Abstract: Tumor Treating Fields (TTFields) represent a novel, non-invasive therapeutic modality in oncology that employs low-intensity, intermediate-frequency alternating electric fields to disrupt mitotic processes and induce cancer cell death. This review integrates mechanistic, preclinical, and emerging clinical evidence supporting the integration of TTFields with immunotherapeutic strategies in pancreatic ductal adenocarcinoma (PDAC). Although immunotherapy has transformed the treatment landscape across multiple malignancies, its efficacy in PDAC remains limited due to the tumor’s dense stroma, immunosuppressive microenvironment, and low immunogenicity. Preclinical investigations suggest that TTFields may potentiate immune-based therapies by enhancing antigen presentation, modulating the tumor microenvironment, and attenuating mechanisms of immune resistance. We highlight studies evaluating TTFields in combination with immune checkpoint inhibitors, adoptive cellular therapies, and cancer vaccines, emphasizing their potential synergistic effects in PDAC. Clinically, the phase II PANOVA-2 trial demonstrated feasibility and encouraging survival outcomes with TTFields in combination with gemcitabine and nab-paclitaxel, providing the rationale for the ongoing phase III PANOVA-3 trial and the phase II PANOVA-4 trial which combines TTFields with chemotherapy and atezolizumab. Additional clinical experiences in glioblastoma and non-small cell lung cancer further substantiate the broader applicability of TTFields as an immunomodulatory adjunct. Remaining challenges include optimizing treatment sequencing, identifying predictive biomarkers, and managing TTFields-associated toxicities. Collectively, current evidence positions TTFields as a promising strategy to augment immunotherapy in PDAC, warranting further translational and clinical investigation to establish its role in reshaping therapeutic paradigms.

Abstract: Elodea species, including Egeria densa, are globally recognized as invasive aquatic macrophytes that significantly disrupt aquatic ecosystems through the formation of dense floating mats thereby inducing anoxic conditions. However, their potential as a source of high-value bioactive compounds remains mostly under-explored. This study aimed to evaluate the phytochemical profile and antioxidant capacity of Elodea methanolic extract to assess its potential for industrial application. Total soluble protein was quantified via the Bicinchoninic Acid (BCA) assay, while total phenolic content (TPC) and total flavonoid content (TFC) were determined using colorimetric methods. The antioxidant activity of the extract was assessed using the Ferric Reducing Antioxidant Power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays, supplemented by qualitative screening for secondary metabolites. Results revealed that the extract possessed a mean soluble protein content of 4.5 ± 0.19 mg BSA eq./g FW, a TPC content of 4.015 ± 0.3 mg GAE/g FW, and a 1.86 ± 0.12 mg QE/g FW. Qualitative screening of the extract revealed the presence of alkaloids and flavonoids. The extract displayed high reducing power (FRAP: 196.6 ± 0.5 µmol Fe²⁺ g⁻¹ FW) and significant radical scavenging activity with an IC50 of 65.5 µg/mL, comparable to the commercial standard, ascorbic acid (IC50 of 61.98 µg/mL). These findings showed that E. densa is rich in pharmacologically active bioactive components; alkaloids, proteins, phenolics, and flavonoids, thereby highlighting its potential as a reservoir of natural antioxidants. It also suggests that the biochemical synergy between soluble proteins and phenolics drives the high antioxidant efficacy of the extract. The results also suggest that E. densa biomass, often overlooked as an invasive species may serve as a sustainable source of bioactive metabolites in the pharmaceutical or food preservative industries.

Abstract: This article proposes a novel and replicable computational methodology named CoLiRa (Computational Literature Review & Analysis) Framework to quantitatively analyze and map the evolution of a scientific field. As a multi-stage approach, the CoLiRa Framework first uses Latent Dirichlet Allocation (LDA) to identify core research topics from a body of literature. Second, it applies cluster analysis (K-Means and Multidimensional Scaling) to map the conceptual structure of the field’s key terms. Finally, it uses linear regression analysis to quantitatively assess the development trends of these topics over time. We demonstrate our proposal through a semi-systematic literature review on the semantic enrichment of tabular data, which covers studies (up to 2024) that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis of this case study revealed three core research topics and found no statistically significant evidence of a shift in topic prevalence, indicating a stable research ecosystem. This work thus offers a validated computational approach for conducting literature reviews and mapping research trends.

