The duality is one of the most interesting properties of the Laplace and Fourier transforms associated to the integer order derivative. Here, we will generalize it for fractional derivatives and extend the results to the Mellin, Z and discrete-time Fourier transforms. The scale and nabla derivatives are used.

This addendum contains clarifications and a sharpening of some of the results on the VIT transform framework developed in [1]. The focus is on the right-coefficient and left- coefficient forms of the transform, the extraction of a first-order term from a left polynomial fraction, and the application to linear time-varying systems.

In this article, the concept of fuzzy $(\sigma,\mathcal{Z})$-contractive mapping has been introduced in fuzzy metric spaces which is an improvement over the corresponding concept recently introduced by Shukla et al. [Fuzzy Sets and system. 350 (2018) 85--94]. Thereafter, we utilized our newly introduced concept to prove some existence and uniqueness theorems in $\mathcal{M}$-complete fuzzy metric spaces. Our results extend and generalize the corresponding results of Shukla et al.. Moreover, an example is adopted to exhibit the utility of newly obtained results.

Computation of Discrete Fourier Transform (DFT) is a challenging task. Especially, on computational machines/embedded systems where resources are limited. The importance of Fourier Transform (FT) cannot be denied in the field of signal processing. This paper proposes a technique that can compute Discrete Fourier Transforms for a matrice or vector with the help of matrix multiplication. Moreover, this paper discusses the trivial methods used for computation of DFT along with methods based on matrix multiplication used to compute discrete Fourier Transform in addition to the shortcomings. The proposed method can help in the calculation of a Discrete Fourier Transformation matrix by truncation of values from the proposed generic method which can help in computing DFT of varying lengths of vectors. On legacy computing machines and programming environments, having support for matrix multiplication, the proposed methodology can be implemented.

Saif et al. (J. Math. Comput. Sci. 21 (2020) 127-135) considered modified Laplace transform and developed some of their certain properties and relations. Motivated by this work, in this paper, we define modified Sumudu transform and investigate many properties and relations including modified Sumudu transforms of the power function, sine, cosine, hyperbolic sine, hyperbolic cosine, exponential function, and function derivatives. Moreover, we attain two shifting properties and a scale preserving theorem for the modified Sumudu transform. We give modified inverse Sumudu transform and investigate some relations and examples. Furthermore, we show that the modified Sumudu transform is the theoretical dual transform to the modified Laplace transform.

The development of photocatalysts with a wide spectral response and effective carrier separation capability is essential for the green degradation of tetracycline. In this work, a magnetic recyclable Z-scheme ZnO/ZnFe2O4 heterojunction (ZZF) was successfully constructed by the solid phase method using MIL-88A(Fe)@Zn as the precursor. Appropriate band gap width and Z-scheme charge transfer mechanism make ZZF have excellent visible light absorption performance, efficient charge separation and strong redox ability. Under visible light irradiation, the degradation efficiency of TCH for optimal sample ZZF2 can reach 86.3 % within 75 min in deionized water, and achieve 92.9 % within 60 min in tap water, which exhibits superior stability and reusability after five cycles. Moreover, the catalyst in the water can be conveniently recovered by magnetic force. After visible light irradiation for 70 minutes, the temperature of the reaction system increased by 21.9 °C. Its degradation constant (35.53×10-3 min-1) increases 5.1 times that at room temperature (6.95×10-3 min-1). Thermal energy perfects the kinetic driving force of the reactants and promotes carrier migration to produce more •O2- and •OH. This work provides a potential candidate and inspires the design of photocatalyst for tetracycline hydrochloride degradation.

Search for appropriate materials with favorable staggered energy band arrangements is important and of great challenge to fabricate Z-scheme photocatalysts with high activity in visible light. In this study, we demonstrated a facile and feasible strategy to construct highly active organic-inorganic Z-scheme hybrids (P-BMO) with linear pyrene-based conjugated polymer (P17-E) and Bi₂MoO₆ via in-situ palladium-catalyzed cross-coupling reaction. Characterization results revealed C-O chemical bond formed at the heterointerface between P17-E and Bi₂MoO₆ after in-situ polycondensation and endowed the hybrids with observably improved photogenerated carries transfer capability. Visible light driven photocatalytic removal of ciprofloxacin and Cr(VI) were significantly enhanced after the incorporation of P17-E into Bi₂MoO₆ whether with the morphology of nanosheets, nanobelts or microspheres. Moreover, this P-BMO hybrids were also found to exhibit sustainable excellent photocatalytic performance after four runs of photocatalytic evaluation test, suggesting its high activity and stability. To better eliminate the redox ability enhancement of P-BMO, a reasonable Z-scheme electrons transferring mechanism between P17-E and Bi₂MoO₆ was proposed and proved by the determination of •O₂^– and •OH and Pt nanoparticles photodeposition experiments. This work might provide a viable source and insight into the design of Z-scheme photocatalysts with excellent redox ability for environmental remediation.

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

Water and sanitation facilities in sub-Saharan Africa and Africa in general are appalling and for the most part absent. Progress continues with respect to the development of plant materials as potent adsorbents, disinfectants, coagulants, flocculants, wetland species and lots more as substitutes for the dangerous chemical disinfectants. This research presents the potential of phyto-active components of Zanthoxylum zanthoxyloides and Gongronema latifolium as effective biocides against water microbial contaminants. Dry powder of Zanthoxylum zanthoxyloides and Gongronema latifolium were extracted and prepared into different concentrations with ethyl acetate and chloroform, ranging from 25 to 500 mg/ml. These fractions were then examined for antimicrobial activities against inherent bacterial and fungal water contaminants using disc diffusion assay. Fractions were afterwards screened for phytochemical active compounds using standard methods. Crude extracts of the different plant examined selectively comprise saponins, tannins, reducing sugars, anthraquinones, flavonoids, terpenoids, phlobatanins and alkaloids. All plant extracts showed broad spectrum antibiosis against selected gram positive and gram negative bacteria including E. coli, P. aeruginosa, Klebsiella sp, S. pneumoniae and B. cereus, as well as tested fungi, including A. niger, A. flavus, Trichoderma sp and Candida sp. While all extracts exhibited maximum antibiosis at 500 mg/ml, the chloroform extracts compared well than ethyl acetate extracts. The overall results revealed that antimicrobial activities of the plant extracts are dose dependent with comparative activity greater than that of commercial antibiotics at the concentration of extracts tested. E.coli was the most susceptible microbial isolate tested and represents the potential of the extract against a group of coliform which are important indicators of microbial pollution in water. Other microbial isolates also recorded sensitivity to extracts tested at varying degrees. The findings indicate that microbes tested were mostly susceptible to chloroform extract of Z. zanthoxyloides and G. latifolium except for the activity of ethyl acetate extract of Z. zanthoxyloide against E.coli. Results of phytochemical screening of the extract also showed the varied presence of alkaloids, saponins, tannins, flavonoids, terpenoids and anthraquinones The results indicated that plant materials investigated can be developed as effective biocides against water microbial contaminants

Kim-Kim (Russ. J. Math. Phys. 2017, 24, 241-248) defined the degenerate Laplace transform and investigated some of their certain properties. Motivated by this study, in this paper, we introduce the degenerate Sumudu transform and establish some properties and relations. We derive degenerate Sumudu transforms of power functions, degenerate sine, degenerate cosine, degenerate hyperbolic sine, degenerate hyperbolic cosine, degenerate exponential function, and function derivatives. We also acquire a relationship between degenerate Sumudu transform and degenerate gamma function. Moreover, we investigate a scale preserving theorem for the degenerate Sumudu transform. Furthermore, we show that the degenerate Sumudu transform is the theoretical dual transform to the degenerate Laplace transform.

In this work, a mixture of Elzaki transform and projected differential transform method is applied to solve a nonlinear wave-like equations with variable coefficients. Nonlinear terms can be easily manipulated by using the projected differential transformation method. The method gives the results show that the proposed method is very efficient, simple and can be applied to other applications.

