Partitioning Technique Research Articles

An automatic grid partitioning technique of large grids based on CloudPSS

The rapid development of power electronics technology makes the traditional grid power generation, transmission, and consumption gradually show the trend of “power electronics”. With the increase in the number of power electronic converters, the accurate electromagnetic transient (EMT) simulation of model power electronic networks is under tremendous computational pressure. Aiming at solving the problem of slow calculation of large-scale power electronics simulation, scientists have widely used parallel computation of sub-networks to accelerate EMT simulation. This paper proposes an automatic grid partitioning algorithm for large power grids based on merging neighbor nodes with the basic principle of the transmission line decoupling (TLD) method. The algorithm can effectively reduce the communication time and the waiting time due to the uneven computational load during parallel simulation. Finally, the method is implemented in the CloudPSS platform with various scales of grids, and the test results show that the algorithm can realize the EMT simulation efficiently and accurately.

An adaptive element subdivision method based on the affine transformations and partitioning techniques for evaluating the weakly singular integrals

In this paper, an adaptive element Subdivision Method based on the Affine transformations and Partitioning techniques (APSM) is presented for evaluating the weakly singular integrals with arbitrary shape of elements. The basic idea consists in partitioning the input surface element via affine transformation and then in generating a set of high-quality patches by adaptive binary-tree subdivision. The proposed method has some advantages over the conventional methods based on elements subdivision, i.e. the adaptive element subdivision, an improvement of the accuracy and a straight-forward implementation. By using the domain partitioning technique, the surface element can be divided into several element projection and subdivision regions under affine transformations. It is far more efficient to separately perform the element subdivision for different regions where the desirable patches are required. The ultimate patches in the vicinity of the singular point are calculated numerically by the coordinate transformation technique, while the remaining patches are evaluated accurately by the standard Gaussian quadrature. Several numerical examples are presented in order to validate the accuracy, efficiency and availability of the proposed method.

Product Pairing Selection for Promotion using Partitioning Method

Slow-moving product is harmful to the business. The slow-moving products take up space and tie up the company’s capital and leave the company with fewer funds to invest in its business. Several factors can cause this issue. There are several methods ranging from statistics to heuristic methods for a company to identify slow-moving inventory but all of them rely on data. In this paper, a partitioning technique from the graph network is proposed to partition the inventories or products into a few clusters. It can help the company to identify what group does the product belongs to and at the same time suggest to the company which product can be paired up or bundled up together to clear up aging and slow-moving products. The partition technique is proposed, and the algorithm is coded using the Visual C++ programming language. The simulation results show that the proposed method can partition the task graph onto smaller subgraphs. The subgraphs called cluster consists of the nodes or products with similar purchase volume (the strong connection between the two nodes). Implementing the partitioning technique could help the companies or managers select the appropriate product to be paired together when doing the promotion.

Computation of some important degree-based topological indices for γ- graphyne and Zigzag graphyne nanoribbon

Topological indices serve as mathematical tools for characterising the molecular structure of a compound, and are useful to anticipate its properties. Actually, these are quantitative measures that can provide valuable information regarding the structure of a molecule, such as its connectivity and symmetry. By analysing these indices, researchers can make predictions about the behaviour of the molecule, such as its reactivity, solubility, and toxicity, among others. The γ-Graphyne is a fascinating carbon allotrope that has recently gained significant attention due to its unique electronic, optical, and mechanical properties. As a result, there has been increasing interest in exploring its potential applications in various fields of science and technology. The molecular descriptors for the characterisation of γ-Graphyne have not yet been investigated. Therefore, it is of much importance to predict its molecular topology to well understand the physicochemical properties. In this work, a graph theory-based edge partitioning technique is used to model the molecular topology of γ-Graphyne and Zigzag graphyne nanoribbon, and mathematical closed-form expressions for some of its essential degree-based molecular descriptors are derived. These computed indices have been illustrated with the help of graphical representations and numerical tables.

