Localization Performance Research Articles

Improving Local Government Performance Through the Use of Contract Workers: A Case From South Korea

Policymakers in many countries have employed market approaches to supplement traditional civil service systems, and using contract employees is a form of this labor resourcing strategy. Although business studies have suggested that contract-based employment may enhance organizational performance, few studies have explored this relationship in the field of public administration. This study examines whether the use of contract employees is associated with organizational performance in the public sector. Based on the New Public Management (NPM) literature, this study develops hypotheses and tests them using two-way fixed effects models with 18 months of longitudinal data (2018–2019) from 2,912 neighborhood offices in South Korea. The results reveal that local governments with a greater number of contract employees are more likely to outperform doorstep health service delivery. The implications for the management of contract workers in the public sector are discussed.

A metaheuristic-based optimization model for flight procedure design

In recent years, the continuous increase in air traffic has led to a saturation of available airspace. The new localizer performance with vertical guidance (LPV) instrument approach procedures (IAPs) offer additional capacity at aerodromes and in airspaces. To support their implementation, a specifically developed model focused on the improved IAP design process and optimized solution generation is proposed. The model provides a fully automated search of the solution space by employing the bee colony optimization metaheuristic algorithm. The resulting list of alternatives is ranked by the objective function score, which is affected by the balancing values of the complexity indicators and the achieved obstacle clearance height. The model’s capabilities are successfully validated against the challenging operational environment of Podgorica Airport. The model was initially developed to conduct IAP feasibility studies; however, it can also be used in the initial stages of airport planning projects or for IAP criteria validation.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Deciphering the Role of Inorganic Nanoparticles' Surface Functionalization on Biohybrid Microbial Photoelectrodes.

Shedding light on the interaction between inorganic nanoparticles (NPs) and living microorganisms is at the basis of the development of biohybrid technologies with improved performance. Au NPs have been shown to be able to improve the extracellular electron transfer (EET) in intact bacterial cells interfaced with an electrode; however, detailed information on the role of NP-surface properties in their interaction with bacterial membranes is still lacking. Herein, we unveil how the surface functionalization of Au NPs influences their interaction with photosynthetic bacteria, focusing on cell morphology, growth kinetics, NPs localization, and electrocatalytic performance. We show that functionalization of Au NPs with cysteine in the zwitterionic form results in a uniform NPs distribution in purple bacteria, specifically locating the NPs within the outer-membrane/periplasmic space of bacterial cells. These biohybrid cells, when coupled with an electrode, exhibit enhanced EET and increased (photo)current generation, paving the way for the future development of rationally designed biohybrid electrochemical systems.

Effect of Bio-Inspired Cutout Shapes at the Leading Edge of Propellers on Noise and Flight Efficiency

In recent years, the application of bio-inspired structures has garnered attention for enhancing the performance of fluid machinery. In this study, we experimentally investigated the effects of introducing a bio-inspired cutout structure to the propellers of drones, aiming to improve thrust efficiency and reduce noise levels. Our results demonstrated reductions in noise levels compared to conventional propellers. Parametric studies revealed that the roundness of the structure significantly influenced both flight efficiency and noise levels, suggesting its importance for replicating the inherent fluid characteristics found in nature. Additionally, optimal parameters for noise reduction, such as the length of the cutout, angle of incision relative to the flow direction, and the distance between the gap were identified. Although no improvements in flight efficiency were observed, most of the models investigated exhibited only around a 5% reduction in efficiency compared to the standard propellers, suggesting practical applicability for scenarios such as nighttime drone operations in urban areas. The noteworthy reduction in sound pressure levels in the mid- to high-frequency range achieved by the bio-inspired propellers in this study holds the potential to address the issue of drone noise pollution and encourage drone operations in urban areas. Moreover, the confirmed decrease in sound pressure at specific frequencies and the suggested controllability hint at the possibility of enhancing sound source localization performance using drones.

