Abstract: Current paradigms in Self-Supervised Learning (SSL) achieve state-of-the-art results through complex, heuristic-driven pretext tasks such as contrastive learning or masked image modeling. This work proposes a departure from these heuristics by reframing SSL through the fundamental principle of Minimum Description Length (MDL). We introduce the MDL-Autoencoder (MDL-AE), a framework that learns visual representations by optimizing a VQ-VAE-based objective to find the most efficient, discrete compression of visual data. We conduct a rigorous series of experiments on CIFAR-10, demonstrating that this compression-driven objective successfully learns a rich vocabulary of local visual concepts. However, our investigation uncovers a critical and non-obvious architectural insight: despite learning a visibly superior and higher-fidelity vocabulary of visual concepts, a more powerful tokenizer fails to improve downstream performance, revealing that the nature of the learned representation dictates the optimal downstream architecture. We show that our MDL-AE learns a vocabulary of holistic object parts rather than generic, composable primitives. Consequently, we find that a sophisticated Vision Transformer (ViT) head, a state-of-the-art tool for understanding token relationships, consistently fails to outperform a simple linear probe on the flattened feature map. This architectural mismatch reveals that the most powerful downstream aggregator is not always the most effective. To validate this, we demonstrate that a dedicated self-supervised alignment task, based on Masked Autoencoding of the discrete tokens, resolves this mismatch and dramatically improves performance, bridging the gap between generative fidelity and discriminative utility. Our work provides a compelling end-to-end case study on the importance of co-designing objectives and their downstream architectures, showing that token-specific pre-training is crucial for unlocking the potential of powerful aggregators.

Abstract:

Traditional paradigms of nation-building and state-building have dominated political theory and international policy for decades, yet their explanatory and prescriptive power remains limited in postcolonial and conflict-affected contexts. Recurrent instability, institutional fragility, and governance failure are often interpreted as operational deficiencies, yet this article contends that the root cause is primarily epistemological. Existing frameworks fragment political life into discrete domains—institutions, identity, legitimacy—while remaining anchored in Westphalian assumptions that fail to capture the dynamic, adaptive nature of political communities. This article introduces Nationesis, a novel transdisciplinary science dedicated to the study of nations as living, adaptive systems whose persistence depends on regenerative processes rather than mere stabilization. Nationesis integrates insights from political theory, comparative constitutionalism, postcolonial scholarship, and systems science to provide a unified analytical framework encompassing institutions, collective meaning, historical memory, leadership intelligence, and legitimacy. Using the Democratic Republic of the Congo as a paradigmatic case of systemic complexity, the article demonstrates why conventional paradigms systematically misread patterns of persistence, fragility, and renewal. The study concludes that the future of political order relies not on institutional replication alone but on a community’s capacity to regenerate meaning, legitimacy, and collective coherence under systemic strain. Nationesis thus offers a transformative lens for political theory, global constitutionalism, and the science of sustainable political communities.

Abstract: We present a fully controlled thermodynamic study of the two-dimensional dipolar $Q$-state clock model on small square lattices with free boundaries, combining exhaustive state enumeration with noise-free evaluation of canonical observables. We resolve the complete energy spectra and degeneracies $\{E_n,c_n\}$ for the Ising case ($Q=2$) on lattices of size $L=3,4,5$, and for clock symmetries $Q=4,6,8$ on a $3\times3$ lattice, tracking how the competition between exchange and long-range dipolar interactions reorganizes the low-energy manifold as the ratio $\alpha = D/J$ is varied. Beyond a finite-size characterization, we identify several qualitatively new thermodynamic signatures induced solely by dipolar anisotropy. First, we demonstrate that ground-state level crossings generated by long-range interactions appear as exact zeros of the specific heat in the limit $C(T \rightarrow 0,\alpha)$, establishing an unambiguous correspondence between microscopic spectral rearrangements and macroscopic caloric response. Second, we show that the shape of the associated Schottky-like anomalies encodes detailed information about the degeneracy structure of the competing low-energy states: odd lattices ($L = 3,5$) display strongly asymmetric peaks due to unbalanced multiplicities, whereas the even lattice ($L = 4$) exhibits three critical values of $\alpha$ accompanied by nearly symmetric anomalies, reflecting paired degeneracies and revealing lattice parity as a key organizing principle. Third, we uncover a symmetry-driven crossover with increasing $Q$: while the $Q=2$ and $Q=4$ models retain sharp dipolar-induced critical points and pronounced low-temperature structure, for $Q \ge 6$ the energy landscape becomes sufficiently smooth to suppress ground-state crossings altogether, yielding purely thermal specific-heat maxima. Altogether, our results provide a unified, size- and symmetry-resolved picture of how long-range anisotropy, lattice parity, and discrete rotational symmetry shape the thermodynamics of mesoscopic magnetic systems. We show that dipolar interactions alone are sufficient to generate nontrivial critical-like caloric behavior in clusters as small as $3\times3$, establishing exact finite-size benchmarks directly relevant for van der Waals nanomagnets, artificial spin-ice arrays, and dipolar-coupled nanomagnetic structures.

