Analysis of post-stroke anxiety (PSA) factors during the COVID-19 pandemic in Indonesia

This study aims to determine the incidence of PSA and the factors that influence it. The study was conducted on 78 patients with neurologic stroke at ANNA Medika Madura Hospital from May to July 2021 and aged over 40 years using simple random sampling. Measuring tools using Hamilton Anxiety Rating Scale, vascular disorders, stroke disorders, and Neurophysiological disorders. Analysis using the Structural Equation Modeling with the software Partial Least Square. Based on the results of statistical tests, it was found that demographic factors (T-statistics 1.756), vascular disorders factor (T-statistics 2.873), stroke disturbance factor (T-statistics 2.164), and neurophysiological disorders factor (T-statistic 3.070). The factor that had the greatest influence on PSA was the neurophysiological factor, with the most influencing subfactor being the cognitive function of stroke patients indicated with MMSE results. Stroke patients with decreased cognitive function will affect the effectiveness of the rehabilitation program. This will lead to an increase in their anxiety with the subsequent effect being a decrease in the quality of life in stroke patients. So rated important for the power of Health assess cognitive function is to reduce PSA.

Long Term Post-Fire Vegetation Dynamics in North-East Mediterranean Ecosystems. The Case of Mount Athos Greece

Fire is an ecological and disturbance factor with a significant historical role in shaping the landscape of fire-prone environments. Despite the large amount of literature regarding post-fire vegetation dynamics, the north-east Mediterranean region is rather underrepresented in the literature. Studies that refer to the early post fire years and long term research are rather scarce. The current study is conducted in the socially and geographically isolated peninsula of Mount Athos (Holly Mountain) in northern Greece, and it studies vegetation dynamics over a period of 30 years since the last fire. Field data were collected 11 years since the event and were used to identify the present plant communities in the area, using TWINSPAN, and the factors affecting their distribution using CART. Four Landsat (TM, ETM, OLI) images are employed for the calculation of NDVI, which was found effective in detecting the intercommunity variation in the study area, and it is used for long term monitoring. The study includes four communities, from maquis to forest which are common in the Mediterranean region covering a wide altitudinal range. The results suggest that fire affects the various communities in a different way and their recovery differs significantly. While forest communities recover quickly after fire, maintaining their composition and structure, the maquis communities may need several years before reaching the pre-fire characteristics. The dry climatic conditions of the study area are probably the reason for the slow recovery of the most fire prone communities. Given that climate change is expected to make the conditions even drier in the region, studies like this emphasize the need to adopt measures for controlling wildfires and preventing ecosystem degradation.

Investigations of a Weathered and Closely Jointed Rock Slope Failure Using Back Analyses

This study presents the case of a landslide triggered by a high groundwater level caused by several days of continuous rainfall in the northeastern region of Taiwan. The slope where this landslide occurred consists of closely jointed and weathered bedrock. By means of finite element limit analysis and the Hoek–Brown failure criterion, this study performed a slope failure simulation similar to the actual landslide and deduced the reasonable value range for the combination of key Hoek–Brown failure criterion parameters through back analyses. The results indicate that the key parameters affecting the bedrock’s slope stability were the geological strength index (GSI) and the disturbance factor (D), whereas the effects of the unconfined compressive strength (σci) were less significant. The results of the back analysis reveal that the suitable D-value range and GSI of closely jointed and weathered sandstone in the northeastern region of Taiwan are 0.8 to 0.9 and 20 to 30, respectively. These back-analyzed value ranges can serve as a reference for broader applications in the preliminary stability analysis of similar rock slopes where it is difficult to perform in situ investigation.

Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options

Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre-existing local growing stock volume maps based on lidar data, a recent national-level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing-based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data.

Impact of Disturbances on Species Diversity and Regeneration of Nepalese Sal (Shorea robusta) Forests Managed under Different Management Regimes

Sal (Shorea robusta) forests, a dominant forest type in Nepal, experience different disturbance intensities depending on management regimes. This study compares the impact of disturbance on Nepalese Sal forests, which are managed on three major management regimes: protected area, state-managed forest, and buffer zone community forest. Using a systematic sampling approach, we sampled 20 plots, each covering 500 square meters, and nested plots within each main plot to measure pole and regeneration for each management regime. We recorded forest characteristics including tree species, counts, diameter, height, crown cover, and disturbance indicators. We compared forest attributes such as diversity indices, species richness, and stand structure by management regime using analysis of variance and regression analysis. The forest management regimes were classified into three disturbance levels based on disturbance factor bundles, and the buffer zone community forest was found to have the highest disturbance while the protected forest had the lowest disturbance. Species richness, diversity, evenness, abundance, density and basal area were higher, but regeneration was lower in protected area and state-managed forest compared to the buffer zone community forests. This suggests positive impacts of moderate disturbance on regeneration. The management plan should prioritize the minimization of excessive disturbance to balance forest conservation and provide forest resources to local users.

