Search Results (541)

The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning sub-Hz to kHz ranges. This review provides a PV-focused and mitigation-oriented analysis that addresses this gap. First, it clarifies the mechanisms of WBOs by mapping oscillatory drivers such as phase-locked loop dynamics, constant power control, converter–grid impedance resonance, and high-frequency switching effects to their corresponding frequency bands, alongside their engineering implications. Second, analysis methods are systematically evaluated, including eigenvalue and impedance-based models, electromagnetic transient simulations, and measurement- and data-driven techniques, with a comparative assessment of their strengths, limitations, and practical applications. Third, mitigation strategies are classified across converter-, plant-, and system-levels, ranging from adaptive control and virtual impedance to coordinated PV-battery energy storage systems (BESS) operation and grid-forming inverters. The review concludes by identifying future directions in grid-forming operation, artificial intelligence (AI)-driven adaptive stability, hybrid PV-BESS-hydrogen integration, and the establishment of standardized compliance frameworks. By integrating mechanisms, methods, and mitigation strategies, this work provides a comprehensive roadmap for addressing oscillatory stability in PV-dominated weak grids. Full article

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data. Full article

Wine phenolics serve as robust chemical signatures correlated to grape variety, processing, and regional identity. This study explores the potential of machine learning algorithms, combined with the phenolic profiles of Albanian wines, to classify them according to grape variety. Geographic origin analysis was conducted as a preliminary exploration. The dataset of phenolic compounds included white and red wines, spanning the 2017 to 2021 vintages. Using five supervised algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and K-Nearest Neighbors—a high classification accuracy was achieved, with SVM reaching 100% under Leave-One-Out Cross-Validation (LOOCV). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation were applied. Random Forest feature importance consistently highlighted trans-Fertaric acid and Procyanidin B3 as dominant discriminants. Parallel coordinates plots demonstrated clear varietal patterns driven by phenolic differences, while PCA and hierarchical clustering confirmed unsupervised grouping consistent with wine type and maceration level. Permutation testing (1000 iterations) confirmed the non-randomness of model performance. These findings show that a small set of phenolic markers can offer high classification accuracy, supporting chemically based wine authentication. Although the dataset is relatively small, thorough cross-validation, non-redundant modeling, and chemical interpretability provide a solid foundation for scalable methods. Future work will expand the dataset and explore sensor-based phenolic measurement to enable rapid authentication in wine. Full article

Damage control surgery (DCS) is a staged surgical strategy for rapid control of life-threatening bleeding, followed by physiological stabilization and delayed definitive repair. Abdomino-pelvic packing (APP)—placing compressive material within the pelvis and/or abdomen to tamponade bleeding—is a cornerstone of DCS as a temporizing measure to achieve hemostasis and stabilization in critically unstable patients. This narrative review synthesizes current evidence on DCS with a focus on APP—a technique historically developed in trauma and orthopedic surgery for exsanguinating pelvic bleeding but adaptable to gynecologic and obstetric emergencies. We outline the historical evolution, physiological basis, and stepwise protocol of DCS, adapted to specialty-specific conditions such as postpartum hemorrhage, placenta accreta spectrum, uterine rupture, and hepatic rupture in HELLP syndrome, as well as oncologic surgeries (debulking, exenteration, lymphadenectomy) and benign procedures (trocar-entry injuries in laparoscopy, presacral bleeding in sacrocolpopexy, and retroperitoneal hemorrhage in deep-infiltrating endometriosis). Modern adjuncts—including early tranexamic acid, topical hemostatic agents, and multidisciplinary coordination—have transformed packing from a last-resort maneuver into an integrated component of staged hemorrhage control. In OB/GYN, APP allows for successful hemostasis in 75–90% of cases, with significantly lower mortality rates than trauma surgery. In conclusion, APP as a potentially life-saving maneuver within DCS requires integration into standardized, institution-wide hemorrhage protocols in OB/GYN. Training, simulation, and guideline adoption are critical, particularly in resource-limited settings where advanced interventions for catastrophic bleeding are inaccessible. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

