Performance Gap Research Articles

Severe plastic deformation of Mn-Al permanent magnets

Manganese-aluminum permanent magnets are promising candidates to fill the cost and performance gap between lower-performance bonded ferrites and high-performance magnets based on rare-earth elements. This is due to the favorable combination of saturation magnetization and magnetocrystalline anisotropy in the Mn-Al τ phase combined with low raw material cost. However, the τ phase is metastable and prone to decomposition at high temperatures, making processing by conventional milling and sintering difficult. Severe plastic deformation (SPD) is an alternative processing route to control the microstructure of a material by applying very high amounts of strain. In this study, equal-channel angular extrusion (ECAE) and high-speed high-pressure torsion (HS-HPT) were both tested as SPD processing routes. ECAE improved magnetic energy product, (BH)max, by 220% by refining the grain size and imparting a high density of dislocations. HS-HPT enabled a rapid phase transformation from the high-temperature ε phase to the τ phase but lowered Hci, making it better suited to soft magnet processing.

Human-centric lighting asset management for LED bulbs: a context-driven approach on prognostics and maintenance strategy development in public libraries

ABSTRACT Traditional asset management of lighting systems typically focuses on functionality, cost, and lifespan. In contrast, a human-centric approach prioritizes social sustainability and user well-being by ensuring lighting assets “provide the right light at the right time” for diverse activities. Light-emitting diode (LED) bulbs, known for energy efficiency and longevity, have become a preferred choice, yet public libraries often struggle to manage these assets sustainably, remaining in a reactive “fix/replace when it breaks” stage. Current predictive methods, such as artificial intelligence and machine learning, rely on laboratory data that often overlook real-world contexts, leading to performance gaps. This paper presents a context-driven, human-centric methodology for LED prognosis and maintenance strategies in public libraries, employing limited degradation data from LED testing. Advanced analytical techniques, including Markov Chain Monte Carlo (MCMC) and Deviance Information Criterion (DIC), support a shift from function-based to performance-based reliability assessment. By incorporating Mean Time of Exposure (MTOE) and Critical Integrated Levels (CILs), the approach defines optimal maintenance inspection intervals. This research enhances sustainable LED lighting management in public libraries, offering a framework adaptable to broader applications and aligned with human-centric goals..

Shape-Aware Adversarial Learning for Scribble-Supervised Medical Image Segmentation with a MaskMix Siamese Network: A Case Study of Cardiac MRI Segmentation

The transition in medical image segmentation from fine-grained to coarse-grained annotation methods, notably scribble annotation, offers a practical and efficient preparation for deep learning applications. However, these methods often compromise segmentation precision and result in irregular contours. This study targets the enhancement of scribble-supervised segmentation to match the accuracy of fine-grained annotation. Capitalizing on the consistency of target shapes across unpaired datasets, this study introduces a shape-aware scribble-supervised learning framework (MaskMixAdv) addressing two critical tasks: (1) Pseudo label generation, where a mixup-based masking strategy enables image-level and feature-level data augmentation to enrich coarse-grained scribbles annotations. A dual-branch siamese network is proposed to generate fine-grained pseudo labels. (2) Pseudo label optimization, where a CNN-based discriminator is proposed to refine pseudo label contours by distinguishing them from external unpaired masks during model fine-tuning. MaskMixAdv works under constrained annotation conditions as a label-efficient learning approach for medical image segmentation. A case study on public cardiac MRI datasets demonstrated that the proposed MaskMixAdv outperformed the state-of-the-art methods and narrowed the performance gap between scribble-supervised and mask-supervised segmentation. This innovation cuts annotation time by at least 95%, with only a minor impact on Dice performance, specifically a 2.6% reduction. The experimental outcomes indicate that employing efficient and cost-effective scribble annotation can achieve high segmentation accuracy, significantly reducing the typical requirement for fine-grained annotations.

Inadequate foundational decoding skills constrain global literacy goals for pupils in low- and middle-income countries.

Learning to read is the most important outcome of primary education. However, despite substantial improvements in primary school enrolment, most students in low- and middle-income countries (LMICs) fail to learn to read by age 10. We report reading assessment data from over half a million pupils from 48 LMICs tested primarily in a language of instruction and show that these pupils are failing to acquire the most basic skills that contribute to reading comprehension. Pupils in LMICs across the first three instructional years are not acquiring the ability to decode printed words fluently and, in most cases, are failing to master the names and sounds associated with letters. Moreover, performance gaps against benchmarks widen with each instructional year. Literacy goals in LMICs will be reached only by ensuring focus on decoding skills in early-grade readers. Effective literacy instruction will require rigorous systematic phonics programmes and assessments suitable for LMIC contexts.

Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation

In the realm of dermatology, the complexity of diagnosing skin conditions manually necessitates the expertise of dermatologists. Accurate identification of various skin ailments, ranging from cancer to inflammatory diseases, is paramount. However, existing artificial intelligence (AI) models in dermatology face challenges, particularly in accurately diagnosing diseases across diverse skin tones, with a notable performance gap in darker skin. Additionally, the scarcity of publicly available, unbiased datasets hampers the development of inclusive AI diagnostic tools. To tackle the challenges in accurately predicting skin conditions across diverse skin tones, we employ a transfer-learning approach that capitalizes on the rich, transferable knowledge from various image domains. Our method integrates multiple pre-trained models from a wide range of sources, including general and specific medical images, to improve the robustness and inclusiveness of the skin condition predictions. We rigorously evaluated the effectiveness of these models using the Diverse Dermatology Images (DDI) dataset, which uniquely encompasses both underrepresented and common skin tones, making it an ideal benchmark for assessing our approach. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, we conducted domain adaptation using additional skin image datasets such as HAM10000. This adaptation significantly improved model performance across all models.

Performance Shortfall, Top Management Team Faultlines, and R&D Investment: Evidence from China

With the increasing uncertainty of the world economic situation, enterprises are facing the pressure of downward performance, and R&D has been an important strategic choice to solve the performance fall problem of high-tech enterprises. The article tries to explore the changes in the R&D investment intensity of enterprises under different performance lag levels and investigate the influence of the combination of characteristics of top management team(TMT) members on the strategic decision-making process. It is found that the relationship between the degree of performance disparity and R&D investment intensity is inverted U-shaped; the strength of the fracture faultline of the top management team negatively regulates the relationship between the degree of performance disparity and R&D investment and makes the inverted U-shaped curve flat. A further study of specific characteristic fracture bands revealed that team fracture bands based on tenure characteristics had a significant negative moderating effect, while professional and functional background fracture bands had a significant positive moderating effect. This study not only theoretically expands the understanding of the performance shortall and TMT disconnection on the deep-seated motivation of enterprise R&D investment, but also provides practical references for solving the problem of performance gap, optimizing the allocation of TMT human resources, and rationally promoting enterprise R&D investment to achieve transformation.

Foreign Market Investment as a Locus of Response to Adverse Performance

AbstractPerformance gap recognition derives from social comparison with rivals. We propose that the attribution of these gaps to foreign direct investment (FDI) inadequacy – and thus the likelihood of responding with FDI – is also guided by such social comparisons. Specifically, the performance gap becomes a stronger trigger for foreign market investment when underperforming peers are less engaged in FDI than outperforming counterparts, as this highlights the role of FDI scarcity in explaining the gap. Similarly, a firm's lower FDI involvement increases the performance gap's capacity to trigger FDI by making FDI inadequacy a more plausible explanation, whereas extensive existing FDIs shift the blame away from FDI insufficiency. By examining the locus of investment activity under the performance gap, we show that the associated FDI‐triggering capacity is limited to institutionally proximate environments where the upside potential for addressing the performance gap can be recognized upfront through current models, methods, and resources. Institutionally distant investments, whose potential evolves through experimentation with the local context, become increasingly unlikely as a firm falls behind its rivals. These findings suggest that a firm's response to the performance gap is shaped by inferences about the causes of underperformance and the clarity of an alternative's capacity to address the gap.

Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

AbstractTo improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.

Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature

Archetype-based housing stock models of summer indoor temperature can support the development of policies to manage the climate change-driven increase in cooling demand and heat-related health impacts. Calibration can reduce the performance gap of such models, however, work on this topic is limited. Motivated by the growing importance of this underexplored research area, this paper introduces a framework for the Bayesian calibration of archetype-based housing stock models of summer indoor temperature. The framework includes data-driven procedures to classify dwellings into homogeneous groups and specify prior probability distributions. To demonstrate its application, an established bottom-up model based on EnergyPlus was calibrated using data collected from 193 dwellings monitored during the 2009 4M survey in Leicester, England. Post-calibration, the root-mean-square error reduced from 2.5°C to 0.6°C and remaining uncertainties were quantified. The application of this modular framework may be extended to models of energy use and other indoor environmental parameters.

When words are your scalpel, what and how information is exchanged may be differently salient to assessors.

