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Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model. [ABSTRACT FROM AUTHOR]

[Display omitted] Ghana has been a pioneer in energy efficiency in the sub-Saharan region, becoming the first country to introduce minimum energy performance standards and energy labelling for lighting, air conditioners and refrigerating appliances in 2008. It also introduced an innovative and not yet widely adopted policy of banning the import of used air conditioners and refrigerating appliances, mostly from Europe. While the impact of this policy package has already been assessed in terms of energy savings in Ghana, it is still unclear whether the import ban on used products makes sense in terms of environmental impact. Ghana's ban may seem surprising given that current European product policies in the context of the Green Deal aim to extend the lifetime of products and encourage their reuse to make them more sustainable. In this paper, different scenarios covering new and used refrigerating appliances as well as different end-of-life management options were examined in a life cycle analysis. The results show that exporting used refrigerators from Europe to West Africa increases their environmental impact per functional unit in many categories, even if it extends the lifetime of the product. The informal end-of-life treatment of refrigerators by open burning has a massive impact on human toxicity. As energy consumption during the use phase is the main determinant of most environmental impacts, energy efficiency policies (minimum energy efficiency performance standards and labelling) combined with an import ban on used refrigerating appliances are the best approach to reduce these impacts. It is therefore recommended that the Ghanaian policy package be replicated in other countries in the region, but also that exporting countries take action, as the fight against environmental dumping is a shared responsibility. • Used refrigerators exported to West Africa increase greenhouse gas emissions. • Energy efficiency reduces the environmental impact of refrigerators. • Import ban on used refrigerating appliances is justified. • Informal end-of-life treatment of refrigerators has a major impact on human toxicity. [ABSTRACT FROM AUTHOR]

Phototoxicity is an acute toxic reaction induced by topical skin exposure to photoreactive chemicals followed by exposure to environmental light and thus chemicals that absorb UV are recommended to be evaluated for phototoxic potential. There are currently three internationally harmonized alternative test methods for phototoxicity. One of them is the in vitro Phototoxicity: RhE Phototoxicity test method (OECD TG498). Korean center for the Validation of Alternative Methods (KoCVAM) developed an in vitro phototoxicity test method using a KeraSkin™ reconstructed human epidermis model (KeraSkin™ Phototoxicity Assay) as a ' me-too ' test method of OECD TG498. For the development and optimization of KeraSkin™ Phototoxicity Assay, the following test chemicals were used: 6 proficiency chemicals in OECD TG498 (3 phototoxic and 3 non-phototoxic), 6 reference chemicals in OECD Performance Standard No. 356 (excluding the proficiency test chemicals, 3 phototoxic and 3 non-phototoxic) and 13 additional chemicals (7 phototoxic and 6 non-phototoxic). Based on the test results generated from the test chemicals above, the overall predictive capacity of KeraSkin™ Phototoxicity Assay was calculated. In particular, the assay exhibited 100 % accuracy, 100 % sensitivity, and 100 % specificity. Therefore, it fulfills the requirements to be included as a ' me-too ' test method in OECD TG498. • Statistical analysis of 25 chemicals showed KeraSkin™ Phototoxicity Assay with 100% sensitivity, specificity, and accuracy. • Predictive capacity for OECD PS No. 356 reference chemicals 100% sensitivity, specificity, and accuracy. • Based on the ROC curve, phototoxicity prediction cut-off set at ≥ 30% decrease in cell viability, consistent with OECD TG498. • KeraSkin™ Phototoxicity Assay satisfied requirements for inclusion as ' me-too ' method in OECD TG498. [ABSTRACT FROM AUTHOR]

Exploring the semantic correspondence of image-text pairs is significant as it bridges vision and language. Most prior works focus on global semantic alignment or local semantic alignment, by developing a fine neural network that facilitates the corresponding alignment but neglects the semantic information and relative position information between image regions, or text words, which will lead to a non-meaningful alignment. To this end, a Heterogeneous Graph Fusion Network (HGFN) is proposed to explore the correlation score of vision-language for improving the accuracy of cross-modal image-text retrieval in this paper. Specifically, we first construct an undirected fully-connected graph based on the semantic or relative position information for each image, as well as a textual graph with neighborhood information of the text. Then, we present a graph fusion module to integrate the features of heterogeneous graphs into a unified hybrid representation, in which the graph convolutional network is utilized to gather neighborhood information to alleviate potentially non-meaningful alignment. In addition, we also propose a novel "Dynamic top- K negative" strategy for the selection of negative examples in the training process. Experimental results demonstrate that HGFN achieves comparable performance with state-of-the-art approaches on the Flickr30K and MSCOCO datasets. • Cross-modal image-text retrieval problems can be treated as graph-graph matching. • A graph fusion module is designed to fuse the visual graph and textual graph. • A novel strategy for selecting negative examples is proposed. • Competitive with state-of-the-art performance on two standard benchmarks. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptive (UDA) is a widely used approach in machine learning strategies that helps move information from a source domain with rich supervised data to a target domain with limited supervised data. In recent years, self-training derived from semi-supervised learning provides a powerful tool for unsupervised domain adaptive by training unlabeled target domain data using pseudo-tags. However, in the case of domain shift, the pseudo-labels generated by standard self-training are no longer reliable due to the influence of the class-distribution shift in a specific domain, resulting in the noise of the pseudo-label error blocking the upper bound of the accuracy. To solve these problems, a domain adaptive method based on regularization joint self-training (RJS) is proposed in this paper. RJS consists of two key steps: First, the generation and optimization of pseudo-tags are trained jointly on the source domain to achieve automatic cross-domain generalization of pseudo-tags. Secondly, the regularization method is introduced as a confidence rule for false labels in initial model training, which implicitly prevents the model from remembering false labels. To verify the effectiveness of the proposed method, experiments were performed on VisDA-2017, Office-31, Office-Home, and DomainNet datasets, and the accuracy rates reached 87.1%, 89.3%, 76.9%, and 57.3%, respectively. In addition, the average classification performance of RJS on VisDA-2017 improved by about 12.6% compared to FixMatch, the standard self-training method. Experimental results demonstrate that RJS achieves state-of-the-art performance on multiple standard UDA benchmarks and confirm its effectiveness in the presence of domain-specific class distribution shifts. [ABSTRACT FROM AUTHOR]

Block cipher has been one of the most reliable options by which data security is achieved. The strength of block cipher against various attacks is purely dependent on its confusion property, which is gained through the S-Boxes. In recent years, S-Boxes based on chaotic maps have become popular due to their favorable characteristics for cryptography. However, vulnerabilities have been discovered in these constructions, leading to concerns about their reliability. In this research, we first generate dynamic S-Boxes, and then based on the newly generated S-Boxes, a ROI-based medical image encryption scheme is proposed to address the challenges posed by the large size of DICOM images. Rather than encrypting the entire DICOM image, proposed encryption scheme only encrypts the ROI part where the relevant information is present, while leaving the black background unencrypted. This approach reduces the size of the encrypted data and improves the efficiency of the encryption process, while maintaining the privacy and confidentiality of sensitive medical data. The proposed ROI-based medical image encryption scheme is evaluated using standard performance metrics, including encryption speed, image quality tests, correlation-coefficient analysis, randomness of encrypted images, key sensitivity analysis, encryption sensitivity analysis, decryption sensitivity analysis, and resistance against common attacks such as differential and linear attacks. The proposed dynamic S-Box construction technique is evaluated using standard S-Box criteria, which include nonlinearity score, bit independence criterion, strict avalanche criteria, linear approximation probability, and differential approximation probability. Results demonstrate that the proposed technique gains high levels of encryption efficiency and security, making it a fast solution for secure medical image transmission in real-world applications. [ABSTRACT FROM AUTHOR]

