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.

Abstract: As the global cancer burden rises, adults with advanced cancer face significant physical and psychosocial symptoms requiring early integration of palliative and supportive care. Nurses in oncology, emergency, and community settings are central to symptom assessment, care coordination, communication, and advance care planning, yet their roles in early integration remain underexplored. This scoping review mapped nursing contributions to early palliative and supportive care for adults with advanced cancer and described related patient, caregiver, and system outcomes. It was based on a search of PubMed, CINAHL, Scopus, and ScienceDirect for English‑language studies (January 2016–November 2025) involving nursing‑relevant interventions. Thirteen studies were included: trials, observational studies, qualitative research, reviews, and a meta‑analysis. Six domains emerged. Early integration consistently improved quality of life and reduced symptom burden. Nurse‑led interventions increased end‑of‑life discussions and advance directive completion. Telehealth and telephone follow‑up proved feasible for symptom management. Studies noted moderate palliative competence but gaps in communication and structural support. Caregiver‑focused interventions enhanced caregiver quality of life and self‑efficacy. Conclusions: Nurses are pivotal in early palliative care. Expanding structured nurse‑led models, strengthening communication training, and addressing organizational barriers are essential to deliver timely, person‑centered care.

Abstract: Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods, to address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A mul-ti-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thick-ness. Based on the measured data, an improved matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the re-quirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually dete-riorates with increasing service mileage, and that flange height wear is the dominant fac-tor affecting the wear grade. These findings are consistent with actual operating condi-tions. The proposed method integrates high-precision multi-parameter measurement with wear condition evaluation, providing a reliable technical basis for wheel condition moni-toring and predictive maintenance in rail transit.

Abstract: Background: Esophagectomy is associated with substantial postoperative morbidity, with infectious complications remaining a leading cause of mortality. Septic shock represents the most severe infectious complication; however, data on its perioperative predictors and long-term impact after esophagectomy are limited. Methods: We conducted a retrospective observational study including consecutive adult patients who underwent esophagectomy with curative intent for esophageal cancer between January 2015 and December 2024 at a tertiary referral center. Postoperative septic shock was defined according to Sepsis-3 criteria. Demographic, clinical, surgical, laboratory, and oncological variables were analyzed. Independent risk factors for septic shock were identified using multivariate logistic regression. Overall survival was assessed using Kaplan–Meier analysis. Results: Among 106 patients, 19 (17.9%) developed postoperative septic shock. These patients had a lower body mass index, reduced preoperative and postoperative albumin levels, and a higher incidence of advanced lymph node involvement. Septic shock was strongly associated with severe postoperative complications, including anastomotic leakage, hemorrhagic shock, acute respiratory distress syndrome, acute kidney failure, and increased rates of PICU readmission. In multivariate analysis, lower albumin levels at PICU admission (OR 0.54; 95% CI 0.29–0.99) and advanced nodal stage (OR 4.98; 95% CI 1.36–18.3) were independently associated with the development of septic shock. Patients who developed septic shock had significantly higher in-hospital mortality (31.6% vs. 1.1%, p < 0.001) and markedly reduced long-term survival, even among those discharged alive. Conclusions: Postoperative septic shock after esophagectomy is a devastating complication with a profound negative impact on both short- and long-term survival. Hypoalbuminemia and advanced lymph node involvement are independent predictors of septic shock. These findings underscore the importance of multidisciplinary perioperative optimization strategies, including nutritional assessment and tailored surgical planning, to mitigate the incidence and consequences of this life-threatening complication.

Abstract: Laser-induced microjet assisted ablation is an emerging technology in the field of laser processing. However, the influence of solid boundaries on jet behavior and the associated material removal mechanism remain unclear. To address this, the present study systematically investigates the effect of the incidence angle on the processing efficiency and material removal mechanism in laser-induced microjet ablation. By controlling the laser power and liquid layer thickness, the dynamic behavior of the microjet, material removal performance, and surface morphology evolution under different inclination angles were explored. Based on video analysis and OpenCV processing, the regulation of jet morphology and impact mode by the incidence angle was revealed. Combined with white light interferometry and ultra-depth-of-field three-dimensional microscopy, the ablation depth and material removal rate were quantitatively characterized. The results show that under normal incidence, the maximum material removal rate of 0.091 mm³/s was achieved at 9 W, while further increases in power led to a decrease in removal rate due to bubble aggregation. When the sample was tilted to 15°, the material removal rate reached 0.163 mm³/s, representing a 106.3% improvement compared to that at 0°, and the ablation depth also peaked with an average maximum depth of 12.2 μm and a single-point maximum of 54.357 μm. Furthermore, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were employed to elucidate the microstructural features and elemental distribution under different process parameters. Through multi-parameter experiments, this study achieves process parameter optimization and clarifies the material removal mechanism influenced by different incidence angles, providing both a process reference and theoretical basis for efficient micro-machining of aerospace materials.

