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ABSTRACT Mechanisms governing the migratory decisions of birds have long fascinated ecologists and sparked considerable debate. Identifying factors responsible for variation in migration distance, also known as differential migration, has been a popular approach to understanding the mechanisms underlying migratory behaviour more generally. However, research progress has been slowed by the continued testing of overlapping, non‐mechanistic, and circular predictions among a small set of historically entrenched hypotheses. We highlight the body size hypothesis and suggest that the predictions commonly tested have impeded progress because body size relationships with migration distance are predictions made by several distinct hypotheses with contrasting mechanisms. The cost of migration itself has not been adequately accounted for in most hypotheses, and we propose two flight efficiency hypotheses with time‐ and energy‐minimizing mechanisms that allow individuals to mitigate the risks inherent to longer migrations. We also advance two conceptual versions of the social dominance hypothesis based on two distinct underlying mechanisms related to distance minimization and food maximization that will help clarify the role of competition in driving migratory decisions. Overall, we describe and refine 12 mechanistic hypotheses proposed to explain differential migration (along with several other special‐case hypotheses), seven of which have underlying mechanisms related to food limitation as past research has identified this to be an important driver of differential migration. We also thoroughly reviewed 145 publications to assess the amount of support for 10 critical assumptions underlying alternative hypotheses for differential migration in birds. Our review reveals that surprisingly few studies explicitly evaluate assumptions within a differential migration context. Generating and testing strong predictions and critical assumptions underlying mechanisms of alternative hypotheses will improve our ability to differentiate among these explanations of differential migration. Additionally, future intraspecific progress will be greatest if investigators continue to focus on mechanisms underlying variation in migration distance within rather than among demographic classes, as previous research has found differing mechanisms to be responsible for differential migration among demographic classes. Interspecifically, a thorough comparative analysis that seeks to explain variation in migration distance among species would broaden both our understanding of the mechanisms regulating current differential migration patterns and those that led to the evolution of migration more generally. Collectively, we provide a framework that, together with advances in animal‐borne tracking and other technology, can be used to advance our understanding of the causes of differential migration distance, and migratory decisions more generally. [ABSTRACT FROM AUTHOR]

Two-dimensional liquid foam systems comprised of an odd number N of bubbles arranged in a staircase configuration are studied as they are packed within a confined straight channel. Energy minimization is employed to characterize the permitted topology and geometry of the system at equilibrium. It is known that in an infinite staircase, bubble films are flat. The configuration for a finite number of bubbles, however, deviates from that of an infinite staircase at the edges. It is known that films near the edges are not flat and structures with a few bubbles (up to N=3) are well characterized. To study what happens as more bubbles are added, and how the behaviour approaches that of an infinite staircase, structures comprised of N=[5,7,...] bubbles are studied here. For these N values, minimum and maximum bubble areas are found that can pack in a staircase configuration within a straight channel. However, for an infinite staircase and also for N=3 , there are maximum areas, but no minima. Staircases with very short films, which make the structure susceptible to break in various ways, are also identified. [ABSTRACT FROM AUTHOR]

