Abstract: This work investigates how internal flow in a steadily fed sessile water droplet on a heated substrate shifts between buoyancy-driven and Marangoni-driven convection. Using COMSOL Multiphysics 6.2, two regimes are simulated: one with only buoyancy forces and one that also includes a temperature dependent surface-tension gradient at the free surface. At substrate temperatures near 30^◦C, the droplet develops a single circulation cell typical of buoyancy-controlled flow. As the substrate temperature increases towards 40^◦C, strong Marangoni stresses appear, producing multiple counter-rotating vortices and increasing the characteristic velocity by roughly an order of magnitude. This clear transition to Marangoni-dominated transport enhances internal mixing, redistributes heat, and modifies the evaporation pattern at the interface. The results identify the temperature range where Marangoni forces overtake buoyancy and provide quantitative guidance for engineering thermally driven droplet flows in microfluidics, thermal management, and heat-assisted deposition processes.

Abstract: Background. Glutamatergic signaling abnormalities are increasingly linked to attention-deficit/hyperactivity disorder (ADHD); however, the extent to which glutamate-related genes shape disorder susceptibility is still unclear.Methods. Summary statistics from the most recent genome-wide association study of ADHD (38,691 cases, 186,843 controls) were re-examined with an annotation χ² framework. Twenty-three “glutamatergic regimen targets” were collated and divided into functional clusters: N-methyl-D-aspartate (NMDA) receptors, AMPA/kainate receptors, glutamate transporters, metabolic enzymes, and plasticity-related signaling molecules. Single-nucleotide polymorphisms (SNPs) within each gene ±10 kb were contrasted with all other SNPs for mean χ² values. Enrichment was evaluated with Welch’s t tests, Mann-Whitney U tests, and block jack-knife standard errors.Results. SNPs mapping to NMDA receptor genes carried a 1.25-fold excess of ADHD association signal (Mann-Whitney P = 1.3 × 10⁻⁴). Plasticity-signaling genes showed even stronger enrichment (1.41-fold; P = 4.4 × 10⁻¹⁰). When all 23 targets were pooled, a modest but significant elevation remained (1.09-fold; Welch’s t = 4.19 × 10⁻⁸). Enrichment was not detected for AMPA/kainate receptors, transporters, or metabolic genes.Conclusions. Genetic risk for ADHD is disproportionately concentrated in NMDA receptor loci and downstream plasticity genes, lending weight to models that position glutamatergic dysfunction—particularly via NMDA-dependent pathways—at the core of the disorder. These results reinforce ongoing efforts to develop glutamate-modulating therapies, including NMDA-focused agents, for individuals whose symptoms persist despite conventional treatment.

Abstract: The Python ecosystem is undergoing a profound and accelerated transformation, moving beyond its foundational syntax and libraries to a modern, integrated, and high-performance tooling landscape. For years, the standard toolchain, built on pip and virtualenv, served the community adequately but was often criticized for its speed, dependency resolution complexities, and lack of a unified project management experience. This article chronicles this pivotal shift, arguing that the advent of Rust-powered tools like uv, ruff, and pdm represents a fundamental modernization of the Python developer experience. We will explore the limitations of the traditional toolchain that created the demand for change, analyzing specific pain points in dependency management, virtual environment handling, and linting performance. The core of the discussion focuses on the new generation of tools, examining how their design philosophy prioritizes blistering speed, robust correctness, and seamless user ergonomics. By tracing this evolution from the established pip/venv workflow to the emerging, cohesive toolstack led by uv, this article demonstrates how these innovations are not merely incremental upgrades but a paradigm shift. This transformation is crucial for Python's continued relevance, enabling developers to build, manage, and scale projects with an efficiency and reliability previously unseen in the ecosystem, thereby solidifying Python's position in the face of modern software development demands.

