Search Results (20,310)

This paper presents the development and characterization of a reference high-voltage sensing chain for the calibration and conformity assessment of instrument transformers with Class 0.1-WB3, in the extended frequency range up to 150 kHz, according to IEC 61869. The sensing chain, composed of a high-voltage divider, precision attenuators and high-pass filters, has been specifically developed and characterized. The chain features two parallel measurement paths: the first path, comprising the high-voltage divider and attenuator, is optimized for measuring the fundamental frequency superimposed with high-amplitude harmonics; the second path, consisting of the high-voltage divider followed by a high-pass filter, is dedicated to measuring very-low-level superimposed harmonic components by enhancing the signal-to-noise ratio. These two paths are integrated with a digitizer to form a complete and modular measurement chain. The expanded uncertainty of measurement has been thoroughly evaluated and confirms the chain’s ability to support assessment of instrument transformers with Class 0.1-WB3 compliance. Additionally, the chain architecture enables a future extension up to 500 kHz, addressing the growing need to evaluate instrument transformers under high-frequency power quality disturbances and improving the sensing capability in this field. Full article

Virtual commands serve as the essential framework for establishing interaction between the virtual pilot and the MCP in autopilot scenarios. Conventional proportional-integral-derivative (PID) controllers are insufficient to ensure accurate flight trajectories due to system hysteresis. To overcome this limitation, a quaternary correlation prediction compensation PID (QCPC-PID) approach is introduced for computing virtual heading commands in autopilot tasks. The method integrates multi-feature statistics, entropy-based predictive compensation, and quaternary correlations. First, flight trajectory error statistics are dynamically calculated using signed error distances to assess deviation levels. Second, a predictive structure based on information entropy is applied to enhance PID compensation. Third, quaternary correlation dependence is established to generate virtual heading commands. The findings confirm the effectiveness of the method in improving flight convergence. The incorporation of predictive structures and quaternary correlations is critical for achieving predictive compensation during PID tuning, thereby reducing flight trajectory deviations. The quaternary correlation prediction compensation method ensures superior performance of PID control in modeling heading adjustment behavior under autopilot conditions. Full article

This study examines the early impact of Industry 4.0 (I4.0) implementation on the financial performance of Croatian companies, focusing on indicators of profitability, efficiency, and activity. The research investigates whether firms adopting I4.0 technologies achieve superior results compared to traditional companies. A unique feature of this study is its integration of primary data—collected via an online survey of Croatian enterprises—with secondary data from publicly available financial reports. Statistical methods, including Analysis of Variance (ANOVA) and linear regression, were employed to test the hypotheses. The results show that I4.0 adopters perform significantly better in terms of net profit margin, return on assets, business efficiency, and supplier bonding days, while no significant difference was found in days sales outstanding. This paper contributes to the literature by offering one of the first empirical analyses of early-stage I4.0 adoption in the context of a transition economy, using firm-level financial data. The findings provide valuable insights for managers, policymakers, and investors aiming to understand the tangible business benefits of digital transformation. The results also highlight the importance of supporting I4.0 adoption strategies to enhance competitiveness and recovery in post-pandemic economic conditions. Full article

This study presents the first comprehensive IUCN-based assessment for all 88 Limonium species occurring in Greece, aiming to close a critical conservation gap for this highly diverse and important genus in the country. To identify patterns of extinction risk, we applied the IUCN Red List Categories and Criteria, integrating data on endemism, ploidy, and anthropogenic threats. Moreover, we employed spatial analysis to identify conservation hotspots, and we statistically analyzed how threat status changes across geographic space. Our results show that 51 species (58.0%) are threatened, with endemics (62.3%) exhibiting a significantly higher risk than non-endemics. A greater proportion of diploid species were also found to be threatened compared to their polyploid counterparts. Longitude was identified as a key spatial predictor of threat, with risk concentrated in southern and western coastal zones. The most prevalent threats are coastal development (56.9% of threatened species) and invasive species (33.3%). This work provides a vital baseline for Limonium conservation, highlighting the urgent need for a dual conservation strategy that combines efficient in situ actions with ex situ measures for the most imperiled species. Full article

