Sampling Distribution Research Articles

Learning Robust and Sparse Principal Components with the α-Divergence.

In this paper, novel robust principal component analysis (RPCA) methods are proposed to exploit the local structure of datasets. The proposed methods are derived by minimizing the α-divergence between the sample distribution and the Gaussian density model. The α-divergence is used in different frameworks to represent variants of RPCA approaches including orthogonal, non-orthogonal, and sparse methods. We show that the classical PCA is a special case of our proposed methods where the α-divergence is reduced to the Kullback-Leibler (KL) divergence. It is shown in simulations that the proposed approaches recover the underlying principal components (PCs) by down-weighting the importance of structured and unstructured outliers. Furthermore, using simulated data, it is shown that the proposed methods can be applied to fMRI signal recovery and Foreground-Background (FB) separation in video analysis. Results on real world problems of FB separation as well as image reconstruction are also provided.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

AbstractDownscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

A Novel Image Processing Technique for Weighted Particle Size Distribution Assessment

Objective The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. Significance of review This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D 10, D 50 , D 90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. Key findings The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. Conclusions The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

High-strain rate deformation of aluminum during the Taylor test

The aim of the study is to develop a methodology for assessing changes in the microstructure of aluminum under dynamic deformation in a rather wide range of the strain rate and strain degree. The distribution of the microstructure and the strength properties in the cross-section of pure aluminum samples (A99) after dynamic deformation according to the Taylor test were studied. The tests were carried out at room temperature using a PG-20 light-gas cannon, at sample throwing speeds of 127 and 165 m/sec. An interference microscope (Leica IM DRM) and a scanning electron microscope (Jeol JSM-6490) were used to study the aluminum microstructure; the microhardness measurements were carried out on an HVS-1000 device to study the uniformity of the strain distribution in samples. It is shown that three characteristic areas can be distinguished in aluminum samples after Taylor test: the elastic deformation zone, the plastic deformation zone, and the zone of severe plastic deformation, which is located in the area of collision of the sample with a steel barrier. It is shown that dynamic deformation reduced the grain structure from 1 – 1.1 mm to 2.5 – 3 μm at high impact velocities. An elongated grain shape is observed in the collision zone. The proposed method provided determination of the critical strain degree necessary for the onset of grain fragmentation and allowed us to explain the formation of zones of weak and severe plastic deformation. It is shown that the critical strain degree corresponding to the beginning of grain fragmentation increases from 0.18 to 0.21 with an increase in the throwing speed of the sample from 127 to 165 m/sec. In the zone of weak deformation, plastic deformation proceeds by intragrain riveting and the initial stages of grain fragmentation. In the zone of severe plastic deformation, a fine-grained microstructure is formed, which leads to an increase in the microhardness of aluminum in accordance with the Hall – Petch equation.

Calibrated Empirical Neutrosophic Cumulative Distribution Function Estimation for Both Symmetric and Asymmetric Data

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets.

INTEGRATED APPROACH TO YOUTH MENTAL HEALTH

This study aimed to analyze the mental health issues among students at the National University “Yuriy Kondratyuk Poltava Polytechnic”, taking into account the international experiences in implementing psychosocial diagnostic programs. Based on this analysis, recommendations were formulated for the effective implementation of programs aimed at enhancing the mental well-being of the Ukrainian student community. Materials and Methods: A total of 812 students from various specialties at Yuriy Kondratyuk Poltava Polytechnic National University participated in the study. The age range of the participants was between 18 and 22 years old, with 47% (382 individuals) being male and 53% (430 individuals) female, ensuring gender balance in the sample distribution and representing a diverse cross-section of respondents. The survey was conducted anonymously using a Google form, thereby upholding the principle of confidentiality in psychological research. Result and discussion. The survey revealed a correlation between military operations and an escalation in anxiety disorders among civilians and refugees within the Ukrainian context. However, there remains an incomplete understanding of the mental well-being of the youth demographic. Presently, numerous studies indicate a decline in the psycho-emotional state of Ukrainian students. A significant proportion of respondents reported feelings of depression, nervousness, loneliness, and anger, with an uptick in cases involving alcohol, tobacco, and sedative abuse. An investigation into the mental health of women within educational institutions, including female students and teachers, unveiled heightened levels of fear and diminished resilience to stressful situations. This methodological approach facilitated the organization of information, enabling an objective comprehension of the issue and the formulation of specific recommendations for future action. Notably, there is a pressing need to implement programs aimed at enhancing both mental and physical health. Initiatives incorporating physical activity, balanced nutrition, and relaxation techniques have demonstrated potential in reducing stress levels and fostering improved mental well-being among students. Conclusions: The proposed recommendations are geared towards enhancing mental health standards and mitigating the impact of psychological challenges on academic performance, social integration, and overall life satisfaction.