Abstract: The two-dimensional Fuzzy Transform was applied in image compression. The quality of the image compressed with this methodis better than that obtained using methods based on fuzzy relation equations and comparable with that of the JPEG method, with better compression execution times. In this paper we propose a variation of the method based on the two-dimensional Fuzzy Transform of image compression, in which the image is first partitioned into blocks and then each block is compressed with the higher compression rate. The advantage of this method consistsinto a greater compression of the image, guaranteeing a high quality of the reconstructed image.The results show that our method is better than the Fuzzy Transform method improving also the quality of the reconstructed image.

As a rapidly expanding family of two-dimensional (2D) materials, MXenes have recently gained considerable attention due to their appealing properties. Here, by developing a solution-based coating method that enables transfer-free and layer-by-layer film coating, we investigate the layer-dependent nonlinear optical absorption of Ti₃C₂T_x films ‒ an important member of the MXene family. By using the Z-scan technique, we characterize the nonlinear absorption of the prepared MXene films consisting of different numbers of monolayers. The results show that there is a strong and layer-dependent nonlinear absorption behavior, transitioning from revisable saturable absorption (RSA) to saturable absorption (SA) as the layer number increases from 5 to 30. Notably, the nonlinear absorption coefficient β varies significantly within this range, changing from ~7.13 × 10² cm/GW to ~-2.69 × 10² cm/GW. We also characterize the power-dependent nonlinear absorption of the MXene films at various incident laser intensities, and a decreasing trend in β is observed for increasing laser intensity. These results reveal the intriguing layer-dependent nonlinear optical properties of 2D MXene films, highlighting their versatility and potential for implementing high-performance nonlinear photonic devices.

The gravitational constant is an unchanging quantity included in the law of universal gravitation. The value of this constant is known much less accurately than other fundamental physical constants, which is due to the experimental difficulties of measuring it. Here we report a simple method for the theoretical calculation of the gravitational constant using the elementary charge, speed of light, Z boson mass, and relativistic mechanics.

In previous studies we used Laurent Schwartz’ theory of distributions to rigorously introduce discretizations and periodizations on tempered distributions. These results are now used in this study to derive a validity statement for four interlinking formulas. They are variants of Poisson’s Summation Formula and connect four commonly defined Fourier transforms to one another, the integral Fourier transform, the Discrete-Time Fourier Transform (DTFT), the Discrete Fourier Transform (DFT) and the Integral Fourier transform for periodic functions—used to analyze Fourier series. We prove that under certain conditions, these four Fourier transforms become particular cases of the Fourier transform in the tempered distributions sense. We first derive four interlinking formulas from four definitions of the Fourier transform pure symbolically. Then, using our previous results, we specify three conditions for the validity of these formulas in the tempered distributions sense.

By utilizing the Fourier transform, we present a practical method for evaluating a function of a derivative applied to any other arbitrary function of a single complex variable. As an illustration of this approach, we compute the actions of the displacement and squeeze operators on an arbitrary function, as well as the propagation of paraxial fields, Airy and number states, without the need for algebraic quantum operator techniques or the Fresnel integral.

This paper reviews the unilateral and bilateral, one- and two-dimensional Laplace transforms. The unilateral and bilateral Laplace transforms are compared in the one-dimensional case, leading to the formulation of the initial-condition theorem. This problem is solved with all generality in the one- and two-dimensional cases with the bilateral Laplace transform. General two-dimensional linear systems are introduced and the corresponding transfer function defined.

High Efficiency Video Coding (HEVC) uses an 8-point filter and a 7-point filter, which are based on the discrete cosine transform (DCT), for the 1/2-pixel and 1/4-pixel interpolations, respectively. In this paper, discrete sine transform (DST)-based interpolation filters (IF) are proposed. The first proposed DST-based IFs (DST-IFs) use 8-point and 7-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. The final proposed DST-IFs use 12-point and 11-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. These DST-IF methods are proposed to improve the motion-compensated prediction in HEVC. The 8-point and 7-point DST-IF methods showed average BD-rate reductions of 0.7% and 0.3% in the random access (RA) and low delay B (LDB) configurations, respectively. The 12-point and 11-point DST-IF methods showed average BD-rate reductions of 1.4% and 1.2% in the RA and LDB configurations for the Luma component, respectively.

The scalp is a unique anatomical region with distinct challenges for reconstruction, especially in cases of melanoma excision. This study explores the utilization of the O-Z flap technique as an alternative approach for patients with melanoma, where traditional free flap reconstruction may not be feasible due to age or underlying health conditions. However, in this case, the patient had a history of angina and was unwilling to undergo a long operation due to his advanced age. This study highlights the importance of following NCCN guidelines for melanoma resection margins based on tumor thickness and underscores the value of staged resection planning to minimize deficits. This requires an individualized approach as each patient may have different circumstances (general health condition, age, compliance, etc.). So we planned a staged excision first. In the first surgery, we would resect the tumor minimally and then determine the exact size of the tumor. Then, in the second surgery, we resected it with a safety margin according to the guidelines. The O-Z flap, a local flap with an "O"-shaped defect and "Z"-shaped incision line, is introduced as a reconstructive option. The surgical procedure, advantages, and limitations of this approach are discussed. This study provides valuable insights into achieving successful scalp reconstruction while considering aesthetic outcomes and patient-specific factors.

Building heterojunction is promising strategy for the achievement of highly efficient photocatalysis. In this study, a novel SnIn4S8@ZnO Z-scheme heterojunction with tight contact interface was successfully constructed through a facile two-step hydrothermal method. The phase composition, morphology, and photophysical properties of SnIn4S8@ZnO were studied through a series of characterization methods, respectively. Methylene blue (MB) was selected as the target pollutant for photocatalytic degradation and the degradation process was fitted with pseudo-first order kinetics. The as-prepared SnIn4S8@ZnO heterojunctions exhibited outstanding photocatalytic activities for the degradation of MB. The optimized sample (ZS800), in which the molar ratio of ZnO to SnIn4S8 was 800, displayed the highest photodegradation efficiency of MB (91%) within 20 min. Furthermore, the apparent rate constant of photodegradation for MB of ZS800 (0.121 min−1) was 2.2 times higher than that of pure ZnO (0.054 min−1). The improvement of photocatalytic activity could be attributed to the effective spatial separation of photogenerated charge carriers through a Z-scheme heterojunction with intimate contact interface. This work will provide a new insight into constructing excellent ZnO-based photocatalytic systems for wastewater purification.

This study addresses the motivations behind Generation Z's attendance at conferences and aims to contribute to a better understanding of their conference participation behavior. The study identified six latent factors that influence conference attendance motivation: Green and Digital Conference Experience, Educational and professional opportunities, Conference costs, Destination and conference stimuli, Stimulating factors, and Conference accessibility. These factors align with Generation Z's unique characteristics and preferences, such as their environmental consciousness, reliance on digital technologies, desire for continuous learning, career advancement, and experiences. The study also found that destination characteristics and educational and professional opportunities are significant motivators for Generation Z's conference attendance across different cultural contexts. Additionally, Generation Z's strong interest in sustainability and financial challenges highlights the need for eco-friendly practices and affordable registration fees to make conferences more appealing and accessible. The study further explores the impact of socio-demographic characteristics, revealing the gender and past conference attendance differences in motivation. Finally, the study discusses the implications for the conference industry and suggests areas for future research to enhance the understanding of conference attendance motivation among Generation Z.

A robust bank industry is a major player in the stability of an economy. The operational efficiency and stability of banks are therefore paramount. By way of financial ratios and Z-score, this study analysed UT Bank’s financial performance prior to the 2017 bank sector reforms in Ghana. Annual financials over a ten year period (2007-2016) were used. Debt management practices of UT Bank per the results obtained were quite on the hind side and unimpressive. This was reflected in the poor leverage and risk management variables. UT Bank would have been unable to meet creditors’ claims considering the mean average values of debt-to-assets and debt-to equity ratios of 0.76 and 0.90 respectively. The entire bank sector will benefit if credit management practices of individual banks are refreshed and worked on. The bank industry regulator should tighten its supervisory and monitoring role over banks to help detect early signs of non-performing banks. The study further recommends that statutory lending limits of banks be re-enforced to uphold the threshold of 10 percent for unsecured loans and 25 percentage for secured loans of net owned funds of the bank.