Due to the enormous amount of user-generated content being generated on the web, labeling such data is a time-consuming and expensive endeavor. As a result, we have limited annotated data and the vast majority of data are unlabeled. Analysis reveals that extracting (external) knowledge from unlabeled data and integrating it with knowledge extracted from labeled data is a beneficial task for text information processing, in particular text classification. In this paper, we present a hybrid approach for classifying sentiments that employs external knowledge, which is categorized as either general-purpose sentiment knowledge or domain-related knowledge. General-purpose sentiment knowledge is extracted from sentiment lexicons, whereas domain-related knowledge is extracted from unlabeled data from the same or related domains. Similar domains for a given domain are identified based on their similarity score in terms of overlapping features. The proposed approach utilizes both forms of external knowledge and combines them with logistic regression to train an improved classification model. The classification model uses the conventional gradient descent algorithm for optimization, and its convergence analysis indicates that it is convex and converges to the global optimum. The proposed approach is empirically evaluated and compared to three baselines and one state-of-the-art method using standard performance evaluation metrics on a multi-domain sentiment dataset. The experiment results are encouraging, demonstrating that the proposed approach considerably outperforms the baseline approaches and outperforms the state-of-the-art approach by up to 2% in terms of both f-score and accuracy. • A hybrid approach for document-level sentiment classification that makes use of external knowledge. • A domain-specific knowledge extraction approach from unlabeled text documents. • A detailed convergence analysis of the proposed optimization model used in the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel dimensionality reduction term (i.e. TripletPCA and ContrastivePCA) designed to enhance the robustness of pair-based loss functions in metric learning against image corruptions. Notably, our approach achieves this without the need for any data preprocessing steps. Our method can be seamlessly integrated into existing models, such as a block with Plug & Play framework. To the best of our knowledge, our TripletPCA and ContrastivePCA represent the first attempts to incorporate dimensionality reduction directly into pair-based metric learning losses for end-to-end metric learning. By projecting image features into low-dimensional vectors, our proposed loss functions effectively retain the essential components of images while mitigating the impact of corrupted features. Consequently, our metric learning loss function accurately computes feature distances through the projection. Experimental results demonstrate that our dimensionality reduction term can be easily incorporated into various types of existing deep neural networks. This integration leads to a substantial improvement in performance on standard benchmark datasets for corrupted image classification tasks. On several corruption datasets, we achieve an average performance improvement of 10.55% compared to existing baseline methods. Our code is available at https://github.com/Juryun/TDRL. [ABSTRACT FROM AUTHOR]

Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to attend to features relevant at different locations and time steps. To address this, we propose a novel progress-aware spatio-temporal transformer speaker (PASTS) model that uses the transformer as the core of the network. PASTS uses a spatio-temporal encoder to fuse panoramic representations and encode intermediate connections through steps. Besides, to avoid the misalignment problem that could result in incorrect supervision, a speaker progress monitor (SPM) is proposed to enable the model to estimate the progress of instruction generation and facilitate more fine-grained caption results. Additionally, a multifeature dropout (MFD) strategy is introduced to alleviate overfitting. The proposed PASTS is flexible to be combined with existing VLN models. The experimental results demonstrate that PASTS outperforms previous speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset. • Spatio-temporal transformer encoder extracts trajectory features effectively. • Speaker progress monitor improves instruction alignment and generation. • Multifeature dropout strategy boosts generalization in unseen environments. • Stronger speaker model provides more accurate pseudo labels for data augmentation. [ABSTRACT FROM AUTHOR]

Studies on performance impact due to the high-dimensional geometric uncertainties face the "curse of dimensionality" problem, making the cost of uncertainty quantification (UQ) unaffordable. To reduce the number of uncertainties, a sensitivity-based deep dimensionality reduction (SBDDR) method is introduced, verified and applied to the UQ studies of a low-pressure turbine cascade considering geometric variations. The basis modes of geometric variations are extracted from more than one thousand casting turbine blades using principal component analysis (PCA) and ordered by eigenvalues. However, the selection of primary basis mode by PCA neglects the performance impact of geometric variations. In the study, total-order sensitivities of performance parameters to the basis modes are calculated by Sobol analysis, in which fast performance prediction methods are employed. The total-order sensitivities are given in detail, by which the basis modes previously obtained by PCA are reordered. Furthermore, considering different numbers of basis modes ordered by PCA and SBDDR, the statistical means and standard deviations of performance changes are calculated and compared with those obtained by the direct Monte Carlo simulation (MCS). The results show that the SBDDR-based statistics are closer to the MCS ones, especially in the cases considering a limited number of basis modes. [ABSTRACT FROM AUTHOR]

• Providing a complete and comprehensive procedure in the design of SAPF systems and their control principles. • Applying PR controller for current control of SAPF for effective harmonic compensation and improve system performance. • Utilization of GA for tuning proposed PR control parameters to reach the most optimal state of control system adjustment. • Mathematic and stability analysis of the proposed PR control used in the SAPF system. • Practical implementation and experimental test of the SAPF device based on the proposed PR control method. Current harmonics produced by nonlinear loads have destructive effects that affect the power quality of the power system and cause serious problems throughout the network. Active Power Filter (APF) is one of the best and most common solutions to reduce or eliminate harmonic disturbances; But due to the complexity and sensitivity of these filters, the accurate and fast performance of the control technologies used in it is the important and basic requirement. Therefore, by performing accurate and optimal adjustments for APF control, system performance and power quality level can be improved significantly. In this article, the design principles of Shunt Active Power Filter (SAPF) are introduced and the optimal control method of this system is explained by providing appropriate modeling. Also, as a suitable structure for the current control of the SAPF system, a Proportional-Resonant (PR) controller optimized by Genetic Algorithm (GA) is proposed. The PR controller with the capability of accurate tracking of harmonic currents and fast dynamic response is highly compatible with the SAPF system. In general, the goal is to achieve better and more appropriate performance to reduce harmonic disturbances and create a robust, faster, more accurate, flexible, stable, and optimal performance of these systems in detecting and generating compensation signals. The simulations have been done in MATLAB/SIMULINK, and also the experimental tests of this device have been done and validated in a real DSP-based SAPF. Finally, according to the results obtained under steady-state conditions with a favorable dynamic response, the Total Harmonic Distortion (THD) of the grid current has been significantly reduced from 20.2 % to 4.2 % for the R-L load and from 56.1 % to 4.5 % for the R-C load with a power factor of almost unity. These results confirm the optimal and standard performance of the proposed method. [ABSTRACT FROM AUTHOR]

