Recent Previous Work Research Articles

PASE: Pro-active Service Embedding in The Mobile Edge

Mobile edge computing offers ultra-low latency, high bandwidth, and high reliability. Thus, it can support a plethora of emerging services that can be placed in close proximity to the user. One of the fundamental problems in this context is maximizing the benefit from the placement of networked services, while meeting bandwidth and latency constraints. In this study, we propose an adaptive and predictive resource allocation strategy for virtual-network function placement comprising services at the mobile edge. Our study focuses on maximizing the service provider’s benefit under user mobility, i.e., uncertainty. This problem is NP-hard. Therefore, we propose a heuristic solution: we exploit local knowledge about the likely movements of users to speculatively allocate service functions. We allow the service functions to be allocated at different edge nodes, as long as latency and bandwidth constraints are met. We evaluate our proposal against a theoretically optimal algorithm as well as against recent previous work, using widely used simulation tools. Through an extensive simulation study, we demonstrate that under realistic scenarios, an adaptive and proactive strategy coupled with flexible placement can achieve close-to-optimal benefit.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

Fourier-Hankel-Abel Nyquist-limited tomography: A spherical harmonic basis function approach to tomographic velocity-map image reconstruction.

A new method for the fully generalized reconstruction of three-dimensional (3D) photoproduct distributions from velocity-map imaging (VMI) projection data is presented. This approach, dubbed Fourier-Hankel-Abel Nyquist-limited TOMography (FHANTOM), builds on recent previous work in tomographic image reconstruction [C. Sparling and D. Townsend, J. Chem. Phys. 157, 114201 (2022)] and takes advantage of the fact that the distributions produced in typical VMI experiments can be simply described as a sum over a small number of spherical harmonic functions. Knowing the solution is constrained in this way dramatically simplifies the reconstruction process and leads to a considerable reduction in the number of projections required for robust tomographic analysis. Our new method significantly extends basis set expansion approaches previously developed for the reconstruction of photoproduct distributions possessing an axis of cylindrical symmetry. FHANTOM, however, can be applied generally to any distribution-cylindrically symmetric or otherwise-that can be suitably described by an expansion in spherical harmonics. Using both simulated and real experimental data, this new approach is tested and benchmarked against other tomographic reconstruction strategies. In particular, the reconstruction of photoelectron angular distributions recorded in a strong-field ionization regime-marked by their extensive expansion in terms of spherical harmonics-serves as a key test of the FHANTOM methodology. With the increasing use of exotic optical polarization geometries in photoionization experiments, it is anticipated that FHANTOM and related reconstruction techniques will provide an easily accessible and relatively low-cost alternative to more advanced 3D-VMI spectrometers.

An innovative and low-cost system for in situ and real-time cure monitoring using electrical impedancemetry for thermoset and CFRP laminate

This paper is divided into five sections. The first one introduces the context of our study dealing with the monitoring of the cure of structural composites made of carbon fibers and thermoset matrix (CFRPs). The second one presents a brief state of the art. The third one deals with the materials characterized in this study, and the design and the production of a new bench dedicated to electrical impedance (EI) and thermal measurements. The fourth one compares the results obtained on CFRP and unreinforced matrix samples using conventional differential scanning calorimetry and EI measurements. A conclusion and some prospectives close the paper. In a recent previous work, we studied phases transitions in CFRP during their curing by EI showing that changes in the electrical complex impedance Z can easily be related to those of conventional parameters such as the thermoset matrix degree of cure (DOC) α. This work was carried out using a commercial single-channel impedance analyzer: HIOKI. In addition to its high acquisition cost, this device presented certain limitations in terms of measurement channels (only one) and data acquisition rate (60 s for a frequency sweep). These facts led us to develop a new EI measurement bench for monitoring the impedance changes in CFRP part for aeronautical applications (airframe structures). The innovative new multi-channel (8) bench we designed and manufactured is based on the Digilent PmodIA module (never used previously for this purpose) and an analog-front-end developed for EI measurements. It costs only 15% of the HIOKI analyzer and has a data acquisition rate 48 times higher (1,23 s versus 60 s). All this while maintaining an equivalent impedance measurement range: 100 mΩ to 1 MΩ. The proposed approach makes it possible not only to monitor the DOC α, but also to detect potential cure cycle issues. This latter demonstration was carried out on a new composite material (vacuum-bag oven-cured), NC66/1808NA (supplied by CTMI), which, to our knowledge, had never been characterized in the literature. Compared with preliminary results, similar behavior is obtained on two different CFRPs using two different benches. This clearly underlines the value and quality of the study. Experimental validation of our approach will contribute to CFRP structural health monitoring.

