Search Results (22,569)

With the continuous growth of data-intensive applications and artificial intelligence workloads, traditional dynamic random access memory (DRAM) is increasingly struggling to meet demands in terms of capacity scale, energy consumption constraints, and data retention after power failure. Consequently, non-volatile memory (NVM) has emerged as a crucial technology for bridging the gap between the memory and storage layers. However, due to inherent differences in write life, read–write performance variations, and consistency guarantee after failure, the systematic application of NVM still faces a series of challenges. Addressing these issues, this paper takes as its starting point the adaptation of medium characteristics and system design, and summarizes the research progress in aspects such as write optimization, consistency and security coordination mechanisms, data structure modification under hybrid memory architecture, and cross-layer resource collaboration. It also conducts an in-depth analysis of representative solutions and evaluation methods. The review results show that current research has shifted from improving a single performance bottleneck to multi-mechanism collaborative optimization. Various technical approaches have proven complementary in alleviating write amplification, enhancing persistence efficiency, and optimizing access patterns. This paper demonstrates that achieving stable and scalable application of NVM requires establishing a more systematic collaborative design concept between durability, security, and performance. As AI training workloads and big data analytics place increasing demands on memory bandwidth and persistence, the techniques surveyed here provide a foundational basis for next-generation memory-centric computing infrastructures. Full article

The work uses a comprehensive approach based on the information and communication concept of construction management templates to minimize information asymmetry between construction stakeholders when implementing innovative technologies. An analysis of the regulatory framework and patent research of existing analogs of wall structures was conducted. It was theoretically substantiated that the use of removable reusable formwork for monolithic walls made of expanded polystyrene concrete allows significant reduction in cost and logistics costs. A technology for erecting heat-insulating walls made of expanded polystyrene concrete (EPC) has been developed, which involves preliminary preparation of the insulation with the application of a protective reinforced layer. This allows avoiding performing labor-intensive and dangerous operations at height. A design of a noise-proof wall with sound-absorbing hollow-forming elements has been proposed, improving acoustic characteristics while saving materials. Thermophysical tests of fragments of walls made of expanded polystyrene concrete with a density of D250 (thickness of 260 mm) confirmed the need for additional insulation for heat transfer resistance for regulatory compliance. Acoustic studies have proven the effectiveness of using hollow-forming elements to increase the airborne noise insulation index and to reduce material consumption. All this helped to develop and patent the polystyrene concrete wall technology. For the first time, the concept of implementing the technological process of expanded polystyrene concreting of monolithic walls into construction management and production using construction management templates was proposed. This allowed the transformation of technological operations into a flow of objective data to minimize information asymmetry between project participants. It was theoretically proven that the objectification of production indicators through construction management templates is a base for measuring the commercial value and investment attractiveness of the technology being implemented. Full article

Neurodegenerative diseases are a serious problem for modern society, and their treatment remains an important issue discussed by the scientific community. One of the promising potential directions for modulating neurodegenerative processes is the use of acylhydrazones, a class of compounds that combine different bioactive fragments linked by an acylhydrazone moiety. So far, the biological properties of these compounds have been proven. They show antibacterial, antiviral, antifungal, antiparasitic, anticancer, anti-inflammatory and antioxidant activity. Many research papers focus on designing acylhydrazones that will find use in the treatment of neurodegenerative diseases by inhibiting the enzymatic activity of acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), β-secretase 1 (BACE1) and monoamine oxidase (MAO), as well as inhibiting β-amyloid aggregation, exhibiting metal chelation and antioxidant properties. Recent studies have described the acylhydrazone-based dual (multi-target) inhibitors, which have demonstrated encouraging outcomes during in vitro evaluations. This review covers recent articles published in the years 2020–2025 and offers a comprehensive overview of the biological properties of the acylhydrazones and their multifunctional derivatives on neurodegenerative processes and/or neuroprotection, while emphasizing their universal nature, structural versatility and role as leading structures in the search for new drugs. Full article