Over 200 globular clusters in the Milky Way and still none with super-Solar metallicity

A large number of globular clusters in the Milky Way have been studied in recent years, especially in hidden regions such as those of the Galactic bulge. The main goal of this work is to understand what we can learn if we include these new objects into the Milky Way globular cluster (GC) system that we know today. We compiled a catalog of 37 recently discovered globular clusters. Most of them are located in the Galactic bulge, but we also included some of the GCs for comparison. We used a range of distributions for investigating the Galactic GC system based on the metallicity, luminosity function, and age. We considered three samples. We first treated the new GC sample separately from the known and well characterized GCs. Consequently, we merged these two samples, thereby upgrading the Milky Way GC system. Furthermore, we performed a comparison between our clusters sample and the field star population. We found a double-peaked distribution for the luminosity function, which shows an elongated faint end tail. Considering the "merged" sample, the luminosity function peaks at $M_ V up 1.3$ mag and at V up 0.48$ mag. The metallicity distributions also display a bimodality trend. In this case, we compare our new sample compilation with previously published ones, finding that the distributions are in good general agreement. We also constructed the metallicity distribution for the field star sample and, by comparing it with that of the GCs, we learned that a high percentage of field stars show Fe/H $>0$; whereas we did not detect any GCs in the same metallicity range. To understand this inconsistency, we constructed the age-metallicity diagram for both samples, noting that the old and metal-poor population (age $ Gyr and Fe/H -1.0$) is represented by Gcs, while the young and metal-rich population (age $<8$ Gyr and Fe/H $>-1.0$) corresponds to field stars. From the analysis of the GC luminosity function and metallicity distribution, we can conclude that many GCs, probably those that are very faint, have survived strong dynamical processes that are typical of the bulge regions. Moreover, we cannot exclude the possibility that some of them have been accreted during past merging events, especially the metal-poor component, whereas the metal-rich population may be related to the formation of the bulge and/or disk. Finally, the difference that we notice between the cluster and field star samples should be explored in the context of the evolutionary differences among these two stellar populations.

Refined Aircraft Positioning Based on Stochastic Hybrid Estimation with Adaptive Square-Root Unscented Particle Filtering

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame.

Calibration‐free image analysis method for grain‐size distribution of small natural sand samples

AbstractNatural sand samples collected from bedload or suspension transport can be too small to be sieved. However, related transported mass to sediment size is crucial to calibrate sediment transport models. Image analysis technique can be an easy and cheap alternative. The present study proposes a general law valid for natural sand grains and small gravels that statistically relates elongation to flatness of particles and gives after some assumptions on grain shape and orientation of grains at the sieve mesh passage, the closest distribution of mass to sieving technique from images of grains given by a flatbed scanner. The proposed model is based on the hypothesis of an ellipsoidal grain shape and a preferential grain crossing aligned with the sieve diagonal. Any sand either homogeneous or heterogeneous from various origins (fluvial, marine and quarry) can be analysed directly without any further need for calibration.

Freeze-Casting of Alumina and Permeability Analysis Based on a 3D Microstructure Reconstructed Using Generative Adversarial Networks.

In this study, alumina ceramics with hierarchical pores were successfully fabricated using freeze casting. Experimental studies show that both the solid loading of the slurry and the thermal insulation layer at the interface of the slurry and cooling plate can influence the pore characteristics of cast samples. In order to examine the pore characteristics and evaluate the permeability of the freeze-cast samples fabricated under different conditions, a generative adversarial network (GAN) method was employed to reconstruct the three-dimensional (3D) microstructure from two-dimensional (2D) scanning electron microscopy (SEM) images of the samples. Furthermore, GAN 3D reconstruction was validated against X-ray tomography 3D reconstruction results. Based on the GAN reconstructed microstructures, the permeability and pore distribution of the various samples were analyzed. The sample cast with 35 wt.% solid loading shows an optimal permeability.

Invariant Content Representation for Generalizable Medical Image Segmentation.