An Attribute-Based Access Control Model for Internet of Things Using Hyperledger Fabric Blockchain

The Internet of Things (IoT) is emerging from its infancy and establishing itself as a component of the future Internet. However, the ability to manage a huge number of IoT devices is one of the IoT technical challenges. To implement access control, traditional schemes typically rely on a trusted central organization. Traditional centralized access control systems of the IoT lead to the shortcomings of a single point of failure, low overall system efficiency, and ethical and privacy issues. To overcome such challenges, an attribute-based access control model using Hyperledger Fabric blockchain (ABAC-HLFBC) is proposed in this paper. By adopting ABAC, it is no longer to create access control lists (ACLs) or assign roles to all system users. Instead, ABAC grants access based on the attributes presented by the target. No one is permitted access unless he/she possesses sufficient attributes that correspond to the access policy. The Hyperledger Fabric Raft consensus mechanism has been used to verify the transaction on the proposed model because it has a demanding feature for faster and less complicated consensus in comparison to the Kafka ordering service. To evaluate the proposed model, it has been tested against the most recent previous work, called fabric-iot model. The proposed model has been tested and evaluated in two parts. The first part tests the cost time using a client test program written in Golang. The second part tests the latency and throughput using the Hyperledger Caliper benchmark tool. Results show that the proposed model efficiently outperforms the previous work in terms of the performance metrics mentioned above.

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

Double-Layer Energy Efficient Synchronous-Asynchronous Circuit-Switched NoC

A network-on-chip (NoC) offers high performance, flexibility and scalability in communication infrastructure within multi-core platforms. However, NoCs contribute significantly to the overall system’s power consumption. The double-layer energy efficient synchronous-asynchronous circuit-switched NoC (CS-NoC) is proposed to enhance the power utilization. To reduce the dynamic power consumption, single-rail asynchronous protocols are utilized. The two-phase and four-phase encoding algorithms are analyzed to determine the most efficient technique. For the data layer, the two asynchronous protocols reduced the power consumption by 80%, with an increase in latency when compared with the fully synchronous protocol. However, the two-phase single-rail protocol had better performance compared with the four-phase protocol by 38%, with the same power consumption and a slight increase in area of 5%. Based on this conducted analysis, the asynchronous two-phase layer had significant power reduction yet operated at a moderate frequency. Therefore, the proposed NoC is divided into two data transfer layers with a single control layer. The data transfer layers are designed using synchronous and asynchronous protocols. The synchronous layer is designated to high-frequency loads, and the asynchronous layer is confined to low-frequency loads. The switching between the layers creates a trade-off between the maximum allowed frequency and the power consumption. The proposed NoC reduces the overall power consumption by 23% when compared with recent previous work. The NoC maintains the same system performance with an 8% area increase over the fully synchronous double-layer in the literature.

Ethene Conversion at a Zeolite‐Supported Ir(I) Complex. A Computational Perspective on a Single‐Site Catalyst System

