Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel-cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy duty trucks and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e. g. the conversion and storage of surplus renewable electricity via electrolysis, become important.

Background: Dental education blends theoretical and practical training to prepare students for independent patient care. This study examines the confidence levels of dental students in Umeå regarding independent performance of general dentistry tasks, and whether this confidence changes after clinical training. Methods: Surveys were conducted before and after a summer clinical internship, with s tudents rating their comfort levels on a scale from 0 to 10 for various procedures. Results: Results from 64 initial surveys and 54 follow-up surveys showed no significant difference in confidence before and after the internship. However, confidence levels varied based on whether students had practiced specific tasks during the internship. Students felt least confident in managing acute dental trauma and treating cavities in young children, while they were most confident in tasks like obtaining radiographic surveys and debriding periodontally compromised dentition. Tooth extraction and dental fillings were the most practiced procedures during the internship. Conclusions: The study concludes that the internship between the ninth and tenth semesters does not notably boost students' confidence in specific dental procedures, possibly due to the short duration of the training. Further exploration, including input from clinical supervisors, is suggested to optimize clinical training in dental education.

The purpose of this paper is to prove strong convergent theorems for Browder's type iterations and Halpern's type iterations of a family of nonexpansive mappings in a complete geodesic space with curvature bounded above by a positive number. Moudafi's viscosity type methods are also discussed without the nice projection property.

Cronbach’s (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores - the empirical basis for the well-known g-factor - as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

This paper delves into the application of digital twin monitoring techniques for enhancing offshore floating wind turbine performance, with a detailed case study. We explore the practical implementation of digital twins and their efficacy in optimizing operations and predictive maintenance. Our findings emphasize digital twins' potential as a valuable tool for renewable energy, driving efficiency and sustainability in offshore floating wind installations.

A semi–numerical approach proposed many years ago for describing gravitational collapse in the post–quasi–static approximation , , , , is modified in order to avoid the numerical integration of the basic differential equations the approach is based upon. For doing that we have to impose some restrictions on the fluid distribution. More specifically, we shall assume the vanishing complexity factor condition, which allows for analytical integration of the pertinent differential equations and leads to physically interesting models. Instead, we show that neither the homologous nor the quasi–homologous evolution are acceptable since they lead to geodesic fluids, which are unsuitable for being described in the post–quasi–static approximation. Also, we prove that, within this approximation, adiabatic evolution also leads to geodesic fluids and therefore we shall consider exclusively dissipative systems. Besides the vanishing complexity factor condition, additional information is required for a full description of models. We shall propose different strategies for obtaining such an information, which are based on observables quantities (e.g. luminosity and redshift), and/or heuristic mathematical ansatz. To illustrate the method, we present two models. One model is inspired in the well known Schwarzschild interior solution, and another one is inspired in Tolman VI solution.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

Given a fuzzy normed space $ \left( X,N \right) $‎, ‎we will introduce the notion of fuzzy near best approximation within a relative distance $ \rho \geq 0 $‎. ‎Some basic properties are characterized and also many examples for illustration are presented‎.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. However, first, it is necessary to know how and from which aspects to compare users. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official TwitterAPI. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for the comparison of the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

The European Union's recent decision to renew the authorization for the use of glyphosate until 15 December 2033 stimulates the scientific discussion all around the world regarding its toxicity or otherwise for humans. Glyphosate is a chemical used by millions of tons in the last 50 years worldwide to drying weeds in cultivated fields, greenhouses, roadsides. Concern has been raised in many quarters about the possible presence in the food chain and the consequent adverse effects on health: both aspects that argue in favor of toxicity and those that instead may indicate limited toxicity of glyphosate are here discussed. The great debate that has been generated requires further investigations and field measurements to understand its fate once dispersed in the environment and its concentration in the food chain. Hence the need for validated analytical methods that are available to analysts in the field. In the present review, methods for the analytical determination of glyphosate and its main metabolite, AMPA, are discussed, with a specific focus on chromatographic techniques applied to cereal products. The experimental procedures are explained in any detail, including cleanup, derivatization, and instrumental conditions to give the laboratories involved enough information to proceed with the implementation of this line of analysis. The prevalent chromatographic methods used are LC-MS/MS, GC-MS/SIM, and GC-MS/MS but sufficient indications are also given to those laboratories that wish to use the simpler HPLC-FLD, HPLC-UV, GC-NPD or GC-FPD techniques for screening purposes. Concentrations from literature measured in wheat, corn, barley, rye, oats, soybean, and cereal-based foods are reported together with the regulatory status in various parts of the world and the accumulation mechanism. As for the accumulation in cereals, available data show that glyphosate tends to accumulate more in wholemeal flours than in refined ones, that its concentration in the product strictly depends on the treatment period (the closer it is to the time of harvesting, the higher the concentration) and that in cold climates the herbicide tends to persist in the soil for a long time.

Hasselmann and coauthors proposed the discrete interaction approximation (DIA) as the best tool replacing the nonlinear evolution term in a numerical wind-wave model. Much later, Polnikov and Farina radically improved the original DIA by means of location all the interacting four wave vectors, used in the DIA configuration, exactly at the nodes of the numerical frequency-angular grid. This provides nearly two-times enhancing the speed of numerical calculation for the nonlinear evolution term in a wind-wave model. For this reason, the proposed version of the DIA was called as the fast DIA (FDIA). In this paper we demonstrate all details of the FDIA concept for several frequency-angular numerical grids of high resolution, with the aim of active implementation the FDIA in modern versions of world-wide used wind-wave models.

In this paper, we propose a new covering-based set in which the lower and the upper approximation operations are defined by neighborhood systems. We discuss this new type of covering-based set systematically in two steps. First, we study the basic properties of this covering-based set, such as the properties of normality, contraction, and monotone. Second, we discuss the relationship between the new type of covering-based set and the other ten sets proposed.

In this paper, we propose a new covering-based set in which the lower and the upper approximation operation are defined by neighborhood systems. We discuss this new type of covering-based set systematically in two steps. First, we study the basic properties of this covering-based set, such as the properties of normality, contraction, and monotone. Second, we discuss the relationship between the new type of covering-based set and the other ten sets proposed.

