Search Results (177)

This systematic review evaluates recent scientific and technological advances in water quality monitoring and pollution alarms for ports, based on records retrieved from seven databases following the PRISMA protocol. A total of 414 documents were screened, resulting in 141 articles (TRL 3) and 56 patents (TRL 4–5). Bibliometric, patentometric, and thematic analyses were conducted using Bibliometrix and ORBIT^®. Results show sustained growth in both academic and technological outputs, with a patent Compound Annual Growth Rate (CAGR) of 32%, compared with 13% for scientific publications, indicating accelerated translation from research to innovation. The conversion rate from scientific research to patenting increased from 14% (2010–2015) to 47% (2020–2023). Analysis of patent legal status reveals that 52% of patent families remain valid (48% granted; 4% pending), while 33% are lapsed, 13% revoked, and 2% expired, reflecting the dynamic and emerging character of the field. Technological ownership is highly concentrated, with China accounting for nearly all active patents, whereas scientific production is more geographically distributed. Thematic analysis identifies four main scientific clusters: environmental monitoring, chemical pollutants, seashore hazards, and eutrophication. The main technological domains of the patents are analysis of biological materials, control, and environmental technologies. Emerging areas of focus at TRL 3 and TRL 4–5 include microplastics, climate-change impacts, aquaculture risks, real-time sensing, IoT-enabled platforms, machine-learning analytics, autonomous monitoring systems, and bioindicator-based early-warning tools. This review provides a quantitative roadmap to support sustainable port operations, coastal ecosystem protection, and progress toward multiple synergistic United Nations Sustainable Development Goals (SDGs). Full article

Climate change significantly affects hydrological responses, yet studies addressing future water availability in the Paraopeba River Basin (PRB), an important tributary of the São Francisco River Basin in Brazil, remain limited, particularly under CMIP6 scenarios and using distributed hydrological modeling approaches. In this context, this study evaluated the hydrological responses of the PRB, under climate change using the MHD-INPE. Future projections were based on an ensemble of seven climate models from the NEX-GDDP-CMIP6 collection, considering a baseline period (1992–2014), three future periods 17(2040–2060, 2061–2080 and 2081–2100) and two socioeconomic scenarios (SSP245 and SSP585). The model satisfactorily reproduced observed streamflow during the baseline period. Under the SSP585 scenario, the projections indicate stronger alterations in water availability, with a potential intensification of flood and drought events, as reflected by reductions in minimum streamflows (Q₉₀) and increases in maximum streamflows (Q₁₀), particularly in sub-basins 4 and 5, where Q₉₀ reductions approach 30% and Q₁₀ increases reach 11.7%. Additionally, a decrease in Q_7,10 values was observed, which enabled the analysis of the Conflict Index (Icg), indicating that water withdrawals currently granted may exceed the limits established by existing legislation in future scenarios (Igc > 1). Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements. Full article