AbstractApplying a quantum mechanics/molecular mechanics scheme involving DFT calculations, a model study of mechanisms for ethene transformations at zeolite‐supported Ir(I) complexes is presented and the results compared to those of recent experiments and previous work on the isostructural Rh(I) complexes. Starting from the 2‐ligand complex [Ir(C2H4)2]+, in the presence of H2, the ethene conversion mechanisms studied yield solely ethane while the dimerization to 1‐butene via either the Cossee‐Arlman (CA) mechanism or the metallacycle (MC) mechanism was determined to be kinetically too demanding. Therefore, turning to 3‐ligand models, the calculations showed that the diethyl complex [Ir(C2H4)(C2H5)2]+ strongly favors ethene hydrogenation over dimerization (via a CA mechanism), with crucial activation free energies of 27 kJ mol−1 and 113 kJ mol−1, respectively. The alternative route to dimerization via a MC mechanism is also not operative because the C−C coupling barrier is higher by 30 kJ mol−1 (in absolute terms) than the hydrogen activation in the CA mechanism. Thus, when Rh is substituted by Ir, the computational results allowed to rationalize the experimentally determined switching from ethene dimerization to ethane formation due to the significantly higher calculated barrier, by ∼50 kJ mol−1 relative to Rh, of C−C coupling in the Ir system. The present study illustrates the advantage of describing the active site in a single site catalysis system, yet it also highlights the potential complexity of such systems as revealed by comparing 2‐ to 3‐ligand models as well as models with different metal centers, Rh vs Ir, in the light of conversion rates via the energetic span concept.

Permutationally Restrained Diabatization by Machine Intelligence.

Simulations of electronically nonadiabatic processes may employ either the adiabatic or diabatic representation. Direct dynamics calculations are usually carried out in the adiabatic basis because the energy, force, and state coupling can be evaluated directly by many electronic structure methods. However, although its straightforwardness is appealing, direct dynamics is expensive when combined with quantitatively accurate electronic structure theories. This generates interest in analytically fitted surfaces to cut the expense, but the cuspidal ridges of the potentials and the singularities and vector nature of the couplings at high-dimensional, nonsymmetry-determined intersections in the adiabatic representation make accurate fitting almost impossible. This motivates using diabatic representations, where the surfaces are smooth and the couplings are also smooth and-importantly-scalar. In a recent previous work, we have developed a method called diabatization by deep neural network (DDNN) that takes advantage of the smoothness and nonuniqueness of diabatic bases to obtain them by machine learning. The diabatic potential energy matrices (DPEMs) learned by the DDNN method yield not only diabatic potential energy surfaces (PESs) and couplings in an analytic form useful for dynamics calculations, but also adiabatic surfaces and couplings in the adiabatic representation can be calculated inexpensively from the transformation. In the present work, we show how to extend the DDNN method to produce good approximations to global permutationally invariant adiabatic PESs simultaneously with DPEMs. The extended method is called permutationally restrained DDNN.

A molecular phylogeny of the circum-Antarctic Opiliones family Neopilionidae

The Opiliones family Neopilionidae is restricted to the terranes of the former temperate Gondwana: South America, Africa, Australia, New Caledonia and New Zealand. Despite decades of morphological study of this unique fauna, it has been difficult reconciling the classic species of the group (some described over a century ago) with recent cladistic morphological work and previous molecular work. Here we attempted to investigate the pattern and timing of diversification of Neopilionidae by sampling across the distribution range of the family and sequencing three markers commonly used in Sanger-based approaches (18S rRNA, 28S rRNA and cytochrome-c oxidase subunit I). We recovered a well-supported and stable clade including Ballarra (an Australian ballarrine) and the Enantiobuninae from South America, Australia, New Caledonia and New Zealand, but excluding Vibone (a ballarrine from South Africa). We further found a division between West and East Gondwana, with the South American Thrasychirus/Thrasychiroides always being sister group to an Australian–Zealandian (i.e. Australia + New Zealand + New Caledonia) clade. Resolution of the Australian–Zealandian taxa was analysis-dependent, but some analyses found Martensopsalis, from New Caledonia, as the sister group to an Australian–New Zealand clade. Likewise, the species from New Zealand formed a clade in some analyses, but Mangatangi often came out as a separate lineage from the remaining species. However, the Australian taxa never constituted a monophyletic group, with Ballarra always segregating from the remaining Australian species, which in turn constituted 1–3 clades, depending on the analysis. Our results identify several generic inconsistencies, including the possibility of Thrasychiroides nested within Thrasychirus, Forsteropsalis being paraphyletic with respect to Pantopsalis, and multiple lineages of Megalopsalis in Australia. In addition, the New Zealand Megalopsalis need generic reassignment: Megalopsalis triascuta will require its own genus and M. turneri is here transferred to Forsteropsalis, as Forsteropsalis turneri (Marples, 1944), comb. nov.

