Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

Acute encephalopathy with biphasic seizures and reduced diffusion (AESD) is characterized by biphasic seizures following febrile viral infections and delayed reduced diffusion of the cerebral white matter on magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) (bright tree appearance, BTA). However, hypoxic encephalopathy with biphasic seizures and AESD-mimicking imaging findings has not been reported. Herein, we report an case of hypoxic encephalopathy due to suffocation with concomitant biphasic seizures and BTA, mimicking AESD. On day 1, a 5-month-old female was found face down with breathing cessation and deteriorating consciousness level. The electroencephalography (EEG) revealed periodic epileptic discharges, suggesting possible nonconvulsive status epilepticus. Despite the improvements in consciousness level and EEG abnormalities on day 2, her consciousness level deteriorated again with generalized tonic-clonic seizures on day 3, and head MRI-DWI revealed restricted diffusion predominantly in the subcortical areas, suggesting BTA. Treatment for acute encephalopathy resolved clinical sei-zures and EEG abnormalities. The persistence of abnormal EEG, reflecting abnormal excitation and accumulation of neurotoxic substances caused by hypoxia, may have contributed to the devel-opment of AESD-like findings. As hypoxic encephalopathy causes AESD-like biphasic seizures, monitoring the consciousness level, seizure occurrence, and EEG abnormalities even after acute symptoms have temporarily improved following hypoxia is essential.

For the first time, few-layer graphene (FLG) nanosheets were synthesized by the method of self-propagating high-temperature synthesis (SHS) from biopolymers (starch and lignin). We suggested that biopolymers (lignin, tree bark) and polysaccharides, in particular starch, could be an acceptable source of native cycles for the SHS process. The carbonization of biopolymers under the conditions of the SHS process was chosen as the basic method of synthesis. Chemical reactions, under the conditions of the SHS process, proceed according to a specific mechanism of nonsothermal branched-chain processes, which are characterized by the joint action of two fundamentally different process-accelerating factors - avalanche reproduction of active intermediate particles and self-heating. The method of obtaining FLG nanosheets included the thermal destruction of hydrocarbons in a mixture with an oxidizing agent. We used biopolymers as hydrocarbons and ammonium nitrate as an oxidizing agent. Thermal destruction was carried out in the mode of SHS, heating the mixture in a vessel at a speed of 20–30 oC/min to 150-200 oC and keeping at this temperature for 15–20 min with the discharge of excess gases into atmosphere. A combination of spectrometric research methods, supplemented by electron microscopy data, has shown that the particles of the carbonated product powder in their morphometric and physical parameters correspond to FLG nanosheets. An X-ray diffraction analysis of the indicated FLG nanosheets was carried out, which showed the absence of formations with a graphite crystal structure in the final material. The surface morphology was also studied and the features of the IR absorption of FLG nanosheets were analyzed. It is shown that the developed SHS method makes it possible to obtain FLG nanosheets with linear dimensions of tens of microns and a thickness of not more than 1-5 graphene layers (several graphene layers).

The Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. The disease damage prompt assessment is crucial for pest management. However, the disease detection current methods are limited at the field scale. Therefore, an alternative approach that can enhance or supplement traditional techniques is needed. In this study, we investigated the potential of hyperspectral methods to identify the changes between fungi-infected and uninfected plants of Vitis vinifera in early detecting the Armillaria disease. The hyperspectral imaging sensor Specim-IQ was used to acquire images of leaves of the Teroldego Rotaliano grapevine cultivar. We analysed three groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the Near infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to internal leaf structure changes. Asymptomatic plants emerged from the other groups due to a smaller reflectance in the red-edge spectrum (around 705nm). Hypothetically associated with the presence of secondary metabolites involved in plant defence strategy. Furthermore, significant differences were observed in the wavelengths close to 550 nm in diseased plants versus asymptomatic. We used linear discriminant analysis from a machine learning context to classify the leaves based on the most significant variables (vegetation indices and single bands), with resulting overall accuracies of 85% and 84% respectively in healthy vs. diseased and healthy vs. asymptomatic. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis on woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAV), could be a promising tool for a cost-effective, non-destructive method of Armillaria disease early diagnosis and mapping in the field, contributing to a significant step forward in precision viticulture.

Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard ‘occlusion factor’, estimated from a manual count of fruit load on a sample of trees (referred to as the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruit. Five approaches to correct for occluded fruit based on canopy images were compared using data of three mango orchards in two seasons. However, no attribute correlates to the number of hidden fruit were identified. Several image features obtained through segmentation of fruit and canopy areas, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest fruit count on trees. The supervised machine learning methods for direct estimation of fruit load per tree delivered an improved prediction outcome over the reference method for data of the season/orchard from which training data was acquired. For a set of 2017 season tree images (n=98 trees), a R2 of 0.98 was achieved for the correlation of the number of fruits predicted by a Random forest model and the ground truth fruit count on the trees, compared to a R2 of 0.68 for the reference method. The best prediction of whole orchard (n = 880 trees) fruit load, in the season of the training data, was achieved by the MLP model, with an error to packhouse count of 1.6% compared to the reference method error of 13.6%. However, the performance of these models on new season data (test set images) was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model. This outcome was attributed to variability in tree architecture and foliage density between seasons and between orchards, such that the characters of the canopy visible from the interrow that relate to the proportion of hidden fruit are not consistent. Training of these models across several seasons and orchards is recommended.

Drought-induced plant mortality has increased globally during the last decades and is forecasted to influence global vegetation dynamics. Timely information on plant water dynamics is essential for understanding and anticipating drought-induced plant mortality. The most common metric that has been used for decades for measuring water stress is leaf water potential (Ψ_L), which is measured destructively. To obtain information on water dynamics from trees and forested landscapes, remote sensing methods have been developed. However, the spatial and temporal resolution of the existing methods have limited our understanding of water dynamics and diurnal variation of Ψ_L within single trees. Thus, we investigated the capability of terrestrial laser scanning (TLS) intensity in observing diurnal variation in Ψ_L during a 50 hour monitoring period and aimed to improve understanding on how large part of the diurnal variation in Ψ_L can be captured using intensity observations. We found that TLS intensity at 905 nm wavelength was able to explain 78% of the variation in Ψ_L for three trees of two tree species with a root-mean square error of 0.137 MPa. Based on our experiment with three trees, time-series of TLS intensity measurements can be used in detecting changes in Ψ_L, and thus it is worthwhile to expand the investigations to cover a wider range of tree species and forests and further increase our understanding of plant water dynamics at wider spatial and temporal scales.

Tree-killing bark beetles are the most economically important insects in conifer forests worldwide. Yet despite >200 years of research, the drivers of population eruptions or crashes are still not fully understood, precluding reliable predictions of the effects of global change on beetle population dynamics and impacts on ecosystems and humans. We critically analyze potential biotic and abiotic drivers of population dynamics of the European spruce bark beetle (Ips typographus) and present a novel ecological framework that integrates the multiple drivers governing this bark beetle system. We call for large-scale collaborative research efforts to improve our understanding of the population dynamics of this important pest; an approach that might serve as a blueprint for other eruptive forest insects.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

To enhance pomegranate production on marginal gravelly lands, our study evaluated standardized planting techniques in an 8-year-old orchard. We employed trenching, wider pit excavation, pit digging, and auger digs with dimensions of 1 and 2 meters. Utilizing native soil from barren land, with or without spent wash, and mixing it with black soil up to 1 meter deep, we assessed growth parameters, leaf nutrients, and fruit yield. The trench and wider pit methods outperformed others, yielding greater above-ground biomass (>7.03 t tree-1), root biomass (2.5 t tree-1), and cross-sectional area (3.3 m² tree-1). Additionally, trench planting enhanced leaf phosphorus (0.28%) and potassium (1.8%) levels, fruit juice content (48.5%), total soluble solids (16.05°), and fruit yield (>9.3 t ha-1). The trench method also fostered longer roots at 90–150 cm radial distance and deeper roots at 40–60 cm depth. In summary, the trench and wider pit methods, combined with a soil mixture, are recommended for sustainable, high-quality fruit production in shallow gravelly terrains, thereby improving food security and the livelihoods of farmers in arid regions.

Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Total genomic DNA (gDNA) was generated using CTAB method from each cultivar. The most effective 50 hyper-variable SSR markers were selected. Highly specific DNA fingerprints were identified in the candidate line ‘Azeem Chaunsa’ compared with three standard cultivars using SSR-PCR. An agglomerative hierarchical clustering method was used to construct dendrogram based on the UPGMA clustering method. Cultivar identification diagram (CID) was constructed to evaluate association among standard cultivars and Azeem Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. The genetic similarity coefficients were recorded between the cultivars of mango ranged from 0.49 to 0.67. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future.

Alzheimer’s disease manifests itself as a complex pathological condition with neuroinflammation, oxidative stress and cholinergic dysfunction being a few of the many pathological changes. Due to the complexity of the disease current therapeutic strategies aim at a multitargeted approach often relying on a combination of sub-stances with versatile and complementary effects. In the present study, unique combination of α-lipoic acid, citicoline, extracts of leaves from olive tree and green tea, Vitamin D3, selenium and an immune supporting complex was tested in a scopolamine-induced dementia in rats. Using behavioral and biochemical methods we assessed the effects of this combination on learning and memory, and elucidated the mechanisms under-lying its effects. Our results showed that as compared to the components, experimental combination was most efficient in improving short- and long-term memory assessed by the step-through method as well as spatial memory, assessed by T-maze and Barnes maze underlying decrease in AChE activity and LPO, in-creases in SOD activity in cortex; activities of catalase and GPx, levels of BDNF and pCREB in the hippocam-pus. No significant histopathological changes or blood parameter changes were detected, making the experi-mental combination an effective and safe candidate in a multitargeted treatment of AD.

Human-caused anthropogenic greenhouse emissions impact climate globally. In this pilot study, we aim to reveal the impact of oil and gas emissions from local oil deposits in the regime of their natural degradation and development and eco-physiological changes of pine forests in Tatarstan Republic, Russia over the past century. We performed stable carbon isotope analysis in pine tree rings (13Cptrw) to reveal the impact of oil and gas emissions from the giant oil field reservoirs located in Leninogorsk region (UVRT) and the natural reserve site in Raifa. Our results show decreasing 13Cptrw at UVRT in 1943 when oil extraction was started, and in 1970, when it reached the maximum production. We found that 13Cptrw from UVRT indicates on developing unfavourable drier conditions and suppressed tree growth caused by both human-induced oil and deposit infrastructure and natural processes compared to undisturbed Raifa. The 13Cptrw from Raifa is more negative compared to UVRT, which can be explained by higher forest sensitivity to human-induced impact. From an ecophysiological point of view decreasing intercellular to ambient CO2 concentration at the leaf level and increasing intrinsic water use efficiency along with decreasing tree-ring width at UVRT (1970-2021) indicate on development of drought conditions.

Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. This study investigated the molecular evaluation of a new mango cultivar ‘Azeem Chaunsa’ using a set of the most effective 50 hyper-variable polymorphic SSR markers. Highly specific DNA fingerprints were identified in the genome of this mango cultivar, ‘Azeem Chaunsa’ compared with three standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. An agglomerative hierarchical clustering method was used to construct dendrogram based on the ‘Unweighted Pair- Group Method with Arithmetic Mean’ (UPGMA). The genetic similarity coefficients were recorded between the mangos cultivars ranged from 0.49 to 0.67. Cultivar identification (CID) evaluates association among standard cultivars and Azeem Chaunsa and further concludes significant variations. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future mango breeding programmes in the context of climate change.