The late Paleogene White River Group is a post-Laramide sedimentary succession that occurs within Laramide intermontane basins and atop some basement-cored uplifts. Detrital zircon U-Pb dates of tuffaceous sandstones from the uppermost White River Group in eastern Wyoming and western Nebraska yield a maximum depositional age of 23.07 ± 0.46 Ma, which overlaps the emplacement of the Markagunt Gravity Slide in Utah’s Marysvale Volcanic Field at 23.05 + 0.22/− 0.20 Ma. The Marysvale Volcanic Field (~31–18 Ma) lies at the east margin of the Nevadaplano, a longstanding highland in the Sevier Hinterland later dismembered by basin and range extension. Strata both proximal and distal to the Marysvale Volcanic Field show an increase in Marysvale provenance up to the emplacement of the Markagunt Gravity Slide. After emplacement, distal sediment sourcing of Miocene strata in the Great Plains shifted back to older Paleogene volcanic fields farther west in Nevada. This temporal relationship suggests that the collapse of the Marysvale Volcanic Field associated with the emplacement of the Markagunt Gravity Slide forced drainage reorganization and sediment sourcing during the transition from White River to Arikaree Group sedimentation. Full article

Generative artificial intelligence (AI) is increasingly used to support decision-making in manufacturing quality assurance (QA), but its adoption raises concerns regarding governance, traceability, and auditability. This paper proposes a proactive framework that integrates generative AI into quality management and auditing while preserving standards alignment and human oversight. The framework structures quality activities across supplier, in-process, and post-market domains and across three hierarchical levels—product, process, and operation—to link quality outcomes with documentary evidence requirements. A proof-of-concept (PoC) study in electronics manufacturing focused on New Product Introduction (NPI) planning and compared two parallel workflows: an expert QA team and a generative AI-assisted chatbot workflow. Within a fixed time window, both workflows produced an aligned Process Failure Mode and Effects Analysis (PFMEA), Control Plan, supplier Production Part Approval Process (PPAP) request package, and internal audit evidence pack. Three independent experts evaluated the integrated deliverable package using five indices covering documentation quality and audit readiness, detection and containment logic, process capability and stability, governance and provenance safeguards, and execution (time) efficiency. Compared with the expert package, the generative AI–assisted workflow produced more traceable, governance-rich documentation (ownership, versioning, clause-to-evidence links) and reduced manual audit-evidence consolidation, supporting quality planning and change-control readiness. Full article

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring. Full article

Background and Objectives: Anemia is a frequent complication of chronic kidney disease (CKD), primarily attributed to erythropoietin deficiency. Interstitial fibrosis (IF), which disrupts the renal interstitium where erythropoietin-producing cells reside, may contribute to anemia independent of glomerular filtration rate (GFR). However, data in primary glomerulonephritis (PGN) are limited and conflicting. Materials and Methods: In this nationwide multicenter registry analysis (TSN-GOLD), 2794 adults with biopsy-proven PGN were included. Interstitial fibrosis was graded semi quantitatively (0–3). Anemia was defined according to KDIGO/WHO criteria. Multivariable logistic regression models were constructed to evaluate the independent association between IF severity and anemia, adjusting for age, sex, eGFR, log-transformed proteinuria, hypertension, diabetes mellitus, and biopsy diagnosis. Interaction between IF and eGFR was assessed. A predefined subgroup analysis was performed in patients with preserved renal function (eGFR ≥ 60 mL/min/1.73 m²). Results: Anemia was present in 34.4% of patients. Although moderate-to-severe IF was more frequent among anemic patients (p < 0.001), IF severity was not independently associated with anemia in multivariable analysis (p-trend = 0.72). Female sex and lower eGFR were independently associated with anemia. A statistically significant IF×eGFR interaction was observed (p = 0.0029), indicating effect modification across renal function levels. The model demonstrated moderate discrimination (AUC = 0.705). In patients with preserved renal function, IF severity was not associated with anemia. Conclusions: In this large multicenter cohort of PGN patients, interstitial fibrosis severity was not independently associated with anemia after adjustment for renal function and clinical covariates. These findings suggest that the association between interstitial fibrosis and anemia in PGN appears largely mediated by renal functional status rather than fibrosis severity alone. Full article

