Search Results (329)

The integration of digital technologies is catalysing a fundamental transformation of modern energy systems, enhancing operational efficiency, adaptability, and sustainability. Despite significant progress, the existing literature often addresses digital innovations in isolation, with limited consideration of their synergistic potential within Advanced Energy Systems (AES). This paper presents a systematic review of key digital technologies—such as artificial intelligence, the Internet of Things, blockchain, and digital twins—employed in AES, providing a critical assessment of their individual functionalities, interdependencies, and collective contributions to the energy sector. The analysis highlights the capacity of integrated digital solutions to augment system intelligence, strengthen operational resilience, and increase flexibility across various layers of the energy infrastructure. In addressing persistent challenges—including demand-side variability, supply intermittency, and regulatory complexity—the coordinated implementation of these technologies enables real-time optimization, predictive maintenance, and data-informed decision-making. The findings demonstrate that the synergistic deployment of digital technologies not only enhances system performance but also contributes to measurable improvements in reliability, cost-effectiveness, and environmental sustainability. The review concludes that establishing a cohesive and interoperable digital ecosystem is essential for the development of future-ready energy systems that are robust, efficient, and responsive to the evolving dynamics of the global energy landscape. Full article

Maintaining body symmetry in sports characterized by high lateralization is crucial for optimizing long-term athletic performance and mitigating injury risk. This study aimed to evaluate the extent of morphological asymmetry in anthropometric features among elite professional fencers. Additionally, the presence of functional asymmetry and its associations with morphological asymmetry were assessed. Thirty-two Polish adult female fencers, aged 18–33 yrs, were examined. Data collection involved a questionnaire survey, anthropometric measurements, calculation of anthropological indices, and assessment of functional asymmetry. For the 24 bilateral anthropometric features, small differences were found in seven characteristics: foot length, subscapular skinfold thickness, upper arm circumference, minimum and maximum forearm circumference, upper limb length, and arm circumference in tension. Morphological asymmetry index did not exceed 5%. Left-sided lateralization of either the upper or lower limbs was associated with significantly high asymmetry, specifically indicating larger minimum forearm circumferences in the right limb. Continuous, individualized monitoring of morphological asymmetry and its direction in athletes is essential, demanding concurrent consideration of functional lateralization. This ongoing assessment establishes a critical baseline for evaluating training adaptations, reducing injury susceptibility, and optimizing rehabilitation strategies. Deeper investigation of symmetry within non-dominant limbs is warranted to enhance our understanding. Full article

Background: A growing body of evidence suggests that diet can modify Parkinson’s disease (PD) outcomes, although there is disagreement about what should be included and excluded in such a diet. Existing evidence suggests that adherence to the MIND and Mediterranean (MEDI) diets are associated with reduced PD symptoms, but only a few variables from the adherence scales are responsible for the statistically observed improvement. Objectives: The goal was to use patient-reported outcomes in a large cohort to identify the foods and dietary patterns (PRO diet) most strongly associated with the fewest PD symptoms over time, and to develop a composite adherence scale to enable comparisons between MEDI, MIND, and PRO. Methods: Data were obtained from the prospective longitudinal natural history study and from Modifiable Variables in Parkinsonism (MVP)—a study designed to identify behaviors associated with patient-reported outcomes (PRO-PD). Upon the completion of the binary and food frequency data collection, using various predictive models and considering congruence with historical data, the PRO diet was created via an iterative process. Our goal was to create a new scale and compare its performance to the existing MIND and MEDI scores. The comparison was made at baseline, using the regression models for PRO-PD and the different scales as the predictors. The models were compared via the Akaike Information Criterion (AIC). To examine whether baseline adherence levels predicted subsequent symptom trajectories, the baseline PRO diet adherence and subsequent slope of progression were evaluated. Results: Data from 2290 individuals with PD were available for this analysis. The Mediterranean and MIND diets showed almost identical effects. For both the diets, the effect they had on non-motor symptoms was about twice the effect on motor symptoms. The slopes for the total PRO-PD for MEDI, MIND, and PRO-21 were −64.20467, −64.04220, and −28.61995, respectively. The AIC value differences were substantial (>2), indicating meaningful improvements in the model fit for total PRO-PD, as follows: MEDI: 28,897.24, MIND: 28,793.08, and PRO-21: 27,500.71. The subset of individuals who were most adherent to the PRO-21 diet at baseline had the slowest subsequent progression, as measured by a 43% reduced PRO-PD slope, compared to the less adherent groups. Conclusions: The PRO-21 outperformed the MIND and MEDI diets in the model fit, overcoming the ceiling effects and showing orders of magnitude and superior explanatory power for variance in PD outcomes, despite the smaller per-unit effect sizes. However, its rigorous demands may introduce barriers related to cost, feasibility, and sustainability, underscoring the need for future intervention trials to assess real-world feasibility, adherence, side effects, and clinical impact. Full article