Domain generalization (DG) for medical image segmentation due to privacy preservation prefers learning from a single-source domain and expects good robustness on unseen target domains. To achieve this goal, previous methods mainly use data augmentation to expand the distribution of samples and learn invariant content from them. However, most of these methods commonly perform global augmentation, leading to limited augmented sample diversity. In addition, the style of the augmented image is more scattered than the source domain, which may cause the model to overfit the style of the source domain. To address the above issues, we propose an invariant content representation network (ICRN) to enhance the learning of invariant content and suppress the learning of variability styles. Specifically, we first design a gamma correction-based local style augmentation (LSA) to expand the distribution of samples by augmenting foreground and background styles, respectively. Then, based on the augmented samples, we introduce invariant content learning (ICL) to learn generalizable invariant content from both augmented and source-domain samples. Finally, we design domain-specific batch normalization (DSBN) based style adversarial learning (SAL) to suppress the learning of preferences for source-domain styles. Experimental results show that our proposed method improves by 8.74% and 11.33% in overall dice coefficient (Dice) and reduces 15.88 mm and 3.87 mm in overall average surface distance (ASD) on two publicly available cross-domain datasets, Fundus and Prostate, compared to the state-of-the-art DG methods.The code is available at https://github.com/ZMC-IIIM/ICRN-DG .

RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating

Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots.

Balanced Adversarial Tight Matching for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is an attractive research area in software testing. It identifies defects in projects with limited labeled data (target projects) by utilizing predictive models from data-rich projects (source projects). Existing CPDP methods based on transfer learning mainly rely on the assumption of a unimodal distribution and consider the case where the feature distribution has one obvious peak. However, in actual situations, the feature distribution of project samples often exhibits multiple peaks that cannot be ignored. It manifests as a multimodal distribution, making it challenging to align distributions between different projects. To address this issue, we propose a balanced adversarial tight-matching model for CPDP. Specifically, this method employs multilinear conditioning to obtain the cross-covariance of both features and classifier predictions, capturing the multimodal distribution of the feature. When reducing the captured multimodal distribution differences, pseudo-labels are needed, but pseudo-labels have uncertainty. Therefore, we additionally add an auxiliary classifier and attempt to generate pseudo-labels using a pseudo-label strategy with less uncertainty. Finally, the feature generator and two classifiers undergo adversarial training to align the multimodal distributions of different projects. This method outperforms the state-of-the-art CPDP model used on the benchmark dataset.

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

IntroductionSound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.MethodIn this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.ResultIt can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.DiscussionThis enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.

Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Singular Value Decomposition-Based Penalized Multinomial Regression for Classifying Imbalanced Medulloblastoma Subgroups Using Methylation Data.

Medulloblastoma (MB) is a molecularly heterogeneous brain malignancy with large differences in clinical presentation. According to genomic studies, there are at least four distinct molecular subgroups of MB: sonic hedgehog (SHH), wingless/INT (WNT), Group 3, and Group 4. The treatment and outcomes depend on appropriate classification. It is difficult for the classification algorithms to identify these subgroups from an imbalanced MB genomic data set, where the distribution of samples among the MB subgroups may not be equal. To overcome this problem, we used singular value decomposition (SVD) and group lasso techniques to find DNA methylation probe features that maximize the separation between the different imbalanced MB subgroups. We used multinomial regression as a classification method to classify the four different molecular subgroups of MB using the reduced DNA methylation data. Coordinate descent is used to solve our loss function associated with the group lasso, which promotes sparsity. By using SVD, we were able to reduce the 321,174 probe features to just 200 features. Less than 40 features were successfully selected after applying the group lasso, which we then used as predictors for our classification models. Our proposed method achieved an average overall accuracy of 99% based on fivefold cross-validation technique. Our approach produces improved classification performance compared with the state-of-the-art methods for classifying MB molecular subgroups.

Hybrid-index-based array configuration optimization for Michelson interferometric imaging

Array configuration design is a critical issue for a high quality of the snapshot point spread function (PSF) and restored image in Michelson imaging interferometer. In classic design, the optimized configurations usually address the few specifications and single objective, which is unable to balance the requirements of both non-redundancy and sampling distribution. In this paper, we formalize mathematically the composite metric to trade-off the multiple demands of observation, and propose the hybrid-index-based array layout optimization strategy. The simulation results demonstrate that, in comparison with the typical distribution, the optimized array using the proposed optimization framework enables the acquisition of more comprehensive spectrum information while utilizing an equal number of apertures, providing superior imaging quality in different observation situations. Furthermore, the designed optimized array masks and the compared conventional array masks were fabricated and used for our experimental validation, further verifying the feasibility of this strategy. This array configuration optimization framework may not only find applications to Michelson interferometric imaging, but also provide a positive impact on all u-v sampling-based imaging modes, including radio interferometry, magnetic resonance imaging, and photonic integrated interferometric imaging.

