Search Results (9,284)

X-ray and EUV solar flare emission cause increases in the Earth’s dayside ionospheric electron density. While the response of the lower ionosphere to X-rays is well studied, the delay between EUV flare emission and the response of the ionospheric F-region has not been investigated. Here, we calculate the delays between incident He II 304 Å emission, and the TEC response for 10 powerful solar flares, all of which exhibit delays under 1 min. We assess these delays in relation to multiple solar and geophysical factors, and find a strong negative correlation (∼−0.85) between delay and He II flux change and a moderate negative correlation (∼−0.55) with rate of increase in He II flux. Additionally, flare magnitude and the X-ray-to-He II flux ratio at peak He II emission show strong negative correlations with delay (∼−0.80 and ∼−0.75, respectively). We also identify longer delays for flares occurring closer to the summer solstice. These results may have applications in upper-ionospheric recombination rate calculations, atmospheric modelling, and other solar–terrestrial studies. We highlight the importance of incident EUV and X-ray flux parameters on the response time of the ionospheric electron content, and these findings may also have implications for mitigating disruptions in communication and navigation systems. Full article

The growing complexity of production systems in the technology sector demands advanced tools to ensure efficiency, flexibility, and cost-effectiveness. This study presents the development of a simulation model for a selective soldering line at a technology manufacturing company in Poland, created during an engineering internship. Using FlexSim 24.2 software, the real production process was replicated, including input/output queues, manual insertion (MI) stations, soldering machines, and quality control points. Special emphasis was placed on implementing dynamic process logic via ProcessFlow, enabling detailed modeling of token flow and system behavior. Through experimentation, various configurations were tested to optimize process time and the number of soldering pallets in circulation. The results revealed that reducing pallets from 12 to 8 maintains process continuity while offering cost savings without impacting performance. An intuitive operator panel was also developed, allowing users to adjust parameters and monitor outcomes in real time. The project demonstrates that simulation not only supports operational decision-making and resource planning but also enhances interdisciplinary communication by visually conveying complex workflows. Ultimately, the study confirms that simulation modeling is a powerful and adaptable approach to production optimization, contributing to long-term strategic improvements and innovation in technologically advanced manufacturing environments. Full article

The paradigm-shift idea of murburn concept is no hypothesis but developed directly from fundamental facts of cellular/ecological existence. Murburn involves spontaneous and stochastic interactions (mediated by murzymes) amongst the molecules and unbound ions of cells. It leads to effective charge separation (ECS) and formation/recruitment of diffusible reactive species (DRS, like radicals whose reactions enable ATP-synthesis and thermogenesis) and emission of radiations (UV/Vis to ELF). These processes also lead to a chemo-electromagnetic matrix (CEM), ascertaining that living cell/organism react/function as a coherent unit. Murburn concept propounds the true utility of oxygen: generating DRS (with catalytic and electrical properties) on the way to becoming water, the life solvent, and ultimately also leading to phase-based macroscopic homeostatic outcomes. Such a layout enables cells to become simple chemical engines (SCEs) with powering, coherence, homeostasis, electro-mechanical and sensing–response (PCHEMS; life’s short-term “intelligence”) abilities. In the current review, we discuss the coacervate nature of cells and dwell upon the ways and contexts in which various radiations (either incident or endogenously generated) could interact in the new scheme of cellular function. Presenting comparative evidence/arguments and listing of systems with murburn models, we argue that the new perceptions explain life processes better and urge the community to urgently adopt murburn bioenergetics and adapt to its views. Further, we touch upon some distinct scientific and sociological contexts with respect to the outreach of murburn concept. It is envisaged that greater awareness of murburn could enhance the longevity and quality of life and afford better approaches to therapies. Full article

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article

Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to simultaneously realize the functions of active power control, reactive power compensation, and active power filtering. In the outer power control loop, according to the distribution rule of decoupled average active power components in three phases, stability control for the sum of cluster average active power flows is realized by injecting positive-sequence active current, so as to control the average cluster voltage (i.e., the average of all the DC-link capacitor voltages), and by injecting negative-sequence current, the cluster average active power flows can be controlled individually to balance the three cluster voltages (i.e., the average of the DC-link capacitor voltages in each cluster). The negative-sequence reactive power component is considered to realize the reactive power compensation. In the inner current control loop, the fundamental and high-order harmonic components are uniformly controlled in the positive-sequence dq frame using the PI + VPIs (vector proportional integral) controller, and the harmonic filtering function is realized while the fundamental positive-sequence current is adjusted. Experiments performed on the 380 V/50 kVA laboratory HVAC-PET verify the effectiveness of the proposed control strategy. Full article

