Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

In this work, we'll present the power spectra density, auto-correlation function, dentrended fluctuation analysis and multifractal spectrum are performed to the daily light curves of Blazars monitored by Fermi LAT. Some signs of power law are found.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

A superior variant of dried jujube in China is ‘Huizao’ (HZ) jujube. Nonetheless, detailed evaluations of the texture quality of HZ fruits have been the subject of few studies. Texture is a significant indicator of the sensory and processed quality of a fruit. Here, we differentiate and characterize the texture quality of HZ fruits from the four primary producing regions in southern Xinjiang, as well as develop a system for assessing the texture quality of HZ fruits. Furthermore, five texture indices of HZ fruits from 109 plantations were exhaustively evaluated utilizing principal component analysis, Pearson correlation analysis, and hierarchical clustering analysis. Consequently, by the 10th, 30th, 70th, and 90th percentiles, each index was classified into five grades—lower, medium, high, and higher. Correlation investigation indicated strong relationships between the springiness, chewiness, cohesiveness, gumminess, and hardness of HZ fruits. Springiness, hardness, and adhesiveness are essential indicators that influence the texture of HZ fruits. We used clustering analysis to separate the six texture indices into three major categories (hard factor, stick-elastic factor, and adhesive factor), with indices representing hardness, springiness, and adhesiveness, respectively. Furthermore, according to the factor molecules, the texture quality of HZ fruits in the four production areas was evaluated as Bazhou (1.24) > Hotan (0.773) > Kashi (-0.577) > Aksu (-0.852). RDA analysis of six texture quality parameters and 24 climate conditions identified higher mean temperature (TEM) and lower relative humidity (RHU) as the primary factors contributing to the improved texture quality of HZ fruits in Xinjiang. This study identified the texture quality characteristics of HZ fruits in four major producing areas of Southern Xinjiang, and it offered a theoretical foundation for optimizing the dominant producing areas and regional production of HZ varieties in Xinjiang.

The age-related loss of muscle mass and strength is referred to as “sarcopenia”. The combined effect of sarcopenia and osteoporosis is a devastating threat to the old adult, accounting for reduced mobility, risk for falls and subsequent fractures. In previous studies, we observed a high degree of inter-individual variability of paraspinal muscle fatty infiltration, one most relevant index of muscle wasting. This aspect solicited us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different age (age range 23-85 years) and physio-pathological status (healthy young, pre-menopause, post-menopause, and os-teoporosis), were used to set up the method. It is based on fractal analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from our hyperbola model function, used to fit the GBA (gliding box algorithm) curvilinear plot. Succolarity was estimated by parameter µ, for the four main directions, through an algorithm implemented on purpose. Results show that fractal lacunarity, by quantifying muscle fatty infiltration, can discriminate be-tween osteoporosis and healthy aging while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease.

Entropy as a mathematical concept is the first of its own kind, and second to none. Barely one or two papers in literature have investigated fractal dimensions of very basic entropies, such as Shannon, Tsallis, and Rényi entropic expressions. This was a thoughtful inspiration to delve into a step forward towards a unified information- theoretic fractal theory. The storytelling of this current paper starts by introducing my third entropy formula, namely Ismail’s entropy, , as a novel generalization to Shannonian entropy with a visionary link to both long- and short-range interactions, (LRIs), (SRIs) respectively. Clearly, this would generate a fourfold unification with statistical mechanics as well as contemporary physics. Hence, this adds more credibility and weight for this study. The fractal dimension of , is identified in this paper. As an essential part of this work, it was influential to spotlight the importance of fractal geometry to space industry as well as education, which is crucial to develop our societies. Thus, some potential fractal applications to CubeSat Technologies and Education are highlighted. So, will the deep search for more ground-breaking research has come to an end. Certainly, not. This was another inspiration to propose new open problems to widen research gates to the scientific community for more research pathways. Consequently, the paper ends with closing remarks combined with some challenging open problems and the next phase of research.

In this paper, we make research on fractal characteristics of the superposition of fractal surfaces in the view of fractal dimension. We give the upper bound of the lower and upper Box dimension of the graph of the sum of two bivariate continuous functions and calculate the exact values of them under some particular conditions. Further, it has been proved that the superposition of two continuous surfaces cannot keep the fractal dimensions invariable unless both of them are two-dimensional. A concrete example of numerical experiment has been provided to verify our theoretical results. This study can be applied to the fractal analysis of metal fracture surfaces or computer image surfaces.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

The role played by non extensive thermodynamics in physical systems has been under intense debate for the last decades. With many applications in several areas, the Tsallis statistics has been discussed in details in many works and triggered an interesting discussion on the most deep meaning of entropy and its role in complex systems. Some possible mechanisms that could give rise to non extensive statistics have been formulated along the last several years, in particular a fractal structure in thermodynamics functions was recently proposed as a possible origin for non extensive statistics in physical systems. In the present work we investigate the properties of such fractal thermodynamical system and propose a diagrammatic method for calculations of relevant quantities related to such system. It is shown that a system with the fractal structure described here presents temperature fluctuation following an Euler Gamma Function, in accordance with previous works that evidenced the connections between those fluctuations and Tsallis statistics. Finally, the fractal scale invariance is discussed in terms of the Callan-Symanzik Equation.

Vertex-degree-based (VDB) topological indices have been applied in QSPR/QSAR. As an important category, the general logarithmic VDB topological index $T_{lnf}(G)$, is defined as the summation of $ln{f(d(u),d(v))}$, where the summation over all $uv\in E(G)$. In this paper, we give the sufficient conditions for that (1) the path $P_{n}$ is the only tree with the minimal $T_{lnf}$; (2) the star $S_n$ is the only tree with the maximal and the minimal $T_{lnf}$, respectively. As applications, the minimal and maximal trees of some logarithmic VDB indices are determined.

The fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing kThe fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing knowledge for the information- theoretic fractal dimensions. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

Rainfall, or more generally the precipitation process (flux), is a clear example of chaotic variables resulting from a highly nonlinear dynamical system, the atmosphere, represented by a set of physical equations such as the Navier-Stokes equations, energy balances and hydrological cycle among others. As a generalization of the Euclidean (ordinary) measurements, chaotic solutions of these equations are characterized by fractal dimensions, which are non-integer values that represent the complexity of variables like the precipitation. However, observed precipitation is measured as an aggregate variable over time, thus physical analysis of the observed fluxes is very limited. Therefore, this review aims to go through the different approaches used in the identification and analysis of the complexity of the observed precipitation, taking advantage of its geometry footprint. To address the review, it ranges from classical perspectives of fractal-based techniques to new perspectives at temporal and spatial scales as well as for classification of climatic features, including monofractal dimension, multifractal approaches, Hurst exponent, Shannon entropy and time scaling in intensity-duration-frequency curves.

The Fractal Analysis of Medical Images is an investigation field in those that numerous results of important value have been obtained. This work describes the basic foundations of the Fractal Analysis and its application has more than enough medical images, leaning on in five scientific investigations.

The global incidence of cutaneous squamous cell carcinoma (cSCC), a prevalent and aggressive skin cancer, has risen significantly, posing a substantial public health challenge. This study investigates the tumor microenvironment (TME) of cSCC by focusing on the spatial distribution patterns of immune and vascular markers (CD31, CD20, CD4, and CD8) using fractal dimension (FD) analysis. Our analysis encompassed 141 cases, including 100 invasive cSCC and 41 specimens with pre-invasive lesions exclusively, and the rest being peripheral pre-invasive lesions from the invasive cSCC class. The FD values for each marker were computed and compared between pre-invasive and invasive lesion classes. The results revealed significant differences in FD values between the two classes for CD20 and CD31 markers, suggesting distinct alterations in B cell distribution and angiogenic activity during cSCC progression. However, CD4 and CD8 markers did not exhibit significant changes individually. Still, the CD4/CD8 ratio showed a significant difference, indicating a potential shift in the immune landscape as lesions progress from pre-invasive to invasive stages. These findings underscore the complexity and heterogeneity of the TME in cSCC and highlight the potential of FD analysis as a quantitative tool for characterizing tumor progression. Further research is needed to elucidate the implications of these differences in the clinical management of cSCC.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

Mineral nutrition is essential for plant growth and interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on the trees’ mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mango. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of ‘Kensington Pride’ (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RE_D) was compared with the resorption efficiency of healthy (RE_S) leaves of KP mangoes. The concentrations of the total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was low and deficient level in LD trees. Moreover, DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and overall nutrient imbalances were exacerbated in HD trees. RE_D was significantly lower than RE_S for the total N, P, S, Cu, Fe, and Zn but significantly higher than RE_S for K. The study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infection.

Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

We develop a quantitative microscopic theory of decaying Turbulence by studying the dimensional reduction of the Navier-Stokes loop equation for the velocity circulation. We have found an infinite dimensional manifold of solutions of the Navier-Stokes loop equation\cite{M93, M23PR} for the Wilson loop in decaying Turbulence in arbitrary dimension $d >2$. This family of solutions corresponds to a fractal curve in complex space $\mathbb C^d$, described by an algebraic equation between consecutive positions plus a nonlinear periodicity condition. We derive the constrained SDE for the evolution of the fractal curve at a fixed moment of physical time as a function of an auxiliary stochastic time. We expect this stochastic process to cover our fixed manifold of the solutions of the decaying Turbulence. The energy density of the fluid decays as $\mathcal E_0/t$, where $\mathcal E_0$ is an initial dissipation rate. Presumably, we have found a new phase of extreme Turbulence yet to be observed in real or numerical experiments.

Abstract: A new method for constructing exact solutions of the General Relativity equations for a dusty matter with fractal property is proposed. This method allows to find the solution of the GR-equations in terms of matter velocities : the connection coefficients and the Ricci tensor of space-time are expressed in terms of matter velocities; the metric tensor and the matter density are found as functions of velocity from the GR-equations. The connection coefficients and the Ricci tensor are invariant with respect to the discrete scaling transformation of velocity , where is constant. Therefore, the found solution can be used to simulate the fractal properties of the cosmic web in terms of matter velocities. This solution includes isotropic and anisotropic distributions of matter density. In an isotropic case, there is a class of exact solutions including both the well-known Friedmann’s solution and a solution with a periodic distribution of the matter density in space. This last solution may be used to simulate the quasi-periodic distribution of matter in the cosmic web. It is possible that, the cosmic web and its fractal properties are the space-time primary properties. These properties are described with a deformation tensor of the space-time.

Unlike trees, shrubs (i.e., multiple-stemmed woody plants) do not need evenly spaced large diameter structural roots and thus can spread further per unit belowground biomass. We therefore hypothesized that compared to trees, shrubs respond more to asymmetric distributions of nutrients and reach nutrient-rich patches of soil faster and with less below-ground biomass. To test these hypotheses we planted individual seedlings of shrubs (Cornus racemosa, Rhus glabra, and Viburnum dentatum) and trees (Acer rubrum, Betula populifera, and Fraxinus americana) in the centers of sand-filled rectangular boxes. In one direction we created stepwise gradients of increasing soil nutrients with slow-release fertilizer; in the other direction no fertilizer was added. Seedlings were harvested when their first root reached the plexiglass-covered fertilized end of their box; time taken as well as above- and below-ground biomass by nutrient segment were determined. There were no consistent differences between the shrubs and trees in belowground architectural plasticity (= ratio of biomass in fertilized and unfertilized volumes of soil) or in the rate and efficiency of lateral growth. Interspecific variation appeared more related to edaphic characteristics of the native habitats than to growth form. The fastest and most efficient roots were produced by the shrub (R. glabra) and the tree (B. populifolia) that are characteristic of poor soils. Root foraging by R. glabra was also facilitated by rapid rhizomatous expansion. The tested shrubs and trees did not consistently differ in nutrient foraging efficiencies or rates but further study is warranted with larger plants and more species.

When describing the tribological behaviour of technical surfaces, the need for full length scale microtopographic characterization often arises. The self-affine of surfaces and the characterization of self-affine by a fractal dimension and its implantation into tribological models are commonly used. The goal of our present work was to determine the frequency range of fractal behaviour of surfaces by analysing the microtopographic measurements of a brake plunger. We also wanted to know if the bifractal and multifractal behaviour can be detected in real machine parts. As a result, we developed a new methodology for determining the fractal range boundaries to separate the nano- and micro-roughness. In order to reach our goals, we used atomic force microscope (AFM) and stylus instrument to provide measurements in wide frequency range (19 nm - 3 mm). As a result of the power spectral density (PSD) based fractal evaluation, it was found that the examined surface could not be characterized by a single fractal dimension and that we developed a methodology for stopping the margin of validity of each fractal dimension, and we also developed a methodology for determining the limits of validity of certain fractal dimensions.

I want to find an extension of the Hausdorff Measure and Fractal Measure which allows me to compute a unique, satisfying average for functions defined on non-fractal, measurable sets in the sense of Carathedory which has no gauge function.

Huanglongbing (HLB), or citrus greening, is a devastating disease impacting citrus trees worldwide, with severe effects particularly noted in Florida. Current strategies to combat HLB have focused on aggressive replanting despite the high susceptibility of young trees to infection. In this context, it is critical to explore agronomic practices that can enhance the health and resistance of young citrus trees to HLB. Here, we demonstrate that treatment with homobrassinolide (HBr), a type of brassinosteroid, on newly planted citrus trees can delay HLB infection and improve tree health amidst the high psyllid pressure conditions endemic to Florida. Our study reveals a significant reduction in HLB infection rates in HBr-treated trees compared to control trees, with only 25% of treated trees testing positive for HLB by six months, in contrast to 100% infection in untreated trees. This delay in infection may be attributed to HBr inducing an immune response and negatively impacting psyllid performance, as subsequently demonstrated in a greenhouse experiment. Our findings suggest that HBr applications could serve as a viable strategy to enhance the resilience of citrus production against HLB, underscoring the need for further investigation into their mechanisms of action and potential role in a comprehensive pest and disease management strategy.

This study examined the impact of differences in fruit load of olive trees on the rate of stem refilling. The experiment was conducted in a commercial olive orchard over the consecutive years of 2021 and 2022, during which the biennial bearing cycle of olive trees resulted in minimal fruit production in 2021 and maximal production in 2022. The rate of stem refilling was determined by quantifying the differences in water volume traversing the apex and base of the principal branches of experimental trees using sap flow sensors. Concurrent measurements of tree water status were also conducted using psychrometers, while soil moisture content dynamics were continuously recorded. Results indicate that alternate bearing significantly influences the stem refilling process, while soil moisture availability also plays a pivotal role. Water reserves were more intensively utilized during the year of full production (“ON” year) primarily to fulfill the water demands of developing fruits. This replenishment occurred in various periods during the day, including early morning, afternoon, and night, when stomatal conductance measurements indicated stomatal closure. The study suggests a more intense replenishment of reserves during the night in "ON" year trees, irrespective of soil moisture, water vapor pressure deficit, or phenological fruit development stage.

A dataset collected from Central-South Finland is used to investigate the mortality of boreal trees. The mortality rate is found to be the order of three times that predicted by earlier Nordic mortality models, being in the upper range of international literature observations. Small subpopulations of any tree species tend to die out. Mortality of downy birch increases with stand basal area, as well as with stand age. Mortality of Norway spruce and silver birch increases after 100 years, while that of Scots pine is invariant to age. As the relative loss rate of basal area is insensitive to stand basal area, the mortality of trees does not strongly regulate thinning practices.

In this work we present a novel steganography algorithm, based on Fractal Transform domain, combined with the principles of Cryptography. The proposed method uses a private key, which will indicate which segment of the image will be taken and encoded with the Fractal Transform, to then insert the secret bits in the transformed coefficients, thus generating the stego image.

So called Higuchi’s method of fractal dimension estimation is widely used and the term Higuchi’s fractal dimension even occurs in many publications. This paper deals with this method from mathematical point of view. Terms distance and dimension and its basic properties are explained and Higuchi’s dimension according the original source is defined. Definition of Higuchi’s dimension was comparated with mathematical definition of the distance and dimension. It is showed, that the definition of the Higuchi’s dimension does not satisfy axioms of distance and dimension. So called Higuchi’s method and Higuchi’s dimension are mathematically incorrect. Therefore, all results achieved by this method are scientifically unreliable.

Level index was introduced in 2017 for rooted trees which is a component of Gini index. In the origin, Gini index is a tool for economical investigations but Balaji and Mahmoud defined the graph theoretical applications of this index for statistical analysis of graphs. Level index is an important component of Gini index. In this paper we define a new graph polynomial which is called level polynomial and calculate the level polynomial of some classes of trees. We obtain some interesting relations between the level polynomials and some integer sequences.

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

SiC ceramics are excellent materials applied at high temperatures because of their light weight, excellent high temperature strength, and high thermal shock resistance. For better engineering properties, we made SiC with fractal lattices. Stress-strain behavior and modulus changes from room temperature to 1,250oC were analyzed using LAMMPS S/W, a molecular dynamics program. As a result of this study, it was confirmed that the modulus of elasticity of SiC crystals changed in the range of about 475 GPa to 425 GPa as it increased from room temperature to 1,250 oC, The stress-displacement characteristics of SiC crystals, which could not be measured at a high temperature of 1,000 oC or higher, could be ensured.