Host load prediction in cloud computing using Long Short-Term Memory Encoder–Decoder

Cloud computing has been developed as a means to allocate resources efficiently while maintaining service-level agreements by providing on-demand resource allocation. As reactive strategies cause delays in the allocation of resources, proactive approaches that use predictions are necessary. However, due to high variance of cloud host load compared to that of grid computing, providing accurate predictions is still a challenge. Thus, in this paper we have proposed a prediction method based on Long Short-Term Memory Encoder–Decoder (LSTM-ED) to predict both mean load over consecutive intervals and actual load multi-step ahead. Our LSTM-ED-based approach improves the memory capability of LSTM, which is used in the recent previous work, by building an internal representation of time series data. In order to evaluate our approach, we have conducted experiments using a 1-month trace of a Google data centre with more than twelve thousand machines. Our experimental results show that while multi-layer LSTM causes overfitting and decrease in accuracy compared to single-layer LSTM, which was used in the previous work, our LSTM-ED-based approach successfully achieves higher accuracy than other previous models, including the recent LSTM one.

New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile

Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy. In our recent previous work, we proposed a new wrapper gene selection method based-on firefly algorithm named FF-SVM. In this work, we will improve the classification performance of FF-SVM algorithm by proposed a new hybrid gene selection algorithm. Our new biomarker gene discovery algorithm for microarray cancer gene expression analysis that integrates f-score filter method with Firefly feature selection method alongside with SVM classifier named FFF-SVM is proposed. The classification accuracy for the selected gene subset is measured by support vector machine SVM classifier with leave-one-out cross validation LOOCV. The evaluation of the FFF-SVM algorithm done by using five benchmark microarray datasets of binary and multi class. To show result validation of the proposed we compare it with other related state-of-the-art algorithms. The experiment proves that the FFF-SVM outperform other hybrid algorithm in terms of high classification accuracy and low number of selected genes. In addition, we compare the proposed algorithm with previously proposed wrapper-based gene selection algorithm FF-SVM. The result show that the hybrid-based algorithm shoe higher performance than wrapper based. The proposed algorithm is an improvement of our previous proposed algorithm.

Learning Physically Based Humanoid Climbing Movements

AbstractWe propose a novel learning‐based solution for motion planning of physically‐based humanoid climbing that allows for fast and robust planning of complex climbing strategies and movements, including extreme movements such as jumping. Similar to recent previous work, we combine a high‐level graph‐based path planner with low‐level sampling‐based optimization of climbing moves. We contribute through showing that neural network models of move success probability, effortfulness, and control policy can make both the high‐level and low‐level components more efficient and robust. The models can be trained through random simulation practice without any data. The models also eliminate the need for laboriously hand‐tuned heuristics for graph search. As a result, we are able to efficiently synthesize climbing sequences involving dynamic leaps and one‐hand swings, i.e. there are no limits to the movement complexity or the number of limbs allowed to move simultaneously. Our supplemental video also provides some comparisons between our AI climber and a real human climber.

Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap

Statistical inference methodology in dynamic factor models (DFMs) is extended to the multiple testing context based on a central limit theorem for empirical Fourier transforms of multivariate time series. This theoretical result allows for employing a vector of Wald-type test statistics which asymptotically follows a multivariate chi-square distribution under the global null hypothesis when the observation horizon tends to infinity. Multiplicity-adjusted asymptotic multiple test procedures based on Wald statistics are compared with a model-based bootstrap procedure proposed in recent previous work. Monte Carlo simulations demonstrate that both the asymptotic multiple chi-square test with an appropriate multiplicity adjustment and the bootstrap-based multiple test procedure keep the family-wise error rate approximately at the predefined significance level. The estimation algorithm as well as the implementation of the testing procedures are described in detail and a real-life application is performed on European commodity data.