Abstract: Thermal water–supplied swimming pools are increasingly used worldwide for recreation, wellness, and therapeutic purposes. However, their management presents unique challenges due to the complex physicochemical properties of thermal and mineral waters and the need to ensure microbiological safety while preserving their natural characteristics. This scoping review examines the main health benefits and safety issues associated with thermal pools and provides a comparative analysis of regulatory frameworks governing these facilities in 39 countries worldwide. Particular attention is dedicated to microbiological hazards, chemical risks related to disinfection practices, and the potential formation of disinfection byproducts (DBPs) in treated thermal waters. The review also discusses emerging contaminants (CECs), including pharmaceuticals and personal care products (PPCPs), and the potential role of thermal water environments in the spread of antibiotic and antimicrobial resistance. The analysis highlights significant global heterogeneity in regulatory approaches, especially regarding disinfection strategies and water quality monitoring. Interactions between natural water composition, anthropogenic contaminants, and disinfection processes may create chemically complex mixtures whose toxicological implications remain insufficiently studied. Adopting a One Health perspective, this review emphasizes the need for integrated management strategies and more harmonized regulatory frameworks to ensure the safe and sustainable use of thermal water pools.

Abstract: Background: For the past four decades, mammography – an X-ray of the breast – has served as the gold standard for the early detection of breast cancer. Within the past 15 years, two other imaging modalities have emerged, receiving regulatory approval from the FDA, and challenging 2D mammography. These are digital breast tomosynthesis (DBT), often referred to as 3D mammography, and automated breast ultrasound (ABUS) which produces 3D images of the whole breast. Since ABUS has three distinct advantages over DBT – ultrasound waves are safe, there is no painful breast compression, and ultrasound can penetrate dense breast tissue to reveal cancers not seen by DBT – this leads to the question: Which of the two modalities performs best for early detection of breast cancer? Methods: First, to provide historical context, the development, testing, patenting and FDA approval of the two modalities were reviewed. Next, employing publicly available databases such as PubMed and Google Scholar, a search was conducted to identify articles in which both DBT and ABUS images were gathered and independently compared for various clinical measures, including sensitivity, specificity, and accuracy. Results: Based on 7 published articles in which 5,550 women were examined, ABUS was equal to or better than DBT for sensitivity, and often outperformed DBT for the other measures. Importantly, ABUS was better than DBT in detecting more invasive cancers and for women with dense breasts. Conclusions: Although the FDA has mandated that ABUS may only be used as an adjunct to DBT, for women younger than 40, and especially those with a family history of breast cancer, ABUS should ideally be applied first.

Abstract: This work presents a comprehensive investigation of the thermal decomposition of nickel oxalate dihydrate as a precursor for the synthesis of porous NiO, with particular emphasis on microstructural formation and evolution. The transformations occurring at successive stages of the reaction were examined using SEM, TEM, N₂ adsorption, TG–DSC–MS, and in situ powder XRD, enabling the mechanisms of pore formation to be elucidated. The decomposition results in the formation of a porous pseudomorph composed of NiO nanoparticles with an average size of approximately 4 nm. The resulting microstructure exhibits hierarchical porous architecture. During dehydration, macropores are generated as a result of crystal fragmentation into blocks several hundred nanometers in size. Subsequent oxalate decomposition leads to the formation of mesoporous aggregates composed of nanometer-sized particles. The factors governing the parameters of the porous microstructure are analyzed. Owing to its hierarchical pore system, the obtained NiO demonstrates significant potential for applications in heterogeneous catalysis, gas sensing, electrodes for super-capacitors and Li-ion batteries, and photoelectrochemical devices. In such systems, macropores facilitate mass transport by reducing diffusion resistance, while mesopores provide a high accessible surface area for adsorption and catalytic reactions.