Fire blight, caused by Erwinia amylovora, is a severe disease impacting pome fruit production worldwide, including in Saudi Arabia. This study evaluated antibiotic sensitivity and the potential of chemical and elicitor treatments to suppress E. amylovora isolates collected from various regions in Saudi Arabia. In the in vitro assays, at low antibiotic levels (10 µg/mL streptomycin and 25 µg/mL oxytetracycline), all Saudi Arabian strains exhibited minimal inhibition (zones ≤ 14 mm). Two isolates displayed partial tolerance at an intermediate oxytetracycline concentration (50 µg/mL). True sensitivity (zones > 18 mm) was mainly observed at the highest tested oxytetracycline dose (100 µg/mL). Regarding copper sulfate, all isolates showed no inhibition between 0.02 and 0.08 mM, while all isolates exhibited intermediate susceptibility at 0.16 mM. The second experimental phase examined in planta effects of streptomycin, salicylic acid (SA), and their combination on disease development in artificially inoculated apple (Malus domestica) shoots under greenhouse conditions. Both streptomycin and SA significantly reduced fire blight incidence (by 75%) and symptom severity, while the combined treatment yielded the greatest reduction in shoot necrosis and bacterial load. This is the first report demonstrating that SA, particularly when used in combination with streptomycin, can effectively suppress fire blight in Saudi Arabia. These results stress the importance of integrating resistance inducers into fire blight management strategies to counter the rise in antimicrobial resistance. Full article

Bolus tracking is a critical component of swallowing analysis, as the speed, course, and integrity of bolus movement from the mouth to the stomach, along with the presence of residue, serve as key indicators of potential abnormalities. Existing machine learning approaches for videofluoroscopic swallowing study (VFSS) analysis heavily rely on annotated data and often struggle to detect residue, which is visually subtle and underrepresented. This study proposes an unsupervised architecture to segment both bolus and residue, marking the first successful machine learning-based residue segmentation in swallowing analysis with quantitative evaluation. We introduce an unsupervised convolutional autoencoder that segments bolus and residue without requiring pixel-level annotations. To address the locality bias inherent in convolutional architectures, we incorporate positional encoding into the input representation, enabling the model to capture global spatial context. The proposed model was validated on a diverse set of VFSS images annotated by certified raters. Our method achieves an intersection over union (IoU) of 61% for bolus segmentation—comparable to state-of-the-art supervised methods—and 52% for residue detection. Despite not using pixel-wise labels for training, our model significantly outperforms top-performing supervised baselines in residue detection, as confirmed by statistical testing. These findings suggest that learning from negative space provides a robust and generalizable pathway for detecting clinically significant but sparsely represented features like residue. Full article

Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the potential origins of life. To enable efficient sample acquisition under vacuum and microgravity conditions, this study introduces a two-stage gas-driven asteroid sampling strategy. This approach mitigates the challenges posed by low-gravity environments and irregular asteroid topography. A coupled computational fluid dynamics–discrete element method (CFD–DEM) framework was employed to simulate the gas–solid two-phase flow during the sampling process. First, a model of the first-stage gas-driven sampling device was developed to establish the relationship between the inlet angle of the gas nozzle and the sampling efficiency, leading to the optimization of the nozzle’s structural parameters. Subsequently, a model of the integrated two-stage gas-driven sampling and sample-delivery system was constructed, through which the influence of the second-stage nozzle inlet angle on the total collected sample mass was investigated, and its design parameters were further refined. Simulation outcomes were validated against experimental data, confirming the reliability of the CFD–DEM coupling approach for predicting gas–solid two-phase interactions. The results demonstrate the feasibility of collecting asteroid regolith with the proposed two-stage gas-driven sampling and delivery system, thereby providing a practical pathway for extraterrestrial material acquisition. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article