Abstract: Turbidity, a key indicator of water quality, arises from suspended and colloidal particles that reduce clarity, hinder disinfection, disrupt aquatic ecosystems, and undermine consumer confidence. With increasing pressures from global water pollution, effective turbidity control is critical for protecting public health, supporting industrial operations, and maintaining environmental sustainability. It is also essential for the stable performance of water treatment processes, including biologically mediated systems such as slow sand filtration. A wide range of treatment techniques, spanning conventional approaches to advanced emerging technologies, are available for turbidity removal; however, existing reviews often consider these methods in isolation, limiting comparative insight. This review presents a mechanism-based classification framework that integrates both traditional and modern approaches. Treatment methods are classified according to their underlying mechanisms, including particle destabilization, aggregation, and separation; adsorptive and transformation processes; and hybrid or assisted systems that combine multiple mechanisms. For each category, the review examines fundamental principles, operational mechanisms, turbidity removal efficiencies, advantages, and limitations, supported by relevant case studies. A comparative discussion highlights the strengths and constraints of different methods, providing a comprehensive reference to guide the selection and optimization of turbidity control strategies across diverse water matrices and treatment objectives.

Abstract: Motivated and inspired by Truesdell's seminal article [``Two measures of vorticity," Journal of Rational Mechanics and Analysis {\bf 2}, 173--217 (1953)], recently the present author has introduced the turbulence kinematical vorticity number $\widetilde{\cal V}_{K}$ to measure the mean rotationality of turbulence [``On the classical Bradshaw--Richardson number: Its generalized form, properties, and application in turbulence," Physics of Fluids {\bf 30}, 125110 (2018)]. In this work, first, within the general framework of the Cauchy equation of motion, we derive the general equation of motion for the turbulence kinematical vorticity number $\widetilde{\cal V}_{K}$ in turbulent flows of incompressible non-Newtonian fluids, which depicts the underlying dynamical character of $\widetilde{\cal V}_{K}$ and in laminar flows reduces to the general equation of motion for the kinematical vorticity number---the Truesdell number ${\cal V}_{K}$. Second, we obtain an inequality which places the relevant dynamical restriction upon the mean Cauchy stress tensor, the Reynolds stress tensor, and the mean body force density vector in the ensemble-averaged Cauchy equation of motion for turbulence modelling. Moreover, we derive the general Reynolds stress transport equation for turbulence modelling of incompressible non-Newtonian fluids based on Cauchy's laws of motion, which includes as a special case the classical Reynolds stress transport equation for an incompressible Newtonian fluid derived from the Navier--Stokes equation.

Abstract: Stroke causes major long-term disability, with balance impairment significantly affecting quality of life. Personalized prognosis and treatment selection, particularly between TeleRehabilitation (TR) and Conventional Rehabilitation (CR), are crucial. However, current predictive models often lack multimodal integration or tailored recommendations. This paper introduces Causal-MMFNet, a novel deep learning framework. It integrates diverse multimodal time-series data to simultaneously predict balance recovery and allocate individualized treatments in stroke rehabilitation. Key innovations include a dynamic cross-modal attention fusion mechanism, an Individual Treatment Effect (ITE) estimation module for counterfactual outcomes, and causal consistency regularization. Evaluated on the StrokeBalance-Sim dataset, Causal-MMFNet consistently outperforms baselines and state-of-the-art multimodal frameworks, demonstrating superior accuracy and reliability across established metrics. Ablation studies confirm component contributions, while dynamic attention reveals adaptive modality prioritization. The framework's treatment allocation significantly improves patient outcomes, with uncertainty estimation providing clinical confidence. Causal-MMFNet offers a robust, causally-aware solution for personalized decision support in stroke rehabilitation, enhancing patient recovery and optimizing resource allocation.