Boltzmann codes are essential tools for studying cosmology. Most of the codes are based on a seminal work by Ma and Bertschinger. We found that the formalism employed in those codes has at least three possible problems which need to be understood. i) The equation of motion for baryons are gauge incompatible, ii) they break the Bianchi identity, and iii) it is not clear from the equations of motion which physical system is considered. In this work we revisit the baryon physics and address all the above mentioned issues, based solely on conservation of stress-energy tensor, resulting in taking into account a correction that is usually neglected and that is numerically small. We also study the tight coupling approximation up to the second order without choosing any gauge. We implement the improved baryon equations in a Boltzmann code and investigate the change in the estimate of cosmological parameters by performing an MCMC analysis. While in this paper we study the Lambda-CDM model only, our baryon equations can be easily implemented in other models and various modified gravity theories.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

This study explores the complexities involved in the extraction and purification of catechins, prominent bioactive compounds found in plant extracts and tea. It addresses challenges arising from the intricate molecular structure of catechins and their inherent variability across botanical sources. Despite these obstacles, catechins present significant potential, particularly in healthcare. Their potent antioxidant properties have been extensively investigated and associated with numerous health benefits, positioning them as promising therapeutic agents for combating chronic diseases. Furthermore, catechins have diverse applications across various industries, including food and beverage, cosmetics, agriculture, and materials science. This abstract emphasizes the importance of continued research and innovation in catechin extraction and utilization, which hold promise for advancing human health and technological progress across multiple domains.

The aim of the article is to verify differences in the perception of selected factors of sustainability among small and medium-sized enterprises (SMEs) in Central Europe. The subjects of analysis are factors such as corporate social responsibility (CSR), business risk sources (market, financial, personnel, and operational), reputation and social media, national support, social changes, and crisis events in business. The questionnaire was completed by 1,090 SMEs from the business environments of Visegrad Group countries. Statistical hypotheses were verified using Z-scores for two population properties. The most negatively perceived factors among owners and managers in the SME segment are national support and legislative changes. The most positively perceived factor is reputation and social media. The country of doing business is a significant factor. The perception of crisis events in business is most negative among Czech SMEs. More than 80% of Hungarian SMEs perceive their companies as sustainable. In comparison with Slovak and Polish SMEs, Hungarian perceptions are more positive in the context of sustainability. The country of doing business is the strongest factor in evaluating CSR indicators. The positive perception of CSR in Czech SMEs (43.9%) is lower than in other countries.

Many farms are replacing the lighting with light emitting diode (LED) bulbs because of the technological modernization. We aimed to investigate the effects of incandescent light (IL) and LED lighting on Cobb500 broiler chickens for six weeks. Production parameters (body weight, feed consumption, feed conversion ratio), calculated slaughter values (yield%, relative breast%, thigh%) and breast meat quality parameters (pH at 45 min and 24 h postmortem, color, drip loss, kitchen equipment losses, shear force, meat composition) were recorded. Non-stop recordings were used to analyze the behavior of the birds in several periods of the rearing. LED group was significantly better in body weigh parameter between week 1-5 and feed conversion ratio between week 2-3. The most significant difference in behavior was observed in the middle of the rearing period. The chickens in the LED group spent more eating time, drinking time, interacting, and resting less. There was no difference in the meat quality parameters, only shear force was significantly lower in the LED group (1781.95±0.33 g/s vs. 2098.82±0.42 g/s). Based on our results, LED lighting can bring positive changes in animal production efficiency, behavior, and important characteristics for meat consumers.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

We develop an 1/n expansion method for the systematic approxi- mation of the solutions of classical spin systems (tri-dimensional vec- tors) which can describe general spin-spin interactions and phases with complex magnetization pattern. This method is applied to study the phase transitions observed in pyrochlore crystals (e.g. Gd₂ Ti₂ O₇ ) and the results have qualitative agreement with the experimental ones.

Purpose of this article is to introduce a modification of Phillips operators on the interval $\left[ \frac{1}{2},\infty \right) $ via Dunkl generalization. This type of modification enables a better error estimation on the interval $\left[ \frac{1}{2},\infty \right) $ rather than the classical Dunkl Phillips operators on $\left[ 0,\infty \right) $. We discuss the convergence results and obtain the degrees of approximations. Furthermore, we calculate the rate of convergence by means of modulus of continuity, Lipschitz type maximal functions, Peetre's $K$-functional and second order modulus of continuity.

Purpose of this article is to introduce a modification of Phillips operators on the interval $\left[ \frac{1}{2}% ,\infty \right) $ via Dunkl generalization. This type of modification enables a better error estimation on the interval $\left[ \frac{1}{2},\infty \right) $ rather than the classical Dunkl Phillips operators on $\left[ 0,\infty \right) $. We discuss the convergence results and obtain the degrees of approximations. Furthermore, we calculate the rate of convergence by means of modulus of continuity, Lipschitz type maximal functions, Peetre's $K$-functional and second order modulus of continuity.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

The scratch test consists in pushing a tool across the surface of a material at a given penetration depth or applied load. It has several applications in Science and Engineering including strength testing, damage and quality control of thin films and coatings. Despite numerous attempts in scientific literature, the application of scratch test to characterizations of fracture properties remains a heavily controversial topic. Therefore, this investigation arises as an experimental study in order to assess the fracture toughness using scratch test. A study on the effect of cryogenic treatment, performed after tempering, on fracture toughness, via scratch test experiments, of different tool steels has been made. A laboratory investigation on AISI D2, AISI M2 and X105CrCoMo18 steel confirms the possibility of increasing the fracture toughness by carrying out the cryogenic treatment after the usual heat treatment

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

The purpose of this paper is threefold: (i) to introduce and study the Euler-Lagrange prolongations of flatness PDEs solutions (best approximation of flatness) via associated least squares Lagrangian densities and integral functionals on Riemannian manifolds; (ii) to analyze some decomposable multivariate dynamics represented by Euler-Lagrange PDEs of least squares Lagrangians generated by flatness PDEs and Riemannian metrics; (iii) to give examples of explicit flat extremals and non-flat approximations.