Construct Functional Oligonucleotides Simultaneous Targeting FEN1 and MIR-21 Integrate Photothermal Therapy to Inhibit Cancer Cell Growth

Abundant investigators have devoted their efforts to proceed the development of novel strategies aimed at anti-tumor through regulating gene expression negatively. Flap structure-specific endonuclease 1 (FEN1) received widespread attention in becoming a potential target for breast cancer. In this study, a prominent protocol based on small molecular nucleic acids was reasonably demonstrated. Various oligonucleotides containing FEN1 cleavage site and anti-miRNA21 sequences were designed and synthesized as a kind of disturbance factor to FEN1 and miRNA21. In order to obtain an ideal therapeutic outcome, graphene oxide (GO) was also employed, which rendered the whole system with photothermal therapy (PTT) effect. Finally, a novel trinity treatment system came into being with the characteristic of combined therapy involving miRNA21 silencing, FEN1 inhibition and PTT. In short, it is our hope that the designed multifunctional system could provide a promising use for the development of anti-tumor therapy in the foreseeable future.

Mechanical Parameters of Deep-Buried Coal Goaf Rock Mass Based on Optimized GSI Quantitative Analysis

In order to obtain the accurate mechanical parameters of deep-buried coal goaf rock mass, the limitation of geological strength index (GSI) in concealed rock mass is analyzed. Based on the test result and analysis of the current normative standards, the classification indexes of rock mass structural are optimized based on discontinuity distance d and rock mass integrity index K v . The ratio of rock mass saturated strength to dry strength, η, is introduced, quantization formula of structural surface conditions is proposed, and the influence of groundwater and rock types is included in structural surface condition classification. The GSI system is improved to better suit all types of deep-buried and water-rich rock masses. Furthermore, the rock mass disturbance factor D’s quantitative formula is listed according to the Hoek–Brown (HB) criterion. Taking the goaf roof under railway as an example, the parameters of deep-buried rock mass are obtained based on the improved quantitative GSI system and HB criterion. This research provides a scientific reference for achieving geological parameters and engineering designing in goaf areas.

Parametric Evaluation of Simultaneous Effects of Damaged Zone Parameters and Rock Strength Properties on GRC

The convergence-confinement method via the ground reaction curve (GRC) is used as the common practice of tunnel design which demands accurate determination of the stress state and material strength behavior in different zones around the tunnel section. Besides, formation of the excavation/blast-induced damaged zone (EDZ/BDZ) adds more complexity to the problem due to variation of elasticity modulus of the rock mass in this zone. As a result, advanced numerical methods via finite element/difference commercial packages or user-coded, semi-numerical techniques are required to develop the GRC, which demands a high degree of proficiency and knowledge of computational plasticity and geomechanics. In this study, a new, simple, and accurate method is proposed for prediction of GRC of circular tunnels constructed in the damaged, elastoplastic rock masses obeying softening in the plastic zone. The effects of deterioration caused by the drilling/blast in the EDZ were taken into account by assuming a reduced and varying Young’s modulus using the disturbance factor, in the form of Hoek–Brown failure criterion and the Geological Strength Index (GSI). Besides, effects of intermediate principal stress and the exponential decaying dilation parameter are taken into account thanks to adoption of the unified strength criterion (USC) as the material strength criteria. To do so, genetic algorithm (GA) via the method of evolutionary polynomial regression (EPR) is used to find a relationship between a number of 19 affecting parameters on the GRC as the input, and the internal support pressure as the target of prediction. Verification analysis was performed to verify the validity of the results using field measurements data as well as other advanced numerical studies found in the literature. Lastly, variation of the support pressure with simultaneous changes in the affecting input parameters was investigated using multivariable parametric study.

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.

Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.