Methane emissions from abandoned coal mining operations represent a persistent environmental and safety challenge in post-mining regions undergoing urban redevelopment. As urban infrastructure expands over former underground workings, the uncontrolled migration of mine gas can compromise public safety, exacerbate greenhouse gas emissions, and undermine sustainable development goals. This study investigates the origin of methane emissions detected in an urban area of the municipality of Lupeni, Romania, following the commissioning of a new natural gas distribution pipeline installed within a historically mined perimeter. The emissions had not been previously reported and were unexpectedly discovered during technical inspections conducted after the gas network installation, highlighting the absence of historical data on gas presence in this area. This is the first documented case of an accidental discovery of methane emissions in an urban perimeter overlying historical coal mine workings in Romania, granting this study a pioneering status, both scientifically and in terms of urban risk management. The findings emphasize that administrative mine closure does not equate to risk closure, as latent methane emissions may reactivate during urban transformations (e.g., excavations, utility upgrades, drainage changes). To ensure a scientifically sound and sustainable risk assessment, an integrated diagnostic framework was applied, combining chronological field monitoring with chromatographic gas composition analysis. This methodology enabled precise attribution of the methane source to abandoned underground mine workings, excluding the public gas network as a contributor. Based on this diagnosis, a controlled drainage and methane recovery system was implemented, resulting in the elimination of detectable concentrations at all monitoring points by February 2025. The captured methane was redirected for local energy use, transforming an environmental liability into a usable resource. This intervention supports circular economy principles and aligns with EU climate and energy transition goals. The proposed methodological framework provides a replicable model for identifying and managing residual mine gas in post-industrial urban environments. Although emission fluxes were not quantified, concentration-based screening enabled risk diagnosis, prioritization, and targeted intervention. These findings are relevant to EU Regulation (2024/1785) on methane emission reduction, emphasizing the need to include post-mining methane (AMM) in urban planning and environmental monitoring strategies. Limitations of the study include the absence of baseline data and the inability to calculate total methane flux. However, the results support immediate and practical risk mitigation and highlight the need for future work focused on long-term monitoring and emission quantification. This case provides critical insights for other post-mining cities in Central and Eastern Europe facing similar challenges at the intersection of legacy coal infrastructure and modern urban development. This study is designed as a concentration-based diagnostic and risk-oriented case study and does not aim to quantify methane emission fluxes. Full article

In this paper, we investigate the grant-free (GF) accessing for cyber–physical systems (CPSs) over space–air–ground integrated networks (SAGINs) by jointly considering system stability and power consumption. The problem of GF access for CPSs over SAGINs is modeled as a Markov decision process where preamble sequences are chosen to minimize power consumption while guaranteeing system stability. To solve this problem, a distributed multi-agent deep reinforcement learning framework based on factorization technology is proposed. In addition, a local network based on hierarchical reinforcement learning is designed to prevent the explosion of the dimension of the action space, in turn reducing the computational complexity of the proposed algorithm. Finally, the simulation results validate the performance superiority of the proposed scheme in terms of convergence, power consumption and stability compared with the baseline schemes. Full article

Electrospinning, Forcespinning^®, and melt electrowriting are becoming increasingly important fiber-based fabrication technologies for tissue engineering and drug delivery applications. Despite their scientific and industrial relevance, their patent landscape has not been systematically examined, which limits the understanding of technological dynamics and translational applications. This study addresses this gap through a patentometric analysis conducted within a Competitive Technology Intelligence framework. A total of 3557 active and granted Extended Patent Families from 2020 to 2024 were analyzed to identify temporal patterns, geographic distribution, key innovators, industrial sectors, and primary application areas. The results showed that the overall patent activity increased until 2022 before experiencing a slight decline. China dominates the landscape, accounting for approximately 62% of applications filed, largely driven by academic institutions such as Shanghai University. Leading industries include special-purpose machinery, medical and dental technology, and textiles. According to International Patent Classification codes, filament formation (D01D5/00) is prevalent, while electrospinning—specifically IPC D04H1/728—represents the most active and influential of the three technologies. These findings exhibit the technological dynamics shaping fiber-based fabrication platforms and underscore their growing relevance in pharmaceutical innovation. The identified trends position these technologies as foundational for next-generation biomaterial design, offering valuable insights for researchers, industry stakeholders, and policymakers. Full article