This research work deals with the modeling and control of hybrid photovoltaic (PV) - Wind micro-grid using Quasi Z-Source inverter. This inverter provides better buck/boost characteristics, able to regulate the phase angle output, less harmonic content, no requirement of the filter and has high power performance characteristics over conventional inverter as major benefits. A SEPIC converter as dc-dc switched power apparatus is employed for maximum power point tracking (MPPT) functions which provides high voltage gain throughout the process. Moreover, a modified power ratio variable step (MPRVS) based perturb & observe (P&O) method has been proposed in the PV MPPT action which forces the operating point close to maximum power point (MPP). Practical responses justify the performance of hybrid PV-Wind micro-grid with Quasi Z-Source inverter structure.

We examine a linear q-difference differential equation which is singular in complex time t at the origin. Its coefficients are polynomial in time and bounded holomorphic on horizontal strips in one complex space variable. The equation under study represents a q-analog of a singular partial differential equation, recently investigated by the author, which comprises Fuchsian operators and entails a forcing term that combines polynomial and logarithmic type functions in time. A sectorial holomorphic solution to the equation is constructed as a double complete Laplace transform in both time t and its complex logarithm log t and Fourier inverse integral in space. For a particular choice of the forcing term, this solution turns out to solve some specific nonlinear q-difference differential equation with polynomial coefficients in some positive rational power of t. Asymptotic expansions of the solution relatively to time t are investigated. A Gevrey type expansion is exhibited in a logarithmic scale. Furthermore, a formal asymptotic expansion in power scale is displayed, revealing a new fine structure involving remainders with both Gevrey and q-Gevrey type growth.

The fractional Hilbert transform, a generalization of the Hilbert transform, has been extensively studied in the literature because of its extensive use in optics, engineering, and signal processing. In the present work, we aimed to expand the fractional Hilbert transform to a space of generalized functions known as Boehmians. We introduce a new fractional convolution operator for the fractional Hilbert transform to prove a convolution theorem similar to the classical Hilbert transform and also to extend the fractional Hilbert transform to Boehmians. We also construct a suitable Boehmian space on which the fractional Hilbert transform exists. Further, we investigate convergence of the fractional Hilbert transform for the class of Boehmians and discuss the continuity of the extended fractional Hilbert transform.

As an implementation of inverse Radon transform, the Second-order Divided-difference Back Projection (SDBP) technique has been proposed in this paper, by which the accurate CT reconstruction can be theoretically realized. A computational model for CT reconstruction filter expressions has been derived on the basis of SDBP technique. The method reveals the correlation between the convolution kernel for data restoration and the filter expression. By substituting kernel functions into the computational model, the amplitude sequences of a variety of filters can be calculated. On the proposed filter construction method, the decomposition form of filters has been discovered, where a series of basic filters have been acquired. The properties of any filter depend on the decomposed basic filters. Basic filters can be used to compose filters in actual needs.

This study aimed to investigate the atherogenicity (quality) of LDL particles in patients with acute and recovered from COVID-19 infection. The participants were adults, aged 18 years or older of both sexes. Those with positive RT-PCR results at baseline were included in the Acute COVID-19 group (n=33), and those with negative RT-PCR six months after acute infection, were included in the Recovered COVID-19 group (n=30). The LDL quality was evaluated using three validated methods: Z-scan, UV-visible spectroscopy, and Lipoprint. The Recovered COVID-19 group showed significantly higher numbers of large LDL particles (less atherogenic) than the Acute COVID-19 group (P<0.05). We also found that COVID-19 infection was associated with the oxidative modification of LDL particles. D-dimer and CRP levels were correlated with Z-scan results and antioxidant-amount estimate. Moreover, we noticed that the infection left a sequel in LDL quality, even after six months of recovery. These findings highlight the importance of monitoring lipids during and after recovery from COVID-19 infection, and their potential deleterious effect on the LDL profile might correlate with the progression of atherosclerosis and poor clinical outcomes.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

Broadband reverse saturable absorption is systematically investigated via Z-scan, transient absorption spectrum (TAS). The excited state absorption and negative refraction of Orange IV are observed in the Z-scan experiment at 532 nm. Meanwhile, two-photon induced excited-state absorption and pure two-photon absorption are observed at 600 nm and 700 nm with the pulse width of 190 fs, respectively. An ultrafast broadband absorption in visible wavelength region is observed via TAS. The different nonlinear absorption mechanism at multiple wavelengths is discussed and interpreted from the results of TAS. In addition, the ultrafast dynamics of negative refraction in the excited state of Orange IV is investigated via degenerate phase object pump probe, from which the weak long-lived excited state is extracted. All studies indicate that Orange IV has the potential to be further optimized into a superior broadband reverse saturable absorption material and also has certain reference significance for the study of optical nonlinearity in organic molecules containing azobenzene groups.

Introduction: In general, everyone believes that the smallest preterm infants should achieve normal intrauterine growth rates, but many thinks that this is not possible with current nutrition guidelines. There is resistance to giving enough nutrition for fear of "toxicity". The difference in weight Z-score between birth and a corrected gestational age (CGA) at discharge is assess in postnatal growth in our unit. Material and methods: An observational study was done between January 2018 and December 2020 where all cases that had ≤ 29 weeks of GA at birth and survived to 36 weeks corrected GA or that were discharged home. An aggressive nutrition protocol including parenteral as well as enteral nutrition was followed. Patients and their weight trajectory was plotted on the Fenton 2013 growth curve. The patients who had had a smaller WZP difference were also plotted. Results: A total of 32 cases were found. The median change in Z-score between birth and discharge of the whole group was -0.52 (IQR 0.53). Six of 32 (19%) had a more than one WZP, all of whom had severe pathologies. The median decline in Z score for this group with poor growth was 1.24 (IQR 0.22). There were 26 cases with a < 1 WZP (81%) and a median Z score fall of 0.39 (IQR 0.55). No important complications secondary to the ingested volumes or parenteral nutrition were reported. Conclusion: The group of cases with a > 1 WZP drop had severe pathologies. All the other cases had adequate growth parallel to normal weight growth charts and a few cases had some catch-up growth. The study showed that it is possible for many preterm infants to achieve normal intrauterine growth rates if they are given enough nutrition, but bigger multicenter studies are needed to confirm these findings.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

This scientific paper presents a comprehensive investigation into transient Fourier analysis in some electrical engineering applications. The article deals with two effective approaches for solving transient analysis whose application is rather novel. The first contribution uses combined the Fourier series analytic Laplace-Carson (L­C) transform methods in the complex domain using complex time vectors, thereby significantly simplifying the calculation of the original function. As the inverse transform goes back into the time domain, it employs the Cauchy-Heaviside (C­H) method. The second contribution uses the Fourier transform (F­Τ)for transient analysis of a power converter electrical circuit with the passive and the active load. The method of complex conjugated amplitudes is used for steady-state analysis. Both contributions represent a new approach to this paper. The starting point is the Fourier series/expansions, computation of the Fourier coefficients, then continuing by solving the steady and transient states of the system and confirming the transient states using the Fourier transform. To validate our findings, the worked-out analytical results are verified by modelling in Matlab/Simulink environment.

We examine a family of linear partial differential equations both singularly perturbed in a complex parameter and singular in complex time at the origin. These equations entail forcing terms which combine polynomial and logarithmic type functions in time and that are bounded holomorphic on horizontal strips in one complex space variable. A set of sectorial holomorphic solutions are built up by means of complete and truncated Laplace transforms w.r.t time and parameter and Fourier inverse integral in space. Asymptotic expansions of these solutions relatively to time and parameter are investigated and two distinguished Gevrey type expansions in monomial and logarithmic scales are exhibited.

