Multiple Deployment Research Articles

The Impact of Relationships Within Combat Units on Post-Deployment Suicide Risk

Multiple deployment factors may affect suicidality in combat veterans. The relationships between combat deployments and suicidality are complex and not completely understood. Studies of stress in the military and psychological effects of military actions are mostly focused on stressors relating to combat operations. However, many studies suggest that interactions within combat units affect post-deployment psychiatric conditions, suicidal ideation, and behavior. The goal of this article is to review and discuss how relationships within combat units may influence post-deployment suicide risk. Studies of the relationships within combat units are generally focused on two aspects: unit cohesion and harassment/abuse. Considerable evidence suggests that service members who report strong unit cohesion have a lower risk of post-deployment psychiatric disorders and suicidal behavior. Studies examining deployment sexual and non-sexual harassment and abuse have found that combat veterans who experience harassment and abuse during deployment are at heightened post-deployment suicide risk. Sound post-deployment social support and the efficient treatment of psychiatric disorders may mitigate the suicide risk associated with adverse relationships within combat units. Improvements in units’ cohesion and the prevention of harassment/abuse during a military deployment are necessary to reduce post-deployment psychiatric pathology, including suicidal behavior.

Migración de serverbox hacia un sistema de microservicios aplicando arquitectura vertical

Efficient data management is essential in public administration for the effective operation of municipal cadastral systems. In 2010, the Multifinality Cadastral Modernization Program was implemented, a partnership between the Puebla municipality government and Sistemas de Información Geográfica S.A. de C.V. (SIGSA), which included the ServerBox platform. Over time, the monolithic architecture of ServerBox showed limitations in maintenance, updates, and change deployment. This project hypothesizes that migrating ServerBox to a distributed microservices architecture, using a vertical approach, can overcome these challenges and improve operational areas of the system. The proposed design establishes a modular and scalable structure that facilitates system maintenance and evolution. The implementation includes the development of TagHelpers in ASP.NET Core to simplify HTML element creation and the use of snippets to streamline coding. Additionally, a JSON configuration file is defined to manage multiple deployment environments. This strategic approach to migrating ServerBox to a distributed and vertical architecture offers significant advantages in terms of scalability, maintenance, and software development efficiency, and can serve as a guide for similar projects in public administration.

Enhancing Indoor Localization Accuracy through Multiple Access Point Deployment

This study addresses the limitations of wireless local area networks in indoor localization by utilizing Extra-Trees Regression (ETR) to estimate locations based on received signal strength indicator (RSSI) values from a radio environment map (REM). We investigate how integrating numerous access points can enhance indoor localization accuracy. By constructing an extensive REM using RSSI data from various access points collected by a mobile robot in the intended interior setting, we evaluate several machine learning regression techniques. Our research pays special attention to an optimized ETR model, validated through 10-fold cross-validation and hyperparameter tuning. We quantitatively evaluate the efficiency of our suggested multi-access-point approach using root mean square error (RMSE) for REM evaluation and location error metrics for accurate localization. The results show that incorporating multiple access points significantly improves indoor localization accuracy, providing a substantial improvement over single-access-point systems when assessing interior radio frequency environments.

A float-based Ocean color vicarious calibration program

Ocean color satellites require a procedure known as System Vicarious Calibration (SVC) after launch as the pre-launch and on-orbit calibration accuracy is insufficient. The current approach for determination of post-launch SVC uses a single fixed measurement location and may be susceptible to unexpected biases in satellite processing algorithms. Here we describe a novel SVC program which is based on a high resolution and high accuracy radiometric system integrated with an autonomous profiling float (providing a buoyancy engine, physical observations, and communication). This float + radiometer (HyperNav) system can be shipped via air, land, ocean and is deployable from small boats. This SVC program relies on multiple deployment sites with associated facilities to collect a significant amount of SVC quality data in a relatively short time. It has centralized logistics and command-and-control centers ensuring easy access to information regarding the status of each asset and to ensure floats stay within a certain ocean area. The development of the program has been associated with the launch of NASA’s PACE satellite and has been executed by academic institutions in collaboration with an industrial partner. Other approaches for a future float-based operational SVC program are discussed.

