Efficiency Tradeoff Research Articles

Hydrodynamic trade-offs in potential swimming efficiency of planispiral ammonoids

AbstractAmmonoid cephalopods were Earth's most abundant oceanic carnivores for hundreds of millions of years, yet their probable range of swimming capabilities is poorly constrained. We investigate potential hydrodynamic costs and advantages provided by different conch geometries using computational fluid dynamics simulations. Simulations of raw drag demonstrate expected increases with velocity and conch inflation, consistent with published experimental data. Analysis at different scales of water turbulence (via Reynolds number) reveals dynamic trade-offs between conch shape, size, and velocity. Among compressed shells, the cost of umbilical exposure makes little difference at small sizes (and/or low velocity) but is profound at large sizes (and/or high velocity). We estimate that small ammonoids could travel one to three diameters per second (i.e., a typical ammonoid with a 5-cm-diameter shell could travel 5–15 cm/s), but that large ammonoids faced greater discrepancies (a 10 cm serpenticone likely traveled <30 cm/s, while a 10 cm oxycone might achieve >40 cm/s). All of these velocities are proposed only for short bursts of jet propulsion, lasting only a few seconds, in the service of dodging a predator or conspecific rival. These analyses do not include phylogeny, taxonomy, second-order conch architecture (ribs, ornament, etc.), or hydrostatic consequences of internal anatomy (soft body, suture complexity). For specific paleoecological context, we consider how these results inform our reconstruction of Jurassic ammonite recovery from the end-Triassic mass extinction. Greater refinements will come with additional simulations that measure how added mass is influenced by individual shape-trait variations, ornament, and subtle body extensions during a single jet motion.

Reinforcement Learning for Efficient Design Space Exploration With Variable Fidelity Analysis Models

Abstract Reinforcement learning algorithms can autonomously learn to search a design space for high-performance solutions. However, modern engineering often entails the use of computationally intensive simulation, which can lead to slower design timelines with highly iterative approaches such as reinforcement learning. This work provides a reinforcement learning framework that leverages models of varying fidelity to enable an effective solution search while reducing overall computational needs. Specifically, it utilizes models of varying fidelity while training the agent, iteratively progressing from low- to high fidelity. To demonstrate the effectiveness of the proposed framework, we apply it to two multimodal multi-objective constrained mixed integer nonlinear design problems involving the components of a ground and aerial vehicle. Specifically, for each problem, we utilize a high-fidelity and a low-fidelity deep neural network surrogate model, trained on performance data generated from underlying ground truth models. A tradeoff between solution quality and the proportion of low-fidelity surrogate model usage is observed. Specifically, high-quality solutions are achieved with substantial reductions in computational expense, showcasing the effectiveness of the framework for design problems where the use of just a high-fidelity model is infeasible. This solution quality-computational efficiency tradeoff is contextualized by visualizing the exploration behavior of the design agents.

Maintaining Academic Integrity in Programming: Locality-Sensitive Hashing and Recommendations

Not many efficient similarity detectors are employed in practice to maintain academic integrity. Perhaps it is because they lack intuitive reports for investigation, they only have a command line interface, and/or they are not publicly accessible. This paper presents SSTRANGE, an efficient similarity detector with locality-sensitive hashing (MinHash and Super-Bit). The tool features intuitive reports for investigation and a graphical user interface. Further, it is accessible on GitHub. SSTRANGE was evaluated on the SOCO dataset under two performance metrics: f-score and processing time. The evaluation shows that both MinHash and Super-Bit are more efficient than their predecessors (Cosine and Jaccard with 60% less processing time) and a common similarity measurement (running Karp-Rabin greedy string tiling with 99% less processing time). Further, the effectiveness trade-off is still reasonable (no more than 24%). Higher effectiveness can be obtained by tuning the number of clusters and stages. To encourage the use of automated similarity detectors, we provide ten recommendations for instructors interested in employing such detectors for the first time. These include consideration of assessment design, irregular patterns of similarity, multiple similarity measurements, and effectiveness–efficiency trade-off. The recommendations are based on our 2.5-year experience employing similarity detectors (SSTRANGE’s predecessors) in 13 course offerings with various assessment designs.