Delineating chilling and forcing periods is one of the challenging topics in understanding how temperatures drive the timing of budburst and bloom in fruit tree species. Here, we investigated this question on olive trees, using flowering data collected over six years on 331 cultivars in the worldwide collection of Marrakech, Morocco. Using a Partial Least Squares approach on a long-term phenology (29 years) of ‘Picholine Marocaine’ cultivar, we showed that the relevance of delineating the chilling and forcing periods depends more on the variability of inter-annual temperatures than on the long-term datasets. In fact, chilling and forcing periods are similar between those delineated by using datasets of 29 years and those of only 6 years (2014–2019). We demonstrated that the variability of inter-annual temperatures is the main factor explaining this pattern. We then used the datasets of six years to assess the chill and heat requirements of 285 cultivars. We classified Mediterranean olive cultivars into four groups according to their chill requirements. Our results, using the Kriging interpolation method, indicated that flowering dates of most of these cultivars (92%) were governed by both chilling and forcing temperatures. Our investigations provided first insights to select adapted cultivars to global warming.

Global climate change requires urgent solutions. Ambitious tree-planting initiatives, many already underway, aim to sequester enormous quantities of carbon, partly compensating for the anthropogenic CO₂ emissions that are a major cause of rising global temperatures. However, poorly planned and executed tree-planting could actually increase CO₂ emissions and have long-term, deleterious impacts on biodiversity, landscapes and livelihoods. Here, we highlight the main environmental risks of large-scale tree planting and propose ten golden rules, based on some of the most recent ecological research, to implement forest ecosystem restoration that maximizes rates of both carbon sequestration and biodiversity recovery, while simultaneously improving livelihoods. These are: i) Protect existing forest first; ii) Work together (involving all stakeholders); iii) Maximize biodiversity recovery to meet multiple goals; iv) Select appropriate areas; v) Use natural regeneration wherever possible; vi) Select species to maximise biodiversity; vii) Use resilient plant material (with appropriate genetic variability and provenance); viii) Plan ahead for infrastructure, capacity and seed supply; ix) Learn by doing (using an adaptive management approach); and x) Make it pay (ensuring the economic sustainability of the project). We focus on the design of long-term strategies to tackle the climate and biodiversity crises and support livelihood needs. We emphasize the role of local communities and their dependence on benefits from successful reforestation programmes that restore ecosystem functioning and deliver a diverse range of forest products and services. While there is no simple and universal recipe for forest restoration, it is now crucial to build on the public and private interest in this topic to ensure interventions provide effective, long-term carbon sinks and maximise benefits for biodiversity and people.

This article reevaluates the Woesean concept of crossing a ‘Darwinian threshold’ from pre-genomic communality, as prevailing in an ancestral ‘progenote’ state, to vertically stable lineages of autonomous and self-similar cells. This transition from collective trunk-line evolution to Darwinian speciation is dependent on the generation of modular organismal genomes. The same general principle should be valid at subcellular levels, allowing the emergence of semi-autonomous genomic agents, such as viruses and plasmid-carrying endogenous vesicles with organelle-like properties. As compartmentalized agents of endogenous nature could start with smaller genomes than those required for fully autonomous cells, it is conjectured that stable subcellular lineages emerged earlier than their cellular counterparts. Referring to the recent ‘pre-endosymbiont hypothesis’, it is proposed that free-living bacteria (the first ‘prokaryote’ cells) arose by ‘lineage escape’ from plasmid-bearing organelle-like compartments, evolving inside the internally complexifying ‘paracells’ of the progenote community. The double-membrane envelopes of diderm bacteria may have resulted from cell-biological processes facilitating cellular lineage escape. The later emergence of archaeal cells (resembling bacteria in ‘prokaryote’ appearance with unichromosomal genomes) and eukaryotic organisms (with compartmented cells and multichromosal genomes) can also be interpreted in terms of this modified progenote hypothesis. Conceivably, the multichromosomal genomes of eukaryotes were bundled in endogenous nuclear compartments to organize a ‘nuclear-cytoplasmic lineage’, which became vertically stable by perfecting mitosis/meiosis-like divisions and yet retained some intra-species population confluence by sexual division-fusion cycles.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