Plants produce specific structures constituting a barrier hindering penetration of pathogens, while they also produce substances inhibiting pathogen growth . These compounds are secondary metabolites, such as phenolics, terpenoids, sesquiterpenoids, resins, tannins and alkaloids. Bioactive compounds are secendary metabolities from trees and shrubs are used in medicine, herbal medicine and cosmetology. To date fruits and flowers of exotic trees and shrubs have been primarily used as sources of bioactive compounds. In turn, search for new sources of bioactive compounds is currently focused on native plant species due to its availability. Application of such raw material needs to be based on knowledge of their chemical composition, particularly health-promoting or therapeutic compounds. Research conducted to date on European trees and shrubs has been scarce. This paper presents results of literature studies conducted to systematise knowledge on bioactive compounds found in trees and shrubs native to central Europe. The aim of this review providing available information on the subject is to indicate gaps in the present knowledge.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

In IP networks, packet forwarding is destination-based and hop-by-hop, and routes are built as needed. Kwong et al. introduced a protection routing in which packet delivery to the destination node can proceed uninterrupted in the event of any single node or link failure. He then shows that “whether there is a protection routing to the destination” is NP-complete. Tapolcai find that two completely independent spanning trees, abbreviated as CISTs, can be used to configure the protection routing. In this paper, we propose dual protection routing trees, denoted as dual-PRTs to replace CISTs, which are less restrictive than CISTs. Next, we propose a transformation algorithm that uses dual-PRTs to configure the protection routing. Taking complete graphs Kn, complete bipartite graphs Km,n, hypercubes Qn, locally twisted cubes LTQn as examples, we provide a recursive method to construct dual-PRTs on them. This article will show that there are no two CISTs on K3,3, Q3, and LTQ3, but there exist dual-PRTs that can be used to configure the protection routing. As shown in the performance evaluation of simulation results, for both Qn and LTQn, we get the average path length of protection routing configured by dual-PRTs is shorter than that by two CISTs.

The chestnut tree (Castanea sativa Mill.) is gaining importance in the mountainous regions of southern Europe due to the high value of its fruits. It is essential to establish effective cultivation protocols, considering that this species is still relatively understudied. In this study, we present the outcomes of the initial establishment of a chestnut orchard conducted through a nutrient omission trial for four years. The treatments included a fertilization plan with nitrogen, phosphorus, potassium, and boron (NPKB), the control, and four other treatments corresponding to the omission of each nutrient (-NPKB, N-PKB, NP-KB, NPK-B). The -NPKB and NP-KB treatments showed significantly lower trunk circumferences and canopy volumes compared to the other treatments. The NPK-B treatment resulted in the lowest fruit production, with a total accumulated yield (2020-2022) of 0.56 kg tree–1, a value significantly lower than that of NPKB (1.12 kg tree–1) and N-PKB (1.19 kg tree–1). The assessment of nutrient concentrations in the leaves revealed plants with deficient levels of B and K in treatments that did not receive these nutrients. Conversely, N levels in the leaves in the -NPKB treatment fell within the sufficiency range (20 to 28 g kg–1). This suggests that the sufficiency range should be adjusted to higher values, given the treatment's effect on tree growth. It was also observed that the -NPKB treatment led to lower soil organic matter compared to the other treatments, likely due to reduced herbaceous vegetation development under the canopy, leading to decreased organic substrate deposition in the soil. The main findings of this study are that N and K were crucial elements for the optimal growth of chestnut trees, while B played a significant role in fruit production.

This paper intoduces a non-standard research technique to clarify how complex phenomena, such as those that are abundantly present in human physiology, can be faithfully described using fractal dynamical models with and without stochastic forces. This method for conducting research involves tracing the his- torical evolution of understaning an empirical medical process faciltated by the fractal calculus perspective. Herein we trace the analysis of the time series for heart rate varability (HRV) developed for diagnosing the cardiovascular health of a patient. This is done herein by introducing four (one empirical which en- tails three theoretical fractal models) distinct but related fractal models each one introduced to solve a particular problem arising from a fundamental defect in the previous model but in generalizing a model at one stage to resolve the problem associated with the defect another is invariably introduced by the re- placement model. It is through the utilization of the fractional calculus that the necessity for rethinking how to systematically incorporate additonal layers of complexity are revealed ultimately resulting in a 'complete' description of its empirical dynamics in fractal terms.

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Cur-rently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader's mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments.

The residual multiparticle entropy (RMPE) of a fluid is defined as the difference, Δs, between the excess entropy per particle (relative to an ideal gas with the same temperature and density), s_ex, and the pair-correlation contribution, s₂. Thus, the RMPE represents the net contribution to s_ex due to spatial correlations involving three, four, or more particles. A heuristic “ordering” criterion identifies the vanishing of the RMPE as an underlying signature of an impending structural or thermodynamic transition of the system from a less ordered to a more spatially organized condition (freezing is a typical example). Regardless of this, the knowledge of the RMPE is important to assess the impact of non-pair multiparticle correlations on the entropy of the fluid. Recently, an accurate and simple proposal for the thermodynamic and structural properties of a hard-sphere fluid in fractional dimension 1 < d < 3 has been proposed [Santos, A.; López de Haro, M. Phys. Rev. E 2016, 93, 062126]. The aim of this work is to use this approach to evaluate the RMPE as a function of both d and the packing fraction ϕ. It is observed that, for any given dimensionality d, the RMPE takes negative values for small densities, reaches a negative minimum Δs_min at a packing fraction ϕ_min, and then rapidly increases, becoming positive beyond a certain packing fraction ϕ₀. Interestingly, while both ϕ_min and ϕ₀ monotonically decrease as dimensionality increases, the value of Δs_min exhibits a nonmonotonic behavior, reaching an absolute minimum at a fractional dimensionality d ≃ 2.38. A plot of the scaled RMPE Δs/|Δs_min| shows a quasiuniversal behavior in the region −0.14 ≲ ϕ − ϕ₀ ≲ 0.02.

In the current work, the stable queueuing system's Generalised Z-Entropy (GZE) of Rényian formalism is examined in its fractal dimension. Notably, fractal dimension’s resulting behaviour that corresponds to the GZE parameters is examined through numerical tests. This research makes a substantial generalization in the literature by fusing fractal geometry and information theory to shed light on how entropy and complexity interact. More fundamentally, the significant role of fractal dimension to advance Big Data Analytics(BDAs) is highlighted. Closing remarks combined with open problems and the next phase of research are provided.

The production and consumption of nuts are increasing in the world due to strong economic returns and the nutritional value of their products. With the increasing role and importance given to nuts (i.e., walnuts, hazelnut, pistachio, pecan, almond) in a balanced and healthy diet and in the prevention of various diseases, breeding of the nuts species has also been stepped up. Most recent fruit breeding programs have focused on scion genetic improvement. However, the use of locally adapted grafted rootstocks also enhanced the productivity and quality of tree fruit crops. Grafting is an ancient horticultural practice use in nut crops to manipulate scion phenotype and productivity and overcome biotic and abiotic stresses. There are complex rootstock breeding objectives and physiological and molecular aspects of rootstock–scion interactions in nut crops. In this review, we provide an overview of these, considering the mechanisms involved in nutrient and water uptake, regulation of phytohormones, and rootstock influences on the scion molecular processes, including long-distance gene silencing and trans-grafting. Understanding the mechanisms resulting from rootstock × scion × environmental interactions will contribute to developing new rootstocks with resilience in the face of climate change, but also of the multitude of diseases and pests and of the possible increase of their aggressiveness. They will also have to offer the premises of economic production, respectively yield and the quality, according to multiple destinations of nuts in the current consumption and food industry, but also the increasing exigencies of the consumer market and the profile industry.

In the context of precision agriculture (PA), geomatic surveys exploiting UAV platforms allow the trees’ dimensional characterization and crown identification. This paper focuses on the use of low-cost UAV photogrammetry to estimate the trees’ height, as part of a project for the phytoremediation of contaminated soils. Two study areas (Area 1; Area 2) have been chosen, having different characteristics in terms of mean trees’ height (5 m; 0.7 m), to test the procedure even in a challenging context. Three campaigns have been performed in Area 1 at different altitudes (30 m, 40 m, 50 m), and one UAV flight is available in Area 2 (42 m of altitude). The implemented workflow involves the elaboration of the UAV point clouds and DSMs using the standard SfM approach, the vegetation filtering, the generation of the DTM and a GIS-based analysis to obtain the CHMs for the extraction of the trees’ heights based on a local maxima approach. UAV-derived heights have been compared with in-field measurements obtaining promising results in Area 1, confirming the applicability of the procedure for trees’ height extraction, while the application in the context of low trees was more problematic.