Using the results of virtual ligand screening (VLS) of a representative set of 66834 commercially available drug-like ligands and 8400 di- and tripeptides in the central cavity of Transthyretin (TTR) amyloid fibrils, it has been shown that despite the great chemical diversity, among commercially available drug-like organic compounds and ultrashort peptides (USPs), only 7 USPs are able to bind in the central cavity of TTR amyloid fibrils, thus preventing the growth of amyloid fibrils. The results of VLS also show that the relaxation of ligand steric strains in the obtained complexes not only significantly improves docking scores but also radically (>50%) changes the main result of VLS, the molecular composition of 1% of the best ligands. Thus, the relaxation of steric strains after VLS can more than double the effectiveness of VLS in the development of new drugs. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a reduction-based algebraic multigrid (AMG) method that is suitable for solving general (non)symmetric linear systems and is naturally robust from pure advection to pure diffusion. Initial motivation comes from a new reduction-based AMG approach, ℓAIR (local approximate ideal restriction), that was developed for solving advection-dominated problems. Though this new solver is very effective in the advection-dominated regime, its performance degrades in cases where diffusion becomes dominant. This is consistent with the fact that in general, reduction-based AMG methods tend to suffer from growth in complexity and/or convergence rates as the problem size is increased, especially for diffusion-dominated problems in two or three dimensions. Motivated by the success of ℓAIR in the advective regime, our aim in this paper is to generalize the AIR framework with the goal of improving the performance of the solver in diffusion-dominated regimes. To do so, we propose a novel way to combine mode constraints as used commonly in energy-minimization AMG methods with the local approximation of ideal operators used in ℓAIR. The resulting constrained ℓAIR algorithm is able to achieve fast scalable convergence on advective and diffusive problems. In addition, it is able to achieve standard low complexity hierarchies in the diffusive regime through aggressive coarsening, something that was previously difficult for reduction-based methods. [ABSTRACT FROM AUTHOR]

NanoTube Construct is a web tool for the digital construction of nanotubes based on real and hypothetical single-layer materials including carbon-based materials such as graphene, graphane, graphyne polymorphs, graphidiyene and non-carbon materials such as silicene, germanene, boron nitride, hexagonal bilayer silica, haeckelite silica, molybdene disulfide and tungsten disulfide. Contrary to other available tools, NanoTube Construct has the following features: a) it is not limited to zero thickness materials with specific symmetry, b) it applies energy minimisation to the geometrically constructed Nanotubes to generate realistic ones, c) it derives atomistic descriptors (e.g., the average potential energy per atom, the average coordination number, etc.), d) it provides the primitive unit cell of the constructed Nanotube which corresponds to the selected rolling vector (i.e., the direction in which the starting nanosheet is rolled to form a tube), e) it calculates whether the Nanotube or its corresponding nanosheet is more energetically stable and f) it allows negative chirality indexes. Application of NanoTube Construct for the construction of energy minimised graphane and molybdenum disulfide nanotubes are presented, showcasing the tool's capability. NanoTube Construct is freely accessible through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/diagonal/nanotube/).

Measure-valued structured deformations are introduced to present a unified theory of deformations of continua. The energy associated with a measure-valued structured deformation is defined via relaxation departing either from energies associated with classical deformations or from energies associated with structured deformations. A concise integral representation of the energy functional is provided both in the unconstrained case and under Dirichlet conditions on a part of the boundary. [ABSTRACT FROM AUTHOR]

The paper investigates a problem concerning the equilibrium of a solid body containing a thin rigid inclusion and a crack. It is assumed that the body is hyperelastic, therefore, it is described within the framework of finite strain theory. One of the peculiarities of this problem is a global injectivity constraint, which prevents the body, the crack faces and the inclusion from both mutual and self penetration. First, the paper deals with the differential formulation of the problem. Next, we consider energy minimization, showing that the latter provides the weak formulation of the former. Finally, the existence of the weak solution is demonstrated through the use of the variational technique. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'. [ABSTRACT FROM AUTHOR]

With the growing demand for automation in agriculture, industries increasingly rely on drones to perform crop monitoring and surveillance. In this regard, fixed-wing unmanned aerial systems (UASs) are viable platforms for scanning a large crop field, given their payload capacity and range. To achieve maximum coverage without landing for battery replacement, an algorithm for producing a minimal required energy survey path is essential. Hence, an energy-aware coverage path planning algorithm is proposed herein. The constraints for a fixed-wing UAS to fly at low altitudes while achieving full coverage of the crop field are first analyzed. Then, the full path is decomposed into straight-line and U-turn primitives. Finally, an algorithm to calculate a combination of straight-line segments and U-turns is proposed to obtain the path with minimum required energy consumption. The genetic algorithm is used to efficiently determine the order of the straight-line paths to traverse. Case studies show that the proposed algorithm can produce planning results for a convex-polygon-shaped crop field. [ABSTRACT FROM AUTHOR]