This study presents a comprehensive assessment of the energy systems in eight Eastern European countries—Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia—focusing on their energy transition, security of supply, decarbonisation, and energy efficiency. Using principal component analysis (PCA) and clustering techniques, we identify three different energy profiles: countries dependent on fossil fuels (e.g., Poland, Bulgaria), countries with a balanced mix of nuclear and fossil fuels (e.g., the Czech Republic, Slovakia, Hungary), and countries focusing mainly on renewables (e.g., Slovenia, Croatia). The sectoral analysis shows that industry and transport are the main drivers of energy consumption and CO₂ emissions, and the challenges and policy priorities of decarbonisation are determined. Regression modelling shows that dependence on fossil fuels strongly influences the use of renewable energy and electricity consumption patterns, while national differences in per capita electricity consumption are influenced by socio-economic and political factors that go beyond the energy structure. The Decarbonisation Level Index (DLI) indicator shows that Bulgaria and the Czech Republic achieve a high degree of self-sufficiency in domestic energy, while Hungary and Slovakia are the most dependent on imports. A typology based on energy intensity and import dependency categorises Romania as resilient, several countries as balanced, and Hungary, Slovakia, and Croatia as vulnerable. The projected investments up to 2030 indicate an annual increase in clean energy production of around 123–138 TWh through the expansion of nuclear energy, the development of renewable energy, the phasing out of coal, and the improvement of energy efficiency, which could reduce CO₂ emissions across the region by around 119–143 million tons per year. The policy recommendations emphasise the accelerated phase-out of coal, supported by just transition measures, the use of nuclear energy as a stable backbone, the expansion of renewables and energy storage, and a focus on the electrification of transport and industry. The study emphasises the significant influence of European Union (EU) policies—such as the “Clean Energy for All Europeans” and “Fit for 55” packages—on the design of national strategies through regulatory frameworks, financing, and market mechanisms. This analysis provides important insights into the heterogeneity of Eastern European energy systems and supports the design of customised, coordinated policy measures to achieve a sustainable, secure, and climate-resilient energy transition in the region. Full article

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation. Full article

Water resource carrying capacity is a key measure of sustainability, commonly employed to evaluate how well water resources can sustain economic and social growth. With China’s rapid economic growth and modernization, water resources in certain regions are now being used at or beyond their sustainable threshold. This study evaluates the present state of water resource carrying capacity in Taizhou City, located in southeastern China. Using relevant data from 2012 to 2022 on society, economy, water resources, and ecology, the weights of the evaluation indicators were determined using both the entropy weight method and principal component analysis. Subsequently, a comprehensive evaluation model for water resource carrying capacity was developed by applying the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. The comprehensive proximity index for water resource carrying capacity in Taizhou City averaged 0.4864 between 2012 and 2022, indicating a moderate level overall but exhibiting a declining trend, suggesting an approaching threshold of utilization limits. The range was between 0.3461 and 0.7143. In 2017, the comprehensive proximity index was 0.3461 (low water resource carrying capacity level, with water resources already suffering damage and various subsystems developing uncoordinatedly). However, the comprehensive proximity index for water resource carrying capacity improved significantly from 2018 to 2022. A combination of rising industrial water demand and a decrease in both the absolute volume and proportional allocation of water for ecological purposes drove the overall decline in the progress rate in 2017. Taizhou City has formulated strict water resource management policies and measures, resulting in a decrease in indicators such as industrial water consumption, residential water consumption, and industrial wastewater discharge, as well as an increase in indicators such as ecological water consumption and ecological water utilization rate. As a result, the comprehensive water resource carrying capacity saw a notable rise during 2018–2019. The study results provide a reference for the rational use of water resources in Taizhou City and are of certain significance for promoting the coordinated economic and social development of Taizhou City. Full article

The geriatric syndrome known as frailty is characterized by diminished physiological reserves and heightened susceptibility to unfavorable health consequences. As the world’s population ages, it is crucial to detect frailty early and accurately in order to reduce hazards, including falls, hospitalization, and death. In particular, functional tests are frequently used to evaluate physical frailty. However, current evaluation techniques are limited in their scalability and are prone to inconsistency due to their heavy reliance on subjective interpretation and manual observation. In this paper, we provide a completely automated, impartial, and comprehensive frailty assessment system that employs computer vision techniques for assessing physical frailty tests. Machine learning models have been specifically designed to analyze each clinical test. In order to extract significant features, our system analyzes the depth and joint coordinate data for important physical performance tests such as the Walking Speed Test, Timed Up and Go (TUG) Test, Functional Reach Test, Seated Forward Bend Test, Standing on One Leg Test, and Grip Strength Test. The proposed system offers a comprehensive system with consistent measurements, intelligent decision-making, and real-time feedback, in contrast to current systems, which lack real-time analysis and standardization. Strong model accuracy and conformity to clinical benchmarks are demonstrated by the experimental outcomes. The proposed system can be considered a scalable and useful tool for frailty screening in clinical and distant care settings by eliminating observer dependency and improving accessibility. Full article

