Search Results (483)

Intel Software Guard Extensions (SGX) have been widely studied and adopted in privacy-preserving information systems to enhance the security and privacy guarantees of sensitive data computation. By constructing a protected enclave within the processor, SGX provides hardware-enforced confidentiality and integrity for sensitive data and critical code. Nevertheless, due to inevitable interactions between trusted enclaves and untrusted host environments, SGX applications remain vulnerable to memory corruption attacks. Existing detection techniques exhibit fundamental limitations, including the lack of systematic induction of SGX-specific memory corruption behaviors, the absence of fine-grained parameter-level taint modeling during call-chain construction, and relatively inefficient call-chain exploration strategies over large search spaces. To address these issues, we propose ParaTaintGX, an analysis framework that integrates parameter-level taint states into vulnerability detection. ParaTaintGX constructs fine-grained call-chain nodes that capture both functions and the taint states of their parameters. It further introduces a Multi-node Heuristic Priority Search Algorithm to guide call-chain exploration. In addition, a backtracking-based pruning strategy is applied during path analysis to efficiently identify memory corruption vulnerabilities. Our evaluation demonstrates that ParaTaintGX discovers 12 vulnerabilities across 10 open-source SGX projects, outperforming the best baseline tool by two vulnerabilities. It achieves 19.35% precision, surpassing the most precise existing tool by 8.37 percentage points. These results highlight its superior detection capability and precision. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

Frequent large-scale construction projects have rendered subway box structures vulnerable to displacements. This study examined the adequacy of foundation reinforcement for a subway box structure exhibiting displacement behavior. A displacement function was derived from the optical leveling data, and a three-dimensional numerical analysis was performed by applying the computed subgrade elastic modulus as a boundary condition. The analysis produced estimates of uplift and subsidence at the nodes along both the transverse and longitudinal directions of the structure. To determine the required amount of reinforcement (grouting volume), the nodal reinforcement depth obtained from the analysis was applied to a grid-based volumetric calculation method. The nodal intervals were subdivided to the maximum feasible extent, and rectangular grids with sufficient resolution were established to ensure accurate reinforcement-volume calculation. The reinforcement volumes estimated through the numerical analysis were compared with actual field values to assess the adequacy of the foundation reinforcement. Some differences were observed, which were attributed to field constraints that prevented reinforcements at certain required locations. Based on these findings, additional reinforcements can be applied at the analytically identified locations to ensure the structural safety of the subway box structure. Full article

Adaptive reuse projects frequently involve substantial plan-level reorganization; however, the reconfiguration of spatial hierarchy within interior layouts remains insufficiently examined at the building scale. Background: This study investigates how spatial hierarchy is restructured during the adaptive reuse of an industrial building converted into a hotel, focusing on configurational implications of program-driven design decisions within unchanged architectural boundaries. Methods: Visibility-based Space Syntax analyses were conducted using visual integration, connectivity, and mean depth measures. Rather than relying on floor-level averages, a control-point-based comparative protocol enabled systematic pre- and post-intervention comparisons linked to plan-level architectural interventions under identical analytical parameters. Results: The findings indicate selective amplification of spatial accessibility and visual integration at defined circulation nodes on the ground floor, while upper floors exhibit contraction of visibility fields and increased relational depth. These shifts indicate a floor-specific redistribution of spatial hierarchy rather than uniform configurational transformation. Conclusions: The results suggest that spatial transformation in adaptive reuse can be interpreted as a design-driven recalibration of configurational relationships within fixed architectural boundaries. Without pursuing statistical generalisation, the study proposes a case-bound analytical protocol that may inform examination of comparable adaptive reuse contexts where program transformation occurs within stable spatial envelopes. Full article

Kubernetes, an open-source project initiated by Google for managing and organizing containers in cloud platforms, has become the preferred choice for deploying large-scale containerized microservice architectures. Kubernetes employs a scheduler that considers constraints defined by workload owners and cluster managers to identify the most suitable node to host a given task. Although it can be configured in a multitude of ways, the default scheduler that comes with Kubernetes is not fully capable of efficiently handling the demands of Horizontal Pod Autoscaling (HPA), particularly when deploying a large number of similar pods simultaneously. This article focuses on the optimization of the Kubernetes scheduler to allocate and manage resources more efficiently in rapid Pod autoscaling scenarios. The scheduling mechanisms of Kubernetes offer considerable potential for improvement. This article introduces a custom scheduler that reduces redundant scoring steps using a caching mechanism, thereby accelerating the scheduling process for horizontal scaling of pods. The article begins with an in-depth literature review, followed by the development of novel algorithms to address existing gaps in the default scheduler. The custom scheduler is then subjected to rigorous simulation and testing phases to ensure its robustness and efficiency. Experimental results demonstrate the effectiveness of the proposed approach in improving the scheduling performance for HPA in Kubernetes. Full article