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

Turbidity currents pose significant threats to offshore seabed infrastructures, including subsea hydrocarbon production facilities and submarine communication cables. These powerful underwater flows can damage pipelines, potentially causing hydrocarbon spills that endanger local communities, the environment, and negatively impact energy production infrastructures. Therefore, a comprehensive understanding of the spatio-temporal development and destructive force of turbidity currents is essential. While numerical computation of 3D flow, sediment transport, and substrate exchange is possible, field-scale simulations are computationally intensive. In this study, we develop a simplified morphodynamic approach to model the flow properties of channelized turbidity currents and the associated trends of sediment accretion and erosion. This model is applied to the Baco–Malaylay submarine system to investigate the dynamics of a significant turbidity current event that impacted a submarine pipeline offshore the Philippines. The modeling results align with available seabed assessments and observed erosion trends of the protective rock berm. Our simplified modeling approach shows good agreement with simulations from a fully 3D numerical model, demonstrating its effectiveness in providing valuable insights while reducing computational demands. Full article

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

This research considers a preliminary comparative technical evaluation of two micro-gas turbine (MGT) systems in combined heat and power (CHP) mode (100 kWe), aimed at supplying heat to a residential community of 15 average-sized buildings located in Central Europe over a year. Two systems were modelled in Ebsilon 15 software: a natural gas case (benchmark) and an ammonia-fueled case, both based on the same on-design parameters. Off-design simulations evaluated performance over variable ambient temperatures and loads. Idealized, unrecuperated cycles were adopted to isolate the thermodynamic impact of the fuel switch under complete combustion assumption. Under these assumptions, the study shows that the ammonia system produces more electrical energy and less excess heat, yielding marginally higher electrical efficiency and EUF (26.05% and 77.63%) than the natural gas system (24.59% and 77.55%), highlighting ammonia’s utilization potential in such a context. Future research should target validating ammonia combustion and emission profiles across the turbine load range, and updating the thermodynamic model with a recuperator and SCR accounting for realistic pressure losses. Full article

This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV’s mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model’s adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks. Full article

By 2050, it is estimated that approximately 200 million people will be displaced due to the impacts of climate change. Vulnerability to climate change is shaped not only by environmental factors but fundamentally by systemic power relations and structural conditions present at both the places of origin and destination. In Latin America, climate-displaced persons predominantly settle in marginalised neighbourhoods, where widely accepted informality facilitates their rapid arrival but obstructs genuine progress and full integration as urban citizens. This paper critically examines the prevailing myths that justify the persistence of informality, revealing the socioeconomic challenges faced by climate migrants in the region. These four dominant myths are (1) Latin America’s inherently low productivity levels; (2) concessions by the ruling class enabling excluded groups to merely survive; (3) the perceived privilege of marginalised neighbourhoods to generate income outside formal legal frameworks, which supports their social capital; and (4) the limited benefits associated with formalisation. Debunking these myths is essential for developing effective public policies aimed at reducing informality and promoting inclusive urban integration, ultimately benefiting both climate migrants and host communities. Full article

In power distribution networks, the growing integration of renewable energy sources (RESs) presents a challenge for the electricity system and its operators, who need to make the energy sector more flexible and resilient. In this context, this paper proposes a novel flexibilization service for the distribution system leveraging the role of renewable energy communities (RECs), an emerging entity with the potential to facilitate the sustainable energy transition through Virtual Islanding operation. The concept of Virtual Islanding is investigated in the paper and a methodology for its validation is developed. Its effectiveness is then assessed using an IEEE-standard 33-node network with significant penetration of RESs, considering the presence of multiple RECs to prove its benefits on electrical distribution networks. The results showcase the advantages of the VI paradigm both from technical and sustainability viewpoint. Full article

Environmental DNA (eDNA) analysis has emerged as a transformative tool in environmental monitoring, enabling non-invasive detection of species and microbial communities across diverse ecosystems. This study systematically reviews the role of bioinformation technology in eDNA analysis, focusing on methodologies and applications across air, soil, groundwater, sediment, and aquatic environments. Advances in molecular biology, high-throughput sequencing, bioinformatics tools, and field-deployable detection systems have significantly improved eDNA detection sensitivity, allowing for early identification of invasive species, monitoring ecosystem health, and tracking pollutant degradation processes. Airborne eDNA monitoring has demonstrated potential for assessing microbial shifts due to air pollution and tracking pathogen transmission. In terrestrial environments, eDNA facilitates soil and groundwater pollution assessments and enhances understanding of biodegradation processes. In aquatic ecosystems, eDNA serves as a powerful tool for biodiversity assessment, invasive species monitoring, and wastewater-based epidemiology. Despite its growing applicability, challenges remain, including DNA degradation, contamination risks, and standardization of sampling protocols. Future research should focus on integrating eDNA data with remote sensing, machine learning, and ecological modeling to enhance predictive environmental monitoring frameworks. As technological advancements continue, eDNA-based approaches are poised to revolutionize environmental assessment, conservation strategies, and public health surveillance. Full article