Search Results (212)

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

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

Background and Aim: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with poor prognosis despite maximal surgical resection, radiotherapy (RT), and temozolomide (TMZ) per the Stupp protocol. IDH-wild-type GBM, the predominant molecular subtype, frequently harbors EGFR amplification and is resistant to therapy, while MGMT promoter methylation predicts improved TMZ response. This study aimed to assess the prognostic impact of EGFR and MGMT status on survival and recurrence patterns in IDH-wild-type GBM. Materials and Methods: We retrospectively analyzed 218 patients with IDH-wild-type GBM treated at the Azienda Ospedaliero-Universitaria Senese (2016–2024). All patients underwent maximal safe surgical resection whenever feasible. The cohort includes patients who received gross total resection (GTR), subtotal resection (STR), or biopsy only, depending on tumor location and clinical condition, followed by intensity-modulated RT (59.4–60 Gy) with concurrent and adjuvant TMZ. EGFR amplification was assessed via FISH/NGS and immunohistochemistry; MGMT promoter methylation was determined using methylation-specific PCR. Progression-free survival (PFS), overall survival (OS), and recurrence patterns (in-field, marginal, out-field) were evaluated using Kaplan–Meier, Cox regression, and logistic regression analyses. Results: Among patients (64.7% male; mean age 61.8), 58.7% had EGFR amplification and 49.1% showed MGMT methylation. Median OS and PFS were 14 and 8 months, respectively. EGFR non-amplified/MGMT methylated tumors had the best outcomes (OS: 22.0 months, PFS: 10.5 months), while EGFR-amplified/MGMT unmethylated tumors fared worst (OS: 10.0 months, PFS: 5.0 months; p < 0.001). MGMT methylation was an independent positive prognostic factor (HR: 0.48, p < 0.001), while EGFR amplification predicted worse survival (HR: 1.57, p = 0.02) and higher marginal recurrence (OR: 2.42, p = 0.01). Conclusions: EGFR amplification and MGMT methylation significantly influence survival and recurrence dynamics in IDH-wild-type GBM. Incorporating these biomarkers into treatment planning may enable tailored therapeutic strategies, potentially improving outcomes in this challenging disease. Prospective studies are needed to validate biomolecularly guided management approaches. Full article

In a two-year experiment, we examined whether increasing plant diversity in the margins of processing tomato fields could attract pollinators and natural enemies of pests compared to weed flora, and questioned the effect on crop yield. Two plant mixtures sown in winter (WM) and spring (SM) were compared with weed vegetation along a tomato crop (CT) and an adjacent irrigation channel (CC). Flower cover was higher in the sown mixtures than the weedy margins, and brought in more visits of pollinating bees (including potential tomato pollinators) than the latter. Flowering species were mainly Eruca vesicaria (WM, SM), Coriandrum sativum and Lathyrus sativus (WM), Fagopyron esculentum and Phacelia tanacetifolia (SM), and Ammi majus, Rapistrum rugosum (CC, CT). Parasitoids (Eulophidae, Braconidae, Scelionidae) were more abundant in the sown and CC margins compared to the CT margin, while the abundance of predators (Aeolothripidae, Orius sp., Thomisidae) was similar among all types of margins. Fruit weight was higher in the field with the sown margins, while pest incidence in the crop was not affected by the margin type. Our findings provide new insights into the contribution of managed and existing field margins in attracting beneficial arthropods, and their implications on yield. Full article

Urban space is the social field in which religious diversity in contemporary Italy becomes most evident and where religious groups compete for visibility, recognition, and places of worship. The sites of so-called minorities can be observed as indicators of a plural religious geography. Peaceful and conflictual dynamics are both expressed precisely through external recognition, which may be horizontal—religious and social—when between peers or vertical therefore juridical. This study presents the findings of research conducted in the city of Turin, an emblematic case within the Italian religious landscape for the management of religious diversity and interreligious dialogue initiatives. The analysis focuses on the Romanian Orthodox Church located in the historic center, which we interpret as a shared religious place. This case shows how a spatial and material perspective can offer an innovative approach to the field of interreligious dialogue. Places of worship are crucial spaces for interreligious dialogue: they serve as laboratories of local peace-building and experiments in coexistence, mutual respect, encounter, and conflict mediation. However, in a frame of multiple secularities, the ambiguity of both the national and regional legal systems contribute to marginality of the religious dimension in the city’s urban planning policies, ignoring the important role these places play as spaces of cohesion, identity, inclusion. Full article

Mineral nutrition management in sweet cherry orchards remains a critical challenge due to the lack of site-specific fertilization guidelines, particularly in Greece, a significant cherry-producing country. This study aimed to develop a predictive framework for total nutrient losses in sweet cherry orchards by proposing simplified estimations using fresh fruit yield as the sole input variable. Field experiments were conducted in two orchards with distinct rootstocks (MxM 14 and CAB-6P), analyzing soil properties, leaf nutrient status, and uptake patterns on different plant components. Results indicated that despite differences in soil texture and pH, nutrient availability was generally sufficient, with only Fe and Zn marginally below optimal levels in leaf tissue. Principal Component Analysis (PCA) revealed distinct nutrient distribution patterns, with N evenly distributed across fruits, peduncles, and prunings, while K was concentrated in fruits and peduncles, and Ca and Mg predominantly in fallen leaves. Notably, K was redistributed from leaves to fruits under high yields, evidenced by negative correlations between leaf biomass and K uptake. Strong relationships (r² > 0.8) were found between fresh fruit yield and uptake of N, P, K, Mg, B, and Cu, enabling reliable predictions of total nutrient losses. Estimated annual nutrient removals were 85.6 kg ha⁻¹ N, 8.94 kg ha⁻¹ P, 42.7 kg ha⁻¹ K, and 12.0 kg ha⁻¹ Mg, with significant fractions retained in prunings and fallen leaves (e.g., 51.8 kg ha⁻¹ N, 6.2 kg ha⁻¹ P). The developed yield-based models provide a practical tool for optimizing fertilization strategies, while our findings highlight the potential for nutrient recycling through sustainable residue management. Full article

Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production. Full article

Perihilar cholangiocarcinoma (pCCA) is the most common subtype of a rare malignancy arising from the biliary tract. Its challenging diagnosis results in delayed treatment, most often when the disease is locally advanced or widespread. Management includes surgery followed by systemic chemotherapy; however, a negative resection margin (R0) is the mainstay for achieving an adequate survival benefit, in the absence of metastatic disease. While minimally invasive surgery (MIS) initially is adopted across every surgical field, laparoscopy’s inherent limitations hinder its implementation for the treatment of pCCA and results in skepticism even for the robotic approach. However, since its initial feasibility phase fifteen years ago, comparable results to open surgery have been reported regarding its safety and oncologic outcomes, in highly selected patients. Moreover, the robotic approach seems to be associated with favorable outcomes regarding post-operative complications, length of hospital stay, and estimated blood loss. International guidelines for the diagnosis and management of pCCA, centralization, definition of a learning curve for MIS, and more comparative studies assessing long term outcomes and randomization are key elements to ensure patient’s safety and technical efficiency. The aim of our review is to provide an updated perspective of the existing literature in the utilization of MIS for patients with pCCA. Full article

Water conservation is critical for global maize production, particularly in arid regions where water scarcity, exacerbated by climate change, threatens conventional irrigation sustainability. Optimizing irrigation strategies to reconcile water productivity and yield remains a key scientific challenge in water-limited agriculture. This four-year study (2018–2021) evaluated integrated irrigation management that combined frequency and volume adjustments. A field experiment compared three strategies: high-frequency limited irrigation (HL: 2400 m³·hm⁻²), low-frequency conventional irrigation (LC: 2400 m³·hm⁻²), and high-frequency conventional irrigation (HC: 4800 m³·hm⁻²). The four-year mean yield showed that HL (10,793.78 kg·hm⁻²) had a non-significant 18.2% numerical advantage over LC (9129.11 kg·hm⁻², p > 0.05). The WUE for HL reached 3.63 kg·m⁻³, representing an 18.6% numerical increase compared to LC (3.06 kg·m⁻³; p > 0.05). Physiological parameters (plant height + 2.6%, leaf area + 9.9%, SPAD + 1.5%) showed marginal improvements in HL, yet lacked both statistical significance (p > 0.05) and strong yield correlation. Multi-year analyses confirmed no statistically distinguishable differences between strategies (p > 0.05), demonstrating that irrigation frequency adjustments alone cannot reliably enhance drought resilience. These findings caution against advocating for HL as a superior practice, given the statistical equivalence between HL and LC despite water savings, and the non-significant yield gap between HL and HC. Future research must establish causality through models integrating real-time soil–crop–climate feedback prior to recommending altered irrigation regimes. Full article

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning. Full article

Radiomic feature (RF) analysis of computed tomography (CT) images may aid the diagnosis and staging of ovine pulmonary adenocarcinoma (OPA). We assessed the RF characteristics of OPA tumours in JSRV-infected sheep compared to non-tumour lung tissues, examined their stability over time, and analysed RF variations in the nascent tumour field (NTF) and nascent tumour margin field (NTmF). In monthly CT scans, lung tissues were automatically segmented by density, and lung tumours were manually segmented. RFs were calculated for each imaging session, selected according to stability and reproducibility, and adjusted for volume dependence where appropriate. Comparisons between scans within sheep were facilitated through fiducial registration and spatial transformations. Initially, 9/36 RFs differed significantly from non-tumour lung tissue of similar density. Predominant RF changes included ngtdm_Complexity, glrlm_RunLNUnif_VN, and gldm_SmDHGLE. RFs in lung tumour segments showed time-dependent changes, whereas non-tumour lung tissue of similar density remained consistent. OPA lung tumour RF characteristics are distinct from those of other lung tissues of similar density and evolve as the tumour develops. Such characteristics suggest that radiomic analysis offers potential for the early detection and management of JSRV-related lung tumours. This research enhances the understanding of OPA imaging, potentially informing better diagnosis and control measures for naturally occurring infections. Full article

Bacterial contamination of the surgical site in horses is a major risk factor for the development of surgical site infections (SSIs), which increase morbidity, mortality, the hospitalisation period, antibiotic use, and management costs. While contamination is a prerequisite for infection, its progression to clinical infection depends on additional factors that compromise host defences. The present study, conducted at the Veterinary Teaching Hospital of the University of Perugia over an 11-month period, investigated bacterial contamination in 70 surgeries (53 clean and 17 clean-contaminated) at the end of the procedure. To exclude pre-existing contamination, a sterile swab was collected after surgical scrub, and only cases that entered surgery with a sterile operative field were considered. A swab, biopsy, and fine-needle aspiration from the wound margins were then performed at the end of the surgery to conduct a qualitative assessment of the bacterial contamination of the surgical sites. Risk factors for surgical field contamination were analysed separately for clean and clean-contaminated procedures. Specifically, for clean-contaminated surgeries, the presence of emergency conditions, surgery duration, and intra-operative complications were evaluated. For clean surgeries, risk factors included the type of operating room, surgical duration, tissue involved, use of local anaesthetics, and placement of surgical drapes. The results revealed bacterial contamination rates of 49.1% in clean surgeries and 41.2% in clean-contaminated surgeries. Coagulase-negative staphylococci were the most frequently isolated bacteria, followed by Burkholderia cepacia, Bacillus sp., and Stenotrophomonas maltophilia. A statistical analysis showed no significant results on the predictive factors of the contamination evaluated. However, the observed trends suggest the importance of further investigating these risk factors in a larger sample size. These results emphasise the importance of effective prophylactic measures to limit surgical site contamination. Future research will focus on optimising pre-operative and intra-operative prophylaxis strategies to reduce bacterial contamination to sub-pathogenic levels, thereby enhancing post-operative outcomes. Full article

This research makes a strong focus on improving fluid dynamics inside the reservoir after stimulation for enhancing oil and gas well performance, particularly in terms of increasing the Gas–oil ratio (GOR) and injectivity leading to a better productivity index (PI). Advanced stimulation operation using new formulated emulsified acid treatment greatly improves the reservoir permeability, allowing for better fluid movement and less formation damage. This, in turn, results in injectivity increases of at least 2.5 times and, in some situations, up to five times the original rate, which is critical for sustaining reservoir pressure and ensuring effective hydrocarbon recovery. The emulsified acid outperforms typical 15% HCl treatments in terms of dissolving and corrosion rates, as it is tuned for the reservoir’s pressure, temperature, permeability, and porosity. This dual-phase technology increases injectivity by five times while limiting the environmental and material consequences associated with spent and waste acid quantities. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficient penetration within the rock pores to enhance oil flow and sweep. This increase in performance is linked to the creation of the wormholing impact of the emulsified acid, resulting in improved fluid dynamics and optimized reservoir efficiency, as shown by the enhanced gas–oil ratio (GOR) in the four mentioned cases. A critical component of attaining such improvements is the capacity to effectively analyze and forecast reservoir behavior prior to executing the stimulation in real life. Engineers can accurately forecast injectivity gains and improve fluid injection tactics by constructing an advanced predictive model with low error margins, decreasing the need for time-consuming and costly trial-and-error approaches. Importantly, the research utilizes sophisticated neural network modeling to forecast stimulation results with minimal inaccuracies. This predictive ability not only diminishes the dependence on expensive and prolonged trial-and-error methods but also enables the proactive enhancement of treatment designs, thereby increasing efficiency and cost-effectiveness. This modeling approach based on several operational and reservoir factors, combines real-time field data, historical well performance records, and fluid flow simulations to verify that the expected results closely match the actual field outcomes. A well-calibrated prediction model not only reduces uncertainty but also improves decision making, allowing operators to create stimulation treatments based on unique reservoir features while minimizing unnecessary costs. Furthermore, enhancing fluid dynamics through precise modeling helps to improve GOR management by keeping gas output within appropriate limits while optimizing liquid hydrocarbon recovery. Finally, by employing data-driven modeling tools, oil and gas operators can considerably improve reservoir performance, streamline operational efficiency, and achieve long-term production growth through optimal resource usage. This paper highlights a new approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. The use of real-time monitoring has boosted the study by enabling for exact measurement of post-injectivity performance and oil flow rates, hence proving the efficacy of these advanced stimulation approaches. The study offers unique insights into unconventional reservoir growth by combining numerical modeling, real-world data, and novel treatment methodologies. The aim is to investigate novel simulation methodology, advanced computational tools, and data-driven strategies for improving the predictability, reservoir performance, fluid behavior, and sustainability of heavy oil recovery operations. Full article

This study investigated and compared the spatiotemporal evolution and driving factors of groundwater storage anomalies (GWSAs) under the dual pressures of climate change and urban expansion in two contrasting river basins of China. Integrating GRACE and GLDAS data with multi-source remote sensing data and using attribution analysis, we reveal divergent urban GWSA dynamics between the humid Yangtze River Basin (YZB) and semi-arid Yellow River Basin (YRB). The GWSAs in YZB urban grids showed a marked increasing trend at 3.47 mm/yr (p < 0.05) during 2002–2020, aligning with the upward patterns observed in agricultural land types including dryland and paddy fields, rather than exhibiting the anticipated decline. Conversely, GWSAs in YRB urban grids experienced a pronounced decline (−5.59 mm/yr, p < 0.05), exceeding those observed in adjacent dryland regions (−5.00 mm/yr). The contrasting climatic regimes form the fundamental drivers. YZB’s humid climate (1074 mm/yr mean precipitation) with balanced seasonality amplified groundwater recharge through enhanced surface runoff (+6.1%) driven by precipitation increases (+7.4 mm/yr). In contrast, semi-arid YRB’s water deficit intensified, despite marginal precipitation gains (+3.5 mm/yr), as amplified evapotranspiration (+4.1 mm/yr) exacerbated moisture scarcity. Human interventions further differentiated trajectories: YZB’s urban clusters demonstrated GWSA growth across all city types, highlighting the synergistic effects of urban expansion under humid climates through optimized drainage infrastructure and reduced evapotranspiration from impervious surfaces. Conversely, YRB’s over-exploitation due to rapid urbanization coupled with irrigation intensification drove cross-sector GWSA depletion. Quantitative attribution revealed climate change dominated YZB’s GWSA dynamics (86% contribution), while anthropogenic pressures accounted for 72% of YRB’s depletion. These findings provide critical insights for developing basin-specific management strategies, emphasizing climate-adaptive urban planning in water-rich regions versus demand-side controls in water-stressed basins. Full article