Abstract: Instructor clarity is a central component of instructional communication and has been consistently associated with positive academic outcomes; however, less evidence exists regarding the mechanisms through which it influences student interest in higher education contexts. The present study examined a structural model in which instructor clarity predicts student interest both directly and indirectly through students’ academic satisfaction and state motivation. A total of 258 undergraduate students from the University of Extremadura enrolled in the Bachelor’s Degree in Early Childhood Education and the Bachelor’s Degree in Primary Education participated in the study. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), including an assessment of the model’s predictive capability. The results indicated that instructor clarity was positively associated with academic satisfaction, state motivation, and student interest, with the first two variables acting as complementary mediators in these relationships. The model demonstrated high predictive power and strong predictive validity with respect to student interest. Overall, the findings suggest that instructor clarity constitutes a relevant mechanism in shaping student interest by structuring the academic experience and fostering positive motivational states, highlighting the importance of promoting clear teaching practices in university faculty training and evaluation processes to enhance students’ learning outcomes.

Abstract:

The retromer is a highly conserved complex that mediates the trafficking of cargo proteins to plasma membrane or trans-Golgi network. In pathogenic microorganisms, retromer-dependent transport contributes to the delivery of virulence factors and promotes infection. The retromer consists of a sorting nexin dimer (SNX) and a cargo-selection complex (CSC), formed by Vps26, Vps35, and Vps29. In Entamoeba histolytica, the parasite causative of human amoebiasis, the retromer functions as a Rab7A GTPase effector and participates in phagocytosis and cytotoxicity. Although we previously characterized the roles of EhVps26 and EhVps35, the function of EhVps29 remained unclear. In this study, we analyzed the subcellular localization and functional role of EhVps29 in adhesion, phagocytosis, and cytopathic effect. EhVps29 localized to the plasma membrane, cytosol, vesicles, tubules, Golgi-like structures, MVBs and, for the first time, in the nucleus. Immunofluorescence and western blot assays demonstrated that EhVps29 modulates the localization of the EhVps26, EhADH adhesin and EhCP112 cysteine protease. The Ehvps29 gene silencing and overexpression confirmed its involvement in virulence-associated processes. Immunoprecipitation and confocal microscopy results showed the interaction among EhVps29, EhVps36 and EhADH ESCRT machinery members. Our results indicate that EhVps29 is involved in parasite virulence and protein trafficking through recycling or degradation pathways.

Abstract: Automatic speech recognition has advanced rapidly for high-resource languages, yet performance remains limited for low-resource languages such as Bangla, particularly in telehealth settings. Most systems rely on a standardized 16 kHz sampling rate, a design choice despite evidence that Bangla contains sibilant fricatives and other phonetic cues with substantial high-frequency energy that may be suppressed under bandwidth and latency constraints. This study evaluates audio sampling rate as a controllable signal-level parameter for Bangla telehealth ASR to identify an empirically grounded operating range balancing transcription accuracy, execution time, and network bandwidth. Twenty real-world Bangla doctor–patient consultations recorded at 32 kHz were deterministically resampled to 55 configurations between 8 kHz and 32 kHz and transcribed using a fixed cloud-based ASR system. Session-level Word Error Rate, execution latency, payload bandwidth, and high-frequency phonetic content were analyzed using a composite sibilant-likelihood score. WER decreased from 0.338 at 8 kHz to a local minimum of 0.232 at 18.75 kHz, with gains plateauing beyond this range despite substantial bandwidth increases. Elbow-point, Pareto frontier, weighted scoring, and Minimum Acceptable Trade-off analyses converged on an optimal region between 17.25 and 18.75 kHz, demonstrating that sampling-rate optimization improves ASR accuracy without proportional resource costs in telehealth settings.

Abstract: Peptides are short chains of amino acids with a unique pharmacological niche between small-molecule drugs and large proteins. Their use in sports medicine is rapidly expanding, driven by patient demand for accelerated injury recovery and performance enhancement. While numerous peptide drugs have undergone a rigorous approval process that evaluates both safety and efficacy, a parallel "gray market" of unapproved compounds has emerged, operating largely outside regulatory oversight. Our objective is to present the pharmacological mechanisms, safety profiles, and regulatory status of prominent approved and unapproved peptides marketed direct to patients, including AOD-9604 (Anti-Obesity Drug 9604), BPC-157 (Body Protection Compound 157), CJC-1295, FS-344 (Follistatin-344), GHK-Cu (Glycyl-L-histidyl-L-lysine copper), ipamorelin, MOTS-C (Mitochondrial ORF of the 12S rRNA type-c), sermorelin, SS-31 (Elamipretide), tesamorelin (Egrifta), thymosin beta-4, and TB-500 (thymosin beta-4 fragment). Many unapproved peptides demonstrate favorable tissue repair and metabolic outcomes in animal models, but rigorous human safety data is scarce, and there is potential for serious harm. This narrative review focuses on peptide utilization in sports medicine and alternative treatments for specific peptides. We provide a framework to navigate patient discussions about peptides to better facilitate evidence-based practices for musculoskeletal healing and athletic performance. We also discuss the placebo effect as a mediator of peptide efficacy, and how social media amplifies this effect.