Abstract: Technology adoption theories developed in institutionally mature contexts assume stable hierarchies among determinants, with perceived usefulness typically dominating. This paper qualifies this assumption by proposing that adoption hierarchies are institutionally contingent. Drawing on institutional voids theory and digital finance research, the paper develops a framework identifying three adoption regimes that function as ideal types which may overlap within contexts: (a) institutional trust dominant, where strong market￾supporting institutions enable usefulness-centered adoption; (b) vendor trust compensatory, where institutional voids elevate vendor-specific trust to primary importance; and (c) infrastructure-constrained, where basic access functions as a direct behavioral determinant. The framework extends technology acceptance theory by specifying when hierarchies change, theorizing trust as a compensatory mechanism, infrastructure as a hard constraint based on physical feasibility rather than perceptions, and a digital leveling effect explaining selective cultural influence. We derive propositions and outline a research agenda for cross-country and longitudinal validation, with implications for technology acceptance theory and digital financial inclusion practice.

Abstract: Drought stress severely limits maize growth and productivity worldwide. In this study, we examined the effects of foliar-applied carbon nanoparticles (CNPs) on morphological and physiological traits in maize plants exposed to drought stress for 25 days. Two maize hybrids one drought-tolerant (LG 36745 PRO4) and one drought-sensitive (AG 8088 PRO2) were treated with 0 or 1.0 mL L⁻¹ of a CNP-based nanofertilizer and exposed to three drought levels: 0 MPa (control), -0.4 MPa (moderate stress), and -0.8 MPa (severe stress). The experiment followed a 2 × 2 × 3 factorial design with four replicates. Results indicated that drought stress adversely affected most morphological and physiological traits, particularly in the drought-sensitive hybrid. However, foliar CNP application showed strong potential to alleviate drought's adverse effects in maize under moderate and severe stress, primarily by preserving plant water status, enhancing water use efficiency, carboxylation efficiency, photosynthetic rate, and early growth in challenging environments. These findings will provide the basis for future research on management practices adopted to control drought and ensure the development of modern and sustainable agriculture.

Abstract: This paper examines how veterinary science intertwines with the different ontologies of resilience. As resilience has increasingly become an influential yet conceptually diverse framework, its different ontologies shape and are shaped by veterinary science thinking. This paper will begin with a brief overview of the origins of the resilience concept and its three major ontologies: engineering, psychological and ecological resiliencies. Following these different ontologies, the paper then explores animal level resilience, where engineering framings emphasise disease response and production stability, while welfare-oriented perspectives focus resilience on the affective experience and the lived realities of animals. It then considers veterinary professional resilience, highlighting how emotional labour, workload pressures and structural constraints shape wellbeing across the profession. Finally, it analyses how veterinary science contributes to socio ecological resilience through One Health approaches in public health, food systems and climate adaptation. Across these domains, resilience is often framed as a desirable at-tribute, yet it remains a value laden concept that can obscure inequities or normalise preventable harms. This paper calls for critical, justice-oriented engagement with resilience to ensure it supports ethically grounded veterinary practice and promotes healthier-happier animals, more equitable systems, and sustainable professional environments.

Abstract: Objective: To compare fetal MAPSE and TAPSE values in preeclamptic pregnancies with those in healthy pregnancies and to examine the changes in these parameters according to the severity of preeclampsia. Methods: This prospective case–control study enrolled 77 women with preeclampsia and 81 healthy pregnant controls. Fetal MAPSE and TAPSE were obtained under standardized conditions by experienced operators using M-mode ultrasonography. Results: Fetal mitral annular plane systolic excursion (MAPSE) and tricuspid annular plane systolic excursion (TAPSE) values were found to be significantly lower in the preeclampsia group compared with the control group (p < 0.001). In analyses evaluating preeclampsia cases within themselves, fetal MAPSE and TAPSE values were found to be more significantly reduced in the preeclampsia with severe features group compared to the preeclampsia without severe features group. Conclusion: Fetal MAPSE and TAPSE values, measured by M-mode ultrasonography, were found to be significantly lower in the preeclampsia group compared to the control group. The more pronounced decrease in these values, particularly in preeclampsia with severe features cases, suggests that MAPSE and TAPSE measurements may be early indicators of fetal cardiac adaptation to the impaired intrauterine environment.