In this paper, we get the sharpest known to date lower bounds for the minimal Green energy of the compact harmonic manifolds of any dimension. Our proof generalizes previous ad-hoc arguments for the most basic harmonic manifold, i.e. the sphere, extending it to the general case and remarkably simplifying both the conceptual approach and the computations. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increased interest in using image processing, computer vision, and machine learning in biological and medical imaging research. One area of this interest is the diagnosis of brain tumors, which is considered a difficult and time‐consuming task traditionally performed manually. In this study, we present a method for tumor detection from magnetic resonance images (MRI) using a well‐known graph‐based algorithm, the Boykov–Kolmogorov algorithm, and the α‐expansion method. This approach involves pre‐processing the MRIs, representing the image positions as nodes, and calculations of the weights between edges as differences in intensity. The problem is formulated as an energy minimization problem and is solved by finding the 0,1 score for the image. Post‐processing is also performed to enhance the overall segmentation. The proposed method is easy to implement and shows high accuracy, precision, and efficiency in the results. We believe that this approach will bring significant benefits to scientists and healthcare researchers in qualitative research and can be applied to various imaging modalities for future research. [ABSTRACT FROM AUTHOR]

We formulate a plausible conjecture for the optimal Ehrhard-type inequality for convex symmetric sets with respect to the Gaussian measure. Namely, letting J_{k-1}(s)=\int ^s_0 t^{k-1} e^{-\frac {t^2}{2}}dt and c_{k-1}=J_{k-1}(+\infty), we conjecture that the function F:[0,1]\rightarrow \mathbb {R}, given by \begin{equation*} F(a)= \sum _{k=1}^n 1_{a\in E_k}\cdot (\beta _k J_{k-1}^{-1}(c_{k-1} a)+\alpha _k) \end{equation*} (with an appropriate choice of a decomposition [0,1]=\cup _{i} E_i and coefficients \alpha _i, \beta _i) satisfies, for all symmetric convex sets K and L, and any \lambda \in [0,1], \begin{equation*} F\left (\gamma (\lambda K+(1-\lambda)L)\right)\geq \lambda F\left (\gamma (K)\right)+(1-\lambda) F\left (\gamma (L)\right). \end{equation*} We explain that this conjecture is "the most optimistic possible", and is equivalent to the fact that for any symmetric convex set K, its Gaussian concavity power p_s(K,\gamma) is greater than or equal to p_s(RB^k_2\times \mathbb {R}^{n-k},\gamma), for some k\in \{1,\dots,n\}. We call the sets RB^k_2\times \mathbb {R}^{n-k} round k-cylinders ; they also appear as the conjectured Gaussian isoperimetric minimizers for symmetric sets, see Heilman [Amer. J. Math. 143 (2021), pp. 53–94]. In this manuscript, we make progress towards this question, and show that for any symmetric convex set K in \mathbb {R}^n, \begin{equation*} p_s(K,\gamma)\geq \sup _{F\in L^2(K,\gamma)\cap Lip(K):\,\int F=1} \left (2T_{\gamma }^F(K)-Var(F)\right)+\frac {1}{n-\mathbb {E}X^2}, \end{equation*} where T_{\gamma }^F(K) is the F-torsional rigidity of K with respect to the Gaussian measure. Moreover, the equality holds if and only if K=RB^k_2\times \mathbb {R}^{n-k} for some R>0 and k=1,\dots,n. As a consequence, we get \begin{equation*} p_s(K,\gamma)\geq Q(\mathbb {E}|X|^2, \mathbb {E}\|X\|_K^4, \mathbb {E}\|X\|^2_K, r(K)), \end{equation*} where Q is a certain rational function of degree 2, the expectation is taken with respect to the restriction of the Gaussian measure onto K, \|\cdot \|_K is the Minkowski functional of K, and r(K) is the in-radius of K. The result follows via a combination of some novel estimates, the L2 method (previously studied by several authors, notably Kolesnikov and Milman [J. Geom. Anal. 27 (2017), pp. 1680–1702; Amer. J. Math. 140 (2018), pp. 1147–1185; Geometric aspects of functional analysis , Springer, Cham, 2017; Mem. Amer. Math. Soc. 277 (2022), v+78 pp.], Kolesnikov and the author [Adv. Math. 384 (2021), 23 pp.], Hosle, Kolesnikov, and the author [J. Geom. Anal. 31 (2021), pp. 5799–5836], Colesanti [Commun. Contemp. Math. 10 (2008), pp. 765–772], Colesanti, the author, and Marsiglietti [J. Funct. Anal. 273 (2017), pp. 1120–1139], Eskenazis and Moschidis [J. Funct. Anal. 280 (2021), 19 pp.]), and the analysis of the Gaussian torsional rigidity. As an auxiliary result on the way to the equality case characterization, we characterize the equality cases in the "convex set version" of the Brascamp-Lieb inequality, and moreover, obtain a quantitative stability version in the case of the standard Gaussian measure; this may be of independent interest. All the equality case characterizations rely on the careful analysis of the smooth case, the stability versions via trace theory, and local approximation arguments. In addition, we provide a non-sharp estimate for a function F whose composition with \gamma (K) is concave in the Minkowski sense for all symmetric convex sets. [ABSTRACT FROM AUTHOR]