Ash trees have considerable economic, cultural and environmental value on the island of Ireland. However, European ash (Fraxinus excelsior L.) is currently under threat from the invasive ascomycete pathogen Hymenoscyphus fraxineus. This pathogen is the causal agent of ash dieback disease, which was initially reported in Poland in 1992. Hymenoscyphus fraxineus has since spread across Europe and the first recorded case of the disease on the island of Ireland was in 2012 at a forestry plantation in Co. Leitrim. The pathogen is now present in all 26 counties in Ireland and 6 counties in Northern Ireland, and it is considered unfeasible to eradicate. The spread of ash dieback disease is reflected in recent policy changes, which focus on management rather than eradication strategies. Since the first formal description of H. fraxineus in 2006, considerable research efforts have been made by the international scientific community to understand the biology of the pathogen and to develop management strategies against it. This review provides an update of current knowledge of H. fraxineus biology and infection. We then explore examples of mitigation techniques that have been trialled in Europe, in order to identify strategies that are feasible for disease management at a local level on the island of Ireland. Finally, we outline five key avenues of research that have the potential to provide breakthroughs in methods to protect valuable F. excelsior resources.

Crapemyrtle (Lagerstroemia indica L.: Lythraceae) is the most popular summer flowering tree in the U.S. Its total value sold has almost doubled since 1998. Consumers prize crapemyrtles for their beauty and pest resistance. However, current crapemyrtle production and use is being threatened by crapemyrtle bark scale (Acanthococcus lagerstroemiae (Kuwana, 1907)) (CMBS), which has been confirmed in 12 U.S. states after its first sighting in Texas in 2004. Our survey results indicate that producers anticipate a significant decrease in the value of crapemyrtle due to CMBS, in the magnitude of 29.93% and 33.79%, in our 2018 and 2019 surveys respectively. Our findings indicate industry demand for CMBS control. We used a non-parametric test to compare the producers’ responses to several questions regarding CMBS-control, among the different producer categories included in our sample. Incorporated businesses showed the most support, followed by part-nerships, and family/individual operations were the least supportive of science-based CMBS control research. Large businesses predicted a more serious decrease in crapemyrtles’ value as compared to smaller businesses. More businesses with high volume of crapemyrtle-related business considered the benefits of CMBS-control to be higher than its cost, as compared to other businesses. We also used a relative importance index to illustrate the ranking of different attrib-utes of crapemyrtles that producers consider while making decisions about growing/purchasing the plants. Flower color was found to be the most important attribute, followed by disease re-sistance. If the issue of CMBS gets out of control, the industry might need to find potential re-placements to crapemyrtle. The most popular landscape plants that can potentially replace crapemyrtle, in the opinion of producers we surveyed, are vitex (Texas lilac) and magnolia.

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

We re-inventoried red wood ant nests (RWA) in 12 study sites (≈1281 ha) in the Westeifel Volcanic Field, Germany, in an area-wide and integrated ecosystem approach after 12 years. Combined with the re-identification of previously mapped nests using a photo database, this approach leads to more accurate nest counts in (re)inventories. A total increase of nests from 1144 (2009) to 1252 (2021), and a dramatic one for the Heidberg site by 52-fold (2009) and 85-fold (2021) compared to a 1984 inventory was observed, contrasting with earlier postulations of a decline in RWA. Early to medium mature (41-80 years) and mature (³81–140 years) spruce forests were the preferred habitat. Large increase in small nest sizes suggests an increase in new nest settlements also in clearings, despite climate-induced forest dieback over the past decade. A decline in biodiversity in the herbaceous layer was observed; highly proliferating blackberries had no negative impact on RWA nests. Monitoring of woodpecker cavities in RWA nests is suggested as an indirect indicator tool for evaluating populations in forests. Positive ground movement rates caused by the Eifel plume are suggested another factor favoring nest settlements. This study contributes to the urgent update of statistically valid data required to a) effectively substantiate the status-quo of RWA occurrences, b) protect RWA as ecosystem engineers, c) advance understanding of Geo-Bio-Interactions in the wake of climate change, and c) contribute to the German government's Insect Conservation Action Program.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

We introduce linear probing hashing schemes that construct a hash table of size $n$, with constant load factor $\alpha$, on which the worst-case unsuccessful search time is asymptotically almost surely $O(\log \log n)$. The schemes employ two linear probe sequences to find empty cells for the keys. Matching lower bounds on the maximum cluster size produced by any algorithm that uses two linear probe sequences are obtained as well.