Trees, often referred to as the "lungs of the Earth," hold immense significance for both the natural world and human society. Their contributions span a multitude of ecological, environmental, economic, and social domains, making them essential components of a healthy and thriving planet. Phytoplasma association not only compromise tree aesthetics but also reduce photosynthetic capacity and overall vigour. Phytoplasma diseases also lead to increased tree mortality, altering ecosystem dynamics.This review article provides an in-depth exploration of phytoplasma diseases affecting trees worldwide, encompassing their epidemiology, symptomatology, impact and management strategies.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

Suitable soil structure and nutrient security are important for plant growth and development, characteristics of soil fractal dimension and distribution of physical and chemical properties and their interactions play an important role in studying the stability of soil structure and water and fertilizer cycles. As a sustainable management model, intercropping has positive benefits for erosion control, spatial optimization of resources, as well as improving system productivity. The effects of four intercropping methods on soil fractal dimension and physicochemical properties were investigated by intercropping Salvia miltiorrhiza with forage and S. miltiorrhiza with forest under typical karst rock desertification habitats in Guizhou. The results showed that soil nutrient content of intercropping was significantly higher than that of monoculture, the organic carbon content of soil grown under forest is higher than other treatments, and there was a non-significant change in soil water content of intercropping compared with monoculture. The soil fine-grained matter of intercropping was significantly higher than that of monoculture, while the soil fractal dimension showed a tendency to become larger with the increase of fine-grained matter. The intercropping planting, due to its component types and spatial and temporal configurations, leads to differences in soil water and fertilizer interactions, which can be combined with other ecological restoration measures to optimize the composite model and jointly promote the restoration and development of ecologically fragile areas.

The decline in global pollinator populations has raised serious concerns about the sustainability of ecosystems and agricultural productivity. Urban environments, often characterized by habitat loss and environmental stressors, are considered challenging habitats for pollinators. However, urban green spaces, including street and ornamental trees, can play a crucial role in supporting pollinators by providing essential food resources. This study aimed to evaluate the sugar and amino acid production of eight urban tree species to determine their potential contributions to pollinator nutrition. Our findings revealed significant variability among species regarding nectar volume, sugar content, and amino acid composition, emphasizing the importance of considering multiple factors when assessing the utility of floral resources. Consequently, Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda demonstrated superior potential in supporting urban pollinator communities. Contrastingly, Sorbus commixta, Styrax japonicus, and Cornus kousa, often favored for their aesthetic appeal, showed less potential. This study underscores the need for urban tree selection that prioritizes species that provide diverse and abundant nutritional resources for pollinators while balancing aesthetic and ecological values. Our results offer a foundation for better-informed urban biodiversity management and contribute to creating pollinator-friendly urban environments.

Reducing the carbon footprint of energy production is one of the most pressing challenges facing humanity today. Lignocellulosic biomass residues from fruit production industries show promise as a viable energy source. This paper presents a study of the Italian context concerning the utilization of orchard lignocellulosic residues for energy production as electricity or bioethanol. The potential of various orchard residues was assessed through chemical and physical analysis, and an equivalent electrical energy of about 6441.62 GWh or an amount of 0.48 Mt/y of bioethanol was obtained based on the average annual dry residue mass availability of about 3.04 Mt/y. These data represent the 9.30% of the national electrical energy production from renewable sources, as well as the 6.21% of the Italian demand for gasoline in 2022. Electricity generation from these residues shown its potential as a reliable and sustainable baseload power source, as well as source of renewable transportation fuel. The studied process could be a valuable reference to expand these concepts on global scale to achieve a greener and more sustainable energy future.

The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DT) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents ("oracles") and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.

Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. We present a two-part data-driven methodology for objectively determining the best EEG bands for a given dataset in this paper. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. Data-driven EEG band discovery may aid in objectively capturing key signal components and uncovering new indices of brain activity.

The recent mountain pine beetle outbreaks have caused widespread mortality among lodgepole pine trees in western North America, resulting in a reduced population of surviving trees. While previous studies have focused on the cascading impacts of these outbreaks on the physiology and growth of the surviving trees, there remains a need for a comprehensive study into the interactions among various physiological traits and the growth in post-outbreak stands. Specifically, the relationship between chemical (primarily terpenes) and anatomical (mainly resin ducts) defences, as well as the allocation of non-structural carbohydrates (NSCs) to support these defence modalities, is poorly understood. To address these gaps, we conducted a field survey of surviving lodgepole pine trees in post-mountain pine beetle outbreak stands in western Canada. Our retrospective analysis targeted to determine correlations between the post-outbreak concentrations of monoterpenes, diterpenes, and NSCs in the phloem, and the historical resin duct characteristics and growth traits before and after the outbreak. We detected strong correlations between post-outbreak concentrations of monoterpenes and historical resin duct characteristics, suggesting a possible link between these two defence modalities. Additionally, we found a positive relationship between NSCs and the total concentrations of monoterpenes and diterpenes, suggesting that NSCs likely influence the production of these terpenes in lodgepole pine. Furthermore, historical tree growth patterns showed strong positive correlations with many individual monoterpenes and diterpenes. Interestingly, while surviving trees had enhanced anatomical defences after the outbreak, their growth patterns did not vary before and after the outbreak conditions. The complexity of these relationships emphasizes the dynamics of post-outbreak stand dynamics and resource allocation in lodgepole pine forests, highlighting the need for further research. These findings contribute to a broader understanding of conifer defenses and their coordinated responses to forest insect outbreaks, with implications for forest management and conservation strategies.

This is a study on fractals existing in music. The application case is the composition DIAPHONIES of Michael Paouris. Sliding-window fractal analysis indicate areas of continuous fractality dipped in non-fractal areas of possibly deterministic and stochastic nature. Diverging cases of pronounced non-fractal areas are addressed and also areas of mixed fractality and non-fractality. Many segments exhibit persistency-P (2

Branching patterns are ubiquitous in nature, consequently over the years many researchers have tried to characterize the complexity of their structures. Due to their hierarchical nature and resemblance to fractal trees, they are often thought to have fractal properties, however their non-homogeneity (i.e., lack of strict self-similarity) is often ignored. In this paper we review and examine the use of the box-counting and sandbox methods to estimate the fractal dimensions of branching structures. We highlight the fact that these methods rely on an assumption of self-similarity that is not present in branching structures due to their non-homogeneous nature. Looking at the local slopes of the log-log plots used by these methods reveals the problems caused by the non-homogeneity. Finally, we examine the role of the canopies (endpoints or limit points) of branching structures in the estimation of their fractal dimensions.

(RADA)4 nanoscaffolds are excellent candidates for use as peptide delivery vehicles: they are relatively easy to synthesize with custom bio-functionality, and assemble in situ to allow a focal point of release. This enables (RADA)4 to be utilized in multiple release strategies by embedding a variety of bioactive molecules in an all-in-one ‘construct’. One novel strategy focuses on the local, on-demand release of peptides triggered via proteolysis of tethered peptide sequences. However, the spatial-temporal morphology of self-assembling nanoscaffolds may greatly influence the ability for enzymes to both diffuse into as well as actively cleave substrates. Fine structure and its impact on overall affect on peptide release is poorly understood. In addition, fractal networks observed in nanoscaffolds are linked to the fractal nature of diffusion in these systems. Therefore, matrix morphology and fractal dimension of virgin (RADA)4 and mixtures of (RADA)4 and matrix metalloproteinase 2 (MMP-2) cleavable substrate modified (RADA)4 were characterized over time. Sites of high (GPQG+IASQ, CP1) and low (GPQG+PAGQ, CP2) cleavage activity were chosen. Fine structure was visualized using established according to established methods. After 2 hrs of incubation, nanofiber networks showed an established fractal nature, however nanofibers continued to bundle in all cases as incubation times increased. It was observed that despite extensive nanofiber bundling after 24 hrs of incubation time, the CP1 and CP2 nanoscaffolds were susceptible to MMP-2 cleavage. The properties of these engineered nanoscaffolds characterized herein illustrate that they are an excellent candidate as an enzymatically initiated peptide delivery platform.

The novel coronavirus has presented specimen-access challenges to geoscientific researchers, including paleobiologists interested in fossil ammonoids. Ammonoid sutures are geometric patterns formed by the intersection of the septa and the shell wall, and have long been a diagnostic tool for ammonite researchers for such applications as species identification, taxonomic relationships, ontogenetic change, functional and evolutionary morphology, and other aspects of ammonoid paleobiology. Without access to specimens in museum and institutional collections, researchers must rely on previously published illustrations and photographs of ammonoid sutures. However, many of these illustrations were published decades ago without an index of scale. Suture tracings lacking a scale bar are not usable by researchers interested in applying the quantitativeness of fractal geometry to the interpretation of septal complexity. Additonally, distortion of the marginal elements of suture geometry occurs due to shell curvature near the venter and umbilicus. The revised approach described here eliminates the problem of missing scale information in fractal analysis of ammonite sutures, making use of just the lateral lobe and adjacent saddle. Our revised method’s non-requirement of a full hemisuture also facilitates comparisons among sutures within an ontogenetic sequence, or sutures from multiple ammonite taxa.