Zero-shot 3D shape understanding aims to recognize "unseen" 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model's ability to fully comprehend 3D shapes and adversely impacts the text–image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image–Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text–image fusion loss, which aids in parameters' regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP. • A differentiable renderer fuses explicit text guidance into rendering process to produce informative multi-view images. • We propose the group-view mechanism and LLM-assisted textual feature learning, enabling efficient text–image fusion. • It achieves state-of-the-art for zero-shot 3D classification, competitive in standard 3D classification. [ABSTRACT FROM AUTHOR]

Visual Attention Networks (VAN) with Large Kernel Attention (LKA) modules have been shown to provide remarkable performance, that surpasses Vision Transformers (ViTs), on a range of vision-based tasks. However, the depth-wise convolutional layer in these LKA modules incurs a quadratic increase in the computational and memory footprints with increasing convolutional kernel size. To mitigate these problems and to enable the use of extremely large convolutional kernels in the attention modules of VAN, we propose a family of Large Separable Kernel Attention modules, termed LSKA. LSKA decomposes the 2D convolutional kernel of the depth-wise convolutional layer into cascaded horizontal and vertical 1-D kernels. In contrast to the standard LKA design, the proposed decomposition enables the direct use of the depth-wise convolutional layer with large kernels in the attention module, without requiring any extra blocks. We demonstrate that the proposed LSKA module in VAN can achieve comparable performance with the standard LKA module and incur lower computational complexity and memory footprints. We also find that the proposed LSKA design biases the VAN more toward the shape of the object than the texture with increasing kernel size. Additionally, we benchmark the robustness of the LKA and LSKA in VAN, ViTs, and the recent ConvNeXt on the five corrupted versions of the ImageNet dataset that are largely unexplored in the previous works. Our extensive experimental results show that the proposed LSKA module in VAN provides a significant reduction in computational complexity and memory footprints with increasing kernel size while outperforming ViTs, ConvNeXt, and providing similar performance compared to the LKA module in VAN on object recognition, object detection, semantic segmentation, and robustness tests. [ABSTRACT FROM AUTHOR]

Objectives: Curative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.Methods: Data were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.Results: With the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.Conclusions: For curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not. [ABSTRACT FROM AUTHOR]

Industrial processes often show typical nonstationarity, resulting from frequent variation of working condition. It is rather challenging to assess the operating performance of nonstationary processes, since the process property is time-variant and the standard of performance levels may differ from working conditions. Besides the nonstationarity, nonlinearity widely exists in industrial processes, making it hard to analyze process characteristics. To solve the above problems, a three-dimensional (3-D) online modeling and assessment method of operating performance is proposed in the present work. Considering that the inner drive of nonstationarity is often the fluctuation of working condition, 3-D space is established, including sample, variable, and working condition axes. Thus, the process can be modeled and assessed online along direction of the working condition indicator to handle both nonstationarity and nonlinearity. Within the 3-D space, the selection of online training data and establishment of assessing model are solved simultaneously through multi-objective optimization, to make the training data suitable for performance assessment and preserve high assessment accuracy. Bayesian inference based criterion is then defined to determine the performance level. Finally, the validation on a three-phase flow facility and a coal mill of a power plant show feasibility and superiority of the proposed method on performance assessment for nonstationary industrial processes with nonlinearity. • Performance assessment is solved for general nonlinearly nonstationary processes. • 3-D online modeling and assessment method is proposed. • Determination of training samples and model are conducted simultaneously. • Solution of optimization involves GA and eigenvalue decomposition. [ABSTRACT FROM AUTHOR]

Background: Simple proxy indicators are needed to assess and monitor micronutrient intake adequacy of vulnerable populations. Standard dichotomous indicators exist for nonpregnant women of reproductive age and 6-23-mo-old children in low-income countries, but not for 24-59-mo-old children or pregnant or breastfeeding women.Objectives: This study aimed to evaluate the performance of 2 standard food group scores (FGSs) and related dichotomous indicators to predict micronutrient adequacy of the diet of rural Burkinabe 24-59-mo-old children and women of reproductive age by physiological status.Methods: A 24-h recall survey was conducted at dry season among 1066 pairs of children and caregivers. Micronutrient adequacy was evaluated by the mean probability of adequacy (MPA) of intake over 11 micronutrients. Proxy indicators were FGS-10 [10 food groups based on the FAO/FHI360 minimum dietary diversity for women (MDD-W) guidelines] and related MDD-W (FGS-10 ≥5); and FGS-7 [7 groups based on the WHO infant and young child (IYC) feeding MDD guidelines] and related MDD-IYC (FGS-7 ≥4).Results: FGS-10 and FGS-7 were similar across children and women (∼3 groups). FGS-10 performed better than FGS-7 to predict MPA in children (Spearman rank correlation = 0.59 compared with 0.50) and women of all 3 physiological statuses (Spearman rank correlation = 0.53-0.55 compared with 0.42-0.52). MDD-W and MDD-IYC performed well in predicting MPA >0.75 in children and MPA >0.6 in nonpregnant nonbreastfeeding (NPNB) women, but a 4-group cutoff for FGS-10 allowed a better balance between sensitivity, specificity, and proportion of correct classification. MPA levels for pregnant and breastfeeding women were too low to assess best cutoff points.Conclusions: MDD-IYC or an adapted MDD-W (FGS-10 ≥4 instead of FGS-10 ≥5) can be extended to 24-59-mo-old children and NPNB women in similar-diet settings. The inadequacy of micronutrient intakes in pregnant and breastfeeding women warrants urgent action. Micronutrient adequacy predictors should be validated in populations where a higher proportion of these women do meet dietary requirements. [ABSTRACT FROM AUTHOR]

A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an overlapping domain decomposition (DD) method, is proposed to efficiently construct surrogate models of parametric linear elliptic problems. A parametric multi-domain formulation is presented, with local subproblems featuring arbitrary Dirichlet interface conditions represented through the traces of the finite element functions used for spatial discretization at the subdomain level, with no need for additional auxiliary basis functions. The linearity of the operator is exploited to devise low-dimensional problems with only few active boundary parameters. An overlapping Schwarz method is used to glue the local surrogate models, solving a linear system for the nodal values of the parametric solution at the interfaces, without introducing Lagrange multipliers to enforce the continuity in the overlapping region. The proposed DD-PGD methodology relies on a fully algebraic formulation allowing for real-time computation based on the efficient interpolation of the local surrogate models in the parametric space, with no additional problems to be solved during the execution of the Schwarz algorithm. Numerical results for parametric diffusion and convection–diffusion problems are presented to showcase the accuracy of the DD-PGD approach, its robustness in different regimes and its superior performance with respect to standard high-fidelity DD methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • The MnO 2 NSs was adopted to establish a label-free ICA for bacteria detection. • The capture antibody imitator MnO 2 NSs as the probe can combine towards S. enteritidis. • The MnO 2 NSs was used to generate signal and had nanozyme property. • The novel ICA has high analytical performances both in standard bacterial solutions and food matrixes. The sensitive and rapid detection of foodborne pathogens is of great significance to global public health security. Here, with two-dimensional nanomaterial MnO 2 NSs as the capture antibody imitator and signal reporter molecules, based on its enzyme-catalyzed signal amplification capability, a label-free, dual-signal output immunochromatographic biosensor was constructed to achieve the sensitive detection of S. enteritidis. The capture antibody imitator of MnO 2 capture probe shows an outstanding capture ability, forming a stable immune complex with S. enteritidis. As an in-situ catalytic amplification colorimetric signal trigger, MnO 2 NSs exert oxidase-like enzyme activity and obtain satisfactory detection sensitivity. It is worth noting that the proposed dual-signal ICA has high analytical performances for S. enteritidis during the concentration range of 103-107 cfu mL−1 both in standard bacterial solutions and food matrixes. [ABSTRACT FROM AUTHOR]

