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Objective: The clinical applicability of shear wave elasticity imaging (SWEI) has been confounded by its appreciable inter-system variability and unsatisfactory sensitivity. While SWEI relies on plane wave imaging (PWI) to achieve real-time rendering, it has been rarely noticed that PWI can affect SWEI's performance. This work is aimed at demonstrating that the use of coherent plane wave compounding (CPWC) can be a factor causing SWEI's underperformance. Methods: We presented a model to formally describe the slow-time behavior of CPWC in motion tracking. This model reveals that CPWC introduces temporal convolution on the observed motion, making the motion sampling process a low-pass filter (LPF). For validation, shear waves were produced in a phantom in the same way but sampled via PWI using different compounding numbers (CN) and pulse repetition frequencies (PRF), with the obtained signals compared with the inferences drawn from our model. Similar experiments were performed to reconstruct two small targets in the phantom in order to appraise the impact of CPWC on SWEI's sensitivity. Discussion: The validation experiment shows that the measurements match well with the model inferences, which verifies the LPF nature of CPWC. The phantom study also shows that either increasing CN or decreasing PRF can cause the loss of high-frequency motion information, leading to blurred target delineation by SWEI. Conclusion: The convolution effect can help understand the variability of SWEI. Researchers should beware this effect when working on SWEI standardization. Clinicians using SWEI should also be cautious because this effect makes it harder to identify small lesions. [ABSTRACT FROM AUTHOR]

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]

Deep reinforcement learning (DRL) has been proven as a powerful approach for robot navigation over the past few years. DRL-based navigation does not require the pre-construction of a map, instead, high-performance navigation skills can be learned from trial-and-error experiences. However, recent DRL-based approaches mostly focus on a fixed navigation target. It is noted that when navigating to a moving target without maps, the performance of the standard RL structure drops dramatically on both the success rate and path efficiency. To address the mapless navigation problem with moving target, the predictive hierarchical DRL (pH-DRL) framework is proposed by integrating the long-term trajectory prediction to provide a cost-effective solution. In the proposed framework, the lower-level policy of the RL agent learns robot control actions to a specified goal, and the higher-level policy learns to make long-range planning of shorter navigation routes by sufficiently exploiting the predicted trajectories. By means of making decisions over two level of policies, the pH-DRL framework is robust to the unavoidable errors in long-term predictions. With the application of deep deterministic policy gradient (DDPG) for policy optimization, the pH-DDPG algorithm is developed based on the pH-DRL structure. Finally, through comparative experiments on the Gazebo simulator with several variants of the DDPG algorithm, the results demonstrate that the pH-DDPG outperforms other algorithms and achieves a high success rate and efficiency even though the target moves fast and randomly. [ABSTRACT FROM AUTHOR]

• Quality Rating and Improvement Systems use indicators to define early care and education quality. • Indicators often offer evidence-informed guidance to support and monitor development and foster mental health. • Development and mental health promotion often applies to center-based and family child care. • Opportunities exist to offer more specific guidance in quality indicators. • Future research can assess equity of child development and mental health promotion indicators. In the United States (U.S.), quality rating and improvement systems (QRIS) are used by many states to incentivize quality in ECE and may be a viable lever for promoting early childhood development and mental health on a population level. We conducted a qualitative review of publicly available data on state QRIS indicators to better understand how states incorporate evidence-informed early childhood development and mental health promotion standards in QRIS. We systematically compared QRIS indicators for 41 U.S. states with child development and mental health promotion quality standards from Caring for Our Children National Health and Safety Performance Standards; Guidelines for Early Care and Education Programs, 3rd Edition , as of March/April 2020. Of those, 39 states included at least one indicator consistent with child development or mental health promotion standards, including practices that can lead to early detection of developmental delays such as developmental monitoring, activities or curriculum addressing developmental domains, and regular communication and resource-sharing with parents/guardians. Opportunities exist within states for incorporating more specific guidance within indicators, such as use of childcare health consultants and advocates, validated screening tools, parent/guardian participation or input in developmental monitoring and screening, and staff training on family engagement. We found that in most states QRIS indicators offer guidance for ECE systems to support and monitor early development and foster mental health, with opportunities to enhance guidance. Findings point to QRIS as a viable opportunity for promotion of early childhood development and mental health standards in ECE systems. [ABSTRACT FROM AUTHOR]

In the course of a more fluctuating power supply due to renewable energy sources, the need for tray columns with flexible operability increases. Accordingly, further research is required to better predict the behavior at load limits – especially for fixed and float valve trays. Within this work, the hydraulic behavior of numerous valve tray configurations was investigated employing a 1.2 m diameter gas/liquid (air/water) pilot test column. Fixed and float valve tray performance has been validated using sieve trays as a reference, which have already been extensively studied in the literature. In order to obtain significant data, weeping and entrainment limits in particular, as well as froth height and pressure drop, were measured quantitatively in the experiments. By varying the geometry parameters, numerous different cases were investigated. Based on all the data obtained, reliable statements can be made about the validity and applicability of calculation models known from the literature. [Display omitted] • A 1.2 m diameter air/water pilot test tray column was set up. • The hydraulic operational limits of the tray column are considered. • Measuring light curtain used as novel non-invasive method measuring froth height. • Quantitative measurements of pressure drop, froth height, weeping and entrainment. • Experimental data is compared to relevant models known from literature. [ABSTRACT FROM AUTHOR]

• A deep learning-based method is proposed for noise robust face super-resolution. • The proposed work introduces the ExFAU to suppress the noise from the SR process. • The multi-scale filters are used to learn the micro and high-level features. • A loss function is designed to preserve the identity and landmark in the face images. • Experimental results reveal the superiority of the proposed method over the state-of-the-art works. A new noise robust face super-resolution model using an attentive ExFeat-based generative adversarial network is proposed in this paper. The proposed model introduces the Exigent Feature Attention Unit (ExFAU) which consists of an Exigent Feature (ExFeat) block with a spatial attention unit to enhance the visual quality of the generated face images. The ExFAU block assists the model in reducing the noise and extracting the micro and high-level facial features. Further, the ExFeat block is followed by a spatial attention unit to focus on specific facial features. This allows us to give more attention to key face attributes related features and less to the remaining features. The proposed model repeats the ExFAU block to focus on different facial components and enhance them to improve the overall quality of the resultant face images. Experimental outcomes exhibit that the proposed model gains state-of-the-art performance on the standard datasets, namely CelebAHQ, Helen, and LFW face. [ABSTRACT FROM AUTHOR]

