Search Results (91)

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

The growing complexity and interdependence of critical infrastructures (CIs), increasingly exposed to natural and technological hazards, call for educational approaches to enhance resilience and risk management. This study examines trends, patterns, and challenges in integrating digital and immersive technologies into education and training for stakeholders in critical infrastructure management. A systematic review of peer-reviewed literature was conducted using Scopus as the primary source, covering the last decade and analyzing the corpus across six dimensions: technological approach, pedagogical model, hazard typology, infrastructure domain, stakeholder category, and implementation phase. Following the PRISMA framework, 5635 records were identified and screened through a multistage process combining rule-based filtering and manual review, resulting in 105 papers meeting the inclusion criteria. The analysis reveals a shift from classroom instruction and physical drills toward immersive, simulation-based, and data-informed learning ecosystems that strengthen situational awareness, procedural accuracy, and decision-making under stress. However, the review identifies persistent gaps in evaluation metrics, cross-sector frameworks, and collaborative learning environments that limit adoption. The findings underscore that digital and immersive technologies can reconfigure education and training frameworks, enabling the formation of Resilient Operators endowed with adaptive cognition, continuous learning capacities, and responsiveness to natural hazard-induced technological risks. Full article

Dangerous goods transport (DGT) is of strategic importance for any economy, and the structure of the fuel and energy industry includes a number of systems and facilities qualified as “critical infrastructure” (CI). Given the current geopolitical situation, sabotage, hybrid or even terrorist activities in the area of logistics and transport pose an increasing threat. At the same time, next to the economic sector, liquid fuels are of great importance to citizens, which is why the transport of this group of goods should be given special importance, ensuring appropriate efficiency and safety parameters, taking into account the risk of intentional, destructive human interference. A significant source of data in the road transport of dangerous goods is the spatial data infrastructure (SDI); digital interactive platforms (DIP) are important here. This scientific research work concerns the application of DIP and related information technologies (IT) in road transport—smart mobility (SM). The main objective of the scientific research work is to develop proposals for effective tools to minimize the overall risk, using publicly available digital interactive platforms. In the implementation of the topic, the following methods were integrated: OKR (Objectives and Key Results), SMART (Specific, Measurable, Achievable, Relevant, Time-bound) and CS (Case Study). The main problem was identified and the main goal of the work was achieved. The results made it possible to present effective risk minimization tools in DGT using DIP. The elaboration was prepared under the research subvention of the AGH University of Krakow, No. 16.16.150.545 in 2026. Full article

The convergence of cyber-physical systems (CPSs), operational technologies (OTs), industrial control systems (ICSs), and quantum computing poses unprecedented challenges for the security and resilience of critical infrastructures (CIs). As quantum capabilities progress, classical cryptographic mechanisms such as RSA and ECC face increasing risks from quantum algorithms (Shor and Grover), while CPS and OT remain constrained by long life cycles, heterogeneity, and limited upgrade capabilities. This study conducts a systematic literature review (SLR) following a GQM-PICO-PRISMA methodological framework to examine 66 primary studies, selected from 1.522 records identified in seven scientific databases and published between 2005 and 2025. The review identifies dominant research domains, ranging from IoT/IIoT security to machine learning-based intrusion detection in CPS/OT environments, and synthesizes key challenges. Findings reveal significant fragmentation in CPS taxonomies, limited integration of post-quantum cryptography (PQC) into OT/ICS protocols, a scarcity of real-world datasets, and insufficient quantum threat modeling (QTM). This work consolidates and structures prior evidence into a literature-derived classification of quantum-era CPS/OT cybersecurity topics and distills a prioritized research agenda for advancing quantum-resilient architectures. Full article

This article addresses issues related to power supply reliability for electronic security systems (ESSs) during their operational lifetime. ESS are deployed both in enclosed building structures, where environmental conditions are stabilised, and across large open areas exposed to natural environmental conditions, such as transport depots, airports, railway stations, ports, and other similar facilities. Laboratory tests on selected power supply units used in ESSs have been conducted by the authors, as well as a theoretical analysis of the reliability of the power supply process. The reliability analysis of the power supply took into account the reliability of delivering electrical energy with specified parameters to all components forming a system aimed at ensuring the safety of electronic security systems (ESSs). Power supply is essential for the correct operation of all modules, components, devices, and alarm control panels (ACPs) within ESSs. In addition to meeting the basic requirements for the provision of electrical power, the system designer must also give particular consideration to power supply reliability, especially in facilities classified as part of the state critical infrastructure (CI). This issue is particularly significant in the case of Fire Detection and Alarm Systems (FASs), which constitute the most critical safety systems responsible for protecting human life and health. Accordingly, this article discusses selected aspects of power supply for representative electronic security systems (ESSs). The subsequent part of this paper presents operational tests of selected ESS power supply units. A further topic addressed in the article is the definition of models of the operational process of power supply systems and the execution of computer simulations. The analysis of the operational process of ESS power supply units, expressed as models and graphs and supported by computer simulations, enabled the formulation of conclusions regarding reliability. The conclusions drawn from this article may be applied in the design, routine maintenance, and operation of ESSs. Full article