Analysis of hydrodynamics and thermal–hydraulic performance of twisted angle fins within an annular conduit

AbstractThis paper investigated the influence of various twisted angles of fin inserts integrated on the inner heated pipe wall of a double-pipe heat exchanger by analysing the thermal–hydraulic performance through numerical simulations. Results showed twisted angle fin (TAF) induced turbulent fluid flow phenomena, generating transverse and longitudinal vortices, along with swirling flow. This collaboration between the vortices and swirling flow effectively prevents heat trapping and enhances heat transfer from the inner pipe wall through the intensive fluid mixing. Despite longitudinal swirling vortices and turbulence significantly enhance local and global heat transfer performance, they led to increased pressure drop. However, TAF twisting reduces pressure drop by streamlining flow. The performance evaluation criterion (PEC) value is a dimensionless quantity that facilitates the comprehensive evaluation of optimal configurations by comparing the increment in Nusselt number and the increment in friction factor. PEC values greater than 1 indicate that the increase in Nusselt number exceeded the increase in friction factor. Among tested configurations, the twisted fin angles of 60° (TAF60) exhibited the highest improvement in Nusselt number and friction factor, also achieving the highest PEC of 1.59712 at Reynolds number of 1194.71. TAF40 and TAF50 were identified as optimal configurations for enhancing the thermal–hydraulic efficiency, with average PEC values of 1.26893 and 1.27030, respectively. Overall, the study indicated a consistent enhancement trend with increasing twisted angles and emphasizes the significant thermal performance improvement of TAF configurations compared to the smooth conduit. Furthermore, the results showed a converging tendency across all TAF configurations in the percentage improvement in Nusselt number and friction factor, as well as PEC compared to the baseline No fin configuration with increasing Reynolds number, suggesting that future improvements in thermal–hydraulic performance are more dependent on geometric angles than fluid flow velocity.

An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving Virtual Interpolation Array.

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.

Carrier Phase-Based Underwater Source Localization for Ultrashort Baseline

In this paper, we propose a phase-based pseudorange estimation and source localization algorithm to improve the localization performances of ultrashort baseline (USBL) systems. The conventional USBL system estimates pseudorange through cross-correlation of received chirps. However, due to the limited sampling rate, precise pseudorange cannot be estimated, resulting in low localization performance. The proposed method calculates the number of wavelengths between the transducer and receiver and detects the initial phase to estimate the pseudorange more accurately than the conventional method. It also improves localization performance by estimating and compensating for phase variations by underwater channels. The source localization performances of the proposed method and the conventional methods were compared and analyzed through computer simulations and ocean experiments, and the proposed method had better localization performances than the conventional methods.

The Square-Root Unscented and the Square-Root Cubature Kalman Filters on Manifolds.

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands.

4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions

AbstractIn this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://go.vision.in.tum.de/4seasons.

See the Unseen: Grid-Wise Drivable Area Detection Dataset and Network Using LiDAR

Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely heavily on illumination conditions and often fail to capture accurate 3D information, while LiDAR-based methods offer accurate 3D data and are less susceptible to illumination conditions. However, existing LiDAR-based methods focus on point-wise detection, so are prone to occlusion and limited by point cloud sparsity, which leads to decreased performance in motion planning and localization. We propose Argoverse-grid, a grid-wise DA detection dataset derived from Argoverse 1, comprising over 20K frames with fine-grained BEV DA labels across various scenarios. We also introduce Grid-DATrNet, a first grid-wise DA detection model utilizing global attention through transformers. Our experiments demonstrate the superiority of Grid-DATrNet over various methods, including both LiDAR and camera-based approaches, in detecting grid-wise DA on the proposed Argoverse-grid dataset. Grid-DATrNet achieves state-of-the-art results with an accuracy of 93.28% and an F1-score of 0.8328. We show that Grid-DATrNet can detect grids even in occluded and unmeasured areas by leveraging contextual and semantic information through global attention, unlike CNN-based DA detection methods. The preprocessing code for Argoverse-grid, experiment code, Grid-DATrNet implementation, and result visualization code are available at AVE Laboratory official git hub.

Civil society, local government and rural health in an eastern Indian state

ABSTRACT This paper explores how civil society mobilization makes a difference in local government performance in delivering rural healthcare. The study was carried out during 2019–2020 in tribal-dominated Nilgiri and Daringbadi blocks of Odisha state in eastern India. Civil society activism is found to interject more programmatic priorities into local governance of health in Nilgiri than in Daringbadi. Success in Nilgiri is linked to its history of subaltern mobilization, the organizational efforts of a pro-poor political party and a comparatively mobilized civil society. Conversely, failure in Daringbadi is linked to a fractured community, little if any history of civil society mobilization and hegemony of the ruling party which uses local government to advance its own political agenda.