Abstract: Background: Helicobacter pylori is a well-established risk factor for gastric carcinogenesis. Increasing evidence suggests that the oral cavity may serve as an extragastric reservoir for the bacterium, potentially contributing to persistent infection and reinfection. Orthodontic appliances can modify oral biofilm ecology and may facilitate bacterial colonization. This study aimed to investigate the association between oral H. pylori colonization and gastric cancer, while exploring the potential modifying role of fixed orthodontic appliances. Materials and Methods: In this cross-sectional observational study, 212 participants were recruited from gastroenterology and dental clinics between January 2023 and March 2025. Oral samples were collected and analyzed for H. pylori DNA using polymerase chain reaction (PCR). Gastric diagnoses were established through endoscopic examination and histopathological evaluation, classifying participants into gastric cancer, precancerous gastric lesions, non-atrophic gastritis, and control groups. Demographic, clinical, and oral health variables were recorded. Multivariable logistic regression models were used to evaluate the association between oral H. pylori detection and gastric cancer while adjusting for potential confounders, including age, sex, smoking status, oral hygiene indicators, and socioeconomic factors. Results: Oral H. pylori DNA was detected more frequently in participants with gastric cancer compared with controls. After adjustment for potential confounders, the presence of oral H. pylori was significantly associated with increased odds of gastric cancer. Interaction analysis suggested that individuals with fixed orthodontic appliances demonstrated higher rates of oral H. pylori detection, supporting the hypothesis that orthodontic biofilm retention may facilitate bacterial persistence within the oral cavity. Conclusions: Our findings support the concept of an oral–gastric microbial axis in H. pylori–associated disease and suggest that the oral cavity may represent a potential reservoir contributing to gastric infection dynamics. The presence of orthodontic appliances may influence oral microbial ecology and could play a role in sustaining H. pylori colonization. These results highlight the importance of interdisciplinary approaches integrating dentistry and gastroenterology in the understanding and management of H. pylori infection and gastric cancer risk.

Abstract: Land use planning plays an important role in advancing sustainable development by integrating environmental, social, and economic dimensions to optimize land utilization and bolster climate resilience. The adoption of efficient practices contributes to the mitigation of land degradation, while strategically planned agricultural systems enhance food security and promote ecological balance. This study focused on the development of an environmental conservation framework for sustainable land use planning in Zambia. Employing a mixed-methods research design, data were collected from a sample of 150 respondents. Quantitative data were analysed using descriptive and inferential statistics, including regression analysis, while qualitative data were subjected to thematic analysis. The research identified key conflicts between agriculture and environmental conservation, including unsustainable farming practices (30.8%), resource competition (24.2%), and deforestation (23.3%). Approximately 40.3% of respondents reported occasional conflicts, while 33% experienced them often. Major barriers to sustainable land development included inadequate financial support (35%) and lack of knowledge (30%). Awareness of sustainable agricultural practices varied, with 38% of respondents indicating high awareness and 35.8% reporting low awareness. Conventional agriculture (35.8%), crop rotation (30%), and conservation agriculture (11.7%) were the most common practices, with crop rotation being the easiest to implement (42.2%), and climate-smart agriculture being the most challenging (37.8%). A chi-square analysis revealed no significant association between awareness levels and perceived barrier impacts (p=0.327). Regression analysis indicated that age negatively correlated with the type of conflict (β=-0.0283, p< 0.001), while location influenced conflict experiences, with certain areas, such as Section D (β=1.3799, p< 0.001) and Section G (β=1.6554, p< 0.001), reporting more frequent conflicts. Additionally, sex had a positive but marginally significant effect (β=0.2640, p=0.062). Qualitative findings highlighted the tension between agricultural production and environmental conservation, with economic pressures driving environmental degradation, such as deforestation and water pollution. Participants also pointed to limited knowledge, training, and financial barriers, including high costs and restricted access to credit, as key obstacles. The study proposed an environmental conservation framework to address these conflicts, integrating sustainable agricultural practices with effective land use planning. The framework advocates a multi-stakeholder approach involving policymakers, farmers, and environmental experts to promote balanced sustainable land use. The findings enhance the body of knowledge by providing empirical evidence on the conflicts between agriculture and environmental conservation in land use planning, highlighting key socio-economic and spatial factors influencing sustainability challenges. The proposed environmental conservation framework offers a practical guide for policymakers and stakeholders to integrate sustainable agricultural practices into land use planning.