Variable assessments of learner performances can occur when different assessors determine different elements to be differently important or salient. How assessors determine the importance of performance elements has historically been thought to occur idiosyncratically and thus be amenable to assessor training interventions. More recently, a main source of variation found among assessors was two underlying factors that were differently emphasised: medical expertise and interpersonal skills. This gave legitimacy to the theory that different interpretations of the same performance may represent multiple truths. A faculty development activity introducing assessors to entrustable professional activities in which they estimated a learner's level of readiness for entrustment provided an opportunity to qualitatively explore assessor variation in the context of an interaction and in a setting in which interpersonal skills are highly valued. Using a constructivist grounded theory approach, we explored variation in assessment processes among a group of palliative medicine assessors who completed a simulated direct observation and assessment of the same learner interaction. Despite identifying similar learner strengths and areas for improvement, the estimated level of readiness for entrustment varied substantially among assessors. Those who estimated the learner as not yet ready for entrustment seemed to prioritise what information was exchanged and viewed missed information as performance gaps. Those who estimated the learner as ready for entrustment seemed to prioritise how information was exchanged and viewed the same missed information as personal style differences or appropriate clinical judgement. When presented with a summary, assessors expressed surprise and concern about the variation. A main source of variation among our assessors was the differential salience of performance elements that align with medical expertise and interpersonal skills. These data support the theory that when assessing an interaction, differential salience for these two factors may be an important and perhaps inevitable source of assessor variation.

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

Synthesis of nano-diamond modified Ti3C2Tx MXene heterostructure for enhanced electromagnetic wave absorption

The highly efficient electromagnetic wave (EMW) absorption materials are considered a promising approach to combatting electromagnetic radiation pollution. We synthesized a novel heterostructure 2D Ti3C2Tx MXene composite film modified by 0D nano-diamond (ND) particles using simple mechanical stirring and a vacuum filtration method. Subsequently, we investigated its microstructure and EMW absorption properties. The ND modification not only regulated the impedance matching of the composite films but also enhanced the diversity of EMW losses, demonstrating excellent microwave absorption performance. With a 40 wt% ND content, the minimum reflection loss (RLmin) of the films reached −43.0 dB, corresponding to a film thickness of 2.5 mm. This study explored a novel and promising microwave absorption film, expanding the application of diamond in microwave absorption, and addressing the theoretical gap in diamond-enhanced MXene microwave absorption performance.

MTCSNet: One-Stage Learning and Two-Point Labeling are Sufficient for Cell Segmentation.

Deep convolution neural networks have been widely used in medical image analysis, such as lesion identification in whole-slide images, cancer detection, and cell segmentation, etc. However, it is often inevitable that researchers try their best to refine annotations so as to enhance the model performance, especially for cell segmentation task. Weakly supervised learning can greatly reduce the workload of annotations, while there is still a huge performance gap between the weakly and fully supervised learning approaches. In this work, we propose a weakly-supervised cell segmentation method, namely Multi-Task Cell Segmentation Network (MTCSNet), for multi-modal medical images, including pathological, brightfield, fluorescent, phase-contrast and differential interference contrast images. MTCSNet is learnt in a single-stage training manner, where only two annotated points for each cell provide supervision information, and the first one is the centroid, the second one is its boundary. Additionally, five auxiliary tasks are elaborately designed to train the network, including two pixel-level classifications, a pixel-level regression, a local temperature scaling and an instance-level distance regression task, which is proposed to regress the distances between the cell centroid and its boundaries in eight orientations. The experimental results indicate that our method outperforms all state-of-the-art weakly-supervised cell segmentation approaches on public multi-modal medical image datasets. The promising performance also shows that a single-stage learning with two-point labeling approach are sufficient for cell segmentation, instead of fine contour delineation. The codes are available at: https://github.com/binging512/MTCSNet.

A novel blockchain-based system for improving information integrity in building projects from the perspective of building energy performance

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

INFLUENCE OF CAPABILITIES FOR RESOURCE MOBILIZATION ON THE PERFORMANCE OF THE LOCAL AUTHORITIES IN ZAMBIA

This study examined the influence of resource mobilization capabilities on the performance of local authorities in Zambia, addressing a decade-long performance challenge despite various government interventions. Using a correlational research design with a sample of 736 employees from 116 local authorities, the study tested the hypothesis that there is no statistically significant influence of resource mobilization capabilities on the financial performance of local authorities. Data collected through a structured questionnaire and analyzed using multiple linear regression revealed a significant positive relationship between capabilities for resource mobilization and local authorities' performance (B = 0.272, t = 6.516, p < .001), with the model explaining 63.7% of the performance variance. While local authorities demonstrated strengths in developing annual work plans, they struggled to provide all required public services. The study concludes that resource mobilization capabilities significantly influence local authorities' performance in Zambia and recommends enhancing resource mobilization strategies, improving service delivery capabilities, promoting knowledge sharing among local authorities, investing in technology-enabled systems for revenue collection and management, and implementing standardized performance management practices to address the identified performance gaps and improve overall public service delivery.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0745/a.php" alt="Hit counter" /></p>

Intellectual Differences Between Boys and Girls, 35 Years of Evolution in France from WISC-R to WISC-V