In this review paper, we present a pellucid proof of how x tanh(x/µ) approximates |x|and is better than √x² +µ  when we are concernedwith accuracy.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

The fractional derivatives are a generalization the derivatives of integer order and find applications in studying memory processes in various scientific fields. Numerical methods are used to solve and analyze fractional models of real world problems. In this paper, we consider an approximation of the Caputo fractional derivative and its asymptotic formula, whose generating function is the polylogarithmic function. In the paper, we prove the convergence of the approximation and derive an estimate for the error and order. We consider an application of the approximation for the construction of finite difference schemes for numerical solution of the two-term ordinary fractional differential equation and the time-fractional Black-Scholes equation for option pricing. The properties of the approximation are used to prove the convergence of the methods used for numerical solution of the fractional differential equations. The theoretical results for the error and order of the methods are illustrated by the results of numerical experiments.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

The current work reveals the fine tuning between stability zones and randomness of Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary queueing D/M/1 system. More specifically, this clearly provides more insights into developing a contemporary PSFFA theory that unifies non-stationary queueing theory with chaos theory and fields in both theoretical physics and chaotic systems. This opens new grounds for stability analysis of non-stationary queueing systems.

Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

The SBA-15 loaded with methylene blue was successfully prepared by sample adsorption of methylene blue in SBA-15 and is characterized by an XRD, SEM, FTIR, and BET. The photocatalytic activity for methyl orange (MO) degradation was investigated under visible light irradiation under LEDs. Experimental results demonstrate that the SBA-15-MB exhibited higher photocatalytic activity than SBA-15 under visible light irradiation. The degradation efficiency of MO (50 ppm) by SBA-15-MB could reach 80% within 180 min under 25W LED visible light. The photocatalytic activity of SBA-15 could be attributed to the excitation of methylene blue by visible light, the transfer of electrons from methylene blue to SBA-15, and the production of radicals at the surface of SBA-15.

This study investigates the valorization potential of yellowfin tuna tails for the production of high-value commercial products. Muscle and skins were firstly mechanically separated by using a specifically designed device. The muscle was then used as a nitrogen source for the growth of the proteolytic enzymes-producer Bacillus subtilis, whereas the skins were employed for gelatin extrac-tion. The proteases from Bacillus subtilis were partially purified and used to produce antioxidant peptides from gelatin obtained. The gelatin obtained formed a gel upon cooling, with gelling and melting temperatures at 16 ºC and 22 ºC, respectively, and Bloom Strength of ≈ 160. RSM was used to determine the optimal hydrolysis conditions to obtain the highest antioxidant activity (35.96%, measured as DPPH radical scavenging activity), which were 50°C / 6.5 IU of enzyme. The findings emphasize the importance of an integrated approach to maximize the value of tuna by-products, promoting sustainability in the frame of circular bioeconomy. Overall, these results contribute to the efficient utilization of tuna by-products, waste reduction, and enhance economic viability of the tuna industry.

We investigate \texorpdfstring{$L_2$}{}-approximation problems in the worst case setting in the weighted Hilbert spaces \texorpdfstring{$H(K_{R_{d,\a,\g}})$}{} with weights \texorpdfstring{$R_{d,\a,{\bm \ga}}$}{} under parameters \texorpdfstring{$1\ge \ga_1\ge \ga_2\ge \cdots\ge 0$ and $1<\az_1\le \az_2\le \cdots$}{}. Several interesting weighted Hilbert spaces \texorpdfstring{$H(K_{R_{d,\a,\g}})$}{} appear in this paper. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. Multivariate approximation; information complexity; tractability; weighted Hilbert spacesWe discuss tractability of \texorpdfstring{$L_2$}{}-approximation problems for the involved Hilbert spaces, which describes how the information complexity depends on \texorpdfstring{$d$}{} and \texorpdfstring{$\va^{-1}$}{}. As a consequence we study the strongly polynomial tractability (SPT), polynomial tractability (PT), weak tractability (WT), and \texorpdfstring{$(t_1,t_2)$}{}-weak tractability (\texorpdfstring{$(t_1,t_2)$}{}-WT) for all \texorpdfstring{$t_1>1$}{} and \texorpdfstring{$t_2>0$}{} in terms of the introduced weights under the absolute error criterion or the normalized error criterion.

In this paper, we study a co-variational inequality problem involving Yosida approximation operators in real uniformly smooth Banach space and define an iterative method to obtain its solution. We prove some properties of Yosida approximation operator for our purpose, that is strongly accretivity and Lipschitz continuity. An existence as well as convergence result is proved for co-variational inequality problem involving Yosida approximation operators using the concept of nonexpansive sunny retraction.

In many Bayesian computations, first, we obtain the expression of the joint distribution of all the unknown variables given the observed data. In general, this expression is not separable in those variables. Thus, obtaining their marginals for each variable and computing the expectations are difficult and costly. This problem becomes even more difficult in high dimensional quandaries, which is an important issue in inverse problems. We may then try to propose a surrogate expression with which we can do approximate computations. Often a separable expression approximation can be useful enough. The Variational Bayesian Approximation (VBA) is a technique that approximates the joint distribution $p$ with an easier, for example separable, one $q$ by minimizing Kullback–Leibler Divergence $KL(q|p)$. When $q$ is separable in all the variables, the approximation is also called Mean Field Approximation (MFA) and so $q$ is the product of the approximated marginals. A first standard and general algorithm is alternate optimization of $KL(q|p)$ with respect to $q_i$. A second general approach is its optimization in the Riemannian manifold. However, in this paper, for practical reasons, we consider the case where $p$ is in the exponential family and so is $q$. For this case, $KL(q|p)$ becomes a function of the parameters $\thetab$ of the exponential family. Then, we can use any other optimization algorithm to obtain those parameters. In this paper, we compare three optimization algorithms: standard alternate optimization, a gradient-based algorithm and a natural gradient algorithm and study their relative performances on three examples.

In this paper, the definition of information entropy of Smoluchowski coagulation equation for Brownian motion is introduced based on coagulation probability. The expression of entropy is the function of geometric average particle volume and standard deviation with lognormal distribution assumption. The asymptotic solution with moment method shows that the entropy is a monotone increasing function of time, which is equivalence to the entropy based on particle size distribution. the result reveals that the present definition of entropy of Smoluchowski coagulation equation are inadequate because the particle average volume at equilibrium cannot be determined from the principle of maximum entropy. This provides a basis for further exploring the global properties of Smoluchowski coagulation equation.

Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. We proposed in this paper to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different climate scenarios compared to the approach using the expected growth and yield. The SAA methods requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

In this paper, we study a three step iterative scheme to approximate fixed points of Suzuki's generalized non-expansive mappings. We establish some weak and strong convergence results for such mappings in uniformly convex Banach spaces. Further, we show numerically that iterative scheme (1.8) converges faster than some other known iterations for Suzuki's generalized non-expansive mappings. To support our claim, we give an illustrative example and approximate fixed points of such mappings using Matlab program. Our results are new and generalize several relevant results in the literature.

We present smooth approximations to $ \vert{x} \vert $ using sigmoid functions. In particular $ x\,erf(x/\mu) $ is proved to be better smooth approximation for $ \vert{x} \vert $ than $ x\,tanh(x/\mu) $ and $ \sqrt{x^2 + \mu} $ with respect to accuracy. To accomplish our goal we also provide sharp hyperbolic bounds for error function.

We shall discuss three methods of inverse Laplace transforms. A Sinc-Thiele approxi- mation, a pure Sinc, and a Sinc-Gaussian based method. The two last Sinc related methods are exact methods of inverse Laplace transforms which allow us a numerical approximation using Sinc methods. The inverse Laplace transform converges exponentially and does not use Bromwich contours for computations. We apply the three methods to Mittag-Leffler functions incorporating one, two, and three parameters. The three parameter Mittag-Leffler function represents Prabhakar’s function. The exact Sinc methods are used to solve fractional differential equations of constant and variable differentiation order.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

This work presents a novel approach to construct surrogate models of parametric Differential Algebraic Equations based on a tensor representation of the solutions. The procedure consists in building simultaneously, for every output of the reference model, an approximation given in tensor-train format. A parsimonious exploration of the parameter space coupled with a compact data representation allows to alleviate the curse of dimensionality. The approach is thus appropriate when many parameters with large domains of variation are involved. The numerical results obtained for a nonlinear elasto-viscoplastic constitutive law show that the constructed surrogate model is sufficiently accurate to enable parametric studies such as the calibration of material coefficients.

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.

This paper provides the first-time ever analytic modelling for Pointwise Stationary Fluid Flow Approximation(PSFFA) model of the non-stationary

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

We construct a novel family of summation-integral type hybrid operators in terms of shape parameter $\alpha\in \lbrack 0,1]$ in this paper. Basic estimates, rate of convergence, and order of approximation are also studied using the Korovkin theorem and the modulus of smoothness. We investigate the local approximation findings for these sequences of positive linear operators utilising Peetre's K-functional, Lipschitz class, and second-order modulus of smoothness.

This letter aims to show the connection between the sinc approximation for high-energy absorption cross section and the shadow radius of the spherically symmetric black hole. This connection can give a physical interpretation of the absorption cross section in the eikonal limit parameters. Moreover, the use of this alternative way, one can extract its shadow radius from the absorption cross section in high energy limits to gain more information about the black hole spacetime. Our results indicate that the increasing the value of the shadow radius of the black hole, exponentially increase the the absorption cross section of the black hole in high-energy limits which can be captured by the Event Horizon Telescope (EHT) collaboration.

In this work, we consider the properties of the two-term Machin-like formula and develop an algorithm for computing digits of π by using its rational approximation. In this approximation, both terms are constructed by using a representation of 1/π in the binary form. This approach provides the squared convergence in computing digits of π without any trigonometric functions and surd numbers. The Mathematica codes showing some examples are presented.

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

In this paper we give approximations to the Mittag-Leffler functions in terms of elementary functions using different methods. This allowed us to establish a practical method we called integerization principle. This principle states that many fractional nonlinear oscillators may be solved by means of the solution to some integer-order Duffing oscillator equation. The accuracy of the obtained results is illsutrated in concrete examples. Formulas for estimating the errors in the approximations are also provided.

4-component relativistic atomic and molecular calculations are typically performed within the no-pair approximation where negative-energy solutions are discarded, hence the symmetry between electronic and positronic solutions is not considered. These states are however needed in QED calculations, where furthermore charge conjugation symmetry becomes an issue. In this work we shall discuss the realization of charge conjugation symmetry of the Dirac equation in a central field within the finite basis approximation. Three schemes for basis set construction are considered: restricted, inverse and dual kinetic balance. We find that charge conjugation symmetry can be realized within the restricted and inverse kinetic balance prescriptions, but only with a special form of basis functions that does not obey the right boundary conditions of the radial wavefunctions. The dual kinetic balance prescription is on the other hand compatible with charge conjugation symmetry without restricting the form of the radial basis functions. However, since charge conjugation relates solutions of opposite value of the quantum number κ, this requires the use of basis sets chosen according to total angular momentum j rather than orbital angular momentum ` . As a special case, we consider the free-particle Dirac equation, where the solutions of opposite sign of energy are related by charge conjugation symmetry. We note that there is additional symmetry in those solutions of the same value of κ come in pairs of opposite energy.

This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic pulses. A receiver collects reflections from the air-ground interface and from the gradients of dielectric permittivity in the half-space. This scenario is of interest for ground penetrating radar (GPR) applications. For the analytical description of the received signal, we developed and implemented a novel time-domain version of the coupled-wave Wentzel–Kramers–Brillouin approximation. Our solution is in very good agreement with finite-difference time-domain results, radically accelerates calculations, and effectively accounts for the protracted return signals observed in the lower part of the GPR spectrum. The paper includes results showing the application of the proposed technique to two case studies: in particular, the method was employed for the post-processing of experimental radargrams collected on Lake Chebarkul, in Russia, and to simulate GPR probing of the Moon surface, to detect smooth gradients of the dielectric permittivity in lunar regolith.

This article presents a harmonic analysis of the stator currents of a squirrel-cage in-15 duction motor fed by a voltage source inverter with PWM space vector control (SV-PWM). The 16 influence of PWM switching frequency and dead time (dead-band) of controlled transistors on 17 THD and electromagnetic torque ripple is shown. The aim is to determine the lowest switching 18 frequency of the transistors for which the drive will operate correctly. Characteristics were de-19 termined as functions in the form of THD ( ), where the least square approximation was 20 used for stator current measurements when the PWM switching frequency is changed. The ap-21 proximations were realized for simulation and experimental results. To clarify the results, the 22 operation of hardware PWM circuits in microcontrollers was analyzed.