This study provides the first comprehensive, field-inventory-based assessment of urban ecosystem services within a Russian university campus, focusing on the woody vegetation of the Lobachevsky State University of Nizhny Novgorod. Utilizing a detailed field tree inventory combined with the i-Tree framework (including i-Tree Eco, i-Tree Canopy, UFORE, and i-Tree Hydro models), we quantified the campus’s capacity for carbon storage and sequestration, air pollutant removal, and stormwater runoff mitigation. The campus green infrastructure, comprising 1887 trees across 32 species with a density of 145.5 stems per hectare, demonstrated significant ecological value. Results show a carbon storage density of 26.61 t C ha⁻¹ and an annual gross carbon sequestration of 11.43 tons. Furthermore, the campus trees removed 1213.7 kg of air pollutants annually (a deposition rate of 9.35 g m⁻²), with ozone, particulate matter, and sulfur dioxide showing the highest deposition. The campus also retained 956.1 m³ of stormwater annually. These findings, particularly the high carbon sequestration rates, are attributed to the dominance of relatively young, fast-growing tree species. This research establishes a critical baseline for understanding urban ecosystem services in a previously under-researched geographical context. The detailed, empirical data offers crucial insights for urban planners and policymakers in Nizhny Novgorod and beyond, advocating for the strategic integration of ecosystem services assessments into campus planning and broader urban green infrastructure development across Russian cities. The study underscores the significant role of university campuses as vital components of urban green infrastructure, contributing substantially to environmental sustainability and human well-being. Full article

This paper presents a comparative study on the transdisciplinary design of resilient urban subcenters, examining the interplay between human-led and artificial intelligence (AI)-generated design approaches. By employing holistic design methods, we prepare and present revitalization projects for two areas of urban space. Our goal was to create a resilient urban subcenter that contributes to the development of a resident. The first revitalized site reflects the multicultural past of the city. The second project addresses the need to revitalize a subcenter reserved for residents. In the non-AI approach, holistic design is implemented across various universities, fields, and academic disciplines—the humanities, social sciences, engineering, and the arts. Transdisciplinary teams of sociologists, engineers, interior designers, architects, urban geographers, and acousticians transcend workshop limitations as well as cognitive boundaries, promoting the creation of new, unconventional knowledge. The AI-integrated approach employs artificial intelligence in a dual capacity: both as a generator of alternative design visions and as an analytical tool for assessing technological readiness. The findings contribute to the evolving discourse on sustainable urban development and the transformative potential of technology in transdisciplinary design practices. Full article

Determining a high-precision national geoid is a fundamental step in modernizing Kazakhstan’s vertical reference system. However, the country’s vast territory, complex topography, and limited coverage of modern terrestrial and airborne gravimetric surveys present significant challenges. In this context, Soviet-era gravimetric maps at a 1:200,000 scale remain the only consistent nationwide data source, yet their reliability has not previously been rigorously assessed within modern gravity standards. This study presents the first comprehensive validation of Soviet-era gravimetric surveys using two independent approaches. The first approach is about the comparison of gravity anomalies with the global geopotential models EGM2008, EIGEN-6C4 and XGM2019e_2159. The second approach is about the direct evaluation against absolute gravity measurements from the newly established Qazaqstan Gravity Reference Frame (QazGRF). The analysis demonstrates that, after applying systematic corrections, the Soviet-era gravimetric survey retains high information content. The mean discrepancy with QazGRF measurements is 0.7 mGal with a standard deviation of 2.5 mGal, and more than 90% of the evaluated points deviate by less than ±5 mGal. Larger inconsistencies, up to 20 mGal, are confined to mountainous and geophysically complex regions. In addition, several artifacts inherent to the global models were identified, suggesting that the integration of validated regional gravimetric data can also support future improvements of global gravity models. A key finding was the detection of an artifact in the global models on sheet M43. Its presence was confirmed by comparison with terrestrial gravimetric data and inter-model differences. It was established that the anomaly is caused by inaccuracies in the terrestrial “fill-in” component of the EGM2008 model, which subsequently inherited by later global solutions. The results confirm that Soviet gravimetric maps, once critically re-evaluated and tied to absolute observations, can be effectively integrated with global models. This integration delivers reliable, high-resolution inputs for regional gravity-field modeling. It establishes a robust scientific and practical foundation for constructing the first national geoid of Kazakhstan and for implementing a unified state coordinate and height system. It also helps enhance the accuracy of global geopotential models. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

