Small Space Research Articles

Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys

Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements. This study introduces a multi-objective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading. The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus, a low thermal expansion, a high heat capacity, and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading. Contrasting two optimization procedures, we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship. Conversely, the Pareto front is much broader in the performance space when these properties have antagonistic relationships. Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

15-minute cities, ‘walkability’ and last millimeter problems

The 15-minute city concept is about planning and building cities so that most essential activities can be accessed within 15-minutes of a residence on foot or by bicycle. The possibilities and limitations of the concept for disabled persons have yet to be fully considered. Here I centre disability in a hopeful critique of concepts central to 15-minute city implementation. I argue that greater attention should be given to what I call last millimeter problems, barriers within small spaces (e.g. subway platform gaps) disabled persons face in transportation systems and the built environment that can make any city, including a 15-minute city, an impassable city.

In-Plane Vibrations of Elastic Lattice Plates and Their Continuous Approximations

This paper presents an analytical study on the in-plane vibrations of a rectangular elastic lattice plate. The plane lattice is modelled considering central and angular interactions. The lattice difference equations are shown to coincide with a spatial finite difference scheme of the corresponding continuous plate. The considered lattice converges to a 2D linear isotropic elastic continuum at the asymptotic limit for a sufficiently small lattice spacing. This continuum has a free Poisson’s ratio, which must be lower than that foreseen by the rare-constant theory, to preserve the definite positiveness of the associated discrete energy. Exact solutions for the in-plane eigenfrequencies and modes are analytically derived for the discrete plate. The stiffness characterising the lattice interactions at the boundary is corrected to preserve the symmetry properties of the discrete displacement field. Two classes of constraints are considered, i.e., sliding supports at the nodes, one normal and the other parallel to the boundary. For both boundary conditions, a single equation for the eigenfrequency spectrum is derived, with two families of eigenmodes. Such behaviour of the lattice plate is like that of the continuous plate, the eigenfrequency spectrum of which has been given by Rayleigh. The convergence of the spectrum of the lattice plate towards the spectrum of the continuous plate from below is confirmed. Two continuous size-dependent plate models, considering the strain gradient elasticity and non-local elasticity, respectively, are built from the lattice difference equations and are shown to approximate the plane lattice accurately.

Finite element analysis based verification of shape estimation performance according to displacement conditions and module types of FBG bending sensor

This study proposes a novel, versatile sensor application solution suitable for various environments by verifying the shape estimation performance of three different module types of an FBG bending sensor based on finite element analysis. FBG bending sensors, including the individual attachment type, triangular stacking type, and band type, were attached to the structure. Subsequently, two simple plane displacement and one complex 3D displacement were applied to the structure to derive the strain for nine cases through finite element analysis. The shape estimation performance was then verified through a simulation based on the previously derived strain. As a result of verifying the shape estimation performance, it was confirmed that all three module types had high performance under simple plane displacement condition, whereas individual attachment types had better performance under complex 3D displacement condition. However, it’s important to note that the triangular stacking type and band type, with their small spacing between FBGs, have been developed to overcome the limited attachment conditions of the individual attachment type and to streamline the manufacturing process of the FBG bending sensors. Therefore, it is possible to select the FBG bending sensor according to the installation conditions of the structure to be applied and the required sensor performance by comprehensively considering the performance verification results of this study and the characteristics of the module types.

Outdoor and nature-based teaching in small, urban primary school contexts

The current evidence base for outdoor and nature-based education in a context of education for sustainability is limited and unrepresentative of the realities of small, urban schools, which tend to be characterised by small outdoor spaces devoid of nature. To address this research gap, we carried out two studies in Malta, as an example of a densely populated and heavily urbanised context. A survey conducted among educators in Maltese primary schools explored the occurence and frequency of outdoor teaching and factors that influence attitudes, perceptions, and practice of outdoor teaching. A complementary study mapped built, hard and soft landscapes of all primary school footprints in Malta. The two studies converge to shed light on possible systemic or physical factors leading to a low incidence of outdoor teaching and in particular, of nature-based teaching in small, urban schools. Nonetheless, our findings show strong interest in such teaching approaches amongst study participants. Our study thus establishes a research priority to identify factors that could support outdoor teaching in such contexts.