This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

The 5-hydroxymethylfurfural (HMF) is a six-carbon heterocyclic organic compound with two substituents an aldehyde group and an alcohol group. It is formed from saccharides as a result of two reactions: the Maillard reaction and the caramelization of saccharides. It has both a negative and a positive effect on the human body, which makes it, even more, an attractive research object. The objective of the study was to determine HMF content in honey samples available on the Polish market using three varied methods and compare the results obtained. The methodology used included: two spectrophotometric methods (by White and Winkler) and the HMF determination method based on the LC-MS/MS technique developed uniquely for this study. The determined HMF content in all tested honey samples did not exceed the applicable standard. All results are within the scope of the European Council Directive 2001/110/EC of 20 December 2001, i.e. they do not exceed HMF content in honey, which is 40 mg/kg.

In this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares. In time-sensitive applications or in embedded systems with limited resources, the runtime of the approximate function is as crucial as its accuracy. The proposed algorithm search to find the Optimal Piecewise Polynomial (OPP) with minimum computational cost while ensuring the error below a specified threshold. This was accomplished by using smooth piecewise polynomials with optimal order and number of intervals. The computational cost only depends on polynomial complexity, i.e., the order and the number of intervals at runtime function call. For optimal approximation, computational costs for all the possible combinations of piecewise polynomials were calculated and tabulated as ascending order for the specific target CPU off-line. Each combination was optimized through constrained least squares and random selection method for given sample points. Afterward, whether the approximation error is below the predetermined value is examined. When the error is permissible, the combination is selected as the optimal approximation or the next combination was examined. To verify the performance, several representative functions were examined and analyzed.

The theory of harmonic based function is discussed here within the framework of umbral operational methods. We derive a number of results based on elementary notions relying on the properties of Gaussian integrals.

In this paper, we study a differential operator of parabolic type with a variable and unbounded coefficient, defined on an infinite strip. Sufficient conditions for the existence and compactness of the resolvent are established, and an estimate for the maximum regularity of solutions of the equation Lu=f∈L_2(Ω) is obtained. Two-sided estimates for the distribution function of approximation numbers are obtained. As is known, estimates of approximation numbers show the rate of best approximation of the resolvent of an operator by finite-dimensional operators. The paper proves the assertion about the existence of positive eigenvalues among the eigenvalues of the given operator and finds two-sided estimates for them.

Our goal in this paper is to study the relationship between the linear approximation of Einstein's equations to the Klein-Gordon’s equation. The part one presents what the Klein-Gordon’s equation and the integration of the theory of quantum information in it. The Part two deals with the Stress Energy tensor quantum, wherein the detail I linearized gravity of Einstein equation, and wherein I develop the tensor quantum energy pulse from the equivalence of equation einstein the linearized gravity and the Schrödinger equation relativistic described by Klein-Gordon’s equation.

In this lecture notes, we will introduce Asymptotics, then we will give a short glimpse on Perturbation theory (regular versus singular), which plays a crucial role especially in theoretical physics. Our goal is to find Asymptotic series that approximates the values of integrals depending on some parameter or the solutions of differential equations. These methods that lead to obtain more effective algorithms of numerical evaluation are called: Asymptotic Methods.

This paper presents the light scattering matrices of atmospheric aggregated hexagonal ice particles appearing in cirrus clouds. In this work aggregates consist of the same particles with different spatial orientation and number of these particles. Two types of particle shape were studied: (1) hexagonal column; (2) hexagonal plate. For both shapes we study compact and non-compact cases of arrangement of particles in aggregates. As a result, four sets of aggregates were made: (1) compact columns; (2) non-compact columns; (3) compact plates; (4) non-compact plates. Each set consists of eight aggregates with different number of particles from 2 to 9. For practical reason the bullet-rosette and the aggregate of hexagonal columns with different sizes was also calculated. The light scattering matrices were calculated for the case of arbitrary spatial orientation within the geometrical optics approximation for sets of compact and non-compact aggregates and within the physical optics approximation for two additional aggregates. It was found that light scattering matrix elements for aggregates are depend on arrangement of particles they are consisted.

In this paper, we introduce a new iteration scheme, named as the S**-iteration scheme, for approximation of fixed point of the nonexpansive mappings. This scheme is faster than Picard, Mann, Ishikawa, Noor, Agarwal, Abbas, Thakur, and Ullah iteration schemes. We show the stability of our instigated scheme and give a numerical example to vindicate our claim. We also put forward some weak and strong convergence theorems for Suzuki's generalized nonexpansive mappings in the setting of uniformly convex Banach spaces. Our results comprehend, improve, and consolidate many results in the existing literature.

A complete MAPLE procedure is designed to implement effectively an algorithm for approximating the trigonometric functions. The algorithm gives a piecewise polynomial approximation on an arbitrary interval, presenting a special partition that we can get its parts, subintervals with ending points of finite rational numbers, together with corresponding approximate polynomials. The procedure takes a sequence of pairs of interval-polynomial as its output that we can easily explore in some useful ways. Examples on calculating approximate values of the sine function with arbitrary accuracy for both of rational and irrational arguments as well as drawing the graph of the piecewise approximate functions will be presented. Moreover, from the approximate integration of integrands of the form $x^m\sin x$ on $[a,b]$, another MAPLE procedure is proposed to find the desired polynomial estimates in norm for the best $L^2$-approximation of the sine function in the vector space $\mathcal{P}_{\ell}$ of polynomials of degree at most $\ell$, a subspace of $L^2(a,b)$.

In this paper, we proposed the numerical method called the variational iteration orthogonal collocation method (VIOCM), for the approximate solution of the deadly Corona virus model using Mamadu-Njoseh polynomials as basis functions. The proposed method is an elegant mixture of the variational iteration method (VIM) and the orthogonal collocation method (OCM). It was observed that the proposed method converges rapidly to the exact solution even as N increases. This suggests that the use of orthogonal polynomials as trial functions for the SEIR model is indeed an effective approximant as it produces the analytic solution at just few iterations. Resulting numerical evidences were compared with the standard variational iteration method as available in literature. All computational frameworks were executed with MAPLE software.

