the Republic of Türkiye and hosted at storiesofturkiye.com—across 34 target languages, covering the cultural, historical, and geographical heritage of Türkiye for young readers worldwide. The pipeline comprises four sequential stages: (1) structured PDF decomposition into text and image assets using PyMuPDF, (2) context-aware translation and editorial refinement via a locally hosted large language model (LLM) running under LM Studio, (3) multilingual text-to-speech (TTS) synthesis with optional zero-shot voice cloning using the Chatterbox model, and (4) automated generation of flip-book–style HTML5 web publications. The resulting system produces 15 languages with full audio-text output and an additional 19 languages with text-only output, reaching over 34 distinct linguistic communities through the diplomatic education network of Türkiye's overseas representations. We describe the architectural decisions, prompt engineering strategies, AI hallucination mitigation, and cross-lingual voice transfer challenges encountered, and we reflect on the broader implications of LLM-driven educational content localisation at scale.

Abstract: In this study, we employed the 5-meter Accuracy Digital Elevation Model (DEM) developed by the Geospatial Information Authority of Japan, to analyze the spatial distribution of Yayoi-period archaeological sites. Rather than relying on conventional regional cross-tabulations—such as prefecture-level classifications—this approach adopts a Geographic Information System (GIS)–based analysis that enables higher spatial precision as well as more intuitive and visually accessible interpretation. Through this methodology, we aim to reconstruct the geographical conditions of ancient Japan at the end of the Yayoi period, approximately 1,800 years ago, and to offer a new perspective on the long-standing debate concerning the location of Yamatai (Yamataikoku). The results of analyses using the 5m DEM substantially increase the likelihood that Yamatai was located in northern Kyushu. In addition, northern Kyushu exhibits highly distinctive patterns of land use that vary markedly by region. The areas surrounding present-day Asakura City and Ogori City appear to have been specialized primarily for military purposes. In contrast, the Yoshinogari site—one of the largest Yayoi-period settlements in Japan—shows a pronounced specialization in agriculture, particularly large-scale wet-rice cultivation. The area corresponding to modern Fukuoka City, meanwhile, functioned as a major urban center in which both military and agricultural functions were concentrated. Furthermore, the “Jimmu’s Eastern Expedition” undertaken by the first Emperor Jimmu cannot necessarily be dismissed as a purely legendary event; it likely reflects certain historical facts. By introducing a GIS-based approach that has been relatively underutilized in previous research, this study serves as a pilot project while simultaneously representing an ambitious attempt to expand the horizons of visualization in ancient Japanese historical studies.

Abstract:

Bacteriophages (phages) represent promising therapeutic agents. Their use in treatments is challenged by the rapid rise of resistant bacterial clones. To overcome this problem, phages can be trained in vitro to adapt them to the possible resistance that may arise. Here, we co-evolved phages with their hosts under different conditions and assessed the outcomes using qPCR. The co-evolution experiment yielded a panel of bacterial clones that were either adapted to a phage, a competing phage, or to a cocktail of both. The adaptation of a phage was done either in the continuous presence of an evolutionarily naïve host, or in a cocktail with a competing phage, or both conditions, or neither conditions. We assessed each obtained phage ability to infect evolved bacterial clones in the panel we created, and we used qPCR to enable high-throughput assessment. This allowed us to evaluate 500 phage-bacteria interactions. While all phages benefitted from the presence of evolutionary naïve hosts, the screening suggests that optimal training conditions are phage-specific, based on the four phages tested. For Enterobacter cloacae phages EC151 and EC152, the most extensive infectivity in our experiments was observed when a competing phage and/or an evolutionarily naïve host was included during adaptation. For Stenotrophomonas maltophilia phages StM171 and StenM174, the presence of an evolutionarily naïve hosts appeared beneficial in both replicates; co-adaptation with a competing phage led to a complete loss of StM171 infectivity in both experiments, but benefited StenM174. Phages passaged for 10 passages consistently infected a broader range of bacterial clones than those sampled after 5 passages. Sequencing of 8 phages obtained after adapting EC152 identified recurring mutations in a transcriptional regulator, and in some cases, in the baseplate and tail fiber genes.