Ferroptosis is an iron-dependent programmed cell death (PCD) implicated in cancer therapy response, yet its transcriptional control remains unevenly characterized and often centered on a limited subset of transcription factors (TFs) rather than systematically addressing TF families. The Activating enhancer-binding Protein-2 (AP-2) family of TFs is a plausible but understudied regulatory node linking oncogenic programs to ferroptosis, with prior research limited to AP-2α and AP-2γ, suggesting anti-ferroptotic and pro-tumorigenic roles. Thus, the present study aimed to provide a family-wide analysis of the relationships between AP-2 and ferroptosis across tumors in which this PCD type is considered biologically and clinically relevant. The research integrates ferroptosis gene modules with AP-2 targetomes, tumor–normal expression comparisons, survival stratification, ferroptosis scoring, cross-cohort functional analyses, and signaling pathway projection extending canonical ferroptosis circuits with AP-2–associated non-canonical elements. Consistent associations between AP-2 expression, prognosis, and ferroptosis score were observed in five tumor cohorts: cervical squamous cell carcinoma, glioblastoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, and thyroid carcinoma. In addition, cross-cohort clustering highlighted genes enriched in redox- and lipid-metabolism programs linked to apoptosis and autophagy-dependent death. Among the candidates emerging from these analyses, ferroptotic markers (LOX, PTGS2, and NQO1) and AP-2–linked nodes such as CD36, DUOX1, EPHA2, MUC1, PTPRC, SNAI2, and TP63 warrant targeted functional and binding validation to infer whether these associations reflect direct AP-2 regulatory mechanisms. Most importantly, AP-2–centered research appears to be a valuable area for guiding studies of tumor-specific ferroptosis vulnerability or resistance. Full article

Anonymous communication networks such as the Invisible Internet Project (I2P) are essential for safeguarding privacy and ensuring freedom of expression, necessitating robust performance and security evaluation in controlled environments. Network testbeds offer a reliable alternative to real-world testing. This paper proposes a dynamic modeling framework based on Graph Attention Network (GAT). We introduce a Region-Centric Initialization (RCI) strategy to establish an initial observation anchor, followed by a GAT-based Locality-Aware (GAT-LA) sampling mechanism that treats representative node selection as a dynamic learning task. Experimental results demonstrate that the GAT-LA mechanism significantly outperforms static methods in maintaining long-term similarity to real-world I2P performance metrics. The integrated stability penalty mechanism effectively suppresses excessive topological fluctuations, ensuring temporal smoothness across evolutionary cycles. Furthermore, the RCI strategy provides high engineering flexibility by supporting both automated scoring and target-oriented manual configuration. This paper presents a scalable methodology for dynamic network simulation with enhanced statistical alignment, providing a practical reference for security research within resource-constrained anonymous network ranges or testbeds. Full article

This study examines the ripple effects of parkification, the transformation of post-industrial landscapes into public parks and green infrastructure, in Greenville at the Upper State region of South Carolina. As many Southern mill towns contend with industrial decline, environmental degradation, and complex land-use legacies, parkification has emerged as a pragmatic response to constraint rather than a conventional redevelopment strategy. Framed as a process rather than an isolated design outcome, parkification is understood here as a generative mechanism that produces cumulative spatial, ecological, and institutional change beyond individual project boundaries. Using a mixed-methods approach that integrates spatial and temporal mapping, archival research, site analysis, and semi-structured interviews with key stakeholders and decision-makers, this study traces how parkification unfolds across time and scale. Three interconnected case studies in Greenville, Falls Park on the Reedy, Conestee Nature Preserve, and the Swamp Rabbit Trail, are examined to address how post-industrial parkification contributes to greenway network formation and broader urban–regional transformation in the American South. The findings reveal that parkification consistently emerged from conditions of environmental constraint, including contamination, flooding, infrastructural legacies, and limited redevelopment feasibility. Early parkification projects functioned as generative landscape nodes that catalyzed the expansion of green space and connectivity rather than remaining isolated amenities. By establishing visible, accessible, and publicly valued landscapes, these projects enabled the extension of trails, river corridors, and preserved infrastructures, contributing to the formation of an interconnected regional greenway system. Institutional alignment among civic organizations, public agencies, and landscape professionals further supported the scaling and replication of parkification. Together, these findings position parkification as a process-based landscape strategy capable of driving the spread of green areas and long-term urban connectivity in post-industrial regions. Full article