The key objective of the present paper is to propose a numerical scheme based on the homotopy analysis transform technique to analyze the time-fractional nonlinear predator-prey population model. The population model is coupled fractional order nonlinear partial differential equations often employed to narrate the dynamics of biological systems in which two species interact, first is a predator and the second is a prey. The proposed scheme provides the series solution with great freedom and flexibility by choosing appropriate parameters. The convergence of results is free from small or large parameters. Three examples are discussed to demonstrate the correctness and efficiency of the used computational approach.

This article focuses on obtaining the analytical solutions for parabolic Volterra integro- differential equations in d-dimensional with different types frictional memory kernel. Based on theories of Laplace transform, Fourier transform, the properties of Fox-H function and convolution theorem, analytical solutions of the equations in the infinite domain are derived under three frictional memory kernel functions respectively. The analytical solutions are expressed by infinite series, the generalized multi-parameter Mittag-Leffler function, Fox-H function and convolution form of Fourier transform. In addition, the graphical representations of the analytical solution under different parameters are given for one-dimensional parabolic Volterra integro-differential equation with power-law memory kernel. It can be seen that the solution curves subject to Gaussian decay at any given moment.

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

Objective:The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. Methods:The boundary edge pixels are detected using Kirsch’s edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier.Results:The proposed method with PCA and CANFES classification approach obtains 97.6% of se, 98.56% of sp, 98.73% of Acc, 98.85% of Pr, 98.11% of FPR and 98.185 of FNR, then the proposed Glioma brain tumor detection method using CANFES classification approach only.

In this paper, a technique of image watermarking using multiple images as watermarks is presented. The technique is based on transform domain functions including discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) with an image as the host signal i.e. the watermarks will be used as proofs of the authenticity of the host image. The technique is executed by performing multilevel DWT followed by applying DCT and SVD to both the host and watermark. Multiple watermarks are used for the insurance of better security level. The scheme is immune to common image processing operations & some attacks and exhibits PSNR of 108.3781dB, normalized cross correlation (NCC) over 0.99 and normalized correlation (NC) over 0.99.

In many application problems, F-transform algorithms are very efficient. In F-transform techniques, we replace the original signal or image with a finite number of weighted averages. The use of weighted average can be naturally explained, e.g., by the fact that this is what we get anyway when we measure the signal. However, most successful applications of F-transform have an additional not-so-easy-to-explain feature: the partition requirement, that the sum of all the related weighting functions is a constant. In this paper, we show that this seemingly difficult-to-explain requirement can also be naturally explained in signal-measuring terms: namely, this requirement can be derived from the natural desire to have all the signal values at different moments of time estimated with the same accuracy.

Context and aim: Environmental awareness is the first step to climate change risk perceptions and environmental behaviour. However, this pathway is not always possible within different population segments and trust contexts. This study explores the conditional effect of trust on environmental behaviour in the Zed Generation in Albania. The consideration of the Zed generation due to two opposing traits, such as social courage and anti-social, make them an interesting topic to understand, especially for their importance in online communities. Method: The youth engagement environmental programme and the climate change risk perceptions framework are adopted in an input-output design. Environmental awareness (input) is the initiating factor, and climate change risk perception and community engagement as mediators of environmental behaviour(output). Individual and institutional trust is the moderating factor that inhibits/enhances engagement and environmental behaviour. Results: The moderation-mediation show that climate change risk perceptions did not mediate environmental behaviour. Environmental awareness directly affects it. However, low institutional trust inhibits the general trust that, in turn, lowers the impact of awareness in climate change perceptions and community engagement toward environmental protection. Practical recommendation: The application of the input-output model shows that the pathway that directly affects environmental behaviour among Zed Generation in Albania is not Climate change risk perception. In low-trust contexts, positive environmental behaviour can be generated through community engagement. Policymakers and educational programmes in low-middle-income countries might consider community engagement to mitigate trust issues and effectively deal with environmental problems with a global segment such as the Zed generation.

We investigate a family of nonlinear partial differential equations which are singularly perturbed in a complex parameter and singular in a complex time variable at the origin. These equations combine differential operators of Fuchsian type in time and space derivatives on horizontal strips in the complex plane with a nonlocal operator acting on the complex parameter known as the formal monodromy around 0. Their coefficients and forcing terms comprise polynomial and logarithmic type functions in time and are bounded holomorphic in space. A set of logarithmic type solutions are shaped by means of Laplace transforms relatively to time and parameter and Fourier integrals in space. Furthermore, a formal logarithmic type solution is modeled which represents the common asymptotic expansion of Gevrey type of the genuine solutions with respect to the complex parameter on bounded sectors at the origin.

The theory of the continuous two-dimensional (2D) Fourier Transform in polar coordinates has been recently developed but no discrete counterpart exists to date. In the first part of this two-paper series, we proposed and evaluated the theory of the 2D discrete Fourier Transform (DFT) in polar coordinates. The theory of the actual manipulated quantities was shown, including the standard set of shift, modulation, multiplication, and convolution rules. In this second part of the series, we address the computational aspects of the 2D DFT in polar coordinates. Specifically, we demonstrate how the decomposition of the 2D DFT as a DFT, Discrete Hankel Transform (DHT) and inverse DFT sequence can be exploited for efficient code. We also demonstrate how the proposed 2D DFT can be used to approximate the continuous forward and inverse Fourier transform in polar coordinates in the same manner that the 1D DFT can be used to approximate its continuous counterpart.

The theory of the continuous two-dimensional (2D) Fourier Transform in polar coordinates has been recently developed but no discrete counterpart exists to date. In this paper, we propose and evaluate the theory of the 2D discrete Fourier Transform (DFT) in polar coordinates. This discrete theory is shown to arise from discretization schemes that have been previously employed with the 1D DFT and the discrete Hankel Transform (DHT). The proposed transform possesses orthogonality properties, which leads to invertibility of the transform. In the first part of this two-part paper, the theory of the actual manipulated quantities is shown, including the standard set of shift, modulation, multiplication, and convolution rules. Parseval and modified Parseval relationships are shown, depending on which choice of kernel is used. Similar to its continuous counterpart, the 2D DFT in polar coordinates is shown to consist of a 1D DFT, DHT and 1D inverse DFT.

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f (wx+b) or f (wx). This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it is time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as . Because I perfected the basic unit of ANNs— neuron, there are so many networks to try. This article gives the basic architecture for reference in future research.

Z-ligustilide is a main active ingredient in the volatile oil of Angelica sinensis, a traditional Chinese medicine. Cisplatin is a commonly used chemotherapy drug for the treatment of lung cancer. Efficacy is often limited by the development of drug resistance after long-term treatment. Here, we investigated the effect of the combination of Z-ligustilide and cisplatin (Z-ligustilide+cisplatin) on resistance of cisplatin-resistant lung cancer cells and its action mechanism. Cell viability was analyzed by cell counting kit-8 (CCK-8) assay. Cell cycle and cell apoptosis were examined by flow cytometry assay. mRNA and protein levels of factors related to cell cycle and apoptosis were analyzed by real-time PCR and western blot. Liquid chromatography-mass spectrometry (LC-MS) analysis and RNA sequencing in A549, A549/DDP and A549/DDP cells treated with the Z-ligustilide+cisplatin were performed. The expression of PLPP1 was analyzed by the Cancer Genome Atlas (TCGA). The correlation between PLPP1 and prognosis was evaluated by immunohistochemistry and Kaplan-Meier (KM) plotter analysis. We found that Z-ligustilide+cisplatin could decrease the cell viability of cisplatin-resistant lung cancer cells. Z-ligustilide+cisplatin induced cell cycle arrest, and promoted cell apoptosis of cisplatin-resistant lung cancer cells. Metabolomics combined with transcriptomics revealed that Z-ligustilide+cisplatin inhibited phospholipid synthesis by upregulating the expression of PLPP1. Furthermore, PLPP1 expression was positively correlated with good prognosis. Knockdown of PLPP1 abolished the effects of Z-ligustilide+cisplatin on cell cycle and apoptosis. Z-ligustilide+cisplatin inhibited the activation of AKT by reducing the levels of PIP3 levels. Z-ligustilide+cisplatin induced cell cycle arrest and promoted cell apoptosis of cisplatin-resistant lung cancer cells by inhibiting PLPP1-mediated phospholipid synthesis.