Discussion on the ecological theory and technological approaches of ecosystem quality improvement and stability enhancement.

Improving ecosystem quality and stability is one of the urgent tasks of national ecological environment construction. However, the ecological theory of ecosystem quality and stability has not been well clarified. Based on the summary of influencing factors and interaction between ecosystem quality and stability, we discussed the ecolo-gical theory on the evolution of ecosystem quality and stability from the perspectives of self-organization of biological agglomeration and structure nesting, correlation of ecological elements and coupling of ecological processes, ecosystem integrity and function emergence, ecological service spillover and efficiency tradeoff, synergy and interactions between resource supply capacity and environmental suitability, as well as interactions between spontaneous change and human activities. Technologies approaches and management strategies were proposed from the aspects of ecosystem macro-pattern adjustment, protected natural areas system construction, regional complex ecosystem comprehensive management, degraded ecosystem restoration, damaged ecosystem reestablishment, typical ecosystem process management.

The NET effect: negative emissions technologies and the need–efficiency trade-off

Non-technical summary When developing and deploying negative emissions technologies (NETs), little attention has been paid to where. On the one hand, one might develop NETs where they are likely to contribute most to global mitigation targets, contributing to a global climate solution. On the other hand, one might develop NETs where they can help support development on a regional basis, justified by regional demands. I defend these arguments and suggest that they reflect the values of efficiency and responding to need, respectively. To the extent that these values conflict, they introduce what I call the Need-Efficiency Trade-off Effect (‘NET Effect’). Technical summary Unlike other geoengineering methods, the effectiveness of negative emissions technologies (NETs) tends to be sensitive to regional siting. This paper argues that this point raises morally and legally important implications by identifying a trade-off between ‘efficiency’ and ‘need’. First, it introduces two arguments justifying NETs: one focused on contributions to global mitigation and the other focused on contributions to regional development. Second, reflecting the two arguments, the paper discusses the moral values of efficiency and need, respectively. For instance, if the strategy is to try to use NETs to maximize expected mitigation contributions to reflect efficiency, then deployment should occur in regions with the best prospects for success (e.g. Western countries). However, if the strategy is to try to use NETs to improve the chances of simultaneous development and mitigation to respond to need, then deployment should occur in regions with limited development and expected growth of demand for NETs (e.g. Asian countries). When these values conflict, I call that a Need-Efficiency Trade-off Effect (‘NET Effect’). The paper concludes by considering the NET Effect in the context of bioenergy with carbon capture and storage as well as direct air carbon capture and storage. Social media summary Should negative emissions technologies be deployed in Western countries for most climate action or Asian where needed for development?

Digital Linearization of Wideband Envelope Tracking Power Amplifiers for Mobile Terminals

This article presents a low-complexity linearization method to compensate for multisource nonlinear distortion in wideband envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed linearization approach consists in an envelope leakage cancellation (ELC) system operating in the dynamic supply path and a 2-D digital predistortion (2-D-DPD) linearizer acting on the baseband complex modulated signal. In a first step, an envelope generalized memory polynomial (EGMP) behavioral model is proposed to compensate for the unwanted radio frequency (RF) leakage that appears at the output of ET modulator (ETM). Then, a baseband 2-D-DPD linearizer based on a slow envelope-dependent generalized memory polynomial (SED-GMP) behavioral model is used to further enhance the linearization performance. The proposed method is validated on a system-on-chip (SoC) ET PA board for mobile applications. The reported experimental results show how the proposed digital linearization approach can mitigate the linearity and efficiency trade-off in ET PAs. Consequently, the out-of-band linearity requirement of −36 dBc of adjacent channel power ratio (ACPR) is met for 5G-NR test signals with bandwidths ranging from 60 to 200 MHz at 2.55 GHz (band B41), with an overall ET PA power efficiency ranging from 10.1% and up to 16.5%, depending on the signal bandwidth.

