Localization Performance Research Articles

Dynamic SelectOut and voting-based federated learning for enhanced medical image analysis

Abstract Federated Learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed healthcare systems. When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. Local medical imaging datasets frequently show diversity in distribution and data imbalances. In certain situations, traditional FL techniques like FedProx and Federated Averaging (FedAvg), which depend on data size to weight contributions, might not be the optimal choice. We improve parameter aggregation and client selection unpredictability and increase the model's adaptability to imbalanced and heterogeneous datasets, our proposed FedVoteNet model introduces SelectOut techniques based on Voting methodology. Based on how much their local performance has improved from the last communication cycle, we arbitrarily remove clients. Additionally eliminated are clients whose model weights when combined with the global model adversely affect its performance. After each communication cycle, clients that improve both their local performance and their contribution to the global model are awarded higher voting values. This encourages more significant and effective contributions from clients by providing incentives for them to actively increase the diversity of their training data. We assess our approach on a dataset of medical images, including MRI scans, and find that the FL model performs noticeably better (F1 Score=0.968, Sensitivity=0.977, Specificity=0.945, and AUC=0.950). The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. To sum up, our proposed approach uses voting and dynamic SelectOut techniques to improve FL performance on a variety of uneven, distributed, and varied datasets. This strategy has a lot of potential to improve FL across a range of applications, especially those that prioritize data privacy, diversity, and performance.

Automation of Local Regression Model Building for Spectroscopic Data

ABSTRACTThe concept of using local calibration for spectroscopic analysis has been discussed since the late 1980s. Since that time, many papers have described modifications to different aspects of the local modeling methodology. In this paper, we briefly discuss some of the modifications and describe an approach for the unattended automation of local model development. Ways to reduce calculation time are discussed. Four example spectroscopic datasets using Raman, FT‐NIR, and dispersive NIR are analyzed, and the local model prediction performance is compared to standard PLS prediction performance. Using independent prediction sets, local modeling is shown to improve prediction performance by 17% to 55%.

Optimization Of MSME Sector for Accelerating Regional Development: An Empirical Case Study of Jabodetabek Agglomeration Area

This study elaborating the optimization of MSMEs as a key driver of regional development in the Jabodetabek Agglomeration Area, emphasizing their vital role in shaping local economy performance. Using a quantitative approach that integrates collecting secondary data and processing with STATA Software, this research highlights its finding to find which variables that most determine MSME performance in Jakarta agglomeration area. MSMEs generate employment, promote market stability, and foster innovation, offering affordable and competitive alternatives that enhance access to goods and services for diverse income groups. By controlling inflation through price-competitive products. Policymakers can leverage the potential of MSMEs to achieve sustainable regional development by reducing reliance on external markets and enhancing local economic resilience. This research results provide findings that MSMEs not only address immediate economic needs but also establish a foundation for long-term regional sustainability. Their contributions to economic inclusivity, innovation, and resilience make them a cornerstone of balanced, sustainable, and inclusive economic growth in the Jabodetabek region.

Synergizing Plasmonic Local Heating and 3D Nanostructures to Boost the Solar-to-Vapor Efficiency Beyond 100.

Solving the global challenge of freshwater scarcity is of great significance for over one billion people in the world. Solar water evaporation based on plasmonic nanostructures is one of the most promising technologies due to its high efficiency. However, the efficiency of this plasmonic nanostructure-based technology can hardly achieve 100%. Therefore, it is highly desired to develop new solar converters utilizing plasmonic local heating and reasonable structure design to break the limit of solar-to-vapor efficiency for freshwater production. Here, a plasmonic sponge is developed as a solar evaporation converter with excellent full-solar-spectrum absorption, good heat localization performance, and fast evaporation kinetics through 3D nanostructures, achieving a 131% solar-to-vapor efficiency. Distinct from the traditional 2D localized heating-based evaporation and nonmetallic 3D water evaporation, the 3D plasmonic sponge can simultaneously achieve highly efficient local heating and super large water-air interfaces for boosting solar-to-vapor efficiency. The 3D plasmonic sponge can be also used as a universal converter for purifying seawater, metal ion solutions, organic pollutant solutions, and strong acid and strong alkaline solutions. The full-solar-spectrum absorption, high efficiency, and universality in water purification suggest that the novel 3D plasmonic solar converter can bring a significant way to alleviate the crisis of freshwater resources.

