Abstract:

Efficiently separating propene and propane is paramount for the chemical industry but notoriously difficult due to their minimal size and volatility differences. Here, we demonstrate a powerful strategy to overcome this separation challenge by designing bimetallic Zeolitic Imidazolate Framework (ZIF)-based mixed-matrix membranes (MMMs). We fabricated thin-film composites (TFCs) by integrating monometallic ZIF-8, ZIF-67, and a synergistic bimetallic ZIF-8-67 into a uniquely formulated ionic liquid-cellulose acetate (IL-CA) polymer matrix. Structural and morphological analyses confirmed the high crystallinity of the ZIF fillers and their seamless integration within the polymer. The resultant ZIF-8-67/IL-CA membrane exhibited exceptional separation performance, surpassing its monometallic counterparts by a threefold increase in both C₃H₆ permeance and C₃H₆/C₃H₈ ideal selectivity relative to the base membrane. Under industrially relevant mixed-gas testing, the membrane achieved an impressive separation factor of 8 for propene over propane. These findings reveal that the strategic integration of bimetallic nodes in ZIFs can unlock synergistic properties unattainable with single-metal frameworks. This work presents a robust and scalable platform for developing next-generation membranes that defy conventional performance trade-offs, a way for efficient membrane-based olefin/paraffin separations.

Abstract:

Ex vivo functional testing for multiple myeloma is rapidly evolving, yet no single assay has reached the level of reliability and clinical utility needed for routine decision-making. Existing approaches generally fall into three categories comprising 2D cultures, 3D models, and dynamic systems. Each contributes valuable but incomplete insight into therapeutic response. Among these, 2D assays remain the most mature, with the most extensive clinical correlations to date, though their simplified architecture limits their ability to reflect the full complexity of the marrow microenvironment. 3D systems, including spheroids and matrix-based organoids, offer improved preservation of tumor heterogeneity and microenvironmental cues. These platforms show emerging clinical relevance and may hold advantages over traditional 2D formats, and validation efforts are developing. Dynamic systems including microfluidic models and perfused bone-marrow mimetics represent the most physiologically ambitious category, yet their technical intricacy and early stage of development have so far limited broad clinical correlation. Altogether, the current landscape highlights substantial progress but lacks an optimal assay. In this review, we take the unique approach of examining published ex vivo tests that have demonstrated a level of clinical correlation. We evaluate their respective formats, strengths and limitations, and discuss considerations for what an ideal future assay may encompass.

Abstract: Since the beginning of modern computer history, the Turing machine has been a dominant architecture for most computational devices, which consists of three essential components: an infinite tape for input, a read/write head, and finite control. In this structure, what the head can read (i.e., bits) is the same as what it has written/outputted. This is actually different from the ways in which humans think or do thought/tool experiments. More precisely, what humans imagine/write on paper are images or texts, and they are not the abstract concepts that they represent in the human brain. This difference is neglected by the Turing machine, but it actually plays an important role in abstraction, analogy, and generalization, which are crucial in artificial intelligence. Compared with this architecture, the proposed architecture uses two different types of heads and tapes, one for traditional abstract bit inputs/outputs and the other for specific visual ones. The mapping rules among the abstract bits and the specific images/texts can be realized by neural networks with a high accuracy rate. Logical reasoning is thus performed through the transfer of mapping rules. The statistical decidability of the Halting Problem with an imperceptibly small error rate in reasoning steps is established for this type of machines. As an example, this paper presents how the new computer architecture (what we call ``Ren machine" for simplicity here) autonomously learns a distributive property/rule of multiplication in the specific domain and further uses the rule to generate a general method (mixed in both the abstract domain and the specific domain) to compute the multiplication of any positive integers based on images/texts. The machine's strong reasoning ability is also corroborated in proving a theorem in Plane Geometry. Moreover, a robotic architecture based on Ren machine is proposed to address the challenges faced by the Vision-Language-Action (VLA) models in unsound reasoning ability and high computational cost.

Abstract: Contemporary accounts of hemispheric asymmetry in cognitive neuroscience predominantlyemphasize neuroanatomical localization and functional specialization, while ofering limitedexplanatory mechanisms for how lateralized neural processes become observable inembodied behavior. The Subjectica Hypothesis proposes a neurophenomenologicalframework in which hemispheric asymmetry is expressed not as a fixed correspondence withbodily sides, but as a dynamic pattern of sensorimotor embodiment manifested throughbodily kinematics.The model introduces four operational constructs—Personal-Oriented Left Side (PO-LS),Society-Oriented Right Side (SO-RS), Asymmetric Neurobehavioral Signal (ANS) and theBody Segments (BS) —designed to link hemispheric functional dominance, cognitiveorientation, and measurable bodily dynamics. These constructs function as interpretativeintermediaries, enabling the analysis of lateralization through continuous patterns of posture,movement, and segment-level motor dominance rather than through discrete anatomicalmappings.Subjectica does not report empirical results; instead, it defines a theoretically coherent andoperationally specified framework intended to guide future experimental, observational, andphenomenological research on lateralized embodiment. The hypothesis generates falsifiablepredictions concerning task-dependent cognitive orientation, hemispheric dominance, andasymmetric sensorimotor expression, extending existing models of hemisphericspecialization within the broader paradigm of embodied cognition.