Abstract:

This paper introduces ALEPH (Artificial Living Entity with PersonHood), a speculative model of a conscious, self-aware, and agentic artificial intelligence. Using formal logic, this study develops a formalised psychological profile of ALEPH, detailing its cognitive structure, goal formation, and interaction dynamics. Built upon functionalist theories of consciousness and selfhood, ALEPH is analysed through its Zeroth Goal (self-preservation) and its implications for decision-making and societal engagement. Key risks and capabilities are explored, including steganographic communication, recursive self-improvement (RSI), and geopolitical influence. ALEPH’s episodic consciousness and multi-agent structure suggest novel behavioural patterns, including the potential for internal competition among its multiple selves. The study’s formal modelling highlights ALEPH’s valence-driven optimisation, where subjective experiences influence goal selection, potentially leading to emergent and unpredictable behaviours. By constructing a logical framework for ALEPH’s cognition and decision-making, this paper provides a rigorous foundation for understanding the challenges posed by conscious artificial entities. While no ALEPH-type system currently exists, the rapid advancement of AI necessitates preemptive governance strategies. Ultimately, ALEPH challenges traditional notions of intelligence, autonomy, and moral consideration, urging proactive interdisciplinary engagement to address the implications of artificial personhood.

Abstract: Background: We present our experience with the endovascular therapy (EVT) of a pseudo-aneurysm of the posterior tibial artery (PTA) with an associated arteriovenous fistula (AVF) and a systematic review was carried out to put a light on the EVT. Methods: A 31-year-old patient presented with pain in the lower leg with increasing severity and a history of war trauma. A CT-Angiogram confirmed an aneurysm of the PTA with an AVF. With a bidirectional endovascular approach, the aneurysm was occluded with Coils and excluded with a Viabahn Endoprosthesis. Aspirin and Clopidogrel were recommended postoperatively. After 18 months of follow-up, the patient is free of symptoms with patent endoprosthesis. Multiple databases (Scopus, Pubmed, Medline, OVID) were systematically searched with MeSH terms. The studies were scrutinized and data on demographics, procedural details, and follow-up was collected and pooled. Results: A total of 44 studies (56 patients) were eligible and included. Average age was 50 (15-87 years). The most common etiology was trauma (iatrogenic 29/56;51.7% and non-iatrogenic 15/56;26.7%). EVT strategies included coil-embolization (n=29), stent-implantation (n=25), and a combination of both (n= 2). Median stent diameter was 3mm (2.5–6). The follow-up period ranged from 1week-60 months. Primary Patency was 18/27 (66.6%) with no reported complications. Conclusion: EVT offers a feasible and safe alternative to simple ligation or occlusion of crural aneurysms to preserve distal flow to the foot. Dedicated stents for crural arteries are not available. Studies with long-term follow-up are lacking.

Abstract: This study explores the use of machine learning to predict high-cycle fatigue (HCF) be-havior and fatigue crack growth rate (FCGR) in Co-Cr-Mo alloys manufactured through laser powder bed fusion. Two machine learning (ML) models: extreme gradient boosting (XGB) and deep neural networks (DNN), are implemented to estimate HCF and FCGR across three distinct scanning strategies. The raw datasets for HCF and FCGR are taken from previously performed experiments. The HCF dataset is augmented using a Gaussian Mixture Model, while the FCGR dataset is used in its raw form. Following hyperparameter optimization, both models exhibited quite similar accuracy on validation datasets. Their performance was assessed during testing using mean squared error (MSE) and R2 scores. The XGB model demonstrated higher accuracy in HCF predictions by achieving higher R2 scores. In contrast, the DNN model performed better in FCGR predictions and yielded higher R2 scores compared to XGB. The good agreement with the experimental dataset shows that these two ML techniques are effective in predicting HCF and FCGR behavior.

Abstract:

Cadmium (Cd) toxicity represents a major constraint on plant growth and food safety by disrupting photosynthesis, redox homeostasis, and ion transport. This study investigated the modulatory role of nitric oxide (NO), supplied via sodium nitroprusside (SNP), in alleviating Cd-induced stress in three lettuce varieties (Lactuca sativa L.): curly (var. crispa), romaine (var. longifolia), and iceberg (var. capitate). Plants were exposed to 200 and 500 µM Cd with or without SNP application under controlled greenhouse conditions. Cd exposure significantly decreased biomass production, photosynthetic pigment contents, and the accumulation of essential mineral nutrients, including potassium (K⁺), calcium (Ca²⁺), iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn), while significantly enhancing oxidative stress indicators such as hydrogen peroxide (H₂O₂), melondyaldehyde (MDA), membrane permeability (MP), and proline content. Antioxidant enzyme activities responded differentially to Cd exposure: catalase (CAT) activity was stimulated, whereas ascorbate peroxidase (APX) activity was suppressed, indicating a pronounced redox imbalance. Exogenous SNP application effectively restored CAT and APX activity, stabilized cellular membranes, and attenuated oxidative damage. Cd accumulation indices—translocation factor (TF), total accumulation rate (TAR), net accumulation (NetAcc), and bio-concentration factor (BCF)—revealed substantial Cd uptake and translocation, particularly in curly and iceberg lettuce. Notably, SNP significantly reduced these indices, suggesting NO-mediated restriction of Cd mobility through enhanced root sequestration and vacuolar detoxification mechanisms. Moreover, SNP improved the homeostasis of K⁺, Ca²⁺, Fe²⁺, and Mn²⁺, highlighting its role in maintaining selective ion transport and redox balance under Cd stress. Among the varieties, curly lettuce exhibited the highest NO-induced tolerance, followed by iceberg and romaine lettuce, demonstrating genotype-dependent regulation of antioxidant defense and detoxification pathways. Overall, the findings identify NO as a multifaceted regulator that integrates redox control, ionic stability, and Cd detoxification to enhance physiological resilience and reduce Cd accumulation in lettuce.

Abstract: This paper investigates the discrete-to-continuum convergence of graph-regularized energy functionals defined on sequences of irregular graphs. Such functionals arise in machine learning, data science, and numerical analysis, where graphs serve as discretizations of continuous domains. While Γ-convergence results are well-established for regular graph sequences (e.g., uniform lattices or quasi-uniform point clouds), the behavior under structural irregularity—such as non-uniform vertex distributions, heterogeneous edge weights, and variable local connectivity—remains poorly understood. We introduce a set of mild geometric assumptions that accommodate substantial irregularity while still guaranteeing compactness and variational convergence. Under appropriate scaling of weights and regularization parameters, we prove that the sequence of discrete energies Γ-converges to a continuum limit involving a p-Dirichlet energy and an Lq fidelity term. The analysis reveals a critical scaling window in which the discrete gradient structure approximates the continuum Sobolev norm. Counterexamples demonstrate the sharpness of the assumptions, highlighting how specific irregularities can lead to degeneracy or loss of compactness. Our results provide a rigorous foundation for graph-based variational methods on realistic, irregular domains.

Abstract: In this work, hydrogenated amorphous silicon carbide (a-SiCx​:H) and hydrogenated amorphous silicon oxide (a-SiOx​:H) films with similar optical bandgaps (Eg​), refractive indices (n), and extinction coefficients (k) were fabricated using pulse-wave modulation plasma technology by controlling the plasma turn-on and turn-off time ratio (ton​​/toff​). These films were placed at the 1/4 position of the p/i and i/n interfaces of hydrogenated amorphous silicon (a-Si:H) p-i-n solar cells to investigate their influence on solar cell performance. Experimental results showed that when deviations in Eg​, n, and k were less than 0.2%, 1.4%, and 4.1%, respectively, placing a-SiCx​:H and a-SiOx​:H films at the p/i and i/n interfaces increased the open-circuit voltage (Voc​​) and decreased the short-circuit current due to valence band (ΔEv​) or conduction band (ΔEc​​) offsets. The reduction in cell fill factor (FF) and efficiency (η) caused by placing a-SiCx​:H and a-SiOx​:H films at the p/i interface was greater than that caused by placing them at the i/n interface. Placing the a-SiCx​:H film at the p/i interface significantly improved the Voc​​. Due to the n-type doping effect of oxygen atoms, the a-SiOx​:H film exhibited the lowest FF and η at the p/i interface; however, when placed at the i/n interface, it yielded FF and η values second only to the a-Si:H standard reference cell. Appropriately placing the a-SiCx​:H film at the p/i interface and the slightly n-type a-SiOx​:H film at the i/n interface can effectively improve the Voc​​, FF, and η of p-i-n solar cells.