Incidence and temporal evolution of delayed peridevice leak after left atrial appendage closure

BackgroundPeridevice leak (PDL) after left atrial appendage closure (LAAC) portends adverse outcomes. ObjectiveThe purpose of this study was to assess the incidence, predictors, clinical implications, and temporal evolution of PDL after LAAC. MethodsThis single-center retrospective study included all patients who underwent LAAC with Watchman FLX and had no PDL detected at the time of implantation. The primary end point was the incidence of new PDL at initial imaging. The composite secondary end point included continued oral anticoagulation after initial imaging, device-related thrombus, stroke or transient ischemic attack, major bleeding, and need for PDL closure at longest follow-up. Temporal evolution of PDL was assessed in patients with available surveillance imaging. ResultsOf the 355 patients who completed imaging at 47 days (interquartile range [IQR] 44–50 days), 139 (39%) had new PDL with a mean leak size of 3.2 ± 1.4 mm (median 3.0 mm; IQR 2.0–4.0 mm; range 1.0–9.0 mm). Multiple deployment attempts and larger device size were positive predictors of PDL, while increased contrast volume administration was a negative predictor of PDL. The composite secondary end point occurred in 42 (30%) patients with PDL and 33 (15%) patients without PDL (P < .001). Of the 139 patients with PDL, 43 (31%) had surveillance imaging where the leak size regressed from 3.7 ± 1.8 mm at 46 days (IQR 44–51 days) to 1.7 ± 2.0 mm at 189 days (IQR 158–285 days) (P < .001). The leak size regressed in 33 (77%), remained stable in 4 (9%), and progressed in 6 (14%) cases. ConclusionDespite design improvements, LAAC with Watchman FLX demonstrates a significant incidence of PDL with meaningful clinical implications. Regardless of initial size, most leaks regressed over time.

Novel Selective Maintenance Approach to Ensure Mission Reliability of Armored Vehicles Considering Multiple Deployment Roles in Distinct Operating Profiles

Given the gravity of the element of surprise in modern warfare, military forces worldwide are constantly attempting to achieve and maintain operational readiness of their critical military equipment. Selective Maintenance (SM) is considered an effective approach for achieving system operational readiness. Effective use of the SM approach for military equipment requires considering various military-specific factors such as multiple deployment roles, distinct operating profiles, human reliability, and the use of refurbished or non-OEM spares. This makes the SM approach for military systems very challenging. This paper presents an approach that formulates the SM problem intending to achieve and maintain the required level of operational readiness for predefined future missions from a military viewpoint. This approach employs a novel methodology that estimates the mission reliability of military equipment while modeling the combined impact of several important military-specific factors. This complex yet necessary integration of various military-specific factors makes the present approach accurate and apt to the exact modus operandi of the armed forces. The developed approach is demonstrated for the maintenance of armored vehicles deployed on distinct missions under different operating conditions. Numerical investigations illustrate the efficacy of the present approach and highlight its advantages over the conventional maintenance approach.

FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing

Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC) environments to process the proliferation of data generated by edge devices. By collaboratively optimizing the global machine learning models on distributed edge devices, FL circumvents the need for transmitting raw data and enhances user privacy. Despite practical successes, FL still confronts significant challenges including constrained edge device resources, multiple tasks deployment, and data heterogeneity. However, existing studies focus on mitigating the FL training costs of each single task whereas neglecting the resource consumption across multiple tasks in heterogeneous FL scenarios. In this paper, we propose Heterogeneous Federated Learning with Local Parameter Sharing (FedLPS) to fill this gap. FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders. To further reduce resource consumption, a channel-wise model pruning algorithm that shrinks the footprint of local models while accounting for both data and system heterogeneity is employed in FedLPS. Additionally, a novel heterogeneous model aggregation algorithm is proposed to aggregate the heterogeneous predictors in FedLPS. We implemented the proposed FedLPS on a real FL platform and compared it with state-of-the-art (SOTA) FL frameworks. The experimental results on five popular datasets and two modern DNN models illustrate that the proposed FedLPS significantly outperforms the SOTA FL frameworks by up to 4.88% and reduces the computational resource consumption by 21.3%. Our code is available at: https://github.com/jyzgh/FedLPS.

Promoting Sustainable Development Goals by Optimizing City-Level Solar Photovoltaic Deployment in China.

Solar photovoltaic (PV) installations, which enable carbon neutrality, are expected to surge in the coming decades. This growth will support sustainable development goals (SDGs) via reductions in power-generation-related environmental emissions and water consumption while generating new jobs. However, where and to what extent PVs should be utilized to support SDGs must be thoroughly addressed. Here, we use multiple PV deployment scenarios to compare the benefits of PVs and related SDGs progress in 366 prefectural-level cities in China. We developed an assessment framework that integrates a PV allocation model, an electricity system optimization model, and a benefit assessment approach. We identify vast differences in PV distribution and electricity transmission and elucidate trade-offs and synergies among the SDGs under various PV implementation scenarios. The water conservation-oriented scenario yields substantial carbon reduction, air pollutant mitigation, and water saving cobenefits, leading to the greatest SDGs improvements. Prioritizing employment creation enhances job-relevant SDGs but inhibits environmental resource benefits. SDGs in less developed cities present greater progress across all scenarios. This study highlights the need to consider spatial heterogeneity and the potential trade-offs between different SDGs and regions when designing energy transition strategies.