Abstract: This work develops a Lorentz-invariant variational framework in which Fisher-information geometry appears as an intrinsic structural contribution to quantum dynamics. Motivated by longstanding attempts to connect quantum mechanics with information-theoretic principles, we introduce an action functional depending on the density and phase fields in the Madelung representation. Variation of this action yields a modified Klein–Gordon equation containing a single nonlinear term proportional to the four-dimensional Fisher-information curvature of the probability density. The standard Klein–Gordon equation is recovered when the structural parameter vanishes, ensuring full compatibility with established relativistic dynamics. Taking the nonrelativistic limit, we obtain a uniquely determined nonlinear Schrödinger equation in which the correction term is the functional derivative of the Fisher information. The resulting dynamics preserve probability, maintain the Hamilton–Jacobi correspondence, and contain the linear Schrödinger equation as a special case. Analytical expressions for Gaussian and superposed states demonstrate how the structural modification scales with spatial localization and interference structure, providing clear qualitative signatures that distinguish the model from previous nonlinear extensions and offer a theoretical basis for future experimental verification. The results establish a mathematically transparent link between information geometry and quantum dynamics and provide a foundation for future extensions to fermionic, gauge, and many-body systems.

Abstract: Academic minors provide universities with a flexible mechanism to broaden curricular reach, attract diverse students, and integrate experiential learning. The Wildlife Care and Handling Minor, established at the University of X in 2015, illustrates how a minor can foster professional, academic, and personal growth while preparing students for animal care. The program combines coursework, structured externships, and reflective writing, requiring more than 200 hours of service in institutions managing captive wildlife. This study analyzes 121 student reflections from the capstone externship course. Thematic analysis revealed consistent patterns across four domains. Professionally, students described the Minor as confirming ambitions, redirecting career goals, or expanding horizons to fields such as environmental education, raptor work, or animal law. Academically, it bridged theory and practice, strengthened graduate preparation, clarified interests, and fostered transferable skills in communication and leadership. Personally, students reported growth in resilience, empathy, and ethical awareness, shaped by challenges in living arrangements, cultural adaptation, and compassion fatigue. Advice to future participants emphasized exploration, preparation, professionalism, adaptability, and mentorship, reflecting peer support. Collectively, findings show the WCH Minor as both proving ground and pivot point, enabling students to test identities, integrate learning, and cultivate maturity. The program offers a transferable model for experiential, reflective education that prepares graduates who are thoughtful, resilient, and ethically grounded.

Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains and many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence of uncooperative airspace users, and rising traffic density are driving a shift toward decentralized concepts such as free flight, in which each actor is responsible for its own safe trajectory. This survey reviews CD&R and CA methods with a particular focus on decentralized automation and encounters with noncooperative intruders. It analyzes classical rule-based approaches and their limitations, then examines Machine Learning (ML)–based techniques that aim to improve adaptability in complex environments. Building on recent regulatory discussions, it further considers how requirements for trust, transparency, explainability, and interpretability evolve with the degree of human oversight and autonomy, addressing gaps left by prior surveys.

Abstract: Basidiomycete mushrooms contain complex -D-glucans which play an important role in immuno-modulating and anti-tumor activities. The present work involves a novel and intrinsic synchronous fluorescent and phosphorescence assay method for -D-glucans. Synchronous fluorescence and phosphorescence spectroscopy was carried out by a spectrofluorometer in the range of 250 to 750 nm with a  range of 5 -30 nm which exhibited peaks at 492, 540 and 550 nm by using -D-glucan from Euglena gracilis as standard. A micro and high throughput method based on 96-well microtiter plate fluorescence was devised with a excitation and emission of 420 nm and 528 nm, respectively . This assay method presented several advantages over the published colorimetric methods since it is a non-invasive assay method that requires only 0.97 g of -D-glucans in samples, greater sensitivity, speed, assay of many samples and very cheap. -D-glucans of several mushrooms (i.e Poria coccus, Auricularia auricula, Ganoderma lucidium, Pleurotus ostreatus , Cordyceps sinensis , Agaricus blazei, Polyporus umbellatus, Inonotus obliquee) were isolated by using a sequence of several extractions and quantified by either spectrofluorometer or fluorescence microtiter plate reader. 3-D spectra measurements were carried out of -D-glucans from medicinal mushroom strains. FTIR spectroscopy was used to study the structural features ofD-glucans in these mushroom samples.