Abstract: Contactless monitoring of vital signs such as Heart Rate (HR) and Respiratory Rate (RR) has gained significant attention, with vibration-based sensors like geophones showing promise for accurate, non-invasive monitoring. However, most existing systems are developed with healthy subjects and may not generalize well to extreme physiological ranges, such as those observed in infants or patients with arrhythmia. Moreover, the underlying mechanisms of cardiorespiratory vibration dynamics remain insufficiently understood, limiting clinical adoption of these systems. To address these challenges, we present a programmable cardiorespiratory testbed capable of generating realistic HR and RR signals across a wide range (HR: 40–240 bpm, RR: 8–40 bpm). Our system uses a voice coil motor that acts as the vibration source, driven by a Raspberry Pi based control circuit. Unlike similar systems that use separate modules for heart and lung signals, our setup generates both signals using a single motor. The synthetic signals exhibit a strong correlation of 0.85 compared with data from 75 human subjects. We use this system to design signal processing based algorithms for vital signs monitoring and demonstrate their robustness for extreme physiological ranges. The proposed system enhances the understanding of cardiorespiratory vibration dynamics while significantly reducing the time and effort required to collect real-world data.

Abstract: This study develops a multi-source feature-fusion framework that combines transaction histories, mobile-behavior data, credit-bureau information, and merchant-level attributes. The feature space contains over 4,800 engineered variables derived from 3.5 million customer records. A three-stage selection pipeline—correlation filtering, mutual-information ranking, and stability-selection LASSO—reduces dimensionality by 92%. The selected features train a LightGBM model optimized for early-stage (0–30 day) delinquency prediction. The model achieves an ROC-AUC of 0.91 and reduces false-negative early defaults by 37.5% compared with baseline logistic regression. Feature-importance patterns reveal strong interactions between merchant category instability and device-behavior anomalies. The results show the effectiveness of multi-source feature fusion for fine-grained default prediction.

Abstract: Aeropalynology the monitoring and interpretation of airborne pollen has become increasingly relevant in Latin America as allergic rhinitis and asthma rise alongside rapid urbanization, land‑use change, and climate variability. Yet the region’s capacity remains heterogeneous: long‑standing traditions in the Southern Cone coexist with emerging programs in tropical and Andean settings, and many series are not translated into standardized products useful for clinical care or public health. We conducted a structured literature review guided by PRISMA 2020 to synthesize the historical evolution, current monitoring infrastructure, dominant pollen taxa, and translational outputs reported across Latin American countries. Evidence indicates that Mexico currently represents the most mature aeropalynological ecosystem in the region, supported by multi‑site monitoring, open weekly reporting (REMA), multiple city‑level pollen calendars, and emerging computational approaches for pollen identification. Across countries, recurrent high‑impact taxa include Cupressaceae/Juniperus, Fraxinus, Platanus, Olea, Poaceae, Urticaceae, Chenopodiaceae–Amaranthaceae, Rumex, Ambrosia, and Parietaria, with local dominance shaped by biogeography and urban vegetation. Key gaps include limited long‑term continuity outside a few cities, variable methodology (sampler type, taxonomic resolution, units, thresholds), and scarce linkage of pollen exposure metrics with clinical outcomes. Future priorities include harmonized volumetric monitoring, interoperable data standards, routine publication of pollen calendars and thresholds, integration with meteorology for forecasting, and expansion of digital decision‑support tools to improve prevention and management of allergic respiratory diseases in Latin America.

Abstract: Brain cancer metastasis is one of the most common neurological complications associated with various types of cancer, particularly lung cancer, breast cancer, and melanoma. Approximately 20% of cancer patients develop brain metastasis. Current therapeutic strategies are limited and often lack effectiveness, with patient survival typically averaging less than 15 months. As a result, brain metastasis remains one of the leading causes of cancer-related mortality worldwide. Therefore, understanding the mechanisms behind brain metastasis is crucial for improving treatment outcomes. In this review, we provide an overview of the epidemiology, mechanisms, diagnostic approaches, prognostic factors, and treatment strategies associated with brain cancer.