With climate change expected to intensify hazards across Europe, empowering communities and strengthening local adaptation is urgent. The challenge is bolstering the resilience of critical infrastructure (CI), which faces substantial risks. Transitioning from predominantly “grey” infrastructure to integrated “green-grey” solutions provides an effective way to safeguard societal and infrastructural assets against hazards and environmental degradation. Although several frameworks developed by international networks and regional authorities exist, they often fail to fully address the nuanced challenges of CI climate proofing, disaster risk reduction, and biodiversity protection. In response to these limitations and to address key societal challenges, the work here introduces an innovative, integrative blueprint framework. This framework synthesises existing approaches to CI climate adaptation, systematically strengthening resilience with nature-based solutions (NBS). The framework is partially applied and validated through the Public-Private-Civil Partnership (PPCP^®) approach, and operationalised in two climatically distinct but heatwave-prone regions: Egaleo (Greece) and Helsinki (Finland). These Labs have promoted more inclusive policymaking by supporting collaboration among key stakeholders, encouraging knowledge sharing and co-designing strategies to advance NBS implementation for heatwave mitigation. The approach facilitated the design of interconnected activities and simplified technical details. Adapting methods to local needs, such as site visits and participatory mapping, has led to concrete outcomes. The prefeasibility analysis outcomes and the targeted NBS-based strategies identified from these areas ensure that solutions are culturally relevant, technically feasible, and collectively owned, incorporating local knowledge and fostering long-term sustainability. Full article

The integration of biomethane into the natural gas infrastructure is a critical element of energy-sector decarbonization, yet optimal infrastructure development scenarios remain insufficiently compared using unified decision frameworks. This study evaluates three biomethane market integration scenarios—direct connection to the gas system, biomethane injection points (compressed biomethane transported by trucks to the gas system), and off-grid delivery using the multi-criteria decision-making method TOPSIS. Environmental, economic, and technical dimensions are jointly assessed. Results indicate that direct connection to the system provides the most balanced overall performance, achieving the highest integrated score (Ci = 0.70), driven by superior environmental and technical characteristics. Biomethane injection points demonstrate strong economic advantages (Ci = 0.49), particularly where capital investments need to be reduced or there is limited access to the gas system, but show weaker environmental and technical performance. Off-grid solutions perform poorly in integrated assessment (Ci = 0.00), reflecting limited scalability and high logistical complexity, although niche applications may remain viable under specific conditions. Sensitivity analysis confirms the robustness of these rankings across a wide range of weighting assumptions, strengthening the reliability of the findings for policy and infrastructure planning. This study provides one of the first integrated multi-criteria assessments explicitly incorporating virtual pipeline logistics, offering a transferable decision-support framework for sustainable biomethane development in diverse regional contexts. Full article

Source plasma centers sustain hematology therapeutics by safeguarding testing, traceability, and cold-chain integrity before fractionation. Despite regulatory requirements (21 CFR 606/640; EU Directive 2005/62/EC), published pre-operational validation frameworks demonstrating deviation-readiness before first collections remain sparse. We conducted a simulation-based pre-operational validation of an electronic quality management system (eQMS) with an Incident → Deviation → Corrective Action and Preventive Action (CAPA) pathway at a new source plasma center, performing 20 chairside mock runs, 3 freezer-alarm drills, and a document-control stress test. Primary endpoints were anomaly rate, alarm-response time relative to a 15 min service-level agreement (SLA), and deviation-closure SLA compliance. Analyses were descriptive and designed to demonstrate system functionality, not long-term process stability. Minor anomalies occurred in 6/20 mock runs (30.0%; 95% CI 11.9–54.3); no major/critical events were observed (0/20; 95% CI 0–16.8). Deviation-closure SLAs were met in 6/6 tests (100%; 95% CI 54.1–100). Alarm-response times averaged 7.0 min (SD 1.0; range 6–8 min; 95% CI 4.5–9.5), and all drills met the 15 min vendor SLA, illustrating a preliminary readiness margin (Cpu ≈ 2.7) rather than a statistically stable capability estimate. Simulation-based pre-operational validation produced inspection-ready documentation and quantitative acceptance criteria aligned to U.S./EU expectations, supporting reproducible multi-site deployment. By protecting cold-chain integrity and traceability before first collections, the validated QMS helps preserve supply reliability for plasma-derived therapeutics central to hematology care and establishes the measurement infrastructure for post-operational performance validation. Full article