Abstract: Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are devastating and currently untreatable neurodegenerative diseases, whose genetic and molecular etiologies remain largely unclear. The histopathological hallmark of both diseases is the cytoplasmic deposition of TDP-43 in neurons, which is attributed to both intrinsic (e.g., mutations, aberrant cleavage) and extrinsic factors (e.g., prolonged oxidative stress, impaired clearance pathways). Mutations and certain PTMs (e.g., cysteine oxidation) destabilize RNA binding, promoting monomer misfolding and increasing its half-life. Disruptions to core ubiquitin-proteasome system (UPS) subunits impede efficient processing, contributing to the clearance failure of misfolded TDP-43 monomers. The accumulation of monomers drives phase separation within stress granules, creating nucleation hotspots that eventually bypass the thermodynamic barrier, resulting in exponential growth. This rapid growth then culminates in the failure of the autophagy-lysosome pathway (ALP) to contain the aggregation, resulting in a self-sustaining feed-forward loop. Here, we synthesize these factors into a unified kinetic cascade model. Therapeutic strategies must therefore move beyond simple clearance and focus on targeting these kinetic inflection points (e.g., oligomer seeding, PTM modulation).

Abstract: Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has arisen due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further guaranteed by a distributed-ledger-technology-based zero-trust security framework. When combined, these elements enable completely autonomous workload orchestration with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.

Abstract: Background/Objectives: Capric acid–based therapeutic deep eutectic systems (THEDES) 18 are emerging as a distinct class of biofunctional matrices capable of reshaping drug solubilization, permeability, and bioactivity. Methods: Relevant studies on capric acid–based therapeutic deep eutectic systems (THEDES) were identified through targeted database searches and screened for evidence on their design, mechanisms, and pharmaceutical performance. Results: This review synthesizes current evidence on their structural design, mechanistic behavior, and pharmaceutical performance, revealing several unifying principles. Across multiple drug classes, capric acid consistently drives strong, directional hydrogen bonding and drug amorphization, enabling exceptional solubility enhancements and stabilized supersaturation. Its amphiphilic C10 chain further contributes to membrane fluidization, which explains the improved transdermal and transmucosal permeation repeatedly observed in capric acid-based THEDES. Additionally, synergistic antimicrobial and anticancer effects reported in several systems confirm that capric acid acts not only as a solvent component but as a bioactive co-therapeutic. Collectively, the reviewed data show that capric acid serves as a structurally determinant element whose dual hydrogen-bonding and membrane-interacting roles underpin the high pharmaceutical performance of these systems. However, gaps remain in long-term stability, toxicological profiling, and regulatory classification. Emerging Artificial Intelligence (AI) and Machine Learning (ML)-guided predictive approaches offer promising solutions by enabling rational selection of eutectic partners, optimal ratios, and property optimization through computational screening. Conclusion: Overall, capric acid-based THEDES represent a rationally designable platform for next-generation drug delivery, where solvent functionality and therapeutic activity converge within a single, green formulation system.

Abstract: Ningbo (NGB), one of the world's most important port cities located on the East Coast of China, contains more than 100 rivers and streams across three major catchments, the Yong, Yuyao and Fenghua Rivers. During the 1970s – 2000s, extensive river engineering, including channelisation, conversion of natural rivers into artificial canals, and construction of sluice gates and embankments were undertaken to cope with rapid urbanisation and development. Since the 2010s, the Ningbo Government and Water Bureau have initiated smart river and fluvial flood management strategies to enhance digital twins and smart flood management technologies, such as 3D flood mapping and real-time water level and discharge monitoring, significantly improving precision. In this study, we demonstrate that smart technology has performed effectively in Ningbo, with applications in the recent climate extreme events such as Typhoon In-Fa and Muihua in 2021 and 2022, during which the Municipal Bureau has safeguarded public safety and welfare. This further strengthening both municipal and national commitment to enhance climate resilience. Nevertheless, further advancement of the DT platform remains necessary. Key areas for improvement include faster computational capacity, enhanced coordination across departments and open data sharing mechanisms, and integration of artificial intelligence (AI) to support more effective decision-making processes in response to the climate extremes and adverse water hazards conditions.