Among the non-traditional manufacturing processes, electro-thermal based processes are used to machine the difficult to cut materials. Among thermal based unconventional machining processes, laser machining and plasma arc cutting (PAC) are most significant processes that find applications in various industries including aerospace, automotive, shipbuilding, chemical processes equipment manufacturing and fabrication. In this manuscript, the parametric optimization of PAC is extensively reviewed. Bibliometric analysis of PAC was performed using Web of science and Scopus database wherein research publications were analyzed between year of 1961 to 2023. It was observed from the analysis that minimization of energy consumption in thermal based non-conventional machining methods is not extensively studied. Therefore, literature was reviewed to discuss about energy consumption and reduction of energy requirements in laser and PAC machining. [ABSTRACT FROM AUTHOR]

Hierarchical (first-order) structured deformations are studied from the variational point of view. The main contributions of the present research are the first steps, at the theoretical level, to establish a variational framework to minimize mechanically relevant energies defined on hierarchical structured deformations. Two results are obtained here: (i) an approximation theorem and (ii) the assignment of an energy to a hierarchical structured deformation by means of an iterative procedure. This has the effect of validating the proposal made in Deseri and Owen (J. Elast. 135:149–182, 2019) to study deformations admitting slips and separations at multiple submacroscopic levels. An explicit example is provided to illustrate the behavior of the proposed iterative procedure and relevant directions for future research are highlighted. [ABSTRACT FROM AUTHOR]

A simple, transparent, two-dimensional, nonlinear model of cell reorientation is constructed in this paper. The cells are attached to a substrate by "focal adhesions" that transmit the deformation of the substrate to the "stress fibers" in the cell. When the substrate is subjected to a deformation, say an in-plane bi-axial deformation with stretches λ 1 and λ 2 , the stress fibers deform with it and change their length and orientation. In addition, the focal adhesions can detach from the substrate and reattach to it at new nearby locations, and this process of detachment and reattachment can happen many times. In this scenario the (varying) fiber angle Θ in the reference configuration plays the role of an internal variable. In addition to the elastic energy of the stress fibers, the energy associated with the focal adhesions is accounted for by a wiggly energy ϵ a cos Θ / ϵ , 0 < ϵ ≪ 1 . Each local minimum of this energy corresponds to a particular configuration of the focal adhesions. The small amplitude ϵ a indicates that the energy barrier between two neighboring configurations is relatively small, and the small distance 2 π ϵ between the local minima indicates that a focal adhesion does not have to move very far before it reattaches. The evolution of this system is studied using a gradient flow kinetic law, which is homogenized for ϵ → 0 using results from weak convergence. The results determine (a) a region of the λ 1 , λ 2 -plane in which the (referential) fiber orientation remains stuck at the angle Θ and does not evolve, and (b) the evolution of the orientation when the stretches move out of this region as the fibers seek to minimize energy. [ABSTRACT FROM AUTHOR]