Marek’s Disease (MD) has a significant impact on both the global poultry economy and animal welfare. The disease pathology can include neurological damage and tumour formation. Sexual dimorphism in immunity and known higher susceptibility of females to MD makes the chicken Z chromosome (GGZ) a particularly attractive target to study the chicken MD response. Previously, we used a Hy-Line F6 population from a full-sib advanced intercross line to map MD QTL regions (QTLRs) on all chicken autosomes. Here we mapped MD QTLRs on GGZ in the previously utilized F6 population with individual genotypes and phenotypes, and in eight elite commercial egg production lines with daughter-tested sires and selective DNA pooling (SDP). Four MD QTLRs were found from each analysis. Some of these QTLRs overlap regions from previous reports. All QTLRs were tested by indiviuals from the same eight lines used in the SDP and genotyped with markers located within and around the QTLRs. All QTLRs were confirmed. The results exemplify the complexity of MD resistance in chickens and the complex distribution of P-values and LD pattern and their effect on localization of the causative elements. Considering the fragments and interdigitated LD blocks while using LD to aid localization of causative elements, one must look beyond the insignificant markers, for possible distant markers and blocks in high LD with the significant block. The QTLRs found here may explain at least part of the gender differences in MD tolerance, and provide targets for mitigating the effects of MD.

In this article the possible impact on the present state of particle physics theory is discussed of two unrecognized theoretical elements. These elements are the awareness that (a) the quark is a Dirac particle with a polarisable dipole moment in a scalar field and that (b) Dirac’s wave equation for fermions, if derived from Einstein’s geodesic equation, reveals a scaling theorem for quarks. It is shown that recognition of these elements proves by theory quite some relationships that are up to now only empirically assessed, such as for instance, the mass relationships between the elementary quarks, the relationship between the bare mass and the constituent mass of quarks, the mass spectrum of hadrons and the mass values of the Z boson and the Higgs boson.

To improve the voltage gain of step-up converters, cascaded technique is considered as a possible solution in this paper. By considering the concept of cascading two Z-source networks in a conventional boost converter, the converter takes the advantages of both impedance source and cascaded converters. However, by applying some modifications, the proposed converter provides high voltage gain while the voltage stress of switch and diodes are still low. Moreover, the low input current ripple of the converter makes it absolutely appropriate for photovoltaic applications in order to expand the lifetime of PV panels. After analyzing the operation principles of the proposed converter, simulation and experimental results of a 100W prototype are presented to verify the proposed converter performance.

High concentrations of Na⁺ or [Co(NH₃)₆]³⁺ can induce the B to Z conformational transition in alternating (dC-dG) oligo and polynucleotides. The use of short DNA oligomers (dC-dG)₄ and (dm⁵C-dG)₄ as models can allow a thermodynamic characterization of the transition. Both form right handed double helical structures (B-DNA) in standard phosphate buffer with 115 mM Na⁺ at 25 ^oC. However, at 2.0 M Na⁺ or 200 mM [Co(NH₃)₆]³⁺, (dm⁵C-dG)₄ assumes a left handed double helical structure (Z-DNA) while the unmethylated (dC-dG)₄ analogue remains right handed under those conditions. We have previously demonstrated that the enthalpy of the transition at 25 ^oC for either inducer can be determined using isothermal titration calorimetry (ITC) [Ferreira, J. M. & Sheardy, R. D., Biophys. J. 2006, 91, 1–7]. Here, ITC is used to investigate the linkages between temperature, water activity and DNA conformation. We found that the determined enthalpy for each titration varied linearly with temperature allowing determination of the heat capacity change (DC_p) between the initial and final states. As expected, the DC_p values were dependent upon the cation (i.e. Na⁺ vs [Co(NH₃)₆]³⁺) as well as the sequence of the DNA oligomer (i. e., methylated vs unmethylated). Osmotic stress experiments were carried out to determine the gain or loss of water by the oligomer induced by the titration. The results are discussed in terms of solvent accessible surface areas, electrostatic interactions and the role of water.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

This paper considers the solution of the equations for ruin probabilities in infinite continuous time. Using the Fourier Transform and certain results from the theory of complex functions, these solutions are obtained as com- plex integrals in a form which may be evaluated numerically by means of the inverse Fourier Transform. In addition the relationship between the re- sults obtained for the continuous time cases, and those in the literature, are compared. Closed form ruin probabilities for the heavy tailed distributions: mixed exponential; Gamma (including Erlang); Lognormal; Weillbull; and Pareto, are derived as a result (or computed to any degree of accuracy, and without the use of simulations).

Discrete Double Hilbert Exponential sums along polynomials. This is preprint version (not peer reviewed) And we now check the proof of (ii) of Lemma 3.2 whether it is really true.

A family of linear singularly perturbed Cauchy problems is studied. The equations defining the problem combine both partial differential operators together with the action of linear fractional transforms. The exotic geometry of the problem in the Borel plane, involving both sectorial regions and strip-like sets, gives rise to asymptotic results relating the analytic solution and the formal one through Gevrey asymptotic expansions. The main results lean on the appearance of domains in the complex plane which remain intimately related to Lambert W function, which turns out to be crucial in the construction of the analytic solutions. On the way, an accurate description of the deformation of the integration paths defining the analytic solutions and the knowledge of Lambert W function are needed in order to provide the asymptotic behavior of the solution near the origin, regarding the perturbation parameter. Such deformation varies depending on the analytic solution considered, which lies in two families with different geometric features.

The work is devoted to the study of a family of linear initial value problems of partial differential equations in the complex domain, dealing with two complex time variables. The use of a truncated Laplace-like transformation in the construction of the analytic solution allows to overcome a small divisor phenomenon arising from the geometry of the problem and represents an alternative approach to the one proposed in a recent work by the first two authors. The result leans on the application of a fixed point argument and the classical Ramis-Sibuya theorem.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

This paper is a slightly modified, abridged version of a previous work ``Parametric Gevrey asymptotics in two complex time variables through truncated Laplace transforms'' motivated by our contribution in the conference ``Formal and Analytic Solutions of Diff. (differential, partial differential, difference, q-difference, q-difference-differential) Equations on the Internet'' (FASnet20). It aims to clarify and give further detail at some crucial points concerning the asymptotic behavior of the solutions of the problems studied in that work.

This paper is a slightly modified, abridged version of a previous work "Parametric Gevrey asymptotics in two complex time variables through truncated Laplace transforms'' motivated by our contribution in the conference "Formal and Analytic Solutions of Diff. (differential, partial differential, difference, q-difference, q-difference-differential) Equations on the Internet'' (FASnet20). It aims to clarify and give further detail at some crucial points concerning the asymptotic behavior of the solutions of the problems studied in that work.

Several depth image based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility, and vulnerability to signal or geometric distortion. This paper proposes a template based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.

We report on recent experimental results on β decay into self-conjugate (N=Z) nuclei with mass number 58≤A≤70. Super-allowed β decays from the Jπ=0+ ground state of a Z=N+2 parent nucleus are to the isobaric analogue state, through so-called Fermi transitions, and to Jπ=1+ states, by way of Gamow–Teller (GT) transitions. The operator of the latter decay is a generator of Wigner’s SU(4) algebra and as a consequence GT transitions obey selection rules associated with this symmetry. Since SU(4) is progressively broken with increasing A, mainly as a consequence of the spin–orbit interaction, this symmetry is not relevant for the nuclei considered here. We argue, however, that the pseudo-spin–orbit splitting can be small in nuclei with 58≤A≤70, in which case nuclear states exhibit an approximate pseudo-SU(4) symmetry. To test this conjecture, GT decay strength is calculated with use of a schematic Hamiltonian with pseudo-SU(4) symmetry. Some generic features of the GT β decay due to pseudo-SU(4) symmetry are pointed out. The experimentally observed GT strength indicates a restoration of pseudo-SU(4) symmetry for A=70.