The proliferation of cage-less robotic applications is justifying this research which proposes a method to process the output of safety sensors with the aim of maximising the productivity of the robot in a collaborative scenario. Particularly, the Speed and Separation Monitoring (SSM) strategy, which prescribes the robot to reduce its speed proportionally to the vicinity of the human, will be investigated. In state-of-the-art industrial implementations, SSM is implemented in a very conservative way, without exploiting the capabilities of modern sensing devices. This work proposes a methodology to improve the performance of SSM algorithms while dealing finite and quantised 2D cost-effective sensing capabilities. The strategy is verified experimentally as applied on a palletising application with a Comau SmartSix industrial robot, showing slightly improved performance with respect to standard practice. [ABSTRACT FROM AUTHOR]

This paper addresses fusion of labeled multi-Bernoulli (LMB) densities in a peer-to-peer sensor network subject to misbehaviors due to abnormal conditions of the signal propagation channel (i.e. low signal-noise-ratio) or to malicious attacks. In such situations unintentional or intentional modifications of the transmitted messages can occur which degrade the performance of standard fusion methods. In order to overcome the aforementioned difficulties, we propose a resilient approach to distributed fusion of LMB densities in which each node exploits the local density as trusted source of information to detect the modified/false data coming from neighboring nodes. The implementation issues of the proposed method as well as possible cyber-attack models in the context of multi-object filtering are also discussed. The performance of the proposed method is verified via simulation experiments concerning fusion under cyber-attacks. • Resilient fusion of labeled multi-Bernoulli densities. • Unified view of existing multi-object fusion rules under label mismatching. • Possible cyber-attacks models aiming at destroying labeled multi-Bernoulli fusion. • Implementation details of resilient fusion of labeled multi-Bernoulli densities. [ABSTRACT FROM AUTHOR]

Venturi flow meters are pressure differential devices that perform accurate measurements with minimal pressure losses. They are widely used and well-documented in high-Reynolds applications. However, there is a lack of information regarding their performance outside the standards. The premise of the present study is to characterize a single-phase subsonic compact truncated venturi flow meter experimentally and then extend the research using Computational Fluid Dynamics (CFD). The computational work results in a design of experiment with 216 unique geometries at three different flow conditions (648 cases). This allows for characterizing the pressure losses and discharge coefficient as a function of diameter ratio (0.3–0.8), recovery cone truncation (0 %–50 %), and divergent angle (5°–20°). The thorough analysis of the results yields an intricate flow interaction and dependency on the studied parameters. The study covers throat Reynolds numbers from 7 ∙ 10 3 to 7 ∙ 10 4. In this regime, uncertainty in mass flow measurement increases notably (up to 20 %) with a decrease in mass flow. Pressure losses are sensitive to the three studied parameters, while the discharge coefficient can be considered to be independent of divergent angle and truncation. Truncating the Venturi at 50 % can increase the pressure loss coefficient by over 15 %. The optimal divergent angle and truncation to minimize losses for a prescribed length are provided for the instances where the installation space is constrained. This manuscript includes design guidelines relevant to all experimentalists. • Novel Venturi design guidelines for biomedical and aerothermal applications • Computational power used to assess the performance of 216 Venturi geometries • Experimental assessment of truncated Venturi at Low Reynolds • Topological parameterization yields reduced order models of pressure losses • Optimal divergence angle is provided for each combination of length and diameter [ABSTRACT FROM AUTHOR]

Multi-dimensional classification (MDC) task can be considered the most inclusive description of all classifications tasks, as it joins multiple class spaces and their multiple class members into a single compound classification problem. The challenges in MDC arise from the possible class dependencies across different class spaces, as well as the imbalance of labels in training datasets due to lack of all possible combinations. In this paper, we propose a straightforward, yet efficient, MDC deep learning classifier, named Deep Self-Organizing Cube (DSOC) that can model dependencies among classes in multiple class spaces, while consolidating its ability to classify rare combinations of labels. DSOC is formed of two n -dimensional components, namely the Hypercube Classifier and the multiple DSOC Neural Networks connected to the hypercube. The multiple neural networks component is responsible for feature selection and segregation of classes, while the Hypercube classifier is responsible for creating the semantics among multiple class spaces and accommodate the model for rare sample classification. DSOC is a multiple-output learning algorithm that successfully classify samples across all class spaces simultaneously. To challenge the proposed DSOC model, we conducted an assessment on seventeen benchmark datasets in the four types of classification tasks, binary, multi-class, multi-label and multi-dimensional. The obtained results were compared to four standard classifiers and eight competitive state-of-the-art approaches reported in literature. The DSOC has achieved superior performance over standard classifiers as well as the state-of-the-art approaches in all the four classification tasks. Moreover, in terms of Exact Match accuracy metrics, DSOC has outperformed all state-of-the-art approaches in 77.8% of the cases, which reflects the superior ability of DSOC to model dependencies and successfully classify rare samples across all dimensions simultaneously. [ABSTRACT FROM AUTHOR]

In situ synthesis of perovskite nanocatalysts within the air electrode remarkably improves the performance of high-temperature solid oxide cells. [Display omitted] • Perovskite (Sm 0.5 Sr 0.5)CoO 3 nanocatalysts are synthesized for solid oxide cells. • Degradation mechanism of infiltrated nanocatalysts is clarified. • Infiltrated nanocatalysts more than doubled fuel cell and electrolyzer performance. • Infiltrated nanoparticles do not degrade during long-term testing at > 650 °C. • Process scale-up feasibility is verified using practical large cells (144 cm2) High-temperature solid oxide cells (SOCs) offer one of the most efficient and versatile routes for producing electric power and H 2. However, the practical use of nanomaterials in SOCs has been limited by their lack of thermal stability. In this study, we present an infiltration technique that enables the in situ synthesis of extremely small, thermally stable perovskite (Sm 0.5 Sr 0.5)CoO 3 nanocatalysts on the inner surface of porous SOC electrodes. We identified certain impurity phases, such as SrCO 3 , that cause fatal degradation and eliminated them using a rational complexation strategy optimized for individual constituent cations. Consequently, we fabricated ∼ 20 nm diameter, highly pure, single-phase nanocatalysts that achieved more than double the performance of a cell with standard (La,Sr)(Co,Fe)O 3 – and (La,Sr)CoO 3 -based air electrode. The cells stably operated during long-term tests in both the power generation and H 2 production modes, with negligible degradation. Furthermore, we successfully scaled up this process to fabricate large-scale commercial cells using a fully automated process. The key findings of this study will resolve critical barriers with high-temperature nanomaterials and accelerate the commercialization of SOC technology. [ABSTRACT FROM AUTHOR]