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.

Interferons (IFNs) are cytokines involved in the immune response that act on innate and adaptive immunity. These proteins are natural cell-signaling glycoproteins expressed in response to viral infections, tumors, and biological inducers and constitute the first line of defense of vertebrates against infectious agents. They have been marketed for more than 30 years with considerable impact on the global therapeutic protein market thanks to their diversity in terms of biological activities. They have been used as single agents or with combination treatment regimens, demonstrating promising clinical results, resulting in 22 different formulations approved by regulatory agencies. The 163 clinical trials with currently active IFNs reinforce their importance as therapeutics for human health. However, their application has presented difficulties due to the molecules’ size, sensitivity to degradation, and rapid elimination from the bloodstream. For some years now, work has been underway to obtain new drug delivery systems to provide adequate therapeutic concentrations for these cytokines, decrease their toxicity and prolong their half-life in the circulation. Although different research groups have presented various formulations that encapsulate IFNs, to date, there is no formulation approved for use in humans. The current review exhibits an updated summary of all encapsulation forms presented in the scientific literature for these cytokines IFNα, IFNß, and IFNγ, from the year 1996 to the year 2021, considering parameters such as: encapsulating matrix, route of administration, and encapsulation.

A key step in the study of tree pathology is the identification of appropriate method for inoculating pathogens of diseases in branch and trunk. Pathogens of diseases in branch and trunk are commonly inoculated through punching, burning, and toothpick inoculation. However, there is a lack of comparative analysis of the inoculation outcomes of these three methods. In this work, Six‐year‐old P. alba var. pyramidalis were inoculated with V. sordida, using punching, burning, and toothpick techniques, respectively, to investigate the differences in the effectiveness of these inoculation methods. Results reveal that the incidence rate was 93.55% in the toothpick inoculation group, significantly higher than 80.65% in the burning inoculation group (p = 0.007), while punching inoculation exhibited significant pathological responses in the early stages, with spontaneous healing in the later stage. Addi-tionally, toothpick inoculation was more efficient in inducing Valsa canker when inoculating the pathogen at the bottom of the tree, with lower intra‐ and inter‐row spacing (stand density) providing better outcomes than higher intra‐ and inter‐row spacing. The results of this study demonstrate that toothpick inoculation is an optimal option for studying the artificial inoculation of V. sordida in six‐year‐old P. alba var. pyramidalis, providing technical support for research on poplar diseases and offering a theoretical basis for the inoculation of other diseases in branch and trunk.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.

Elucidating physicochemical processes in the degradation of pollutants may optimize their removal from water sources. This work presents a set of elementary steps in the photocatalytic degradation of carbamazepine (CBZ), assuming a steady state approximation in an Advanced Oxidation Process (AOP) combining short-wave ultraviolet radiation (UVC), homogeneous reagent (H₂O₂) and heterogeneous (TiO₂) catalyst. Elementary steps include excitation of both reagent/catalysts by UVC photons, adsorption of CBZ on the excited TiO₂, or its oxidation by hydroxyl radicals. Assuming the steady state approximation on the intermediate products (excited TiO₂, CBZ- excited TiO₂ complex, and hydroxyl radicals), leads to rate laws for degradation of CBZ, in which UVC radiation, TiO₂, and H₂O₂ are pseudo first order at all concentrations or intensities having no direct influence on CBZ pseudo-order, whereas CBZ shifts from pseudo first order at low concentrations to pseudo-zero order at high concentrations. Several experiments to test the mechanism were conducted, by varying CBZ, H₂O₂, and TiO₂ concentrations, and UVC radiation intensities. Measured results indeed fit the suggested mechanism for the first three, but irradiation intensity appears to shift CBZ influence from pseudo-second to pseudo-first order with increased intensities. Corrected elementary steps are proposed to fit the results.

Up-conversion nanoparticles (UCNP) under near-infrared (NIR) light irradiation have been well investigated in the field of bio-imaging. However, the low up-conversion luminescence (UCL) intensity limits applications. Plasmatic modulation has been proposed as an effective tool to adjust the luminescence intensity and lifetime. In this study discrete dipole approximation (DDA) was explored concerning guiding the design of UCNP@mSiO2-Au structures with enhanced UCL intensity. The extinction effects of gold shells could be changed by adjusting the distance between the UCNPs and the Au NPs by synthesized tunable mesoporous silica (mSiO2) spacers. Enhanced UCL was obtained under 808 nm irradiation. Theoretical predictions could not be demonstrated to full extend by experimental data, indicating that better models for simulation need to better take into account inhomogeneities in particle morphologies.

Modern concepts in irreversible thermodynamics are applied to system transformation and degradation analyses. Phenomenological entropy generation (PEG) theorem is combined with the Degradation-Entropy Generation (DEG) theorem for instantaneous multi-disciplinary, multi-scale, multi-component system characterization. A Transformation-Phenomenological Entropy Generation (TPEG) theorem and space materialize with system and process defining elements and dimensions. The near-100% accurate, consistent results and features in recent publications demonstrating and applying the new TPEG methods to frictional wear, grease aging, electrochemical power systems cycling—including lithium-ion battery thermal runaway—metal fatigue loading and pump flow, are collated herein, demonstrating the practicality of the new and universal PEG theorem, and the predictive power of models that combine and utilize the PEG and DEG theorems. The methodology is useful for design, analysis, prognostics, diagnostics, maintenance and optimization.

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

As an emerging discipline, Design Morphology, with the advantage of "Form Study", has been integrated with many disciplines, and gradually formed its collaborative innovation paradigm. The inclusion of "Ecology" into Design Morphology is expected to promote the research of Design Morphology with the help of the systematic thinking and methods of ecology. The ecosystem of Design Morphology includes the natural ecosystem and the quasi-ecosystem closely related to human beings, and also put forward the concept of "Form Community" for the first time in the design field. In fact, this is exactly the research scope of Design Morphology. Advocating the ecological view of Design Morphology, can not only help to design researchers improve their values and world view, with new thinking and method to engage in "Form Study", but also contribute to the theoretical construction and thinking expansion of Design Morphology, and play a positive role in promoting interdisciplinary collaborative innovation led by Design Morphology. In addition, it can be used to evaluate the overall research and future development trend of Design Morphology, and provide the new research ideas and approaches for the development of design.

