Abstract:

It is known that snowcover properties change rapidly due to effect of weather and radiation, detailed models mapping effect of weather and radiation processes to evolution of snowpack have been developed. These models are capable of accurately simulating entire evolution of snowpack at a specific point if a sufficiently detailed time-series of weather and radiation parameters affecting the point is known. In this study we consider the reverse problem of finding the weather and radiation parameters that lead to changes in snowpack parameters, we have used a simulation approach to study the feasibility of finding this reverse map. We mapped a time-series of snowcover states to their corresponding time-series of weather and radiation states using a machine learning model. The data of snowcover states was generated using a well known and rigorously validated snowcover simulation model (SNOWPACK). The results of our experiments show that snow surface time-series contains important information about the meteorological time-series affecting it. We were able to find the meteorological parameters from the simulated data under certain conditions, we expect these results to generalize with actual data. There maybe important applications of these results in optimization of weather data collection systems, weather interpolation algorithms and downscaling algorithms, combining the snowpack data with weather observations can lead to improvements in these algorithms. This study makes a preliminary feasibility study of the reverse problem, our results are positive and encourage further field work using actual data.

Abstract: Introduction: The educational promotion of inclusive recreational spaces remains a key challenge for public policies, particularly in contexts where gaps persist between regulation and implementation. Objective: This study analyzed the impact of public policies on the promotion of inclu-sive recreational spaces in the canton of Milagro, focusing on accessibility, pedagogical use, and social inclusion. Methodology: A mixed-methods approach was applied, with a non-experimental, cross-sectional, descriptive-correlational design and an explanatory component. The sample included teachers, administrators, technical staff, and community members. Data were collected through questionnaires and semi-structured interviews. Results: Findings showed a favorable regulatory framework but only partial local im-plementation. Significant relationships were identified between policy implementation and accessibility, educational use, and inclusion. Gaps were also observed in teacher training and adaptation to functional diversity. Discussion: Results align with previous studies emphasizing the need to integrate pub-lic policies, education, and territorial planning. Conclusions: Strengthening policy implementation and coordination is essential to consolidate inclusive recreational spaces with sustainable educational impact.

Abstract: Robin's criterion equates the Riemann hypothesis with the inequality $\sigma(n) < e^{\gamma}\,n\,\log\log n$ for every $n > 5040$. By a theorem of Robin, if the Riemann hypothesis is false then infinitely many colossally abundant numbers fail this inequality, so the existence of a counterexample to Robin's criterion is equivalent to the existence of a colossally abundant counterexample. Combining structural properties of colossally abundant numbers with explicit estimates for the Chebyshev theta function and the Mertens product due to Aoudjit, Berkane, and Dusart, we prove an unconditional and effective upper bound on any such counterexample. Specifically, if $n > 5040$ is colossally abundant and violates Robin's inequality, then $n < N_{k}^{Y_{k}}$, where $p_{k}$ is the largest prime factor of $n$, $N_{k} = \prod_{i=1}^{k} p_{i}$ is the primorial of order $k$, and $Y_{k} \to 1^{+}$ is an explicit constant. Together with the lower bound $n \geq N_{k}$ that holds for any Hardy--Ramanujan integer, this confines every hypothetical colossally abundant counterexample to a narrow explicit window above the corresponding primorial. We deduce a conditional reformulation of the Riemann hypothesis localized to this window. This work refines the approach taken in the author's earlier article ``Robin's criterion on divisibility'', published in The Ramanujan Journal.