• We developed a ML model to recommend first FSH dosage for all types of patients. • The model performance surpassed the clinicians' in both development and validation. • The model can serve as quality check, second opinion or learning tool for trainees. Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model? Observational study (2011–2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011–2019) and 774 in the validation phase (2020–2021). Predictor variables included age, BMI, AMH, AFC and previous live births. Performance was measured with a proposed score based on the number of MII oocytes retrieved and dose received, recommended, or both. The included cycles were from women aged 37.7 ± 4.4 years (18–45 years), with a BMI of 23.5 ± 4.2 kg/m2, AMH of 2.4 ± 2.3 ng/ml, AFC of 11.3 ± 7.6, and an average number of MII obtained 6.9 ± 5.4. The model reached a mean performance score of 0.87 (95% CI 0.86 to 0.88) in the development phase, significantly better than for doses prescribed by clinicians for the same patients (0.83, 95% CI 0.82 to 0.84; P = 2.44 e-10). Mean performance score of the model recommendations was 0.89 (95% CI 0.88 to 0.90) in the validation phase, also significantly better than clinicians (0.84, 95% CI 0.82 to 0.86; P = 3.81 e-05). The model was shown to surpass the performance of standard practice. This machine learning model could be used as a training and learning tool for new clinicians, and as quality control for experienced clinicians. [ABSTRACT FROM AUTHOR]

The compliance with international standards does not necessarily ensure a long-term reliability of photovoltaic modules. Field tests are required to make the standard tests more adjusted to the evaluation of new concepts. Numerous researches focus on long-term performance of standard modules, but only few studies are dedicated to lightweight modules. Therefore, the field operation of a PV system consisting of 28 lightweight modules of 4 different types (denoted as P1, P2, P3 and P4) has been being monitored for a year in warm temperate climate conditions. Over this period, the modules were tested by visual inspection, thermography and electroluminescence imaging and their electrical parameters were measured to compare results with the outcome of simulations in PVsyst. Visual inspections revealed yellowing and delamination of a few P2 and P4 modules because of issues with EVA foil. Thermography exposed many hot spots and failure of substrings in case of P2 modules. Also, the electroluminescence imaging showed numerous defects, mostly various micro cracks, which affected all modules' types. Nevertheless, excluding P2 modules, the others generated higher yield than estimated by the simulations. Analysing year-to-year degradation, results for P3 and P4 were in line with results predicted for standard PV modules in similar climates. [ABSTRACT FROM AUTHOR]

Despite myriads of mathematical and experimental studies using various heat exchanger-metal hydride assemblies, an adept comparison of reactors on standard performance defining parameters, i.e., reactor weight, external energy utilization, and alloy material, are not studied or reported for industrial storage containers and onboard applications. This lacuna is a critical barrier in developing efficacious hydrogen storage reactors for various applications. This article chronicles the 3-D design and optimization of three basic heat exchanger setups of solid-state hydrogen storage systems (shell and tube, spiral tube, and tubular) on critical parameters. For 5 kg LaNi 5 , a tubular design with small tube diameters has resulted in a shorter reaction time (120 s) for 90% saturation at 303 K and 15 bar inlet conditions; however, the reactor's gravimetric density was exceptionally high. In contrast, the reactor with three spiral tubes accomplished an exemplary reaction rate at a reasonable gravimetric density. This spiral arrangement became the base for developing a novel reactor that integrated heat pipes to reduce the sorption time. The proposed design achieved a specific output energy rate of 527 W/kg for 90% saturation in 366 s at a supply pressure of 15 bar, which is 23.7% higher than the reactor without heat pipes. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Auscultation for an extended period of time using a wearable stethoscope enables objective computerized analysis and longitudinal assessment of lung sounds. However, this auscultation method differs from bedside auscultation in that clinicians are not present to optimize the quality of auscultation. No prior studies have compared these two auscultation methods.Objective: The aim of this study was to compare intermittent auscultation using a conventional stethoscope with continuous auscultation using a wearable stethoscope for wheeze detection in patients who present with acute respiratory distress.Methods: Patients presenting to the emergency department with acute respiratory distress were enrolled. The Strados Remote Electronic Stethoscope Platform (RESP™) was used for continuous auscultation, and intermittent auscultation was performed using a U.S. Food and Drug Administration-cleared electronic stethoscope. A recording was made with an electronic stethoscope. Subsequently, continuous recording was made using RESP™, which continued until the patient was admitted or discharged from the emergency department. The number of captured wheezes in each recording was counted and validated by two board-certified physicians.Results: From May 2018 to May 2019, 43 patients were enrolled in the study. Three patients were excluded from analysis due to incomplete audio recording data. The mean length of recording was 62.3 min for continuous auscultation and 0.7 min for intermittent auscultation; 77.5% (31 of 40) of intermittent recordings contained wheezes, in contrast to 85% (34 of 40) of continuous recordings.Conclusions: Extending the duration of auscultation using a wearable stethoscope in a noisy clinical environment showed comparable performance to standard of care intermittent auscultation in identifying patients who have wheezes. [ABSTRACT FROM AUTHOR]