Abstract: Background: Oral administration remains the preferred delivery route for supplements and bioactive compounds, yet the mucosal barrier restricts systemic exposure of most therapeutic agents to a small fraction of the administered dose. Enzymatic degradation, pH-dependent instability, and limited paracellular transport collectively constrain bioavailability, particularly for hydrophilic nutrients, peptides, and mineral ions. Framework: This paper proposes a three-mechanism framework to explain how a class of surface-active mineral oxide delivery systems may enhance mucosal absorption through established biological pathways. The framework is grounded entirely in peer-reviewed physiology and pharmacology independent of any proprietary formulation data. Mechanisms: The three proposed mechanisms are: (1) a low-dose, transient reactive oxygen species (ROS) pulse that reversibly modulates tight junction proteins, expanding paracellular permeability within a well-characterized physiological range; (2) surface-mediated delivery of mineral oxide species directly to mucosal epithelial cells, bypassing the portal circulation route that limits conventional oral mineral delivery; and (3) activation of the NRF2-KEAP1-ARE pathway in mucosal cells, upregulating endogenous antioxidant defenses through hormetic adaptation. These mechanisms are supported by the established biology of hormesis, tight junction physiology, mineral pharmacology, and redox signaling. Comparison to Existing Approaches: Liposomal encapsulation protects cargo from gastric degradation but achieves typical oral bioavailability of 1 to 15 percent for peptide cargo and faces instability challenges in the gastrointestinal environment. The mineral oxide delivery approach described here modifies the absorption environment itself rather than encapsulating cargo, representing a mechanistically distinct and potentially complementary strategy. Conclusion: This framework offers a biologically plausible, citation-supported basis for enhanced oral and sublingual delivery of nutrients and peptide compounds. Controlled clinical pharmacokinetic studies are needed to confirm these mechanisms and quantify the magnitude of bioavailability enhancement in human subjects.

Abstract: Sudan faces a looming threat of multiple overwhelming infectious disease outbreaks in 2026, driven primarily by conflict, displacement, and weakened health systems. Likely climate events, including floods, droughts, and heat waves, increase the likelihood and potential impact of outbreaks. Many epidemic-prone diseases have been identified by the WHO as health risks with high likelihood in the first third of 2026, including cholera, dengue fever, measles, acute enteric diseases (typhoid, rotavirus), acute respiratory tract infection, hepatitis E, meningococcal diseases, and Polio type 2; and classified as either very high risk or high risk based on the potential to cause morbidity/mortality. This is alongside other non-epidemic health crises such as malaria, malnutrition, and sexual and gender-based violence. There is an urgent need to strengthen key interventions, including the provision of safe water and sanitation and hygiene promotion for water-borne disease prevention and control, integrated vector surveillance and control for vector-borne diseases like dengue and malaria, and addressing low vaccination coverage through the re-establishment of routine immunizations and vaccination campaigns for diseases like diphtheria, pertussis, and meningitis. Strong community engagement (CE) remains central to preventing looming outbreak threats, alongside a commitment to coordination, funding, and implementation support for the Government of Sudan by UN agencies, including UNICEF and WHO, and humanitarian and development agencies, while ensuring localization. Collaborative efforts addressing vulnerabilities, healthcare preparedness, and community participation are required to limit the impact of the looming threat.

Abstract: Miniaturization of photonic integrated circuits is a long-standing problem in optical engineering. Nowadays, the most promising material platform for integrated photonics are anisotropic van der Waals materials due to overcoming the light diffraction limit. Here, we numerically study v-groove channel waveguides formed in a 50-nm-thick slab of the in-plane hyperbolic in visible and near-infrared ranges van der Waals material MoOCl₂. At the telecom wavelength 1550 nm, a channel supports a guided mode with an effective index 1.0206 and a decay length of 13.7 µm. We also design a Mach–Zehnder–type interferometric layout with a maximum splitter angle of approximately 7° for demonstration of a possible practical application in a telecom range and in-plane angular channel modes propagation characteristics. We demonstrate that using MoOCl₂ instead of gold leads to a tenfold reduction in the linear dimensions of the photonic integrated circuit. Therefore, we envision that by combining the extraordinary material properties of MoOCl2 with the v-shaped geometry of waveguides, one can make the integration density of photonic devices close to electronics.