Enhancing fault tolerance and scalability in multi-region Kafka clusters for high-demand cloud platforms

This study examines strategies for enhancing fault tolerance and scalability in multi-region Kafka clusters, essential for supporting high-demand cloud environments. As cloud-based applications expand globally, achieving seamless data streaming across regions requires advanced configurations in Apache Kafka. This paper provides a thorough analysis of key approaches, including replication strategies, dynamic resource management, and real-time monitoring techniques tailored for multi-region deployments. Through a comprehensive literature review and real-world case studies, the study identifies critical challenges in managing latency, data consistency, and resilience within distributed Kafka clusters. Findings reveal that fault tolerance can be significantly improved through hybrid replication models that balance latency and data integrity, while advanced partitioning and load balancing techniques optimize Kafka’s scalability under fluctuating demands. The integration of container orchestration tools such as Kubernetes has also proven effective in automating resource scaling and failover across distributed environments. Furthermore, the paper highlights future research directions, including edge computing integration, predictive scaling, and enhanced security protocols to address evolving data privacy requirements. In conclusion, while multi-region Kafka deployments offer robust solutions for distributed data streaming, achieving optimal performance and resilience requires a combination of adaptive replication, proactive resource management, and secure, compliant data flows. Future research should focus on refining edge-compatible solutions and regulatory-compliant frameworks to sustain Kafka’s role in global, real-time data processing.

Analysis of acoustic radiation characteristic of laminated paraboloidal shell based on Jacobi-Ritz-spectral BEM

This research presents a semi-analytical method to investigate the acoustic characteristics of a composite laminated paraboloidal shell in a light fluid. First of all, the mathematical model of a paraboloidal shell structure is constructed based on the Ritz method and multi-section partition technique. Secondly, Jacobian polynomial and Fourier series are innovatively used to expand displacement tolerance functions of a paraboloidal shell structure, and the external acoustic fluid is modeled using Helmholtz integral formula. Lastly, artificial spring technology is employed to effectively realize complex boundary conditions such as stiff fixing and basic support of paraboloidal shell structures. The validity and dependability of Jacobi-Ritz-spectral BEM are verified by comparing it with the results of the coupled FEM/BEM. In the meantime, this research also discusses the effects of boundary conditions, structural parameters, and fiber ply angle on acoustic radiation characteristics.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

The Goal of Intraoperative Blood Loss in Major Hepatobiliary Resection for Perihilar Cholangiocarcinoma: Saving Patients From a Heavy Complication Burden.

To determine the goal of intraoperative blood loss in hepatectomy for perihilar cholangiocarcinoma. Although massive bleeding can negatively affect the postoperative course, the target value of intraoperative bleeding to reduce its adverse impact is unknown. Patients who underwent major hepatectomy for perihilar cholangiocarcinoma between 2010 and 2019 were included. Intraoperative blood loss was adjusted for body weight [adjusted blood loss (aBL)], and the overall postoperative complications were evaluated by the comprehensive complication index (CCI). The impact of aBL on CCI was assessed by the restricted cubic spline regression. A total of 425 patients were included. The median aBL was 17.8 (interquartile range, 11.8-26.3) mL/kg, and the CCI was 40.6 (33.7-49.5). Sixty-three (14.8%) patients had an aBL<10 mL/kg, nearly half (45.4%) of the patients were in the range of 10 ≤aBL<20 mL/kg, and 37 (8.7%) patients had an aBL >40 mL/kg. The spline regression analysis showed a nonlinear incremental association between aBL and CCI; CCI remained flat with an aBL under 10 mL/kg; increased significantly with an aBL ranging from 10 to 20 mL/kg; grew gradually with an aBL over 20 mL/kg. These inflection points of ~10 and 20 mL/kg were almost consistent with the cutoff values identified by the recursive partitioning technique. After adjusting for other risk factors for the postoperative course, the spline regression identified a similar model. aBL had a nonlinear aggravating effect on CCI after hepatectomy for perihilar cholangiocarcinoma. The primary goal of aBL should be <10 mL/kg to minimize CCI.

Dynamic Student Data Management Using Resource Optimization Technology in Higher Education Platforms

This study combines intelligent resource optimization technology to build a dynamic student data management model and suggests a fuzzy hierarchical network representation model based on isomorphism and homogeneity in order to increase the effectiveness of dynamic student data management in colleges and universities. The important semantics of nodes in the network are also captured in this study using fuzzy k-kernel decomposition as a technique for multigranularity partitioning. Based on the SIR model, FHNE compares the production of the sequence to the process of information transmission in the random walk stage, which increases the node sequence’s accuracy. According to the research, the dynamic student data management system that is used in the higher education platform that is suggested in this study can significantly increase the effectiveness of managing student data.