The absorption and scattering properties of the water medium cause various types of distortion in underwater images, which seriously affects the accuracy and effectiveness of subsequent processing. The application of supervised learning algorithms in underwater image enhancement is limited by the difficulty of obtaining a large number of underwater paired images in practical applications. As a solution, we propose an unsupervised representation disentanglement based underwater image enhancement method (URD-UIE). URD-UIE disentangles content information (e.g., texture, semantics) and style information (e.g., chromatic aberration, blur, noise, and clarity) from underwater images and then employs the disentangled information to generate the target distortion-free image. Our proposed method URD-UIE adopts an unsupervised cycle-consistent adversarial translation architecture and combines multiple loss functions to impose specific constraints on the output results of each module to ensure the structural consistency of underwater images before and after enhancement. The experimental results demonstrate that the URD-UIE technique effectively enhances the quality of underwater images when training with unpaired data, resulting in a significant improvement in the performance of the standard model for underwater object detection and semantic segmentation. • URD-UIE accomplishes underwater image decomposition: domain-invariant content & domain-specific style. • We use disentangled cycle translation for structural consistency & diverse styles in recovered images. • Our method employs diverse loss functions ensuring realistic enhanced images via disentangled features. • URD-UIE trains unsupervised, providing valuable improvements for subsequent underwater tasks. [Display omitted] [ABSTRACT FROM AUTHOR]

Advancements in battery technology have dramatically increased demand for improvements in separator design, as the separator plays a critical role in ensuring the safety and electrochemical performance of the cells. Current separators, either in commercial usage or under investigation, have yet to meet the high stability and lifespan performance standards necessary to prevent deterioration in the efficiency and reliability of the battery technologies. Recently, considerable effort has been devoted to developing functionalized separators, ranging from designing a variety of new materials and modification methods, and increasingly, to optimizing advanced preparation processes. In order to understand how the mechanisms of separator performance are affected by different properties, we will first summarize recent research progress and then have in-depth discussions regarding the separator's significant contribution to enhancing the safety and performance of the cell. We then provide our design strategy for future separators, which not only meets the requirements of different type of batteries, but also aims for multifunctionality. We hope such a perspective could provide new inspiration in the development of separator research for future battery technologies. [ABSTRACT FROM AUTHOR]

In this paper, we present a computationally efficient economic NMPC formulation, where we propose to adaptively update the length of the prediction horizon in order to reduce the problem size. This is based on approximating an infinite horizon economic NMPC problem with a finite horizon optimal control problem with terminal region of attraction to the optimal equilibrium point. Using the nonlinear programming (NLP) sensitivity calculations, the minimum length of the prediction horizon required to reach this terminal region is determined. We show that the proposed adaptive horizon economic NMPC (AH-ENMPC) has comparable performance to standard economic NMPC (ENMPC). We also show that the proposed adaptive horizon economic NMPC framework is nominally stable. Two benchmark examples demonstrate that the proposed adaptive horizon economic NMPC provides similar performance as the standard economic NMPC with significantly less computation time. • Computationally efficient economic NMPC formulation with adaptive horizon length. • Nominal stability of the adaptive horizon economic NMPC is shown. • Two examples demonstrate that the proposed adaptive horizon economic NMPC provides similar performance as the standard economic NMPC with significantly less computation time. [ABSTRACT FROM AUTHOR]

• Standards of care play an important role in the clinical course of bacteraemia. • Definitive antibiotic treatment must be tailored according to susceptibility. • Timely CVC removal reduces mortality in bacteraemia. • ID consultations might enhance adherence to standards of care and improve quality of care. Despite high mortality rates, physicians can alter the course of the Staphylococcus aureus bacteraemia (SAB) by following recommended standards of care. We aim to assess the adherence of these guidelines and their impact on mortality. Substudy from a prospective cohort of hospitalized patients with SAB from three hospitals from Peru. Hazard ratios were calculated using Cox proportional regression to evaluate the association between 30-day mortality and the performance of standards of care: removal of central venous catheters (CVC), follow-up blood cultures, echocardiography, correct duration, and appropriate definitive antibiotic therapy. 150 cases of SAB were evaluated; 61.33% were MRSA. 30-day attributable mortality was 22.39%. CVC removal was done in 42.86% of patients. Follow-up blood cultures and echocardiograms were performed in 8% and 29.33% of cases, respectively. 81.33% of cases had appropriate empirical treatment, however, only 22.41% of MSSA cases were given appropriate definitive treatment, compared to 93.47% of MRSA. The adjusted regression for all-cause mortality found a substantial decrease in hazards when removing CVC (aHR 0.28, 95% CI: 0.10 - 0.74) and instituting appropriate definitive treatment (aHR 0.27, 95% CI: 0.08 - 0.86), while adjusting for standards of care, qPitt bacteraemia score, comorbidities, and methicillin susceptibility; similar results were found in the attributable mortality model (aHR 0.24, 95% CI: 0.08 - 0.70 and aHR 0.21, 95% CI: 0.06 - 0.71, respectively). Deficient adherence to standards of care was observed, especially definitive treatment for MSSA. CVC removal and the use of appropriate definitive antibiotic therapy reduced the hazard mortality of SAB. [ABSTRACT FROM AUTHOR]

• For HIV genotyping, the undetected minority populations by standard Sanger sequencing (SS) were not always minor. • 9–15% of cases with treatment failure/interruption had underestimated drug resistant variants accounting for ≥15–25% total viral population. • The study suggested that 10-15% threshold deep sequencing may be considered a suitable substitute for SS on HIV drug resistant mutation detection. The clinical utilisation of deep sequencing in HIV treatment has been hindered due to its unknown correlation with standard Sanger genotyping and the undetermined value of minority drug resistance mutation (DRM) detection. To compare deep sequencing performance to standard Sanger genotyping with clinical samples, in an effort to delineate the correlation between the results from the two methods and to find the optimal deep sequencing threshold for clinical utilisation. We conducted a retrospective study using stored plasma collected from August 2014 to March 2018 for HIV genotyping with the commercial Sanger genotyping kit. Samples with available Sanger genotyping reports were further deep sequenced. Drug resistance was interpreted according to the Stanford HIV drug resistance database algorithm. At 15–25% minority detection thresholds, 9–15% cases had underestimated DRMs by Sanger sequencing. The concordance between the Sanger and deep sequencing reports was 68–82% in protease-reverse transcriptase region and 88–97% in integrase region at 5–25% thresholds. The undetected drug resistant minority variants by Sanger sequencing contributed to the lower negative predictive value of Sanger genotyping in cases harbouring DRMs. Use of deep sequencing improved detection of antiretroviral resistance mutations especially in cases with virological failure or previous treatment interruption. Deep sequencing with 10–15% detection thresholds may be considered a suitable substitute for Sanger sequencing on antiretroviral DRM detection. [ABSTRACT FROM AUTHOR]