Foreign species can deteriorate the environment and the economy of a country. To automatically monitor biosecurity threats at country borders, this paper investigates compact deep networks for accurate and real-time object segmentation for hyperspectral images. To this end, knowledge distillation (KD) approaches compress the model by distilling the knowledge of a large teacher network to a compact student network. However, when the student is over-compressed, the performance of standard KD methods degrades significantly due to the large capacity gap between the teacher and the student. This gap can be addressed by adding medium-sized teacher assistants, but training them incurs significant computation and hence is impractical. To address this problem, this paper proposes a new framework called Knowledge Distillation from Multi-head Teacher (KDM), which distills the knowledge of a multi-head teacher to the student. By encapsulating multiple teachers in a single network, our proposed KDM assists the learning of a very compact student and significantly reduces the training time. We also introduce Bio-HSI, a new large benchmark hyperspectral image dataset of 3,125 high-resolution images with dense segmentation ground truth. This new, large dataset can be expected to advance research on deep models for hyperspectral image segmentation. Evaluated on this dataset, the student trained via our KDM has 762 times fewer parameters than the state-of-the-art segmentation model (i.e., HRNet), while achieving competitive accuracy. [ABSTRACT FROM AUTHOR]

Aim Of the Study: While out-of-hospital cardiac arrest (OHCA) is associated with poor survival, early bystander CPR (B-CPR) and telephone CPR (T-CPR) improves survival from OHCA. American Heart Association (AHA) Scientific Statements outline recommendations for T-CPR. We assessed these recommendations and hypothesized that meeting performance standards is associated with increased likelihood of survival. Additional variables were analyzed to identify future performance measurements.Methods: We conducted a retrospective cohort study of non-traumatic, adult, OHCA using the Singapore Pan-Asian Resuscitation Outcomes Study. The primary outcome was likelihood of survival; secondary outcomes were pre-hospital Return of Spontaneous Circulation (ROSC) and B-CPR.Results: From 2012 to 2016, 2,574 arrests met inclusion criteria. Mean age was 68 ± 15; of 2,574, 1,125 (44%) received T-CPR with 5% (135/2574) survival. T-CPR cases that met the Lerner et al. performance metrics analyzed, demonstrated no statistically significant association with survival. Cases which met the Kurz et al. criteria, "Time for Dispatch to Recognize Need for CPR" and "Time to First Compression," had adjusted odds ratios of survival of 1.01 (95% CI:1.00, 1.02; p = <0.01) and 0.99 (95% CI:0.99, 0.99; p = <0.01), respectively. Identified barriers to CPR decreased the odds of T-CPR and B-CPR being performed. Patients with prehospital ROSC had higher odds of B-CPR being performed. EMS response time < 8 minutes was associated with increased survival among patients receiving T-CPR.Conclusion: AHA scientific statements on T-CPR programs serve as ideal starting points for increasing the quality of T-CPR systems and patient outcomes. More work is needed to identify other system performance measures. [ABSTRACT FROM AUTHOR]

MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through xml configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License. [ABSTRACT FROM AUTHOR]

This study aims to characterize some safety aspects by examining the geometries of the infrastructure, currently used by societies, against the accumulation of hazardous hydrogen clouds during an accidental leak in areas with limited ventilation. Using ANSYS FLUENT as a modeling tool, the influence of garage roof shape; pyramidal and domed roof compared with the basic model (flat roof), for different leak times, on dispersion and stratification of hydrogen layers, is analyzed. As a result, the domed roof promotes to have a lower hydrogen concentration and presents two remarkable peaks of the Richardson number (Ri) with the highest value more than 2 × 105, which is three times higher than the flat roof. Besides, the influence of the leak time on the dynamic of the flow, concentration, and stratification process are observed: the mole fraction of hydrogen is more than 0.25 after 1 h of leak, whereas it is lower than 0.05 after 100 s. The volume flow and therefore the flammable volume increase. This study highlights the importance of geometrical and sizing parameters on the characteristics of hydrogen leaks and subsequently gives insights to establish performance standards for the availability and reliability of safety critical systems. • Assessment of hydrogen leaking in a confined garage with different roof shapes. • Effects of pyramidal, domed, and flat roofs on hydrogen stratification are studied. • The dispersion and accumulation of hydrogen clouds are studied as function of leaks' time. • The domed shape promotes hydrogen stratification and exhibits lower concentration. • The concentration increases with the leaks' time and the gas cloud becomes dangerous. [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]

• Individual effect of acid blocking AEM and monovalent selective CEM in selective ED was studied. • The recovered acid concentration increased by using acid blocking AEM, but limited by high Cu2+ flux. • Monovalent selective CEM further improved the acid concentration by breaking the H+ transfer balance. • Acid concentration was increased with electric current and dilute volume in SED. • H+ concentration difference plays crucial role in improving the current efficiency of SED. Electrodialysis (ED) could concentrate salt solutions, but the leakage of H+ ions across the anion-exchange membrane (AEM) obstructed their application in waste acid recovery. Selective electrodialysis (SED) with acid blocking AEM and monovalent selective cation-exchange membrane (CEM) has the ability of reclaiming acid with high concentration efficiently. This study investigated the individual effect of the two selective ion-exchange membranes by comparing the acid reclamation performance of configurations with standard AEM and CEM (A-C), acid blocking AEM and standard CEM (SA-C) and acid blocking AEM and monovalent selective CEM (SA-SC) for etching solution treatment after diffusion dialysis. Substituting acid blocking AEMs for the standard ones could significantly increase the recovered acid concentration. An acid concentration plateau was reached quickly, but Cu2+ ions transfer through the CEMs continuously due to the low H+/Cu2+ ratio in the dilute of SA-C configuration. The monovalent selective CEM obstructed the migration of Cu2+ through the CEM and broke the balance of H+ migration into and out of the dilute through CEM and selective AEM individually. The configuration of SA-SC has the highest current efficiency and the configuration of A-C has the lowest. The utilization of selective membranes increased the purity of the recovered acid, and decreased the specific energy consumption simultaneously. The acid concentration could be further improved by higher current or enlarged dilute volume in SA-SC configuration. During the ED process, H+ concentration difference between the concentrate and the dilute plays a crucial role on the current efficiency for acid recovery. [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]