Abstract: Racing homing pigeons (Columba livia) have been selectively bred for centuries for superior flight capacity. Yet, the quantitative structure of flight performance traits and the extent to which sex influences these parameters remain poorly characterized, par-ticularly in Turkish populations. This study aimed to evaluate flight performance in racing pigeons raised in the South Marmara region of Turkiye using three key kine-matic traits (flight duration, speed, and distance) and to explore the multivariate structure and individual variation of these parameters through an integrative machine learning framework. Data were compiled from 166 individually registered pigeons (77 females, 89 males), totaling 781 race records used for pattern analysis. A composite Flight Performance Score (FPS) was constructed using min–max normalized compo-nent variables, and its internal consistency was assessed via Cronbach's alpha and principal component analysis. Univariate comparisons revealed no statistically signif-icant sex-related differences in any of the three flight parameters (P > 0.05 for all traits). Principal component analysis confirmed substantial overlap between male and female individuals in multivariate trait space, and Random Forest classification failed to dis-criminate between sexes above chance level (accuracy = 0.490; ROC-AUC = 0.500), col-lectively indicating that sex is not a dominant determinant of flight performance in this population. Internal consistency analysis revealed that flight duration, speed, and dis-tance are functionally independent dimensions (Cronbach's α = 0.135; r = −0.749 be-tween duration and speed), with their variance structure being effectively two-dimensional (PC1: 60.1%; PC2: 39.7%), supporting the equal-weighting scheme applied in FPS construction. Pattern analysis of race records identified four biologically distinct flight performance profiles, characterized by differential trade-offs among flight duration, speed, and distance, suggesting that individual-level performance strategy, rather than sex, is the primary axis of variation in this dataset. These findings challenge common breeder assumptions about sex-based differences in performance and highlight the multidimensional, individual-specific nature of flight performance in racing pigeons.

Abstract: The transition to a circular economy requires the safe management of sewage sludge through nutrient and energy recovery. However, the presence of pharmaceuticals and personal care products (PPCPs) poses a critical obstacle, as these compounds tend to accumulate in the sludge matrix through sorption processes, thereby shifting the environmental problem from the water stream to the sludge stream. This manuscript provides a comprehensive review of the scientific literature on technical alternatives for valorizing sewage sludge and removing emerging contaminants. The study evaluates the limitations of conventional biological methods, such as anaerobic digestion and composting, which exhibit variable efficacy and are often insufficient to degrade recalcitrant molecules, including some commonly used pharmaceuticals. On the contrary, thermal treatments (pyrolysis, gasification, and hydrothermal processes) are considered robust alternatives capable of achieving removals exceeding 90-99% thanks to the thermal degradation of contaminants. Furthermore, the article emphasizes the innovative potential of utilizing carbon-based byproducts (biochar and hydrochar) as adsorbents or catalysts to enhance the removal of PPCPs within the treatment infrastructure itself. The integration of advanced thermal technologies is essential to mitigate the risks of contaminant transfer to the food chain and ensure a safe and sustainable nutrient cycle.

Abstract: Background: Immune checkpoint inhibitors (ICI) targeting the PD-1/PD-L1 and CTLA-4 axes have transformed the treatment landscape of genitourinary (GU) malignancies, yielding durable responses in subsets of patients with urothelial carcinoma, renal cell carcinoma (RCC), and selected cases of metastatic castration-resistant prostate cancer (mCRPC). However, most patients exhibit primary resistance, and those who initially respond frequently develop acquired resistance, substantially limiting long-term clinical benefit. A systematic understanding of the biological mechanisms driving resistance, the identification of robust predictive biomarkers, and the design of rational combination strategies are essential to extend the therapeutic reach of ICI-based regimens. Methods: An integrative review was conducted following the Whittemore and Knafl (2005) framework. Systematic searches were performed in PubMed/MEDLINE, Cochrane Central Register of Controlled Trials, Embase, and Web of Science, covering the period January 2020 to April 2026. Studies were selected according to predefined inclusion criteria encompassing original research, clinical trials, systematic reviews, and narrative reviews reporting on resistance mechanisms, predictive biomarkers, or emerging therapeutic strategies for ICI in GU cancers. Methodological quality was assessed using RoB 2, ROBINS-I, and AMSTAR-2 as appropriate. Results: A total of 32 studies met eligibility criteria. Three interconnected resistance categories were identified: 1) tumor cell-intrinsic mechanisms, including low tumor mutational burden (TMB), loss of antigen presentation via major histocompatibility complex class I (MHC-I) downregulation, activation of the Wnt/β-catenin and JAK/STAT pathways, and epigenetic reprogramming; 2) tumor microenvironment (TME)-mediated immunosuppression, driven by myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), cancer-associated fibroblasts (CAFs), and immunosuppressive cytokines including TGF-β and VEGF; and 3) acquired post-treatment resistance involving T-cell exhaustion and upregulation of alternative immune checkpoints. Among validated biomarkers, PD-L1 expression demonstrated variable predictive utility across GU cancer types, while TMB-high status (≥10 mutations/megabase) predicted improved response to pembrolizumab across solid tumors. Emerging therapeutic strategies include ICI plus tyrosine kinase inhibitor (TKI) combinations, antibody-drug conjugates (ADCs), MDSC-targeted interventions, therapeutic vaccines, and radiotherapy sensitization. Conclusion: ICI resistance in GU cancers is a multidimensional phenomenon with distinct biological drivers across tumor subtypes. Precision combinations targeting both intrinsic tumor factors and the immunosuppressive TME represent the most promising avenue to overcome resistance. Standardization of composite biomarker platforms is urgently needed to individualize ICI selection in clinical practice.