Does decentralization boost electoral participation? Revisiting the question in a non-western context

Since the late 1970s, intergovernmental relationships worldwide have undergone an unequivocal transformation, with powers increasingly shifting toward subnational governments. One of the expected outcomes of decentralization reforms is a (re)vitalization of participation in subnational decision-making. If available empirical evidence tends to support this claim, it has remained primarily restricted to Western countries. This article leverages asymmetric decentralization arrangements in Ukraine to re-examine the effect of decentralization on voter turnout in a diverging setting. It conducts matching and regression analyses using an original city-level dataset that combines electoral, census, administrative, financial and geospatial data. The results converge on a significantly positive effect on voter turnout in local elections of more than 4 percentage points. Additional analyses concerning the mechanisms suggest that the effect might be driven by citizens’ awareness of electoral stakes. Interestingly, the increase in voter turnout appears concomitant with lower perceived local government performance.

Extracellular Vesicle Characteristics in Local Fluid and Plasma Measured by Nanoparticle Tracking Analysis Can Help Differentiate High-Grade Serous Carcinoma from Benign Ovarian Pathology

Background: High-grade serous carcinoma (HGSC) is the most lethal of gynecological cancers in developed countries. It usually presents late with non-specific symptoms and most cases are diagnosed at an advanced stage, with 5-year overall survival being around 40%. Biomarkers for screening and early diagnosis of this aggressive disease are, thus, a research priority. Extracellular vesicles (EVs) that reflect the cell of origin and that can be isolated from local fluid and plasma by minimally invasive liquid biopsy are such promising biomarkers. Besides EV concentration and molecular profile, which have been the main focus of research for many years, recent studies have also called attention to EV size distribution. The aim of our study was to evaluate the potential of EV concentration and size distribution in local fluid and plasma as diagnostic biomarkers for HGSC. Methods: Paired pretreatment ascites and plasma samples from 37 patients with advanced HGSC and paired pretreatment free peritoneal fluid (FPF) and plasma samples from 40 controls with benign ovarian pathology (BOP) were analyzed using nanoparticle tracking analysis (NTA). Results: We observed a significant difference in EV concentration in local fluid, but not in plasma, between HGSC patients and the control group. We also found a significant difference in EV size distribution in both local fluid and plasma between HGSC patients and the control group. The receiver operating characteristics (ROC) curve analysis of EV characteristics showed excellent diagnostic performance for the mode, D10, and D50 in local fluid and acceptable diagnostic performance for EV concentration and mean EV size in local fluid, as well as for the mode and D10 value in plasma. Conclusions: The results of our study show that EV concentration in local fluid and more importantly EV size distribution in both local fluid and plasma are significantly changed in the presence of HGSC. Future research of size-dependent molecular profiling of EVs could help identify novel diagnostic biomarkers for HGSC.

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention

Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.

Comparison between endogenous and exogenous nitrogen of nitrogen-doped carbon catalyst in the process of activating PMS

BackgroundAdvanced oxidation processes (AOPs) based on sulfate radicals (SO4•–) have proven to be highly effective in degrading organics in wastewater. Carbon-based materials have emerged as promising catalysts for activating persulfate, which generates environmentally friendly sulfate radicals (SO4•–), for remediation purposes. The nitrogen doping technique is an effective method for site-specific regulation and can significantly enhance the performance of carbon-based catalysts, which could promote the application of carbon-based catalysts in the future. Endogenous and exogenous nitrogen sources can provide nitrogen sources for N doping. However, there are few reports on the comparison of the structure and catalytic mechanism of these two types of N-doped biochar. It is also of great significance to reveal the mechanisms of constructing catalytic sites using endogenous and exogenous nitrogen. MethodsHerein, the preparation of endogenous nitrogen-doped biochar (BC) was achieved by using soybean as the precursor material, which is rich in natural nitrogen-containing components of proteins. Subsequently, the BC was doped by mixing it with urea and pyrolysis, resulting in the preparation of exogenous nitrogen-doped biochar (NBC). Significant findingsThe characterization of XRD and HRTEM showed that g-C3N4 formed in NBC. The results of catalytic degradation and quenching experiments demonstrate that the exogenous nitrogen-doped catalysts have a better performance than endogenous nitrogen-doped catalysts. The OFL removal rate in BC/PMS was higher than that in the BC/PMS system (71.68% vs. 61.83 %). The kobs in NBC/PMS are also higher than that in BC/PMS (0.00943 min−1vs. 0.01369 min−1). The NBC could be a promising catalyst for PMS activation in practical application. DFT results showed that the g-C3N4 generated from exogenous nitrogen can improve PMS activation performance in the g-C3N4/graphene bilayer structure. The influence on the charge distribution of surrounding carbon materials makes endogenous nitrogen doping a good choice for optimizing the local performance of the material in the absence of natural nitrogen components.