The contribution of small-scale sporting events to tourism sustainable development in local communities has been well recognized in the literature. Small-scale running events in particular are essential in the promotion of sustainable tourism in the community due to their localized impact. Although research has largely focused on the demand side—including participants, spectators, and host communities—limited attention has been given to the supply side, particularly the perspectives of sport event organizers and their role in implementing sustainable practices. This study aims to address this gap and answer key research questions that concern the factors which contribute to the success of these events in relation to their economic, social, and environmental impacts through the lens of the events’ primary decision-makers, their organizers. This study employs an exploratory research method, conducting interviews with twenty-five organizers of diverse Greek running events that represent a variety of geographical contexts and event characteristics of the country, based on their long-standing presence. Findings contribute to sustainable sport tourism literature by developing a theoretical framework which encompasses the three dimensions of sustainability, highlighting inclusivity as a key component within the social dimension. Results indicate that organizers perceive their running events as having a positive economic impact and enhancing social cohesion. They are also aware of the potential environmental impact, like trail preservation, and consequently implement responsible measures to protect the environment. Organizers share a variety of specific strategies and practical suggestions aimed at improving the sustainability of running events, such as managing waste effectively or improving accessibility for diverse participants, offering transferable insights for other event planners and local authorities. This paper showcases the key role of organizers in developing partnerships to implement effective sustainable sport event practices in the destination. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

Scattered light makes up a significant amount of recorded intensities during tomographic imaging, thereby leading to severe misinterpretation and artifacts in the reconstructed volume images. Correcting artificial intensities that stem from scattered light, therefore, is of primary interest and demands quantitative measurements. While numerous methods have been developed to reduce X-ray scattering artifacts, fewer methods deal with optical scattering. In this study, a measurement method for determining optical scattering in scintillators is presented with the aim of further developing correction algorithms. A theoretical model based on internal multiple reflections was developed for this purpose. This model assumes an additive exponential kernel with a certain scattering length to the system’s point spread function. This assumption was confirmed, and the scatter length was estimated from three new different kinds of experiments (hgap, rect, and LSF) on the BM18 beamline of the European synchrotron. The experiments further revealed significant differences in scattering proportion and length when different coatings are applied to the front and back faces of crystalline LuAG scintillators. Anti-reflective coatings on the backside show an effect of reducing the scattering magnitude while reflective coatings on the front side increase the proportion of the unscattered signal and, thus, show proportionally less scattering than black coating or no front coating. In particular, roughened black coating is found to worsen optical scattering. In summary, our results indicate that a combination of reflective (front) and anti-reflective (back) coatings yields the least optical scattering and, hence, the best image quality. Full article

This paper presents a compact 24 GHz radar with a 4-transmit (4Tx) and 4-receive (4Rx) CMOS radar IC, integrated with a 4 × 4 Tx array and four 1 × 4 receive Rx array antennas, optimized for enhancing small drone detection. By employing the hybrid beamforming technique based on analog beamforming on the transmit side and independent four-channel digital reception, the proposed radar achieves high spatial resolution and robust target tracking. The proposed radar features an elevation scan range of ±45° with an azimuth fan-beam half-power beamwidth (HPBW) of 80° for a comprehensive detection field. Tests with a small drone measuring 20.3 × 15.9 × 7 cm³, positioned at various elevation angles of up to 45° and azimuth angles of up to ±60° at a distance of 4 m from the radar, verified its detection capability and highlighted the radar’s effectiveness in tracking small aerial targets. This architecture emphasizes the advantages of analog beamforming on Tx and multi-channel Rx, addressing the increasing demands for precise drone detection and monitoring in both civilian and defense domains. Full article

According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM. Full article

The rapid development of 5G and next-generation wireless systems has increased the demand for antennas that support circular polarization (CP), wide frequency coverage, and a compact size. Achieving wideband CP performance in a low-profile and simple structure remains a key challenge for modern antenna designs. In response to this, this paper presents a compact wide-slot antenna with a single feed, offering a wide operational bandwidth and circularly polarized radiation. The proposed design is excited by a 50 Ohm microstrip feedline, and it is fabricated on an (54 × 50 × 1.6 mm³) FR4 dielectric substrate. On the bottom side of the dielectric substrate, the ground plane is engraved to form a square-shaped radiating slot. The shape of the tuning stub of the antenna is modified in order to attain a wide impedance bandwidth and an axial ratio bandwidth (ARBW). The modifications include inserting a rectangular strip and thin horizontal strips into the tuning stub after tapering its upper corner. On the other hand, the radiating slot is appended by two rectangular stubs. The radiation of the resulted structure has right-hand circular polarization (RHCP). The measured results of the proposed antenna show a −10 dB impedance bandwidth equal to 78% (2.65 GHz, 2.08–4.73 GHz), whereas its broadside 3 dB ARBW is 71.6% over the frequencies (2.31 GHz, 2.07–4.38 GHz), which is compatible with various wireless communication applications. Furthermore, the peak value of the measured gain is equal to 4.68 dB, and its value is larger than 2 dBi along the operational bandwidth of the antenna. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