Exploring community resilience based on co-produced micro-regeneration projects in China: Two case studies

ABSTRACT This paper attempts to answer the question: How can community micro-regeneration projects in Chinese cities be situated and contextualized as particular kinds of practices to build resilient communities? Amidst rapid urbanization and social transformation over the past four decades, historical areas in Chinese cities have faced neglect, exacerbating inequalities and marginalization. The emerging strategy of micro-regeneration, emphasizing public participation and co-production, addresses this issue by revitalizing small community spaces using local resources and empowering grassroots efforts. This research contextualizes micro-regeneration within the global discourse on community resilience, highlighting communities’ agency and collaborative strength in adapting to change. Through the analysis of co-productive micro-regeneration cases in Beijing and Shanghai, this study offers insights into resourcefulness and the transformative potential of resilience, shedding light on China’s urban regeneration shift toward co-production and heightened social focus.

Analysis of deformation and multi-angle tip contact in a combined mechanism of thin-walled membrane forces and inextensible layers in soft actuators

Soft actuators have extensive applications in operations, are generally made of elastomers, and generate large deformation. It makes accurately predicting their deformation behavior challenging. This study considers the combined effects of chamber sidewall expansion and inextensible layer strain mechanisms on the actuator's bending and tip contact force. We separately model the expansion of the chamber sidewall and the strain in the inextensible layer using the expanding membrane and strain beam theory. The theoretical model presented to predict the contact area of the sidewall expanding membrane under multiple bending conditions. The analytical models for three actuator states are established: 1) Free space; 2) Tip contact at multiple angles; and 3) Grasping state. The proposed theoretical model accurately predicts deformation, force characteristics, and the requisite pressure for object grasping, and its applicability extends to similar soft actuators. Comparative analyses with finite element methods (FEM) and experimental results validate the model's computational efficiency, reduced design variables, and accurate prediction of diverse deformations and tip contact forces. Notably, the model outperforms with shorter computation times. The large and small chamber spacing errors are approximately 10 % and 5 %, respectively.

Effect of Elastic Resistance on Exercise Intensity and User Satisfaction While Playing the Active Video Game BoxVR in Immersive Virtual Reality: Empirical Study.

One of the main contemporary forms of physical activity (PA) involves exercises and games in an immersive virtual reality (VR) environment, which allows the user to practice various forms of PA in a small space. Unfortunately, most of the currently available VR games and workout applications are mostly based on upper body movements, especially the arms, which do not guarantee sufficiently high exercise intensity and health benefits. Therefore, it is worth seeking solutions to help increase the exercise load during PA in VR. The main aim of this study was to evaluate the effect of elastic arm resistance in the form of latex resistance bands of different elasticity levels on the intensity of students' PA while playing the BoxVR game. We further assessed the satisfaction of this form of exercise and its associations with PA intensity. A total of 21 healthy and physically fit men (mean age 22.5, SD 2.0 years) were included in the study. The tests consisted of 3 10-minute games. One game was run with no load and the other two were run with 1.5-meter latex resistance bands (low and high resistance). The order of the tests was randomized and the participants rested for 20 minutes after each exercise. Exercise intensity was estimated using objective (heart rate monitoring) and subjective (Borg scale) methods. The Physical Activity Enjoyment Scale was used to assess satisfaction with the PA. The effect of elastic resistance on exercise intensity and user enjoyment was estimated using ANOVA for repeated measures. The ANOVA results indicated that incorporation of elastic resistance caused a significant change (F2,40=20.235, P<.001; η²p=0.503) in the intensity of PA in VR, which was low while playing without resistance and then increased to a moderate level with additional resistance. The use of elastic bands also changed participants' perceptions of the enjoyment of exercise in VR (F2,40=9.259, P<.001; η²p=0.316). The students rated their satisfaction with PA in VR on a 7-point scale highly and similarly when exercising without an upper limb load (mean 6.19, SD 0.61) and with slight elastic resistance (mean 6.17, SD 0.66), whereas their satisfaction declined significantly (mean 5.66, SD 0.94) when incorporating a higher load. The intensity of PA among students playing the BoxVR game is at a relatively low level. With the added resistance of elastic bands attached to the upper limbs, the intensity of the exercise increased to a moderate level, as recommended for obtaining health benefits. Participants rated the enjoyment of PA in VR highly. The use of slight elastic resistance did not negatively affect satisfaction with the BoxVR game, although user satisfaction declined with a higher load. Further research should be undertaken to increase the effectiveness of exercise in VR so that regular users can enjoy the health benefits.