Collaborative route optimization and resource management strategy for multi-target tracking in airborne radar system

In this paper, a collaborative route optimization and resource management (CRO-RM) strategy is developed for multi-target tracking (MTT) in airborne radar system. The mechanism of the proposed CRO-RM strategy is to collaboratively coordinate the kinematic velocity, heading angle, transmit power, waveform pulselength and bandwidth of the airborne radar, in order to achieve accurate target state estimation while adhering to platform limitations and system resources budget constraints. The analytical expression for the Bayesian Cramér-Rao lower bound (BCRLB) on resources optimization and route planning parameters is derived and utilized as the optimization criterion for the CRO-RM strategy. It is shown that the resulting optimization problem is nonlinear and nonconvex since the above adaptable parameters are coupled in the optimization function and constraints. By exploiting the partition technique and cyclic minimization method, embed with the barrier function method for route planning, the semi-definite programming (SDP) for transmit power allocation and the finite grid search for waveform parameters selection, a partition-based four-step solution method is proposed to tackle the optimization problem effectively. Simulation results demonstrate that the proposed CRO-RM strategy achieves the best MTT performance compared with other benchmarks.

ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems

Recent advances in memory technology have enabled near-data processing (NDP) to tackle main memory bottlenecks in modern systems. Prior works partition applications into segments (e.g., instructions, loops, functions) and execute memory-bound segments of the applications on NDP computation units, while mapping the cache-friendly application segments to host CPU cores that access a deeper cache hierarchy. Partitioning applications between NDP and host cores causes inter-segment data movement overhead, which is the overhead from moving data generated from one segment and used in the consecutive segments. This overhead can be large if the segments map to cores in different parts of the system (i.e., host and NDP). Prior works take two approaches to the inter-segment data movement overhead when partitioning applications between NDP and host cores. The first class of works maps segments to NDP or host cores based on the properties of each segment, neglecting the performance impact of the inter-segment data movement. Such partitioning techniques suffer from inter-segment data movement overhead. The second class of works maps segments to host or NDP cores based on the overall memory bandwidth savings of each segment (which depends on the memory bandwidth savings within each segment and the inter-segment data movement overhead between other segments). These works do not offload each segment to the best-fitting core if they incur high inter-segment data movement overhead. Therefore these works miss some of the potential NDP performance benefits. We show that mapping each segment (here basic block) to its best-fitting core based on the properties of each segment, assuming no inter-segment data movement, can provide substantial performance benefits. However, we show that the inter-segment data movement reduces this benefit significantly. To this end, we introduce ALP, a new programmer-transparent technique to leverage the performance benefits of NDP by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alleviating</i> the performance impact of inter-segment data movement between host and memory and enabling efficient partitioning of applications between host and NDP cores. ALP alleviates the inter-segment data movement overhead by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proactively and accurately</i> transferring the required data between the segments mapped on host and NDP cores. This is based on the key observation that the instructions that generate the inter-segment data stay the same across different executions of a program on different input sets. ALP uses a compiler pass to identify these instructions and uses specialized hardware support to transfer data between the host and NDP cores at runtime. Using both the compiler and runtime information, ALP efficiently maps application segments to either host or NDP cores considering 1) the properties of each segment, 2) the inter-segment data movement overhead between different segments, and 3) whether this inter-segment data movement overhead can be alleviated proactively and in a timely manner. We evaluate ALP across a wide range of workloads and show on average 54.3% and 45.4% speedup compared to executing the application only on the host CPU or only the NDP cores, respectively.

A method of defense against cache timing attack in non-volatile memory

Attackers of modern computer architecture found that cache access latency difference between cache hit and cache miss is a point where secure data are overlooked. To prevent such data leakage, cache partitioning technique is utilized for defenders via cache hit isolation. Although this approach is effective in increasing resistance against cache timing attack, it is not suitable for emerging memory system, which is based on non-volatile memories, because it overlooks the weaknesses of the write operations. This paper proposes a secure-aware partitioning guide architecture to improve performance and write endurance by removing the necessity of cache flushing. During changing cache partitioning status, the write counts are considered for the new status and no cache lines are evicted in the proposal. As a result, the lifetime is extended by 1.77 times and the penalty of cache flushing is saved by 7.8%.

