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Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

Green hydrogen is a clean, versatile, and future-oriented fuel that can play a key role in the energy transition, decarbonization of the economy, and climate protection. It offers an alternative to fossil fuels and can be used in various applications, including power generation, industry, and transportation. However, due to its wide flammability range, small molecular size, and high diffusivity, special attention must be paid to ensuring safety during its use, particularly in leakage control. This paper provides a review and analysis of equipment leakage testing methods used for natural gas, with a view to applying these methods to the leakage testing of gas meters intended for hydrogen-containing gas mixtures and pure hydrogen. Tests of simulated leaks were carried out using two common methods: the bubble method and the pressure decay method, for three different gases: nitrogen (most commonly used for leak testing), helium, and hydrogen. The results obtained from the tests and analyses made it possible to verify and select optimum leak-testing methods for gas meters designed for measuring fuels containing hydrogen. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

The stability of nitrogen gas foam hinders its applicability in petroleum applications. Fly ash nanoparticles and clay improve the N₂ foam stability, and flue gas foams provide a cost-effective solution for carbon capture, utilization, and storage (CCUS). This study examines the stability, volume, and bubble structure of foams formed using two anionic surfactants, sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS), along with the cationic surfactant cetyltrimethylammonium bromide (CTAB), selected for their comparable interfacial tension properties. Analysis of foam stability and volume and bubble structure was conducted under different CO₂/N₂ mixtures, with half-life and initial foam volume serving as the evaluation criteria. The impact of fly ash and clay on SDS-N₂ foam was also evaluated. The results showed that foams created with CTAB, SDBS, and SDS exhibit the greatest stability in pure nitrogen, attributed to low solubility in water and limited gas diffusion. SDS showed the highest foam strength attributable to its comparatively low surface tension. The addition of fly ash and clay significantly improved foam stability by migrating to the gas–liquid interface, creating a protective barrier that reduced drainage. Both nano fly ash and clay improved the half-life of nitrogen foam by 11.25 times and increased the foam volume, with optimal concentrations identified as 5.0 wt% for fly ash and 3.0 wt% for clay. This research emphasizes the importance of fly ash nanoparticles in stabilizing foams, therefore optimizing a foam system for enhanced oil recovery (EOR). Full article

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model substantially enhances system robustness and adaptability. To quantify nodal vulnerability and importance, the study introduces two novel evaluation indicators: the Electric Potential–Closeness Fusion Indicator (EPFI) for power networks and the Pressure Difference–Closeness Comprehensive Indicator (PDCI) for natural gas systems. Leveraging these indicators, three coupling paradigms—assortative, disassortative, and random—are systematically constructed and analyzed. System resilience is assessed through simulation experiments incorporating three attack strategies: degree-based, betweenness centrality-based, and random node removal. Evaluation metrics include network efficiency and the variation in the size of the largest connected subgraph under different coupling configurations. The proposed framework is validated using a hybrid case study that combines the IEEE 118-node electricity network with a 20-node Belgian natural gas system, operating under a unidirectional gas-to-electricity energy flow model. Results confirm that the disassortative coupling configuration, based on EPFI and PDCI indicators, exhibits superior resistance to network perturbations, thereby affirming the effectiveness of the model in improving the robustness of integrated energy systems. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Essential oils (EOs) constitute intricate mixtures of volatile phytochemicals that have garnered significant attention due to their multifaceted biological effects. Notably, the presence of bioactive constituents capable of inhibiting tyrosinase enzyme activity and scavenging reactive oxygen species (ROS) underpins their potential utility in skin-related applications, particularly through the modulation of melanin biosynthesis and protection of skin-relevant cells from oxidative damage—a primary contributor to hyperpigmentation disorders. Zingiber officinale Rosc. (ginger) and Humulus lupulus L. (hop) are medicinal plants widely recognized for their diverse pharmacological properties. To the best of our knowledge, this study provides the first report on the synergistic interactions between essential oils derived from these species (referred to as EOZ and EOH) offering novel insights into their combined bioactivity. The purpose of this study was to evaluate essential oils extracted from ginger rhizomes and hop strobiles with respect to the following: (1) chemical composition, determined by gas chromatography–mass spectrometry (GC-MS); (2) tyrosinase inhibitory activity; (3) capacity to inhibit linoleic acid peroxidation; (4) ABTS^•+ radical scavenging potential. Furthermore, the study utilizes both the combination index (CI) and dose reduction index (DRI) as quantitative parameters to evaluate the nature of interactions and the dose-sparing efficacy of essential oil (EO) combinations. GC–MS analysis identified EOZ as a zingiberene-rich chemotype, containing abundant sesquiterpene hydrocarbons such as α-zingiberene, β-bisabolene, and α-curcumene, while EOH exhibited a caryophyllene diol/cubenol-type profile, dominated by oxygenated sesquiterpenes including β-caryophyllene-9,10-diol and 1-epi-cubenol. In vitro tests demonstrated that both oils, individually and in combination, showed notable anti-tyrosinase, radical scavenging, and lipid peroxidation inhibitory effects. These results support their multifunctional bioactivity profiles with possible relevance to skin care formulations, warranting further investigation. Full article