In the mid to late Tang Dynasty, the compilation and reproduction of the tripiṭaka (Tripitaka) was directly intervened and controlled by the authorities, surpassing its own religious function and becoming an important political expression form with Buddhist elements. In the 10th century, with the collapse of the Tang Empire, the Liao, Song, and Western Xia coexisted within its original territory. At the same time, the political situation in the Korean Peninsula and Vietnam has undergone drastic changes. Within this period, the Song and Liao empires formed a dual core pattern, and the printing and issuing of the Tripitaka with strong political attributes became an important means of competition between the two sides. From the end of the 10th century to the early 11th century, the Song was the first to complete the Kaibao Canon, maintaining the monopoly of the printed Tripitaka during this period and taking it as an important way to establish the world order dominated by the Song and consolidate its relationship with neighboring political powers. Japan, which was not part of the tribute system dominated by the Song, became a target that Emperor Taizong of Song vigorously sought. Under the guidance of this political logic, the Song court, which advocated Daoism, also wanted to voluntarily issue the newly printed Daoist Canon to Vietnam, to exert its religious and political influence. After the mid-11th century, with the establishment of the Khitan Canon, the competition began between the Liao and Song dynasties over the printing and distribution of the Tripitaka. As one of the dual core patterns, the Liao attempted to challenge the monopoly position of the Song’s Kaibao Canon and the cultural hegemony behind it. In this situation, Goryeo, which was wavering between the Liao and Song, became the battlefield of competition between the two empires, presenting complex changes in terms of whether to accept the Tripitaka and their reception standards. In the late 11th century, the Goryeo and the Western Xia almost coincidentally embarked on the so-called “third path”—the construction of their own independent Tripitaka. Due to their limited positions in the political landscape of East Asia at that time, these Tripitakas were not granted to neighboring regimes outside their borders after completion, and therefore did not have international significance. It should be noted that although the positioning of the Goryeo Canon was different from that of the Song and Liao editions, it still belonged to the competition among the same language; the emergence of the Tangut Canon marked a new possibility and opened the way for future Tibetan and Manchu Canons. Briefly, the printed Tripitaka was deeply involved in the multilateral interactions of political powers in East Asia from the 10th to the 13th Century. Full article

Ranking research groups plays a crucial role in various contexts, such as ensuring the fair allocation of research grants, assigning projects, and evaluating journal editorial boards. In this paper, we analyze the distribution of h-indexes within research groups and propose a single metric to quantify their overall performance, termed the α-index. This index integrates two complementary aspects: the homogeneity of members’ h-indexes, captured by the Gini coefficient (g), and the h-group, an extension of the individual h-index to groups. By combining both uniformity and collective research output, the α-index provides a consistent and equitable metric for comparative evaluation, essentially calculated as the average relative h-group multiplied by $(1-g)$ and normalized by the maximum value of this quantity across all analyzed groups. We describe the full procedure for computing the index and its components and illustrate its application to computer science conferences, where program committees are compared through a resampling procedure that ensures fair comparisons across groups of different sizes. Additional results are presented for postgraduate programs, further demonstrating the method’s applicability. Correlation analyses are used to establish rankings; however, our primary goal is to recommend a fairer index that reduces deviations from those currently used by governmental agencies to evaluate conferences and graduate programs. The proposed approach offers a more nuanced assessment than simply averaging members’ h-indexes and can be applied broadly–for example, to university departments and research councils–contributing to a more equitable distribution of research funding, an issue of increasing importance. Full article

This paper analyzes a finite-capacity Markovian queueing system with two customer types, each assigned to a separate buffer, and a single alternating server whose service priority is dynamically controlled by a queue-length-based threshold policy. The arrivals of both customer types follow independent Poisson processes, and the service times are generally distributed. The server alternates between the two buffers, granting service priority to buffer 1 when its queue length exceeds a specified threshold immediately after service completion; otherwise, buffer 2 receives priority. Once buffer 1 gains priority, it retains it until it becomes empty, with all priority transitions occurring non-preemptively. We develop an embedded Markov chain model to derive the joint queue length distribution at departure epochs and employ supplementary variable techniques to analyze the system performance at arbitrary times. This study provides explicit expressions for key performance measures, including blocking probabilities and average queue lengths, and demonstrates the effectiveness of threshold-based control in balancing service quality between customer classes. Numerical examples illustrate the impact of buffer capacities and threshold settings on system performance and offer practical insights into the design of adaptive scheduling policies in telecommunications, cloud computing, and healthcare systems. Full article