Abstract: Cloud cover remains a persistent challenge in optical remote sensing, limiting the usability of optical satellite imagery for continuous Earth observation. Synthetic Aperture Radar (SAR) data, in contrast, provides cloud-penetrating, all-weather imaging but lacks the spectral richness and is less visually interpretable compared to optical observations. Bridging these complementary modalities, this study investigates SAR-to-optical image translation using the Pix2Pix conditional generative adversarial network (cGAN). While existing research predominantly focuses on reconstructing only the visible (RGB) or near-infrared bands, this work employs the winter subset of the SEN12-MS dataset to address the full spectral range. The objectives are threefold: (i) to validate SAR-to-optical translation across all 13 Sentinel-2 spectral bands; (ii) to assess the reliability and reconstructability of each individual band; and (iii) to evaluate the performance of the translation model for cloud removal. Experimental results show that the model effectively learns the SAR-to-optical mapping and achieves high reconstruction quality across all spectral bands, though bandwise analysis reveals that reconstruction accuracy varies with spectral characteristics. When applied to the SEN12-MS-CR dataset, the model successfully generates cloud-free optical imagery that closely matches reference data, achieving performance competitive with state-of-the-art models such as DiffCR. Overall, the findings confirm the viability of SAR-to-optical translation for producing spectrally consistent, cloud-free optical imagery, thus enhancing the temporal continuity of Earth observation data. Two ablation studies further analyze the impact of different loss functions and the exclusion of 60 m bands.

Abstract: As a third-generation semiconductor material, indium phosphide (InP) exhibits complex anisotropic etching characteristics, showing significantly varying etching morphologies under different temperature, concentration, and surfactant conditions. This complexity poses challenges in controlling the etching evolution process and predicting its three-dimensional structures. To address the simulation of InP etching structures and surface morphology, this study first establishes an atomic model of the InP etching system and analyzes how different atomic structures influence crystal plane etching rates. Subsequently, based on the microscopic activation energy theory, we propose an atomic removal determination function (InP-RPF) for InP etching substrates, numerically elucidating the relationship between macroscopic crystal plane etching rates and microscopic atomic removal probabilities. Furthermore, we develop an evolutionary Monte Carlo etching system model (InP-EMC), employing evolutionary algorithms to continuously optimize the energy parameters in the InP-RPF function, thereby adjusting the removal probabilities of various atomic types on the substrate and validating the simulated etching rates. Experimental comparisons demonstrate that the InP-EMC model accurately constrains atomic removal probabilities using limited crystal plane etching rate data, achieving simulation accuracy exceeding 90% for full-crystal-plane etching rates, mask etching structures, and surface morphology characteristics.

Abstract: Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re) and its laminar separation bubbles significantly complicate aerodynamic predictions. This study provides an experimental and numerical data for a custom-designed airfoil tested at Re = 68k–159k and angles of attack α = 0°–17.5°. Lift, drag, and pressure coefficient (C_p) distributions were measured experimentally. The XFOIL, the fully turbulent 3D k-ω SST, and the γ-Re_θ transition RANS models were validated against the experimental data using multiple quantitative metrics. The γ-Re_θ model demonstrated superior performance, achieving lift Maximum Absolute Percent Error of 1.6–3.4%, near-zero bias, and coefficient of determination > 0.99. It accurately captured the laminar separation bubble pressure plateau at mid-chord, with mean gross-averaged C_p percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. In contrast, the k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL showed poor reliability in transitional flow regimes. Sensitivity analyses confirmed the robustness of the γ-Re_θ model across the tested Re and α ranges. The generated experimental dataset, combined with the validated transition-sensitive RANS approach, provides a strong foundation for low-Re airfoil and DAWT diffuser design. Future work should extend experimental measurement below Re = 5x10⁴ and above 2x10⁵, including post-stall conditions and system level designing.

Abstract: Neuromyelitis optica spectrum disorder (NMOSD) is a severe autoimmune astrocytopathy of the central nervous system, characterized by recurrent optic neuritis and longitudinally extensive transverse myelitis. Due to overlapping features with multiple sclerosis (MS), misdiagnosis remains common, yet treatment strategies differ fundamentally between these conditions. This narrative review synthesises recent advances in diagnostic and prognostic biomarkers for NMOSD, focusing on serological, cellular, molecular, and microbiological indicators.A targeted literature search was conducted in PubMed, Scopus and Web of Science (January 2015 – June 2025) using keywords related to NMOSD and biomarkers. Of over 500 initially identified articles, 72 key references were selected based on scientific relevance, methodological quality and clinical applicability, consistent with a narrative review approach.Established biomarkers – aquaporin‑4 immunoglobulin G (AQP4‑IgG) and myelin oligodendrocyte glycoprotein antibodies (MOG‑IgG) – remain central to diagnosis and phenotypic classification. Emerging markers, including glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), cytokines (interleukin‑6, CXCL13, CXCL10), complement components (C3, C4, sC5b‑9), microRNAs, metabolomics profiles, gut microbiome signatures and viral serology (Epstein–Barr virus, human herpesvirus 6), show promise in differentiating NMOSD from MS and MOGAD, predicting relapses, and guiding therapy. Integration of antibody assays with markers of astrocytic and axonal injury, immune activation and microbiological signatures could improve diagnostic accuracy and enable personalised treatment. However, clinical application remains limited by assay variability, lack of standardised cut-offs and small study populations. Future progress requires validated multimarker panels and harmonised measurement platforms across diverse populations.