Stacked Common-Base vs Common-Emitter mmWave PA Cells and 68–105 GHz Broadband Asymmetrical PA in 250nm InP HBT

In this paper, we perform a comparative analysis between stacked common-emitter (SCE) and stacked common-base topologies (SCB) for high efficiency and broadband millimeter-Wave (mmWave) power amplifiers (PAs) in 250 nm InP-based heterojunction bipolar transistor (HBT) technology. We propose an analytical approach to design stacked PA cells accounting for complex load impedance and intra-matching between stacked transistors to allow optimal loadpull operation of both the devices in the stack. We demonstrate how SCB cell can allow higher gain, mitigated power and efficiency trade-off and linearity, when compared to more well-established SCE cells at higher mmWave frequencies. The designed SCB and SCE cells achieve a measured gain of 11.8 dB/6dB gain, 33%/34% peak PAE, 16.8%/14.5% PAE at 6-dB back-off, and Psat of 18.7 dBm/19.6 dBm at 90 GHz. The SCB PA demonstrates superior linearity, and achieves an EVM of 2.38% at 11.8-dBm average power supporting 3 Gbps 64-QAM. The SCB PA achieves 17.9-18.9-dBm Psat across 80–110 GHz and is one of the highest efficiency, broadband and linear PAs in W-band using InP technology. Utilizing the PA cells, we demonstrate an asymmetrical broadband power combining circuit for high efficiency combining across a large relatively bandwidth. The PA operates across a 68–105 GHz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{sat,3dB}$ </tex-math></inline-formula> bandwidth with 21.5 dBm peak saturation power and 24% peak PAE.

Efficiency and Security Trade-offs of Secure Multi-Party Computation for Machine Learning

Machine learning methods require massive data collection to produce accurate predictions, raising privacy concerns. Secure Multi-Party Computation (SMPC) is one of the possible techniques to preserve data privacy during the computation process. Despite recent advances, the efficiency and scalability of SMPC in combination with machine learning remains an uncertain area. Moreover, various implementations of protocols exist that were optimized for the particular use cases, making the decision towards a suitable solution more complicated. The question of which protocol should be used under what circumstances naturally arises. In this case, when is defined as a security requirement and participant number. This work focuses on exploring the feasibility of using SMPC and machine learning in different settings. The work presents a comparison of selected multi-party computation protocols with different numbers of participants, malicious, and semihonest security models. Each relevant group of settings was constructed for the given input parameters, and the same inference scenario is performed within those settings. The inference scenario involves using a model trained on private statistical data inside a simulated multi-party computation session with a given number of participants and a security model. The results revealed a noticeable increase in computation time when moving from two to three participants, and the addition of a fourth participant led to a less substantial increase in computation time. Furthermore, as the number of participants continued to increase, the increase pattern became less aggressive. The study also noted that transitioning from a semi-honest to a malicious setting resulted in a significant increase in computation time. The results achieved help to outline the trade-offs between the protocol, the security model, and the number of participants, and help to choose the most efficient approach for future work.

Scalable Fuzzy Keyword Ranked Search Over Encrypted Data on Hybrid Clouds

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold d, the storage cost will grow exponentially or the precision of search results will greatly decrease as <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$d$</tex></formula> increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this paper, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over the encrypted database, in which our scheme achieves fuzzy keyword search for any similarity threshold d with constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation. In satellite communication systems, channel acquisition and energy consumption are two critical issues. To this end, we investigate the tradeoff between the total energy efficiency (TEE) and minimum EE (MEE) for robust multigroup multicast satellite communication systems in this paper. Specifically, under the total power constraint, we investigate the robust beamforming aimed at balancing the TEE-MEE, so as to achieve the balance between the fairness and total performance on the system EE. For this optimization problem, we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate. Then, the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure. In addition, depending on the solution rank value, Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions. Finally, simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE, thus realizing a tradeoff between fairness and system EE performance. It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