The growing number of Internet of Things (IoT) devices has driven the need for energy-efficient communication in long-range, low-power networks like LoRa. LoRa offers wide coverage with minimal transmission power. However, radio communication remains the main energy consumer in end devices. Data compression can mitigate this issue by reducing packet size and transmission frequency. This work presents a comprehensive evaluation of classical and cutting-edge lossless compression algorithms applied to LoRa networks. Evaluated algorithms include Huffman, LZW, BSC, CMIX, PAQ8PX, GMIX, and LSTM-compress. Experiments were conducted using a Raspberry Pi 5 integrated with an RFM95W LoRa module and INA219 sensors to measure real-time power consumption, CPU load, and memory usage. Results show that classical methods, particularly LZW, achieve the best energy efficiency and reduce LoRa transmission energy by up to 7.41%. In contrast, cutting-edge machine learning (ML)-based algorithms, such as CMIX and PAQ8PX, achieve higher compression ratios but exhibit excessive computational and memory overhead, resulting in negative energy gains. Metadata overheads, including dynamic Huffman tables (28–128 bytes), also affect payload efficiency for small packets. These findings indicate that LZW is the most practical choice for energy-constrained LoRa nodes. At the same time, modern compressors, including ML-based ones, are better suited for gateways or edge servers with higher computational capacity. An open-source implementation of the experimental framework and scripts used in this study is available in the project’s public GitHub repository. Full article

Electromobility is increasingly recognized as a cornerstone of sustainable transport, yet its adoption remains uneven across regions. This study develops an integrated framework that combines geospatial analysis, multi-criteria decision-making (MCDM), and power system evaluation to identify and prioritize fast-charging sites at the national scale. Applied to Costa Rica’s national road network (NRN), encompassing both urban centers and peripheral regions, the framework integrates spatial suitability, socioeconomic priorities, and grid readiness across projected electric vehicle (EV) penetration scenarios. Critically, power system simulations reveal voltage instability at distribution nodes (as low as 89.88% p.u.) under 3% EV penetration despite 99% renewable generation, demonstrating that grid capacity, not planning methodology, constitutes the primary barrier to electric mobility adoption. This finding, derived from the first national-scale analysis that integrates equity-driven spatial prioritization with comprehensive grid validation using real fleet projections, challenges conventional assumptions in transport-focused infrastructure planning. The framework provides a transferable tool for countries seeking to align EV infrastructure planning with sustainability and decarbonization objectives, while highlighting that grid reinforcement must precede, not follow, the deployment of fast-charging infrastructure. Full article

With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approaches tend to work well only if detailed network parameters are available, while data-driven approaches can suffer from overfitting and may not generalize well. We created the PHY-GAT-SAC framework to address these issues. Physics-regularized reinforcement learning uses bidirectional graph attention, which combines a physics-informed model with a safety projection method that relies on sensitivity matrices. This makes it so that the voltage regulation is practical, interpretable, and secure. The framework works with two combined branches. One branch takes care of the nonlinear mapping from power injections to voltage states using a forward graph encoder and a reverse consistency constraint. At the same time, another branch extracts features directly from the voltages to improve the perception of system violation risk. The framework has a sensitivity-based safety layer as well. This layer projects every control action into a feasible area formed by linearized voltage restrictions, thus securing operation safety. Experiments on an IEEE 33-node system show that the framework works well. A safety layer guarantees a safe operating range without exact impedance values. And PHY-GAT-SAC greatly lowers voltage violations compared to multi-agent deep reinforcement learning. By successfully combining physics with learning, this study gives a unified framework for merging graph neural networks and reinforcement learning within intricate grid management. Full article