Abstract: Background Patellofemoral pain syndrome (PFPS) is a prevalent overuse injury among recreational cyclists worldwide. Despite its ubiquity, little is known about the lived experiences of people with PFPS, especially in Saudi Arabia, where healthcare and cultural factors may have a special impact on how the condition is managed. The aim of this study is to explore the lived experiences of recreational cyclists with patellofemoral pain syndrome in Al Madinah, Saudi Arabia. Method: A qualitative, descriptive, phenomenological design was employed. Eleven male recreational cyclists aged 28–44 years diagnosed with PFPS were purposely recruited from Al Madinah Physical Therapy Centre. Female participants were excluded due to cultural constraints in sports participation. The participants consented to participate in the study and to be audio recorded. Data were collected using audio-recorded semi-structured interviews using an interview guide. Data were transcribed verbatim and thematically analysed using Atlas.ti version 24. Results: The following themes emerged from our findings: characteristics of patellofemoral pain, functional activities that exacerbate knee pain, psychological and physical effects, coping mechanisms, community and psychosocial constraints, and strategies for managing knee pain. Conclusion: Patellofemoral pain syndrome imposes significant multidimensional burdens on recreational cyclists in Al Madinah, exacerbated by cultural practices. Physiotherapy offers targeted interventions for pain relief, functional restoration, and participation enhancement, underscoring the need for culturally sensitive management programs.

Abstract: An integrated structural parameter optimization method is presented to improve the vibration performance of medical rotating systems. Key geometric and material parameters were selected using sensitivity analysis, and a response surface model was constructed based on 48 finite element simulations. The optimization objective was to minimize vibration displacement under operational speeds between 300 and 900 rpm. Results indicate that optimized designs reduced maximum vibration displacement by 31.2% while maintaining structural stiffness within ±5% of the original design. The proposed framework provides an effective pathway for vibration reduction without introducing additional damping devices.

Abstract: Background:The Standard Model (SM) has been successful, yet it fails to explain the origin of fermion masses and mixing parameters. Methods: In this study we construct the single-fermion framework “Information Flux Theory (IFT),” derived from the Unified Evolution Equation. IFT preserves gauge symmetry while replacing Standard Model fields with a single fundamental operator, yielding analytic solutions without adjustable parameters. Results: IFT reproduces all SM particle masses—including the 125 GeV Higgs mass—and the CKM matrix within current experimental precision, requiring neither additional particles nor fine-tuning. Conclusion: These results demonstrate that IFT can fully replace the Standard Model with a single-fermion description, providing a conceptually simpler yet phenomenologically complete foundation for particle physics. Supplement: This paper includes proofs for two Clay Millennium Problems: the Yang–Mills mass gap and the Navier–Stokes equations. Note Added: Furthermore, as a result of this series of studies, the origin of gravity has now been clarified.

Abstract: The adoption of static and dynamic code analysis techniques within modern software development environments is critical for early vulnerability detection and comprehensive quality assurance. Static code analysis scrutinizes source code without execution to uncover potential defects, security vulnerabilities, and coding standard violations early in the lifecycle. Dynamic code analysis complements this by examining the software's runtime behavior to identify issues such as memory leaks, race conditions, and interaction faults that only manifest during execution. The integration of both methodologies into automated security toolchains within continuous integration/continuous delivery (CI/CD) pipelines enables rapid feedback, efficient remediation, and elevated code quality. This combined approach fosters a culture of proactive security and accelerates the delivery of robust, secure software applications.