To substitute of conventional manure and peat with alternatives sourcing from environmental conservation concerns, several promising alternatives has been attracting scientific parties’ interest, recently. However, among them compost perform the best, mostly and support carbon sequestration and mitigation against climate change. The article describes the made locally produced 70% in volume olive pruning branches compost (COMP) performance in two trials as an organic amendment in pepper production and an olive sapling substrate during 2019-2021 organic management in Turkey. The application of COMP to pepper trial conducted using factorial randomised block design with 4 replications and 6 treatments increased total organic matter and soil organic carbon, significantly (p<0.05) as compared to non-used plots in two locations. The olive sapling trial was conducted using a randomised plot design with 4 replications and 4 treatments. After the 12 months of growth, compost had the largest architecture rooted plants significantly different (p<0.05). Fresh volume (cm³) of COMP used saplings were obtained 35% less than 40% peat treatment, significantly (p<0.05) while 6^th month measurement was found as 40%. It is concluded that to enhance circular economy recycling and composting olive pruning branches is lucrative for the country to reduce external input usage in organic horticultural production.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

The import and export of tree seed carries with it risks of inadvertent introduction of pests and pathogens to hitherto unaffected regions. Although trade in seed of specified trees is regulated, phytosanitary requirements for most tree species are minimal, even those related to the most important forest tree species in a given region. A better understanding of the microbiome associated with seed intended for commercial production or ornamental use, and their potential risk with the transport from the source origin of distributors, will help regulatory agencies implement measures to prevent new and emerging risks. In this study we used high throughput sequencing to show that highly diverse fungal communities were associated with seed of 14 different Pinus species obtained from seed banks (seed orchards) and retail sources (online distributors) in North America and Europe. Fungal diversity differed among the 23 seedlots tested. Community composition did not relate to the species of Pinus nor the country of origin. Assigned potential functions based on sequence identity using FUNGuild provided an overall understanding of the likely life strategies associated fungal OTUs. Of those sequences classified to a trophic level, 453 were plant pathogens, with the Dothideomycetes having the highest prevalence. The most common plant pathogens detected included Sydowia polyspora, Lasiodiplodia theobromae, Diplodia intermedia and Diplodia sapinea. The evidence presented here illustrates an urgent need for plant protection authorities, practitioners and the general public to recognize the potential risk of introducing harmful pathogens through innocent transport of seed.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.

The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

Clock Network Design (CDN) is a critical step while designing any Integrated-Circuits (ICs). It holds vital importance in the performance of entire circuit. Due to continuous scaling, 3D ICs stacked with TSV are gaining importance, with an objective to continue with the Moore's law. Through-Silicon-Via (TSV) provides the vertical interconnection between two die, which allows the electrical signal to flow through it. 3D ICs has many advantages over conventional 2D planar ICs like reduced power, area, cost, wire-length etc. The proposed work is mainly focused on power reduction and obstacle avoidance for 3D ICs. Various techniques have already been introduced for minimizing clock power within specified clock constraints of the 3D CND network. Proposed 3D Clock Tree Synthesis (CTS) is a combination of various algorithms with an objective to meet reduction in power as well as avoidance of obstacle or blockages while routing the clock signal from one sink to other sink. These blockages like RAM, ROM, PLL etc. are fixed during the placement process. The work is carried out mainly in three steps- first is Generation of 3D Clock tree avoiding the blockages, then Buffering and Embedding and finally validating the results by SPICE simulation. The experimental result shows that our CTS approach results in significant 9% reduction in power as compare to the existing work.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.