California is currently navigating the confluence of two acute systemic challenges: a chronic housing affordability deficit and increasing grid instability driven by climate-induced volatility and the aggressive transition to variable renewable energy. This review posits that the answer lies in a novel technology convergence: the strategic integration of Accessory Dwelling Units (ADUs) with residential Battery Energy Storage Systems (BESSs) utilizing the “Photovoltaic-Energy Storage-Direct Current-Flexibility” (PEDF) architecture. We identify this ADU + BESS + PEDF nexus as a critical innovation that transforms the dwelling unit from a passive consumption endpoint into an active highly efficient DC-coupled “prosumer” node capable of providing critical grid services. Unlike traditional AC-coupled systems, the PEDF framework minimizes conversion losses and maximizes grid-interactive flexibility, establishing the ADU as a decentralized asset for grid stabilization. To validate this paradigm, I employ a stochastic financial simulation using the RShiny framework to assess the economic viability of prefabrication-based deployment strategies under Senate Bill 9 (SB 9) provisions for three investment scenarios: Acquisition-to-Rent, Acquisition–Development–Resale, and Long-Term-Asset-Retention. Benchmarked against a baseline of traditional in situ constructions globally, our results indicate that modular prefabrication reduces project timelines by 30–50% and cradle-to-site embodied carbon by up to 47%. Furthermore, financial modelling—benchmarked at a 7.5% nominal discount rate without discretionary state incentives—confirms that “Acquisition–Development–Resale” strategies yield Internal Rates of Return (IRR) exceeding 20%, while “Long-Term-Asset-Retention” achieves stabilized positive cash flow, validating the economic competitiveness of sustainable densification. Despite identifying implementation barriers—specifically the “split-incentive” dilemma in rental markets and emerging data sovereignty constraints—this review concludes that the BESS-powered PEDF-architecture ADU represents the fundamental atomic unit of a resilient, low-carbon urban dwelling infrastructure, necessitating aligned policy support to achieve scalable deployment. Full article

Background: Perineural invasion (PNI) is a recognized pathway for cancer spread and is associated with poor outcomes in gastric cancer. However, the initial morphological characteristics of tumor–nerve interactions in early gastric cancer, particularly at the ultrastructural level, remain insufficiently defined. Case Presentation: We report a case of a 49-year-old man diagnosed with type IIc early gastric cancer. Histological examination revealed a combined poorly cohesive carcinoma (PCC)-NOS/signet-ring cell (SRC) histotype. Tumor invasion reached the middle third of the submucosa and was accompanied by a mature desmoplastic reaction, with metastases identified in two perigastric lymph nodes (pT1bN1M0). Transmission electron microscopy (TEM) revealed unmyelinated nerve fibers embedded within the submucosal desmoplastic stroma, in close proximity to infiltrating neoplastic cells. Several tumor cells exhibited cytoplasmic projections ranging from single extensions to multiple prominent pseudopods, resulting in an amoeboid morphology. Notably, an unmyelinated nerve process was observed within a cytoplasmic invagination of an individual tumor cell. Conclusions: Taken together, these ultrastructural findings provide novel and previously undescribed morphological evidence of a specific interaction between amoeboid tumor cells and peripheral unmyelinated nerve fibers within the submucosal desmoplastic stroma of early gastric cancer. The biological and clinical significance of this interaction in the early stages of perineural invasion warrants further investigation. Full article

The growing interest in precision agriculture has led, in recent years, to an increase in the adoption of Internet of Things (IoT) technologies in the service of smart agriculture to optimize agricultural production processes through the monitoring of environmental conditions and prevent food loss. This work stems from research conducted as part of the Tech4You project, where the enabling digital technologies developed in Spoke 6 contribute to the advanced solutions envisaged by Spoke 3 to facilitate the transition to a sustainable agrifood system. In particular, we present the design and evaluation of a multi-hop Device-to-Device (D2D) communication architecture that leverages Long Range (LoRa) technology, specifically designed for monitoring vineyards in the context of passito wine production. The proposed framework addresses the challenge of monitoring mobile containers for grapes during the drying phase, a critical stage in which inadequate temperatures and humidity can promote the growth of fungi and the formation of mycotoxins. The integration of simulation-based performance evaluation with a multi-layer system architecture is presented in this work. The objective is to compare the performance of different routing strategies in choosing data forwarding paths to the gateway. The simulation results show that the proposed routing strategy, which is based on learning but also focuses on energy consumption, offers good performance. In particular, it achieves packet delivery rates of over $92\%$ and preserves over $95\%$ of active nodes after 2 h of operation. Energy-aware routing strategies also perform well compared to those that only consider the distance from the destination, but overall, the proposed strategy achieves a better trade-off on the metrics analyzed. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article