Abstract: Missing data remains a pervasive challenge in air quality data analysis, where inappropriate imputation techniques can introduce hidden biases and compromise the reliability of time-series models such as AutoRegressive Integrated Moving Average (ARIMA). This paper examines the impact of linear interpolation and mean/median imputation on the performance of the ARIMA model and biases in the prediction of particulate matter 2.5 (PM2.5) concentration, together with a detailed analysis of ARIMA generated error metrics and their implications for the accuracy and reliability of the prediction. The findings reveal that package-default imputation significantly influences ARIMA forecasts, while mean/median imputation consistently delivers superior predictive performance, highlighting its robustness for handling missing environmental data. Moreover, imputation during the data transformation stage exerts a greater influence on model outcomes than methods applied at later analysis stages.

Abstract: Automated research has just crossed a threshold, becoming increasingly visible through public-facing instruments like AUTORESEARCH https://github.com/karpathy/autoresearch. In this position paper, we use this system to highlight a broader methodological shift: the human role is moving from experimenter to research director. As agents cheaply generate and execute experimental branches, the primary unit of scientific accountability shifts from a successful run to an admissible claim—a concept we call the claim-governance thesis. NLP makes this shift especially apparent due to its dynamic evaluation, contamination risks, and normative trade-offs. Because current agents excel at short-horizon search but lack long-horizon evidential discipline, a traditional paper and final checkpoint no longer sufficiently convey the scientific object. We therefore propose a research-director bundle—comprising an objective sheet, program boundaries, discovery trace, verification ledger, provenance bundle, and role map—as a practical minimum artifact set for evaluating automated research.

Abstract: Magnetic resonance imaging (MRI) acquired at low magnetic field strengths typically suffers from reduced signal-to-noise ratios (SNR), which leads to noticeable signal degradation compared with high-field MRI. As a result, reconstructing high-field-like images from low-field MRI data is a challenging task due to the inherently ill-posed nature of the problem. In addition, obtaining paired low-field and high-field MR images is often difficult in practical scenarios.To address these challenges, we propose a novel meta-learning framework with a two-stage mechanism. In the first stage, an optimal-transport-based meta-learner models the degradation process from high-field to low-field MRI and generates pseudo-paired datasets consisting of high-field and low-field images. In the second stage, a base learner solves the inverse problem of recovering high-field-like images from low-field MRI through an iterative regularization strategy, where the learned joint distribution of the pseudo-paired data serves as a prior.Experimental results demonstrate the capability of the proposed approach to generate 1.5T-like images from 0.5T MRI data. Both qualitative visualization and quantitative evaluations, conducted by comparing the reconstructed images with registered real 1.5T images, show that the proposed method produces images with SNR and contrast comparable to those of true 1.5T scans, even under a three-fold acceleration setting. Furthermore, the proposed method achieves superior performance compared with several mainstream approaches, including CycleGAN and Score-MRI.

Abstract: Large Language Models (LLMs) serving as automatic evaluators (LLM-as-a-Judge) have become essential for assessing Retrieval-Augmented Generation (RAG) systems. However, in multilingual settings, these judges exhibit significant calibration drift across languages, producing scores that are neither comparable nor aligned with human judgments. We present CalibJudge, a post-hoc calibration framework that addresses this challenge through: (1) language-specific temperature scaling, (2) uncertainty quantification, and (3) selective abstention. We evaluate CalibJudge on the MEMERAG benchmark covering five languages. Our experiments demonstrate that CalibJudge improves correlation with human annotations by up to 21.3% relative improvement in Kendall's while reducing cross-lingual fairness gaps by 42% and achieving 88% balanced accuracy at 70% coverage.