Tethered UAV Deployment Strategies: The Coverage and Energy Efficiency Trade-Off

Tethered UAV Deployment Strategies: The Coverage and Energy Efficiency Trade-Off

A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution

Deep learning methods have shown outstanding performance in many applications, including single-image super-resolution (SISR). With residual connection architecture, deeply stacked convolutional neural networks provide a substantial performance boost for SISR, but their huge parameters and computational loads are impractical for real-world applications. Thus, designing lightweight models with acceptable performance is one of the major tasks in current SISR research. The objective of lightweight network design is to balance a computational load and reconstruction performance. Most of the previous methods have manually designed complex and predefined fixed structures, which generally required a large number of experiments and lacked flexibility in the diversity of input image statistics. In this paper, we propose a dynamic residual self-attention network (DRSAN) for lightweight SISR, while focusing on the automated design of residual connections between building blocks. The proposed DRSAN has dynamic residual connections based on dynamic residual attention (DRA), which adaptively changes its structure according to input statistics. Specifically, we propose a dynamic residual module that explicitly models the DRA by finding the interrelation between residual paths and input image statistics, as well as assigning proper weights to each residual path. We also propose a residual self-attention (RSA) module to further boost the performance, which produces 3-dimensional attention maps without additional parameters by cooperating with residual structures. The proposed dynamic scheme, exploiting the combination of DRA and RSA, shows an efficient trade-off between computational complexity and network performance. Experimental results show that the DRSAN performs better than or comparable to existing state-of-the-art lightweight models for SISR.

Analysis of Optimal Spectral-energy Efficiency Tradeoff Linear Precoding Massive MIMO 5G and Beyond

In this article, we consider cellular wireless communication with a large number base station antennas deployement, called Massive-MIMO systems, are inspected. Massive MU (multi-user), (MIMO) multiple-input-multiple-output technique gives a significant SE (spectral efficiency) using easy techniques precoding while high EE (energy efficiency) is expected to be offered. Our paper works on two famous schemes, linear precoding Zero-Forcing and Maximum-RatioTransmission taking into account of system circuit power consumption, where that important aspect was ignored in the literature. Both EE (energy efficiency) and SE (spectral efficiency) are considered to be the important key indicators in 5G and beyond communications, which need to be taken into account at the same moment with respect to nowadays environmental consideration. To realize an optimal tradeoff between SE-EE, that precoding schemes are been formulated under CSI-imperfection (channel state information) in a realistic way. Simulation results illustrate that our proposed method achieves better optimal balance between Energy Efficiency and Spectral Efficiency. Under Matlab experimentation, simulation result shows an optimal performance, while tradeoff aspect is observed clairly between SE and EE using such two precoders algorithms. Although such tradeoff SE-EE coincide at first but later on their interests become conflicting and divergent then leading EE to so gradually decrease while SE continues increasing logarithmically. ZF pre-coding method is the appropriate scenario to achieve higher pre-coder performance.

Effect of Coolant Temperature on Performance and Emissions of a Compression Ignition Engine Running on Conventional Diesel and Hydrotreated Vegetable Oil (HVO)

To meet future goals of energy sustainability and carbon neutrality, disruptive changes to the current energy mix will be required, and it is expected that renewable fuels, such as hydrotreated vegetable oil (HVO), will play a significant role. To determine how these fuels can transition from pilot scale to the commercial marketplace, extensive research remains needed within the transportation sector. It is well-known that cold engine thermal states, which represent an inevitable portion of a vehicle journey, have significant drawbacks, such as increased incomplete combustion emissions and higher fuel consumption. In view of a more widespread HVO utilization, it is crucial to evaluate its performance under these conditions. In the literature, detailed studies upon these topics are rarely found, especially when HVO is dealt with. Consequently, the aim of this study is to investigate performance and exhaust pollutant emissions of a compression ignition engine running on either regular (petroleum-derived) diesel or HVO at different engine thermal states. This study shows the outcomes of warm-up/cool-down ramps (from cold starts), carried out on two engine operating points (low and high loads) without modifying the original baseline diesel-oriented calibration. Results of calibration parameter sweeps are also shown (on the same engine operating points), with the engine maintained at either high or low coolant temperature while combustion phasing, fuel injection pressure, and intake air flow rate are varied one-factor at a time, to highlight their individual effect on exhaust emissions and engine performance. HVO proved to produce less engine-out incomplete combustion species and soot under all examined conditions and to exhibit greater tolerance of calibration parameter changes compared to diesel, with benefits over conventional fuel intensifying at low coolant temperatures. This would potentially make room for engine recalibration to exploit higher exhaust gas recirculation, delayed injection timings, and/or lower fuel injection pressures to further optimize nitrogen oxides/thermal efficiency trade-off.