Companion cropping involves growing two or more species together, benefiting the conservation of natural resources and improving fertilization and nutrient cycling by balancing chemical and organic sources. Studies, as in the case of tree tomato and Hass avocado, have shown a significant increase in yield compared to monocultures, highlighting the viability of this practice. In addition to their environmental benefit, companion crops offer economic advantages to producers by obtaining multiple products in a single harvest, strengthening food security and the economy of rural families. This study evaluated three levels of fertilization and interactions between fruit trees at different altitudes, observing differential behavior in the variables evaluated. The combination of cape gooseberry and blackberry showed significantly positive results, with more leaves and fewer pests, demonstrating the benefits of companion plants. A trend towards the combined use of chemical and organic fertilizers was observed, a potential strategy to reduce costs and improve crop growth. The results were shared with producers interested in implementing companion cropping, encouraging the adoption of this more sustainable and profitable agricultural practice. Companion crop agriculture represents a viable and promising strategy for a more efficient and sustainable agriculture, with both environmental and economic benefits.

This paper uses the numerical results of surveys sent to Huelva’s (Andalusia, Spain) households to determine the degree of knowledge they have about the urban water cycle, needs, values, and attitudes regarding water in an intermediary city with low water stress. In previous research, we achieved three different households’ clusters. The first one grouped households with high knowledge of the integral water cycle and a positive attitude to smart devices at home. The second cluster described households with low knowledge of the integral water cycle and high sensitivity to price. The third one showed average knowledge and predisposition to have a closer relationship with the water company. This paper continues with this research line, applying Classification and Regression Trees (CART) to determine which hierarchy of variables/factors/ independent components obtained from the surveys are the decisive ones to predict the range of household water consumption in Huelva. Positive attitudes towards improved cleaning habits for personal or household purposes are the highest hierarchy component to predict the water consumption range. Second in the hierarchy, the variable Knowledge Global Score about the integral urban water cycle, associated with water literacy, also contributes to predicting the water consumption range. Together with the three clusters obtained previously, these results will allow us to design water demand management strategies (WDM) fit for purpose that enable Huelva’s households to use water more efficiently.

Trees provide key ecosystem services, but the health and sustainability of these plants is under increasing biotic and abiotic threat, including from the growing incidences of non-native invasive plant pests (including pathogens). The island of Ireland (Ireland and Northern Ireland) is generally accepted to have a high plant health status, in part due to its island status and because of the national and international regulations aimed at protecting plant health. To establish a baseline of the current pest threats to tree health for the island of Ireland, the literature and unpublished sources were reviewed to produce a dataset of pests of trees on the island of Ireland. The dataset contains 396 records of pests of trees on the island of Ireland, the majority of pests being arthropods and fungi, and indicating potentially more than 44 non-native pest introductions. The reliability of many (378) of the records was judged to be high, therefore the dataset provides a robust assessment of the state of pests of trees recorded on the island of Ireland. We analyse this dataset and review the history of plant pest invasions, including (i) discussion on notable native and non-native pests of trees, (ii) pest interceptions at borders and (iii) pests and climate change. The dataset establishes an important baseline for the knowledge of plant pests on the island of Ireland, and will be a valuable resource for future plant health research and policy making.

Leaf temperature (T_leaf) influences photosynthesis and respiration. Currently, there is a growing interest on including lianas in productivity models due to their increasing abundance, and their detrimental effects on net primary productivity in tropical environments. Therefore, understanding the differences of T_leaf between lianas and trees is important for future of forest on whole ecosystem productivity. Here we determined the displayed leaf temperature (T_d= T_leaf – ambient temperature) of several species of lianas and their host trees during ENSO and non-ENSO years to evaluate if the presence of lianas affects the T_d of their host trees, and if leaves of lianas and their host trees exhibit differences in T_d. Our results suggest that close to midday, the presence of lianas does not affect the T_d of their host trees; however, lianas tend to have higher values of T_d than their hosts across seasons, in both ENSO and non-ENSO years. Although lianas and trees tend to have similar physiological-temperature responses, differences in T_d could lead to significant differences in rates of photosynthesis and respiration based temperature response curves. Future models should thus consider differences in leaf temperature between these life forms to achieve robust predictions of productivity.

Olive trees have a unique reproductive pattern marked by biennial fruiting. This study delves into the repercussions of alternate fruit bearing on the water relations of olive trees and the associated ecophysiological mechanisms. The experiment spanned two consecutive years: the "ON" year, characterized by a high crop load, and the "OFF" year, marked by minimal fruit production. Key ecophysiological parameters, including sap flow, stomatal conductance, and photosynthetic rate, were monitored in both years. Pre-dawn water potential was measured using continuous stem psychrometers and the pressure chamber technique. Biochemically, non-structural carbohydrate concentrations (starch, sucrose, and mannitol) and the carbon-stable isotope ratio (δ13C) in olive leaves were examined. Results unveiled heightened leaf gas exchange during the "ON" year, leading to increased water consumption and photosynthetic rates. This escalated water usage during the "ON" year significantly reduced leaf water potential. Sucrose and starch concentrations were notably elevated in the "ON" year, with no significant impact on mannitol concentration. Regarding the carbon stable isotope ratio, leaves from the "OFF" year exhibited significantly higher δ13C abundance, indicative of heightened resistance to the CO2 pathway from the atmosphere to carboxylation sites compared to the "ON" year plants.

The methods used to introduce CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas-mediated genome editing into fruit species, as well as the impacts of the application of this technology to activate and knock out of target genes in different fruit trees species including tree development, yield, fruit quality, and tolerance to biotic and abiotic stresses have been firstly described in this review. The application of this gene editing technology could allow the development of new generations of fruit crops with improved traits by targeting different genetic segments or even could facilitate the introduction of traits in elite cultivars without changing other traits. However, at this moment, the scarcity of efficient regeneration and transformation protocols in some species, the fact that many of those procedures are the genotype-dependent or the convenience of segregating the transgenic parts of the CRISPR system represent the main handicaps limiting the potential of genetic editing techniques for fruit trees. Finally, latest news on the legislation and regulations about the use of plants modified through CRISPR/Cas systems has been also discussed.

Many tropical invasive species have allelopathic effects that contribute to their success in native plant communities. Pyrolyzed biomass (“biochar”) can sorb toxic compounds, including allelochemicals produced by invasive plants, potentially reducing their inhibitory effects on native species. Strawberry guava (Psidium cattleianum) is among the most important allelopathic invasive species in tropical islands and recognized as the most serious invasive species threat in the global biodiversity hotspot of Mauritius. We investigated the effects of additions of locally produced biochar on native tree species in a field experiment conducted in areas invaded by strawberry guava within Mauritius’ largest national park. Growth and survivorship of native tree species were monitored over 2 ½ years in plots subjected to four treatments: non-weeded, weeded, weeded + 25 t/ha biochar and weeded + 50 t/ha biochar. Native tree growth and survivorship were strongly suppressed by strawberry guava. Biochar treatments dramatically increased native tree performance, with more than a doubling in growth, and substantially increased native tree survivorship and species diversity, while suppressing strawberry guava regeneration, consistent with growth-promoting properties and sorption of allelochemicals. We conclude that biochars, including “sustainable biochars” produced from locally accessible biomass using low-tech pyrolysis systems, have considerable potential to counteract effects of allelopathic invaders and increase the capacity for native species regeneration in tropical island ecosystems.

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound Workflow net is trivial; however, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, when the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree re-discoverability.