Introduction: Despite proven efficacy of anti-TNF agents in inflammatory bowel disease, primary non-response affects up to one-third of patients, while secondary loss of response occurs at 13–21% per patient-year, often requiring dose optimization or switching to alternative advanced therapies. Methods: The present single-center cohort study at the University Hospital of Ioannina included biologic-naïve patients receiving anti-TNF therapy as their first biologic treatment. First anti-TNF treatment failure was defined as discontinuation due to persistent IBD activity despite maximal dose optimization (infliximab 10 mg/kg every 4 weeks, adalimumab 40 mg weekly). Patients with measurable anti-drug antibodies prior to anti-TNF dose intensification or discontinuation were excluded. Of 528 anti-TNF-treated patients, 286 (173 with CD, 113 with UC) met the inclusion criteria and were included in the final statistical analysis. Results: Anti-TNF failure occurred in 32.7% of Crohn’s (CD) and 32.9% of ulcerative colitis (UC) patients. Multivariable Cox regression identified complicated phenotype (stricturing or/and penetrating CD; HR = 1.9, p = 0.032) and concomitant corticosteroid use at anti-TNF initiation (HR = 2.03, p = 0.012) as independent predictors of anti-TNF failure in CD. Age at CD diagnosis showed a trend for statistical significance (HR = 1.02, p = 0.061), and after stratification, age at diagnosis ≥ 40 years conferred higher risk (HR = 1.93, p = 0.016), alongside persistent effects of complicated phenotype (HR = 1.83, p = 0.027) and corticosteroid use (HR = 2.01, p = 0.013). In UC patients, female sex predicted anti-TNF failure (HR = 2.13, p = 0.025). IBD-related bowel resection occurred in 26.6% of patients with CD and in 5.3% of patients with UC. Conclusions: Anti-TNF failure remains common despite optimization. Identifying immunogenicity-independent predictors may enable personalized treatment strategies and improve outcomes. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

This study investigated the impact of incorporating calcined eggshell and carbon nanotube (CNT) on the properties of polyvinyl alcohol (PVOH) blends. Prior to solution casting, eggshell waste underwent a calcination process and then the samples were prepared via solution cast method. Mechanical properties study revealed a significant enhancement in tensile strength and elongation at break with increasing loads of calcined eggshell and CNT. Higher tensile strength was observed with increasing CNT loading for PVOH blends added with 1 phr and 3 phr calcined eggshell, owing to the reinforcing role of CNT in the composite matrix. In contrast, the tensile strength at 0.3 phr CNT is lower than at 0.2 phr CNT due to CNT agglomeration, which weakens the interfacial adhesion with the PVOH matrix and hinders effective stress transfer during deformation. SEM images depicted well-dispersion and interaction effect of calcined eggshell particles and CNT particles at low loading levels. The good interaction effect between calcined eggshell and PVOH matrix (which both exhibit hydrophilic behaviour) is mainly attributed to the presence of hydrogen bonding in the polymer matrix, as proven in FTIR analysis. XRD analysis revealed significant peaks in the 2θ range of 19° to 21°, suggesting that increased amounts of calcined eggshells influenced the crystallite size of the original PVOH matrix. In summary, the addition of calcined eggshell and CNT at low loading levels markedly enhanced the mechanical, physical, and thermal properties of the composite material. Full article

In this paper, an efficient and accurate framework for nonlinear spacetime fractional diffusion equations is proposed. The methods are based on the spectral deferred correction technique, which employs a compact difference scheme as the preconditioner via the Picard integral collocation formulation. The nonlinear term is incorporated into the preconditioner in a way similar to linear systems without using Newtonian methods. The preconditioner is proven to be a stable operator, and the resulting spectral deferred correction method maintains an arbitrary order of accuracy and excellent stability. Due to the dense property of the central finite difference approximation of the fractional Laplacian ${(-\Delta )}^s$, a dual accelerated algorithm for the exact computation of the matrix–vector product is presented by introducing the discrete sine transform. The numerical results demonstrate that the proposed new methods are highly efficient and precise. Full article

This study presents a comprehensive geochemical and isotopic investigation of urban stream sediments draining into the Keban Dam Lake in Eastern Türkiye. A total of 15 sediment samples were collected along a ~35 km transect, spanning rural-to-urban transition zones. PAAS-normalised REE patterns revealed coherent light REE behaviour and positive Eu anomalies (Eu/Eu* = 1.57–2.01), except sample K8 (Eu/Eu* = 0.91), indicating contributions from plagioclase-bearing lithologies. Enrichment Factor (EF) calculations based on scandium normalisation showed notable enrichment in Li, Zr, Nb, and REEs, reflecting felsic source rocks and mineralogical sorting. Multivariate statistical analyses (PCA and HCA) revealed distinct groupings of elements associated with lithogenic sources (Be, Sc, and Y) and anthropogenic inputs (Li, Sn, and Rb). Spatial clustering of samples into rural, transitional, and urban zones supported this differentiation, suggesting increasing anthropogenic influence downstream. Sr isotopic data (⁸⁷Sr/⁸⁶Sr = 0.7045–0.7057) and Pb isotope ratios (²⁰⁶Pb/²⁰⁴Pb = 18.914–18.947) suggest dominantly geogenic control, with slightly more radiogenic signatures in urban sediments. These integrated geochemical and isotopic results provide the provenance model for the Keban catchment, highlighting how natural lithological sources and urbanisation jointly shape sediment composition and metal distribution. The findings also provide a useful geochemical baseline for environmental monitoring, sediment quality assessment, and sustainable watershed management in the Keban Dam Lake basin. Full article