Abstract: Megacity mobility research has long relied on aggregate statistical indicators (composite traffic-planning indices andaccessibility surfaces) that capture the system at rest but are disconnected from the operational decisions shaping mobilityminute to minute. In parallel, Agentic AI offers a reference paradigm of semantically interoperable autonomous agents that negotiate and coordinate across open networks. We propose a unifying Agentic AI reference architecture for urbantransportation that maps any composite traffic index and any accessibility surface onto agent utility functions, negotiation-protocol primitives, and shared semantic ontologies. The architecture is instantiated in simulation only: no live agent-to-agent endpoints, no runtime large language models, no cross-organisation interoperability experiment; these are explicitly listed as next steps. A 12-borough London case study, evaluated over $N=30$ seeds on an origin-destination matrix calibrated to the 2021 UK Census commuter-flow aggregates, benchmarks four regimes (historic, adaptive, MaxPressure, and our Agentic policy) across four scenarios covering equity, corridor prioritisation, incident response, and their combination. The Agentic regime reduces the accessibility-deficit Gini coefficient by \textbf{23--58\,\%}, travel-time coefficient of variation by up to \textbf{41\,\%}, and mean travel time by \textbf{4--9\,\%} relative to the historic baseline; in the joint equity-plus-incident scenario it attains the per-column best within $95\,\%$ confidence on travel-time coefficient of variation and mean travel time while improving on every metric over the historic baseline. Amicroscopic testbed in the SUMO simulator on a $4\!\times\!4$ signalised grid ($120$~runs across three demand regimes and five peripheral-boost values) traces an explicit equity--efficiency Pareto frontier; in the saturation and over-saturation regimes the agentic policy matches or beats a SCOOT-style adaptive controller on mean travel time and throughput at every boost level, with travel-time variance reduced by up to a third.

Abstract: This study challenges the static treatment of the "green premium" in commercial real estate. We introduce and empirically test the Sustainability Value Decomposition Framework, which disaggregates sustainability-related valuation effects into signalling, performance, and market-access components: Signalling (the value of the label), Performance (the value of operational efficiency), and Market Access (the value of regulatory compliance). Using a mixed-methods approach that combines econometric analysis of 111 institutional office transactions in London with an instrumental-variable strategy, we strengthen identification of the certification effect while recognising the remaining assumptions required for causal interpretation. Our 2SLS model estimates an IV-based premium of 9.5% for top-tier BREEAM certification, conditional on the validity of the local planning-authority instrument. Mediation analysis suggests that EPC ratings, interpreted as market-perceived energy-performance and compliance proxies, statistically account for approximately 65.2% of the observed BREEAM-value association. We also provide a sizeable "brown discount" in a major European market, documenting a 24.7% valuation penalty for assets non-compliant with minimum energy standards, consistent with a Market Access effect. These findings suggest that sustainability-related pricing in mature office markets increasingly reflects market-perceived performance and regulatory risk, rather than certification labels alone.