The interlaminar strength of mechanically interlocked polymer-metal-interfaces is strongly dependent on the surface structure of the metal component. Therefore, this contribution assesses the suitability of the fractal dimension for quantification of the surface structure as well as interlaminar strength prediction of aluminum/polyamide 6 polymer-metal-hybrids. Seven different surface structures, manufactured by blasting, combined blasting and etching, thermal spraying and laser ablation, are investigated. The experiments are carried out on a novel butt-bonded hollow cylinder testing method that allows shear and tensile strength determination with one specific specimen geometry. The fractal dimension of the metal surfaces is derived from cross-sectional images. For comparison, the surface roughness slope is determined and related to the interlaminar strength. Finally, a fracture analysis is conducted. For the investigated material combination, the experimental results indicate that the fractal dimension is an appropriate measure for predicting the interlaminar strength

Sehezar area is located in southern Tonokabon in Mazandaran province, north of Iran, near the Tarom – Hashdtjin belt. The existence of granitoid masses in the region can be important in terms of the potential of mineralization. Geochemical anomaly separation from the background is one of the important steps in mineral exploration. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods (like univariate, bivariate, multivariate statistics), spatial statistical methods and fractal and multi-fractal methods. To identify the anomalous area, 71 stream sediment samples were collected from the area and analyzed by the ICP-MS method, and then interpreted. Initially, data were normalized and afterwards, univariate analysis (threshold limit and screening (P.N) methods) was used, in which results of the probable and definite anomaly of the threshold method were confirmed by the P.N screening method. Finally, the maps of the anomal zones were drawn. Then, bivariate analysis (Pearson correlation coefficients) and multivariate analysis on normal data were performed on SPSS software, in which factor analysis and cluster analysis were used for multivariate analysis. As a result of using the factor analysis method, six factors were identified and factor maps were drawn by the Surfer software. Also, by using cluster analysis, the variables were divided into two groups. In order for a better separation of the geochemical anomaly from the background, in addition to the threshold method, the Concentration - Area fractal method was used. Here, the fractal geometry using full-logarithmic graphs of the Concentration - Area obtained is capable of separating the stairs of different sections (background, threshold, and anomaly) with respect to the angle coefficient of the Concentration - Area plot. Then, in conclusion, results of these methods were compared and investigated, and finally, the anomalies area maps of the Au, Ag, Cu, Fe, W elements were drawn by Concentration - Area fractal and threshold methods and anomalous zones were introduced.

The establishment of an isolation layer in submarine mining has long presented an enduring challenge. In the context of this research, we conducted a similarity simulation test to preliminarily ascertain the interplay between the thickness and extent of the isolation layer. Subsequently, we introduce an innovative approach that integrates fractal theory and the bonded block model (BBM) to simulate undersea isolation layer mining. The validation of this method hinges on on-site borehole scanning and displacement monitoring, which serve to depict the intricate fractal evolution of fractures and predict the optimal thickness of the isolation layer. Our findings affirm the robustness and validity of this method. Evaluation of the fractal dimensions of fractures reveals that a critical threshold of 1.7 exists to prevent structural failure of the isolation layer, while a limit of 1.5 is necessary to avert significant water ingress. Remarkably, the correlation dimension of the settlement time series closely aligns with the fractal dimension of the fractures, underscoring the feasibility of ensuring the safety of isolation layer mining through real-time settlement monitoring.

In this article, the new Hermite–Hadamard type inequalities are studied via generalized s-convexity on fractal sets. These inequalities derived on fractal sets are shown to be the generalized s-convexity on fractal sets. We proved that the absolute values of the first and second derivatives for the new inequalities are the generalization of s-convexity on fractal sets.

In this article, the new Hermite–Hadamard type inequalities are studied via generalized s-convexity on fractal sets. These inequalities derived on fractal sets are shown to be the generalized s-convexity on fractal sets. We proved that the absolute values of the first and second derivatives for the new inequalities are the generalization of s-convexity on fractal sets.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

Urban trees are often more sun- and wind-exposed than their forest-grown counterparts. These environmental differences can impact how many species grow – impacting trunk taper, crown spread, branch architecture, and other aspects of tree form. Given these differences, windthrow models derived from traditional forest production data sources may not be appropriate for urban forest management. Additionally, visual abnormalities historically labeled as “defects” in timber production may not have a significant impact on tree failure potential. In this study, we look at urban tree failures associated with Hurricane Irma in Tampa, Florida, USA. We used spatial analysis to determine if patterns of failure existed among our inventoried trees. We also looked at risk assessment data to determine which visual defects were the most common and the most likely to be associated with branch or whole-tree failure. Results indicate that there was no spatial pattern associated with the observed tree failures – trees failed or withstood the storm as individuals. While some defects like decay and dead wood were associated with increased tree failure, other defects like weak branch unions and poor branch architecture were less problematic.

The fractal dimension of geomagnetic variations at the Baigazan magnetic station at Russian Altay for 2011-2013 was calculated using the Higuchi method. The daily variation of Higuchi Fractal Dimension (HFD) for the D,H,Z-components of the geomagnetic field has been investigated, its contribution to the variability of HFD is from 30 to 40 percent of the total variance. A correlation analysis of the fractal dimension of the variations of the D,H,Z-components with Auroral Electrojet (AE) index and solar wind characteristics was carried out. A negative correlations with logarithms of AE-index, interplanetary magnetic field (IMF) strength and solar wind velocity were found. About 25 percent of the fractal dimension variance is controlled by the variability of these characteristics. Pair and partial correlation coefficients for these parameters were calculated for every month of 2011-2013.

The discovery of a simple numerical formula for the projection of all the atomic mass of life-sustaining CONHSP bioatoms leads to the emergence of a set of Nested CODES unifying all the biological, genetic and genomic components by unifying them from bioatoms up to 'to whole genomes. In particular, we demonstrate the existence of a digital meta-code common to the three languages ​​of biology that are RNA, DNA and amino acid sequences. Through this meta-code, genomic and proteomic images appear almost analogous and correlated. The analysis of the textures of these images then reveals a binary code as well as an undulatory code whose analysis on the human genome makes it possible to predict the alternating bands constituting the cariotypes of the chromosomes. The application of these codes to perspectives in astrobiology, in Cancers basic research and the emergence of binary codes and regions of local stability (voting process), whose fractal nature we demonstrate, is illustrated.

This paper addresses the Collatz Conjecture, an open question in mathematics that postulates all positive integers will eventually reach one when a pair of specific operations are repeatedly applied. Despite its apparent simplicity, the conjecture lacks a formal proof. To tackle this enigma, we introduce Algebraic Inverse Trees (AITs), data structures that trace inverse operations of the Collatz sequence. This new approach not only elaborates our unique methodology but also sheds light on the underlying complexities of the Collatz Conjecture.

Background & Aims: Globally, liver diseases account for 2 million deaths per year. For those with advanced liver disease the only curative approach is liver transplantation. However, less than 10% of those in need get a liver transplant due to limited organ availability. To circumvent this challenge, there has been a great focus in generating a bioengineered liver. Despite its essential role in liver functions, a functional biliary system has not yet been developed. In this framework, exploration of epithelial cell self-organogenesis and microengineering-driven geometrical cell confinement allow to envision the bioengineering of a functional biomimetic intrahepatic biliary tract. Approach: Three-dimensional (3D) bile ducts were built in vitro by restricting cell adhesion to two-dimensional (2D) patterns to guide cell self-organization. Tree shapes mimicking the configuration of the human biliary system were micropatterned on glass slides, restricting cell attachment to these areas. Different tree geometries and culture conditions were explored to stimulate self-organogenesis of normal rat cholangiocytes (NRCs) used as a biliary cell model, either alone or in coculture with human umbilical endothelial cells (HUVECs). Results: Pre-seeding the micropatterns with HUVECs promoted luminogenesis with higher efficiency to yield functional branched biliary tubes. Lumen formation, apico-basal polarity, and preservation of the cholangiocyte phenotype were confirmed. Moreover, intact and functional biliary structures were detached from the micropatterns for further manipulation. Conclusion: This study presents physiologically relevant 3D biliary duct networks built in vitro from 2D micropatterns. This opens opportunities for investigating bile duct organogenesis, physiopathology, and drug testing.

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways on how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry point as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

An extension of the models used to generate fractal traffic flows is presented by means of the formulation of a model that considers the use of one-dimensional chaotic maps. Based on the disaggregation of the temporal series generated by the model, a valid explanation of behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.

In the event of an abrupt sunlight reduction scenario there is a time window that occurs between when food stores would likely run out for many countries (~6 months or less) and ~1 year when resilient foods are scaled up. A promising temporary resilient food is leaf protein concentrate (LPC). Although it is possible to extract LPC from tree biomass (e.g. leaves and needles), neither the yields nor the toxicity of the protein concentrates for humans from the most common tree species has been widely investigated. To help fill this knowledge gap, this study uses high-resolution mass spectrometry and an open source toolchain for non-targeted screening of toxins on five common North American coniferous species: Western Cedar, Douglas Fir, Ponderosa Pine, Western Hemlock, and Lodgepole Pine. The yields for LPC extraction from the conifers ranged from 1% to 7.5%. The toxicity screenings confirm that these trees may contain toxins that can be consumed in small amounts and additional studies including measuring the quantity of each toxin are needed. The results indicate that LPC is a promising candidate to be used as resilient food, but future work is needed before LPCs from conifers can be used as a wide-scale human food.