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article

Eurasian aspen (Populus tremula L.) is a tree species with recognised ecological and economic importance for both natural and plantation forests. For the fast cloning of selected aspen genotypes, the method of plant propagation through in vitro culture (micropropagation) is often recommended. The efficiency of this method is related to the use of shoot-inducing chemical growth regulators, among which cytokinins, a type of plant hormone, dominate. Although cytokinins can inhibit rooting, this effect is avoided by using cytokinin-free media. This study sought to identify concentrations and combinations of growth regulators that would stimulate one type of P. tremula organogenesis (either shoot or root formation) without inhibiting the other. The investigated growth regulators included cytokinin 6-benzylaminopurine (BAP), auxin transport inhibitor 2,3,5-triiodobenzoic acid (TIBA), auxins indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA), gibberellin biosynthesis inhibitor paclobutrazol (PBZ), and a gibberellin mixture (GA₄/₇). Both BAP and TIBA increased shoot number per P. tremula explant and decreased the number of adventitious roots, but TIBA, in contrast to BAP, did not inhibit lateral root formation. However, for the maintenance of both adventitious shoot and root formation above the control level, the combination of PBZ and GA_4/7 was shown to be especially promising. Full article

The naturally occurring radioactive gas radon presents a major public health danger mainly affecting people who spend time in poorly ventilated buildings. The periodic table includes radon as a noble gas which forms through uranium decay processes in soil, rock, and water. The accumulation of radon indoors in sealed or poorly ventilated areas leads to dangerous concentrations that elevate human health risks of lung cancer. The research examines environmental variables affecting radon concentration indoors by studying geothermal installations and their drilling activities, which potentially increase radon emissions. The study was conducted in the basement of the plumbing educational building at the Aristotle University of Thessaloniki to assess the potential impact of geothermal activity on indoor radon levels, as the building is equipped with a geothermal heating system. The key findings based on 150 days of continuous data showed that radon levels peak during the cold days, where the concentration had a mean value of 41.5 Bq/m³ and reached a maximum at about 95 Bq/m³. The reason was first and foremost poor ventilation and pressure difference. The lowest concentrations were on days with increased human activity with measures that had a mean value of 14.8 Bq/m³, which is reduced by about 65%. The results that are presented confirm the hypotheses and the study is making clear that ventilation and human activity are crucial in radon mitigation, especially on geothermal and energy efficient structures. Full article

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation. Full article

Plants and insects are developing strategies to avoid each other’s defense systems. Host plants may release volatile compounds to attract the natural enemies of herbivores; insect pests may also select host plants that are deterrent to natural enemies to avoid such predation. Here we investigated whether the host plant preference of Hyphantria cunea correlates with the attractiveness of these plants to Chouioia cunea, a parasitoid wasp that serves as the primary natural enemy of H. cunea. We found Morus alba was the preferred host plant for female H. cunea. Although M. alba provided suboptimal nutritional value for H. cunea growth and development compared to other plants, it attracted fewer C. cunea relative to alternative host plants. Gas chromatography–mass spectrometry (GC–MS) coupled with gas chromatography–electroantennographic detection (GC-EAD) analysis identified six distinct compounds among the herbivore-induced plant volatiles (HIPVs) produced following H. cunea feeding. Notably, M. alba was the sole plant species that did not emit tridecane. These results suggest that H. cunea utilizes M. alba as a reduced predation enemy-free space, thereby minimizing parasitization by C. cunea. Our research emphasizes the importance of considering adaptive responses of herbivores within the context of multi-trophic relationships, rather than solely focusing on optimizing herbivore growth on the most nutritionally suitable plant host. Full article