• Assessed conflict monitoring on two novel picture-word interference tasks. • Tasks examined conflict monitoring across animal and object categories. • Conflict effects observed in both tasks. • Task differences observed between animals and objects. • Task performance related to performance on standard measures of cognitive control. Conflict monitoring has been studied extensively using experimental paradigms that manipulate perceptual dimensions of stimuli and responses. The picture-word interference (PWI) task has historically been used to examine semantic conflict, but primarily for the purpose of examining lexical retrieval. In this study, we utilized two novel PWI tasks to assess conflict monitoring in the context of semantic conflict. Participants included nineteen young adults (14F, age = 20.79 ± 3.14) who completed two tasks: Animals and Objects. Task and conflict effects were assessed by examining behavioral (reaction time and accuracy) and neurophysiological (oscillations in theta, alpha, and beta band) measures. Results revealed conflict effects within both tasks, but the pattern of findings differed across the two semantic categories. Participants were slower to respond to unmatched versus matched trials on the Objects task only and were less accurate responding to matched versus unmatched trials in the Animals task only. We also observed task differences, with participants responding more accurately on conflict trials for Animals compared to Objects. Differences in neural oscillations were observed, including between-task differences in low beta oscillations and within-task differences in theta, alpha, and low beta. We also observed significant correlations between task performance and standard measures of cognitive control. This work provides new insights into conflict monitoring, highlighting the importance of examining conflict across different semantic categories, especially in the context of animacy. The findings serve as a benchmark to assess conflict monitoring using PWI tasks across populations of varying cognitive ability. [ABSTRACT FROM AUTHOR]

The intercomparison of personal dose equivalent (H p (10)) among nine economies was carried out at 27 Individual Monitoring Service (IMS) Laboratories in Southeast and East Asia in 2023. In this programme, a whole-body dose assessment from Cs-137 and N-80 X-rays with two different incident beam angles (0 deg and 30 deg) was performed using an Optically Stimulated Luminescence (OSL) dosimeter. The trumpet curve recommended by IAEA GSG-7 was used as the criterion of acceptance value. The results showed that 95% of all data were within the acceptable value, with 13 items of data from two laboratories being unacceptable. After the intercomparison programme was concluded, these two laboratories implemented system improvement, including reader performance checks, calibration, configuration adjustment, and dose evaluation method enhancement. The results after improvement fell within the acceptable limit, indicating successful system enhancement. This study contributes to the harmonisation of individual monitoring service laboratories in Southeast and East Asia, ensuring that measurement results are traceable to the International System of Units (SI). Each laboratory fulfils the performance standard requirements to ensure the validity of the results for the individual monitoring service laboratory. Moreover, the complementary study induced by this exercise indicates the necessity of the intercomparison programme and successful outcome of the system improvement. • Personal dose equivalent measurement using OSL dosimeter. • Competency testing between individual Monitoring Service laboratories. • Harmonisation of personal dose assessment in the region. [ABSTRACT FROM AUTHOR]

The categorical Gini correlation, ρ g , was proposed by Dang et al. (2021) to measure the dependence between a categorical variable, Y , and a numerical variable, X. It has been shown that ρ g has more appealing properties than current existing dependence measurements. In this paper, we develop the jackknife empirical likelihood (JEL) method for ρ g. Confidence intervals for the Gini correlation are constructed without estimating the asymptotic variance. Adjusted and weighted JEL are explored to improve the performance of the standard JEL. Simulation studies show that our methods are competitive to existing methods in terms of coverage accuracy and shortness of confidence intervals. The proposed methods are illustrated in an application on two real datasets. • Confidence intervals for the categorical Gini correlation are constructed without knowing the underlying distributions. • The proposed project is easy to implement without estimating the complicated asymptotic variance. • The proposed project is computational efficient. [ABSTRACT FROM AUTHOR]

Depth completion is an effective method for generating dense depth maps from sparse ones. In recent studies, the majority of points, which we call standard points, often exhibit sub-optimal performance. This issue arises from the need to fit only very few points, termed as challenging points, which consist of noises and regions with discontinuous depth in the ground-truth. On the other hand, traditional evaluations can not recognize this situation, and are dominated by these limited challenging points, whose performance improvements may not significantly benefit related tasks. In contrast, standard points, which are critical for these tasks, are not effectively measured. This discrepancy highlights the need for a more targeted approach and evaluation method for depth completion. In order to solve the above problems, we propose a standard-point-enhancing learning paradigm. This paradigm aims to improve the performance on standard points, which consists of a Cascaded Segmentation-to-Regression Networks (CSRNet) and a Mining L1 loss. CSRNet includes two branches: DSNet and DRNet. DSNet uses segmentation to generate a coarse depth map, providing challenging-point-insensitive information. DRNet adopts a coarse-to-fine approach to learn the residual depth map between the coarse depth map and the ground-truth depth map. In addition, our Mining L1 loss leverages the segmentation results to filter out potential challenging points. This approach allows the network to concentrate more effectively on standard points. Lastly, we introduce the Minimum Error (ME) Curves as a new way to measure the performance of predicted depth maps in a flexible and comprehensive manner, irrespective of whether the points are standard or challenging. Experimental results on the KITTI and NYUDv2 datasets show that our approach significantly improves accuracy on the majority of points. • Fitting few points with large errors may not benefit most points. • Fitting few points with large errors easily leads to distortion in 3D space. • Focusing on standard points improves 2D boundaries and 3D geometry. • Flexible evaluation method for more comprehensive performance assessments. [ABSTRACT FROM AUTHOR]