'Xinomavro' (V. vinifera L.) is an important native red wine grape variety in Northern Greece, particularly in PDO regions. Despite its significance, there is limited research on the effects of pruning type and severity on 'Xinomavro' vine physiology, yield, and berry quality across diverse environmental conditions. This study aims to address this knowledge gap and provide growers with crucial information for optimizing vineyard management practices. The study was conducted over two consecutive years (2016 and 2017) in a vineyard in Thessaloniki, Northern Greece. Four treatments ( B12: 12 buds on 6 spurs; B24: 24 buds on 12 spurs; M12: 12 buds on 2 canes; M24: 24 buds on 4 canes) combining two bud load levels (12 or 24 count nodes) and two pruning types (short spurs or long canes) were applied to 'Xinomavro' vines in complete block randomized design. Measurements included vine water status, gas exchange, canopy characteristics, yield components, and berry composition. Bud load and pruning type significantly influenced vine canopy development, microclimate, and yield components. Short pruning with high bud load (B24) resulted in denser canopies and higher yields, while cane pruning (M12 and M24) led to more open canopies and improved berry quality indicators. Treatment effects on berry composition were inconsistent across years but showed a tendency for higher anthocyanin and total phenol content in cane-pruned vines. The study demonstrates that pruning type (short or long fruiting units) may have a greater impact on vine growth, yield, and berry composition than bud load alone in 'Xinomavro' vines. Cane pruning appears to be a more effective strategy for achieving vine balance and potentially improving grape quality under the given experimental conditions.

This work introduces a device for long term systematic monitoring of trees against borers. A widely applied way to detect wood-boring insects is to insert a piezoelectric probe with uncoated waveguide in the tree trunk and listen for locomotion or feeding sounds through headphones. This approach has several shortcomings: a) frequent manual inspection of trees is costly and impractical to be scaled to hundreds or thousands of trees, b) the larvae could be present but inactive during the inspection time and, c) when the trees are in urban environments the background noise can be significant and can mask the feeble sounds of wood-boring insects even with the use of specialized headphones. We introduce a remotely controlled device that records and wirelessly transmits on a scheduled basis short recordings of the internal vibrations of a tree to a server. The user can listen remotely or process the recording automatically to infer the infestation state of the tree with wood-boring insects that feed or move inside the tree. When integrated within the IoT framework this device can scale up to automatically monitoring the trees of the entire city. The proposed approach led to detection results in field trials of the pests Xylotrechus chinensis and Rhynchophorus ferrugineus.

Sustainable urban forests require tree acceptance and support. Two groups of respondents: professionals working in urban green areas and individuals with no professional connection with trees revealed their attitudes towards trees by assessing statements in a survey questionnaire. Tree benefits were perceived as much more important than the annoyance. However, 6% of the non-professionals found only negative aspects of trees, proving to be arboriphobes. No arboriphobes and no “tree sceptics” were among the professionals. Around 40% of the respondents in the two groups found the number of trees in the surrounding areas too low. The nuisance caused by trees was seen as more disturbing by younger and lower educated professionals. Women tended to assess trees as more attractive and as having a stronger influence on social relations than men. Men dominated the “tree indifferent” group. The attractiveness of trees and their impact on social relations were related to the place of residence and the level of education among the non-professionals. The level of education of the non-professionals was also connected to being clustered into one of the four abovementioned groups of respondents. A majority of medium and big city dwellers as well as a minority of villagers were in the “tree liking” cluster.

The rapid urbanization process in recent decades has altered the carbon cycle and exacerbated the impact of climate change, prompting many cities to develop tree planting and green area preservation as mitigation and adaptation measures. While numerous studies have estimated the carbon stocks of urban trees in temperate and subtropical cities, data from tropical regions, including tropical botanic gardens, are scarce. This study aimed to quantify the aboveground biomass and carbon (AGB and AGC, respectively) stocks in trees at the Rio de Janeiro Botanical Garden arboretum, Rio de Janeiro, Brazil. Our survey included 6793 stems with a diameter at breast height (DBH) ≥ 10 cm. The total AGB was 8,047.24 Mg, representing 4,023.62 Mg of AGC. The AGB density was 207.4 Mg.ha-1 (AGC = 103.7 Mg.ha-1), which is slightly lower than the density stored in Brazil's main forest complexes: the Atlantic and Amazon forests, but much higher than in many cities worldwide. Our results suggest that, in addition to their global importance for plant conservation, tropical botanic gardens could function as significant carbon sinks within the urban matrix.

My innovative entropy measure, Ismail entropy, namely, (IE) is an ultimate generalization to numerous generalized entropies within the literature. The fractal dimension of IE is identified in this paper. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

This study reports fractal analysis of one-year radon in groundwater disturbances from five stations in China amid the catastrophic Wenchuan (Mw=7.9) earthquake of May 12, 2008 (day 133). Five techniques are used (DFA, fractal dimensions with Higuchi, Katz, Sevcik methods, power-law analysis) in segmented portions glided throughout each signal. Noteworthy fractal areas are outlined in the KDS, GS, MSS station data, while the portions were non-significant for PZHS and SPS. Up to day 133 critical epoch DFA-exponents have 1.5<=a<2.0 with several above 1.8. Fractal dimensions exhibit Katz’s D around 1.0-1.2, Higuchi’s D between 1.5-2.0 and Sevcik’s D between 1.0-1.5. Several power-law exponents are above 1.7 and numerous above 2.0. All fractal results of KDS-GS-MSS stations are further analysed using a novel computerised methodology which locates the exact out-of-threshold fractal areas and combines the outcomes of different methods per five, four, three, and two (maximum 13 combinations) versus nineteen Mw<=5.5 earthquakes of the area. Most different-technique coincidences are before the great Wenchuan earthquake and its post-earthquakes. This is not only with one method, but with 13 different methods. Other interpretations are also discussed.

Chilean geography is highly variable, not only from a climatic and hydrological point of view, but also a morphological one, showing unpredictable natural patterns with marked contrasts throughout the country, for which sometimes it is considered as a "crazy" geography. In this paper we have investigated this apparent disorganized character by exploring the fractal properties of fluvial networks extracted from basins distributed across the continental territory. Analytical and semi-empirical methods were applied, finding striking patterns of organization in the distributions of Horton parameters and the fractal dimension of the drainage networks. Fractal dimension reveals to be quite dependent on the drainage area of each unit, showing clear groupings by tectonic and climatological factors. Such dimension reveals to be an important geomorphic parameter, if not the only one able to capture the real morphology of a fluvial network. From our results and despite the diversity of landforms, hydrological, climatic and tectonic conditions, Chilean’s geography is perhaps not as crazy and disorganized as believed.

Pruning is an important part in controlling the branching, good fruit size, and prodctivity of fruit trees. and consequently, ‘Valencia’ orange trees were grown many years ago, the trees were suffering from a decline in productivity in the newly reclaimed lands after certain years of plantation. This study elucidated the effect of pruning severity on growth, yield and fruit quality of ‘Valencia’ orange trees. Four levels of pruning were applied on 35 years old ‘Valencia’ orange trees as follows: T1: Control (un-pruned), T2: Light pruning ( 25% of main branches were removed); T3: Moderate pruning (50% of main branches were removed) and T4: Heavy pruning: 75% of main branches were removed. The treatments were done in February 15th (for each season). The results indicated that pruning severity increased vegetative growth paraments such as shoot length and leaf area and decreased canopy volume of Valencia orange trees in comparison to control. Maximum yield was gained from 75% followed by 50% and 25% pruning severity treatment. The pruning treatments improve in fruit weight, size, firmness, juice content, TSS%, TSS/acid ratio and vitamin C content. In addition, pruning produced an increasing in fruit yield per tree as well as yield increasing %. Hence, it might be concluded that pruning at different severity applied in February can be used effectively on ‘Valencia’ orange trees to improve vegetative growth, fruit physical and chemical properties. Besides, increasing fruit yield by nearly 20% in comparison to control.

The location and number data of individual fruit trees are critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for rapidly obtaining the data and reducing human operations errors. This study proposes an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing collected high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision ([email protected]) of the four YOLOv5 models for validation reach 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was counted using YOLOv5x, and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study will provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.

Microgrids of varying size and applications are regarded as a key feature of modernizing the power system. The protection of those systems, however, has become a major challenge and a popular research topic for the reason that it involves greater complexity than traditional distribution systems. This paper addresses the issue through a novel approach which utilizes detailed analysis of current and voltage waveforms through windowed fast Fourier and wavelet transforms. The fault detection scheme involves bagged decision trees which use input features extracted from the signal processing stage and selected by correlation analysis. The technique was tested on a microgrid model developed using PSCAD/EMTDS, which is inspired from an operational microgrid in Goldwind Sc. Tech. Co. Ltd, in Beijing, China. The results showed great level of effectiveness to accurately identify faults from other non-fault disturbances, precisely locate the fault and trigger opening of the right circuit breaker/s under different operation modes, fault resistances and other system disturbances.