Abstract: The Hodgkin-Huxley (HH) model has dominated quantitative neuroscience since 1952. Its authors explicitly acknowledged its phenomenological character and called for a deeper mechanistic account. We propose that this account is the IMH model of nerve conduction. The model rests on three biophysical foundations: (1) the polyelectrolyte gel framework of Ling, in which intracellular K⁺ is adsorbed on protein sites and the resting ionic distribution is a thermodynamically stable Donnan equilibrium requiring no metabolic pump; (2) the Hofmeister ion series, which governs differential adsorption of K⁺ versus Na⁺; and (3) the hydraulic wave equation for a fluid-filled elastic tube, which predicts conduction velocity from myelin elastic modulus rather than sodium channel density. In this framework, the action potential is a coupled ionic-hydraulic phase transition propagating as a pressure wave in the periaxonal space. Electrical events are causally secondary — the electromagnetic shadow of the hydraulic wave, not its cause. We demonstrate that the model resolves a 75-year-old anomaly identified but left unexplained by Huxley and Stämpfli in 1949: positive current enters a node before the membrane potential reaches its maximum, a relation the authors themselves described as "impossible in a system of resistances and capacities." We present nine falsifiable predictions distinguishing the IMH model from HH, covering myelin mechanics, mechanoreceptor adaptation, terminal arborisation geometry as the physical substrate of the Umwelt, motor tremor as hydraulic interference, the temporal basis of conscious perception, and the coupled physical constraints that explain why large-diameter unmyelinated fibres do not exist in nature.

Abstract: We summarize the key aspects of the ongoing octonionic E₈ ×ωE₈ unification program. This summary was fed to Open AI’s GPT-5.4 Pro with a prompt to give a critical appraisal of this program, and to compare it with string theory. We include the prompt and the detailed response from AI in an Appendix.

Abstract: The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet a systematic quantitative overview of its scientific development remains limited. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a data-driven overview of research trends that can support future research prioritization and inform policy and strategic planning for integrated mobility-energy systems.

Abstract: Pavement texture is a critical element affecting road safety and ride quality. It is affected by traffic volume, climate conditions, aggregate properties, and asphalt volumetric properties. This research aims to study the effect of different parameters on pavement texture using statistical and machine learning models. Pavement profile data and multiple variables affecting texture were collected from 192 SPS sections from the Long-Term Pavement Performance (LTPP) database. After data collection, pavement texture data were obtained from the pavement profile using ProVAL software and Python. Thereafter, the pavement texture was clustered into four diverse groups using the Gaussian Mixture Model (GMM), and the research determined cluster-specific profiles by applying centroid-based optimization techniques. Finally, an ordered logistic regression model and different machine learning models using K-nearest neighbor, random forest, extra trees, extreme gradient boosting, cat boosting, neural network, and weighted ensemble algorithm were developed to explore the parameters affecting the texture at diverse levels. The important parameters obtained from the statistical model were International Roughness Index (IRI), Annual Average Daily Truck Traffic (AADTT), temperature, and untreated subgrade, and from machine learning models were precipitation, IRI, AADTT, and 18-kips ESAL. Overall, this study significantly contributed to advancing the understanding and application of diverse impactful factors for pavement surface characteristics, pavement safety, and ride quality.

Abstract: The genus Acanthosaura is characterized by a high level of cryptic species diversity and is subdivided into several species complexes. The phylogenetic relationships within the A. coronata complex remain unresolved due to the presence of cryptic lineages and limited molecular data for several species. In this study, these relationships are clarified using a molecular genetic analysis that integrates newly collected field samples and historical museum specimens with previously uncertain identification. Three mitochondrial genes (cyt b, COI, and ND2) from samples, including fresh collections of A. murphyi from Phu Yen Province (Vietnam) and museum specimens from Vietnam and Myanmar were ana-lyzed. In addition, morphological characters of the examined specimens with diagnostic traits of known species were compared. Phylogenetic analyses confirmed the distinct spe-cies status of A. murphyi and enabled the taxonomic reassignment of previously undeter-mined museum specimens to this species. Specimens from Vietnam and Myanmar formed a single, well-supported clade, suggesting a broader distribution for A. murphyi than previously recognized. It is demonstrated for the first time that A. murphyi belongs to the A. coronata complex, together with A. coronata and A. cuongi, a result consistently supported by both genetic distances and phylogenetic tree topology.