A measurement-based approach to analyze the power consumption of the softwarized 5G core

In light of the ever growing energy needs of the ICT sector, a value that is becoming increasingly important for a mobile network is its power consumption. However, the transition away from legacy network deployments tightly coupled with the underlying hardware and the adoption of the Network Function Virtualization (NFV) paradigm has made more difficult to accurately evaluate their energy and carbon footprint. In this paper, we propose and validate a measurement-based approach to analyze the power consumption of a virtualized 5G core network (5GC) deployment. We design an experimental testbed using commercial off-the-shelf (COTS) hardware and open-source software as a sample architecture simulating an edge computing node and supporting three different virtualization options. We make use of both hardware-based and software-based power meters to investigate the power consumption trends associated with increasing levels of traffic and multiple 5GC deployment types. The results show the feasibility of a real-time power monitoring system and highlight how deployment choices, such as virtualization framework and 5GC software, can significantly impact on the power consumption of the network.

A proactive controller failure recovery mechanism in SD-WAN with multiple controllers

The separation of data and control plane is a key feature of Softwaredefined networking (SDN), which makes network administration more intelligent. The control plane is realized using a logically centralized controller. In SD-WAN, as the network becomes larger, multiple controllers are needed to manage the network. In this scenario, there is a chance for controller failure due to overload. Once the controller fails, the switches lose connection with the controller. The load of the failed controller is to be re-distributed among other controllers. Sometimes this load transfer may cause many other problems like controller chain failure. It may consistently undermine the trustworthiness of the network. In the proposed technique, the multiple controller deployment based on affinity propagation clustering in SD-WAN is extended to include a proactive controller failure recovery mechanism. Whenever the controller load exceeds a pre-defined threshold, a set of switches under the bottleneck controller will be reassigned to a neighbouring controller without exceeding its capacity. The simulation results show that when network traffic increased, the proposed proactive controller failure recovery method balanced the controller load, resolved a cascading controller failure, improved the average throughput, and reduced the average end-to-end delay and packet loss effectively.

Towards energy balancing optimization in wireless sensor networks: A novel quantum inspired genetic algorithm based sinks deployment approach

A Wireless Sensor Network (WSN) with multiple sinks alleviates the bottleneck problem of imbalanced energy consumption and leads to improved scalability and convenience in data collection for diverse range of large-scale applications. However, introducing more than one sink also brings a new set of challenges to overcome. To minimize the uneven energy consumption among sensors for longevity of the WSN, it is required to find optimal locations of the sinks that will permit most of the sensors to forward their data using minimum hops and to determine the optimal route for each sink with consistent route length. Routing in large networks is an NP-Hard problem. Previous schemes have solved this problem using multiple sinks deployment either in planned ways or random manner without simultaneously considering the possibilities of network topological changes, edge cases of sensors, and problem of uneven path length. In this context, this paper proposes a quantum inspired genetic algorithm based multiple sinks deployment approach (Q-GEMS) for energy consumption balancing among sensors to extend the network lifetime. Specially, the exiting approaches have focused on either minimizing energy consumption or reducing the path length. In addition to addressing these constraints, the proposed Q-GEMS is designed to be resilient which adapts to any changes in the planned topologies of deployed sinks in anticipation of sensor failures and external influences. Also to achieve greater energy consumption balance among sensors by equalizing the path length and bring down the cost of WSN by minimizing the number of sinks, novel heuristics have been proposed for positioning the sinks, binding the sensors to sinks, evaluating the quality of solutions and updating the Q-bit population. The experimental results show that the proposed Q-GEMS performs comparatively better than state of the art approaches and achieving network lifetime 5.1%, 21%, and 24% longer than that of EEMS, EESS, and DPSO+PSO respectively.

A - 69 Examining Differences in Neuropsychological Test Performance Based on Deployment Era and Years Served in United States Veterans.