Despite the technological advancements of 3-D city models, the 3-D shape abstraction methods for urban vegetation are recent and still limited to support spatial analysis of individual or groups of trees and forest patches. In this paper, a scheme for individual urban tree abstraction, which retains the semantic complexity of the object, is proposed. Our contribution is three-fold. First, an initial tree structure based on a new 3-D aggregation operator is proposed. Second, we developed Gestalt rules for 3-D urban vegetation mapping. Third, current state-of-the-art deep learning is adapted for the abstraction of individual urban trees. Quantitative and qualitative results show the effectiveness of our proposed approach in accurately reducing the spatial density of trees and the degree of fragmentation of the total green area, which can access information about the location, size, and spatial distribution of mapped urban trees.

For tropical forests to survive anthropogenic climate change, the trees that comprise them will need to tolerate the newly emerging conditions through adaptation or acclimation, or they can avoid climate change through range shifts and “species migrations”. In this review, we show that the rapid pace and extreme severity of modern climate change makes it extremely unlikely that tropical tree species can adapt (with some possible exceptions). We also show that while many tropical tree species are shifting their distributions to higher, cooler elevations, the rate of these migrations are mostly insufficient to offset ongoing changes in temperatures, especially in lowland tropical rainforests where thermal gradients are shallow or nonexistent. We argue that the best hope for tropical tree species to avoid becoming “committed to extinction” is acclimation. While several new methods are being developed to test for acclimation, we unfortunately still do not know if tropical tree species can acclimate or what factors may prevent or facilitate acclimation. Until these questions are answered, our ability to predict the fate of tropical species and tropical forests – and the many services that they provide to humanity – remains critically impaired.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis by selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), the decision tree of C5.0, support vector machine (SVM), the decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

Olive trees have been of economic and cultural value since pre-Roman times, and continue to dominate landscapes and agriculture in many mediterranean regions. Recent mass losses of olive trees in Southern Italy due to an exotic plant pathogen highlight the need for methods that to monitor the olive trees in a landscape or region operationally. Here, we develop a method for counting olive trees from aerial photographs and test it in areas with a high diversity of olive tree ages, sizes, and shapes. This heterogeneity complicates tree counting as centennial trees often have crowns that are split into multiple segments, resembling multiple crowns, while nearby crowns often form a semi-closed canopy comprising multiple trees. Comparisons with reference counts in two 20 ha sites and over three different years indicate the automated counts tend to be reasonably accurate (median error 13%, n = 6), but heavily influenced by a few olive orchards with particularly high planting densities and a relatively closed canopy in which distinguishing individual trees is challenging. Overall, the algorithm estimated tree densities well (counting 82 to 109 trees/ha versus 87 to 104 trees/ha in the reference counts), indicating the method is suitable to track the number of olive trees over large areas.

The nuclear fusion reaction can be catalyzed in a suitable fusion fuel by muons(heavy electron)."For the fractal relation, ranging from DNA knots to solar neutrino flux signals" ever derived f scale-invariant properties distinguished between classical invariant theory & quantum invariant theory subfactors. Accompanying isomorphicity & Connes Fusion Tensor Product retrieved to muon-catalyzed fusions where surroundings of room temperature fusion driven by the balance in mt-Dna fusion &fission.

This article presents a rigorous approach to the Collatz Conjecture, focusing on fundamental properties of Collatz sequences. We establish key properties of the Collatz function and its inverse, including surjectivity and injectivity. The structure of Collatz sequences is analyzed in depth, proving important results such as the Bounded Subsequence Property and the uniqueness of cycles. Central theorems on the properties of Collatz sequences, including the boundedness of all sequences and the nature of the unique cycle, are presented and proved. These results culminate in a complete resolution of the Collatz Conjecture, demonstrating that all Collatz sequences eventually reach the cycle {1, 4, 2}. We provide a rigorous proof of the conjecture, while emphasizing the need for thorough peer review and verification by the mathematical community given the significance of this long-standing problem.

To achieve low-cost and on-demand launches of micro satellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous-oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar-turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of low fuel regression rate; i.e., it achieved a higher fuel-gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up and its total impulse was increased to over 50 kNs for reaching an altitude of 15 km. In addition to the engine, other components were also improved, such as through incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device and data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. The presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments.

Bioenergy produced from perennial feedstocks such as woody biomass could serve as an opportunity to strengthen local and regional economies and also jointly produce various environmental services. In order to assess the potential for biomass- based bioenergy, it’s essential to characterize the interest that potential biomass suppliers have in such an endeavor. In the U.S. Great Plains region, this largely means assessing relevant perceptions of farmers and ranchers. We conducted a series of farmer and rancher oriented focus groups in North Dakota, South Dakota, Nebraska and Kansas to qualitatively explore opinions about the role that trees can play in agriculture and interest in woody biomass systems within existing Northern Great Plains (NGP) farms and ranches. Our findings suggest that farmer and ranchers generally value the role that trees, or tree-based practices like windbreaks can play in agriculture particularly on marginal farmland in terms of conservation or crop protection. Yet relative to the potential of trees as a biomass crop there is a distinct lack of knowledge and skepticism. Farmers and ranchers also noted variable degrees of risk concern and uncertainty regarding investing in tree-based systems, as well as a number of perceived external market related constraints to integrating trees within their managed systems. Most of the participants recognized that if biomass production or an increase in tree planting and management in general were to expand in the NGP region, government programs would likely be required to provide much needed technical guidance and financial incentives. As the NGP regional bioeconomy continues to emerge and expand, private and public investment relative to niche bioenergy feedstocks such as woody biomass should address the type of information needs that farmers and ranchers have relative to integrating biomass production into existing farm and ranch systems.

Fractal Geometric Pyramidal - Spiral- Hexagonal complexes ( GPS-H ) are the hereditary product of chaos and is its molecular solution.. Cancer is like a time machine to allowed us as pathologists to discover and document geometric structures that otherwise, we would not know existed. We have been documenting about cancer, geometric ordering complexes that are absolutely perfect made by an elastic geometry that we have called GPS-H complex: P for pyramid, S for spiral and H for hexagonal these structures have their genesis in the intercellular collisions of tumor cells or in extreme cellular damage by collision which generate DNA breakage. The DNA helices breaking generates the pyramidal and spiral component and in the same way, the amplification of the nitrogenous bases generates the hexagonal component. We are documenting, from different geology and biology scenarios, the existence of fractal invariant geometry of ancestral expansion, that generates order and connects these two systems as of the perspective of collision events at molecular , microscopic or macroscopic scales with the identification of geometric pattern that is modulated by the interaction between the electromagnetic field and material systems. The Geology and Biology converge into a single entity mediated by these geometric complexes to generate life platforms From collision and chaos events emerge at the interface of solid- liquids the GPS-H complexes that facilities water Fluid Interaction with basic geometries into a composite geometry leading in all systems and scales to deformation of the solid states . The deformed solid structures, in turn, acts as a moving wall boundary for the fluid and changes the water flow field in this way the balance and order of the system is restored. GPS-H are the hereditary product of chaos and its molecular solution. Based on the data obtained and analyzed, we verified that these GPS-H complexes are not native to the earth, representing a metastatic seeding that originally comes from the outer space and probably was incorporated into the ancestral DNA of the first multicellular organisms in the interphase of geology collsion events so we can trace it to the first multicellular organisms of the edicarian biota As ancient geometry, the pyramids of Egypt represent the most enigmatic and majestic structures built on earth but until know, it is unknown how they built, it us an unresolved mystery. Like enigmatic are the patterns in biology documented and published about cancer and represented by these fractal GPS-H complexes which have a unique particularity, remain invariable, they do not change from a microscopic to a macroscopic or megascopic scale and even over time they remain unchanged, as here it is documented. They have the quantum property of multiples expansions; thus, it is possible to trace them over time and in different dimensional scales. Based on the properties of this unique complex, it is possible to identify an active morphologic fractal connection between the microscopical level of pyramidal complexes seen in cancer and the megastructures of Egypt pyramids by clearly documenting complexes GPS-H from an ancestral universal geometry in these two different pictures The complexes GPS-H are part of a universal functional quantum geometry that generates a precursor order of life platforms They are the replaceable mobile pieces of a geological- biological puzzle that is assembled in time collectively and spatially to give rise to the multicellular platform that generates life on our planet, which shows an existing evident connection between these geometric complexes that jumps systems and dimensions to transfer and encode information between them. Bioengineering and manipulation of these ancestral GPS-H complexes can be used in alternative therapies in cancer , regenerative and rejuvenation medicine.

The paper continues the study of efficient algorithms for the computation of zeta functions over the complex plane. We aim to apply the modifications of algorithms to the investigation of underlying fractal structures associated with the Riemann zeta function. We discuss the computational complexity and numerical aspects of the implemented algorithms based on series with binomial-like coefficients.