Socio-demography of statuses reflects the demographic and social roles and achievements of an individual(s) in a population. In the theoretical frameworks, little was known about the theory, analytical measures, and evolutionary mechanism of the socio-demographics of statuses of an individual(s). In this paper, three (3) methodological issues regarding the concept of socio-demographics were proposed, these are: (a) identified the basic features that clarify our understanding about the conception of the socio-demographic statuses of an individual(s) as common, universal, and desirable positions, achievements, and advantages every individual strives and competes to attain, achieve, maintain, update, and upgrade which are determined by biological and social environments, driven and regulated by a continuum of competitions and natural selections; (b) presented a new paradigm of analytical measures of an individual’s socio-demographic statuses and the established hierarchical transitions; (c) proposed theoretical assumptions for the evolution of a socio-demographically unique population. Seven (7) factors (the driving forces) and eight (8) conditions responsible for the evolution of socio-demographically unique populations were identified and explained. The evolutionary transitions were organized into six (6) socio-demographic ranks.

Even though the current classic aerospace propulsion methods are greatly improved, some of the most modern methods have already been introduced, among which the propulsion with electric motors. Energy can be obtained from hydrogen burners, honeycomb burners to protect the burning of hydrogen that burns 10 times faster than conventional oil fuels or alcohols. Hydrogen storage is also done in honeycomb cell reservoirs. Getting solar photovoltaic power even during flight is not yet profitable or efficient, but it can be proposed in the future for lighter and/or spacecraft. A great desire is to dismantle water in hydrogen and oxygen directly on the aircraft, so the stored fuel is actually water and the one actually used is the hydrogen removed from the water. In this case, there is no need for hydrogen storage. And other modern methods of propulsion and energy acquisition are briefly presented in the paper.

Given several nonnegative matrices with a single pattern of allocation among their zero/nonzero elements, the average matrix should have the same pattern, too. This is the first tenet of the pattern-multiplicative average (PMA) concept, while the second one suggests the multiplicative (or geometric) nature of averaging. The original concept of PMA was motivated by the practice of matrix population models as a tool to assess the population viability from long-term monitoring data. The task has reduced to searching for an approximate solution to an overdetermined system of polynomial equations for unknown elements of the average matrix (G), hence to a nonlinear constrained minimization problem for the matrix norm. Former practical solutions faced certain technical problems, which required sophisticated algorithms but returned acceptable estimates. Fresh idea to use the eigenvalue approximation ensues also from the basic equation of averaging, which determines the exact value of λ1(G), the dominant eigenvalue of matrix G, too, and the corre-sponding eigenvector. These are bound by the well-known linear equations, which reduce the task to a standard problem of linear programing (LP). The LP approach is realized for 13 fixed-pattern matrices gained in a case study of Androsace albana, an alpine short-lived perennial, monitored on permanent plots during 14 years. A standard software routine reveals the unique exact solution, rather than an approximate one, to the PMA problem, which turns the LP approach into ‘’the best of versatile optimization tools”. The exact solution turns out to be peculiar in reaching zero bounds for certain nonnegative entries of G, which deserves a modified problem formulation separating the lower bounds from zero.

A new trend in cow's milk has emerged in the market called type A1 and A2 milk. These products have piqued the interest of both consumers and researchers. Recent studies suggest that A2 milk may have potential health benefits, which is why researchers are investigating this product further. It is interesting to note that the A1 and A2 milk types have area-specific characteristics compared to breed-specific characteristics. Extensive research has focused on milk derivatives obtained from cow’s milk, primarily through in vitro and animal studies. However, few clinical studies have been conducted in humans, and the results have been unsatisfactory. New molecular techniques for identifying A1 and A2 milk may help researchers to develop new studies that can clarify certain controversies surrounding A1 milk. It is essential to exercise extreme caution when interpreting the updated literature. It has the potential to spread panic worldwide and have negative economic implications. Therefore, this study aims to investigate the new aspects of A1 and A2 milk in different research areas and clarify some aspects regarding these two types of milk.

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

When implementing a control system for a ground-based unmanned vehicle (UV) used in agri-culture, an important factor is the low cost of the equipment used, so the use of cameras without lidars for navigation, avoiding obstacles, and ensuring movement safety seems promising. En-suring the safety of movement of UV consists of developing methods and means that make it possible to bypass static and dynamic obstacles and make an emergency stop if it is impossible to bypass. The article describes methods and software that provide detection of objects surrounding the UV through the use of the YOLOv8 neural network, space free for movement through the use of se-mantic segmentation with the MobileNetV3 network architecture, tools for combining several data sources to build a local perception map, as well as linear and angular speed of the UV based on calculating the optimal direction of movement for avoiding obstacles. Testing of the developed methods and software was realized in the Webots simulation environment. Software is developed with ROS2.

The properties of iterative splitting methods with serial versions have been analyzed since recent years, see [1] and [3]. We extend the iterative splitting methods to a class of parallel versions, which allow to reduce the computational time and keep the bene t of the higher accuracy with each iterative step. Parallel splitting methods are nowadays important to solve large problems, which can be splitted in subproblems and computed independently with the dierent processors. We present a novel parallel iterative splitting method, which is based on the multi-splitting methods, see [2], [10] and [15]. Such a exibilisation with multisplitting methods allow to decompose large iterative splitting methods and recover the bene t of their underlying waveform-relaxation (WR) methods. We discuss the convergence results of the parallel iterative splitting methods, while we could reformulate such an error to a summation of the individual WR methods. We discuss the numerical convergence of the serial and parallel iterative splitting methods and present dierent numerical applications to validate the bene t of the parallel versions.