Abstract: We propose a new clinical entity termed recovery-phase viral pancreatitis (RVP), characterized by delayed-onset acute pancreatitis arising during the recovery phase of viral infection, frequently after symptom resolution or hospital discharge.Although acute pancreatitis associated with viral infections, particularly coronavirus disease 2019 (COVID-19), has been increasingly recognized, most previous studies have focused on pancreatic injury occurring during the acute infectious phase. However, accumulating reports have described delayed pancreatitis developing after apparent clinical recovery, including post-discharge presentations. These delayed manifestations are difficult to explain solely by direct viral cytopathic injury during acute infection and instead suggest a distinct recovery-associated inflammatory process.A focused narrative literature review was performed to identify delayed-onset pancreatitis occurring during the recovery phase of viral infections. Representative cases demonstrated a reproducible temporal clustering of pancreatitis onset approximately 1-3 weeks after viral illness onset, frequently during convalescence or after hospital discharge. Similar delayed recovery-phase patterns were observed not only in SARS-CoV-2 infection but also in dengue virus, Epstein-Barr virus, and hepatitis A virus infections.Potential mechanisms may include transient immune suppression during acute infection, persistent viral antigens or pancreatic viral reservoirs, and delayed immune-mediated pancreatic inflammation during recovery. Although RVP shares certain conceptual similarities with immune reconstitution inflammatory syndrome (IRIS), important mechanistic differences exist because RVP appears to arise during the natural course of viral infection without external immune manipulation or profound baseline immunodeficiency. We believe RVP should be distinguished from classical IRIS.The recognition of RVP may provide a new clinical framework for understanding delayed inflammatory pancreatic injury after viral infection and may improve awareness of post-recovery and post-discharge pancreatitis.

Abstract: Time series generation (TSG) plays a fundamental role in data engineering and knowledge discovery, serving as a key enabler for data augmentation, representation learning, privacy preservation, and scenario simulation analysis in temporal data mining. By synthesizing realistic and controllable time series, TSG directly benefits downstream tasks such as forecasting, anomaly detection, and classification by data augmentation and improving model generalization. This survey presents a comprehensive and systematic review of TSG methodologies, spanning traditional non-deep learning approaches, such as rule-based, statistical, and simulation-based methods, and modern deep generative models, including variational autoencoders, generative adversarial networks, diffusion models, normalizing flows, and large language models. To organize the rapidly growing literature, we introduce a unified multi-level taxonomy to organize the technical landscape of TSG and clarify the relationships among diverse approaches. The taxonomy emphasizes underlying modeling principles rather than individual representative models. Specifically, it categorizes TSG methods according to modeling backbones, generation settings (conditional versus unconditional), sampling regularity, and modality characteristics. Building on this taxonomy, we systematically review advanced methods within each category and discuss how they address key challenges such as irregular sampling, long-sequence modeling, multivariate dependencies, and controllable generation, and how they are applied in real-world scenarios. Furthermore, we present a holistic evaluation framework encompassing five core dimensions, including fidelity, diversity, controllability, downstream performance, and privacy protection. Finally, we identify critical open challenges and outline promising research directions from data characteristics, application scenarios, and modeling paradigm perspectives. This survey aims to serve as a structured reference and roadmap for researchers and practitioners in this rapidly evolving field.