Research Problem: Despite growing recognition that digital transformation strategies affect economic coordination, no study has empirically tested whether different national digital development models create systematically different transaction cost environments, particularly in emerging economies pursuing Sustainable Development Goals (SDGs). Research Gap and Novelty: This study addresses a critical gap by providing the first comprehensive empirical validation of how equity-focused versus scale-intensive digital development strategies influence coordination efficiency outcomes. Unlike previous studies that focus on aggregate digital infrastructure investment or single-country analyses, we develop a novel multi-dimensional Digital Coordination Efficiency Index and systematic development model classification framework to test transaction cost economics (TCE) predictions across diverse emerging economy contexts. Methodology: Using panel data from 16 strategically selected emerging economies (2017–2022) representing distinct development pathways, we apply advanced econometric techniques including comprehensive diagnostic testing, jackknife analysis, and bootstrap procedures to ensure robust causal inference. Key Findings: Development model choice explains 63.4% of variation in digital coordination efficiency compared to only 8.9% explained by GDP per capita—a 7.1-fold improvement in explanatory power—though this finding is based on a limited sample of 16 countries. Countries pursuing equity-focused strategies achieve 15.42 points higher coordination efficiency (p < 0.05) and demonstrate 49.4% superior mobile infrastructure penetration in our sample. The Vietnam–India comparison illustrates how an equity-focused model can systematically outperform a scale-intensive approach, with Vietnam achieving 68.4% higher GDP per capita, though we acknowledge this represents one specific case rather than a universal pattern. Practical Implications: Emerging economies can achieve superior economic outcomes by prioritizing digital inclusion over concentrated innovation, with equity-focused approaches providing measurable coordination advantages that translate into higher GDP growth and better SDG attainment. Multinational corporations should consider coordination capabilities when making location decisions, as equity-focused countries offer superior environments for distributed operations. Full article

Monochromatic mode-filtering-based scattering suppression techniques have been shown to be applicable to all commonly used forms of far- and near-field antenna and RCS measurement techniques. Traditionally, the frequency-domain mode-filtering technique takes a far-field pattern, either measured directly or obtained using a suitable near-field to far-field transformation, as its starting point. The measurement is required to be conducted such that the antenna under test (AUT) is positioned offset from the origin of the measurement coordinate system. This physical offset introduces a phase taper across the AUT pattern and results in far greater interference occurring between the direct and indirect parasitically coupled spurious scattered signals. The method is very general and can be applied to all forms of near- or far-field measurements. However, for the case of a spherical near-field measurement (SNF) approach, it is somewhat cumbersome and tedious as first we must perform a probe-corrected spherical near-field to far-field transformation, which itself involves the computation of a complete set of spherical mode coefficients, and then after the displacement has been applied to the far-electric-fields, a second spherical wave expansion and summation is required to implement the mode-filtering procedure. While this data processing chain has been widely deployed and exhaustively validated, it requires passing through the asymptotic far-field, which inevitably results in additional computational effort, as well as incurring some loss of information, which can impose limitations on further near-field applications. This paper introduces an alternative, novel, rigorous algorithm that applies the displacement of the AUT directly using the vector addition theorem for spherical waves. An efficient implementation has been developed, and it is shown that the new, rigorous algorithm for the translation and filtering can be easily implemented directly within the data processing chain of any standard spherical near-field transformation algorithm, avoiding the need to first transform to the asymptotic far-field and also removing the need for a secondary spherical mode expansion and secondary spherical mode summation. While the vector addition theorem required for the spherical near-field to far-field transformation (SNFFFT) algorithm has been described in detail in the open literature, its implementation has been limited to the case of impinging waves and positive z-directed translations where the magnitude of the displacement is necessarily larger than the minimum sphere radius (MRE). In the current paper, the addition theorem will be derived in a new form that allows the translation to be applied in any desired direction, without the need for additional rotations, as well as being valid for solutions for waves transitioning through the sphere and applicable for the case where the magnitude of the translation is smaller or larger than the radius of the minimum sphere. Full article

In the course of digitalization, the importance of modeling and simulating real-world processes in a computer is rapidly increasing. Simulations are now in everyday use in many areas. For example, simulations are used to gain a better understanding of the real experiment, to plan new experiments, or to analyze existing experiments. Simulations are now also increasingly being used as an essential component of a real measurement, usually as part of an inverse problem. To ensure confidence in the results of such virtual measurements, traceability and methods for evaluating uncertainty are needed. In this paper, the challenges and benefits inherent to virtual metrology techniques are shown using three examples from different metrological fields: the virtual coordinate measuring machine, the tilted-wave interferometer, and the virtual flow meter. Full article