Synthesis of 2-imino and 2-hydrazono thiazolo[4,5-d]pyrimidines as corticotropin releasing factor (CRF) antagonists

Corticotropin-releasing factor (CRF) is an important neuropeptide hormone which controls the body's overall response to stress. It plays a crucial role in regulating the behavioral, cardiovascular, immune and gastrointestinal systems. Over-activation of the CRF system has been implicated in many disorders including anxiety, depression, drug addiction, hypertension, Irritable Bowel Syndrome (IBS), peptic ulcers, inflammation and others. Thus, binding of CRF to its receptors is an attractive target to develop new medications which aim at treating ailments associated with chronic stress. Numerous small-molecule non-peptide CRF receptor antagonists were developed and many are in various stages in clinical trials. Many showed great promise in treatment of anxiety, depression, peptic ulcers, inflammation, IBS and drug addiction. In our recent previous work, the development of two series of pyrimidine and fused pyrimidine CRF antagonists were described. In continuation of our efforts in this direction, in the current manuscript, the synthesis of a third series of CRF receptor antagonists is described. The binding affinities of select compounds for the type 1 receptor of CRF (CRF1R) were determined and compared to a standard CRF antagonist drug antalarmin. A lead compound was identified and further evaluated by measuring its effect on the inhibition of the agonist-stimulated accumulation of second messengers.

Fast Holographic Image Reconstruction Using Phase Shifting-assisted Depth Detection Scheme in Optical Scanning Holography

For the implementation of a real-time holographic camera, fast and automatic holographic image reconstruction is an essential technology. In this paper, we propose a new automatic depth detection algorithm for fast holography reconstruction, which is especially useful in optical scanning holography. The proposed algorithm is based on the inherent phase difference information in the heterodyne signals and works without any additional optical or electrical components. An optical scanning holography setup was made using a heterodyne frequency of 4 MHz with a 500-mm distance and 5-mm depth resolution. The reconstruction processing time was measured as 0.76 s, showing a 62% time reduction compared to a recent previous work.

Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multi-core systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i ) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii ) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energy-management technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29 percent energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work.

Microbiology (Endobacteriology) of Fruit and Vegetable Crops: An Expanded and Continuing Study

Many commercial fruits and vegetables consumed regularly by the public were tested for the presence and identification of bacteria within their inner flesh/pulp. Our recent previous work has shown that many commercial fruits and vegetables contain various species of resident bacteria, a science we have termed ‘<i>endobacteriololgy</i>’. Fruits and vegetables were thoroughly washed and their inner flesh was swabbed onto agar plates. Any colonies that appeared were sent to specialized laboratories for identification by DNA sequence analysis. While many fruits and vegetables contained an inner flesh that was sterile, many others were found to harbor various species of bacteria. Among these were blueberries, rhubarb, pineapple, artichokes, okra, celery, asparagus, figs, anise/fennel, white/Rainier cherries, coconuts, cardoon, turmeric, and others. Leafy vegetables produced particularly large numbers of bacterial colonies, such as radicchio, Boston lettuce, Iceberg lettuce, red and green cabbage, Brussels sprouts, endive, bok choy/pak choy/Chinese cabbage. The presence of these species may or may not imply that consumption of these crops, when uncooked or undercooked, are hazardous to human health, except, perhaps, under certain disease/health conditions.

Ground states of stealthy hyperuniform potentials. II. Stacked-slider phases.

Stealthy potentials, a family of long-range isotropic pair potentials, produce infinitely degenerate disordered ground states at high densities and crystalline ground states at low densities in d-dimensional Euclidean space R^{d}. In the previous paper in this series, we numerically studied the entropically favored ground states in the canonical ensemble in the zero-temperature limit across the first three Euclidean space dimensions. In this paper, we investigate using both numerical and theoretical techniques metastable stacked-slider phases, which are part of the ground-state manifold of stealthy potentials at densities in which crystal ground states are favored entropically. Our numerical results enable us to devise analytical models of this phase in two, three, and higher dimensions. Utilizing this model, we estimated the size of the feasible region in configuration space of the stacked-slider phase, finding it to be smaller than that of crystal structures in the infinite-system-size limit, which is consistent with our recent previous work. In two dimensions, we also determine exact expressions for the pair correlation function and structure factor of the analytical model of stacked-slider phases and analyze the connectedness of the ground-state manifold of stealthy potentials in this density regime. We demonstrate that stacked-slider phases are distinguishable states of matter; they are nonperiodic, statistically anisotropic structures that possess long-range orientational order but have zero shear modulus. We outline some possible future avenues of research to elucidate our understanding of this unusual phase of matter.