RD-OpenMax: Rethinking OpenMax for Robust Realistic Open-Set Recognition.

Open-set recognition (OSR) toward a practical open-world setting has attracted increasing research attention in recent years. However, existing OSR settings are either too idealized or focus on specific scenes such as long-tailed distribution and few-shot samples, which fail to capture the complexity of real-world scenarios. In this article, we propose a realistic OSR (ROSR) setting that covers a diverse range of challenging and real-world scenarios, including fine-grained cases with strong semantic correlation and a large number of species, few-shot samples, long-tailed sample distribution, dynamic inputs (e.g., images, spatio-temporal, and multimodal signals) and cross-domain adaptation. In particular, we rethink the simple and basic OpenMax for the ROSR setting and introduce a novel method, regularized discriminative OpenMax (RD-OpenMax), to handle the challenges in the ROSR setting. RD-OpenMax improves upon the basic OpenMax approach by introducing a covariance attention-based covariance pooling (CACP) module as a global aggregation step before the deep architecture's classifier. This module explores rich statistical information on features and provides discriminative distance scores for OpenMax. To address the instability of extreme value theory (EVT) estimation due to insufficient training samples under few-shot and long-tailed scenarios, we propose a regularized EVT (REVT) method based on Monte Carlo sampling to recalibrate the distribution of distance scores. As such, our RD-OpenMax performs a REVT model of distance scores generated by discriminative CACP representations to distinguish known classes and recognize unknown ones effectively and robustly. Extensive experiments are conducted on more than ten visual benchmarks across several scenarios, and the empirical comparisons show that the ROSR setting challenges existing state-of-the-art OSR approaches. Moreover, our RD-OpenMax clearly outperforms its counterparts under the ROSR setting while performing favorably against state-of-the-arts under the traditional OSR setting.

Valorisation of Agricultural Residue Bio-Mass Date Palm Fibre in Dry-Blended Polycaprolactone (PCL) Bio-Composites for Sustainable Packaging Applications

Abstract Purpose This study experimentally developed and characterised dry-blended Polycaprolactone (PCL)/date palm fibre biodegradable composites for sustainable packaging applications. Date palm fibres are collected from date palm trees as by-products or waste materials. They will be valorised in bio-composite application to promote fibre-based sustainable packaging items over their non-biodegradable synthetic polymer based conventional packaging products. In the dry-blending process, fibre and polymer are mixed with a shear mixer, while, in a melt-blending process, an extruder is used to extrude fibre/polymer blends after applying heating and high shear pressure to melt and mix polymer with fibres. Dry-blending process offers many comparative advantages, such as less equipment, steps, cost, process degradation, energy consumption and hence, lower harmful environmental emissions; while, a proper fibre/polymer mixing is a challenge and it needs to be achieved properly in this process. Therefore, it is important to understand the effects of dry-blending process on manufacturing of PCL/date palm fibre bio-composites for packaging applications, before promoting the dry-blending as a suitable alternative to the melt-blending process. Methods Short chopped fibres were grinded as powders and dry-blended at a ratio of (0 − 10%) (w/w) with PCL polymer using hand and a shear mixer for 30 min, following a compression moulding process to produce bio-composite samples. Tensile, water contact angle, SEM, TGA, DSC and DMA tests and analysis were conducted. The dry-blended PCL/date palm fibre composites’ properties were compared with reported melt-blended samples’ results found in literature. Results Dry-blended samples showed an increase in tensile modulus values (up-to 20%) with fibre inclusion and these values were found close to the melt-blended samples in the literature. Tensile strength and strain values were reduced which could be related to the poor fibre/polymer interface. Fibre addition affected the thermal, thermo-mechanical and crystallisation processes in PCL polymer matrix. Conclusion Dry-blending is capable of producing bio-composites with a very comparable properties to melt-blended counterparts, although a more details study is needed to conduct in future. The results of this study, could be used carefully to design dry-blended PCL/date palm fibre bio-composites for possible packaging applications. The irregular fibre distribution in dry-blended samples could be improved in different ways which should be investigated in future. Graphical Abstract