Abstract: NK cells are crucial for innate immunity and rapidly target abnormal cells through ligand-receptor signaling, without prior sensitization. Vitamin C is a bioenhancer of NK cells; however, its susceptibility to oxidation limits its efficacy in NK cell activation. This study evaluated the efficacy of Aptamin C, a stable conjugate of vitamin C and an aptamer, in enhancing NK cell activation. In in vivo study, 109 participants were ad-ministered either Aptamin C (36.057 mg/day) or placebo for 4 weeks. The results showed significant increases in NK cell cytotoxicity after 2 and 4 weeks in the Aptamin C group. Additionally, cytokine and granule levels associated with NK cell activity peaked in the serum 4 weeks after Aptamin C intake. In in vitro study, NK-92 cells treated with Aptamin C were compared to NK-92 cells treated with vitamin C. The results showed enhanced proliferation, survival, cytotoxicity, and cytotoxic granule production in NK-92 cells treated with Aptamin C compared to cells treated with vitamin C. These findings indicated that Aptamin C effectively promoted NK cell ac-tivation, suggesting its potential as an immunomodulatory supplement in NK cell therapy.

Abstract: This paper presents the results of research aimed at identifying deep-seated natural sealed reservoirs for the isolation of chemically active gases, including CO₂. A comprehensive analysis of geological, geophysical, and hydrogeological data was conducted to identify potential structures within the aquifers of Almaty and the Almaty region (Southern Kazakhstan) capable of capturing and storing carbon dioxide under geographically favorable and economically viable conditions. The study utilizes seismic survey results and drilling data from the eastern part of the Ili Basin, demonstrating the efficacy of seismic exploration in identifying stratigraphic horizons and their structural-tectonic settings. Based on an integrated analysis of available geo-physic information, the lithological and stratigraphic characteristics of the sedimentary cover in the Ili Basin are substantiated. Key caprock sequences and reservoir units for potential storage sites are identified, and recommendations for further geological exploration are provided. Five reflecting horizons were identified within the geological section of the troughs. It was established that the Miocene-Paleogene and Jurassic horizons contain sandstone reservoirs with a thickness exceeding 10 m and enhanced filtration properties. Clay complexes are prevalent in the Upper Jurassic deposits, which can serve as a caprock for these reservoir rocks. Furthermore, the Upper Cretaceous clay sequence may act as a fluid seal for the Neogene-Paleogene sandy horizons. Such conditions meet the requirements for sealed reservoirs for the isolation of chemically active gases, including CO₂. According to hydrogeological studies, seven aquifer complexes are distinguished: Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. The novelty and practical significance of this research lie in obtaining new information on the geological structure of deep horizons in poorly studied areas of the Ili Basin and establishing favorable geological factors for identifying potential sites suitable for carbon dioxide sequestration.

Abstract: Over the past decade, acoustically-actuated magnetoelectric (ME) antennas have been proposed as chip scale radiofrequency (RF) antennas compatible with post Complementary Metal Oxide Semiconductor (CMOS) fabrication processes. These devices have been reported to exhibit antenna gains far exceeding those of conventional electromagnetic (EM) antennas with comparable footprint. However, recent studies have challenged whether this enhanced gain originates from magnetoelastic coupling or from stray radiation sources, like the electric dipole moment in the piezoelectric film or currents in the probing pads. We resolve this controversy through a combined analytical, numerical, and experimental investigation. We model and quantify the radiated power and corresponding gain contributions from (I) magnetoelastic coupling; (II) the strain driven, time-varying electric dipole moment in the piezoelectric layer; and (III) the currents in the probing pads. Our results confirm that the radiation from magnetoelastic coupling exceeds that of the other sources by several orders of magnitude. In addition, we explain how to optimize the return loss and the radiated power of ME antennas when connected to a 50 Ω source, showing that the optimal operating point is the anti-resonance frequency. Based on this finding, we investigate the impact of the electromechanical coupling (k_t²) on gain and-10 dB fractional bandwidth. To corroborate our simulation results, we design, fabricate, and characterize the first two Aluminum Scandium Nitride (AlScN) magnetoelectric Bulk Acoustic Wave (BAW) antennas operating beyond 1.1 GHz. The two prototypes integrate different magnetostrictive materials (FeGaB and FeCoSiB) and exhibit measured realized gains of-31.8 dB and-29.7 dB, with-10 dB fractional bandwidths of 1.28% and 1.27% at 2.62 and 3.08 GHz, respectively. The achieved bandwidths are the highest reported for radiofrequency (RF) ME antennas, owing primarily to the enhanced piezoelectric coefficients of the AlScN. Benchmarking against control structures (unreleased FeGaB and FeCoSiB devices) confirms substantially degraded radiation performance in the absence of a strong magnetoelastic coupling. These results elucidate the working principle of ME antennas and provide RF designers with a rigorous framework for the design and modeling of acoustically actuated ME antennas for wireless communication and sensing.