The increased emergence of multidrug-resistant Helicobacter pylori influences the prevention of stomach cancer. Zingiber officinale is a plant usually used in folk medicine to treat a variety of diseases involving infections, nausea, vomiting, peptic ulcer, dyspepsia, and inflammation. This study was conducted to evaluate the ability of Z. officinale extract to combat resistant H. pylori. The disc diffusion, microdilution, and microplate assays were performed to evaluate the susceptibility to antibiotics, and the antibacterial and antibiofilm activities of the Z. officinale extracts. Using the checkerboard method, the combined effects of the gentamicin and Z. officinale extract were investigated. In addition, anti-inflammatory activity and GC-MS analysis were determined by the modified protocol. According to the findings, H. pylori isolates exhibited resistance rates of 56.33, 50.0, and 45.85 for metronidazole, gentamicin, and tetracycline, respectively. The methanolic extract of Z. officinale had the strongest effectiveness against resistant H. pylori isolates with MIC of 20.0 to 50.0 µg/ml against both H. pylori isolates and the stranded strain NCTC 11637. Z. officinale extract suppress the biofilm formed by H. pylori isolates with a percentage of 92.96% at 50.0µg/ml, compared with 97.19% for gentamicin at the same concentration. According to FICI values, combination of methanolic Z. officinale extract to gentamicin increases bacterial sensitivity to such drugs. Moreover, the Z. officinale extract exhibits strong anti-inflammatory activity. The GC-MS analysis of Z. officinale extract exhibits 17 different chemical compounds. Conclusions: The Z. officinale extract contain the anti-inflammatory compound gingerol as the main constituent which inhibits the growth of H. pylori and its biofilm, are a promising natural therapeutic alternative or enhance antibiotics activity

Cyclic olefin copolymer (COC) is a novel type of thermoplastic polymer gaining the attention of the scientific community in electronic, optoelectronic, biomedicine and packaging applications. Despite the benefits in the use of COC like undoubted optical transparency, chemical stability, good water-vapor barrier and biocompatibility, its original hydrophobicity restricts its wider applicability and optimization of its performances. Presently, we report on the optical, and morphological properties of the films of COC covered with Ti in selected areas. The layer of Ti on COC was deposited by pulsed lased deposition processing. The Ti/COC film was characterized by UV-Vis spectroscopy indicating that its transmittance in the visible region decreased by about 20% with respect to the pristine polymer. The quality of the deposited Ti was assessed with the morphology by scanning electron (SEM) and atomic force microscopies (AFM). The modification of the wettability was observed by the sessile drop method indicating a reduction of the native hydrophilicity.

Conventional radiation therapy uses white X-rays that consist of a mixture of X-ray waves with various energy levels. In contrast, a monochromatic X-ray (monoenergetic X-ray) has a single energy level. Irradiation of high Z elements with a synchrotron generated monochromatic X-ray with the energy at or higher than the K-edge energy of the element results in the production of the Auger electrons that cause DNA damage leading to cell killing. Delivery of high Z elements into cancer cells and tumor mass can be facilitated by the use of nanoparticles. Mesoporous silica nanoparticles (MSNs) have been shown to be effective in delivering high Z elements to cancer cells. A proof of principle experiment was reported that demonstrated the feasibility of this approach. This opens up a possibility to pursue the Auger cancer therapy by the combined use of MSNs loaded with high Z elements and monochromatic X-rays. Similar cancer therapies using other types of quantum beams such as neutron, proton and carbon ion beams can be envisioned.

Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist even after the cessation of meteorological droughts due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorological and agricultural droughts using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did not explain all the variability noted in agricultural droughts. The relationships between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states more so than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for better drought-preparedness planning.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

Texture is essential information for image representation, capturing patterns, and structures. Consequently, texture plays a crucial role in the manufacturing industry and has been extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade the image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform were employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, analyzing the frequency domain enables effective defect detection because texture defects often exhibit characteristic changes in specific frequency ranges. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with those of existing approaches.

We prove that the Legendre coefficients associated with a function f(x) can be represented as the Fourier coefficients of a suitable Abel-type transform of the function itself. Thus, the computation of N Legendre coefficients can be performed efficiently in O(NlogN) operations by means of a single Fast Fourier Transform of the Abel-type transform of f(x). We also show how the symmetries associated with the Abel-type transform can be exploited to further reduce the computational complexity. The dual problem of calculating the sum of Legendre expansions is also considered. We prove that a Legendre series can be written as the Abel transform of a suitable Fourier series. This fact allows us to state an efficient algorithm for the evaluation of Legendre expansions. Finally, numerical tests are presented to exemplify and confirm the theoretical results.

Bearings are widely used in various types of electrical machinery and equipment. As their core components, failures will often cause serious consequences . At present, most methods of parameter adjustment are still manual adjustment of parameters. This adjustment method is susceptible to prior knowledge and easy to fall into the local optimal solution, failing to obtain the global optimal solution and requires a lot of resources.Therefore, this paper proposes a new method of bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by simulated annealing algorithm.The experimental results show that the method proposed in this paper has a more accurate effect in feature extraction and fault classification compared with traditional bearing fault diagnosis methods. At the same time, compared with the traditional artificial neural network parameter adjustment, this paper introduces the simulated annealing algorithm to automatically adjust the parameters of the neural network, thereby obtaining an adaptive bearing fault diagnosis method. To verify the effectiveness of the method, the Case Western Reserve University bearing database was used for testing, and the traditional intelligent bearing fault diagnosis method was compared. The results show that the method proposed in this paper has good results in bearing fault diagnosis. Provides a new way of thinking in the field of bearing fault diagnosis in parameter adjustment and fault classification algorithms

In this paper, we proposed a high accurate and stable Legendre transform algorithm which can reduce the potential instability for very high order at a very small increase in the computational time. The error analysis of interpolative decomposition for Legendre transform is presented. By employing block partitioning of Legendre-Vandermonde matrix and butterfly algorithm, a new Legendre transform algorithm with computational complexity O(N(logN)^2/loglogN) in theory and less than O(N(logN)^4) in practical application is obtained. Numerical results are provided to demonstrate the efficiency and numerical stability of the new algorithm.

The main ideas of F-transform came from representing expert rules. It would be therefore re reasonable to expect that the more accurately the membership functions describe human reasoning, the more efficient will be the corresponding F-transform formulas. We know that an adequate description of our reasoning corresponds to complicated membership functions -- however, somewhat surprisingly, most efficient applications of F-transform use the simplest possible triangular membership functions. There exist some explanations for this phenomenon which are based on local behavior of the signal. In this paper, we supplement this local explanation by a global one: namely, we prove that triangular membership functions are the only one that provide the accurate description of appropriate global characteristics of the signal.

This algorithm is designed to perform Discrete Fourier Transforms (DFT) to convert temporal data into spectral data. This algorithm obtains the Fourier Transforms by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this DFT algorithm is that it can produce spectral data at any user-defined resolution.

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.

In the history of high-performance polymer composite parts with continuous in-plane fibre reinforcement, the comparatively moderate structural-mechanical properties in their out-of- plane-z-direction and the limited load carrying capability between individual reinforcement layers within the laminated structures have always been one of the greatest challenges. Plenty of research work has been carried out to improve load transfer capability as well as fracture toughness for crack opening events between the reinforcement layers by means of out-of-plane fibre reinforcement implementation, for example by sewing or z-pinning technologies. However, the results have not become established across a wide range of applications so far, because the technologies used have led to considerable losses of in-plane strength properties. In this paper first results are presented for a new method of a low fraction z-fibre reinforcement by means of very thin diameter laser drilled holes filled with short carbon fibres, demonstrating potential to improve Mode II fracture toughness.