To determine the influence of years of military service and deployment era on neurocognitive performance. Archival data included 316 veterans (89.6% male; 94.3% White) seen for neuropsychological evaluation at the Portland VA (M/SD: age = 55.54/14.39; education = 13.32/2.44). Participants were administered a comprehensive neuropsychological battery. Two-way between-subjects analyses of variance (ANOVAs) examined the effects of years of service and deployment era on test performance. Bonferroni post-hoc tests compared differences between groups when a significant main effect was observed. Significant main effects were found for deployment era on TOMM Retention trial (F(4, 20) = 4.39, p = 0.04), WAIS-IV Block Design (F(5, 244) = 2.45, p = 0.04), WMS-IV Logical Memory I (F(5, 230) = 3.79, p < 0.01), Trail Making Test Part A (F(5, 242) = 3.54, p < 0.01), Trail Making Test Part B (F(5, 227) = 3.38, p < 0.01) and Grooved Pegboard for nondominant hand (F(4, 86) = 3.22, p < 0.05). Significant main effects were also observed for years of service for Trail Making Test Part A (F(4, 242) = 2.81, p < 0.05) and DKEFS Color-Word Trial 2 (F(4, 77) = 2.67, p < 0.05). A significant interaction was found between deployment era and years served on the BVMT-R total recall score (F(15, 213) = 1.92, p < 0.05). Post-hoc analyses did not reveal significant variance accounted for on neuropsychological tests. While there were significant differences between multiple measures, deployment era and years of service do not impact veteran's cognitive performance.

Multiple Aerial Base Station Deployment and User Association Based on Binary Radio Map

The utilization of the aerial base station (ABS) has been treated as a promising solution to the coverage, deployment and cost savings problems in the wireless networks. Different from the terrestrial networks, in the ABS assisted air-to-ground network, the service positions of ABSs and user association should be jointly optimized, which is challenging due to the unknown propagation environment in the task area. In this paper, a binary radio map (BRM) is constructed to grant the ABSs the location-specific channel knowledge within the entire task area. With the assistance of the BRM, a multiple ABS deployment and user association framework is proposed. The framework consists of an offline optimization stage during which the initial ABS deployment position and user association are jointly optimized based on the BRM, and an online refinement stage during which the ABS positions and user association are refined in real time according to both the observed quality of service (QoS) and virtual QoS emulated by the BRM. The performance of the proposed framework is examined in a complex urban scenario with mobile users. The results show that higher achievable rate performance and lower on-board energy consumption can be realized when compared with the reference schemes.

Zero‐Power Shape Retention in Soft Pneumatic Actuators with Extensional and Bending Multistability

AbstractPower‐free shape retention enables soft pneumatic robots to reduce energy cost and avoid unexpected collapse due to burst or puncture. Existing strategies for pneumatic actuation cannot attain motion locking for trajectories combining extension and bending, one of the most common modes of operation. Here, a design paradigm is introduced for soft pneumatic actuators to enable zero‐power locking for shape retention in both extension and bending. The underpinning mechanism is the integration of a pneumatic transmitter and a multistable guider, which are programmed to interact for balanced load transfer, flexural and extension steering, and progressive snapping leading to state locking. Through theory, simulations, and experiments on proof‐of‐concept actuators, the existence of four distinct regimes of deformation is unveiled, where the constituents first interact during inflation to attain locking in extension and bending, and then cooperate under vacuum to enable fully reversible functionality. Finally, the design paradigm is demonstrated to realize a soft robotic arm capable to lock at desired curvature states at zero‐power, and a gripper that safely operates with puncture resistance to grasp and hold objects of various shapes and consistency. The study promises further development for zero‐power soft robots endowed with multiple deformation modes, sequential deployment, and tunable multistability.

Fracture failure analysis and prevention of carbon fiber thin wall crossbar

The fracture failure of a crossbar occurred in a routine deployment test of a satellite ring antenna truss structure. In this study, the fracture cause of the carbon fiber thin wall crossbar was analyzed using body microscope observation, scanning electron microscope observation, metallographic analysis, hardness analysis, XRD analysis, and other methods. The analysis results show that the failure bar had interlaminar damage and local crack or breakage before this deployment test. This defect has further expanded and intensified in the multiple deployment tests of the circular antenna, resulting in the gradual decline of the local stiffness of the crossbar, and finally the buckling failure. By using the bottom plate reflection method to carry out non-destructive testing on the rod, it can effectively avoid false inspection and missing inspection of defects.

Optimum: Runtime optimization for multiple mixed model deployment deep learning inference

GPUs used in data centers to perform deep learning inference tasks are underutilized. Previous systems tended to deploy a single model on a GPU to ensure that inference tasks met throughput and latency requirements. The rapid increase in one GPU’s resources, as well as the emergence of scenarios such as small models and small batches, have exacerbated the issue of low GPU utilization. In this case, a mixed model deployment-based solution can significantly improve GPU utilization while also providing greater flexibility to the inference system’s upper layer. The selection of model combinations and optimization strategies in mixed model deployment, however, remain unresolved issues. This paper proposes Optimum, the first model-combination planning and runtime optimization framework for mixed model deployment. Facing enormous search spaces, Optimum uses performance prediction for model combination selection with low search overhead. The predictor is based on a multilayer perceptron. Its input features are the profiling results of the model engine, and the output is the performance degradation. The runtime optimization strategies allow Optimum to perform performance optimization and fine-grained tradeoff. The Optimum prototype is based on CUDA multi-stream and TensorRT. The test results show that we have a flat 10.3% performance improvement over mainstream single-model deployments. We have a performance improvement of up to 7.09% over the state-of-the-art with an order of magnitude reduction in search overhead.