Nowadays, wireless sensor networks (WSNs) prove their potential in our daily day-to-day life. However, due to high congestion, energy management becomes the key challenge for WSNs. To increase the lifespan of WSNs, a unique clustered routing strategy is presented in this study. It offers an effective solution for the hot-spot effect and idle-listening issues. Outcomes help in lessening energy consumption. The developed algorithm is based on the principle of balanced energy consumption. Further, the developed WSN involves a node dormancy mechanism. It requires the energy balance technique using the clustering routing mechanism with distance variance. The design of clustering nodes is based on the master-slave principle, where the formation of clustering relies on node position and residual energy. MATLAB provides the simulation results as energy drop of each node to calculate the battery life. According to the achieved results, the developed algorithm can reduce the decay rate which can further lessen the energy consumption of the network. Moreover, it enhances the throughput and prolongs the network lifetime. The paper provides an energy-efficient clustering approach for Wireless Sensor Networks (WSNs) that can directly relate to manufacturing applications by practical solutions to the challenges faced in manufacturing settings, where effective sensor network deployment can lead to significant improvements in production processes and overall operational efficiency. [ABSTRACT FROM AUTHOR]

Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Evidence that the electronic structure of atoms persists in molecules to a much greater extent than has been usually admitted is presented. This is achieved by resorting to N-electron real-space descriptors instead of one- or at most two-particle projections like the electron or exchange-correlation densities. Here, the 3N-dimensional maxima of the square of the wavefunction, the so-called Born maxima, are used. Since this technique is relatively unknown to the crystallographic community, a case-based approach is taken, revisiting first the Born maxima of atoms in their ground state and then some of their excited states. It is shown how they survive in molecules and that, beyond any doubt, the distribution of electrons around an atom in a molecule can be recognized as that of its isolated, in many cases excited, counterpart, relating this fact with the concept of energetic promotion. Several other cases that exemplify the applicability of the technique to solve chemical bonding conflicts and to introduce predictability in real-space analyses are also examined.

Capillary forces generated by capillary adsorption between varied solids have been widely used in micro-objects manipulation. In present study, capillary forces and capillary bridges between an end-adjusted three-finger capillary microgripper with hydrophobic side surface and a plate were investigated. The effects of the separation distance on capillary forces for three configurations of capillary bridges were analyzed experimentally using a customized platform to verify the effectiveness of the established capillary models. The results show that the capillary forces increased sharply in the initial stage, followed by a decrease. The maximum capillary force of 141.1 μN was obtained for the capillary bridge of Structure A with capillary bridge volume of 0.55 μL. The effects of the capillary bridge volume, radial distance between probes and contact angles on the capillary forces were analyzed based on the established simulation model. The results indicate that the variation of capillary force with capillary bridge volume increasing was not monotonic because of the restriction of the probe edge. The capillary force changing was more sensitive to the variation of the contact angle on the plate than the variation of the contact angle on the probe end surface. Additionally, experimental analysis was conducted on variations in capillary forces for two types of the microgripper with variable relative axial distance between probes. The capillary forces increased as the relative axial distance between the probes increased, and then decreased. The maximum capillary force of 159.15 μN was obtained for Type 1 capillary bridge with capillary bridge volume of 0.45 μL. [ABSTRACT FROM AUTHOR]

The existence of minimizers of the Canham–Helfrich functional in the setting of generalized Gauss graphs is proved. As a first step, the Canham–Helfrich functional, usually defined on regular surfaces, is extended to generalized Gauss graphs, then lower semicontinuity and compactness are proved under a suitable condition on the bending constants ensuring coerciveness; the minimization follows by the direct methods of the Calculus of Variations. Remarks on the regularity of minimizers and on the behavior of the functional in case there is lack of coerciveness are presented. [ABSTRACT FROM AUTHOR]