Ecosystem services provide the scientific foundation and optimization objectives for constructing ecological security patterns, and their spatial characteristics directly affect planning decisions such as ecological source identification and corridor layout. However, current methods for constructing ecological security patterns rely excessively on static spatial optimization of landscape structure and ecological processes, while overlooking the dynamic variations in ecosystem service values under climate change. Taking Yunnan Province as a case study, this paper calculates ecosystem service values, analyzes their spatiotemporal variations, and based on ecosystem service value hotspots, applies the MSPA model and circuit theory to identify ecological sources, corridors, pinch points, barrier areas, and improvement areas. On this basis, we construct and optimize the ecological security pattern of Yunnan Province and propose ecological protection strategies. The results show that: (1) From 2000 to 2030, ecosystem service values in Yunnan exhibit significant spatiotemporal heterogeneity. From 2000 to 2020, they first declined and then increased, with aquatic ecosystems contributing the most. Under future climate scenarios, ecosystem service values continue to increase, with the greatest growth under the SSP2-4.5 scenario. The spatial pattern is characterized by higher values in the central region and lower values in the eastern and western areas. (2) In 2020, 56 ecological sources were identified; under the SSP1-1.9 scenario, 61 were identified, while 57 were identified under both SSP2-4.5 and SSP5-8.5 scenarios. These sources are mainly distributed in northwestern Yunnan and the Nujiang and Lancang River basins, presenting a “more in the west, fewer in the east” pattern. (3) In 2020, 132 ecological corridors and 74 pinch points were identified. By 2030, under SSP1-1.9, there are 149 corridors and 84 pinch points; under SSP2-4.5, 135 corridors and 55 pinch points; and under SSP5-8.5, 134 corridors and 60 pinch points. (4) By integrating results across multiple scenarios, an ecological security pattern characterized as “three screens, two zones, six corridors, and multiple points” is constructed. Based on regional ecological background characteristics, differentiated strategies for ecological security protection of territorial space are proposed. This study provides a scientific reference for the synergistic optimization of ecosystem services and ecological security patterns under climate change. Full article

Mental health challenges in academic settings are increasingly recognised, yet research on staff wellbeing remains limited, particularly within African universities. This study provides the first institution-wide assessment of psychological distress and burnout among academic staff at a South African university. A cross-sectional survey using validated tools, the 28-item General Health Questionnaire (GHQ-28 ) and the Oldenburg Burnout Inventory (OLBI) was administered to 157 academic employees, and data were analysed using descriptive statistics, non-parametric tests, and ordinal regression. The median age of participants was 42 years (Interquartile range [IQR] = 34–50; SD = 11.4), and the majority of participants were female (n = 110, 70%). The sample included staff across academic ranks, with lecturers being the most common (n = 64, 41%). Results showed that nearly half of participants (49%) exhibited severe psychological distress, and over a quarter (27%) reported high levels of burnout. Female staff reported significantly higher distress and burnout scores compared to their male counterparts. Less than a third (28%) of participants reported feeling safe to disclose mental health concerns, while over half expressed dissatisfaction with institutional support. Participants indicated strong support for both individual-level services, such as confidential counselling and workshops, and systemic changes, including flexible work arrangements and leadership-driven mental health initiatives. Findings highlight the need for integrated, participatory mental health strategies that are culturally and contextually tailored. These results offer timely evidence to inform the development of institutional strategies, policies, and practices to promote mental health among academic staff. Full article

The optimization of pump setting depth in low-permeability oil wells remains a persistent challenge, as conventional inflow performance relationship (IPR) curves fail to capture the coupled effects of downhole pump intake depth and reservoir productivity. To address this limitation, this study proposes a novel Low-Permeability Intake Inflow Performance Relationship (LIIPR) framework. The method establishes a theoretical link between pump depth and production by integrating low-permeability reservoir inflow models with multiphase wellbore flow calculations. On this basis, a series of derivative concepts and analytical tools are introduced, including (i) a three-zone classification of inflow curves to distinguish effective, inefficient, and abnormal production regimes; (ii) a multi-pump-depth analysis to determine the feasible range and optimal boundaries of pump setting depth; and (iii) a three-dimensional deep-pumping limit map that couples inflow and outflow dynamics through nodal analysis, providing a comprehensive criterion for system optimization. The proposed LIIPR methodology enables accurate identification of optimal pump depth and intake pressure conditions, overcoming the ambiguity of traditional IPR-based approaches. Unlike previous IPR- or EIPR-based methods, LIIPR introduces for the first time a unified inflow–outflow coupling framework that quantitatively links pump intake depth with well productivity. This integration represents a novel theoretical and computational advance for deep-pumping optimization in low-permeability reservoirs. Applications for field cases in Shengli Oilfield confirm the theoretical findings and demonstrate the practical potential of the method for guiding efficient deep pumping operations in low-permeability reservoirs. Full article