• Two novel term weighting schemes are proposed for text classification. • The proposed schemes are named as SQRT_TF-IGM imp and TF-IGM imp. • These schemes are derived from standard inverse gravity moment formula. • SQRT_TF-IGM imp method generally outperformed all other schemes. • The performance gains of the SQRT_TF-IGM imp scheme are statistically significant. Text classification is one of the popular high dimensional classification problems where providing better feature vector representations explicitly improve classification performances. Thus, assigning appropriate weights to features or terms are crucial for obtaining effective feature vector representations. The methods used for weighting terms in text classification are called term weighting schemes. Although there exist some term weighting schemes for text classification, they are not fully effective and researchers still focus on proposing new term weighting schemes. In this study, two novel term weighting schemes namely SQRT_TF-IGM imp and TF-IGM imp derived from standard inverse gravity moment formula are proposed to improve weighting behaviors of existing TF-IGM scheme especially for some extreme cases. The performances of proposed schemes are compared with two standard IGM based schemes and five other state-of-the-art term weighting methods on both unbalanced (Reuters-21578) and balanced (20 Mini Newsgroups and 20 Newsgroups) datasets with KNN, SVM, and NN classifiers. Micro-F1 and macro-F1 are used as success measures. The experiments are conducted with various different feature sizes to examine the effects of the feature size on the success of weighting. The experimental results showed that the proposed SQRT_TF-IGM imp method generally outperformed all schemes including both standard TF-IGM and SQRT_TF-IGM schemes. However, the proposed TF-IGM imp scheme also showed mostly better performance than standard TF-IGM. To demonstrate validity of the proposed weighting scheme having maximum performance, t -test is also used and it can be stated that the performance gains obtained by the proposed SQRT_TF-IGM imp weighting scheme compared to standard SQRT_TF-IGM are statistically significant. [ABSTRACT FROM AUTHOR]

In probabilistic robust optimization problems we generally take as objective function the weighted sum of the expected value and the standard deviation of the performance. Unfortunately, it has been observed that if the weight given to the standard deviation is too high, then the designs obtained may have no physical meaning at all. If this occurs we say that the design obtained is not consistent from the physical point of view. In this work we present a probabilistic robust optimization approach aimed to preserve physical consistency of the problem being addressed. The objective function is written as the p -norm of a vector composed of the expected value and the weighted standard deviation. We then demonstrate that the standard approach is a particular case of this p -norm approach with p = 1. In the proposed approach we take p = 2 and prove that physical consistency is ensured provided the standard deviation is not given more weight than the expected value. A similar proof is not available for the standard approach. This makes statement of the robust optimization problem much easier with the proposed approach. The proposed approach can be adapted into existing computational routines, since the same statistical moments and sensitivities are required. The theoretical results are illustrated in the context of compliance-based topology optimization. In the numerical examples we show that the concept of physical consistency may have a significant impact on the designs obtained. • A discussion on physical consistency of robust optimization is presented. • An approach that is always consistent is proposed. • Theoretical results are illustrated in the context of compliance-based topology optimization. [ABSTRACT FROM AUTHOR]

The need for appropriate standards to calibrate ion arrival times in traveling wave ion mobility (TWIM) remains an issue for collision cross section (CCS) measurements, particularly in the case of multiply charged anions due to the scarcity of calibrants in the negative ion mode. In order to circumvent constraints raised for standards in the conventional power law calibration, the new approach recently implemented in the IMSCal software (Anal. Chem. 93 (2021) 3542–3550) has been evaluated here to derive CCS of multiply charged anions measured by TWIM. The mathematical model developed in IMSCal to describe ion motion in the TWIM cell was trained with different combinations of phosphoric acid clusters and deprotonated polyalanine standards and performance of so-obtained calibrations was evaluated for multiply deprotonated oligothymidine 10mers used as control analytes. Although optimal composition of the calibrant set could not be fully rationalized, one mandatory condition for best CCS accuracy is to include standards of different charge states to properly model the effects of radial distribution of ions in the TWIM cell. Further improvement of calibration robustness requires that one of these charge states equals that of the analytes to account for the effects of velocity relaxation also incorporated in the IMSCal model. Doing so, experimental CCS values could be readily obtained with relative error below ±5% for anions of charge state up to 6–. [Display omitted] • The IMSCal software was tested for determine CCS of multiply charged anions. • High CCS accuracy could be achieved with low constraints on standards. • Selected combination of charge states advantageously replaces large series of calibrants. • CCS could be determined for oligothymidines with up to 6 deprotonated units. [ABSTRACT FROM AUTHOR]

The improvement of energy efficiency of products is a key pillar of climate and energy strategy in the European Union (EU). The first EU product policies were adopted in the late 1970s, and they have evolved to become a coherent set of implementing measures under framework directives that harmonise and refine the regulatory process. After years of weak implementation, considerable progress in terms of scale and ambition has been achieved in the last decade. In 2020, product mandatory measures covered 50% of the EU total final energy consumption, leading to 46 Mtoe energy savings. This paper describes the available policy instruments to promote energy efficiency and remove the market barriers hindering the penetration of the best performing technologies. It offers a review of the progress made over these last 40 years of EU product policies, describing the Energy Labelling, the Minimum Energy Performance Standards (MEPS), the Ecodesign Directive and the voluntary agreements (EU Ecolabel and Green Public Procurement). Moreover, it highlights the remaining challenges and provides policy recommendations to further exploit the EU potential to save energy from products. • Energy efficiency in products is a key pillar of EU climate and energy strategy. • Despite 40 years of EU product policies, their scale has only improved since 2010. • In 2020, mandatory measures covered 50% of the EU final energy consumption. • By 2030, savings expect to reduce the final consumption by 17%. • Policy orientation to further exploit the energy savings of products is provided. [ABSTRACT FROM AUTHOR]

The first section of this work is dedicated to the exposure of possible methodologies and solutions that can be implemented to face hygrothermal ageing effects, avoiding reduction of service-life and increasing safety margins. It's underlined the importance of the material and manufacturing process selection, and its impacts on issues related to the hygrothermal ageing. Nanofiller, Surface and fiber treatments are investigated and the their influence on the moisture uptake is discussed. The second part is dedicated to the technique used to detect and remove moisture. It shows how an early detection and the moisture removal can be useful to elongate the service-life and to avoid irreversible loss of mechanical properties. Particular emphasis is given to innovative structural health monitoring (SHM) systems combined with fiber optic sensors, which allows to check on board, real-time, the status of composite component subjected to moisture aggression, thus keeping high performance standards, while preventing failures. [ABSTRACT FROM AUTHOR]

Phase-field models are widely employed to simulate microstructure evolution during processes such as solidification or heat treatment. The resulting partial differential equations, often strongly coupled together, may be solved by a broad range of numerical methods, but this often results in a high computational cost, which calls for advanced numerical methods to accelerate their resolution. Here, we quantitatively test the efficiency and accuracy of semi-implicit Fourier spectral-based methods, implemented in Python programming language and parallelized on a graphics processing unit (GPU), for solving a phase-field model coupling Cahn–Hilliard and Allen–Cahn equations. We compare computational performance and accuracy with a standard explicit finite difference (FD) implementation with similar GPU parallelization on the same hardware. For a similar spatial discretization, the semi-implicit Fourier spectral (FS) solvers outperform the FD resolution as soon as the time step can be taken 5 to 6 times higher than afforded for the stability of the FD scheme. The accuracy of the FS methods also remains excellent even for coarse grids, while that of FD deteriorates significantly. Therefore, for an equivalent level of accuracy, semi-implicit FS methods severely outperform explicit FD, by up to 4 orders of magnitude, as they allow much coarser spatial and temporal discretization. [Display omitted] [ABSTRACT FROM AUTHOR]

