Robust Approach Research Articles

Trends and insights on multiple intelligences in Islamic schools: A bibliometric analysis

In this comprehensive study, we explore the intersection between Multiple Intelligences (MI) theory and Islamic educational systems, employing bibliometric tools like Descriptive analysis and Author Network Visualization to reveal the intricate contours of academic collaboration and research in this domain. An increasing trend in publications and cross-cultural collaborations, particularly involving the United States, Indonesia, and Turkey, underscores the global relevance of integrating modern psychological theories like MI with Islamic education. Through citation and keyword analysis, we highlight a significant divergence between research on Islamic schools and MI, as well as an emerging interest in applying MI in religious educational contexts. Furthermore, our research identified a seriously alarming gap in the academic literature at this intersection, emphasizing the need for future exploration. In synthesizing the findings, we conclude that the fusion of MI with Islamic pedagogy offers a robust approach to holistic learning in recognizing individual intelligence domains and fostering growth. The integration of these two fields promises to redefine education by melding traditional religious teachings with contemporary insights, creating a pathway for enhanced student understanding, progressive policy formulation, and innovative pedagogical strategies.

Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses.

Sequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

A phase-field fracture model for fatigue behavior in fiber-reinforced composites

A new framework based on the phase field method is developed to model the fatigue fracture behavior of fiber-reinforced composite laminates. The growth path of matrix cracks can be simulated more accurately compared to the classical model. Further, anisotropic fatigue accumulation effects are considered in this model. Comparisons between simulations by the phase field model with uniform fatigue accumulation effects and the proposed method are encouraging. The new framework proposed can more naturally capture different types of crack growth in fiber- reinforced composites. The reliability and potential of this framework to model fatigue behavior for fiber-reinforced composite laminates are demonstrated by investigating several numerical examples. Moreover, the finite element implementation utilizes a simpler and robust approach via the ABAQUS user-defined subroutine UMAT and UMATHT, including the so called AT1, AT2 and PF-CZM models and two solution strategies.

A Robust Zero-Watermarking Scheme in Spatial Domain by Achieving Features Similar to Frequency Domain

In recent years, there has been a substantial surge in the application of image watermarking, which has evolved into an essential tool for identifying multimedia material, ensuring security, and protecting copyright. Singular value decomposition (SVD) and discrete cosine transform (DCT) are widely utilized in digital image watermarking despite the considerable computational burden they involve. By combining block-based direct current (DC) values with matrix norm, this research article presents a novel, robust zero-watermarking approach. It generates a zero-watermark without attempting to modify the contents of the image. The image is partitioned into non-overlapping blocks, and DC values are computed without applying DCT. This sub-image is further partitioned into non-overlapping blocks, and the maximum singular value of each block is calculated by matrix norm instead of SVD to obtain the binary feature matrix. A piecewise linear chaotic map encryption technique is utilized to improve the security of the watermark image. After that, the feature image is created via XOR procedure between the encrypted watermark image and the binary feature matrix. The proposed scheme is tested using a variety of distortion attacks including noise, filter, geometric, and compression attacks. It is also compared with the other relevant image watermarking methods and outperformed them in most cases.

An Objective and Robust Bayes Factor for the Hypothesis Test One Sample and Two Population Means.

It has been over 100 years since the discovery of one of the most fundamental statistical tests: the Student's t test. However, reliable conventional and objective Bayesian procedures are still essential for routine practice. In this work, we proposed an objective and robust Bayesian approach for hypothesis testing for one-sample and two-sample mean comparisons when the assumption of equal variances holds. The newly proposed Bayes factors are based on the intrinsic and Berger robust prior. Additionally, we introduced a corrected version of the Bayesian Information Criterion (BIC), denoted BIC-TESS, which is based on the effective sample size (TESS), for comparing two population means. We studied our developed Bayes factors in several simulation experiments for hypothesis testing. Our methodologies consistently provided strong evidence in favor of the null hypothesis in the case of equal means and variances. Finally, we applied the methodology to the original Gosset sleep data, concluding strong evidence favoring the hypothesis that the average sleep hours differed between the two treatments. These methodologies exhibit finite sample consistency and demonstrate consistent qualitative behavior, proving reasonably close to each other in practice, particularly for moderate to large sample sizes.

Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty

Dynamic blood collection planning is crucial for adjusting strategies in response to varied donors' behavior within the dynamic urban environment, which can be utilized to mitigate wastage and scarcity due to the uncertain supply chain parameters. In this study, we address the challenge of adapting collection planning to dynamic fluctuations in potential blood donors’ behavior by introducing an innovative updatable approach based on the rolling horizon planning approach, which considers uncertain conditions in daily urban life. Taking into account one-day planning and diverse urban areas contexts, we propose an approach that operationally updates relocation and collection strategies throughout the daily fluctuation in urban conditions. We present a robust optimization approach to mitigate uncertainty across various parameters such as donors' behavior, demand, transportation time, and operational cost. Given the impact of dynamic and diverse conditions, we implement our updatable approach within the Tehran blood supply chain, which possesses the largest blood network in northern Iran, providing support to 108 hospitals and numerous laboratories. Deriving insights from our results, we prove a noteworthy 9.65% enhancement in blood collection across the entire network resulting from the implementation of the rolling horizon planning approach. Moreover, adjusting the dynamic conditions, in contrast to non-dynamic conditions, has led to an additional 24.14% increase in blood collection through temporary collection facilities as a main focus in this investigation.

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.

Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

Interface engineering for high-strength and high-ampacity of carbon nanotube/copper composite wires

Demands for lightweight and high strength electrical power transmission wires continue to rise, especially for electric vehicles, aerospace and 5G communication applications. Carbon nanotubes/copper composite wire is regarded as one of the promising candidate material systems for transmitting electricity and thermal energy. However, the improvements of the electrical conductivity and ampacity of CNT/Cu composite fiber often sacrifices the strength heavily. In this paper, a facile and robust approach including densification and Ni discontinuous nanoparticle layer (NiDPL) pre-seeding prior to Cu electroplating, was developed to enhance simultaneously the strength and ampacity of CNT/Cu composite fiber. Benefiting from the densification process, the conductivity and strength of CNT fiber were improved greatly. Further, NiDPL seeds formed using organic Ni salt (such as nickel acetate) were served as active sites for subsequent Cu layer deposition. The NiDPL were constructed on the surface of CNT fiber to improve both the chemical affinity and the electrons transfer between CNT and Cu. Finally, CNT/Cu composite wire with NiDPL achieved both high strength (2.54 GPa) and ampacity (1.68 × 106 A/cm2).

Speckle wavemeter based on a multi-core fiber and compressive imaging.

Random speckle patterns contain valuable information about the incident light. Researchers have successfully constructed spectrometers and wavemeters by utilizing the speckles generated by inter-mode interferences of a multimode fiber (MMF). However, cameras were often employed to record the speckle data in previous reports. The camera's high cost (especially in the near-infrared range), large size, and low response speed limit the applications in optical communications, metrology, and optical sensing. A seven-core fiber (SCF) was fused with an MMF to capture the speckle pattern, where each core coupled part of the speckle field. Furthermore, we take advantage of the space division multiplexing capability of the SCF by incorporating an optical switch. This allows the variety of speckles generated by the incidence of different cores into the MMF. A convolutional neural network (CNN) regression algorithm was designed to analyze the complicated speckle data. The experimental results show that the proposed wavemeter can resolve adjacent wavelengths of 1pm with an error of about 0.2pm. We also discussed how different lengths of MMF influence the wavelength resolution. In conclusion, our research presents a robust and cost-effective approach to a wavelength measurement device by use of a seven-core optical fiber.

Validation of the 5C questionnaire to assess the hesitancy towards adult vaccination among the Hindi speaking population of Northern India