Modelling renewable energy communities: assessing the impact of different configurations, technologies and types of participants

BackgroundEnergy communities (ECs) have emerged as a solution to support governments mitigating climate change and comply with decarbonization goals, while introducing end-users on the energy value chain. In this paradigm, citizens have an active role in reducing electricity demand from the utility grid, by generating, sharing and/or trading locally generated renewable energy, such as solar energy. However, the economic and environmental outputs of energy communities are dependent on a variety of factors, such as technology features (renewable energy generation, existence of flexible equipment and/or energy storage systems), types of participants (consumers and prosumers with different electricity intensity and load profiles), and electricity sharing/trading agreements. As such, assessing the impact these will have on delivering benefits to the energy community and its participants is of paramount importance.MethodsThis work models different energy communities’ design typologies in Lisbon, Portugal considering different types of consumers with heterogenous electricity demand profiles and willingness to participate, multiple technology deployment scenarios (solar systems installation, batteries, and electric vehicles), and electricity trading (collective self-consumption versus peer-to-peer trading).ResultsResults demonstrate community electricity cost savings are up to 42%, with self-sufficiency rate up to 12.5%, which is considerably low due to the participation of high demanding sectors (such as industry or retail). At participants’ individual level, electricity costs savings can reach 48% and 53%, for residential consumers and prosumers, respectively, while for high-demanding participants are slightly lower: 43% for hotel, 44% for retail, 13% for industry and 5% for university. Individual self-sufficiency rates register highest results for the residential prosumers (35% for PV prosumers, 28% for PV + electric vehicles and 54% with PV + batteries) while for other participants results fall between 6% (retail) and 26% (industry).ConclusionsWe conclude that for ECs deployment, individual PV self-consumption assets are not sufficient, thus greater PV sizes and higher adoption rates should be considered, according to consumer and prosumers shares. The share/trade of PV surplus, paired with competitive aggregation tariffs results in positive economic and environmental outputs, for the majority of both consumers and prosumers.

An Integrated Management System for Composed Applications Deployed by Different Deployment Automation Technologies

Automation is the key to enable an efficient, fast, and reliable deployment of applications. Therefore, several deployment automation technologies emerged in recent years whereby each technology has its specific field of application: While some are bound to cloud providers and offer provider-specific functionalities, others enable multi-cloud deployments but mostly do not support provider-specific features. As a consequence, often companies have to use multiple deployment technologies in combination to deploy large applications. However, the management capabilities of most deployment technologies are limited or even non-existent. This issue becomes even more severe if different parts of a single application are deployed by different technologies. To tackle this issue, we present an approach that enables generating automatically executable management workflows for applications that consist of multiple components deployed by different deployment technologies. Our approach builds on top of instance models that are automatically generated based on information retrieved from the different deployment technologies involved. Based on the derived instance model, we generate workflows that manipulate the running application. We prove the technical feasibility by an open-source prototype and discuss a detailed case study.

Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries

Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1–2 h forecast errors—resulting in early or delayed battery discharging relative to the onset of a reliability event—lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery’s availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.

Dynamic Analysis of Bidirectional Solar Arrays with Flexible Panels and Clearance Joints

The dynamic characteristics of a two-step deployable bidirectional solar array system with flexible panels and clearance joints are studied in this paper. Driving mechanisms, closed cable loop configurations, and latch mechanisms are considered in this system where normal contact force is established using a nonlinear spring damping model and the friction effect is determined by a modified Coulomb friction model. With the simulation strategy of activating different constraint states and applying latch torques, the multiple deployment and locking processes of the system are implemented. The results indicate that the second locking causes obvious vibration on the dynamic responses of the yoke and main panels and leads to a complex bidirectional vibration of the main panel connected to the side clearance joints. The equivalent suspension damping property produced by the coupling of the flexibility and clearances can reduce the vibration of the deployable components. Moreover, the attitude and orbital motion results reveal the effect of different deployment methods of the system on the spacecraft attitude. Finally, the trajectories of journal centers and contact forces are analyzed to indicate the foundation of the dynamic behavior of the system.