Inductive open data study on teleworking dissatisfaction in Spain during the Covid-19 pandemic

PurposeThe research paper aims to study dissatisfaction of teleworking employees in Spain during the Covid-19 health pandemic in order to propose three models: sociodemographic profile of the teleworking dissatisfied employee; advantages and disadvantages for the teleworking dissatisfied employee and advantages for the teleworking dissatisfied employee.Design/methodology/approachThis study uses official open data obtained from the Spanish National Statistical Institute (INE, 2022) through Decision Trees statistical multivariable models implementing Classification and Regression Trees and Recursive Partitioning and Regression Trees techniques to determine the variables that can influence the satisfaction or dissatisfaction of the subjects.FindingsThis investigation offers three models with two sociodemographic profiles of dissatisfied teleworking employee, who is a high/middle-level manager/employee around 45 years old, and she/he lives with the partner. Regarding the most important advantage of teleworking, employees consider “use/saving of time” and as disadvantage “worse organization and coordination of work”.Originality/valueThis research provides empirical evidence with inductive reasoning on understanding the challenges of teleworking dissatisfied employees in Spain not only in turbulent times but also in “normalcy” to improve overall teleworker well-being and accomplish company’s and organization’s long-term objectives for better productivity and effectivity. The study has high practical value due to the integral approach incorporating dissatisfaction as a driver that can trigger negative behaviours towards the organizations and that is seldom addressed in the literature. Additionally, this paper could provide some new ideas for accomplishing “Spain Digital 2025” and “Europe’s Digital Decade: 2030” plans on institutional level.

Measuring sustainability, resilience and livability performance of European smart cities: A novel fuzzy expert-based multi-criteria decision support model

Cities of the 21st century are buffeted with challenges, leaving potentially serious consequences on the future of urban living. Smartening development in cities has reinvented hopes of melting down predicaments in the early 2000s'. However, perplexed by the intensifying complexities of smart cities, urban living in smart cities needs to be evaluated with multiple conflicting criteria. Multi-criteria-based evaluations have been an answer to this case when attempting to gauge the composite performance of multiple decision-making entities. Several multi-criteria assessment techniques exist when dealing with selection problems. Nonetheless, the vagueness associated with the methodologies accompanied by uncertainties and complexities is inevitable in multi-attribute assessments. Fuzzy-based multi-criteria models are often an answer to such uncertainties when modelling real-world problems. The study thus presents a novel fuzzy expert-based multi-criteria decision support model, where the Analytical Hierarchy Process (AHP) is combined with the Evaluation based on Distance from Average Solution (EDAS) approach under a spherical fuzzy environment to create a composite index for comprehensive performance monitoring. The case of 35 high-tech European cities was used to empirically validate the proposed novel approach and thus construct a composite index. The composite index considers the intricate facet of integrating the concept of smart cities with sustainability, urban resilience, and livability under a unified framework. The fuzzy c-means partitioning technique was then used to segment smart cities into high, medium, and low-performing classes. A comparative analysis considering several distance-based approaches under a fuzzy environment with the SF-AHP and EDAS methodology is conducted to validate the robustness and stability of the proposed novel decision support model. The results revealed London as the top-ranked smart city that promotes sustainability, resilience, and livability in its current urban development model. Dusseldorf, Zurich, Munich, Oslo, Dublin, Amsterdam, Hamburg, Rome, Moscow, and Stockholm were no exemption from addressing the tritactic goals of sustainability, urban resilience, and livability well into their urban development plan and were placed in the high-performance cluster. The proposed model is efficient to express decision makers' preferences in a larger space and modelling functional parameters including hesitancy independently in the 3-dimensional domain. The model supports decision-makers and relocation analysts to assess the performance of smart cities and set targets to improve performance to remodel urban development to a more sustainable, resilient, and livable pattern.

Anti-Zika Virus Activity and Isolation of Flavonoids from Ethanol Extracts of Curatella americana L. Leaves.