Abstract: The vertebrate respiratory system arose under evolutionary pressures that linked increasing atmospheric oxygen levels to the metabolic demands of mitochondria. This transition—from ancestral gill-based exchange to the highly alveolated mammalian lung—was accompanied by the emergence of a hormonal regulatory axis centered on the glucocorticoid receptor alpha (GRα). Over time, GRα became deeply integrated into the architecture and function of the respiratory system, aligning pulmonary performance with organismal homeostasis across different developmental stages, environmental challenges, and disease states. This review combines evolutionary, embryological, and molecular evidence to explain how GRα shapes respiratory structure and function. We trace the evolution from ancient oxygen-sensing systems to mammalian alveoli and endothelial adaptations, demonstrating how conserved developmental pathways (including WNT, FGF, BMP, and SHH) are repurposed during both organogenesis and repair. Genetic models show that GRα is essential for preparing the lung for postnatal life, coordinating the reciprocal signaling between mesenchyme and epithelium that drives branching, septation, extracellular matrix organization, and the development of functional alveolar units. In the mature lung, GRα maintains the stability of the alveolar–capillary interface and coordinates immune, vascular, and metabolic functions to support efficient gas exchange. Its actions also extend to red blood cell biology and the regulation of stress erythropoiesis, linking pulmonary oxygen management with systemic oxygen delivery. Mechanistically, GRα interacts with circadian and hypoxia pathways and activates mitochondrial programs that enhance energy production and redox homeostasis during stress. By integrating these regulatory layers across developmental and physiological contexts, this review reframes GRα not simply as a stress-response receptor but as a non-redundant systems-level integrator of respiratory homeostasis. Understanding this layered control not only explains the benefits of antenatal corticosteroids but also highlights the therapeutic value of phase-specific, precision modulation of the GC–GRα axis—along with strategies that support GC–GR signaling—to reestablish and maintain homeostasis in acute and chronic pulmonary disorders.

Abstract: This study numerically investigates how welding parameters influence the dynamic stability of medical equipment frames. A set of 30 frame models with varying weld bead sizes and heat inputs was analyzed using finite element modal and harmonic response analysis. The first natural frequency varied between 62 and 91 Hz depending on welding conditions, leading to up to 2.1-fold differences in resonance amplification factors. The findings indicate that welding design plays a critical role not only in static strength but also in dynamic performance and service life of medical equipment.

Abstract: Residual unbalance moments are a major source of vibration in rotating medical devices. This study proposes an adaptive control strategy to suppress residual unbalance moments based on real-time vibration feedback. A rotating structure model with distributed mass eccentricity was established, and adaptive gain tuning was implemented using acceleration signals sampled at 2 kHz. Simulations under three unbalance levels (120, 220, and 350 g·mm) show that the proposed method reduced peak vibration acceleration from 1.42 m/s² to 0.79 m/s² on average, corresponding to a 44.4% reduction. The approach demonstrates strong robustness against speed variation and sensor noise, making it suitable for medical rotating equipment.

Abstract: Contemporary artificial intelligence systems increasingly rely on recursive feedback processes, including self-training, preference optimization, and algorithmic governance loops. Across these settings, practitioners report a recurring failure mode in which system behavior contracts, dominant patterns emerge, and corrective interventions lose effectiveness. While such phenomena are often attributed to convergence or over-optimization, there remains no general empirical criterion for declaring when a system has entered an irrecoverable state. This paper introduces a fully empirical system for detecting entropy collapse in feedback-driven AI systems. Collapse is defined operationally, not by low entropy alone, but by the failure of admissible interventions to restore diversity within a finite observation window. The framework relies exclusively on observable quantities, including empirical state distributions, Shannon entropy, dominance concentration, distributional displacement between iterations, and second-order change in displacement. A recovery-based threshold is constructed from repeated intervention experiments, yielding a system-specific limit beyond which collapse becomes statistically irreversible. An empirical demonstration is provided using a recursive AI setting, illustrating how collapse can be anticipated prior to full stagnation and formally declared once recovery probability falls below a prescribed tolerance. The same empirical indicators generalize naturally to socio-technical systems governed by feedback, offering a common language for diagnosing rigidity, adaptability loss, and governance failure. By grounding entropy collapse in measurable irrecoverability rather than descriptive convergence, the proposed system provides a practical tool for early warning, evaluation, and intervention in AI systems and their societal deployments.