To improve the consumption rate of distributed energy and enhance the self-healing performance of distribution networks, this paper proposes a distribution network optimization method considering carbon emissions and dynamic reconfiguration. Firstly, various measures such as dynamic reconfiguration and distributed energy scheduling are used in upper-level optimization to reduce the network loss and solar curtailment cost of the system and to realize the optimal economic operation of the distribution network. Secondly, based on carbon emission flow theory in lower-level optimization, a low-carbon demand response model with a dynamic carbon emission factor as the guiding signal is established to promote carbon emission reduction on the user side. Then, the second-order cone planning and improved dung beetle optimization algorithm are used to solve the model. Finally, it is verified on the test system that the method can effectively reduce the risk of voltage overruns and enhance the low-carbonization and economy of distribution network operation. Full article

Efficient and accurate grey-box building models, including water-based heating and cooling systems, are crucial for simulating and optimizing the energy demand of building, neighborhood, and network scenarios. However, the numerical effort and the amount of input data required for existing models are still high, and the parameterization of these systems is very labor-intensive. This paper presents a grey-box model that addresses these limitations by requiring minimal input data and offering a highly efficient parameterization method. Using physical principles, the model was validated against a detailed physical building model and measurement data. Our results show that the grey-box model accurately predicts return temperatures (σ = 0.37 K, µ = 0.05 K) and room air temperatures (σ = 0.62 K, µ = 0.28 K). Compared to 8229 s for the detailed physical model, the model requires only 18 s for a one-year simulation. The model also shows robust behavior with alternative weather data and control strategies. The key contribution of this work is the development of a grey-box model that combines high accuracy and numerical efficiency with significantly reduced data and parameterization requirements, with possible applications in large-scale building simulations, demand-side management, short-term energy storage strategies, and model predictive control. Full article

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and robust Demand-Side Management (DSM) solutions. EV charging patterns, such as home, workplace, and public charging, need adapted strategies to match solar generation. This study analyzes a system designed to meet a unitary hourly average energy demand (8760 MWh annually) using an optimization framework that balances PV capacity and battery storage to ensure reliable energy supply. Historical solar data from 22 years is used to analyze seasonal and interannual fluctuations. The results show that solar PV alone can cover around 30% of the demand without DSM, rising to nearly 50% with aggressive DSM measures, using PV capacities of 1.0–2.0 MW. The optimization reveals that incorporating battery storage can achieve near 100% coverage with PV power of 8.0–9.0 MW. Moreover, DSM reduces required storage from 18 to about 10 MWh. These findings highlight the importance of integrating optimization-based energy management strategies to enhance system efficiency and cost-effectiveness, offering a pathway toward a more sustainable and resilient EV charging infrastructure. Full article

Objectives: The present study aimed to investigate changes in forearm muscle activity associated with short-term go-kart driving (680 m) and its potential effect on muscle activation patterns. Methods: Eleven male karting league drivers (mean age: 23.18 ± 1.40 years; body mass: 83.27 ± 10.98 kg; height: 182.73 ± 5.66 cm) volunteered to participate. Electromyographic (EMG) activity was recorded from four muscles: extensor carpi radialis (ECR), extensor carpi ulnaris (ECU), flexor carpi radialis (FCR), and flexor carpi ulnaris (FCU). Baseline EMG was measured before the intervention, followed by two consecutive kart-driving sessions on a 680 m closed track. Post-exercise EMG data were then collected. A repeated-measures analysis of covariance (ANCOVA) was used to analyze the effects of time (pre vs. post) while controlling for cumulative race time as a covariate. Results: A significant time effect with cumulative time as a covariate was observed, particularly in the ECR and ECU muscles on both the left and right sides. Notable findings include increases in maximum and mean activity of the left and right ECR (e.g., ECR right max: F = 51.57; p < 0.001; η² = 0.851) and ECU (e.g., ECU right max: F = 36.170; p < 0.001; η² = 0.801). Additionally, a significant increase was found in the maximum activation of the left FCR (F = 11.019; p = 0.009; η² = 0.550, which remained significant after controlling for total driving time. This heightened activation likely reflects an acute neuromuscular fatigue response to the demands of kart steering, rather than a long-term adaptation. Conclusions: The findings suggest that even short bouts of kart driving can induce measurable changes in neuromuscular activation of the forearm muscles, particularly in those involved in grip control and steering stability. This highlights the physical demands of karting and its potential impact on the upper limb muscle conditioning. Full article