Search Results (1,375)

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

Pulsed electric field (PEF) and high-pressure processing (HPP) are non-thermal treatments, developed to ensure preservation of food products whilst maintaining taste and valuable nutrients. In this study, we investigated their potential for the inactivation of 3 commercial exogenous pectinases (polygalacturonase, pectin transeliminase, pectin esterase) commonly used in juice processing for clarification of juices. The inactivation of these enzymes after processing is mandatory by European law. Clear apple juice was treated with both non-thermal processing methods, as well as with thermal pasteurization as the standard method. For HPP, 3 pressures (250, 450, and 600 MPa) and different holding times (from 2 to 12 min) were tested. For PEF, 3 electric field intensities (10, 13, and 15 kV/cm) and different specific energy values (from 121 to 417 kJ/kg). Standard thermal pasteurization resulted in a complete inactivation of all tested pectinases. HPP treatment only showed marginal effects on polygalacturonase and pectin transeliminase at the highest pressure and holding times, which are beyond levels used in industrial settings. For PEF, dependence upon high electric field strength and specific energy values was evident; however, here too, the effect was only moderate at the levels attainable within the scope of this study. Assuming a continued linear relationship, usable results could be achieved in an industrial setting, albeit under more extreme conditions. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

The Kekeya oilfield is located at the southwestern edge of the Tarim Basin, in the southern margin of the Yecheng depression, at the western end of the second structural belt of the northern foothills of the Kunlun Mountains. It is one of the important oil and gas fields in western China, with significant oil and gas resource potential in the X4-X5 section of the Xihepu Formation. This study focuses on the edge of the alluvial fan depositional system, employing various techniques, including core data and well logging data, to precisely characterize the sand body architecture and comprehensively analyze the reservoir architecture in the study area. First, the regional geological background of the area is analyzed, clarifying the sedimentary environment and evolutionary process of the Xihepu Formation. Based on the sedimentary environment and microfacies classification, the sedimentary features of the region are revealed. On this basis, using reservoir architecture element analysis, the interfaces of the reservoir architecture are finely subdivided. The spatial distribution characteristics of the planar architecture are discussed, and the spatial distribution and internal architecture of individual sand body units are analyzed. The study focuses on the spatial combination of microfacies units along the profile and their internal distribution patterns. Additionally, a quantitative analysis of the sizes of various types of sand bodies is conducted, constructing the sedimentary model for the region and revealing the control mechanisms of different sedimentary architectures on reservoir properties and oil and gas accumulation patterns. This study pioneers a quantitative model for alluvial fan fringe in gentle-slope basins, featuring the following: (1) lobe width-thickness ratios (avg. 128), (2) four base-level-sensitive boundary markers, and (3) a retrogradational stacking mechanism. The findings directly inform reservoir development in analogous arid-climate systems. This research not only provides a scientific basis for the exploration and development of the Kekeya oilfield but also serves as an important reference for reservoir architecture studies in similar geological contexts. Full article

The eastern North Pacific Ocean coastline (from the Salish Sea to the western Aleutian Islands) is highly glaciated with relic sediment deposits scattered throughout a highly contoured and variable bathymetry. Oceanographic conditions feature strong currents and tidal exchange. Sand wave fields are prominent features within these glaciated shorelines and provide critical habitat to sand lance (Ammodytes spp.). Despite an awareness of the importance of these benthic habitats, attributes related to their structure and characteristics remain undocumented. We explored the micro-bathymetric morphology of a subtidal sand wave field known to be a consistent habitat for sand lance. We calculated geomorphic attributes of the bedform habitat, analyzed sediment composition, and measured oceanographic properties of the associated water column. This feature has a streamlined teardrop form, tapered in the direction of the predominant tidal current. Consistent flow paths along the long axis contribute to well-defined and maintained bedform morphology and margin. Distinct patterns in amplitude and period of sand waves were documented. Strong tidal exchange has resulted in well-sorted medium-to-coarse-grained sediments with coarser sediments, including gravel and cobble, within wave troughs. Extensive mixing related to tidal currents results in a highly oxygenated water column, even to depths of 80 m. Our analysis provides unique insights into the physical characteristics that define high-quality habitat for these fish. Further work is needed to identify, enumerate, and map the presence and relative quality of these benthic habitats and to characterize the oceanographic properties that maintain these benthic habitats over time. Full article

An analysis of Albanian Muslims in Italy provides a compelling case study of communities perceived as marginal. Studies of Muslims in Italy tend to focus on the majority and chronologically older groups within the country’s Islamic landscape, particularly those from Asia and Africa. In addition to providing a better understanding of Islam in Italy, a study of the identity and community-building issues of the Albanian community of origin offers many insights into that community’s complexity. Albanians in Italy have a very specific historical and religious heritage; so, analyzing their roots and community-building processes helps us to better understand the development of Islam on the margins of large national organizations and majority groups. This article presents the results of the first national study of Albanian Muslims in Italy. Online interviews and field observations were conducted in 2024 within the Union of Muslim Albanians in Italy (Unione degli Albanesi Musulmani in Italia—UAMI), using the ethnographic method. The Association has fewer members compared with national level organizations. It was founded in 2009 to address specific issues related to the management of Muslim Albanian religious identity. The Association has sought to address the fragmentation of religion and Albanian nationalism, a consequence of a long period of state atheism, and to counter the literalist and radical tendencies in the interpretation of religion that have emerged in Albania since the collapse of the communist regime. In addition to these challenges, the Association has also tackled issues related to the Islamic religion in its local and global dimensions. The analysis of these challenges and the ways to deal with them offers a new framework in the Italian Islamic panorama, despite its marginality. The results of this research point to the emergence of new forms of rooting and belonging characterized by spirituality over orthopraxis. These forms adopt a religious approach open to diversity and pluralism. Full article

Based on ethnographic data collected in Germany, this article compares ultra-conservative Salafi and progressive, LGBTQI-plus Muslim movements and examines their negotiation of religious identity and practice within and in contrast to ‘mainstream Islam’ (e.g., DİTİB). While on the surface these movements appear to be on the fringes of Islam and clearly opposed to each other, a closer look reveals interesting moments of convergence and publicly gained prominence. In doing so, this article explores the actor biography issues that drive affiliation, including negative experiences with mainstream mosques and the search for authentic expression and roots. It analyses the politics of labelling (e.g., ‘Salafi’, ‘liberal’), and how these groups define their target audiences in relation to the perceived mainstream. It examines the negotiation of cultural diversity and Islamic ‘purity’, contrasting Salafi reform with progressive interpretations. Finally, it examines strategies for challenging mainstream institutions. By comparing these groups, the article offers a nuanced insight into Islamic practices at the margins. It sheds light on the various strategies employed to discredit mainstream Islamic institutions, ranging from theological differences to power struggles within the contested religious field. Full article

Traditional intensive agricultural system impedes ecological functions, such as nutrient cycling and biodiversity conservation, resulting in excessive nitrogen discharge, CH₄ emission, and ecosystem service losses. To enhance critical ecosystem services and mitigate environmental externalities in paddy fields, we developed a multi-objective agricultural system (MIA system), which combines two eco-functional units: paddy wetlands and Beitang (irrigation water collection pond). Pilot study results demonstrated that the MIA system enhanced biodiversity and inhibited pest outbreak, with only a marginal reduction in rice production compared with the control. Additionally, the paddy wetland effectively removed nitrogen, with removal rates of total nitrogen and dissolved inorganic nitrogen ranging from 0.06 to 0.65 g N m⁻² d⁻¹ and from 0.02 to 0.22 g N m⁻² d⁻¹, respectively. Continuous water flow in the paddy wetland reduced the CH₄ emission by 84.4% compared with the static water conditions. Furthermore, a simulation experiment indicated that tide flow was more effective in mitigating CH₄ emission, with a 68.3% reduction compared with the drying–wetting cycle treatment. The emergy evaluation demonstrated that the MIA system outperformed the ordinary paddy field when considering both critical ecosystem services and environmental externalities. The MIA system exhibited higher emergy self-sufficiency ratio, emergy yield ratio, and emergy sustainable index, along with a lower environmental load ratio. Additionally, the system required minimal transformation, thus a modest investment. By presenting the case of the MIA system, we provide a theoretical foundation for comprehensive management and assessment of agricultural ecosystems, highlighting its significant potential for widespread application. Full article

Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R² > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m³) and MAE (0.0126 m³). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems. Full article

Anthropogenic infrastructure, such as inland wind turbines commonly found in agricultural fields, has substantially increased subterranean vibratory noise in the past decades. Plants, being rooted in soil, are continuously exposed to these vibrations, yet we have little understanding of how vibrational noise affects plant development and, consequently, plant–insect interactions. Here, we examine the impact of windmill-like vibrational noise on the growth of Pisum sativum and its full-factorial interaction with the generalist herbivore Spodoptera exigua. Plants were exposed to either high or low vibrational noise from seed germination to the seed production stage. We recorded germination, flowering, fruiting time, and daily shoot length. Additionally, we measured herbivory intensity by Spodoptera exigua caterpillars placed on a subset of plants. Plants exposed to high vibrational noise grew significantly faster and taller than those in the low-noise treatment. Additionally, we found a marginally significant trend for earlier flowering in plants exposed to high noise. We did not find a significant effect of vibrational noise on herbivory. Our results suggest that underground vibrational noise can influence plant growth rates, which may potentially have ecological and agricultural implications. Faster growth may alter interspecific competition and shift trade-offs between growth and defense. Understanding these effects is important in assessing the broader ecological consequences of renewable energy infrastructure. Full article

Background: Gastric cancer (GC) is a malignant tumor of the gastrointestinal tract, characterized by high mortality rates and responsible for about one million new cases each year globally. Surgery is the main treatment, but achieving radical resection remains a relevant intraoperative challenge. Fluorescence-guided surgery offers clinicians greater capabilities for real-time detection of tumor nodules and visualization of tumor margins. In this field, the main challenge remains the development of fluorescent dyes that can selectively target tumor tissues. Methods: we examined the expression of the most suitable GC markers, including carcinoembryonic antigen cell adhesion molecule-5 (CEACAM5) and Claudin-4 (CLDN4), in GC cell lines. To further evaluate their expression, we performed immunohistochemistry (IHC) on tumor and healthy tissue samples from 30 GC patients who underwent partial gastrectomy at the Digestive System Surgery Unit, AOU Careggi, Florence. Additionally, we validated anti-CEACAM5 expression on patient-derived organoids. Furthermore, we developed a fluorescent molecule targeting CEACAM5 on the surface of GC cells and assessed its binding properties on patient tissue slices and fragments. Results: in this work, we first identified CEACAM5 as an optimal GC biomarker, and then we developed a fluorescent antibody specific for CEACAM5. We also evaluated its binding specificity for GC cell lines and patient-derived tumor tissue, achieving an optimal ability to discriminate tumor tissue from healthy mucosa. Conclusions: Overall, our results support the development of our fluorescent antibody as a promising tumor-specific imaging agent that, after further in vivo validation, could improve the accuracy of complete tumor resection. Full article

The control of following motion under mesoscale gap flow fields has important applications. The flexible characteristics of the plant, wideband time-varying disturbances caused by the flow field, and requirements of high precision and low overshoot make achieving submicron level accuracy a significant challenge for traditional control methods. This study adopts the control concept of Disturbance Observer Control (DOBC) and uses H_∞ mixed-sensitivity shaping technology to design a Q-filter. Simultaneously, multiple control techniques, such as high-order reference trajectory planning, Proportional-Integral-Derivative (PID) control, low-pass filtering, notch filtering, lead lag correction, and disturbance rejection filtering, are applied to obtain a control system with a high open-loop gain, sufficient phase margin, and stable closed-loop system. Compared to traditional control methods, the new method can increase the open-loop gain by 15 times and the open-loop bandwidth by 8%. We even observed a 150-time increase of the open-loop gain at the peak frequency. Ultimately, the method achieves submicron level accuracy, making important advances in solving the control problem of semiconductor equipment. Full article

Throughout the generations, traditional academic writing skills development has taught students in socio-human programs to express their knowledge and thoughts with an evidence-based foundation, helping them make a special connection with their professional fields. However, a lack of digital learning and writing resources in this process has been identified. This study of the social work field connects digital academic writing, social protection functionality, and research innovations, identifying and exploring open-access (OA) educational and social resources for social work higher education (SWHE). Applying content analyses to online documents and websites, we identified key terms characteristic of social work, following a standard approach on formulating research questions, identifying categories, creating a code book, sampling, and measuring information. The research questions were as follows: How is digital academic writing being developed in social work education programs in Romanian universities? Where do researchers, students, teachers, and professionals gather OA digital information and data for academic innovation? What kind of OA information and data are contained in websites for academic writing? We also used OA socio-educational resource analysis to derive digital, evidence-based, and academic writing codes. The frequencies of these elements in documents and websites were examined. Professional samples of four OA documents and five academic and non-academic Romanian websites with extensions were processed. Furthermore, information from a non-academic official website concerning social protection functionality was observed, identified, and measured. We concluded that academic writing is not included as an independent course in the curricula of Romanian social work programs at universities; this topic is rarely researched. Digital and evidence-based education is also a marginalized topic in socio-human scientific resources. OA information, laws, reports, and statistics were identified. Information on scientific research, academic–non-academic partnerships, descriptions of good practices, and human resources information was lacking. In conclusion, this study contributes to increasing productivity and developing digital academic skills in social work education and research. Full article

This study developed an Energy Quadrilemma Index (EQI) for Latin American countries, analyzing data from six countries from 2014 to 2020. Using the Principal Component Analysis method, this work reduced the dimensionality of 20 indicators grouped into four dimensions: energy security, energy equity, sustainable development, and a new social context axis. The results reveal significant disparities among the countries in the study. For example, Uruguay shows robust indicators, Paraguay exhibits low utilization of the energy it produces, and Chile displays the poorest results in the sustainable development axis. Many countries’ widespread dependence on hydroelectricity makes them vulnerable to water crises. The results show that social, economic, and structural inequalities represent the main barriers to the energy transition, often marginalizing low-income populations. Ensuring a fair and inclusive transition requires implementing targeted policies and solutions adapted to each country’s specific context. Although Costa Rica leads in performance, it faces significant challenges in the field of sustainability. In contrast, Honduras has made some progress with sustainable development but still demonstrates weaknesses in other areas. These results highlight that standardized solutions can exacerbate regional inequalities, demanding approaches more tailored to local needs. This work’s novelty lies in the use of the social context dimension as a feature to assess energy poverty in selected countries. Full article

The hub location problem is constantly examined in the field of air transportation, especially when designing networks for passenger airlines or express cargo providers. The competition that characterizes these businesses combined with the small benefit margins of the industry puts more pressure on finding innovative optimization tools when designing networks, locating hubs, and opening new routes with the minimum cost, usually under strict capacity constraints. This review covers the hub location problem in air transportation and its different mathematical models in preparation for a detailed SLR. Full article