Due to the rapid advancements in coordinate measuring systems, data processing software, and additive manufacturing (AM) techniques, it has become possible to create copies of existing models through the reverse engineering (RE) process. However, the lack of precise estimates regarding the accuracy of the RE process—particularly at the measurement, reconstruction, and computer-aided design (CAD) modeling stages—poses significant challenges. Additionally, the assessment of dimensional and geometrical errors during the manufacturing stage using AM techniques limits the practical implementation of product replicas in the industry. This paper provides an estimation of the errors encountered in the RE process and the AM stage of various models. It includes examples of an electrical box, a lampshade for a standing lamp, a cover for a vacuum unit, and a battery cover. The geometry of these models was measured using a GOM Scan 1 (Carl Zeiss AG, Jena, Germany). Following the measurement process, data processing was performed, along with CAD modeling, which involved primitive detection, profile extraction, and auto-surface methods using Siemens NX 2406 software (Siemens Digital Industries, Plano, TX, USA). The models were produced using a Fortus 360-mc 3D printer (Stratasys, Eden Prairie, MN, USA) with ABS-M30 material. After fabrication, the models were scanned using a GOM Scan 1 scanner to identify any manufacturing errors. The research findings indicated that overall, 95% of the points representing reconstruction errors are within the maximum deviation range of ±0.6 mm to ±1 mm. The highest errors in CAD modeling were attributed to the auto-surfacing method, overall, 95% of the points are within the average range of ±0.9 mm. In contrast, the lowest errors occurred with the detect primitives method, averaging ±0.6 mm. Overall, 95% of the points representing the surface of a model made using the additive manufacturing technology fall within the deviation range ±0.2 mm on average. The findings provide crucial insights for designers utilizing RE and AM techniques in creating functional model replicas. Full article

Urban spatial justice is a critical issue in the context of rapid urbanization. Improving public well-being depends on the efficient use of park green space (PGS) resources. This study evaluates the spatial distribution equity and social equity of PGS in Jinan City, Shandong Province, China, with the aim of optimizing their spatial layout, mitigating poor accessibility due to uneven spatial distribution, and improving the quality of life for all inhabitants. Firstly, based on Sustainable Development Goal 11 (SDG11), we constructed an urban sustainable development index system to quantify residents’ demand levels. The supply level was measured through three dimensions: quantity, quality, and accessibility of PGS utilizing multi-source geospatial data. A coupling coordination degree model (CCDM) was employed to analyze the supply-demand equilibrium. Secondly, Lorenz curves and Gini coefficients were utilized to evaluate the equity of PGS resource distribution to disadvantaged populations. Finally, a k-means clustering algorithm found the best sites for additional parks in low-accessibility regions. The results show that southern areas—that is; those south of the Yellow River—showed greater supply-demand equilibrium than northern ones. With a Gini index for PGS services aimed at vulnerable populations of 0.35, the citywide social level distribution appeared to be relatively balanced. This paper suggests an evaluation technique to support fair resource allocation, establishing a dual-perspective evaluation framework (spatial and social equality) and giving a scientific basis for PGS planning in Jinan. Full article

This study presents a methodological approach to assess radionuclide contamination in the Baltic Sea near Juodkrante, Lithuania, based on measurements of β- and γ-emissions in seawater, foreshore sand, and dune top sand. Existing assessments often lack sufficient site-specific detail and multicompartment analysis, limiting the understanding of localized contamination and radionuclide behavior in coastal environments. Sampling was carried out between 2019 and 2024 at approximately the same geographical coordinates, along transects orientated normally to the shoreline. Given that the dune top remains unaffected by seawater intrusion, while the foreshore sand is subject to regular inundation, the foreshore environment is considered a natural filter that is capable of accumulating radionuclides from seawater. The proposed methodology supports the hypothesis that radionuclide retention in sandy substrates may persist beyond episodic contamination events in seawater, with retention dynamics influenced by environmental factors such as hydrodynamic conditions and aeolian processes. Simultaneous β- and γ-emission analysis enhances the precision of radionuclide quantification, while comparative evaluation of γ-spectra improves the detection of both natural and anthropogenic radionuclides, providing insight into both contemporary and historical contamination processes. The sustainability of the proposed approach lies in its efficient use of time, resources, and effort to monitor radionuclide contamination. Unlike conventional techniques that require energy-intensive seawater processing, this approach uses foreshore sand, which passively accumulates radionuclides through natural wave-driven deposition. Full article