Fitness Landscapes and Evolution of Catalytic RNA.

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

Perturbation theory in the complete degenerate active space (CDAS-PT2).

Methods based on the multireference perturbation theory (MRPT) with the one-electron zeroth-order Hamiltonian are widely used for the description of excited states, for example, due to their relatively low computational cost. However, current methods have a common drawback-use of a model space with low size. In this article, we propose the MRPT method with the model space extended to the complete active space. The one-electron zeroth-order Hamiltonian suitable for this extension is formulated. The proposed method was applied to common models, such as LiF, ethylene, and trans-butadiene. It was shown to have accuracy superior to XMCQDPT2 in most cases, especially in the case of the small active space.

Study on the noise reduction mechanism and application of the noise simulation analysis of care robots

Abstract Currently, robots on the market have varying operational requirements due to their different functions. The care robots are primarily used to serve people under care. Therefore, reducing the amount of noise they make while moving is important. This study measured their noise in an anechoic room. This study found that the most noise is produced when they move forward and backward at full speed. The robot’s vibration signal was measured and compared with the noise source to propose the most effective way to reduce the noise. Due to the robot's small internal space, methods for noise reduction are limited. The care robot’s development must adopt quick customizable 3D printing technologies, produce sound absorption/insulation materials independently, determine the best sound absorption/insulation material and structural configuration, use impedance tubes to measure the sound absorption coefficient and sound insulation performance, and select the appropriate materials and implement them into the robot for actual tests. These results indicate that the noise value of the main contributing frequency can be reduced by 15dB at most. Furthermore, this study also used an acoustic simulation analysis to calculate and simulate the vibration and noise of the care robot. The vibration and noise signals during the robot's operation were measured and compared to provide a reference for manufacturers in design. Finally, experimental materials were added to the simulation and compared with the noise reduction results. The results show similar trends. Therefore, the model developed in this study may provide a fast, low-cost, and feasible method for the noise reduction of care robots.

Multi-task neural networks by learned contextual inputs

This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerful task adaption mechanism, which facilitates a low-dimensional task parameter space. Theoretically, we show that a scalar task parameter is sufficient for universal approximation of all tasks, which is not necessarily the case for more common architectures. Empirically it is shown that, for homogeneous tasks, the dimension of the task parameter may vary with the complexity of the tasks, but a small task parameter space is generally viable. The task parameter space is found to be well-behaved, which simplifies workflows related to updating models as new data arrives, and learning new tasks with the shared parameters are frozen. Additionally, the architecture displays robustness towards datasets where tasks have few data points. The architecture’s performance is compared to similar neural network architectures on ten datasets, with competitive results.