NEŞET ERTAŞ'S FINGERPRINT IN THE HICAZ MAKAM: PITCH/FREQUENCY ANALYSIS

ABSTRACT This study examines the unique pitch and microtone structures in Neşet Ertaş’s renditions of the Hicaz makam, particularly as shaped by the Kırşehir region's musical traditions. Known as one of the most prominent figures in Turkish folk music, Neşet Ertaş brings a distinctive pitch configuration to the Hicaz makam, reinterpreting Kırşehir’s musical heritage with a modern approach. The objective of this study is to determine whether Ertaş’s music reflects a Kırşehir-specific interpretation of the Hicaz makam. To this end, the pitch choices Ertaş used in his Kırşehir-based performances are analyzed in detail. This analysis aims to show that the Hicaz makam in Ertaş's music embodies a localized interpretation, emerging as a unique modal understanding specific to the Kırşehir region. The study aims to contribute to Turkish music culture by analyzing the distinct variations of the Hicaz makam as observed in the Kırşehir region. Its scope is centered on examining the unique musical characteristics of Kırşehir, as presented through Neşet Ertaş’s music. While the universe of the study comprises works performed in the Hicaz makam within Turkish folk music, the sample consists of recordings of Kırşehir folk songs performed by Neşet Ertaş in the Hicaz makam. In the study, frequency analyses of the pitches and microtone values in Ertaş’s music were conducted using signal processing software, including MakamBox. Through this approach, the precise deviations in pitch and microtones used by Ertaş have been identified. Quantitative analyses provide numerical measurements of microtones, which are compared to the Arel-Ezgi-Uzdilek pitch system. In the qualitative analyses, Ertaş’s local performance style and characteristics within the Hicaz makam were interpreted alongside these findings.The study’s findings indicate that Ertaş’s music deviates from the Arel-Ezgi-Uzdilek system, presenting a Hicaz makam profile unique to the Kırşehir region. Particularly, the deviations in the pitches of si flat and do sharp reflect Kırşehir’s distinct musical character. For example, in practice, the si flat pitch is often executed with microtone values of 5.5 and 6 commas, which are not present theoretically, while variations from 3 to 4 commas are observed in the do sharp pitch. These findings reveal that Ertaş’s music is marked by entonations and melodic trajectories unique to the Abdal cultural tradition. The study concludes that Neşet Ertaş’s approach to the Hicaz makam reflects the musical richness of the Kırşehir region and reveals this regional identity through modern analysis tools. The study emphasizes the importance of preserving, analyzing, and incorporating regional musical variations into music education. It recommends increasing the number of music analyses focused on local variations, conducting more comprehensive frequency analyses, and developing tools like MakamBox for in-depth regional makam analysis. These suggestions support a broader perspective on the interpretation of regional differences in Turkish folk music and the preservation of cultural diversity within Turkish music. Ertaş’s music contributes a cultural and artistic value to Turkish music through its Kırşehir-specific interpretation of the Hicaz makam and serves as a vital source for the transmission of this heritage to future generations.

Digitalization's effect on everyday entrepreneurs in the French organic wine industry

Everyday entrepreneurs comprise a substantial portion of the entrepreneur population. This paper focuses on everyday entrepreneurship, and on its ability despite limited resources to seize opportunities. Specifically, it investigates the impact of digital platform on the performance and activities of everyday entrepreneurs in France's organic wine sector. To this end, we construct a difference-in-difference model for the period 2011–2020. We compare the performance and growth of 66 organic wine producers before and after they joined Les petites caves—a digital platform dedicated to the promotion of organic wines. Subsequently, we compare this target sample with a control sample. The results show that, after digitalization, the target sample achieves higher sales; earnings before interest, taxes, depreciation, and amortization; net income; and return on assets and equity than that of the control sample. The effect of digital platform on economic performance is more pronounced for younger ventures. These findings show that recourse to new digital technologies thanks to a bricolage ability contributes to everyday entrepreneurs' operational growth and sustainability and enhances their value creation. This study recommends everyday organic wine entrepreneurs to exploit digital platforms to promote local products, improve marketing and economic performance, and revitalize winemaking territories.