Abstract: The Navier–Stokes equations provide the fundamental continuum description of viscous fluid motion, yet their derivation from discrete interacting systems remains an important theoretical challenge. In this study we propose a network-based master equation framework for fluid dynamics and demonstrate how Navier–Stokes–type equations emerge from interacting systems through a relaxation mechanism. The system is formulated as a set of nodes exchanging mass, momentum, and energy along network edges. The evolution of node states is governed by a master equation that incorporates both conservative fluxes and entropy-producing dissipative interactions. Under appropriate structural assumptions, the resulting discrete dynamics preserve global conservation laws while satisfying a discrete form of the second law of thermodynamics. By analyzing the continuum limit of the network system, we show that the master equation converges to a conservation-law-type partial differential equation. A relaxation extension is then introduced to represent nonequilibrium stresses through auxiliary variables. The resulting relaxation system possesses an extended entropy structure that yields uniform a priori estimates. Using compactness arguments based on the Aubin–Lions theorem, we establish the strong convergence of velocity fields and prove that a subsequence of solutions converges to a Leray–Hopf weak solution of the incompressible Navier–Stokes equations. In particular, the forcing generated by residual stresses vanishes in the limit due to the dissipative structure of the extended system. The present framework provides a unified perspective linking discrete network dynamics, relaxation systems, and continuum fluid mechanics. It suggests a new pathway for understanding how classical hydrodynamic equations may arise from interacting systems beyond the traditional kinetic-theory setting.

Abstract: This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional coupled finite element analyses. Static, cyclic, thermal, and coupled loading tests were conducted to clarify deformation evolution, strain distribution, and damage characteristics of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stabilization, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab centre is identified as the thermally sensitive zone. Under coupled high-temperature and static loading, both strain and settlement show a non-monotonic increase–decrease trend at approximately 1.1–1.3 kN, suggesting a potential threshold for damage initiation. Under cyclic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude. Numerical results further show that geocell reinforcement reduces the central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to a doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the surface course thickness; beyond this range, the marginal reinforcement benefit decreases. The results demonstrate that geocell reinforcement can significantly improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements.

Abstract: Internationalization has become a central feature of contemporary higher education, yet collaborations and doctoral training across institutional and cultural contexts often involve persistent asymmetries. While these are frequently interpreted as temporary coordination problems or individual adaptation challenges, less attention has been paid to how asymmetry is structurally produced and managed within everyday academic practice. This paper examines asymmetry as a structural condition shaping international academic cooperation and doctoral supervision. The study adopts a conceptual and prac-tice-informed analytical approach based on two longitudinal situations at the University of Genoa (Italy): a capacity building partnership with a university in the Global South and the supervision of an international doctoral student within a biomedical research labor-atory. Based on literature on internationalization, supervision, and academic develop-ment, the analysis explores how asymmetries emerge and evolve in practice. Across both cases, asymmetry became visible through misaligned temporalities, uneven distributions of responsibility, and adjustment processes that enabled collaboration and supervision to continue despite unresolved structural tensions. Stabilization occurred primarily through the redistribution of academic labor rather than through convergence of expectations or practices. These dynamics gradually contributed to the normalization, and partial in-visibility, of asymmetry within everyday academic work. The paper argues that rec-ognizing asymmetry as a structural feature of international academic engagement can support more reflexive and negotiation-oriented approaches to collaboration and doctoral education.