Abstract: Background/Objectives: The oscillatory dynamics underlying stage-specific processing in working memory (WM) remain incompletely characterized, particularly under varying memory loads. We examined load-dependent modulation of theta (4-7 Hz), lower alpha (8-10 Hz), and upper alpha (11-13 Hz) absolute power during encoding, maintenance, and retrieval using quantitative EEG in a modified Sternberg task that temporally dissociates these stages. Methods: Forty-five healthy young adults performed trials with memory sets of three, five, or six uppercase consonants followed by a lowercase probe. EEG data were analyzed using cluster-based permutation testing, and brain-behavior relationships were assessed using regression models. Results: Fronto-central theta power increased with memory load and was significantly higher during retrieval than during encoding or maintenance. Greater theta power during retrieval predicted faster reaction times in the three-letter condition. Alpha oscillations showed robust stage effects. Lower alpha power was higher during maintenance than retrieval across loads and exhibited a load effect during maintenance (three > six letters) in occipital regions. Upper alpha power was consistently maximal during maintenance across all loads, involving bilateral fronto-central, parietal, and occipital regions. Critically, under moderate load (five letters), higher upper alpha power predicted a greater probability of correct responses across task stages. Conclusion: These findings demonstrate a functional dissociation between oscillatory bands across temporally separated WM stages: theta activity was retrieval-dominant and associated with response speed, whereas alpha, particularly upper alpha, was maintenance-dominant and supported accuracy under increased mnemonic demand.

Abstract: This paper surveys the emerging field of hybrid quantum–classical (HQC) architectures for medical image analysis, with a particular focus on COVID-19 classification as a rep-resentative application. Despite extensive progress in quantum machine learning, there is still a lack of structured taxonomy-driven analysis that systematically characterizes how quantum components are integrated within hybrid imaging pipelines. This study pro-vides a structured review of representative HQC models, categorizing existing approaches based on the functional role of quantum modules, including front-end processing, feature extraction, and classification layers. A comparative analysis is conducted to examine how these architectural design choices relate to reported performance across different ex-perimental settings. The review shows that hybrid models can achieve strong binary classification performance under simulation conditions, particularly when quantum cir-cuits are used as feature extractors. However, performance in multi-class scenarios remains inconsistent and highly sensitive to encoding strategies, circuit design, and optimization settings. In addition, most existing studies rely on simulator-based evaluations, limited or single-source datasets, and non-standardized benchmarking protocols, which restrict reproducibility and generalization. Real-hardware validation remains scarce, and no consistent evidence of task-level quantum advantage over optimized classical baselines has been demonstrated. Overall, current HQC-based approaches should be considered exploratory rather than clinically validated solutions. Future progress requires stand-ardized benchmarking, multi-institutional validation, hardware-aware evaluation, and the development of interpretable hybrid architectures that respect NISQ-era constraints.

Abstract: The issue of single-use plastic (SUP) waste is a recent sustainability challenge in developing and fragile regions with varying capacities for waste management, enforcement, and regional governance. This paper examines the regulation of SUPs in the Intergovernmental Authority on Development (IGAD) member states as an example of regional environmental governance in contexts of weak institutions. The paper provides a structured qualitative legal analysis of formally enacted legislative and policy measures. It assesses the extent to which national (and some subnational) legal responses comply with key principles of international environmental law, including prevention, the polluter-pays principle, and cooperation. The findings show that the legal responses to SUPs in IGAD are developing, but differ in scope, legal form, consistency, and effectiveness. Some countries employ direct bans, while others regulate plastics through their general environmental and waste management legislation. Prevention measures are prominent, but responsibility management measures (such as extended producer responsibility) are in their infancy, with Kenya, and to a lesser extent Uganda, showing more integrated systems. The paper links the regulation of SUPs to sustainable consumption and production, climate change, marine protection, effective institutions and partnerships, and informs discussions about SDGs 12, 13, 14, 16, and 17. The paper concludes that the most pragmatic way forward for IGAD is progressive regional harmonization, with enhanced common standards, monitoring, producer responsibility, and transboundary cooperation, rather than immediate legal convergence.