• The structural impact on anthocyanins' UV–Vis molar absorptivity was studied; • A quantitative strategy based on the molar relative response factors was proposed; • An online database for 617 anthocyanins was developed to support the approach; • Our strategy provided improved quantitative results for anthocyanins in food. The effects of anthocyanin's substitution groups on the UV–Vis molar absorptivity were examined by analyzing a group of 31 anthocyanidin/anthocyanin reference standards with ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD). The substitution groups on aglycones were found to associate with molar absorptivity variations, often neglected in anthocyanin quantitation, resulting in significant analytical errors. A simple yet comprehensive strategy based on the molar relative response factors (MRRFs) and a single master reference calibration (i.e., cyanidin-3-glucoside) was proposed to quantify anthocyanins in red cabbage, blueberry, and strawberry samples with improved analytical accuracy. The results indicate this approach provides an effective, inexpensive, and accurate analytical method for anthocyanins in food materials without using individual reference standards. MRRFs of 617 anthocyanins/anthocyanidins were calculated, and the information is freely available at https://BotanicalDC.online/anthocyanin/. This study could be critical to developing new reference methods for anthocyanin analysis and harmonizing results and data from various sources. [ABSTRACT FROM AUTHOR]

This study aims to quantitatively analyze the three-dimensional microstructure of ultra-thin HDF paving boards and molding boards, in order to explore the modeling methods of HDF and attempt to predict its mechanical properties more accurately through theoretical and simulation analyses. In this study, non-destructive analysis of the internal microstructure was conducted using computed tomography (CT) imaging technology. The characteristics of microstructural evolution during the hot compression molding process were explored, and all analyzed data were utilized for 3D reconstruction and simulation model construction. Ultra-thin fiberboards with different fiber ratios were modeled through volume compression and experimentally simulated in tension. The results were compared with experimentally determined results for verification purposes. Additionally, differences in surface roughness, water absorption, and expansion of ultra-thin fiberboards with varying fiber ratios were analyzed. The findings indicate that different fiber ratios have a significant impact on the mechanical properties of ultra-thin fiberboards; tensile strength increases with an increase in medium-length fibers content when fibers are pulled or split, highlighting the key role played by the strength of the fibers themselves. Furthermore, it was observed that surface roughness and water-absorbing expansion rate are lower when a higher proportion of fine fibers is present. The results from this study hold great significance for gaining a deeper understanding of the composition, structure, and properties of ultra-thin fiberboards as well as exploring modeling methods for high-density fiberboards. They also provide guidance for optimizing material formulations and preparation processes while improving performance and quality standards for poplar-ultra-thin fiberboards. • We quantitatively analysed the three-dimensional microstructure of ultra-thin fibreboard paving slabs and moulded panels. • We explored the modelling method of high-density fibreboard and obtained a model of high-density fibreboard with a volume fraction of 90%. • We explored the effects of different fibre ratios on the mechanical properties of ultra-thin fibreboards. [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]

Monte Carlo simulations are at the heart of many high-fidelity simulations and analyses for radiation transport systems. As is the case with any complex computational model, it is important to propagate sources of input uncertainty and characterize how they affect model output. Unfortunately, uncertainty quantification (UQ) is made difficult by the stochastic variability that Monte Carlo transport solvers introduce. The standard method to avoid corrupting the UQ statistics with the transport solver noise is to increase the number of particle histories, resulting in very high computational costs. In this contribution, we propose and analyze a sampling estimator based on the law of total variance to compute UQ variance even in the presence of residual noise from Monte Carlo transport calculations. We rigorously derive the statistical properties of the new variance estimator, compare its performance to that of the standard method, and demonstrate its use on neutral particle transport model problems involving both attenuation and scattering physics. We illustrate, both analytically and numerically, the estimator's statistical performance as a function of available computational budget and the distribution of that budget between UQ samples and particle histories. We show analytically and corroborate numerically that the new estimator is unbiased, unlike the standard approach, and is more accurate and precise than the standard estimator for the same computational budget. • Parameter uncertainty and Monte Carlo solver noise both contribute to output variance. • The brute-force approach is to over-resolve the stochastic solver noise towards zero. • The introduced estimator is more accurate and efficient than the brute-force approach. • The bias and variance of both estimators are characterized and compared. • Resource allocation analysis shows that over-resolution is not necessary. • Results are discussed for an example energy-independent radiation transport problem. [ABSTRACT FROM AUTHOR]

For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD. • A method to detect multiple underlying subspaces in the brain associated with a trait. • Multimodal active subspaces fuse structural and functional features of the brain. • Active subspaces retain predictive information for brain disorders. • Experiments with Alzheimer's related scores show successful subspace detection. [ABSTRACT FROM AUTHOR]

• Help in attaining enhances absorption due to multiple reflections as well as concentrating effect within the enclosure. • Reduces the absorption on the side wall temperature and hence, the resultant temperature and the side losses (reduction in both conductive as well radiative heat loss). • A lower heat capacity of the cooking interior due to reduced absorbing surface area which generally do not participate in heat transfer to load but act as surface that loses heat to ambient. • Selection of a TPP that could reflect minimal change- cooker opto-thermal ratio (COR) • Detailed discussion of additionality achieved in terms of performance, economy and carbon saving (environmental aspect) post-retrofit in Indian SBC • Introduced carbon mitigation potential as an objective parameter derived from cooker opto-thermal ratio (COR) Design intervention for improvement in the performance of a solar cooker is required for its mass adoption and popularization. It has remained stagnant for decades for want of a holistic tool which provides metric for prediction of its cumulative optical and thermal(opto-thermal) performance, cooking type/objectives, techno-economic feasibility and climatic impact/carbon mitigation potential. In this work assessment of effectiveness of novel design intervention through retrofitting of internal radiative control in an Indian solar box cooker, one of the most popular family cookers in India, has been investigated using available tools. The solar cooker, with the minimal design intervention, and negligible cost implication shows an improvement of 6.74%, and 6.86% in the values of figures of merit, F 1 and F 2 , respectively, used for the thermal performance evaluation. Here, the standard test procedure for figures of merit mainly accounts for the thermal aspect of the performance as it excludes the use of external booster reflector. Alternatively, the Cooker opto-thermal ratio (COR), a parameter measuring opto-thermal performance, shows a 1.25-fold increase. The retrofitted reflectors enhance localised targeted absorption and reduce thermal losses, resulting in a 25% improvement in COR. The retrofitting process is simple, and easy; and offers superior performance, favourable economics, and potential for carbon mitigation, making it highly attractive. Also, this work demonstrates COR as a tool with higher resolution enabling analysis of the impact of even minimal design intervention. [ABSTRACT FROM AUTHOR]