The electric power grid constitutes a foundational pillar of modern critical infrastructure (CI), underpinning societal functionality and global economic stability. Yet, the increasing convergence of Information Technology (IT) and Operational Technology (OT), particularly through the integration of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS), has amplified the sector’s exposure to sophisticated cyber threats. This study conducts a comparative analysis of five major cyber incidents targeting electric power systems: the 2015 and 2016 Ukrainian power grid disruptions, the 2022 Industroyer2 event, the 2010 Stuxnet attack, and the 2012 Shamoon incident. Each case is examined with respect to its objectives, methodologies, operational impacts, and mitigation efforts. Building on these analyses, the research evaluates the extent to which such attacks could have been prevented or mitigated through the systematic adoption of leading cybersecurity maturity frameworks. The NIST Cybersecurity Framework (CSF) 2.0, the ENISA NIS2 Directive Risk Management Measures, the U.S. Department of Energy’s Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Risk Foundation (CRF) Maturity Model alongside complementary technical standards such as NIST SP 800-82 and IEC 62443 have been thoroughly examined. The findings suggest that a proactive, layered defense architecture grounded in the principles of these frameworks could have significantly reduced both the likelihood and the operational impact of the reviewed incidents. Moreover, the paper identifies critical gaps in the existing maturity models, particularly in their ability to capture hybrid, cross-domain, and human-centric threat dynamics. The study concludes by proposing directions for evolving from compliance-driven to resilience-oriented cybersecurity ecosystems, offering actionable recommendations for policymakers and power system operators to strengthen the cyber-physical resilience of electric generation and distribution infrastructures worldwide. Full article

Background: Foundational learning spaces in South Africa, designed to nurture growth and development, continue to grapple with malnutrition, a persistent barrier to the health, cognitive potential, and wellbeing of preschool-aged children, amidst a nutrition transition. Aim: This study assessed dietary diversity, nutritional status, and their associations among children aged 2–5 years attending funded Early Learning Centres (ELCs) in Johannesburg (Region C). Methods: Using systematic random sampling across 33 nutrition-funded ELCs in Region C, we assessed the nutritional status of children aged 2–5 years with WHO Anthro software (z-score cut-offs for undernutrition: stunting, underweight, thinness; overnutrition: overweight, obesity). Dietary diversity scores (DDSs) were derived from a 24 h recall of 16 food groups, classified by primary nutrient contributions (some groups spanning multiple classes), and categorized as low (≤8) or normal (≥9). Associations between DDS and nutritional indicators were analyzed using Poisson regression to estimate adjusted prevalence ratios (aPRs). Results: Despite structured feeding practices, all ELCs reported inadequate nutritional funding, prompting calls for dietitian support. While 27% sourced groceries from wholesalers, most relied on supermarkets and spaza shops; all had cooking infrastructure, but only 12% had food gardens, and 88% expressed interest in establishing them to improve dietary diversity. The mean DDS was 9.47 (±1.07), and 83% of children had a normal DDS (≥9), with common consumption of cereals (100%), vitamin A-rich vegetables (100%), oils (100%), and leafy greens (96%), but limited intake of protein-rich foods like eggs (7%), legumes (19%), and fish (37%). A dual burden of malnutrition was observed: 31% of children were stunted and 30% were overweight or obese. Multivariable analysis showed that boys had significantly lower odds of stunting compared to girls (aPR = 0.38; 95%CI: 0.01–0.74), while younger age (aPR = 0.61; 95%CI: 0.37–0.85) and low DDS (aPR = −0.15; 95%CI: −0.29–−0.06) were independently associated with increased risk of stunting. Age was positively associated with underweight (aPR = 1.27; 95%CI: 0.58–1.96), and thinness was strongly associated with boys (aPR = 17.00; 95%CI: 15.12–18.74), with a marginal association with age. Conclusions: Integrated nutrition strategies are critical to addressing the dual burden of stunting and being overweight in urban ELCs. Strengthening funding, professional dietetic support, and promoting food gardens can enhance dietary diversity and child health outcomes. Full article

The rapid expansion of electric vehicles in high-altitude Andean cities, such as the Metropolitan District of Quito, Ecuador’s capital, presents unique challenges for electrical infrastructure planning, necessitating advanced methodologies that capture behavioral heterogeneity and mass synchronization effects in high-penetration scenarios. This study introduces a hybrid approach that combines agent-based modelling with Monte Carlo simulation and a TimescaleDB architecture project charging demand with quarter-hour resolution through 2040. The model calibration deployed real-world data from 764 charging points collected over 30 months, which generated 2.1 million charging sessions. A dynamic coincidence factor ($F_C=0.222+0.036\ast e^{(-0.0003n)}$) was incorporated, resulting in a 52% reduction in demand overestimation compared to traditional models. The results for the 2040 project show a peak demand of 255 MW (95% CI: 240–270 MW) and an annual consumption of 800 GWh. These findings reveal that non-optimized time-of-use tariffs can generate a critical “cliff effect,” increasing peak demand by 32%, whereas smart charging management with randomization reduces it by 18 ± 2.5%. Model validation yields a MAPE of 4.2 ± 0.8% and an RMSE of 12.3 MW. The TimescaleDB architecture demonstrated processing speeds of 2398.7 records/second and achieved 91% data compression. This methodology offers robust tools for urban energy planning and demand-side management policy optimization in high-altitude contexts, with the source code available to ensure reproducibility. Full article