The ethnomedicinal plant Curatella americana L. (Dilleniaceae) is a common shrub in the Brazilian Cerrado, whose ethanolic extract showed significant in vitro anti-Zika virus activity by the MTT colorimetric method. Currently, there is no drug in clinical use specifically for the treatment of this virus; therefore, in this work, the antiviral and cytotoxic properties of the ethanolic extract, fractions, and compounds were evaluated. The ethanolic extract of the leaves showed no cytotoxicity for the human MRC-5 cell and was moderately cytotoxic for the Vero cell (CC50 161.5 ± 2.01 µg/mL). This extract inhibited the Zika virus multiplication cycle with an EC50 of 85.2 ± 1.65 µg/mL. This extract was fractionated using the liquid-liquid partition technique, and the ethyl acetate fraction showed significant activity against the Zika virus with an EC50 of 40.7 ± 2.33 µg/mL. From the ethyl acetate fraction, the flavonoids quercetin-3-O-hexosylgallate (1), quercetin-3-O-glucoside (2), and quercetin (5) were isolated, and in addition to these compounds, a mixture of quercetin-3-O-rhamnoside (3) and quercetin-3-O-arabinoside (4) was also obtained. The isolated compounds quercetin and quercetin-3-O-hexosylgallate inhibited the viral cytopathic effect at an EC50 of 18.6 ± 2.8 and 152.8 ± 2.0, respectively. Additionally, analyses by liquid chromatography coupled to a mass spectrometer allowed the identification of another 24 minor phenolic constituents present in the ethanolic extract and in the ethyl acetate fraction of this species.

A Speculative Computation Approach for Energy-Efficient Deep Neural Network

Deep neural networks (DNNs) have been widely used for data processing and analysis nowadays. Many computational techniques have been proposed to improve the energy efficiency of executing DNNs, which is critical for emerging smart edge applications. This article presents a speculative computation approach to improving the energy efficiency of DNN computations. The proposed approach employs the techniques of input channel partitioning and threshold-based negative masking to predict and eliminate unnecessary computations. Moreover, a systematic procedure of threshold optimization is proposed to achieve the best tradeoff between the energy and accuracy performance. Finally, an energy-efficient DNN processor architecture was designed and implemented to support the proposed speculative computation approach. The experimental results show that the proposed DNN processor with speculative computation can enhance the energy efficiency of the processor by 22.8%, with only 0.96% accuracy degradation and 1% implementation overhead.

A Novel Graph-Based Computation Offloading Strategy for Workflow Applications in Mobile Edge Computing

With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.

Voltage-sag mitigation by stability-constrained partitioning scheme

Microgrids have the ability to function in islanded or grid-connected modes of operation. The increased penetration of inverter-based Distributed Energy Resources (DERs) in the system promotes the concept of partitioning the system into self-governing and self-adequate microgrids. Most of the partitioning techniques determine virtual boundaries and did not consider the survivability of the constructed microgrids. In this paper, a stability-constrained partitioning scheme is proposed based on small-signal stability to ensure microgrids' survivability when physically partitioned. Moreover, a sensitivity analysis of active power droop gain is utilized to define a novel index for the microgrid’s marginal stability. The application targeted in this paper is the mitigation of voltage-sag events caused by low impedance faults. The system will be partitioned into clusters of survivable microgrids during faults to isolate the faulted zone that caused the voltage-sag event. By isolating the voltage-sag origin from the rest of the system, voltage-sag mitigation is accomplished. Also, a microgrid re-connection method is proposed. This method allows multiple droop-controlled DERs to adjust the frequency, phase, and magnitude of their output voltages to facilitate seamless re-connection of microgrids. Simulation results are given that show a seamless re-connection of two different microgrids when the proposed method is utilized. The effectiveness of the proposed mitigation algorithm is validated using a modified IEEE 33-bus distribution system simulated on MATLAB/SIMULINK platform.

Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement savings in Mexico.

Individuals regularly struggle to save for retirement. Using a large-scale field experiment ( ) in Mexico, we test the effectiveness of several behavioral interventions relative to existing policy and each other geared toward improving voluntary retirement savings contributions. We find that an intervention framing savings as a way to secure one's family future significantly improves contribution rates. We leverage recursive partitioning techniques and identify that the overall positive treatment effect masks subpopulations where the treatment is even more effective and other groups where the treatment has a significant negative effect, decreasing contribution rates. Accounting for this variation is significant for theoretical and policy development as well as firm profitability. Our work also provides a methodological framework for how to better design, scale, and deploy behavioral interventions to maximize their effectiveness.