Acoustical dissipation in biobased granular packings

Biobased building materials have many advantages, including local availability and functional performance in terms of mechanical, acoustic and hygrothermal properties. Besides, the biobased solutions coming from plants that uptake CO2 during their growth, are good opportunities to reach carbon-neutral building construction. However, mastering their acoustical performance still raises a number of questions directly linked to their microstructure, which is characterised by a high degree of porosity spread over several scales. The work presented here aims to highlight the effect of the porosity distribution in biobased granular packings on their acoustical performances. Several plant aggregates are considered, such as hemp shiv, sunflower pith or reed as a function of their density and water content. Characterizations are performed in normal incidence and compared to fluid equivalent modellings based on various assumptions, to analyze the effective contribution of the various types of pores at the aggregate level, and between the aggregates.

Spectral weighting functions for localization of complex sound. III. The effect of sensorineural hearing lossa).

Spectral weighting functions for sound localization were measured in participants with bilateral mild sloping to moderately severe, high-frequency sensorineural hearing loss (SNHL) and compared to normal hearing (NH) participants with and without simulated SNHL. Each participant group localized three types of complex tones, comprised of seven frequency components spatially jittered and presented from the horizontal frontal field. A threshold-elevating noise masker was implemented in the free field to simulate SNHL for participants with NH. On average, participants with SNHL and NH (in quiet and simulated SNHL) placed the greatest perceptual weight on components within the interaural time difference "dominance region," found previously to peak around 800 Hz [Folkerts and Stecker, J. Acoust. Soc. Am. 151, 3409-3425 (2022)]. In addition to the peak at 800 Hz, both participant groups (including NH participants in quiet) placed near equal weight on 400 Hz, resulting in a broadened "peak" in the dominance region, most likely due to the reduction of audibility to higher frequency components. However, individual weighting strategies were more variable across participants with SNHL than participants with NH. Localization performance was reduced for participants with SNHL but not for NH participants with simulated hearing loss when compared to NH participants in quiet.

Strategic Planning In The Public Sector: Analyzing Its Effects On Local Government Performance

This study investigates the impacts of strategic planning, strategic types, and stakeholder involvement on the performance of Brazilian local governments. Using a sample of 286 municipalities, the research employs multiple regression analysis to evaluate how these variables influence various dimensions of organizational performance, including the management of objectives, operations, people, external relations, and overall performance. The primary goal of the study is to empirically validate the relationship between strategic planning and the performance of local governments, as well as to identify which strategic types are most effective in different contexts. Additionally, it seeks to understand the role of stakeholder involvement in the strategic planning process. The methodology included the collection of primary data through electronic surveys sent to municipal managers, as well as secondary data from the Municipal Management Index (IGM-CFA). The variables analyzed were the Strategic Planning Perception Index (IPE), strategic types (defender, prospector, reactor), and the Stakeholder Involvement Index (IEST). The results indicate that a positive perception of strategic planning is significantly associated with improvements in all areas of performance analyzed. Proactive and innovative strategies (prospectors) showed positive correlations with performance, while reactive strategies showed negative correlations. Stakeholder involvement also contributed positively to the management of objectives, operations, and people, although it showed a negative correlation with the management of externalrelations.

The Wind Power Blade Spoiler Design and Validation Based on CFD

Abstract The power generation capacity of the wind turbine is derived from the aerodynamic properties of the blade, which can be characterized by the local flow state of the blade. In order to improve the aerodynamic performance of the blade and furthermore the power generation efficiency of the wind turbine, the top priority is to find the abnormal area of the local flow along the blade, and take relevant strategies to control the abnormal flow. The CFD-based blade spoiler design is carried out in both the blade root and the blade middlepiece respectively. Meanwhile, the influence of assembled spoilers on the local and whole performance of the blade is verified by the panel method and BEM method. The verification lays the foundation for further flow control optimization design.