Understanding tree and stand growth dynamics in the frame of climate change calls for large-scale analyses. For analysing growth patterns in mountain forests across Europe, the CLIMO consortium compiled a network of observational plots across European mountain regions. Here, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir.First, we sketch the state of the art of existing monitoring and observational approaches for assessing the growth of mountain forests. Second, we introduce the design, measurement protocols, as well as site and stand characteristics, and we stress the innovation of the newly compiled network. Third, we give an overview of the growth and yield data at stand and tree level, sketch the growth characteristics along elevation gradients, and introduce the methods of statistical evaluation. Fourth, we report additional measurements of soil, genetic resources, and climate smartness indicators and criteria, which were available for statistical evaluation and testing hypotheses. Fifth, we present the ESFONET (European Smart Forest Network) approach of data and knowledge dissemination. The discussion is focussed on the novelty and relevance of the database, its potential for monitoring, understanding and management of mountain forests toward climate smartness, and the requirements for future assessments and inventories.In this chapter, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir. We present how to acquire and evaluate data from individual trees and the whole stand to quantify and understand the growth of mountain forests in Europe under climate change. It will provide concepts, models, and practical hints for analogous trans-geographic projects that may be based on the existing and newly recorded data on forests.
This chapter addresses the concepts and methods to assess quantitative indicators of Climate-Smart Forestry (CSF) at stand and management unit levels. First, the basic concepts for developing a framework for assessing CSF were reviewed. The suitable properties of indicators and methods for normalization, weighting, and aggregation were summarized. The proposed conceptual approach considers the CSF assessment as an adaptive learning process, which integrates scientific knowledge and participatory approaches. Then, climate smart indicators were applied on long-term experimental plots to assess CSF of spruce-fir-beech mixed mountain forest. Redundancy and trade-offs between indicators, as well as their sensitivity to management regimes, were analyzed with the aim of improving the practicability of indicators. At the management unit level, the roles of indicators in the different phases of forest management planning were reviewed. A set of 56 indicators were used to assess their importance for management planning in four European countries. The results indicated that the most relevant indicators differed from the set of Pan-European indicators of sustainable forest management. Finally, we discussed results obtained and future challenges, including the following: (i) how to strengthen indicator selections and CSF assessment at stand level, (ii) the potential integration of CSF indicators into silvicultural guidelines, and (iii) the main challenges for integrating indicators into climate-smart forest planning.
Soil samples were collected in an industrial area (Banja Luka, Bosnia and Herzegovina) and analyzed the concentration of 16 polycyclic aromatic hydrocarbons (PAHs). The total concentration of 16 PAHs in surface soil varied within the range of 0.599-2.848 mg/kg and in deeper layer soil samples 0.041-0.320 mg/kg. Two basic sources of PAHs at this location are: pyrogenic and petrogenic sources. Benzo(a)pyrene toxic equivalency factors (TEFs) were used to calculate BaPeq in order to evaluate carcinogenic risk of soil contamination with PAHs. The total BaPeq of seven carcinogenic PAHs in surface soil and deeper soil layer were in the range 23.270-368.63 µg/kg (mean of 151.223 µg/kg), and 15.71-80.24 µg/kg, (mean of 48.08 µg/kg), respectively. These indicated that PAHs in this industrial soil presented relatively high toxicity potential. This study identifies the concentration and estimation of the potential cancer risk caused by contact with soils for adults, adolescents and children. In accordance with the estimated values of incremental life cancer risks (ILCRs), the cancer risk resulting from contact with the contaminated surface soil should be considered high (total ILCR>10 -3 ). The results suggest that current PAHs concentration highly carcinogenic and may hold a serious health risk for local residents and employees.
The COVID-19 pandemic has altered the way business is conducted. The widespread closure of commercial organizations presents opportunities to reset the way business activities are conducted. Regardless of the organization’s size or its status as a domestic or international firm, due diligence is required to find solutions that will allow firms to sustain their business activities in uncertain times. This study addresses this issue and attempts to identify issues that require urgent attention so that organizations can be effective and efficient in their global operations. In this context, the study proposes three imperatives for global/international businesses to sustain their operations in the long term. These imperatives include having a strong reserve fund, access to a local mutual fund, and networking to form alliances in host countries. Other implications are discussed, and we identify areas for future research.