Abstract: Modern cancer therapy is largely grounded in a linear paradigm in which drug–target interaction is assumed to be the dominant determinant of therapeutic outcome. Yet across virtually all tumor types and treatment modalities, clinical responses remain profoundly unpredictable: patients with nearly identical disease characteristics often exhibit radically different outcomes to the same therapy, and biomarker-positive tumors can be either exquisitely sensitive or entirely refractory. These inconsistencies expose fundamental limitations of a single-dimension, drug-centered framework and indicate the presence of deeper system-level determinants.Here, I introduce the concept of the high-dimensional biological algorithm—the nonlinear, multidimensional computational logic by which the tumor–host system integrates all internal and external variables to generate therapeutic outcomes. This algorithm is not mutable code but is embodied in the invariant laws and principles of physics, chemistry, and biology (such as biochemistry, biophysics, molecular biology, cell biology, immunology, physiology) in the tumor-host system. While these governing principles remain constant, the algorithm faithfully processes its inputs; thus, system output is determined entirely by the nature and configuration of those inputs.Within this framework, a drug is not a direct cause of outcome but one input among many entering a system that simultaneously processes biochemical, metabolic, signaling, immunological, structural, and microenvironmental variables. The quantities and qualities of these variables differ between patients, between tumors, within individual tumors, and over time. Consequently, when the same drug is introduced into tumor–host systems with different computational baselines, it enters fundamentally distinct input landscapes. Although processed by the same underlying algorithm, these differing baseline conditions necessarily drive computation along divergent trajectories, yielding heterogeneous therapeutic outcomes.Thus, variability in therapeutic response is not a failure of the drug nor a change in the governing biological algorithm, but a predictable consequence of applying identical inputs to heterogeneous computational baselines. Achieving consistent and durable therapeutic efficacy therefore requires reconditioning the baseline inputs presented to the algorithm, rather than attempting to modify the algorithm itself.A second-dimension input, distinct from molecular targeting, is therefore required to constrain or recondition the computational baseline through which the algorithm processes therapeutic inputs, thereby favoring convergent and therapeutically productive outputs. Concentrated bicarbonate provides a proof-of-concept example: by sharply alkalizing extracellular and intracellular pH, it reconditions global baseline parameters—including enzymatic kinetics, metabolic flux distribution, mitochondrial energetics, immune visibility, and signaling thresholds. In clinical studies, including bicarbonate-integrated TACE and bicarbonate-augmented anti–PD-1 therapy, this conditioning transformed previously variable responses into uniform, high-magnitude outcomes, despite unchanged drugs, doses, and delivery routes.Together, this framework suggests that future progress in oncology will require moving beyond an exclusive focus on drug–target interactions, toward strategies that combine molecular targeting therapies with deliberate manipulation of the computational baseline that governs how therapeutic inputs are interpreted and executed. Integrating pharmacology with system-level input conditioning offers a principled path toward more predictable, controllable, and robust cancer therapies.

Abstract:

Archaea lipids are a source of new biomaterials for pharmaceutical and nanomedical applications; however, their classic extraction method relies on chloroform and methanol, toxic solvents that conflict with green chemistry principles. In this paper we explore the performance of an eco-friendly method for the extraction of total lipids from the haloarchaea Halorubrum tebenquichense. Using the bio-solvents ethyl acetate and ethanol in a two-step procedure, a fraction of total lipids (135 ± 41 mg phospholipids and 1.1 ± 0.4 mg bacterioruberin (BR) / 100 g cell paste) was obtained containing the same composition as that resulting from extraction with the classical solvents as confirmed by Electrospray Ionization Mass Spectrometry, although with lower phospholipid content, thus with a higher proportion of bacterioruberin. The extracted lipids were subsequently utilized for preparation of archaeosomes, which were characterized by uniform size distribution (406 ± 137 nm, 0.63 ± 0.13 polydispersity index), colloidal stability, and negative ζ potential (-38.2 ± 5.4 mV). The photoprotective potential of these archaeosomes was for the first time determined in human keratinocyte (HaCaT) cells exposed to UVB irradiation (270 mJ/cm²). Treatment with archaeosomes significantly (p< 0.05) enhanced cell viability (from ~43 to ~80 %), reduced intracellular ROS generation and proinflammatory cytokine release (TNF-α) and mitigated UVB-induced apoptosis compared to untreated controls, indicating effective cytoprotection. This study demonstrates that ethyl acetate–ethanol-based extraction offers an alternative for archaeal lipid recovery and highlights the potential of archaeosomes as natural photoprotective agents for skincare applications.