Summary: Tensegrity systems are construction system that offers solutions for lighter designs compared with a standard truss system consisting of tension and compression elements. Due to their structure, they are recommended in the design of space structures due to the use of lighter and easy‐to‐assemble structural elements such as cables, ropes, and similar to provide the desired durability. In this study, energy minimization was carried out to solve the structural element displacements. It is expected to reduce the total potential energy of the system with minimization. By minimizing the total potential energy of the tensegrity system models, the displacement of each building element is found for the equilibrium condition. Tensegrity models were analyzed by minimizing energy via the adaptive harmony search (AHS) algorithm. In this research, two distinct tensegrity structure specimens were employed. One comprised a cantilever beam, while the other adopted a cyclic model, forming eight equal octagons from eight nodes at both the base and the top. Additionally, an examination was conducted on a two‐layer iteration of the cyclic model. The method is robust for both space and planar tensegrity structures, allowing the determination of deformed shapes under various loads without design assumptions. The TPO/MA method demonstrates superiority in handling nonlinear and barely stable systems, as evidenced by examples illustrating its efficacy in maintaining structural form under increasing loads and challenging conditions. [ABSTRACT FROM AUTHOR]

Object tracking is one of the fundamental processes for many high level applications in the field of machine vision. Many challenges in this field remain unsolved despite the development of several tracking perspectives in recent years. A tracking system can be defined in four steps including object detection, appearance modeling, data association, and trajectory estimation. The idea of combining these processes is attractive, but it raises new challenges. The focus of this paper is on the integration of these processes by relying on the use of local structural information. Quantitative and qualitative comparison of the results of the experiments obtained from the proposed method with some works done shows the improvement of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel energy minimization approach (called R-NCG) that combines a random batch idea with a nonlinear conjugate gradient method to accelerate the dynamics pre-equilibrium simulation of charged polymers in different systems. By using small but random batches, R-NCG can efficiently approximate the collective dynamics numerically while maintaining the numerical behaviours of standard NCG algorithm, and further improving its performance. Our simulation results suggest that the R-NCG can significantly reduce the computational cost of the minimization process for system with N-body interactions, thus reducing the time complexity to O(N). In other words, R-NCG method improves the problem of the dependence of CPU simulation time on the number of charged particles, and achieves acceptable accuracy in most cases. Our work can be helpful for accelerating the pre-equilibrium simulations of systems with N-body interactions, and confer new insights into the optimization of energy minimization algorithms in large electrostatic systems. In this work, we apply the random batch idea to the nonlinear conjugate gradient (NCG) method for improving the dependence of the CPU time on the number of charged particles in the energy minimization of the standard NCG method. Furthermore, we use the local iteration algorithm proposed in previous work to compute the spatial electrostatic potential distribution, which effectively improves the efficiency of the most time-consuming parts of calculating the electrostatic energy and its gradient values. Numerical results show that for different dielectric systems with N-body interactions, the NCG method based on random batch idea is computationally more efficient than the standard NCG method with acceptable accuracy in most cases (time complexity is reduced to O(N)). [ABSTRACT FROM AUTHOR]

The fairing curves and surfaces are used extensively in geometric design, modeling, and industrial manufacturing. However, the majority of conventional fairing approaches, which lack sufficient parameters to improve fairness, are based on energy minimization problems. In this study, we develop a novel progressive-iterative approximation method for the fairing curve and surface generation (fairing-PIA). Fairing-PIA is an iteration method that can generate a series of curves (surfaces) by adjusting the control points of B-spline curves (surfaces). In fairing-PIA, each control point is endowed with an individual weight. Thus, the fairing-PIA has many parameters to optimize the shapes of curves and surfaces. Not only a fairing curve (surface) can be generated globally through fairing-PIA, but also the curve (surface) can be improved locally. Moreover, we prove the convergence of the developed fairing-PIA and show that the conventional energy minimization fairing model is a special case of fairing-PIA. Finally, numerical examples indicate that the proposed method is effective and efficient. [ABSTRACT FROM AUTHOR]