We demonstrate how a rigorous mathematical approach can be used to extract a current CMB temperature of 2.7276 ±0.0723 K from the 580 type Ia supernova data points in the Union2 database. Using redshift formula: z = Sqrt(Rh/R_t) − 1, incorporated into our new CMB redshift prediction redshift formula, we find that the above current CMB temperature value is a remarkably good match for this particular data set. In addition, we can alternatively start out with the measured current CMB temperature and extract the Hubble constant from all 580 supernovae. We then get an estimated Hubble constant of H0 = 66.8711+0.0019 km/s/Mpc when using the CMB temperature from the recent Dhal et al study [1] in combination with redshifts from all 580 supernovae. Furthermore, we get an estimated Hubble constant value of H_0 = 66.8943+0.0287 km/s/Mpc when using the CMB temperature measured by Fixsen [2]. In summary, our simple model essentially perfectly matches all 580 type Ia supernova redshifts while extracting H_0 or T_0 values. The Hubble constant we extract by this method fits very well with the Hubble constant extracted from the CMB by other studies. We believe that our new approach likely solves the Hubble tension problem, at least inside R_h = ct cosmology.

Plant viral nanoparticles (VNPs) are attractive to researchers in nanomedicine due to their safety, easy production, resistance, and straightforward functionalization. In this paper, we developed and successfully purified a VNP derived from turnip mosaic virus (TuMV), a well-known plant pathogen, that exhibits high affinity for immunoglobulins G (IgG) thanks to its functionalization with the Z domain from staphylococcal protein A via gene fusion. We selected cetuximab as a model IgG to prove the versatility of this novel TuMV VNP by developing a fluorescent nanoplatform to mark tumoral cells of the line Cal33, from tongue squamous cell carcinoma. We observed that the fluorescent VNP-cetuximab bound selectively to Cal33 and were internalized by confocal microscopy, revealing the potential of this nanotool in cancer research.

Formalin-Fixed Paraffin Embedded (FFPE) tissues are a valuable resource in studying different markers and mechanistic molecules (protein, DNA and RNA) in order to understand the etiology of different cancers as well as many other diseases. Degradation and modification of RNA is the major challenge in utilizing FFPE tissue samples in medical research. Recently, non-protein coding transcripts long non-coding RNAs (lncRNAs), have gained significant attention due to their important biological actions and potential involvement in cancer. There is no validated method except qRTPCR or RNAseq to evaluate and study lncRNA expression. We have standardized and are reporting a sensitive Z probe based in situ hybridization method to identify, localize and quantitate lncRNA in FFPE tissues. This assay is sensitive to single transcript and localizes lncRNA in individual cells within tumor. We have characterized a tumor suppressor lncRNA-NRON (non coding repressor of NFAT), which is scarcely expressed, a moderately expressed oncogeneic lncRNA UCA1 (urothelial cancer associated 1), and a highly studied and expressed lncRNA MALAT1 (metastasis associated lung adenocarcinoma transcript1) in different cancers. High MALAT1 staining was found in colorectal, breast and pancreatic cancer. MALAT1 expression increased with the progression of the stage in colorectal cancer and invasiveness in breast cancer.

This paper concentrates on the general birth-death processes with two different types of catastrophes. The Laplace transform of transition probability function for birth-death processes with two-type catastrophes are is successfully expressed with the Laplace transform of transition probability function of the birth-death processes without catastrophe. The first effective catastrophe occurrence time is considered. The Laplace transform of its probability density function, expectation and variance are obtained.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Covid-19 is a lung disease caused by a Coronavirus family virus. Due to its extraordinary prevalence and death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of Coronavirus and the participation of physicians in all countries, Information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient's lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

Nanofluids are a novel class of heat transfer fluid that plays a vital role in industries. In mathematical investigations, these fluids are modeled in terms of traditional integer-order partial differential equations (PDEs). It is recognized that traditional PDEs cannot decode the complex behavior of physical flow parameters and memory effects. Therefore, this article intends to study the mixed convection heat transfer in nanofluid over an inclined vertical plate via fractional derivatives approach. The problem in hand is modeled in connection with Atangana-Baleanu fractional derivatives without singular and local kernel having strong memory. The human blood is considered as base fluid dispersing carbon nanotube (CNTs) (single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes(MWCNTs )) into it to form blood-CNTs nanofluid. The nanofluids are considered to flow in a saturated porous medium under the influence of an applied magnetic field. The exact analytical expressions for velocity and temperature profiles are acquired using the Laplace transform technique and plotted in various graphs. The empirical results indicate that the memory effect decreases with increasing fractional parameters in the case of both temperature and velocity profiles. Moreover, the temperature profile is higher for blood-SWCNTs by reason of higher thermal conductivity whereas, this trend is opposite in case of velocity profile due densities difference.

It's challenging to achieve robust lane detection depending on single frame when considering complicated scenarios. In order to detect more credible lane markings by using sequential frames, a novel approach to fusing vision and Inertial Measurement Unit (IMU) is proposed in this paper. The hough space is employed as the space where lane markings are stored and it's calculated by three steps. Firstly, a basic hough space is extracted by Hough Transform and primary line segments are extracted from it. In order to measure the possibility about line segments belong to lane markings, a CNNs based classifier is introduced to transform the basic hough space into a probabilistic space by using the networks outputs. However, this probabilistic hough space based on single frame is easily disturbed. In the third step, a filtering process is employed to smooth the probabilistic hough space by using sequential information. Pose information provided by IMU is applied to align hough spaces extracted at different times to each other. The final hough space is used to eliminate line segments with low possibility and output those with high confidence as the result. Experiments demonstrate that the proposed approach has achieved a good performance.

This paper predict and effectively control the temperature distribution of the steady-state and transient states of anisotropic four-layer composite materials online, knowing the density, specific heat, heat conductivity and thickness of the composite materials. Based on the transfer function, a mathematical model was established to study the dynamic characteristics of heat transfer of the composite materials. First of all, the Fourier heat transfer law was used to establish a one-dimensional Fourier heat conduction differential equation for each composite layer, and the Laplace transformation was carried out to obtain the system function. Then the approximate second-order transfer function of the system was obtained by Taylor expansion, and the Laplace inverse transformation was carried out to obtain the transfer function of the whole system in the time domain. Finally, the accuracy of the simplified analytical solutions of the first, second and third order approximate transfer functions was compared with computer simulation. The results showed that the second order approximate transfer functions can describe the dynamic process of heat transfer better than others. The research on the dynamic characteristics of heat transfer in the composite layer and the dynamic model of heat transfer in composite layer proposed in this paper have a reference value for practical engineering application. It can effectively predict the temperature distribution of composite layer material and reduce the cost of experimental measurement of heat transfer performance of materials.

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread deployment. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed, which could mitigate environment noise through wavelet transform and extract the amplitude or phase covariance matrix as the feature vector. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. The accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

This paper presents a new methodology that provides the analysis of surface texture changes in areas adjacent to the volcano and its impact product of volcanic activity. To do this, algorithms from digital image processing such as the co-occurrence matrix and the wavelet transform are used. These methods are working on images taken by the Landsat satellite platform sensor 5 TM and Landsat 7 ETM + sensor, and implemented with the purpose of evaluating superficial changes that can warn of surface movements of the volcano. The results were evaluated by similarity metrics for grayscale images, and validated in two different scenarios that have the same type of eruption, but differ, essentially, in climate and vegetation. Finally, the proposed algorithm is presented, setting the parameters and constraints for implementation and use.