Abstract: The Deori community represents one of the most ancient indigenous tribal communities of Assam, with a rich cultural heritage spanning over a millennium. This article provides a comprehensive overview of the Deori people, examining their historical origins, social structure, cultural traditions, linguistic heritage, and contemporary challenges. Drawing on ethnographic research, government records, and academic studies, the paper explores how this Sino-Tibetan community has preserved its distinct identity while navigating centuries of political change, from ancient kingdoms to colonial rule to modern democratic governance. The establishment of the Deori Autonomous Council in 2005 marked a significant milestone in the community's political empowerment, though ongoing demands for Sixth Schedule status reflect continuing aspirations for greater autonomy. The article also examines contemporary efforts to document and preserve the Deori language and culture, particularly through recent collaborations with academic institutions such as the reputed universities and IITs, while addressing the challenges of language endangerment, economic development, and cultural preservation in the 21st century.

Abstract: Artificial intelligence (AI) is increasingly embedded in educational technology systems, yet many current applications primarily optimize short-term performance metrics rather than modeling the developmental processes that shape learning over time. Drawing on learning sciences, dynamic systems theory, learning analytics, and responsible AI scholarship, this paper proposes a trajectory-oriented precision learning framework in which artificial intelligence functions as a human-centered interpretive layer for modeling state-dependent variability in learning. We introduce the Medically Informed Learning and Education (MILE) framework, an architecture that integrates contextual learner signals, longitudinal trajectory modeling, and human-in-the-loop instructional decision support. Instead of classifying learners based on static performance snapshots, the framework models learning as a dynamic developmental process and generates interpretable insights that support educator-guided adaptation. We describe the conceptual architecture of the framework, outline operational design components for educational technology systems, and illustrate potential applications across neurodiverse learners, twice-exceptional profiles, and health-related variability in learning contexts. By repositioning educational AI from static classification toward longitudinal developmental modeling, the proposed approach contributes a theoretically grounded paradigm for precision learning. The framework highlights interpretability, developmental responsiveness, and educator oversight as core design principles for next-generation educational AI systems. Implications for learning analytics, adaptive system design, and ethical governance of AI in education are discussed.

Abstract: Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as AndroidWorld, operate by connecting to a system-level Android emulator and provide evaluation signals based on the state of system resources. In real-world mobile-agent scenarios, however, many third-party applications do not expose system-level APIs to determine whether a task has succeeded, leading to a mismatch between benchmarks and real-world usage and making it difficult to evaluate model performance accurately. To address these issues, we propose MobiFlow, an evaluation framework built on tasks drawn from arbitrary third-party applications. Using an efficient graph-construction algorithm based on multi-trajectory fusion, MobiFlow can effectively compress the state space, support dynamic interaction, and better align with real-world third-party application scenarios. MobiFlow covers 20 widely used third-party applications and comprises 240 diverse real-world tasks, with enriched evaluation metrics. Compared with AndroidWorld, MobiFlow's evaluation results show higher alignment with human assessments and can guide the training of future GUI-based models under real workloads.

Abstract:

The marble industry relies on proprietary commercial names rather than objective visual categories, creating market inefficiencies for stakeholders who select stones based on appearance. Supervised classification methods perpetuate this problem by replicating inconsistent commercial labels instead of discovering intrinsic visual structure. We propose an unsupervised pipeline combining a two-stage training strategy, pure self-supervised pretraining followed by cluster-aware fine-tuning of a DINO Vision Transformer, with UMAP dimensionality reduction and Ward's agglomerative hierarchical clustering. Systematic ablation studies on 1,540 marble images spanning 10 commercial varieties validate each design choice: cluster-aware training at k=10 yields superior embeddings over the self-supervised baseline (Silhouette Score 0.778 vs. 0.761; Davies–Bouldin Index 0.293 vs. 0.364), UMAP compression to five dimensions resolves high-dimensional noise pathologies, and Ward's linkage produces the most compact partitions. The resulting taxonomy reveals three phenomena invisible to commercial classification: cross-category merging of visually indistinguishable stones carrying different market names, intra-category splitting of heterogeneous sub-populations within single varieties, and coherent grouping where commercial and visual boundaries coincide. We further demonstrate that standard extrinsic metrics are misaligned with unsupervised taxonomy objectives when reference labels encode the inconsistencies the method aims to resolve. This work provides a validated methodology for data-driven visual classification in the natural stone industry and a transferable template for domains with unreliable labelling conventions.