Alkali-activated binders (AAB) are low-carbon alternatives to cement used in traditional concrete manufacture. This paper presents a state-of-the-art survey of the historical background, synthesis development, reaction mechanism, and recent advances of the AAB. The parameters influencing the engineering performance of the AABs, including the physicochemical characteristics of the precursor materials, formulation and dosage of alkaline activators, and type and period of curing, were critically reviewed. In this regard, three different precursor material sources are identified: industrial by-products, natural aluminosilicates, and agricultural waste materials, which have various availability, reactivity, cost, and value worldwide. In addition, recent advances in the life cycle assessment of AAB mixtures are compiled. The challenges that AABs face towards commercial applications are also discussed. The selection of appropriate precursors, the production of low-cost alkaline activators, mix design parameters, fundamental mechanisms, engineering properties, construction processing and application, performance standards, and sustainability aspects constitute the challenges. Alkali-activated concrete can be a potential low-carbon substitute for conventional concrete if it ensures true sustainability and not just greenwashing. • Collation of recent technical updates on alkali-activated cementitious composites. • Informs readers of the nuances and challenges of alkali-activated materials. • Recent progress in understanding the reaction kinetics and engineering properties of alkali-activated binders summarized. • An overview of LCA studies conducted on alkali-activated binders. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fast, simple and direct determination of GBL in adulterated beverages. • GBL prediction capability statistically comparable in all samples evaluated. • Suitable methodology for in-situ forensic Analytical Chemistry without solvent and reagents. • Developed procedure in good agreement with the principles of Green Analytical Chemistry. • Simplicity in calibration model required. A green partial least square (PLS) attenuated total reflectance infrared spectroscopy (ATR-FTIR) method has been developed for the prediction of the γ-butyrolactone (GBL) in all type of soft and alcoholic beverages. The method involves the use of a drop (less than 5 µL) of sample and a single calibration model based on aqueous and alcoholic solutions of GBL and beverages free from GBL. A selected wavenumber range from 1225 to 1171 cm−1 and from 999 to 972 cm−1 of the second derivative (SD) spectra of samples and standards were employed. The validation set provided root mean square error of prediction (RMSEP) of the order of 300 µg g−1 GBL with a ratio of standard error of performance to standard deviation value (RPD) of 13.4. The prediction of unknown samples not considered in the calibration nor in the validation sets offered accuracy relative errors between −1.0 and 2.9 % GBL. The proposed PLS-FTIR based method allows a fast and direct determination of GBL in all type of beverages, in less than 2 min and avoiding the use of neither reagents nor solvents, without any sample treatment required, being in agreement with the principles of Green Analytical Chemistry. [ABSTRACT FROM AUTHOR]

The conservation community needs to improve the quality and quantity of evidence to address urgent and complex environmental challenges. The growth of Upstream Conservation Initiatives (UCIs) such as those designed to eliminate deforestation by shifting consumer or corporate behavior, exemplify the tension between the urgency to act and the need to build evidence to enable progressive improvements in implementation. These initiatives—often led by non-governmental organizations (NGOs)—are rarely evaluated rigorously in terms of their causal effect on intended outcomes, contributing to the evidence deficit. Impact evaluations have been useful tools to analyze effectiveness and build evidence in other fields. However, they are seldom used in the conservation field broadly or in UCIs specifically. To help practitioners involved in UCIs make better use of these tools, we introduce the field of impact evaluation, its core concepts, and how it contrasts with and complements traditional performance measurement approaches. Using these concepts, we propose a guidance process which can be used by practitioners at the project design phase to weigh tradeoffs and consider the most appropriate impact evaluation methodologies. We then use this guidance process to analyze two UCIs implemented by an NGO in Indonesia. Through these case studies we show how impact evaluation concepts could have been employed in project design to improve implementation and promote progressive learning, even if actual impact evaluations were infeasible. We conclude by proposing recommendations on how implementers, donors, and academia can build off these ideas to improve learning and strengthen the evidence base. • Impact evaluation is underutilized in the conservation field, limiting evidence. • Upstream Conservation Initiatives (UCIs) can benefit from impact evaluation. • Evaluations of UCIs must balance feasibility, rigor, and relevance. • Concepts from impact evaluation strengthen standard performance measurement. • Implementing organizations can apply principles at low cost to improve evidence. [ABSTRACT FROM AUTHOR]

• With 3D Navigo™ system the targeted approach outperforms the systematic one. • The systematic biopsy detects high risk disease and should not be omitted. • Biopsy may be omitted when no visible lesion is demonstrated on mpMRI. The introduction of multi parameter magnetic resonance imaging (mpMRI) of the prostate in combination with MRI/TRUS fusion and systematic biopsy resulted in improved detection of prostate cancer. The aim of the current study was to document the performance of MRI/TRUS fusion biopsy of the prostate using the Navigo™ software in a contemporary cohort of patients from nonreferral centers. We performed a two centers prospective data collection (2014–2020) for men with clinically suspected Pca and patients on active surveillance for low-risk Pca that were referred for TRUS biopsy after performing mpMRI of the prostate with a visible lesion. The primary outcome was detection of clinically significant cancer (csPca) defined as ISUP grade group ≥2. Patients were stratified according to biopsy technique and PI-RADS category. The study group included 236 patients of whom 129 (54.9%) were diagnosed with Pca and 82 (34.7%) with csPca (GG ≥ 2) on combined biopsy. The overall detection of csPca was 31% for targeted vs. 25.4% for systematic biopsy with an absolute difference of 5.6% in favor of the fusion technique. No significant difference between the two techniques was observed for detection of benign prostate or GG1 disease. The improved performance of the targeted approach was noted only in patients with PI-RADS 4 and 5 lesions. Of the patients with csPca 10 (12%) were diagnosed only by the systematic biopsy while 20 (24%) were detected only in the fusion biopsy. Systematic biopsy of prostate lobe without MRI lesion detected only 2 cases (∼1%) with high grade disease. Detection of csPca by mpMRI/TRUS fusion biopsy using the 3D Navigo™ system is feasible. The targeted approach outperforms the systematic one, however the later technique also detects high risk disease and should be included in the biopsy procedure. The overall detection rate (34.9%) of clinically significant prostate cancer by both targeted and systematic sampling is relatively low. [ABSTRACT FROM AUTHOR]