Abstract: Background/Objectives: Prolonged smartphone use is associated with musculoskeletal and neurological hand symptoms; however, specialized tools for bilateral assessment remain limited. This study aimed to develop and evaluate the psychometric properties of the Smartphone-Related Hand Symptoms (S-HAND) scale. Methods: A total of 456 participants (mean age: 21.8 ± 6.65 years) were included. The sample was randomly divided into two independent subsamples for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Internal consistency was assessed using Cronbach’s alpha and McDonald’s omega coefficients. Test–retest reliability was evaluated in 75 participants using Intraclass Correlation Coefficients (ICC). Measurement precision was determined using the standard error of measurement (SEM) and minimal detectable change (MDC95). Concurrent validity was examined using correlations with general hand pain intensity scores. Results: EFA and CFA supported a three-factor structure consisting of Pain Symptoms, Numbness Symptoms, and Functional Impact. The final CFA model demonstrated excellent fit (CFI = 0.991, TLI = 0.986, RMSEA = 0.049, SRMR = 0.033). Standardized factor loadings ranged from 0.685 to 0.932. Internal consistency was high (Cronbach’s α = 0.857–0.940; McDonald’s ω = 0.863–0.942), and test–retest reliability was excellent (ICC = 0.800–0.907). SEM and MDC95 for the total score were 4.67 and 12.95, respectively. Significant positive correlations with pain intensity scores supported concurrent validity (r = 0.61–0.73, p < 0.001). Conclusions: The S-HAND scale is a reliable and valid instrument for assessing smartphone-related hand symptoms in clinical and research settings.

Abstract: Inflammatory rheumatic diseases exhibit dynamic and heterogeneous inflammatory activity, yet clinical monitoring remains episodic and temporally sparse, limiting early intervention and delaying treatment adjustment. Advances in biosensing technologies, wearable monitoring, and computational modeling offer opportunities to transition toward continuous, data-driven disease assessment. In this review, we synthesize evidence across rheumatology, immunology, biosensing, and digital health to examine how multimodal measurement approaches can support clinically actionable decision-making. We introduce a structured framework—the “Measurement Stack”—that links three components: biological signal domains (systemic, synovial, imaging-derived, and physiological), sensing platforms with distinct temporal and specificity trade-offs, and computational inference layers including feature extraction, multimodal data integration, and predictive modeling. We emphasize that the clinical value of biomarkers depends not on association alone but on actionability, defined by temporal sensitivity, repeatability, robustness to heterogeneity and signal noise, and alignment with clinical decisions. Key methodological considerations include feature engineering for sparse and continuous data, handling missingness and signal drift, calibration-aware validation, temporal and external validation, and decision-curve analysis for clinical utility. A decision-centric mapping aligns measurement and modeling strategies with clinical tasks such as early flare detection, differentiation of flare from infection, therapy switching or tapering, and monitoring of treatment response. By integrating biosensing advances with clinically grounded evaluation standards, this review outlines pathways toward interpretable, deployment-ready monitoring systems enabling proactive and personalized management of inflammatory rheumatic disease.