The quantity of data generated increases daily, which makes it difficult to process. In the case of supervised learning, labeling training examples may represent an especially tedious and costly task. One of the aims of positive and unlabeled (PU) learning is to train a binary classifier from partially labeled data, representing a strategy for combining supervised and semi-supervised learning and alleviating the cost of labeling data fully. Still, the main strength of PU learning arises when the negative data are not directly available or too diverse. Although the generative approaches have shown promising results in this field, they also bring shortcomings, such as high computational cost, training instability, and inability to generate fully labeled datasets. In the paper, we propose a novel Conditional Generative PU framework (CGenPU) with a built-in auxiliary classifier. We develop a novel loss function to learn the distribution of positive and negative examples, which leads to a unique, desirable equilibrium under a nonparametric assumption. Our CGenPU is evaluated against existing generative approaches using both synthetic and real data. The characteristics of various methods, including ours, are depicted with different toy examples. The results demonstrate the state-of-the-art performance on standard positive and unlabeled learning benchmark datasets. Given only ten labeled CIFAR-10 examples, CGenPU achieves classification accuracy of 84%, while current state-of-the-art D-GAN framework achieves 54%. On top of that, CGenPU is the first single-stage generative framework for PU learning. • CGenPU is capable of conditional generation and binary classification. • Auxiliary PU loss enables the generation of positive and negative data distributions. • The objective has a unique, desirable equilibrium under a non-parametric assumption. • CGenPU shows state-of-the-art results in One-vs-One and One-vs-Rest scenarios. [ABSTRACT FROM AUTHOR]

Service composition can quickly build new value-added composite services by combining existing Web services. However, with the complex and changeable Internet environment, composite services need to effectively understand and manage requirements in a flexible and adaptive manner, and also be able to quickly and proactively provide high-quality services through a series of dynamic evolution models. In response to these challenges, in this study, we propose a service composition evolution method that can adaptively select the appropriate service to improve user satisfaction and service quality. First, we introduce a deep clustering method based on the topic model and auto-encoder, which considers the function description documents and parameter information of the services to reduce the search space of the candidate services. Furthermore, we describe a requirement-oriented context-sensitive task model to integrate functional and non-functional user requirements by connecting the context of Web subtasks. Then, we present a dynamic service matching method with QoS threshold judgement to filter and rank the services. We applied the proposed evolution method to the datasets of real-world Web services and measured the performance using standard measurement metrics. The prototype implementation and results of simulation experiments verified the effectiveness of each part of our method. • A novel service composition evolution method is proposed. • A deep clustering method is presented to implement the service clustering process. • RCT model and various evolutionary types are used to satisfy user requirements. • Simulation experiments on prototype system show the effectiveness. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used. [ABSTRACT FROM AUTHOR]

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse the role played by independent directors and their decisions regarding the disclosure of information on corporate social responsibility (CSR). We posit that these directors (i) drive the creation of a CSR committee to advise them on social and environmental issues, and (ii) promote the use of the Global Reporting Initiative guidelines and the International Finance Corporation Performance Standards (jointly, the GRI-IFC strategy) in their CSR disclosure. The GRI-IFC strategy requires the disclosure of key indicators, and is expected to strengthen stakeholder engagement and to enhance the firm's internal monitoring. Our analysis of an unbalanced sample of 750 international companies, spanning the period 2011–2016, in which logistic and ordinal regressions are applied to the panel data, reveals a generalised absence of direct involvement by independent directors in the adoption of the GRI-IFC strategy. However, in many cases these directors promote the creation of a CSR committee to advice the board on the voluntary implementation of the GRI-IFC strategy. Our evidence shows that the CSR committee plays a mediating role in the cause-effect relationship between independent directors and their adoption of the GRI-IFC strategy. [ABSTRACT FROM AUTHOR]

In this work, the application of a new strategy called NWFE ensemble (nonparametric weighted feature extraction ensemble) method is proposed. Subspace-supervised projections based on NWFE are incorporated into the construction of ensembles of classifiers to facilitate the correct classification of wrongly classified instances without being detrimental to the overall performance of the ensemble. The performance of NWFE is investigated with a c-Jun N-terminal kinase-3 inhibitor benchmark dataset using different chemical compound representation models. Compared with the standard method, the results obtained show that the applied method improves the prediction performance using two classifiers based on decision trees and support vector machines. Image 1 • The new nonparametric weighted feature extraction (NWFE) method is proposed. • It improves classifier performance compared to standard ensemble method. • The fast clustering-based feature selection algorithm showed be appropriated to find an optimal subset of descriptors. • The results were validated using different molecular representation, with JNK3 inhibition models as an example. [ABSTRACT FROM AUTHOR]

Abstract We present a stabilized finite element method that generalizes Nitsche's method for enforcing stiff anisotropic cohesive laws with different normal and tangential stiffness. For smaller values of cohesive stiffness, the stabilized method resembles the standard method, wherein the traction on the crack surface is enforced as a Neumann boundary condition. Conversely, for larger values of cohesive stiffness, the stabilized method resembles Nitsche's method, wherein the cohesive law is enforced as a kinematic constraint. We present several numerical examples, in two-dimensions, to compare the performance of the stabilized and standard methods. Our results illustrate that the stabilized method enables accurate recovery of crack-face tractions for stiff isotropic and anisotropic cohesive laws; whereas, the standard method is less accurate due to spurious traction oscillations. Also, the stabilized method could mitigate spurious sensitivity of load–displacement results to displacement increment in mixed-mode fracture simulation, owing to its stability and accuracy. [ABSTRACT FROM AUTHOR]

Abstract Tenova Pulsed Column Kinetics Internals (TPC-KIs) are a newly designed type of pulsed column internals with a novel structure that is expected to achieve less back-mixing, higher holdup and improved mass transfer. This study compared the hydrodynamic performance of TPC-KIs with standard disc and doughnut internals in a 2 m high, 76 mm diameter pulsed solvent extraction column with the water-Alamine 336-Shellsol 2046 system. The effects of the wettability of column internals have also been considered. Dispersed phase holdup and Sauter mean droplet diameter were measured under various pulsation intensities and velocities of both phases, and empirical correlations for holdup and drop size have been refitted within absolute average relative errors of 15%. Tenova kinetics internals have a lower holdup and larger Sauter-mean drop size compared to standard disc and doughnut internals, and these hydrodynamic parameters are predictable using the correlation developed in this study. [ABSTRACT FROM AUTHOR]