Background and objectivesTo enable an accurate assessment of vaccine hesitancy among adults, a validated Hindi vaccine hesitancy questionnaire, encompassing multiple sub-domains of vaccine hesitancy, is a pre-requisite. The following study was conducted to translate and determine the reliability, content, construct, and concurrent validity of the Hindi Adult Vaccine Hesitancy scale (5C) among adults in Northern India.MethodsTranslation-back translation of the 5C tool (which comprises five domains of vaccine hesitancy: confidence, calculation, complacency, constraint, and collective responsibility) to Hindi, followed by cognitive interviews among the target population, was done. Item-wise (I-CVI) and full-scale Content Validity Index (S-CVI) were determined through a panel of experts. The robust econometric approach of Confirmatory Factor Analysis (CFA) was employed and construct validity were tested among 150 participants. Discriminant and Convergent validity were examined using the Average Variance Extracted (AVE). The reliability of the domains was assessed through Composite Reliability (CR).ResultsThe I-CVI and Kappa statistics of all tool items ranged from 0.8 to 1.0. The S-CVI/AVE was calculated to be 0.97. The composite reliability (CR ≥ 0.70) and convergent validity (AVE ≥ 0.50) coefficients were found to be adequate. AVE of the latent variables was greater than the squared values of the latent variable correlations, indicating adequate discriminant validity. 5C tool in the Indian setting demonstrated a ‘good fit’ established through confirmatory factor analysis.Interpretation and conclusionsHindi version of the 5C scale is a valid tool to assess vaccine hesitancy among the Hindi-speaking population North Indian Population. Willingness to take the recommended vaccines was positively correlated with ‘confidence,’ ‘calculation,’ and ‘collective responsibility’ and negatively correlated with ‘complacency’ and ‘constraint’ domains.

A robust LP-based approach for a dynamic surgical case scheduling problem with sterilisation constraints

The purpose of this article is to investigate a practical scheduling problem in which a group of elective surgical cases are scheduled over time, while considering their unpredictable durations and potential delays in the sterilisation of surgical instruments. The primary objectives were to schedule the maximum number of surgeries and decrease overtime for the surgical staff, as well as limit the number of instruments requiring emergency sterilisation. The study was conducted in collaboration with the University Hospital of Angers in France, which also contributed historical data for the experiments. We propose two robust mixed integer linear programming models, which are then solved iteratively through a rolling horizon approach, in which the objective functions are taken into account in lexicographic order. Experiments on randomly generated instances indicated which of the two approaches had better performance. Comparison of the results for a real-world scenario involving actual planning at the hospital indicated a greater than 69% decrease in overtime, and a minimum of 92% fewer stressful situations in the sterilising unit.

Robust Control System for DFIG-Based WECS and Energy Storage in reel Wind Conditions

This research work focuses on addressing the challenges of controlling a wind energy conversion system (WECS) connected to the grid, particularly when faced with variable wind speed profiles. The system consists of a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery storage system connected to the Back-to-Back converter DC link via a DC/DC converter. The non-linearity and internal parametric variation of the wind turbine can negatively impact energy production, battery charging performance, and battery lifespan. To overcome these issues, the study proposes a robust control approach called Integral action Sliding Mode Control (ISMC) to enhance the dynamic performance of the WECS based on DFIG. Additionally, the battery charging and discharging controllers play a crucial role in efficiently distributing power to the grid and storage unit based on the battery's state of charge, extracted energy, and power injected into the grid. Two current regulation modes, buck charging and boost discharging, are employed to ensure proper energy distribution. Furthermore, a storage system energy management algorithm is implemented to ensure battery safety during one of the charging modes. The effectiveness and robustness of the proposed control method were validated through simulations of a 1.5 MW wind power conversion system using Matlab/Simulink. The results confirmed the method's efficiency and efficacy.

Beyond Conventional Monitoring: A Semantic Segmentation Approach to Quantifying Traffic-Induced Dust on Unsealed Roads.

Road dust is a mixture of fine and coarse particles released into the air due to an external force, such as tire-ground friction or wind, which is harmful to human health when inhaled. Continuous dust emission from the road surfaces is detrimental to the road itself and the road users. Due to this, multiple dust monitoring and control techniques are currently adopted in the world. The current dust monitoring methods require expensive equipment and expertise. This study introduces a novel pragmatic and robust approach to quantifying traffic-induced road dust using a deep learning method called semantic segmentation. Based on the authors' previous works, the best-performing semantic segmentation machine learning models were selected and used to identify dust in an image pixel-wise. The total number of dust pixels was then correlated with real-world dust measurements obtained from a research-grade dust monitor. Our method shows that semantic segmentation can be adopted to quantify traffic-induced dust reasonably. Over 90% of the predictions from both correlations fall in true positive quadrant, indicating that when dust concentrations are below the threshold, the segmentation can accurately predict them. The results were validated and extended for real-time application. Our code implementation is publicly available.

