Standard Benchmark Research Articles

Minimizing the cobalt content in black ceramic pigments by Design of Experiments

This paper presents a comprehensive optimization strategy for the synthesis of new black ceramic pigments with low cobalt content while maintaining a single-phase spinel structure. The aim is to achieve comparable hues to those of an industrial benchmark containing five transition metals while minimizing the environmental impact. Exploring all possible compositions deriving from a five–component system (Cr, Mn, Fe, Co and Ni), through traditional methods would be extremely time consuming to guarantee an efficient sampling, requiring high experimental efforts. Hence, to identify the best black compositions, we employed a chemometric approach, the Design of Experiments, aiming to investigate the compositional domain derived by varying the metals stoichiometry within fixed boundaries to identify optimal pigment compositions. The resulting pigments (comprising Cr, Fe and Co), despite the lower cobalt content, exhibited optimal colorimetric properties comparable to the standard benchmark, with experimental ΔE values comparable to the ones predicted by the model. In particular, the pure spinels Cr1.05Fe1.05Co0.9O4 and Cr1.2Fe1.05Co0.75O4 displayed low lightness and chroma (L∗ = 2.4; C∗ = 5 and L∗ = 1.12; C∗ = 1.1, respectively), providing deep and dark black tonality. These compositions exhibit promising colorimetric performance and chemical stability, offering potential benefits for industrial applications.

Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real-world problems to adequately track the optimal decision in real-time.

A study on ecological sustainable cities based on LCA-emergy-carbon footprint and geographic information system (GIS) approach

ABSTRACT Sustainable cities, as an effective response to global climate aberrations, have increasingly garnered the attention of researchers. However, due to the wide range of interpretations and applications associated with sustainable cities, diverse methodologies can yield starkly contrasting results. This paper adopts ecological and low-carbon perspectives to evaluate and discuss urban sustainability. The core of this investigation involves quantitatively measuring a city’s sustainability based on the lifecycle metrics of emergy and carbon footprint. The findings reveal that Yangzhou, recognized as a model ecological city by authorities, exhibits an emergy sustainability index below the standard benchmark (ESI = 0.02 < 1). GIS mapping reveals that the city’s largest ecological service system is situated in the northwest water region. Both the emergy and carbon footprint analyses indicate that urbanized areas exert a detrimental influence on the city’s overall sustainability. Additionally, the administrative authorities in Yangzhou have implemented proactive measures to transition the city towards sustainability. Currently, the impacts of clean energy adoption and urban wetland restoration are particularly pronounced. The application of lifecycle emergy and carbon footprint methods offers a holistic approach for urban managers, enabling sustainable urban planning that harmoniously integrates ecological emergy and low-carbon considerations. This approach is instrumental in the realization of sustainable cities.

Consistency Prototype Module and Motion Compensation for few-shot action recognition (CLIP-CPM[formula omitted]C)

Recently, few-shot action recognition has progressed significantly, as it has learned the feature discriminability and designed suitable comparison methods. Still, there are the following restrictions. (a) Previous works are mainly based on visual mono-modal. Although some multi-modal works use labels as supplementary to construct prototypes of support videos, they cannot use this information for query videos. The labels are not used efficiently. (b) Most of the works ignore the motion feature of video, although the motion features are essential for distinguishing. We proposed a Consistency Prototype and Motion Compensation Network(CLIP-CPM2C) based on CLIP to address these issues. Firstly, in order to make the amount of information between the prototype and the query more similar, we propose a novel method to compensate for the text(prompt) information of query videos when text(prompt) does not exist, which depends on a Consistency Loss. Secondly, we use the differential features of the adjacent frames in two directions as the motion features, which explicitly embeds the network with motion dynamics, and then apply the Consistency Loss to the motion features. Extensive experiments on standard benchmark datasets demonstrate that the proposed method can compete with state-of-the-art results. Our code is available at the URL: https://github.com/cofly2014/CPM-MC.git.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data.

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Global–Local Query-Support Cross-Attention for Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) models aim to segment unseen target objects in a query image with scarce annotated support samples. This challenging task requires an effective utilization of support information contained in the limited support set. However, the majority of existing FSS methods either compressed support features into several prototype vectors or constructed pixel-wise support-query correlations to guide the segmentation, which failed in effectively utilizing the support information from the global–local perspective. In this paper, we propose Global–Local Query-Support Cross-Attention (GLQSCA), where both global semantics and local details are exploited. Implemented with multi-head attention in a transformer architecture, GLQSCA treats every query pixel as a token, aggregates the segmentation label from the support mask values (weighted by the similarities with all foreground prototypes (global information)), and supports pixels (local information). Experiments show that our GLQSCA significantly surpasses state-of-the-art methods on the standard FSS benchmarks PASCAL-5i and COCO-20i.