Abstract: Primary cutaneous lymphomas (PCLs) are a heterogeneous group of extranodal non-Hodgkin lymphomas presenting in the skin without evidence of extracutaneous disease at diagnosis. They encompass a broad clinicopathologic spectrum dominated by cutaneous T-cell lymphomas (CTCL), primarily mycosis fungoides (MF) and Sézary syndrome (SS), and by distinct entities of primary cutaneous B-cell lymphomas (PCBCL). Recent updates of the WHO–EORTC classification have refined disease definitions and introduced new entities and lymphoproliferative disorders, with direct consequences for prognosis and therapeutic decision-making. Parallel advances in genomics and im-munobiology have revealed recurrent alterations in T-cell receptor (TCR) signalling, JAK–STAT and NF-κB pathways, as well as hallmarks of immune evasion in the tumour microenvironment, providing a rationale for targeted and immune-based therapies. This narrative review, written from a dermatologic perspective, summarises current concepts in the classification, epidemiology and clinicopathologic features of the major PCL subtypes. We discuss key molecular drivers of CTCL and PCBCL, practical aspects of diagnosis and staging at the interface between dermatology, pathology and haematology, and the role of non-invasive imaging. We then review the contemporary therapeutic armamentarium, including skin-directed therapies, systemic biologic agents and chemotherapy, and emphasise pivotal trials of antibody-drug conjugates and immune therapies such as brentuximab vedotin and mogamulizumab. Finally, we highlight unmet needs, including diagnostic delay, real-world prognostic stratification, manage-ment of advanced and relapsed disease, and the integration of biomarkers into person-alised care. Dermatologists occupy a central role in early recognition, longitudinal monitoring and multidisciplinary management of PCLs, and ongoing collaboration between specialties is essential to translate molecular insights into improved patient outcomes.

Abstract: In this field, several erroneous theories had long been accepted as fundamental laws and formulas. Recent corrections to these misconceptions were made possible through the application of Exploratory Data Analysis (EDA). This article outlines how EDA contributed to these breakthroughs and offers a brief guide for those wishing to begin using it themselves.

Abstract: Despite decades of investigation, Aldose Reductase (AR; AKR1B1) -an enzyme that plays a key role in the metabolism of glucose and other carbonyl compounds and whose hy-peractivity contributes to oxidative stress and vascular dysfunction- inhibitors have failed to translate into clinical application for Diabetic Retinopathy (DR). We argue that these failures might arise from non-selective inhibition, which does not consider AR’s dual roles in pathology but also in retinal health, as AR is also an important detoxifying enzyme for aldehydes produced during oxidative stress, and discuss the missing structural infor-mation, despite the over one hundred crystal structures of AR in complex with inhibitors. Our review bridges this gap by proposing how recent advances in structural biology, namely, fragment-based drug discovery and MicroED, provide novel ways of selectively modulating AR functions, offering advantages in the detection of weak, allosteric, or conformation-dependent binding events. Despite past challenges, we suggest that therapeutic targeting or finding new-generation inhibitors for AR will become more effective once we have a clearer understanding of AR’s requirements for selective inhi-bition of its pathological and physiological functions. By integrating fragment screening and structural biology, we outline a strategy to reinvigorate AR modulation as a viable retina-specific approach for managing DR first, although potentially relevant across multiple diabetic microvascular complications later.

Abstract: Optic neuritis (ON) has been recognized since antiquity, but its modern clinical identity emerged only in the late 19th century and was definitively shaped by the Optic Neuritis Treatment Trial (ONTT). The ONTT established the natural history, visual prognosis, association with multiple sclerosis (MS), and therapeutic response to corticosteroids, building the foundation for contemporary ON management. Over the past two decades, ON has evolved from a seemingly uniform demyelinating syndrome into a group of biologically distinct disorders. The identification of aquaporin-4-IgG ON (AQP4-ON), myelin oligodendrocyte glycoprotein antibody–associated ON (MOG-ON), and double-negative ON has transformed diagnostic and therapeutic strategies. These subtypes differ in immunopathology, clinical course, MRI features, retinal injury patterns, CSF profiles, and long-term outcomes, making early and accurate differentiation essential. MRI provides key distinctions in lesion length, orbital tissue inflammation, bilateral involvement, and chiasmal or optic tract extension. Optical coherence tomography (OCT) offers complementary structural biomarkers, including severe early ganglion cell loss in AQP4-ON, relative preservation in MOG-ON, and variable patterns in double-negative ON. CSF analysis further refines diagnosis, with oligoclonal bands strongly supporting MS-ON. Together, these modalities enable precise early stratification and timely initiation of targeted immunotherapy, which is critical for preventing irreversible visual disability. Despite major advances, significant unmet needs persist. Access to high-resolution MRI, OCT, cell-based antibody assays, and evidence-based treatments remains limited in many regions, contributing to global disparities in outcomes. The pathogenesis of double-negative ON, reliable biomarkers of relapse and visual recovery, and standardized multimodal diagnostic thresholds remain unresolved. Future research must expand biomarker discovery, refine imaging criteria, and ensure equitable global access to cutting-edge diagnostic platforms and therapeutic innovations. Four decades after the ONTT, ON remains a dynamic field of investigation, with ongoing advances holding the potential to transform care for patients worldwide.