Condensation heat transfer and applicability assessment of a printed circuit heat exchanger as a condenser in a cryogenic CO2 capture and storage system

Global warming is accelerating due to the rapid increase in CO2 emissions and cold energy. CO2 capture and storage systems (CCSSs) using discarded cold energy have been suggested to prevent this problem. To utilize cold energy, a printed circuit heat exchanger (PCHE) with high structural integrity was applied as a condenser in the CCSS. However, studies on condensation heat transfer in PCHE are still lacking, and conventional condensation heat transfer correlations developed in circular-channels do not sufficiently predict heat transfer performance in PCHE. Additionally, evaluating the local heat transfer performance is difficult because PCHE is an all-in-one type heat exchanger. Therefore, in this study, a CO2 condensation experiment in PCHE was conducted using an optical fiber sensor as a measurement, which can evaluate the local heat transfer performance, and a new condensation heat transfer correlation for CO2 in PCHE was developed. The correlation was approximately 20 % smaller than the conventional correlations developed in circular-channels, indicating that CO2 would not be fully condensed if the condenser was designed by conventional correlations. Based on the developed correlation, in-house code was developed and the CCSS using cold energy was compared with the conventional CCSS, which condenses CO2 after pressurizing it to a high pressure. As a result, cold energy based CCSS could replace the conventional CCSS because the CCSS using cold energy was approximately 5.7 million-USD per year more economical than the conventional CCSS when condensing CO2 at 200 tons/h. These results contribute to the design of PCHE-type condensers used in the CCSS.

A local synergy angle of velocity, temperature and pressure fields for enhancement of comprehensive heat transfer performance

The field synergy principles for convective heat transfer and flow resistance are integrated in this study. A local three-field synergy angle of velocity, temperature and pressure fields is proposed to evaluate the local comprehensive performance of heat transfer and pumping power consumption. The angle is based on the polar angle of a plot with dot products of velocity and enthalpy/total pressure gradients, respectively, as coordinates. The polar angle is then shifted by the observation that the enhancement of heat transfer rate at the cost of pumping power decreases from the second quadrant to the forth quadrant. The application of the synergy angle is demonstrated by examples of flow and heat transfer in channels with different fin structures. Inspired by the distribution of the synergy angle, Airfoil-Rect fin and Rhom-Rect fin structures are designed, which can improve the heat transfer rate with constraints of pumping power consumption. The present study provides a possible approach for heat transfer enhancement.

Do women get into less debt? Gendered local political performance in the EU

Gender differences in local financial management are, according to empirical grounds, a reality that predetermines differences in the fiscal performance of municipalities led by women and men. The position of women leaders in public administration and their influence on sources of income, the nature of expenses, and the level of indebtedness have been a popular research issue in recent years, but it includes an almost unexplored gender dimension that needs deeper analysis. For this reason, the main aim of the paper is to examine the relationship between women’s representation in local politics (mayor and councilors) and local indebtedness, emphasizing the assumption about observed better fiscal discipline in municipalities led by female local authorities. The econometric estimations based on the panel data approach are conducted on the sample of twenty-seven European Union countries in the period 2011–2021 using the share of female mayors and female local councilors as explanatory variables. Further, we test the effects of the moment of the EU accession and the degree of fiscal decentralization to offer deeper insights into the problem. Results support the assumption of an inverse relationship between female mayors and local indebtedness, while female local councilors do not cause any statistically significant impact on local indebtedness. Other results show that the number of women in local politics and the degree of fiscal decentralization are important to consider when examining this relationship.

Effect of incomplete line defect size on energy localization and harvesting in phononic crystals.

The high electrical output performance of the phononic crystal (PnC)-based piezoelectric energy harvesting (PEH) system is of great research value in self-powered applications. This work presents the effect of incomplete line defect size on elastic wave energy localization and harvesting. The results show that for a given 7 × 5 supercell when the incomplete line defect reaches the second to sixth layer, the energy localization and harvesting performance show a changing trend of first increasing and then decreasing; when the incomplete line defect reaches the 4th, 5th, 3rd, 2nd, and 6th layers of the supercell, respectively, the performance of PEH systems shows a trend from large to small. Among them, when the incomplete line defect reaches the fourth layer of the supercell, the performance of the PEH system is optimal, and the maximum output voltage and the maximum output electric power are 22.54 V and 12.78 mW, respectively. This work provides valuable insights for improving the performance of PEH devices by using the PnC with incomplete line defects.