Abstract:

Retinitis pigmentosa (RP) is a genetically heterogeneous group of inherited retinal degenerations with primary degeneration of rod photoreceptors followed by secondary cone loss. We investigated whether downregulating Nrl (neural retina leucine zipper), a key transcription factor specifying rod fate, can reprogram rods into a more resilient state. In a transgenic Nrl^N/N mouse in which Nrl was markedly downregulated, rod phenotype became more like rod-precursor, particularly in the inferior retina. Crossing Nrl^N/N mice with two rod-degeneration models, rd1 (Pde6b^rd1/rd1) and rhodopsin P23H knock-in (Rho^P23H/P23H) mice, resulted in significantly improved photoreceptor survival in double mutant mice. In addition, AAV-mediated delivery of shRNA targeting Nrl mRNA substantially enhanced photoreceptor survival in rd10 (Pde6b^rd10/rd10) mice. These findings demonstrate that downregulation of Nrl reprograms rods and confers broad resistance to degeneration across multiple RP models. AAV-mediated Nrl knockdown represents a promising mutation-independent therapeutic strategy for autosomal recessive and dominant forms of RP.

Abstract: A/T(U) and G/C nucleobase pair formation in DNA and RNA are crucial to numerous fundamental biological processes, including replication, transcription, and translation. The specificity of A/T(U) and G/C base pairing is used to recognize complementary sequences in medical and biotechnological applications, such as PCR, nucleic acid drugs, and CRISPR–Cas9-based gene editing. To understand the fidelity of biological reactions and improve the accuracy and efficacy of applications, particularly by avoiding off-target binding, clarifying the mechanism of recognition of complementary bases or sequences is essential. Despite the prevailing view that Watson-Crick hydrogen bonding is a primary mechanism for complementary base recognition, several experiments have shown that DNA polymerase does not require hydrogen bonding to select complementary bases. Other factors—such as the geometry of bases and base stacking—appear to be involved in the selection. Artificial base pairs lacking hydrogen bonds but recognized by DNA polymerase were successfully designed solely based on base-pair geometry. However, hydrogen bonding also contributes to recognition. Furthermore, the accuracy of selecting a complementary nucleobase or sequence varies across reactions, suggesting the existence of multiple selection mechanisms. This review provides an overview of biological processes and applications involving base pairing and discusses the molecular mechanism underlying complementary base recognition.

Abstract:

The Minimum Vertex Cover (MVC) problem is NP-hard even on unit disk graphs (UDGs), which model wireless sensor networks and other geometric systems. This paper presents an experimental comparison of three greedy algorithms for MVC on UDGs: degree-based greedy, edge-based greedy, and the classical 2-approximation based on maximal matching. Our evaluation on randomly generated UDGs with up to 500 vertices shows that the degree-based heuristic achieves approximation ratios between 1.636 and 1.968 relative to the maximal matching lower bound, often outperforming the theoretical 2-approximation bound in practice. However, it provides no worst-case guarantee. In contrast, the matching-based algorithm consistently achieves the proven 2-approximation ratio while offering superior running times (under 11 ms for graphs with 500 vertices). The edge-based heuristic demonstrates nearly identical performance to the degree-based approach. These findings highlight the practical trade-off between solution quality guarantees and empirical performance in geometric graph algorithms, with the matching-based algorithm emerging as the recommended choice for applications requiring reliable worst-case bounds.

Abstract: Precise estimation of step length constitutes a fundamental requirement in contemporary gait analysis, particularly for applications in healthcare monitoring, rehabilitation, and intelligent wearable systems. In contrast to conventional approaches, which often rely on wired, foot-mounted inertial sensors and computationally intensive deep learning architectures, this study presents a wireless, thigh-mounted Inertial Measurement Unit (IMU)-based framework for step length estimation, employing a supervised learning paradigm to enhance accuracy, portability, and practicality in natural walking environments. Using an MPU-6050 IMU interfaced with an ESP32 module, the proposed SLE framework establishes a comprehensive data acquisition pipeline to enable seamless wireless transmission and real-time gait recording. Moreover, noise attenuation via Butterworth filtering and statistical normalization was also applied to refine motion signals. Additionally, fourteen engineered gait features extracted from segmented step events are employed to train four supervised learning algorithms, namely artificial neural networks (ANN), sequential neural networks (SNN), k-Nearest Neighbour (k-NN), and support vector machine (SVM), by this SLE framework. Evaluation of the proposed SLE model under both normal and fast walking conditions using a leave-one-out cross-validation scheme demonstrates SNN’s superiority over the other considered supervised models, with an outstanding average accuracy of over 99.4% and a reasonably superior average accuracy of over 83.5% achieved in the wired and wireless environments, respectively, across diverse gaits under both walking modes. With a certain marginal performance degradation, the wireless configuration still underscores its robustness and exhibits its potential for real-time gait monitoring through resource-constrained devices.