The study of different forms of preconditioners for solving a system of nonlinear equations, by using Newton’s method, is presented. The preconditioners provide numerical stability and rapid convergence with reasonable computation cost, whenever chosen accurately. Different families of iterative methods can be constructed by using a different kind of preconditioners. The multi-step iterative method consists of a base method and multi-step part. The convergence order of base method is quadratic and each multi-step add an additive factor of one in the previously achieved convergence order. Hence the convergence of order of an m-step iterative method is m + 1. Numerical simulations confirm the claimed convergence order by calculating the computational order of convergence. Finally, the numerical results clearly show the benefit of preconditioning for solving system of nonlinear equations.

Whole genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, anchors-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. The paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explored these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects.

This report discusses the application of approximate dynamic programming (ADP) and Krylov subspace methods in solving sequential decision-making problems in machine learning. ADP is used when dealing with large state spaces or when the exact dynamics are unknown. The paper explores various ADP methods such as temporal difference learning and least-squares policy evaluation. The authors also focus on the use of Krylov subspace methods for solving the Bellman equation in ADP. The report provides insights into linear approximation, stochastic algorithms, and the Arnoldi algorithm for orthonormal basis. Overall, the paper highlights the theoretical foundation provided by ADP and its connection with Krylov subspace methods, shedding light on their application in computer science.

This manuscript explores unmanned aerial vehicle DC motor control performance efficacy of deterministic artificial intelligence in comparison to model-following adaptation, particularly a direct self-tuner with filtering. The deterministic artificial intelligence model made use of self-awareness statements to overcome error in response to permutations of the multi-duty cycle square wave that served as the system input. It can be seen that (despite equivalently powerful estimation techniques) deterministic artificial intelligence provided far superior results: a reduction in peak initial transient error of 55%, and a mean error reduction over 81% with over 65% reduction in error standard deviation compared to a state-of-the-art nonlinear adaptive control method. Deterministic artificial intelligence also was able to very closely track at the switching of the input control, while the benchmark nonlinear adaptive control failed to respond as quickly at these points.

Chymotrypsin is an important proteolytic enzyme in human digestion system that cleaves milk proteins, through hydrolysis reaction, which makes it interesting subject to study activity of milk proteases. In this work, we compared detection of chymotrypsin by spectrophotometric, dynamic light scattering (DLS) and quartz crystal microbalance (QCM) methods and determined the limit of chymotrypsin detection (LOD), 0.15 ± 0.01 nM for spectrophotometric, 0.67 ± 0.05 nM for DLS and 1.40 ± 0.30 nM for QCM methods, respectively. We discuss peculiarities and give perspective for implementation of detection methods and note that while the optical detection methods are simple to implement, the QCM method is more robust for sample preparation. We give an overview on methods and instruments for detection of chymotrypsin and other milk proteases.

This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.

This review is dedicated to sustainable practices in liquid chromatography. HPLC and UHPLC methods contribute significantly to routine analytical techniques. Therefore, the transfer of classical liquid chromatographic methods into sustainable ones is of utmost importance in moving toward sustainable development goals. Among other principles to render a liquid chromatographic method green, substitution of the organic solvent component in the mobile phase with a greener one received great attention. This review concentrates on choosing the best alternative green organic solvent to replace the classical solvent in the mobile phase for easy rapid transfer to a more sustainable normal phase or reversed-phase liquid chromatography. The main focus of this review will be on describing the transfer of non-green to green and white chromatographic methods in an effort to elevate the sustainability best practice in analytical chemistry. The greenness properties and greenness ranking in addition to the chromatographic suitability of seventeen organic solvents for liquid chromatography are mentioned to have a clear insight into the issue of rapidly choosing the appropriate solvent to transfer a classical HPLC or UHPLC method into a more sustainable one. A simple guide is proposed for making the liquid chromatographic method more sustainable.

The rapid growth of network services, Internet of Things devices and online users on the Internet have led to an increase in the amount of data transmitted daily. As more and more information is stored and transmitted on the Internet, cybercriminals are trying to gain access to the information to achieve their goals, whether it is to sell it on the dark web or for other malicious intent. Through thorough literature study relating to the causes and issues that are brought from the security and privacy segment of wireless networks, it is observed that there are various factors that can cause the networks to be an insecure; especially factors that revolve around cybercriminals with their growing expertise and the lack of preparation and efforts to combat them by relevant bodies. The aim of this paper is to showcase major and frequent security as well as privacy issues in wireless networks along with specialized solutions that can assist the related organizations or the public to fathom how great of an impact these challenges can bring if every related stakeholder took a step in reducing them. Through this paper it is discovered that there are many ways these challenges can be mitigated, however, the lack of implementation of privacy and security solutions is still largely present due to the absence of practical application of these solutions by responsible parties in real world scenarios.

The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Considering its broad modeling capabilities, Rosetta consistently ranks highly when compared to other leading methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of scientists at more than 60 institutions, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss the methods developed in the last five years, involving the latest protocols for structure prediction, protein–protein and protein–small molecule docking, protein structure and interface design, loop modeling, the incorporation of various types of experimental data, and modeling of peptides, antibodies and other proteins in the immune system, nucleic acids, non-standard amino acids, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the ­­software. Rosetta is available at www.rosettacommons.org.

Notable critics, including Sunday Anozie and Robert Morsberger, have branded Christopher Okigbo's Labyrinths as allusions to the Nigerian crisis of 1965, as well as Igbo traditional religion, the historical and personal idiosyncrasies, and as a literary work from Theocritus and Vergil to Howl. This article veers from such imputations into a hitherto less trodden ground. It subjects the mind of the persona in Labyrinths -from 'Heavensgate' to 'Limits' and 'Silences', and from 'Distances' to 'Path of Thunder' to behavioural patterns. The article concludes that the behavioural patterns not only define how the mind works imaginatively, but that the very imagination wrought in obscurities methodically defines madness.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

During the most recent years, a lot of research has been done in creating robots with more self-governance so that they can overcome the challenges that real world environments present. The robot's limited versatility in real world applications can be overcome by the development of Legged robots. Also, as they permit movement in unavailable territory to robots with wheels, Legged Robots are more advantageous. But the potency of the legged robots explicitly its energy usage among alternate points of view really fall behind robots that use wheels. So, the present status of development, there are as yet a few perspectives that need to be analysed, optimized and enhanced. This paper presents review of literature of various biologically inspired legged robots, various techniques adopted for their analysis and optimization and the analysis and optimization of the ones that are not biologically inspired