• The effect of the shape factor on the efficiency of the flexible photovoltaic modules. • The performance of different shapes of the flexible photovoltaic modules. • The standard flat photovoltaic modules is the most efficient shape of the flexible photovoltaic modules. This study investigates the performance of different shapes of the flexible photovoltaic modules. Three shapes of the flexible modules were compared under similar outdoor conditions: The standard flat, convex, and concave. The comparison was based on the solar radiation input, voltage output, current output, and back of the module's temperature. Results showed that based on the output power and the efficiency for the modules, the standard flat module was of the highest performance, followed by the convex module and then by the concave module. The average output efficiency of the standard flat module was 4.5–6.0% and 0.8–1.2% for the convex module, while it was 0.46–0.53% for the concave module. In addition, a simulated energy analysis was carried out between the standard flat module toward the south direction and the convex module toward the southeast direction. The simulation results showed that an area that holds 50 standard flat modules, could hold 72 convex modules. Moreover, the annual ejected electrical energy into a grid for the standard flat modules and convex modules were 8.61 MWH and 5.462 MWH, respectively. Furthermore, the performance of the standard flat and convex modules were 74.79% and 70.96%, respectively. [ABSTRACT FROM AUTHOR]

• A strong multidisciplinary organizational structure is necessary to drive consensus in selection and successfully implement point of care glucose meters across a large multi-hospital system. • Beyond the standard analytical laboratory evaluation, meter performance should be evaluated at the point of use, employing consensus error grids and defined acceptability criteria. • Structured end-user evaluation is as an essential element of the meter selection process which commonly includes the evaluation of cost, connectivity, indications for use, and ease of implementation. To select and standardize point-of-care (POC) glucose meters across a multi-hospital system. We formed a multidisciplinary POC glucose standardization working group including key stakeholders from each site. A set of selection criteria: usability, clinical and laboratory performance, indications for use, interface connectivity, ease of implementation and ongoing operational costs were used to develop a scoring schema to facilitate a consensus-driven selection process. Method comparison and consensus error grid evaluation against the clinically validated reference methods demonstrated that the analytical performance for all candidate meters was comparable for both the laboratory and clinical evaluation. However, Meter 1 ranked highest in usability evaluations, implementation and streamlined interface connectivity. The meter selection process and implementation were staggered across sites due to complexity of transitioning to a new manufacturer's meter and limitations in vendor support for training and ongoing troubleshooting of interface connectivity. Standardization of POC glucose meters in a large multi-hospital system is a complex undertaking requiring robust, multidisciplinary organizational structure both system-wide and locally, development of consensus-driven selection tools, usability evaluation by end-users, laboratory and clinical evaluation of the analytical performance, and a strong vendor-laboratory partnership during the implementation process. [ABSTRACT FROM AUTHOR]

Lipoprotein(a) [Lp(a)] became besides LDL cholesterol one of the most attractive targets for intervention in cardiovascular disease. Strong genetic evidence supports the causal association between high Lp(a) concentrations and cardiovascular outcomes. Since specific Lp(a)-lowering therapies are under clinical investigation, the interest in measuring Lp(a) has markedly increased. However, the special structure of the lead protein component of Lp(a), named apolipoprotein(a), creates difficulties for an accurate measurement of Lp(a). A highly homologous repetitive structure, called kringle IV repeat with up to more the 40 repeats, causes a highly polymorphic protein. Antibodies raised against apolipoprotein(a) are mostly directed against the repetitive structure of this protein, which complicates the measurement of Lp(a) in molar terms. Both measurements in mass (mg/dL) and molar terms (nmol/L) are described and a conversion from one into the another unit is only approximately possible. Working groups for standardization of Lp(a) measurements are going to prepare widely available and improved reference materials, which will be a major step for the measurement of Lp(a). This review discusses many aspects of the difficulties in measuring Lp(a). It tries to distinguish between academic and practical concerns and warns to make a mountain out of a molehill, which does no longer allow to see the patient behind that mountain by simply staring at the laboratory issues. On the other hand, the calibration of some assays raises major concerns, which are anything else but a molehill. This should be kept in mind and we should start measuring Lp(a) with the aim of a better risk stratification for the patient and to identify those patients who might be in urgent need for a specific Lp(a)-lowering therapy as soon as it becomes available. [Display omitted] • Measurement of Lp(a) in molar terms is desirable but not easy to accomplish. • The repetitive kringle IV repeat structure of apo(a) is the source of measurement problems of Lp(a). • The selection of the calibrators is of key importance and might improve the measurement performance of an Lp(a) assay. • Major efforts to better standardize Lp(a) measurements are under way and should be followed by assay manufacturers. • Despite the assays are not yet perfect, most of them can be used for risk stratification of the patients. [ABSTRACT FROM AUTHOR]

• This clinical practice guideline identified standards with recommendations summarized in Table S1. • Limited high-level evidence addressing standards for LTVEM exist, and further research is needed. • Selected topics for utility of LTVEM exist, although comprehensive criteria addressing minimum standards for performance are needed. • Clinicians, hospital administrators, and insurers benefit from establishing standards for inpatient video-EEG monitoring applied to patient management. The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility. [ABSTRACT FROM AUTHOR]