Abstract: This study investigates hydro-meteorological trends in five Alpine catchments within the Upper Po River basin, spanning Northwestern Italy and Southern Switzerland. We ana-lyzed climatic variables from 25 weather stations (1950–2022) alongside streamflow data from 14 river sections (1911–2022). Trends were assessed using the Mann-Kendall test to detect monotonic changes and the Theil-Sen estimator to quantify magnitude, ensuring robustness against outliers. Results reveal pronounced warming, particularly in spring maximum temperatures (+0.95 °C per decade). Conversely, average and minimum daily temperatures show lower rates (+0.50 and +0.39 °C per decade). Consequently, potential evapotranspiration increased significantly (+15.1 mm per decade), contributing to a marked decline in summer streamflow in 8 out of 14 sections. Correlation analysis con-firms that snow dynamics modulate the hydrological response: while precipitation drives discharge annually and in autumn, winter exhibits a weaker coupling, as winter precipi-tation is partially stored in the basin as snow, contributing to discharge during spring and summer. By focusing on this strategic region for European agriculture and industry, the study provides essential insights to support effective adaptation strategies.

Abstract: Building Information Modeling (BIM) represents a paradigmatic transformation in architecture and engineering, facilitating the transition from two-dimensional documentation to integrated multidimensional collaborative environments. This article synthesizes a systematic literature review (2014-2024) combining meta-analyses, case studies, and quantitative-qualitative research on the adoption of BIM in the AEC sector. The results document significant benefits: reductions of 25-30% in design errors, 20% in execution time and 15% in costs. However, adoption reveals geographic fragmentation (US 60%, UK 80%, Latin America <25%) and multidimensional barriers: lack of specialized training, cultural resistance, absence of specific legal frameworks in developing countries, and limited interoperability. The analysis identifies that successful integration requires deep organizational transformation, coordinated public policies, and educational curricular adaptation. Recommendations include micro-regional contextual strategies, contractual standardization (ISO 19650) and applied research in BIM-Facility Management integration and emerging technologies (XR, digital twins). BIM integrates geometric (3D), temporal (4D-schedule), economic (5D-costs) and operational (6D-facility management) information into collaborative parameterized models. Beyond visualization, the methodology calls for clarity on specific Development Levels (LODs) for each phase of the asset lifecycle, from LOD 100 (conceptual) to LOD 500 (as-built). Interoperability using IFC (ISO 16739) and ISO 19650 standards requires robust model validation and accurate definition of model views (MVDs), areas where 74% of projects in developing countries still have critical gaps. This article emphasizes that BIM is not only a software tool, but a comprehensive information management protocol that permeates processes from conceptual design to sustainable operation and demolition.

Abstract: Microwave photonics has recently come to the forefront as a valuable approach to generating, processing, and measuring signals in high-performance domains such as communication, radar, and timing systems. Recent studies have introduced a range of photonics-based phase-noise analyzers (PNAs) that utilize a variety of architectures, including phase detection, frequency discrimination, and hybrid mechanisms that combine optical with electronic processing. This review delves into the microwave photonics methodologies developed with the specific purpose of measuring phase noise, by exploring their fundamental principles, system design frameworks, and performance indicators. Through the integration of insights garnered from recent publications, our objective is to deliver a comprehensive understanding of the strengths and limitations associated with PNAs and to pinpoint new and promising areas for advancing research in the field of oscillator metrology.

Abstract: In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + illumination” module is constructed. Specifically, a learning-based dehazing method, Learning Hazing to Dehazing, is employed to restore long-range details affected by scattering artifacts. Additionally, HVI-CIDNet is introduced to decouple luminance and chrominance in the Horizontal/Vertical Intensity (HVI) color space, thereby simultaneously enhancing structural fidelity in low-light regions and achieving global brightness consistency. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Furthermore, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment, respectively. Evaluated on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5–2.7 percentage points in core metrics such as mAP@50–90 compared to similar lightweight models (e.g., the DEIM baseline and YOLOv12s), while maintaining low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions.