Recent studies have highlighted the lipid-lowering ability of hawthorn ethanol extract (HEE) and the role played by gut flora in the efficacy of HEE. Our study sought to explore the effects of HEE on non-alcoholic fatty liver disease (NAFLD) in normal flora and pseudo germ-free mice. The results showed that HEE effectively diminished hepatic lipid accumulation, ameliorated liver function, reduced inflammatory cytokine levels and blood lipid profiles, and regulated blood glucose levels. HEE facilitated triglyceride breakdown, suppressed fatty acid synthesis, and enhanced intestinal health by modulating the diversity of the gut microbiota and the production of short-chain fatty acids in the gut. In addition, HEE apparently helps to increase the presence of beneficial genera of bacteria, thereby influencing the composition of the gut microbiota, and the absence of gut flora affects the efficacy of HEE. These findings reveal the potential of hawthorn for the prevention and treatment of NAFLD and provide new perspectives on the study of functional plants to improve liver health. Full article

Nutrition periodization in football training is an important determinant of adaptation to cyclic training loads. Personalizing an athlete’s diet is crucial to ensure optimal performance and body composition, depending on the phase of training. The purpose of this review is to answer the question of how the body composition of football players changes over the training macrocycle and how dietary recommendations should be tailored to specific training periods. The review of scientific evidence was conducted based on the available literature, typing in phrases related to training and nutrition periodization using the PubMed and Google Scholar database methodology tools. A literature search resulted in the selection of 346 sources directly related to the topic of the study, and then those with the highest scientific value were selected. There is a need to adjust energy and nutrient intake according to the different training phases in a football player’s preparation cycle. During the preparatory phase, it is recommended to increase protein and energy intake to support anabolic processes and muscle mass development. During the competitive period, due to the intensity of matches and training, the importance of carbohydrates for glycogen replenishment and recovery is emphasized. The transition phase requires the regulation of caloric intake to prevent adverse changes in body composition. Hydration has been identified as a key element in each phase of training. Cooperation between coaches, nutritionists, and players is essential to optimize sports performance and rapid recovery, and the authors recommend continuous adaptation and nutritional optimization as an integral part of football training. Full article

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment. Full article

An Nd:YAG-based Raman lidar provides a mature technology to derive profiles of the optical properties of aerosols over a wide altitude range. However, the derivation of micro-physical parameters is an ill-posed problem. Hence, increasing the information content of lidar data is desirable. Recently, ceilometers and wind lidar systems, both operating in the near-infrared region, have been successfully employed in aerosol research. In this study, we demonstrate that the inclusion of additional backscatter coefficients from these two latter instruments clearly improves the inversion of micro-physical parameters such as volume distribution function, effective radius, or single-scattering albedo. We focus on the Arctic aerosol and start with the typical volume distribution functions of Arctic haze and boreal biomass burning. We forward calculate the optical coefficients that the lidar systems should have seen and include or exclude the backscatter coefficients of the ceilometer (910 nm) and wind lidar data (1500 nm) to analyze the value of these wavelengths in their ability to reproduce the volume distribution function, which may be mono- or bimodal. We found that not only the coarse mode but also the properties of the accumulation mode improved when the additional wavelengths were considered. Generally, the 1500 nm wavelength has greater value in correctly reproducing the aerosol properties. Full article

The ionosphere equatorial ionization anomaly (EIA) is usually characterized by two plasma density maxima in the Earth’s equatorial region. Merged EIA (MEIA) is a unique phenomenon in the evolution of the EIA. Currently, the occurrence characteristics of MEIA are still not well understood. In this study, we investigate the occurrence characteristics of nighttime MEIA using NASA Global-scale Observations of the Limb and Disk (GOLD) observations between October 2018 and the end of 2023. We found that the occurrence of nighttime MEIA exhibits solar cycle, seasonal, and local time variations. The occurrence rate of the MEIA is inversely dependent on solar activity. Occurrence of the MEIA maximizes near the equinoxes, with a primary (secondary) low occurrence rate near the June (December) solstice. In addition, occurrences of the MEIA are suppressed during the pre-reversal enhancement (PRE), resulting in relatively fewer events. Furthermore, it was found that the occurrence of the MEIA is not significantly dependent on the strength of geomagnetic activity. As far as we know, this study represents the first instance of utilizing observations from GOLD observations to investigate the characteristics of MEIA occurrences and their correlations with solar activity, season, and local time. Full article

Ribonuclease P (RNase P) complexed with an external guide sequence (EGS) represents a promising nucleic acid-based gene targeting approach for gene expression knock-down and modulation. The RNase P-EGS strategy is unique as an EGS can be designed to basepair any mRNA sequence and recruit intracellular RNase P for hydrolysis of the target mRNA. In this study, we provide the first direct evidence that the RNase P-based approach effectively blocks the gene expression and replication of herpes simplex virus 2 (HSV-2), the causative agent of genital herpes. We constructed EGSs to target the mRNA encoding HSV-2 single-stranded DNA binding protein ICP8, which is essential for viral DNA genome replication and growth. In HSV-2 infected cells expressing a functional EGS, ICP8 levels were reduced by 85%, and viral growth decreased by 3000 folds. On the contrary, ICP8 expression and viral growth exhibited no substantial differences between cells expressing no EGS and those expressing a disabled EGS with mutations precluding RNase P recognition. The anti-ICP8 EGS is specific in targeting ICP8 because it only affects ICP8 expression but does not affect the expression of the other viral immediate-early and early genes examined. This study shows the effective and specific anti-HSV-2 activity of the RNase P-EGS approach and demonstrates the potential of EGS RNAs for anti-HSV-2 applications. Full article

The dried Mume fructus (MF) is a special food and herbal medicine with a long history of processing and application. The browning index (BI) of Prunus mume (PM) is pivotal in determining the medicinal value and edible quality of MF. Exploring the BI of PM holds significant importance for both the selection of PM varieties and understanding the formation mechanism of high-quality MF. This study systematically analyzed the physicochemical properties, BI, and quality indicators of four PM varieties (Qingzhu Mei, Yesheng Mei, Nangao Mei, and Zhaoshui Mei) after processing into MF. The results showed significant differences in eight physicochemical indicators among the four PM varieties (p < 0.05). Notably, Qingzhu Mei exhibited the highest titratable acid content, Nangao Mei had the most prominent soluble solid and soluble sugar content, and Zhaoshui Mei showed outstanding performance in reducing sugar, soluble protein, and free amino acids. Regarding drying characteristics, Yesheng Mei and Nangao Mei required a shorter drying time. In terms of BI, Nangao Mei exhibited the greatest degree of browning and its color appearance was darker. When considering quality evaluation, Nangao Mei excelled in rehydration ability and extract content, while Zhaoshui Mei demonstrated outstanding levels of total phenols, total flavonoids, and total antioxidant capacity. Overall, the evaluation suggested that the Nangao Mei variety was more suitable for MF processing. These research results provide a valuable theoretical foundation for understanding the BI of PM varieties and serve as a reference for the selection of PM varieties as raw materials for processing MF. Full article

Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers’ viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers’ behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers’ behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers’ perspectives and intentions regarding GenAI technology. Full article

In the context of an aging population and escalating work pressures, cardiovascular diseases pose increasing health risks. Electrocardiogram (ECG) monitoring presents a preventive tool, but conventional devices often compromise comfort. This study proposes an approach using Ag NW/TPU composites for flexible and breathable epidermal electronics. In this new structure, TPU fibers are used to support Ag NWs/TPU nanocomposites. The TPU fiber-reinforced Ag NW/TPU (TFRAT) nanocomposites exhibit excellent conductivity, stretchability, and electromechanical durability. The composite ensures high steam permeability, maintaining stable electrical performance after washing cycles. Employing this technology, a flexible ECG detection system is developed, augmented with a convolutional neural network (CNN) for automated signal analysis. The experimental results demonstrate the system’s reliability in capturing physiological signals. Additionally, a CNN model trained on ECG data achieves over 99% accuracy in diagnosing arrhythmias. This study presents TFRAT as a promising solution for wearable electronics, offering both comfort and functionality in long-term epidermal applications, with implications for healthcare and beyond. Full article

The right heart catheterisation constitutes the gold standard for pulmonary hypertension (PH) diagnosis. However, echocardiography remains a reliable, non-invasive, inexpensive, convenient, and easily reproducible modality not only for the preliminary screening of PH but also for PH prognosis. The aim of this review is to describe a cluster of echocardiographic parameters for the detection and prognosis of PH and analyse the challenges of echocardiography implementation in patients with suspected or established PH. The most important echocardiographic index is the calculation of pulmonary arterial systolic pressure (PASP) through the tricuspid regurgitation (TR). It has shown high correlation with invasive measurement of pulmonary pressure, but several drawbacks have questioned its accuracy. Besides this, the right ventricular outflow track acceleration time (RVOT-AT) has been proposed for PH diagnosis. A plethora of echocardiographic indices: right atrial area, pericardial effusion, the tricuspid annular plane systolic excursion (TAPSE), the TAPSE/PASP ratio, tricuspid annular systolic velocity (s′), can reflect the severity and prognosis of PH. Recent advances in echocardiography with 3-dimensional right ventricular (RV) ejection fraction, RV free wall strain and right atrial strain may further assist the prognosis of PH. Full article

Within the context of global initiatives to address climate change, the shipping industry is facing increasingly intensified pressure to decarbonize. The industry is engaging in the exploration and implementation of greenhouse gas (GHG) emission reduction measures, including energy efficiency technologies and alternative fuels, with the objective of accelerating the progression towards greenhouse gas mitigation. The application of various GHG emission reduction measures usually requires different levels of investment costs, and economic feasibility is a key factor influencing policy formulation and investment decisions. In this regard, this paper developed a cost-effective model for energy efficiency measures and alternative fuels based on the marginal abatement cost (MAC) methodology. This model can distinguish the differences between energy efficiency measures and alternative fuels in terms of Tank-to-Wake emissions and Well-to-Wake emissions in the GHG emission evaluation system. By taking typical ship types with significant emission contributions as study cases, i.e., bulk carriers (61–63K DWT), container ships (8000 TEU), product tankers (115K DWT), crude oil tankers (315–320K DWT), and Ro-Ro passenger ferries (3500 DWT), the GHG abatement cost-effective performance of major categories of measures such as operational measures, technical measures, renewable energy sources, and alternative fuels were calculated. According to the MAC results, the marginal abatement cost curves were plotted based on the ranking of energy efficiency measures and alternative fuels, respectively. The impacts of bunker fuel prices and carbon market prices on the cost-effectiveness were analyzed. The research results provided the GHG abatement potential of the integrated application of cost-effective energy efficiency measures, the cost-effectiveness ranking of alternative fuels, and the carbon emission price expected to bridge the price gap between alternative fuels and conventional bunker fuel. The presented methodology and conclusions can be used to assist shipping companies in selecting emission reduction measures, and to support maritime authorities in developing market-based measures. Full article

The severity of global climate change is a pressing issue, and carbon emission reduction requires collaboration between producers and consumers. Carbon emission responsibility accounting is critical for distributing the tasks associated with carbon reduction. To examine the current research status and future development trends of carbon emission responsibility accounting, we used the scientific quantitative knowledge graph method and CiteSpace software. We analyzed the data from 4089 studies retrieved from the Web of Science and China National Knowledge Infrastructure databases, focusing on various aspects such as the number of published papers, subjects, research focuses, research content, and future research directions. In 2022, the number of publications was 657. The largest number of published carbon emission responsibility accounting documents was published by scholars from China: there were 708 published articles, 35% of the 2002 articles published in the Web of Science. The reason may be that China, as a developing country, produces more carbon emissions. In order to actively shoulder international responsibilities and slow down global warming, China strengthened its research on carbon emission responsibility, the basic work on carbon emission reduction. This was followed by the United States and England. England showed high levels of research collaboration. “Environmental science” was one of the main subjects in the Web of Science database, representing 43.96% of total publications. Research focuses included input–output analyses and implied carbon export trade. This study summarizes the literature on carbon emission responsibility accounting in terms of research perspectives, accounting principles, and accounting methods. In the future, the accounting of carbon emission responsibility at the city level considering the carbon emission responsibility sharing method including three or more shared parties and accounting for the carbon emission responsibility between the upstream and downstream from the perspective of the industrial chain can be studied. The findings of this study provide guidance to researchers and policymakers for the progression and enhancement of carbon emission responsibility accounting. Full article

Short-range elastic backscatter lidar (SR-EBL) systems are remote sensing instruments for studying low atmospheric boundary layer processes. This work presents a field campaign oriented to filling the gap between the near-surface aerosol processes regarding aerosol radiative properties and connecting them with the atmospheric boundary layer (ABL), centering attention on the residual layer and the ABL transition periods. A Colibri Aerosol Lidar (CAL) instrument, based on the short-range lidar with high spatio-temporal resolution, was used for the first time in the ACTRIS AGORA facility (Andalusian Global Observatory of the Atmosphere) in Granada (Spain). This study showed the possibility of combining lidar and in situ measurements in the lowermost 150 m. The results address, on the one hand, the characterization of the short-range lidar for developing a method to find the calibration constant of the system and to correct the incomplete overlap to further data exploitation. On the other hand, relevant radiative properties such as the temporal series of the aerosol lidar ratio and extinction coefficient were quantified. The campaign was divided in three different periods based on the vehicular emission peak in the early mornings, namely, before, during, and after the emission peak. For before and after the emission peak data classification, aerosol properties presented closer values; however, large variability was obtained after the emission peak reaching the maximum values of extinction and a lidar ratio up to 51.5 ± 11.9 ${\left(\mathrm{Mm}\right)}^{-1}$ and 36.0 ± 10.5 sr, respectively. During the emission peaks, the values reached for extinction and lidar ratio were up to 136.8 ± 26.5 ${\left(\mathrm{Mm}\right)}^{-1}$ and 119.0 ± 22.7 sr, respectively. Full article

Neuroblastoma presents with two patterns of disease: locoregional or systemic. The poor prognostic risk factors of locoregional neuroblastoma (LR-NB) include age, MYCN or MDM2-CDK4 amplification, 11q, histology, diploidy with ALK or TERT mutations, and ATRX aberrations. Anti-GD2 immunotherapy has significantly improved the outcome of high-risk (HR) NB and is mostly effective against osteomedullary minimal residual disease (MRD), but less so against soft tissue disease. The question is whether adding anti-GD2 monoclonal antibodies (mAbs) benefits patients with HR-NB compounded by only soft tissue. We reviewed 31 patients treated at SJD for HR-NB with no osteomedullary involvement at diagnosis. All tumors had molecular genetic features of HR-NB. The outcome after first-line treatment showed 25 (80.6%) patients achieving CR. Thirteen patients remain in continued CR, median follow-up 3.9 years. We analyzed whether adding anti-GD2 immunotherapy to first-line treatment had any prognostic significance. The EFS analysis using Cox models showed a HR of 0.20, p = 0.0054, and an 80% decrease in the risk of relapse in patients treated with anti-GD2 immunotherapy in the first line. Neither EFS nor OS were significantly different by CR status after first-line treatment. In conclusion, adding treatment with anti-GD2 mAbs at the stage of MRD helps prevent relapse that unequivocally portends poor survival. Full article

With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance–transmission and fluorescence excitation–emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018–2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending. Full article

Recovering cobalt from waste batteries is crucial for resource recycling and environmental protection. Here, MOF-OH, a Zr-based MOF, was synthesized and merged into a polyacrylonitrile (PAN) matrix to create MOF-OH-PAN nanofibers (NFs). These NFs showed a high cobalt ion adsorption capacity of 33.1 mg/g, retaining over 90% of the capacity after six cycles. The adsorption mechanism involves Co(II) surface diffusion followed by strong bonding with functional groups. This technology enables efficient cobalt recovery from waste batteries, supporting reuse and reducing resource depletion and environmental pollution. The study provides insights into waste battery resource management, highlighting environmental and economic benefits and contributing to green resource recovery and circular economy initiatives. Full article

The utilization of Effective Microorganisms (EMs) for lake restoration represents a sustainable approach to enhancing water quality and rebalancing the ecology of aquatic ecosystems. The primary objective of this study was to evaluate the effects of two bioremediation treatment cycles employing EM-enriched biopreparations on water quality in the Siemiatycze lakes. Specifically, this research analyzed various parameters, including dissolved oxygen, transparency, chlorophyll-a, pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, and suspended matter (SM), across eleven designated sampling locations. Additionally, this study employed remote sensing techniques, leveraging Sentinel-2 satellite imagery and the Maximum Chlorophyll Index (MCI), to detect and quantify algal blooms, with a particular focus on elevated chlorophyll-a concentrations. This comprehensive approach aimed to provide a holistic understanding of the impact of biotechnological reclamation on aquatic ecosystem restoration and sustainability. The study’s findings indicated a significant improvement in water quality in all lakes, with enhanced water clarity and oxygen profiles. Further, remote sensing studies indicated a reduction in algal blooms, particularly those with high chlorophyll-a concentrations. A considerable decrease in water eutrophication intensity was observed due to diminished nutrient concentrations. The improvements in water parameters are likely to enhance the living conditions of aquatic organisms. These results demonstrate the effectiveness of using EM-enriched biopreparations in the bioremediation of lakes, providing a sustainable approach to enhancing water quality and balancing aquatic ecosystems. Full article

The treatment of large amounts of copper slag is an unavoidable issue resulting from the high demand for copper during the global transition to a sustainable development path. Metal-rich copper slag might serve as a potential source of metals through secondary recovery. In this study, two copper slags (CS1 and CS2) with different metallurgical properties were characterized, focusing on secondary metal recovery. The X-ray diffraction (XRD) results show that fayalite (Fe₂SiO₄) and magnetite (Fe₃O₄) were the main crystalline phases in both CS1 and CS2. In addition, CS2 exhibited a more stable amorphous silicate network than CS1, which was attributed to the differences in the content of Si-O-3NBO linkages. The sequential extraction of Zn, Cu, Fe, and Pb from the slags was also explored, with the Cu content in CS1 being substantially lower than that in CS2. All metals were distributed in the F5 residue fraction. Cu was the most mobile metal as a result of the large proportion of soluble fractions (F1–F3), followed by Zn and Fe. This study explored the chemical speciation of Zn, Cu, Fe, and Pb from copper slags, which has practical implications for secondary metal recovery from such materials. Full article

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification. Full article

Lactic acid bacteria (LAB) are commonly consumed as probiotics to improve gut barrier function and boost the immune system. This study aimed to screen LAB with high immunomodulatory activity using RAW264.7 macrophages. According to the results, Limosilactobacillus reuteri AUc2301 was selected from 84 screened strains that can stimulate RAW264.7 cell proliferation. Limosilactobacillus reuteri AUc2301 significantly enhanced the phagocytosis activity of RAW264.7 cells. In the ELISA test, Limosilactobacillus reuteri AUc2301 significantly promoted the release of interleukin-6, IL-1β, the tumor necrosis factor, and nitric oxide in RAW264.7 macrophages. In addition, Limosilactobacillus reuteri AUc2301 significantly inhibited the excessive release of IL-6, IL-1β, TNF-α, prostaglandin E2 as well as NO and the high expression of cyclooxygenase-2 in RAW264.7 macrophages induced by lipopolysaccharide. In further mechanism studies, Limosilactobacillus reuteri AUc2301 could regulate the nuclear factor-κB signaling pathway in RAW264.7 macrophages. Collectively, the screened Limosilactobacillus reuteri AUc2301 showed good immunomodulatory activity in vitro, and it has the potential to be developed as a novel probiotic. Full article

Throughout the smart city literature, there are mentions of capacities, the application of which is claimed to result in the sustainable achievement of objectives. Because of the often desperate need for smart city objectives to be met, we sought to understand which were the capacities and whether the components of these capacities are explained sufficiently for them to be effective in practice. We applied a four-stage methodology commencing with a search of multiple databases for smart city capacity knowledge. We next assembled the evidence from the items identified in that search using a thematic analysis that identified the capacity to exploit technology, innovate, collaborate, and orchestrate. Next, we followed the threads of knowledge, iteratively allocating the knowledge to each of the four capacities to a typology of what, why, and who. The fourth stage was a cross-capacity analysis that generated further refinement and identified important factors. We identified that capacities are not sufficiently explained. In addition to the need for more levels of detail as to practical implementation, we identified significant underdevelopment of the literature as to the impact of institutional complexity and the influence of stakeholders. We propose research directed at increasing the effectiveness of capacities, define the concept of smart city capacities, propose a framework of the components of capacities, and draw on established stakeholder theory to create a stakeholder influence research framework. Full article

Vitis vinifera L. subsp. sylvestris (sylvestris) is the only native wild grapevine in Eurasia (Europe and western Asia) and is the existing ancestor of the grapevine varieties (for wine and table grape production) belonging to the subsp. sativa. In Slovenia, the prevailing opinion has been that there are no Slovenian sylvestris habitats. This study describes sylvestris in Slovenia for the first time and aims to present an overview of the locations of the wild grapevine in the country. In this project, a sample set of 89 accessions were examined using 24 SSR and 2 SSR markers plus APT3 markers to determine flower sex. The accessions were found in forests on the left bank of the Sava River in Slovenia, on the border between alluvial soils and limestone and dolomite soils, five different sites, some of which are described for the first time. The proportion of female to male accessions differed between sites. At two sites, female plants dominated; at others, the ratio was balanced. The plants’ genetic diversity and structure were compared with autochthonous and unique varieties of subsp. sativa from old vineyards in Slovenia and with rootstocks escaped from nature from abandoned vineyards. Sylvestris was clearly distinguishable from vinifera and the rootstocks. Based on genetic analyses, it was confirmed that Slovenian sylvestris is closest to the Balkan and German sylvestris groups. Meanwhile, a safety duplication of the wild grapevine accessions has been established at the University Centre of Viticulture and Enology Meranovo, Faculty of Agriculture and Life Sciences at the University of Maribor. Full article