In the evolving urban transportation, the emergence of Micro-Mobility (MM), symbolized by Electric Scooters (ESs), has become a pivotal response to private automobiles’ environmental and logistical challenges. However, the limited battery capacity of ESs presents a challenge in realizing their full potential. This paper addresses the problem of optimizing energy consumption in ESs by jointly considering path and speed selection all while considering user dissatisfaction levels. Our approach considers two types of ESs, one with regenerative braking (i.e., able to recharge the battery from kinetic energy of movement) and the other without regenerative braking. In order to build a realistic environment, we considered dynamic factors such as time-varying road congestion, road conditions, and ambient temperature. We considered a comprehensive energy consumption model for the ES that includes parameters such as rolling resistance, air friction, road gradient, auxiliary power and ambient temperature influence. Moreover, we introduced a user dissatisfaction model that accounts for traffic conditions, congestion, and ambient temperature to enhance the user experience. The optimization problem was then formulated and solved with Deep Reinforcement Learning (DRL-DQN) approach considering the time-varying environment, road-specific parameters (i.e., road angle, road shading, road speed limit, and road condition), and user dissatisfaction levels. The DRL approach was designed to make timely and context-aware decisions the minimize the energy consumption of the ES. Rigorous validation and comprehensive testing demonstrate the effectiveness of our approach. We evaluated the proposed solution’s performance against alternative methodologies used by fleet operators in different tests, including energy consumption, average user dissatisfaction, and average trip duration. The results showed that the proposed approach saved nearly 53-67% of energy for regenerative braking cases and 25-55% for non-regenerative braking cases when compared with other approaches and offers high adaptability to the environment and less complexity when compared with the exhaustive solution.

Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses robust $\ell _{1}$ norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.

In this paper, we suggest that the combination of edge computing in the form of data compression with communication at the base stations (BSs) for transmissions to their associated multiple downlink users (DUs) is advantageous for minimizing the total energy consumption. We assume that the individual DUs have minimum rate requirements along with outage probability constraints. Then, we set the resource allocation to minimize the total energy consumption (the sum of compression energy and transmission energy) for the BSs with orthogonal and non-orthogonal multiple access (OMA and NOMA) transmission schemes, while taking into account the quality of service (QoS) constraints of individual DUs. The formulated optimization problems are non-convex and difficult to solve. Therefore, the energy minimization problems are decomposed into smaller problems and low-complexity solutions are obtained. Specifically, for the single-cell scenario we use Lagrange duality theory and Karush–Kuhn–Tucker conditions to obtain closed-form global optimal solutions. It is revealed that the optimal resource allocation at the BS is determined by a DU-specific parameter, named path-loss factor. This finding is then used to obtain the optimal resource allocation for the multi-cell scenario and two iterative algorithms, with guaranteed convergence, are proposed to solve the energy minimization problems for NOMA and OMA transmission schemes. Next, the effectiveness of the proposed approaches are demonstrated with the help of simulation results. It is found that the BSs can exploit the flexibilities in minimum rate requirements and outage probability requirements, and compress the data of individual DUs before transmission in an attempt toward reducing the total consumed energy.