Towards Effective Clustered Federated Learning: A Peer-to-Peer Framework With Adaptive Neighbor Matching

In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance. Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models. In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients. In this paper, without assuming the number of clusters, we propose a peer-to-peer (P2P) FL algorithm named PANM. In PANM, clients communicate with peers to adaptively form an effective clustered topology. Specifically, we present two novel metrics for measuring client similarity and a two-stage neighbor matching algorithm based Monte Carlo method and Expectation Maximization under the Gaussian Mixture Model assumption. We have conducted theoretical analyses of PANM on the probability of neighbor estimation and the error gap to the clustered optimum. We have also implemented extensive experiments under both synthetic and real-world clustered heterogeneity. Theoretical analysis and empirical experiments show that the proposed algorithm is superior to the P2P FL counterparts, and it achieves better performance than the centralized cluster FL method. PANM is effective even under extremely low communication budgets.

Self-supervised anomaly detection and localization for X-ray cargo images: Generalization to novel anomalies

Robust detection of illicit items using X-ray inspection methods has gained increasing importance in recent years due to the large volume of cargo crossing international borders. In addition to detecting the presence of such items, determining their location, size, and shape is challenging due to the unpredictable nature of anomalies, but essential for expediting security inspections. Viewing the illicit items as anomalies relative to expected cargo, we propose a self-supervised learning framework consisting of an encoder–decoder–classifier–segmenter model, a multi-component loss function, coupled with a training strategy to extract discriminative features tailored for detection of the presence of anomalies, as well as localization of such items in X-ray cargo images. Our framework addresses the challenges posed by limited labeled data and offers a model capable of both detecting and localizing anomalies effectively. Moreover, we present a diverse dataset encompassing various cargo scenarios with and without anomalies, providing a robust evaluation environment for this class of problems. Unlike existing approaches, which are trained to detect specific types of objects with a fixed set of illicit items, our framework is adaptable to real-world scenarios where a wide range of illicit items may be present in the cargo. This versatility enhances the practical applicability of our model. We evaluate the performance of our framework on our dataset as well as two other publicly available datasets, demonstrating our method’s strong detection and localization performance even when faced with complex novel anomalies significantly different from those encountered during training.

Automatic exposure strategy network for robust visual odometry in environments with high dynamic range

The success of visual-based localization requires the image features to be sufficiently captured in well-exposed images and reliably tracked among image sequences. In this paper, a learning-based exposure strategy algorithm is developed to improve the performance of visual odometry or simultaneous localization and mapping in scenes with sudden illumination changes and high dynamic range (HDR). The exposure value is predicted by optimizing a novel image information metric, which is calculated based on a multi-scale image histogram. Experimental observations show that the proposed metric is highly related to the localization performance, and achieves better localization accuracy when compared to other state-of-the-art metrics. Using this novel metric, the exposure control network is designed to predict the exposure value for the next image given a sequence of recently captured images as inputs. For different application scenarios, alternative network frameworks are proposed with the options to include an optimization module providing improved exposure values for feedback learning, but with additional computation complexity. The approach is light-weight for real-time video applications and the images captured using the predicted exposure values are well-exposed with better detected and matched features in a variety of challenging HDR scenes. Code will be available in https://github.com/leejieyu13/HDR-AE.

Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

The Gazelle Optimization Algorithm (GOA) is an innovative metaheuristic inspired by the survival tactics of gazelles in predator-rich environments. While GOA demonstrates notable advantages in solving unimodal, multimodal, and engineering optimization problems, it struggles with local optima and slow convergence in high-dimensional and non-convex scenarios. This paper proposes the Hybrid Gazelle Optimization Algorithm with Differential Evolution (HGOADE), which combines Differential Evolution (DE) with GOA to leverage their complementary strengths for addressing limitations. HGOADE initializes a population of candidate solutions using GOA, then enhances these solutions through DE’s mutation and crossover operations. The algorithm subsequently employs GOA’s exploration and exploitation phases to refine the solutions. By integrating DE’s robust exploration capabilities with GOA’s dynamic search patterns, HGOADE aims to improve global and local search performance. The effectiveness of HGOADE is validated through experiments on benchmark functions from the CEC 2017, CEC 2020, CEC 2022 suite, comparing with ten established optimization techniques, including classical GOA, Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Arithmetic Optimization Algorithm (AOA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), and DE. Additionally, the performance of HGOADE is assessed against prominent winners from CEC competitions, specifically CMA-ES, LSHADEcnEpSin, and LSHADESPACMA, using the CEC-2017 test suite. Statistical analyses using the Wilcoxon Rank Sum Test and Wilcoxon Signed-Rank Test, along with the Weighted Aggregated Sum Product Assessment (WASPAS) method, confirm that HGOADE significantly outperforms existing algorithms in terms of solution quality and convergence speed. HGOADE’s superiority is validated through six complex engineering design problems, demonstrating its higher feasibility and effectiveness than GOA and other methods. This paper addresses GOA’s shortcomings and advances metaheuristic optimization by integrating DE strategies, offering valuable insights and practical applications for global optimization and engineering design.