Abstract: Aerodynamic drag is one of the two principal external sources of energy loss in on-road vehicles – the other being rolling resistance – and it critically affects the range of battery-electric and fuel cell-electric vehicles. To ensure accurate early-stage analysis such as vehicle range prediction and sizing of energy storage and powertrain components, it is essential to incorporate realistic representations of air resistance. Despite its importance, due to limited data availability air resistance is often simplified using zero crosswind and "nominal air conditions", which tend to underestimate the actual energy required to overcome aerodynamic drag. This approach also fails to capture the variability introduced by changing environmental conditions, leading to significant discrepancies in energy consumption and, consequently, vehicle range. As a result, evaluating system robustness and conducting meaningful trade-off analyses between different vehicles or vehicles configurations becomes challenging. This study demonstrates how publicly available meteorological data can be utilized to quantify long-term variations in aerodynamic drag. By analyzing multiple years of weather observations, we derive realistic distributions of aerodynamic energy losses – capturing not only mean values but also the full range of variability. These distributions enable probabilistic modeling of vehicle performance, thereby supporting robust system design and informed trade-off decisions across various levels of vehicle architecture. To demonstrate this, we compare two different tractor/semitrailer configurations.

Abstract: Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems' success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study investigates the effects of service quality, system quality, information quality, and data quality on public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality emerged as the strongest predictor of public trust, followed by system quality. Service quality also significantly influenced system quality, information quality, and data quality. In addition, system quality, information quality, and data quality mediated the relationship between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation.

Abstract: The rapid advancement of generative large language models (LLMs) has sparked significant interest in their poten- tial to transform higher education, particularly in fostering student engagement. While these models offer novelopportunities for personalized learning and interactive experiences, their integration into academic settings remains underexplored, with varying implications for pedagogy, ethics, and institutional policy. This systematic literaturereview examines the role of generative LLMs in enhancing student engagement across multiple dimensions, includ- ing their impact on learning outcomes, academic writing, subject-specific applications, and ethical considerations.We synthesize existing research to identify key trends, challenges, and gaps in the current understanding of how these technologies are reshaping educational practices. A rigorous methodological approach was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of the field. The findings reveal that generative LLMs can significantly influence student engagement by facilitating adaptive learning environments and supporting creative problem-solving; however, concerns about academic integrity, equitable access, and pedagogical alignment persist. The review also highlights emerging tools and systems designed to integrate LLMs into education, alongside institutional and student perspectives on adoption. Based on the synthesized evidence, we discuss future directions for research and policy, emphasizing the need for balanced frameworks that harness the benefits of generative AI while addressing its risks A. Chowdhury et al., 2025. This study contributes a structured overview of the current landscape, offering insights for educators, researchers, and policymakers navigating the evolving intersection of AI and higher education.

Abstract: Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of 0.703 and the lowest coefficient of variation of 2.28%, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited.

Abstract: The sustainability-focused issues of the built environment require a change in architectural education not to form-based design methods but to adaptive, systems-based, and performance-oriented thinking. The paper explores a unified pedagogical model that incorporates biomimicry, parametric thinking, and modular design in improving sustainable design learning in architectural studios. The study adopts a qualitative case study method to investigate Architectural Design Studio 4 at the American University of Ras Al Khaimah (AURAK), where third-year architecture students undertake a discovery-based design process that takes three sequential stages. The students explored biological systems to first identify transferable principles, then implemented the principles in parametric modules with computational software like Dynamo and Revit and then reused these systems to create high-rise architecture. The results suggest that biomimicry combined with parametric workflows helps to achieve optimization but not maximization, which allows students to come up with flexible, efficient, and reusable design systems. The modular design approaches were essential in dealing with the architectural complexity, especially in the high-rise application and parametric tools enabled exploration of many variations and informed decisions based on the performance. The undisclosed final design goal promoted critical thinking, conceptualization, and problem-solving. The research provides the literature of architectural education with empirical evidence as it illustrates how an integrated process-based approach can improve the knowledge of sustainability, system logic, and adaptability in students. The study finds that integrating biomimicry and parametric design in modular and discovery-oriented studios is a sound pedagogical approach to equip future architects to deal with modern environmental and technological demands.