Critical Infrastructures (CIs) are the backbone of modern societies, providing essential services whose disruption can have severe consequences. The interdependencies among the CIs, across sectors and national borders, add significant complexity to risk and resilience management. While various EU Directives and EU-funded projects have addressed CI risk management, most efforts have focused on individual infrastructures rather than systemic cross-sector and cross-border approaches. In the EU-funded project ATLANTIS, we address this gap by advancing CI risk and resilience assessment towards a fully integrated European protection framework. We emphasise a holistic, multi-level approach that transcends individual assets, enabling coordination across operators, sectors, and national borders. To this end, we introduce a comprehensive risk assessment methodology that explicitly accounts for interdependencies among CIs and evaluates potential impacts and probabilities of disruptive events. This methodology is underpinned by the tailored data management framework, structured across three integrated layers. To validate the approach, novel tools and methods were implemented and tested in three large-scale pilot exercises, conducted through a series of stakeholder workshops. Results indicated measurable improvements in CI preparedness and awareness, ranging from approximately 5% to 55%, depending on the threat scenario and stakeholder group. The findings demonstrate that our approach delivers added value by supporting enhanced decision-making and fostering consistent, cross-CI communication through a shared platform. This paper presents the key components, cross-CI and multi-threat risk assessment methodology, and testing outcomes of the ATLANTIS project, highlighting its contribution to advancing European CI resilience. Full article

The increasing complexity of Water Distribution Systems (WDSs), driven by urbanization, climate change, and aging infrastructure, necessitates robust methods for risk assessment and visualization. This study presents a practical methodology for mapping the risk of water supply disruption or reduction using five parameters: Probability (P), Consequences (C), Water Pipe category (WP), Inhabitants exposed (I), and response Efficiency (E). The approach enables comprehensive analysis of the risk associated with specific pipeline segments within an Analyzed Supply Area (ASA). The method integrates statistical and operational data, allowing utilities to evaluate vulnerability, identify Critical Infrastructure (CI), and prioritize maintenance. The investigation conducted during the study revealed that cast iron and steel pipes with large diameters (e.g., 400 mm) show the highest failure probability and impact. Despite a calculated risk value (r_LW = 80), effective response measures—including specialized repair teams and equipment—kept the risk acceptable. The results demonstrate that historical failure and response data enhance risk identification and management. The generated risk maps facilitate spatial visualization of high-risk areas, supporting decision-making processes, renovation planning, and emergency preparedness. Integration with GIS tools, including GeoMedia and Google Earth programmes, enables dynamic map creation and simulation of response scenarios. The methodology is scalable and adaptable to any WDS, and potentially to other municipal systems such as wastewater and heating networks. By accounting for both technical and social dimensions of risk, the method supports improved water safety planning and infrastructure resilience. Future development should include real-time data integration and climate-related risk scenarios to increase predictive accuracy and system adaptability. Full article

Escalating extreme wildfires, fueled by the confluence of climate change, land use patterns alterations, ignitions by humans, and flammable fuels accumulation, pose significant and increasingly destructive risks to critical infrastructure (CI). This study presents a comprehensive comparative analysis of wildfire impacts and the corresponding CI resilience strategies employed across the EU and the US. It examines the vulnerability of CIs to the devastating effects of wildfires and their inadvertent contribution to wildfire ignition and spread. The study evaluates the EU’s CER Directive and the US National Infrastructure Protection Plan and assesses European Commission wildfire resilience-related initiatives, including FIRELOGUE, FIRE-RES, SILVANUS, and TREEADS flagship projects. It synthesizes empirical evidence and extracts key lessons learned from major wildfire events in the EU (2017 Portuguese fires; 2018 Mati wildfire) and the US (2023 Lahaina disaster; 2025 Los Angeles fires), drawing insights regarding the effectiveness of various resilience measures and identifying areas for improvement. Persistent challenges impeding effective wildfire resilience are identified, including governance fragmentation, lack of standardization in risk assessment and mitigation protocols, and insufficient integration of scientific knowledge and data into policy formulation and implementation. It concludes with actionable recommendations aimed at fostering science-based, multi-stakeholder approaches to strengthen wildfire resilience at both policy and operational levels. Full article

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article