• Retrofits will be required in 65–85% of covered buildings in most cities. • Heavy (>35%) or deep (>50% savings) energy retrofits will be required in most buildings. • Meeting BPS targets will produce considerable (25–45%) total savings for each city. • Most of the total savings can be achieved by improving a small number of buildings. • Simple payback periods for implementing required retrofits are between 10 and 20 years. Building performance standards (BPS) are a promising policy tool for reducing energy use and greenhouse gas emissions in existing buildings, and have become increasingly popular among major U.S. cities and elsewhere globally. Assessing the scope and extent of the investment required by BPS is critical for shaping their design and preparing jurisdictions for the effort ahead. The goal of this study was to evaluate the feasibility of achieving BPS targets. Municipal benchmarking data for three property types—offices, multifamily housing, and K-12 schools—in 10 U.S. cities was analyzed and compared to the site energy use intensity (EUI) targets in ASHRAE Standard 100–2018. The results show that BPS are a high effort, high reward policy tool with 65–85% of benchmarked buildings not meeting the target and median required reductions of around 30–40% leading to a total energy use reduction of 25–45% for most cities and property types. Median simple payback periods for implementing the required retrofits were estimated between 10 and 20 years. However, the results also show that the majority of BPS savings could be achieved by retrofitting a small number of buildings and these policies could be better tailored to minimize the retrofit effort and maximize the environmental rewards. [ABSTRACT FROM AUTHOR]

Existing scene text recognition methods have incorporated text rectification to lessen text irregularity in images for accurate recognition. Previous text rectification methods aim to convert an irregular text image into a regular form, making it easier to be recognized. In this study, we explore text rectification for text recognition and discover the issues: performance degradation of the recognition network and the unreliable situation of text rectification, which are ignored by all previous works. Therefore, we rethink what is causing two issues, and propose a rectification-based text recognition network to mitigate the above issues. The proposed network consists of text rectification and text recognition, and designs a multi-level feature aggregation module to enhance feature learning for character representation. Concretely, we devise a mixed batch training strategy to address the performance degradation of the recognition network, and design a confidence decoding scheme to avoid the unreliable situation of text rectification. Extensive ablation studies verified the positive role of the feature aggregation module in feature learning and the effectiveness of the proposed training strategy and decoding scheme in addressing the issues. Experimental results outperform the state-of-the-art results on public benchmarks. • We explore the limitation and unreliable cases caused by the rectification network. • Mixed batch and confidence decoding are designed for accurate recognition. • We design feature aggregation to exploit multi-level features. • Our method achieves superior performance on standard datasets. [ABSTRACT FROM AUTHOR]

Obesity, termed as excessive body fat, is a major public health problem. Being able to accurately predict body fat percentage provides an opportunity for making appropriate prevention and diagnosis. Although various assessment techniques have been applied for body fat estimation, high-cost tests are required using special equipment, and the performance of standard machine learning models are typically impacted by noise data. In this paper, we propose a novel fuzzy-weighted approach for improving body fat prediction using easily accessed body measurements in a cost-effective manner. Given the noise data problem, we design a novel fuzzy weighted operation by extending the minimum and maximum values for calculating the fuzzy weights to cover more possibilities. The fuzzy weights are incorporated into the error constraints of a relative error support vector machine model to improve its robustness to noise data. In addition, the Whale Optimization Algorithm (WOA) is applied in our proposed model for parameter optimization. Our proposed fuzzy-weighted approach delivers excellent results for body fat prediction by outperforming other well-known machine learning models. Further, the statistical test based on the 20 runs of experiments not only confirms that our proposed model could significantly outperform the models under comparison, but also show the fuzzy weighted operation and WOA are effective to improve the proposed approach. This offers a viable alternative for medical decision makers to make appropriate prevention, early diagnosis, and early treatment of body fat related diseases when high-cost measurement techniques are not available. • A novel fuzzy-weighted approach is proposed for body fat prediction. • The whale optimization algorithm is applied for parameter optimization. • Evaluation metrics indicate the effectiveness of our proposed approach. • Our approach can be a useful tool for medical decision makers. [ABSTRACT FROM AUTHOR]

Heat pumps and refrigerators are two systems with different usage, but same working principles. Generally, HPs work in steady state condition and specific thermodynamic cycle, but refrigerator or freezer apparatus have variable cycle and work in the unsteady condition. In this study, two different data sources are gathered to analyze thermodynamic differences between two mentioned systems. Initially, an existing heat pump unit is analyzed in different working conditions with different refrigerants and then the unit is converted to a refrigeration device to compare their thermodynamic conditions and treatments. Thermodynamic laws, COP value and efficiency, compressor power consumption, optimum charge amount, and flow rate of secondary fluid are main parameters which are considered to compare heat pump and refrigerator performances by using several refrigerants including R134a, R407c, R22, and R404a. In the case of freezer mode, entropy reduction in compressor and COP L drop to very low values are other substantial issues discussed in this research. It was also revealed that, optimal charge amount of the freezer is 15–25% lower than that of the system when works in HP mode. Experiments showed that, in some cases COP of the heat pump was even more than 100 times greater than that of the freezer. [ABSTRACT FROM AUTHOR]

PBR in the electric power industry has failed to gain the traction that incentive regulation experienced in the telecommunications industry. We do not believe institutional differences between the two industries fully explain why PBR has not gained the footing that the economics literature suggests that it should. It is time to debunk several myths that pervade industry and regulatory thinking on the merits of PBR that have had a deleterious effect on its adoption. [ABSTRACT FROM AUTHOR]

Environmental issues and the prospect of an energy crisis inspire humans to exploit wind power. However, with the increase of wind power penetration level, operating power systems securely and reliably is a serious challenge due to the inherent nature of wind power intermittency. Wind power intermittency has been the major barrier for large scale wind power integration. This paper reviews past research on wind power intermittency, including its impacts on power system, how it is measured, and mitigation solutions. It has been found that as wind power integration increase, the system reverses and costs consequently increase, while the system reliability and CO 2 reductions decrease. In order to mitigate wind power intermittency, studies on intermittency measurements and mitigation solutions are necessary. Existing measurements of wind power intermittency are summarized firstly. Considering the limitations of existing methods, new definitions and metrics are proposed based on our study. Then, various wind power intermittency mitigation solutions are comprehensively reviewed, including wind farms, generation-side, demand-side and energy storage. In the final part of this paper, the further work on wind power intermittency is discussed in detail. In summary, wind power intermittency can be effectively mitigated using various technological and managerial approaches based on an in-depth understanding of intermittency. [ABSTRACT FROM AUTHOR]

Fatigue is one of the factors leading to reduction in productivity, poor quality of work and increased risk of accidents in construction. Existing established methods of assessing fatigue include surveys and questionnaires, which are cumbersome to implement at construction sites. This study presents a novel approach for real time monitoring of physical fatigue in construction workers using wearable sensors. Changes in the heart rate, thermoregulation and electrical brain activity during a simulated construction task were monitored from 12 participants using a heart rate monitor, infrared temperature sensors and an EEG sensor. Borg's RPE was used as a subjective scale to collect the level of fatigue experienced by the participants. Boosted tree classifiers were trained using the features extracted from the heart rate and temperature sensor signals and used to predict the level of physical fatigue. Only physical fatigue was assessed as none of the participants developed any sign of mental fatigue during the study. The results show that physical fatigue can be monitored using wearable sensors. The classification accuracy, based solely on features extracted from average of skin temperature data, was 9% higher than based solely on heart rate data, and combining the information from both sensors resulted in the best accuracy of 82%. The results also show that monitoring thermoregulation from temple can be more useful compared to other studied monitoring sites, the classification accuracy based only on data from the temple was 79%. This accuracy is significantly higher compared to the classification accuracy based only on heart rate data (59%). [ABSTRACT FROM AUTHOR]