A robust and efficient citrus counting approach for large-scale unstructured orchards

CONTEXTAccurately detecting and counting fruits is essential for orchard yield estimation and smart management. However, two drawbacks are inherent in the current citrus counting algorithms: First, the robustness needs to be improved, particularly illumination changes and dense occlusion. Secondly, the actual operating efficiency of the system is low. OBJECTIVETo tackle the above issues, this paper proposed a robust and efficient fruit-counting pipeline based on Unmanned Aerial Vehicle (UAV). METHODSFirst, to obtain UAV video streaming data online, a live broadcast platform and a flight control Application called FlyCounter were developed. Secondly, the Illumination-Adaptive-Transformer network is used to enhance the low-illumination citrus image in real-time. Then, for the specific challenging scenario, a novel model named Fruit-YOLO is designed to accurately detect citrus in data streams. Finally, the DeepSORT is adopted to track and count fruits in video sequences. RESULTS AND CONCLUSIONSThe results indicate that for detection performance, the P, R and mAP of the Fruit-YOLO of input enhanced image are 0.898, 0.854 and 0.929 respectively, which are 1.7%, 4.6% and 3.5% higher than the original image respectively. Regarding counting performance, for the daytime and evening scenes, the ID switch, MOTA and Error Mean of the pipeline are 63.7, 0.86 and 9.81% respectively. The MAPE of the offline and online operations of the pipeline are 4.36% and 12.66% respectively. The time and resources consumed by the system under parallel operations with different numbers of UAVs were analyzed. SIGNIFICANCEIn this study, an online counting pipeline based on UAVs that can work in low-light scenarios is implemented for the first time. The system has good performance in both daytime and nighttime scenarios, enabling efficient counting in orchards and extending operating time.

Polyethylene glycol (PEG) as a broad applicability marker for LC–MS/MS-based biodistribution analysis of nanomedicines

Polyethylene glycol (PEG) conjugation (PEGylation) is a well-established strategy to improve the pharmacokinetic and biocompatibility properties of a wide variety of nanomedicines and therapeutic peptides and proteins. This broad use makes PEG an attractive ‘allround’ candidate marker for the biodistribution of such PEGylated compounds. This paper presents the development of a novel strategy for PEG quantification in biological matrices. The methodology is based on sample hydrolysis which both decomposes the sample matrix and degrades PEGylated analytes to specific molecular fragments more suitable for detection by LC–MS/MS. Method versatility was demonstrated by applying it to a wide variety of PEGylated compounds, including polymeric poly(ethylbutyl cyanoacrylate) (PEBCA) nanoparticles, lipidic nanoparticles (Doxil®, LipImage 815™ and lipid nanoparticles for nucleic acid delivery) and the antibody Cimzia®. Method applicability was assessed by analyzing plasma and tissue samples from a comprehensive drug biodistribution study in rats, of both PEBCA and LipImage 815™ nanoparticles. The results demonstrated the method's utility for biodistribution studies on PEG. Importantly, by using the method described herein in tandem with quantification of nanoparticle payloads, we showed that this approach can provide detailed understanding of various critical aspects of the in vivo behavior of PEGylated nanomedicines, such as drug release and particle stability. Together, the presented results demonstrate the novel method as a robust, versatile and generic approach for biodistribution analysis of PEGylated therapeutics.

An Automated Graph-Based Neural Network Model for Predicting Urban Environmental Air Quality Using Spatio-Temporal Data Optimization

<p>Environmental protection and the need for accurate pollutant forecasting have become increasingly important as worries about environmental issues and the harmful effects of pollution have grown. Predictive accuracy of air pollutants is generally unsatisfactory due to the fact that conventional methodologies prioritise time series analysis over the important spatial transmission dynamics among neighbouring locations. To address this inherent limitation, our proposed solution introduces an innovative Time Series Prediction Network, augmented by the auto-optimization capabilities of a Spatio-Temporal Graph-based Neural Network. This groundbreaking network comprises distinct spatial and temporal modules. The spatial module harnesses a Graph Sampling and Aggregation Network to extract essential spatial information from the data. Simultaneously, the temporal module integrates a Bayesian approach with a Complex Valued Graph Gated Recurrent Unit (BCV-GRU), seamlessly incorporating a graph network into the Gated Recurrent Unit (GRU) to capture temporal intricacies. Moreover, to manage the challenge of model inaccuracy stemming from inappropriate hyperparameters, Bayesian optimization was employed. The efficacy of our proposed method was validated using real PM2.5 data from the USGS website, showcasing a significant enhancement in prediction accuracy. This study puts forth a robust and effective approach for forecasting PM2.5 concentrations, bridging gaps in existing methodologies and contributing substantially to the evolution of environmental prediction models.</p>