Efficient neural implicit representation for 3D human reconstruction

High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional methods for reconstructing 3D human motion frequently require the use of expensive hardware and have high processing costs. This study presents HumanAvatar, an innovative approach that efficiently reconstructs precise human avatars from monocular video sources. At the core of our methodology, we integrate the pre-trained HuMoR, a model celebrated for its proficiency in human motion estimation. This is adeptly fused with the cutting-edge neural radiance field technology, Instant-NGP, and the state-of-the-art articulated model, Fast-SNARF, to enhance the reconstruction fidelity and speed. By combining these two technologies, a system is created that can render quickly and effectively while also providing estimation of human pose parameters that are unmatched in accuracy. We have enhanced our system with an advanced posture-sensitive space reduction technique, which optimally balances rendering quality with computational efficiency. In our detailed experimental analysis using both artificial and real-world monocular videos, we establish the advanced performance of our approach. HumanAvatar consistently equals or surpasses contemporary leading-edge reconstruction techniques in quality. Furthermore, it achieves these complex reconstructions in minutes, a fraction of the time typically required by existing methods. Our models achieve a training speed that is 110× faster than that of State-of-The-Art (SoTA) NeRF-based models. Our technique performs noticeably better than SoTA dynamic human NeRF methods if given an identical runtime limit. HumanAvatar can provide effective visuals after only 30 s of training. Please visit https://github.com/HZXu-526/Human-Avatar for further demo results and code.

Adaptive diversity-based quantum circuit architecture search

Quantum variational algorithms (VQAs) are highly promising to realize quantum advantages on near-term quantum devices. Existing VQAs based on a manually fixed quantum are computationally inefficient due to noise and the limited coupling maps of these devices. Previous work considers various quantum architecture search (QAS) algorithms to autodesign a quantum based on specific questions to improve the performance of VQAs. Compared to manual design, autodesign can more efficiently explore the large space of a possible and achieve better performance. However, two main challenges in utilizing QAS to design quantum circuits efficiently are the tremendous amount of space required for candidate quantum circuits, and the disconnection between quantum devices and autodesign in terms of qubit mapping and quantum noise. To address these issues, we propose an adaptive diversity-based quantum search algorithm to efficiently generate the optimal quantum circuit based on device qubit mapping and noise. By considering the diversity among different candidate circuits and adaptively adding circuit depths, our approach only needs to focus on a small optimization space at each iteration step. In addition, the synchronization of optimizing circuit structure and aligning qubit mapping enables us to generate quantum circuits while avoiding additional mapping overhead. We evaluate the performance of our algorithm on simulators and real quantum devices for quantum eigenvalue problems and classification tasks. Results demonstrate that quantum circuits generated by our method outperform both a fixed hardware-efficient and randomly generated quantum circuits in terms of final performance and resource-saving. Our algorithm provides a flexible way to efficiently generate excellent quantum circuits for significantly improving the performances of VQAs on near-term quantum devices. Published by the American Physical Society 2024

Defining the analytical complexity of decision problems under uncertainty based on their pivotal properties.

Uncertainty poses a pervasive challenge in decision analysis and risk management. When the problem is poorly understood, probabilistic estimation exhibits high variability and bias. Analysts then utilize various strategies to find satisficing solutions, and these strategies can sometimes adequately address even highly complex problems. Previous literature proposed a hierarchy of uncertainty, but did not develop a quantitative score of analytical complexity. In order to develop such a score, this study reviewed over 90 strategies to cope with uncertainty, including methods utilized by expert decision-makers such as engineers, military planners and others. It found that many decision problems have pivotal properties that enable their solution despite uncertainty, including small action space, reversibility and others. The analytical complexity score of a problem could then be defined based on the availability of these properties.

Efficient and scalable prediction of stochastic reaction–diffusion processes using graph neural networks

The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction–diffusion processes which model such systems is highly computationally expensive, the cost increasing rapidly with the size of space. Here, we devise a graph neural network based approach that uses cheap Monte Carlo simulations of reaction–diffusion processes in a small space to cast predictions of the dynamics of the same processes in a much larger and complex space, including spaces modelled by networks with heterogeneous topology. By applying the method to two biological examples, we show that it leads to accurate results in a small fraction of the computation time of standard stochastic simulation methods. The scalability and accuracy of the method suggest it is a promising approach for studying reaction–diffusion processes in complex spatial domains such as those modelling biochemical reactions, population evolution and epidemic spreading.