This study models trachoma transmission in Cameroon using a deterministic approach with integer and fractional-order derivatives, incorporating direct, fly-mediated, and environmental transmission routes. Fitting disease data from 1990–2019, the model forecasts trachoma prevalence until 2035. The research confirms the solution existence and uniqueness, calculates the basic reproduction number ${\mathcal{R}}_0^{\lambda }$ where $\lambda \in (0,1]$ represents the fractional-order parameter, and analyzes equilibrium stability. A stable trachoma-free equilibrium exists when ${\mathcal{R}}_0^{\lambda }<1$, while an endemic equilibrium is proven stable for ${\mathcal{R}}_0^{\lambda }>1$ under specific conditions. Calibration of a fractional model with Cameroon data yielded an ${\mathcal{R}}_0$ of 1.169 (indicating endemicity) and identified an optimal fractional order of $\lambda =0.98$. By calculating the strength number, we found that another epidemic wave could occur in 50 years. Global sensitivity analysis highlighted key parameters affecting trachoma dynamics. A numerical scheme of the model based on the Adams–Bashforth–Moulton method is constructed and its stability demonstrated. It is then used to perform several numerical simulations, first to validate the theoretical results obtained, and then to compare the different models (statistical and deterministic). The conclusion is reached that the disease will persist in the population (${\mathcal{R}}_0>1$), although the statistical model shows that it could disappear by 2030. This proves that, for trachoma dynamics in Cameroon, it is advisable to use a deterministic model. Full article

Building-Integrated Photovoltaics (BIPV) can contribute to decarbonisation, but its large-scale deployment requires accurate energy yield predictions that justify these systems during the decision-making process to ensure cost-effectiveness. In urban contexts, boundary conditions involve modelling strategies that can reliably represent the effect of shading from nearby elements. However, specific tools for proper modelling BIPV are not generally available and the workflow frequently requires the combination of different tools. Nowadays there is still no clear nor unique strategy for modelling BIPV, and expert groups are currently working on benchmarking analyses. This work compares energy yield estimations from two PV simulation software tools, System Advisor Model and PVsyst to seven years of experimental data (2017–2023) from five BIPV façade arrays distributed across three orientations (east, south and west). The main focus was twofold. Firstly, to analyse their management of shadows by following two different shading approaches: their built-in 3D modelling tools and a Digital Surface Model (DSM). Secondly, to evaluate the capability of these tools to simulate the performance of real BIPV systems. Results manifest that conventional and accessible PV software can be suitable for BIPV modelling as long as care is taken to properly assess the effect of shading, especially from urban tree canopies. The novel DSM strategy proposed is proven effective and can be a valid alternative in certain cases when the availability of in situ data is limited. Full article

For studies utilizing methods such as water color parameter inversion and algal bloom classification, abundant spectral bands and high spectral resolution are of great significance. However, for multispectral satellite sensors that are not designed for water color studies (e.g., Sentinel-2 MSI), the number of bands in the visible–near-infrared range is limited, and lacks specific spectral bands with rich spectral information. Hyperspectral reconstruction of multispectral data based on hyperspectral remote sensing reflectance ($R_{rs}$) databases and machine learning algorithms have been proven to be a feasible solution. Based on the in situ measured $R_{rs}$ data, this study constructed a large-sample hyperspectral $R_{rs}$ database covering various optical water types using two Chinese hyperspectral satellites, and compared the spectral reconstruction accuracy of six machine learning algorithms. The results show that expanding the $R_{rs}$ database for model training by integrating hyperspectral satellite data can effectively improve the reconstruction accuracy in waters of different optical types. Comparisons with in situ measured hyperspectral $R_{rs}$ indicate that the reconstructed Sentinel-2 hyperspectral data achieve high accuracy, with the Spectral Angle Mapper (SAM) less than 5° and the correlation coefficient ($r$) higher than 0.7. Furthermore, the reconstructed data can effectively restore spectral information not captured by the original multispectral data, such as the suspended sediment $R_{rs}$ peak at 580 nm and the chlorophyll $R_{rs}$ valley at 680 nm. Through spectral reconstruction, the spectral resolution of Sentinel-2 can be maximized while retaining its advantages of fast revisit capability and high spatial resolution, thereby expanding its application potential in water color remote sensing. Full article