Logistics planning for direct temporary disaster housing assistance under demand uncertainty

In this paper, we propose and study a framework for disaster housing logistics planning under demand uncertainty. Specifically, we utilize a two-stage chance-constrained stochastic programming model to achieve the balance between logistics operational cost and demand fulfillment especially towards extreme disaster scenarios. To do so, we incorporate two operational modalities, one for the ordinary modality and the other for the emergency modality, and the emergency modality is only allowed to be activated for a certain percentage of scenarios that is specified by the decision maker among all scenarios. The set of scenarios is generated according to a spatial regression model for characterizing the disaster housing demand based on a selected number of independent variables related to both the hazard and socioeconomic factors, which is trained offline from historical data. We conduct a numerical experiment based on Hurricane Ian, and our numerical results show the effectiveness of the proposed approach compared to some standard benchmark approaches. We also highlight the managerial insights for disaster housing logistics planning gained through this numerical experiment.

Thermal production of astrophobic axions

Hot axions are produced in the early Universe via their interactions with Standard Model particles, contributing to dark radiation commonly parameterized as ∆Neff. In standard QCD axion benchmark models, this contribution to ∆Neff is negligible after taking into account astrophysical limits such as the SN1987A bound. We therefore compute the axion contribution to ∆Neff in so-called astrophobic axion models characterized by strongly suppressed axion couplings to nucleons and electrons, in which astrophysical constraints are relaxed and ∆Neff may be sizable. We also construct new astrophobic models in which axion couplings to photons and/or muons are suppressed as well, allowing for axion masses as large as few eV. Most astrophobic models are within the reach of CMB-S4, while some allow for ∆Neff as large as the current upper bound from Planck and thus will be probed by the Simons Observatory. The majority of astrophobic axion models predicting large ∆Neff is also within the reach of IAXO or even BabyIAXO.

Benchmarking deep learning-based low-dose CT image denoising algorithms.

Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reductionalgorithms. Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reportedresults. In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardizedsetup. Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal atbest. This study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate theiralgorithms.

Analyzing deep textual facial patterns for human pain sentiment recognition system in smart healthcare framework

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework.

Performance of Low-Dimensional Solid Room-Temperature Photodetectors-Critical View.

In the last twenty years, nanofabrication progress has allowed for the emergence of a new photodetector family, generally called low-dimensional solids (LDSs), among which the most important are two-dimensional (2D) materials, perovskites, and nanowires/quantum dots. They operate in a wide wavelength range from ultraviolet to far-infrared. Current research indicates remarkable advances in increasing the performance of this new generation of photodetectors. The published performance at room temperature is even better than reported for typical photodetectors. Several articles demonstrate detectivity outperforming physical boundaries driven by background radiation and signal fluctuations. This study attempts to explain these peculiarities. In order to achieve this goal, we first clarify the fundamental differences in the photoelectric effects of the new generation of photodetectors compared to the standard designs dominating the commercial market. Photodetectors made of 2D transition metal dichalcogenides (TMDs), quantum dots, topological insulators, and perovskites are mainly considered. Their performance is compared with the fundamental limits estimated by the signal fluctuation limit (in the ultraviolet region) and the background radiation limit (in the infrared region). In the latter case, Law 19 dedicated to HgCdTe photodiodes is used as a standard reference benchmark. The causes for the performance overestimate of the different types of LDS detectors are also explained. Finally, an attempt is made to determine their place in the global market in the long term.

Ru/NiMnB spherical cluster pillar for highly proficient green hydrogen electrocatalyst at high current density

Advanced OER/HER electrocatalytic alternatives are crucial for the wide adaptation of green hydrogen energy. Herein, Ru/NiMnB spherical cluster pillar (SCP), denoted as Ru/NiMnB, is synthesized using a combination of electro-deposition and hydrothermal reaction. Systematic investigation of Ru doping in the NiMnB matrix revealed significant improvements in electrocatalytic performance. The Ru/NiMnB SCPs demonstrate superior OER/HER activity with low overpotentials of 150 and 103 mV at 50 mA/cm2 in 1 M KOH, making them highly competitive with state-of-the-art electrocatalysts. Remarkably, the Ru/NiMnB SCPs exhibit a low 2-E cell voltage of 2.80 V at ultra-high current density of 2,000 mA/cm2 in 1 M KOH, outperforming the standard benchmark electrodes of RuO2 || Pt/C, thereby positioning Ru/NiMnB as one of the best bifunctional electrocatalysts. These SCPs exhibit exceptional high-current characteristics, stability and corrosion resistance, as evidenced by continuous operation at 1,000 mA/cm2 high-current density for over 150 h in 6 M KOH at elevated temperatures under harsh industrial conditions. Only a small amount of Ru incorporation significantly enhances the electrocatalytic performances of NiMnB, attributed to increased active sites and improved intrinsic properties such as conductivity, adsorption/desorption capability and reaction rates. Consequently, Ru/NiMnB SCPs present a promising bi-functional electrode concept for efficient green H2 production.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Towards standarized benchmarks of LLMs in software modeling tasks: a conceptual framework

AbstractThe integration of Large Language Models (LLMs) in software modeling tasks presents both opportunities and challenges. This Expert Voice addresses a significant gap in the evaluation of these models, advocating for the need for standardized benchmarking frameworks. Recognizing the potential variability in prompt strategies, LLM outputs, and solution space, we propose a conceptual framework to assess their quality in software model generation. This framework aims to pave the way for standardization of the benchmarking process, ensuring consistent and objective evaluation of LLMs in software modeling. Our conceptual framework is illustrated using UML class diagrams as a running example.