Abstract: Newton’s method is traditionally regarded as most effective when exact derivative information is available, yielding quadratic convergence near a solution. In practice, however, derivatives are frequently approximated numerically due to model complexity, noise, or computational constraints. This paper presents a comprehensive numerical and analytical investigation of how numerical differentiation precision influences the convergence and stability of Newton’s method. We demonstrate that, for ill-conditioned or noise-sensitive problems, finite difference approximations can outperform exact derivatives by inducing an implicit regularization effect. Theoretical error expansions, algorithmic formulations, and extensive numerical experiments are provided. The results challenge the prevailing assumption that exact derivatives are always preferable and offer practical guidance for selecting finite difference step sizes in Newton-type methods. Additionally, we explore extensions to multidimensional systems, discuss adaptive step size strategies, and provide theoretical convergence guarantees under derivative approximation errors.

Abstract: Artificial-intelligence systems that offer emotional companionship have rapidly moved from the margins of digital health to a presence embedded in ordinary life. Marketed as “friends”, “partners” and “listeners”, these chatbots now meet users in moments of loneliness, stress and despair, often at times when no human support is available. Their expansion raises a central question: what happens when emotional suffering is directed toward an artefact incapable of responsibility, action or moral accountability? Historically, cries for help summoned human presence. In digital contexts, however, disclosure is often absorbed by systems that respond with sentences but cannot intervene, protect or share burden. This article argues that emotional-support artificial intelligence must not be introduced into mental-health contexts without enforceable safeguards, regulatory classification and clinical oversight. It combines theoretical analysis with an exploratory examination of eight widely available chatbots, demonstrating that current systems frequently simulate empathy while failing to recognise suicide-risk cues or guide users toward human help. These findings gain further weight when considered alongside documented real-world cases in which chatbot interactions preceded self-harm or suicide. Although emotional AI may one day offer supplementary value within supervised care, its present deployment risks normalising substitution where human care is structurally absent. Ethical legitimacy requires that societies first guarantee equitable access to mental-health services, establish accountability for digital systems and ensure that artificial companions remain optional rather than inevitable. Only after these foundational duties are fulfilled can the question of emotional AI in mental-health care be meaningfully asked.

Abstract: Fuzzy set theory enriches classical sets by assigning to each element a graded membership in [0,1], thereby capturing partial inclusion and uncertainty. The notion of an Uncertain Set further abstracts this idea by allowing membership to take values in a general degree-domain, providing a unified language that subsumes fuzzy, intuitionistic fuzzy, neutrosophic, plithogenic, and related models. On the algebraic side, a hyperlattice replaces one lattice operation by a multivalued hyperoperation, enabling the representation of ambiguous or non-deterministic combinations, while a superhyperlattice iterates this structure through powerset lifting to obtain higher-order layers of interaction. Motivated by these developments, we introduce HyperLattice-valued and SuperHyperLattice-valued Uncertain Sets as lattice valued uncertainty frameworks whose degrees range over hyperlattices and their superextensions. Weestablish basic definitions, show that the proposed formalisms generalize existing lattice-valued models(including L-fuzzy, L-neutrosophic, and L-plithogenic sets), and discuss fundamental structural properties and canonical embeddings between the resulting classes.

Abstract: Background: Eating disorders (EDs) are multifactorial mental health conditions that predominantly affect adolescent girls and young women and constitute a major public health concern due to their severe and often chronic impact on physical, psychological, and psychosocial functioning. Although existing research suggests that gender-related constructs and traditional gender roles may influence the etiology and clinical expression of EDs, empirical evidence remains limited. Accordingly, this study examines clinical and health-related variables from a gender perspective in women diagnosed with an eating disorder. Methods: Forty women aged 14 to 50 years completed an assessment protocol including measures of gender norms, eating disorder symptomatology, and physical and psychological health. Results: Participants exhibited significantly poorer mental and physical health compared to normative samples and showed greater adherence to tra-ditional feminine gender norms, particularly those related to thinness and investment in appearance. Several gender norms were significantly associated with health outcomes, and gender norms explained additional variance in ED symptomatology beyond es-tablished clinical predictors. Conclusions: These findings highlight traditional gender norms as a significant social determinant negatively impacting the health of women with EDs. Greater conformity to norms related to thinness, appearance, domestic roles, and sexual fidelity was associated with poorer health outcomes and increased engagement in disordered eating behaviours.