The paper proposes a novel computing and net-working framework that can be implemented for the realization of different disaster management applications or real-time surveillance. The framework is based on networks of unmanned aerial vehicles (UAVs) equipped with different sensors including cameras. The framework represents a holistic approach that exploits the distributed architecture of clusters of UAVs and cloud computing resources located on the ground. The proposed framework is characterized by the hierarchical organization among framework elements. In such a framework, each UAV is assumed to be fully autonomous and locally implements a state-of-the-art deep learning algorithms for real-time route planning, obstacle avoidance and object detection on aerial images. The main operating modules of the proposed framework have been presented, with the emphasis on the improvements which the proposed framework can bring in terms of event detection time and accuracy, energy consumption and reliability of application in disaster management systems. The proposed framework can serve as the foundation for the development of more reliable, faster in terms of disaster event detection and energy-efficient disaster management systems based on UAV networks.
This paper considers the application of machine learning models to electric field intensity and magnetic flux density estimation in the proximity of the overhead transmission lines. The machine learning models are applied on two horizontal overhead transmission line configurations at different rated voltages, at height 1 m above ground surface. The obtained results are compared with the results obtained by charge simulation method and Biot-Savart law based method as well as with the field measurement results.
Aim of the study Lateral episiotomy is a widely used procedure, although it is rarely mentioned in the literature and its effects on the pelvic floor are largely unexplored. The purpose of this study is to evaluate the impact of lateral episiotomy on the incidence of urinary incontinence (UI) after vaginal delivery in primiparas. Material and methods The study design is a prospective cohort study. The primiparas were divided into two groups. The first group consisted of women who gave birth with lateral episiotomy, while the second group included women who gave birth with an intact perineum or with perineal tears of first and second degree. Assessments of UI were performed at 5 and 8 months after childbirth using the International Consultation on Incontinence Questionnaire – Short Form (ICIQ-SF) questionnaire followed by the stress test. Results The results revealed no significant differences (p > 0.05) in emergence of stress urinary incontinence (SUI) between the groups at the two time points. There were no statistically significant differences in overall rate of UI, urge urinary incontinence (UUI), or mixed urinary incontinence according to the ICIQ-SF questionnaire. The overall incontinence rate on the first examination was 24% in the episiotomy group and 36% in the perineal laceration group, although the difference was not statistically significant (p = 0.064). On the second examination, rates were similar and without a statistically significant difference. Conclusions Lateral episiotomy has a neutral effect on the onset of UI in primiparous women in the first year after delivery.
Wearable devices and smartphone applications have allowed for the utilization of different at-home treatments. Biofeedback is a mind-body technique that enables users to self-regulate the responses of the autonomic nervous system. This paper has conducted a proof-of-concept study to test multimodal biofeedback treatment with smartphone application and custom-made wearable sensor. While contact-based measurements included skin temperature and skin conductance from the sensor, the smartphone's front camera recorded the patient's face to estimate cardiovascular parameters such as heart rate and heart rate variability. The tested individual completed five biofeedback treatments at home, with activation stress exercises before and after a 5-day experiment. The obtained results show increased finger's skin temperature and heart rate variability during biofeedback sessions, indicating the successful biofeedback treatment.
Positive energy districts (PEDs) are seen as a promising pathway to facilitating energy transition. PEDs are urban areas composed of different buildings and public spaces with local energy production, where the total annual energy balance must be positive. Urban areas consist of a mix of different buildings, such as households and service sector consumers (offices, restaurants, shops, cafes, supermarkets), which have a different annual energy demand and production, as well as a different consumption profile. This paper presents a data modeling approach to estimating the annual energy balance of different types of consumer categories in urban areas and proposes a methodology to extrapolate energy demands from specific building types to the aggregated level of an urban area and vice versa. By dividing an urban area into clusters of different consumer categories, depending on parameters such as surface area, building type and energy interventions, energy demands are estimated. The presented modeling approach is used to model and calculate the energy balance and CO2 emissions in two PED areas of the City of Groningen (The Netherlands) proposed in the Smart City H2020 MAKING CITY project.
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