VCF observer: a user-friendly software tool for preliminary VCF file analysis and comparison

BackgroundAdvancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files.ResultsIn response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application’s interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision–recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists.ConclusionsBy providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries.

Optimal nonlinear Fractional-Order Proportional-Integral-Derivative controller design using a novel hybrid atom search optimization for nonlinear Continuously stirred Tank reactor

Atom search optimization (ASO) algorithm derived from physics molecular dynamics. Lennard-Jones(L-J) and bond length potential of molecules are used to derive the model for optimization. In this paper, ASO is used for developing a nonlinear Fractional Order Proportional Integral Derivative controller (NL-FOPID) for Continuously Stirred Tank Reactor (CSTR). The convergence characteristics of ASO was improved by proposing a novel hybridization approach. The proposed hybridization approach called Hybrid ASO(HASO) guides the Atom search algorithm to optimally replace the atoms that goes out of the boundary of the search space. The designed algorithm is implemented to optimize various unimodal and multi model standard benchmark functions. From results obtained from this extensive simulation, it is indicated that proposed approach increased the convergence rate and also improved the optimization effort of conventional ASO. The proposed algorithm also tested with controller design for nonlinear CSTR. The NLPID and NL FOPID designed by HASO was better than conventional controllers found in the literature.

Dynamic and static mutual fitting for action recognition

Action recognition is intended to classify a video into a certain category by aggregating and summarizing its temporal and spatial information. Existing methods have achieved remarkable performance on the standard datasets. However, how to accurately recognize the action under occluded scenarios remains the major challenge in practical applications and is barely explored. Although previous augmentation methods have made some efforts by introducing additional modalities to deal with occlusion cases, the continuous occlusion related to temporal in the video is rarely focused. To tackle this issue, this paper takes the sample augmentation and feature representation into consideration. Specifically, we design a Dynamic Temporal-aware Erasing (DTE) augmentation strategy to enrich the occlusion samples. The proposed DTE builds upon an explicit analysis conditioned with temporal dimension along with the actors’ trajectories, which ensures the capability of carrying the temporal relation to an arbitrary spatial augmentation setting. Specifically, DTE makes the added augmentation samples more temporally consistent with obtaining the motion trajectories of the actors, enhancing the robustness against occlusions. Besides, we revisit the necessity of diverse backgrounds and propose Dynamic and Static Mutual Fitting (DSMF) to optimize the action recognition model. DSMF incorporates background auxiliary mutual fitting actors to distinguish features, which learns a smooth representation with the global temporal consistency. Extensive experiments conducted on standard benchmarks have proved that the proposed DSMF achieves competitive performance over powerful competitors.

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy.

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

Three-phase power flow using binary tree search algorithm for unbalanced distribution networks

The main objective of this study is to examine a binary tree (BT) three-phase (3Φ) power flow analysis method for unbalanced distribution networks. A simple and efficient 3Φ power flow solution method for unbalanced radial distribution networks was proposed. This method is based on the BT search structure. Large-scale distribution network problems can be evaluated using current-injection techniques and two-port network models. The power flow solution process was simplified into a set of 3 × 3 matrix operations using the BT-based method. The solution complexity remained unchanged, regardless of the size of the power system. In other words, the performance and robustness of the proposed algorithm were maintained at relatively high values with increasing system size. Furthermore, programming complexity was reduced by standardising the solution process. The effectiveness and accuracy of the proposed model and algorithm were validated using four IEEE standard benchmark feeders and a series of random cases.

Advancements in Autonomous Mobile Robot: A Holistic Review of Obstacle Avoidance Methods

The emergence of AMRs has altered our perspective and relationship with automation. At the heart of this transition is navigation and obstacle avoidance, both of which are important needs for deploying AMRs in a variety of scenarios. This comprehensive review looks at the latest advances in navigation and collision avoidance for AMRs, including a wide range of modern techniques and methodologies, algorithms, and technologies that aim to improve functionality. The study provides a detailed analysis of known approaches, such as rule-based approaches, potential fields, reactive navigation systems as behavior systems, and path-following algorithms, that have been developed to address the difficulty in practice. In contrast, technological advancements in machine learning, computer vision sensor fusion, and SLAM techniques, as well as edge computing, are reviewed in light of their unprecedented impact on AMR navigation. Global and local techniques are tackled using universal worldwide optics as well as national adaptations that reveal the unique characteristics of individual countries. The Data Analysis and Processing section emphasizes the importance of technologies that define AMR performance. Due to the constraints imposed by previous studies, it is clear that additional research is required to focus on closing gaps in controlled environments and using standard benchmarks; sensor heterogeneity issues; and practical implementation of theoretical aspects. In a nutshell, this review provides a map of the complex world of AMR navigation and obstacle avoidance. Its primary purpose is to contribute to the continuing debate, promote innovation, and suggest new research avenues in a fast-changing world of autonomous mobile robotics that breaks down traditional deployment constraints.