The French adaptation of the Wechsler Intelligence Scale of Children, 5th edition (WISC-V) was an opportunity to examine if some common representations of gender differences in intellectual abilities are supported by empirical evidence. The WISC-V standardization sample provided data on a wide range of cognitive tests in a large sample of 6- to 16-year-old children representative of the French population. This sample included 517 boys and 532 girls. The WISC-V data were compared to those of the French standardization samples of three previous versions of the WISC (WISC-R, WISC-III, and WISC-IV). These four standardization samples span a 35-year period. The data analysis of the WISC-V standardization sample and the three previous versions of this intelligence scale showed that the performance gaps on intellectual tests between girls and boys have gradually narrowed over time. Almost no gender differences were observed in the WISC-V standardization sample, not only in IQ but also in key facets of intelligence. Data do not support the stereotype that girls are better at verbal tasks and boys are better at visuospatial tasks. However, some statistically significant differences remain, but the magnitude was generally small with no practical implications. The only important difference is in favor of girls and concerns performance on processing speed tasks that require visual discrimination, attentional control, and writing.

ANALISIS MANAJEMEN PELATIHAN DI RUMAH SAKIT

Training management is crucial to ensure that the training process runs smoothly and meets both organizational and employee goals. At Hermina Hospital Bogor, 22.5% of employees have not met the minimum required training hours. The training process faces challenges, including limited space, incomplete attendance, and scheduling issues. This study aimed to analyze the gaps in training at Hermina Hospital Bogor, using both quantitative and qualitative methods from March to May 2023. Qualitative data were gathered through interviews with three key informants, selected via purposive sampling, and observations of the training process. Quantitative data were obtained from implementation surveys and pre-post tests for the Isolation Awareness training, involving 31 participants. Qualitative analysis used content analysis, while the Wilcoxon test was applied for quantitative data. The training process includes needs assessment, planning, implementation, and evaluation. Training needs are identified through gap analysis, covering issues raised in morning meetings, unit evaluation results, and proposals from stakeholders, competencies performance gaps. Implementation challenges include rescheduling and participant absence. Online training faces additional barriers, such as a lack of computers and poor internet connectivity. Improvements in training management can be achieved by developing training facilities, enhancing online tools and e-learning modules, and increasing participant involvement through the Documentation System application.

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Ensuring Quality and Consistency in OPC-53 Cement Testing: A Comprehensive Proficiency Testing Evaluation

The proficiency testing (PT) program is a critical part of quality assurance in laboratories, especially within industries like construction where cement testing is essential. This study provides a comprehensive evaluation of the fifth PT scheme for Ordinary Portland Cement (OPC) grade 53, conducted by the Nodal Laboratory. The key parameters assessed in this program were standard consistency, compressive strength, and soundness using the Le-Chatelier method. Twenty-four laboratories participated, and their performance was analyzed using advanced statistical methods, adhering to ISO 17043:2010 and ISO 13528:2015 standards. The results showed that most laboratories performed satisfactorily, with only one outlier identified in the soundness test. This paper covers all aspects of the PT program, from sample preparation to the distribution of materials, as well as the homogeneity and stability tests conducted on the samples. Statistical evaluation was carried out using Z-scores, which allowed the laboratories to be ranked according to their performance in each parameter. This method highlights discrepancies and provides a clear framework for participating laboratories to improve their testing procedures. Proficiency testing is essential for maintaining high standards in laboratory performance. It helps standardize testing methods across multiple laboratories, ensuring that OPC-53 cement meets strict quality and performance specifications. The program enables laboratories to identify performance gaps and take corrective actions, ensuring the reliability and accuracy of results. Participation in PT programs is a step toward continuous improvement, keeping laboratories aligned with international standards, ultimately benefiting the broader construction industry by ensuring the structural integrity of buildings and infrastructure. This study underscores the importance of regular PT participation. It not only provides a benchmark for laboratory performance but also ensures compliance with ISO standards. This fosters the consistent quality of cement used in large-scale construction, reducing the risk of structural failures and contributing to long-term safety and durability. The findings demonstrate the positive impact of PT programs on overall testing accuracy and laboratory reliability, emphasizing their role in advancing best practices in the construction industry.

Election-Denying Republican Candidates Underperformed in the 2022 Midterms

We combine newly collected election data with records of public denials of the results of the 2020 election to estimate the degree to which election-denying Republican candidates over- or underperformed other Republicans in 2022 in statewide and federal elections. We find that the average vote share of election-denying Republicans in statewide races was approximately 3.2 percentage points lower than their co-partisans after accounting for state-level partisanship. However, we find no such underperformance on aggregate for U.S. House elections, perhaps due to the more-partisan nature of many House districts. Together, the results suggest that the types of candidates in American elections who take more-extreme positions tend to underperform, but that these performance gaps are relatively small in the present, polarized political environment.