Spatiotemporal Dynamics of Ecosystem Services and Their Trade-Offs and Synergies in Response to Natural and Social Factors: Evidence from Yibin, Upper Yangtze River

During the rapid urbanization phase, the trade-off between ecosystem services is the most severe and also the most effective stage to implement ecological management. Exploring the natural—social driving mechanisms for trade-offs contributes to the coordinated development of the social economy and nature. Taking the typical mountainous city (Yibin) that is currently in the rapid urbanization phase and ecologically fragile as an example, utilizing a combination of difference comparison, trade-off–synergy index (TSI), optimal-parameter-based geographical detector model (OPGD), and multi-scale geographically weighted regression (MGWR), we spatially assess the nature and intensity of ES relationships and explore its social–natural driving mechanisms. Our findings reveal the following: (1) Varied geospatial patterns of four ESs—habitat quality (HQ), carbon storage (CS), soil conservation (SC), and water yield (WY)—with the greatest fluctuations in WY. (2) Significant changes in the nature and intensity of ES relationships over time, showing predominant positive synergies between WY-HQ, WY-SC, and HQ-CS, and negative synergies between HQ and SC, and trade-offs between WY-CS and SC-CS. (3) Distinct, time-varying driving factors for different ES relationships: climate and topography for WY, vegetation and topography for CS, topography and economic factors for HQ, and climate and topography for SC. Rapid urbanization has diminished the role of natural factors. (4) The regression coefficients reveal the local mechanisms of various driving factors, based on which targeted recommendations can be proposed. For instance, the establishment of interconnected small wetlands and green spaces in urban areas contributes to the enhancement of multiple ESs. The purpose of this study is to provide scientific insights into the driving mechanisms and optimizations of the key ecosystem services’ relationships in areas that are currently undergoing rapid urbanization.

Study on Classification of Close Coal Seams in Hebao Mining Area Based on Layer Spacing

In order to understand the situation of close coal seam in Baode mining area of Hequ, it is convenient to form the surrounding rock control technology of roadway in close coal seam. Aiming at the inner staggered roadway under the goaf in Hebao mining area, a mechanical model is established, the influence of layer spacing on the inner staggered roadway is analyzed, and a short-distance coal seam classification method based on layer spacing is proposed. The results show that the close distance coal seams in Hebao mining area can be divided into three categories: 0-2m is the close distance coal seam with very small spacing, 2-6m is the close distance coal seam with small spacing, and more than 6m is the close distance coal seam with large spacing.

Quantum optimization algorithm based on multistep quantum computation

We present a quantum algorithm for finding the minimum of a function based on multistep quantum computation, and apply the algorithm for solving optimization problems with continuous variables. We construct the state space of the problem by discretizing the variables of the problem, and divide the state space according to the function values of the vectors of the state space. By comparing the function values of the vectors with a series of threshold values in decreasing order, we construct a sequence of Hamiltonians where the search space of a Hamiltonian is nested in that of the previous one. By applying a multistep quantum computation process for finding the ground state of the last Hamiltonian, the optimal vector of the state space of the problem is located in a small search space and can be determined efficiently. One of the most difficult problems in optimization algorithms is that a trial vector is trapped in a deep local minimum while the global minimum is missed, this problem can be alleviated in our algorithm and the run time is proportional to the number of the steps of the algorithm, provided that the reduction rate of the search spaces is polynomial large. We discuss the implementation of the algorithm and test the algorithm for some test functions.

Development of the manipulator hinge based on the original planetary differential gearbox

For manipulation in small spaces, a compact hinge based on an original planetary differential gearbox is pro-posed. The kinematic diagram of the gearbox is described, the methodology for choosing its optimal parameters is outlined, and compared with wave and traditional planetary gearboxes from leading companies. A joint design is presented, including a planetary differential gearbox, motor, an angle sensor and slip rings. A manipulator built on the basis of the proposed hinges is described.