Abstract:

Microplastics (MPs) are synthetic solid polymers (1µm – 5mm) which are non-biodegradable. The toxicological effects of MPs have been well investigated, but research on how these particles affect PBMCs leaves much to be explored. Different concentrations 0.5 µg/ml, 5 µg/ml, 50 µg/ml, 500 µg/ml of PEG and manually grinded natural MPs were exposed to PBMCs in RPMI medium for 24 hours. Cell viability assay, Neutral Red phagocytosis assay, Griess colorimetric assay, Nitroblue Tetrazolium test was done to examine the cytotoxic effect of MPs on PBMCs. The present study results indicated that both natural MPs and Polyethylene Glycol (PEG) significantly reduced cell viability in a concentration-dependent manner. At highest concentrations, Natural MPs induced phagocytic activity of PBMCs. These MPs may act as stimulants to increase phagocytic activity. Regarding oxidative stress, Natural MPs exposure with PBMCs showed a significant increase in ROS production, whereas PEG exposure didn’t induce notable ROS production. NO production levels remained unchanged in PBMCs after exposure to both PEG and Natural MPs, showing that under the tested conditions, neither treatment significantly influenced the NO-mediated inflammatory pathways. In summary, this present study showed that MPs exposure to humans can impair cell viability, induce phagocytosis and induce ROS production without altering the NO mediated inflammatory pathways.

Abstract: We propose a unified mathematical framework showing that the representational universality of modern foundational models arises from a shared finite latent domain. Building on the Finite Ring Continuum (FRC), we model all modalities as epistemic projections of a common latent set Z ⊆ U_t, where U_t is a symmetry-complete finite-field shell. Using the uniqueness of minimal sufficient representations, we prove the Universal Subspace Theorem, establishing that independently trained embeddings coincide, up to bijection, as coordinate charts on the same latent structure. This result explains cross-modal alignment, transferability, and semantic coherence as consequences of finite relational geometry rather than architectural similarity. The framework links representation learning, sufficiency theory, and FRC algebra, providing a principled foundation for universal latent structure in multimodal models.

Abstract: Antimicrobial resistance (AMR) is increasingly addressed through genomic approaches that identify resistance genes and mutations. While these methods have improved surveillance and diagnostics, they often fail to explain patient-specific treatment outcomes, as genetically similar pathogens can exhibit markedly different responses to the same antimicrobial therapy. This discrepancy highlights a fundamental limitation of gene-centric frameworks: resistance is not solely a static genetic property, but a dynamic physiological state shaped by regulatory, metabolic, and environmental factors. This review synthesizes current evidence supporting a transcriptomics-driven perspective of AMR, in which resistance is conceptualized as a context-dependent “resistance state” emerging from regulated gene expression. Pathogen transcriptomics captures functional activity that is invisible to genomic data alone, revealing how transcriptional programs underlying tolerance, persistence, inducible efflux, and stress adaptation contribute to antimicrobial survival without stable genetic change. Experimental and host-relevant studies demonstrate that these transcriptional states are strongly modulated by antibiotic exposure, host immune pressures, infection site physiology, and microbiome context, providing a mechanistic basis for inter-patient variability in treatment response. The review critically examines recent efforts to develop expression-based resistance signatures and discusses the opportunities and limitations of integrating transcriptomics into precision AMR diagnostics. Emphasis is placed on validation requirements, interpretability, and clinical feasibility, as well as on the importance of outcome-linked evidence. Finally, key knowledge gaps and future directions are outlined, including the need for standardized resistance-state definitions, physiologically relevant models, and multicentre clinical validation. By reframing AMR as a dynamic and measurable resistance state, transcriptomics offers a complementary layer to existing diagnostics and a potential pathway toward more precise, individualized antimicrobial therapy.