Virtual Reality Audio Game for Entertainment and Sound Localization Training

Within the gaming and electronics industry, there is a continuous evolution of alternative applications. Nevertheless, accessibility to video games remains a persistent hurdle for individuals with disabilities, especially those with visual impairments due to the inherent visual-oriented design of games. Audio games (AGs) are electronic games that rely primarily on auditory cues instead of visual interfaces. This study focuses on the creation of a virtual reality AG for cell phones that integrates natural head and torso movements involved in spatial hearing. Its assessment encompasses user experience, interface usability, and sound localization performance. The study engaged eighteen sighted participants in a pre-post test with a control group. The experimental group underwent 7 training sessions with the AG. Via interviews, facets of the gaming experience were explored, while horizontal plane sound source localization was also tested before and after the training. The results enabled the characterization of sensations related to the use of the game and the interaction with the interfaces. Sound localization tests demonstrated distinct enhancements in performance among trained participants, varying with assessed stimuli. These promising results show advances for future virtual AGs, presenting prospects for auditory training. These innovations hold potential for skill development, entertainment, and the integration of visually impaired individuals.

Accountability as a Precursor to Improving the Performance of Local Governments: A Case in Lira District, Mid-North of Uganda

Purpose: The purpose of the study was to find out the effect of accountability on the performance of Lira District Local Government. Methodology: The research was conducted this study the Research Designs was cross-sectional and used using quantitative approach. Questionnaire was used for the purpose of collecting primary data from the respondents. Data was analyzed using the Statistical Package for Social Sciences where conclusions were drawn from tables, figures from the package. The study took on a sample of 70 employees from a population of 76. Findings: The study findings reveled that administrative accountability positively influences the level of performance of Lira district local government, financial accountability does not while stakeholder engagement negatively influences the level of performance of the district. The study concluded that respondents believe administrative accountability boosts performance, service quality, and cost-effectiveness, but note its effectiveness depends on organizational culture and political factors. Financial accountability is seen as beneficial for organizational performance, but its effective implementation faces a number of challenges. Stakeholders see engagement boosting financial performance, productivity, brand loyalty, and customer satisfaction, but note challenges to effective engagement. Unique contribution to theory, practice and policy: This study contributes to the understanding of the practice and policy of local government performance from the perspective of accountability. The study suggested the need for Lira district to implement strong accountability mechanisms tailored to their organizational culture and political environment so as to enhance performance, service quality, and cost-effectiveness. Also, there is need to strengthen financial accountability mechanisms to enhance organizational performance, while addressing the specific challenges that hinder their effective implementation.

A digital design framework for the dimensional optimization of parallel robots based on kinematic and elasto-dynamic performance

The dimensional optimization based on kinematic and dynamic performance indices is proven significant for improving the operational performance of parallel robots. As the pose-dependent performances vary with the dimensional parameters, there are still many challenges in the optimal design of parallel robots, such as the possible conflict amongst different performance indices and computational expensiveness. By considering a 4-DOF high-speed parallel robot as an illustrative example, this paper presents a framework for the optimal design of parallel robots by integrating skeleton modeling, CAD-CAE integration and multi-objective optimization techniques. In this approach, the models for kinematic and elasto-dynamic performance evaluation are first developed using the CAD and CAE techniques, respectively. After evaluating the performance distributions over the task workspace, several reference poses representative of multiple neighboring sample poses are determined to formulate corresponding local performance indices by using the Hard C-Means (HCM) clustering analysis algorithm. This consideration leads to the determination of the optimal dimensions by investigating the Pareto-optimal solutions of a multi-objective optimization problem, allowing the conflict amongst different performance indices to be appropriately handled and the computational time to be saved significantly. The results of the case study show that the proposed approach can significantly enhance the elastic dynamic performance while ensuring that the kinematic performance is not excessively compromised, when compared to the performance of existing physical prototype of the robot. A software package has been developed using the proposed framework, providing a highly accessible way for designing various types of parallel/hybrid robots based on CAD and CAE techniques.