Multi-objective programming for multi-period multi-product closed-loop supply chain network design: a fuzzy robust optimization approach

Multi-objective programming for multi-period multi-product closed-loop supply chain network design: a fuzzy robust optimization approach

A preclinical model of THC edibles that produces high-dose cannabimimetic responses

No preclinical experimental approach enables the study of voluntary oral consumption of high-concentration Δ9-tetrahydrocannabinol (THC) and its intoxicating effects, mainly owing to the aversive response of rodents to THC that limits intake. Here, we developed a palatable THC formulation and an optimized access paradigm in mice to drive voluntary consumption. THC was formulated in chocolate gelatin (THC-E-gel). Adult male and female mice were allowed ad libitum access for 1 and 2 hr. Cannabimimetic responses (hypolocomotion, analgesia, and hypothermia) were measured following access. Levels of THC and its metabolites were measured in blood and brain tissue. Acute acoustic startle responses were measured to investigate THC-induced psychotomimetic behavior. When allowed access for 2 hr to THC-E-gel on the second day of a 3-day exposure paradigm, adult mice consumed up to ≈30 mg/kg over 2 hr, which resulted in robust cannabimimetic behavioral responses (hypolocomotion, analgesia, and hypothermia). Consumption of the same gelatin decreased on the following third day of exposure. Pharmacokinetic analysis shows that THC-E-gel consumption led to parallel accumulation of THC and its psychoactive metabolite, 11-OH-THC, in the brain, a profile that contrasts with the known rapid decline in brain 11-OH-THC levels following THC intraperitoneal (i.p.) injections. THC-E-gel consumption increased the acoustic startle response in males but not in females, demonstrating a sex-dependent effect of consumption. Thus, while voluntary consumption of THC-E-gel triggered equivalent cannabimimetic responses in male and female mice, it potentiated acoustic startle responses preferentially in males. We built a dose-prediction model that included cannabimimetic behavioral responses elicited by i.p. versus THC-E-gel to test the accuracy and generalizability of this experimental approach and found that it closely predicted the measured acoustic startle results in males and females. In summary, THC-E-gel offers a robust preclinical experimental approach to study cannabimimetic responses triggered by voluntary consumption in mice, including sex-dependent psychotomimetic responses.

A preclinical model of THC edibles that produces high-dose cannabimimetic responses.

No preclinical experimental approach enables the study of voluntary oral consumption of high-concentration Δ9-tetrahydrocannabinol (THC) and its intoxicating effects, mainly owing to the aversive response of rodents to THC that limits intake. Here, we developed a palatable THC formulation and an optimized access paradigm in mice to drive voluntary consumption. THC was formulated in chocolate gelatin (THC-E-gel). Adult male and female mice were allowed ad libitum access for 1 and 2 hr. Cannabimimetic responses (hypolocomotion, analgesia, and hypothermia) were measured following access. Levels of THC and its metabolites were measured in blood and brain tissue. Acute acoustic startle responses were measured to investigate THC-induced psychotomimetic behavior. When allowed access for 2 hr to THC-E-gel on the second day of a 3-day exposure paradigm, adult mice consumed up to ≈30 mg/kg over 2 hr, which resulted in robust cannabimimetic behavioral responses (hypolocomotion, analgesia, and hypothermia). Consumption of the same gelatin decreased on the following third day of exposure. Pharmacokinetic analysis shows that THC-E-gel consumption led to parallel accumulation of THC and its psychoactive metabolite, 11-OH-THC, in the brain, a profile that contrasts with the known rapid decline in brain 11-OH-THC levels following THC intraperitoneal (i.p.) injections. THC-E-gel consumption increased the acoustic startle response in males but not in females, demonstrating a sex-dependent effect of consumption. Thus, while voluntary consumption of THC-E-gel triggered equivalent cannabimimetic responses in male and female mice, it potentiated acoustic startle responses preferentially in males. We built a dose-prediction model that included cannabimimetic behavioral responses elicited by i.p. versus THC-E-gel to test the accuracy and generalizability of this experimental approach and found that it closely predicted the measured acoustic startle results in males and females. In summary, THC-E-gel offers a robust preclinical experimental approach to study cannabimimetic responses triggered by voluntary consumption in mice, including sex-dependent psychotomimetic responses.