Massive machine-type communications (mMTC) in future 6G networks will involve a vast number of devices with sporadic traffic. Grant-free access has emerged as an effective strategy to reduce the access latency and processing overhead by allowing devices to transmit without prior permission, making accurate active user detection and channel estimation (AUDCE) crucial. In this paper, we investigate the joint AUDCE problem in wideband massive access systems. We develop an innovative channel prior model that captures the dual correlation structure of the channel using three state variables: active indication, channel supports, and channel values. By integrating Markov chains with coupled Gaussian distributions, the model effectively describes both the structural and numerical dependencies within the channel. We propose the correlated hybrid message passing (CHMP) algorithm based on Bethe free energy (BFE) minimization, which adaptively updates model parameters without requiring prior knowledge of user sparsity or channel priors. Simulation results show that the CHMP algorithm accurately detects active users and achieves precise channel estimation. Full article

Massive random access (MRA) involves massive devices sporadically and randomly sending short-packet messages through a shared wireless channel. It is a crucial scenario in 6G communications to support the Internet-of-Things. Grant-free access, where devices complete transmission without grants, is a promising scheme for MRA. In grant-free access, the design of pilot sequences has a significant effect on joint activity detection and channel estimation (JADCE) and, consequently, system performance. Inter-user interference (IUI), caused by non-orthogonal pilots, is random owing to the random set of active users, and existing studies on pilot design for grant-free access often attempt to reduce the mean IUI. However, the performance of JADCE is affected not only by the mean IUI but also by the tail behavior of the IUI distribution. In this paper, we propose a metric for pilot design, exploiting the distribution of IUI to reflect the impact of pilots on JADCE more precisely. We further develop a pilot design algorithm based on the proposed metric, with modified approximate message passing (AMP) adopted as the JADCE algorithm. Simulation results demonstrate that the proposed pilot design reduces the probability of missed detection of active users and channel estimation error, compared with existing pilot designs. Full article

This paper presents TwinP2G, a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling, including electricity, green hydrogen, natural gas, and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles, including data scientists and energy experts, using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally, TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid. Full article

Delegation of authority remains a critical yet insufficiently addressed capability in Self-Sovereign Identity (SSI) systems. Building on an existing delegation model that introduced the concept of a Verifiable Mandate (VM) for expressing authority and access rights, this paper extends the approach with a rigorous formalization of delegation semantics, enabling unambiguous reasoning over roles, grants, and constraints. The formal model is aligned with standards from the World Wide Web Consortium (W3C), and its constructs are embedded into an extended credential schema that preserves compatibility with the Verifiable Credentials (VC) data model while introducing delegation-specific attributes. A generalized VM schema is defined, supporting both generic and business-specific instantiations, and ensuring structural and semantic interoperability. Policy compliance is operationalized through a policy-based enforcement architecture, where rules are authored in the Rego language and evaluated at runtime by the Open Policy Agent (OPA). The architecture incorporates trusted registries for schema and policy distribution, allowing verifiers to define and enforce context-specific delegation rules in a modular and interoperable manner. Validation through realistic scenarios, such as postal service and academic use cases, demonstrates how formal semantics, schema validation, and language-based policy enforcement can be combined to enable secure, verifiable, and context-aware delegation in SSI ecosystems. Full article

Electrical grids are undergoing a transformation driven by the increasing integration of renewable energy sources on the consumer side. This shift, alongside the electrification of consumption—particularly in areas such as electric mobility—has the potential to significantly reduce CO₂ emissions. However, it is also contributing to a rise in electricity prices due to growing demand and infrastructure costs. Paradoxically, these higher prices serve as a catalyst for further investment in renewable energy technologies by reducing the payback periods of such systems. Recent European legislation has accelerated this transformation by mandating the liberalization of energy markets. This regulatory shift enables the emergence of prosumers—consumers who are also producers of energy—by granting them the right to generate, store, and trade electricity using the existing distribution grid. In this new landscape, photovoltaic systems represent a viable and increasingly attractive investment option for both households and businesses. This study presents an economic evaluation of photovoltaic system investments across different European countries, focusing on key indicators such as payback periods and the impact of local solar irradiation on the resulting electricity price. The analysis provides insight into the varying economic feasibility of distributed solar energy deployment, offering a comparative perspective that supports both policymakers and potential investors in making informed decisions about renewable energy adoption. Full article