We consider a family of nonlinear singularly perturbed PDEs whose coefficients involve a logarithmic dependence in time with confluent Fuchsian singularities that unfold an irregular singularity at the origin and rely on a single perturbation parameter. We exhibit two distinguished finite sets of holomorphic solutions, so-called outer and inner solutions, by means of a Laplace transform with special kernel and Fourier integral. We analyze the asymptotic expansions of these solutions relatively to the perturbation parameter and show that they are (at most) of Gevrey order 1 for the first set of solutions and of some Gevrey order that hinges on the unfolding of the irregular singularity for the second.

This research explores the behaviors of Generation Z consumers in China, particularly focusing on their interaction with companies through social media for the joint creation of value. Adopting a qualitative methodology, the research initially constructs a scale for assessing co-creation of value on social media platforms. Subsequently, it delves into the motivational aspects of users' engagement in these co-creative activities. The study unveils a range of utilitarian and hedonic motivations that drive Generation Z's value co-creation actions on social media. Insights derived from the findings offer both theoretical and practical implications for the field.

Extensive genome structure variation, such as copy number variations (CNVs) and presence/absence variations, is the basis for the remarkable maize genetic diversity; however, the effect of CNVs on maize herbivory defense remains largely underexplored. Here, we report that the naturally occurring duplication of the maize 9-lipoxygenase gene, ZmLOX5, leads to increased resistance of maize to herbivory by fall armyworms (FAW). Previously, we showed that ZmLOX5-derived oxylipins are required for defense against chewing insect herbivores and identified several inbred lines, including Yu796, that contained duplicated CNVs of ZmLOX5, referred to as Yu796-2×LOX5. To test whether introgression of the Yu796-2×LOX5 locus into the herbivore-susceptible B73 background that contains a single ZmLOX5 gene is a feasible approach to increase resistance, we generated a series of near-isogenic lines that contained either 2, 1 or 0 copies of the Yu796-2×LOX5 locus in the B73 background by six back crosses (BC6). Droplet digital PCR (ddPCR) confirmed the successful introgression of the Yu796-2×LOX5 locus in B73. The resulting B73-2×LOX5 inbred line displayed increased resistance against FAW associated with increased expression of ZmLOX5, increased wound-induced production of its primary oxylipin product, the α-ketol, 9-hydroxy-10-oxo-12(Z),15(Z)-octadecadienoic acid (9,10-KODA), and the downstream defense hormones regulated by this molecule, 12-oxo-phytodienoic acid (12-OPDA) and abscisic acid (ABA). Surprisingly, wound-induced JA-Ile production was not increased in B73-2×LOX5, resulting from the increased JA catabolism. Furthermore, B73-2×LOX5 displayed enhanced drought tolerance likely due to increased ABA and 12-OPDA content. Taken together, this study revealed that the duplicated CNV of ZmLOX5 quantitively contributes to maize antiherbivore defense and presents proof-of-concept evidence that the introgression of naturally occurring duplicated CNVs of a defensive gene into the productive, but susceptible, crop varieties is a feasible breeding approach for enhancing plant resistance to herbivory and tolerance to abiotic stress.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator’s effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.

In this research, a computation algorithm is established for a fractional order 2D fuzzy heat equation. In this study, Elzaki transform and HPM fusion is produced. Computing the desired outcome in series yields a fast convergence on an appropriate response. Examples are provided to support the conclusions, which are then compared with a particular approach to show the effectiveness and potential of the suggested approach. Two crisp equations—one for the lower bound solution and one for the upper bound solution are constructed from the input fuzzy fractional heat equation. The contour and surface representations of the approximate and exact results are offered for the lower and upper-bound solutions. The l∞-error norm is used in this study to validate the numerical convergence aspect. Together with the absolute inaccuracy, the approximate and exact solutions are matched. It has been demonstrated that the proposed regime will make it feasible to work with fuzzy fractional partial differential equations in a wide range of dimensions.

This paper studies the pilot control strategy and workload of tilt-rotor aircraft dynamic conversion procedure between helicopter mode and fixed-wing mode. A nonlinear flight dynamics model of tilt-rotor aircraft with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is constructed, considering factors such as conversion corridor limitations, pilot control, flight attitude, engine rated power, and wing stall effects. To assess pilot workload, an analytical method based on wavelet transform is proposed, which examines the mapping relationship between pilot control input's amplitude, constituent frequencies, and control tasks. By integrating the nonlinear optimal control model and the pilot workload evaluation method, the analysis of pilot control strategy and workload during the conversion procedure is conducted, leading to the identification of strategies to reduce pilot workload. The results indicate that incorporating the item of pilot workload in the performance index results in a notable reduction in the magnitude of collective stick inputs and longitudinal stick inputs. Moreover, it facilitates smoother adjustments in altitude and pitch attitude. Additionally, the conversion of the engine nacelle can be achieved at a lower and constant angular velocity. In summary, the conversion and reconversion procedures are estimated to have a low workload (Level 1 ~ Level 2), with relatively simple and easy manipulation for the pilot.

During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.

The twisted Wang transform distribution family, defined as the composition of parameter shifted inverse CDF function with an original CDF function, is found to be most suitable for matching low shape factor distributions, characterizing hard to fit or to simulate reinsurance portfolio losses for some perils from our previous study. Among them, the best form for matching a hard-to-fit empirical loss distribution for a specific peril, is the Exponential Fractional Extra Power 0 Distribution in (0,1) with CDF:.The simplest yet still a good form of this family is the Transformed Hyperbolic Tangent Distribution with CDF:,which has analytical formulas for the moments. The twisted Wang transform distribution family is compared and confirmed to be superior to all other well-known distribution families through extensive numerical optimization practice, distribution forms guesses, and computer-aided exploration experiments.

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

We propose a new Mamdani fuzzy rule-based system in which the fuzzy sets in the antecedents and consequents are assigned in a discrete set of points and approximated by using the extended inverse fuzzy transforms, whose components are calculated by verifying that the dataset is sufficiently dense with respect to the uniform fuzzy partition. We test our system in the problem of spatial analysis consisting in the evaluation of the liveability of residential housings in all the municipalities of the district of Naples (Italy). Comparisons are done with the results obtained by using trapezoidal fuzzy numbers in the fuzzy rules.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

This study develops a new definition of fractional derivative that mixes the definitions of fractional derivatives with singular and non-singular kernels. Such developed definition encompasses many types of fractional derivatives, such as the Riemann-Liouville and Caputo fractional derivatives for singular kernel type as well as the Caputo-Fabrizio, the Atangana-Baleanu and the generalized Hattaf fractional derivatives for non-singular kernel type. The associate fractional integral of the new mixed fractional derivative is rigorously introduced. Furthermore, newly numerical scheme is developed to approximate the solutions of a class of fractional differential equations (FDEs) involving the mixed fractional derivative. Finally, an application to computational biology is presented.

In view of the problem that the hot and cold loads of mushroom houses are affected by multiple factors such as environment, mushrooms, and equipment, which are difficult to accurately model, this paper proposes a short-term load prediction method based on empirical wavelet transform (EWT) and mixed autoregressive integrated moving average (ARIMA) and convolutional bi-directional short- and long-term attention mechanism (CNN-BiLSTM-Attention). The method first uses the Boruta algorithm to filter the input characteristics, and then uses the EWT method to decompose the mushroom house load data into 4 modal components. Then the Lempel-Ziv method is introduced to divide the modal components into two categories of high and low frequencies, and the CNN-BiLSTM-Attention and ARIMA prediction models are constructed separately. Finally, the two types of prediction results are superimposed and reconstructed to obtain the final load prediction value. The test results show that the Boruta algorithm can effectively filter out the characteristics of key influencing factors. Compared with the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively; compared with the ensemble empirical mode decomposition (EEMD) method, the EWT method can increase the decomposition and reconstruction error by 103 orders of magnitude, which can effectively improve the prediction accuracy of the model; compared with the individual neural network model, the prediction effect of the model proposed in this paper has obvious advantages, and the MAE, root mean square error (RMSE) and mean absolute percentage error (MAPE) of the prediction results are reduced respectively. 31.06%, 26.52% and 39.27%.