This paper introduces a novel model for image fusion that is based on a fractional-order osmosis approach. The model incorporates a definition of osmosis energy that takes into account nonlocal pixel relationships using fractional derivatives and contrast change. The proposed model was subjected to theoretical and experimental investigation. The semigroup theory was used to demonstrate the existence and uniqueness of the evolution equation solution. Additionally, the model was validated and tested using numerical experiments and compared to local image fusion methods. The findings demonstrate that the proposed model outperforms the competitive local image fusion models. [ABSTRACT FROM AUTHOR]

In the study, based on the analysis of the technological process of drying apricots on a tunnel-type drying machine, the governing parameters and the control parameters at the exit of dehydrator as well as disturbing factors were determined. Fruit and air control parameters at the outlet of the tunnel-type drying chamber were determined as the final value of the state vector components, the solution of the system of non-linear ordinary differential equations with distributed parameters, describing the static mode of the drying process. With respect to assessing the quality of drying process control, the requirements of minimum energy consumption during drying, achieving maximum productivity and maintaining the desired value of the residual moisture content of the product were used. In order to reach a compromise between these requirements, a Pareto-optimal solution of the problem was proposed, for which a linear fold of the first and second criteria was introduced and the dependence of the minimum value of a new criterion on the fold parameter was considered. Based on the analysis of the results, approximately 5 % reduction in energy consumption with a significant increase in the quality of the dried fruits could be achieved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new model for image segmentation under geometric constraints. We define the geometric constraints and we give a minimization problem leading to a variational equation. This new model based on a minimal surface makes it possible to consider many different applications from image segmentation to data approximation. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer's operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes. [ABSTRACT FROM AUTHOR]

We consider a class of models motivated by previous numerical studies of wrinkling in highly stretched, thin rectangular elastomer sheets. The model is characterized by a finite-strain hyperelastic membrane energy perturbed by small bending energy. In the absence of the latter, the membrane energy density is not rank-one convex for general spatial deformations but reduces to a polyconvex function when restricted to planar deformations, i.e., two-dimensional hyperelasticity. In addition, it grows unbounded as the local area ratio approaches zero. The small bending component of the model is the same as that in the classical von Kármán model. The latter penalizes arbitrarily fine-scale wrinkling, resolving both the amplitude and wavelength of wrinkles. Here, we prove the existence of energy minima for a general class of such models. [ABSTRACT FROM AUTHOR]

The geometric design and shape optimization of complex surfaces are pivotal and knotty techniques in computer aided geometric design (CAGD), and widely used in many complex product manufacturing fields involving surfaces modeling, e.g., for ships, aircraft wing, automobiles, etc. In this paper, an enhanced golden jackal optimization (GJO) algorithm is used to optimize the shape of complex composite shape-adjustable generalized cubic Ball (CSGC-Ball, for short) surfaces. Firstly, the shape design of CSGC-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by meta-heuristic algorithms. In this regard, an enhanced GJO (EGJO), combined with opposition-based learning, spring vibration-based adaptive mutation and binomial-based cross-evolution strategy, is developed to improve the convergence speed and calculation accuracy of the original GJO. The performance of EGJO is assessed on 23 benchmark test functions, IEEE CEC-2019 and 4 actual engineering optimization problems, and the competition and practicability of EGJO algorithm are confirmed. Secondly, the CSGC-Ball surfaces with global and local shape parameters is constructed based on a class of cubic generalized Ball basis functions, and then the conditions of G1 and G2 continuity for the surfaces are derived. The shapes of CSGC-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Finally, the minimum energy-based shape optimization models of CSGC-Ball surfaces with 1th-order and 2th-order geometric continuity are established, respectively. Furthermore, the proposed EGJO is utilized to solve the established optimization models, and the CSGC-Ball surfaces with minimum energy are obtained. Four representative examples are given to demonstrate the excellence and effectiveness of EGJO in solving the shape optimization problems of complex CSGC-Ball surfaces. [ABSTRACT FROM AUTHOR]

We introduce a novel relaxed Kačanov scheme for the computation of the discrete minimizer to the p-Laplace problem with 2 ≤ p <∞. The iterative scheme is easy to implement since each iterate results only from the solve of a weighted, linear Poisson problem. It neither requires an additional line search nor involves unknown constants for the step length. The scheme converges globally, and its rate of convergence is independent of the underlying mesh under certain regularity assumptions on the (discrete) solution. [ABSTRACT FROM AUTHOR]