Elastic scheduling to maximize VM resource utilization constrained to system stability sensitivity

Considering the limitation of computing resources, resource over-reserving, and virtual machine stability, this paper proposes an adaptive dynamic management method for virtual machine resources in cloud/fog computing system. The cloud/fog computing task processing process is modeled in the case of the resource constraints in cloud or fog computing node. According to the tradeoff of resource utilization efficiency and system stability, the virtual machine resources are scheduled elastically to realize the on-demand use of cloud/fog computing resources. The experimental results show that the proposed method can effectively improve the resource efficiency of cloud/fog computing and guarantee the quality of service under the dynamic task workload.

Bi-objective goal programming for balancing costs vs. nutritional adequacy.

Linear programming (LP) is often used within diet optimization to find, from a set of available food commodities, the most affordable diet that meets the nutritional requirements of an individual or (sub)population. It is, however, not always possible to create a feasible diet, as certain nutritional requirements are difficult to meet. In that case, goal programming (GP) can be used to minimize deviations from the nutritional requirements in order to obtain a near feasible diet. With GP the cost of the diet is often overlooked or taken into account using the ε-constraint method. This method does not guarantee to find all possible trade-offs between costs and nutritional deficiency without solving many uninformative LPs. We present a method to find all trade-offs between any two linear objectives in a dietary LP context that is simple, does not solve uninformative LPs and does not need prior input from the decision maker (DM). This method is a bi-objective algorithm based on the NonInferior Set Estimation (NISE) method that finds all efficient trade-offs between two linear objectives. In order to show what type of insights can be gained from this approach, two analyses are presented that investigate the relation between cost and nutritional adequacy. In the first analysis a diet with a restriction on the exact energy intake is considered where all nutrient intakes except energy are allowed to deviate from their prescription. This analysis is especially helpful in case of a restrictive budget or when a nutritionally adequate diet is either unaffordable or unattainable. The second analysis only relaxes the exact energy intake, where the other nutrients are kept within their requirements, to investigate how the energy intake affects the cost of a diet. Here, we describe in what situations the so-called more-for-less paradox takes place, which can be induced by requiring an exact energy intake. To the best of our knowledge, we are the first to address how to obtain all efficient trade-offs of two linear objectives in a dietary LP context and how this can be used for analyses.

An improved stiff-ODE solving framework for reacting flow simulations with detailed chemistry in OpenFOAM

The integration of stiff ordinary differential equation (ODE) systems associated with detailed chemical kinetics is computationally demanding in practical combustion simulations. Despite the various approaches in expediting the computational efficiency, it is still necessary to optimise the cell-wise calculation in operator-splitting type simulations of reactive flow. In this work, we proposed an improved stiff-ODE solver framework targeting to speed up the simulation of reactive flow in OpenFOAM. This framework combines the Radau-IIA and backward differentiation formula (BDF) ODE-integration algorithms, the pyJac-based fully analytical Jacobian formulation, and dense-based LAPACK and sparse-based KLU sophisticated linear system solvers. We evaluate the performance of the efficient solver framework on various benchmark combustion problems across a wide range of chemical kinetic complexities. A comprehensive investigation of the key elements of stiff ODE solvers is conducted in the homogeneous reactor, focusing respectively on the influences of error tolerance, integration time interval, Jacobian evaluation methodology, and linear system solver on the accuracy and efficiency trade-off. More realistic simulation results are presented regarding the one-dimensional laminar flame and three-dimensional turbulent flame. The results indicate that the Radau-IIA is more preferable in both efficiency and accuracy compared with the widely used BDF and Seulex methods for large integration interval, whereas the differences between three methods diminish as the integration time interval decreases. In all cases, it is found that the full analytical Jacobian is more advantageous for small mechanisms of species number around 50–100 while the approximated formulation of Jacobian is recommended for larger ones. Furthermore, the more robust linear system solvers provide significant improvement on computational efficiency with the dense-based LAPACK solver being more suitable for small to moderate-scale mechanisms while sparse-based KLU being superior for large-scale mechanisms. The proposed efficient solver framework in its optimal configuration obtains more than 2.6 times speedup in realistic high-fidelity flame simulation with a 57 species combustion mechanism.

Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System

Recent research has introduced the notion of an Intelligent Reflective Surface (IRS) so as to boost the source–destination communication environment. IRS refers to a plane that consists of a number of passive elements that have a smaller size than the wavelength of the incident signal. Those elements use reflection to beam-form and to forward the incident radio signals toward the intended destination without power consumption. This article examines the performance of an IRS-based non-orthogonal multiple access (NOMA) system, and adopts the idea of using multiple IRS planes. It additionally investigates improving the performance of an IRS-NOMA combination through proposing various solutions that include optimizing the overall number of the IRS elements as a first step. After that, a closed form expression is derived for the considered IRS-NOMA system to determine the optimal number of the reflective elements. Secondly, the paper utilizes a new approach termed as multiple-IRS-based NOMA. This encompasses using multiple reflective planes (multi-IRS) along with IRS-NOMA, to boost the received signal characteristics, especially in situations where it is there a weak or no direct link between the source and the destination. An energy efficiency-spectral efficiency (EE-SE) trade-off is likewise presented, so as to have a complete view of the IRS-NOMA performance, along with the proposed solutions. The obtained simulation results depicts that IRS-NOMA is better than the existing orthogonal techniques. In addition, the results confirm that exploiting multiple IRSs offers a considerable gain in performance, as it enhances the conditions of the propagation environment. In specific, the considered case of a two IRS surfaces-assisted NOMA system offers higher performance levels than the case of a single IRS surface-assisted NOMA system.

True Random Number Generation Capability of a Ring Oscillator PUF for Reconfigurable Devices

This paper presents the validation of a novel approach for a true-random number generator (TRNG) based on a ring oscillator–physical unclonable function (RO-PUF) for FPGA devices. The proposal takes advantage of the different noise sources that affect the electronic implementation of the RO-PUF to extract the entropy required to guarantee its function as a TRNG, without anything more than minimal changes to the original design. The new RO-PUF/TRNG architecture has been incorporated within a hybrid HW/SW embedded system designed for devices from the Xilinx Zynq-7000 family. The degree of randomness of the generated bit streams was assessed using the NIST 800-22 statistical test suite, while the validation of the RO-PUF proposal as an entropy source was carried out by fulfilling the NIST 800-90b recommendation. The features of the hybrid system were exploited to carry out the evaluation and validation processes proposed by the NIST publications, online and on the same platform. To establish the optimal configuration to generate bit streams with the appropriate entropy level, a statistical study of the degree of randomness was performed for multiple TRNG approaches derived from the different implementation modes and configuration options available on the original RO-PUF design. The results show that the RO-PUF/TRNG design is suitable for secure cryptographic applications, doubling its functionality without compromising the resource–efficiency trade-off already achieved in the design.

Range Search over Encrypted Multi-Attribute Data

This work addresses expressive queries over encrypted data by presenting the first systematic study of multi-attribute range search on a symmetrically encrypted database outsourced to an honest-but-curious server. Prior work includes a thorough analysis of single-attribute range search schemes (e.g. Demertzis et al. 2016) and a proposed high-level approach for multi-attribute schemes (De Capitani di Vimercati et al. 2021). We first introduce a flexible framework for building secure range search schemes over multiple attributes (dimensions) by adapting a broad class of geometric search data structures to operate on encrypted data. Our framework encompasses widely used data structures such as multi-dimensional range trees and quadtrees, and has strong security properties that we formally prove. We then develop six concrete highly parallelizable range search schemes within our framework that offer a sliding scale of efficiency and security tradeoffs to suit the needs of the application. We evaluate our schemes with a formal complexity and security analysis, a prototype implementation, and an experimental evaluation on real-world datasets.