SinkFlow: Fast and traceable root-cause localization for multidimensional anomaly events

With the development of various artificial intelligence (AI)–based applications, detecting anomalies and analyzing the root causes from massive data are critical to increasing the usability of AI. Fast, accurate root-cause analysis (RCA) that finds the main reason for an anomaly, as well as reasonable explanations, helps in solving problems effectively. Thus, RCA plays an important role in troubleshooting and fault diagnosis, making its application in data analysis crucial. Previous root-cause-localization approaches for multidimensional anomaly events encompass various techniques to reduce search space and have improved the localization performance. However, they do not effectively balance the requirements in terms of performance, compatibility, and interpretability. To solve these problems, we propose a new root-cause-localization method called SinkFlow. It provides a unified framework event-aggregation Graph (EAG) to describe the constraints of event aggregation and relations between events, so it can be easily generalized to various domains. SinkFlow introduces an applicable measure evaluation method for both fundamental and derived measures to quantify the impact of events. Also, it utilizes an optimal search strategy to reduce the search space based on the anomaly behavioral consistency and deviation significance. Our experimental results on semisynthetic datasets show that SinkFlow achieved better performance than other baselines and ran much faster, achieving a 1.88% increase of the F1-score and only 25% of the time cost of the second best localization method. In addition, SinkFlow offered clear, visible explanations of the localization results to answer the questions of why they are root causes and how the anomaly is formed.

Effects of spectral peaks and notches in head-related transfer function on median plane sound localization with dynamic binaural playback

Spectral peaks and notches in the head-related transfer function (HRTF) are considered pivotal for elevation perception in virtual auditory displays (VAD), especially during static binaural signal playback. However, studies on dynamic binaural signal playback in VAD have shown that the auditory system can still utilize dynamic cues for elevation localization, even when these high-frequency spectral components are missing, although this may compromise localization accuracy. This study investigated the effects of spectral peaks and notches in dynamic playback, examining how distorting these features and their contrasts at various levels (33%, 66%, and 100% removal) influenced elevation localization along different rotational axes (yaw and pitch rotation). The results revealed that at the same distortion level, the impact of these features on median plane localization decreased sequentially from spectral contrast, to peaks, to notches. At a distortion level of 33%, notch removal enabled dynamic playback results that were not significantly different from control conditions. As distortion levels increased to 66% and 100%, localization performance progressively deteriorated, including increased localization errors and up-down confusion with head yaw rotation as well as front-back and up-down confusion with head pitch rotation. Simultaneously, localization performance with head pitch rotation exhibited poorer performance compared to yaw rotation, particularly in cases involving peak removal and contrast compression. The experimental results further revealed that auditory elevation localization benefits from multiple localization cues generated by head movements, including dynamic spectral cues produced during large head rotations when all spectral cues are available or distorted at a level of 33%.

Enhancing the local compressive strength perpendicular to the grain of inorganic-bonded bamboo composite using glued-in rods

The inorganic-bonded bamboo composite (InorgBam) is a novel environmentally friendly engineered bamboo product manufactured from the bamboo fiber bundles glued together with magnesium oxysulfide (MOS) inorganic adhesive. Similar to raw bamboo culms, InorgBam exhibits significant material anisotropy that the compressive strength perpendicular-to-grain typically lower than that parallel-to-grain. To enhance the local compressive performance perpendicular-to-grain of InorgBam, the glued-in rods (GiRs) method was adopted, and the local compression test was conducted with various loading forms and GiRs parameters in this paper. Experimental results indicate that the GiRs method significantly enhances the local compressive strength and the deformation resistance perpendicular to the grain of InorgBam material. The replacement ratio and depth of GiRs are the major impact factors for enhancing the load-carrying capacity. The compressive ultimate load, yield load, and initial stiffness of the GiRs enhanced specimens increased by 167 %, 190 %, and 103 %, respectively. Furthermore, a universal predicted method of the yield load for local compression perpendicular to the grain of InorgBam material enhanced by GiRs was proposed considering the replacement ratio and depth of GiRs. The results herein can provide a basis expansion of the methods in enhancing the compressive properties perpendicular-to-grain of engineered bamboo materials.