Consider the recent upswing in interest around multi-view clustering procedures. Such methods aim to boost clustering efficiency by leveraging information from numerous perspectives. Much research has been devoted to tensorial representation to exploit high-order correlations underlying disparate views while preserving the local geometric structure inside each view. Our research introduces a novel multi-view clustering approach. This approach creates a 3rd-order tensor, assimilating features from all perspectives. We use the t-product in the tensor space to generate the self-representation tensor from the tensorial data. We incorporate the ℓ 1 -based sliced-Laplacian regularization to increase our model's resilience and introduce a fresh column-wise sparse norm: the ℓ 2 , p -norm with 0 < p < 1. This norm displays attributes of invariance, continuity, and differentiability. We present a closed-form answer to the ℓ 2 , p -regularized shrinkage problem, broadening its relevance to other generalized problems. Simultaneously, we propose a tensorial arctan -function as an improved surrogate for the tensor rank. This function has proven more proficient at assessing consistency across multiple viewpoints. By integrating these two components, we formulate an effective algorithm that refines our suggested model, ensuring that the constructed sequence gravitates toward the stationary KKT point. Our team conducts extensive experiments on various datasets to evaluate our model's effectiveness, spanning diverse situations and scales. Results from these experiments emphasize that our approach establishes a novel performance standard. • We integrate ℓ 1 -sliced Laplacian with ℓ 2 , p regularization for multi-view clustering. It reduces redundancy and enhances outlier resilience, achieving an efficient, innovative solution. • An algorithm with augmented Lagrange multipliers tackles multi-view clustering, providing solvable sub-problems with closed-form solutions. • Within the tensor framework adept at leveraging intrinsic cluster structures, our algorithm approaches an optimal Karush–Kuhn–Tucker (KKT) point. • Extensive experiments on four datasets demonstrate our technique's superiority over contemporary methods through thorough comparisons. [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]

Zero-shot learning (ZSL) facilitates the transfer of knowledge from seen to unseen categories through high-dimensional vectors that capture both known and unknown class names. However it encounters challenges with domain shift arising from a lack of sufficient labeled data. Although transductive zero-shot learning (TZSL) addresses this bias by including samples from unseen classes, it still faces obstacles in enhancing TZSL performance. In this study, We introduce the Structure Alignment Variational Autoencoder Generative Adversarial Network (SA-VAEGAN), a novel approach that enhances the alignment between visual and auxiliary spaces. We delved into the underlying causes of domain shift and introduced a structural alignment (SA) strategy to tackle these challenges. The SA model thoroughly accounts for both inter-class and intra-class dynamics, designed to leverage the model's comprehension of high-level semantic relations to disambiguate confusion among similar classes and mitigate intra-class confusion by penalizing atypical visual samples within classes. Assessed across four benchmark datasets, SA-VAEGAN has established a new performance standard, underscoring its efficiency in addressing the domain shift challenge within TZSL tasks, and achieving high accuracy. • We introduce the concept of root-oriented optimization models for the first time. • We deploy a generative model integrated with the structure alignment strategy. • The proposed model outperforms state-of-the-art methods on four benchmarks. [ABSTRACT FROM AUTHOR]

Learning using privileged information (LUPI) has shown promise in improving supervised learning by embedding additional knowledge. However, its reliance on the assumption of readily available privileged information may not hold true in practical scenarios due to limitations in access or confidentiality. To address these challenges, this paper presents a novel weakly privileged learning (WPL) framework, integrating knowledge extraction methods within the LUPI context. An effective strategy is proposed to implement the WPL framework, where knowledge extraction techniques generate a weight matrix as weak privileged information. Extensive experiments employing various existing knowledge extraction techniques demonstrate that the proposed WPL outperforms traditional supervised learning and approaches the performance of standard privileged learning where privileged information is given in advance. This research establishes WPL as a promising learning paradigm, addressing limitations in privileged information availability and advancing the field of machine learning in practical settings. • Introduce a novel weakly privileged learning framework (WPL). • Knowledge extraction methods are used to generate weak privileged information. • Extensive experiments verify the superiority of the proposed methods. [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]

We propose a novel spatial bagging workflow for predictive ensemble machine learning that improves on standard bagging models. Our proposed method integrates spatial bootstrap for bagging with the number of effective sample size, n e f f , for integration of the spatial context of the dataset. We benchmark the improved performance over standard machine learning bagging models with a large number of two-dimensional synthetic datasets with varying degrees of Gaussian noise. For noise free datasets, both methods demonstrate equivalent accuracy; however, spatial bagging achieves this with a significantly smaller sample size, showcasing its improved efficiency. As data noise increases, spatial bagging consistently outperforms standard bagging, displaying an improved Mean Squared Error (MSE) and robustness against overfitting. Our proposed spatial bagging method computes the optimal effective sample size for spatial data, reducing model overfitting. Furthermore, our proposed method requires only the additional step of variogram calculation and modeling, and can be implemented with any predictive machine learning bagging model with minimal code modification. i.e., specification of the number of bootstrap samples as the number of effective data. We recommend using spatial bagging for improved predictions for any spatial data setting across diverse scientific fields, e.g., atmospheric, agricultural, subsurface resources etc. • Ensemble learning methods employ standard bagging, which is prone to overfitting. • We introduce a novel spatial bagging technique utilizing effective sample size. • Spatial bagging mitigates overfitting, enhancing robustness against noise. • Spatial bagging provides a computational advantage by using smaller data. [ABSTRACT FROM AUTHOR]

Tapeworm infection in horses can cause serious health concerns, and recent data have documented treatment failures in the most common species, Anoplocephala perfoliata. The threat of anthelmintic resistance in A. perfoliata is of particular concern because of poor diagnostic performance of standard egg counting techniques for detecting this parasite. This study compared the performance of three diagnostic techniques 1) Mini-FLOTAC, 2) Cornell-Wisconsin, and 3) Proudman and Edwards used to detect and quantify A. perfoliata eggs in naturally infected horses. Eighteen adult female horses from the University of Kentucky's historic parasitology herd were included in this study. Fecal samples were collected from all horses at five collection time points two weeks apart and analyzed with the three techniques. A total of 90 samples were collected and 270 counts determined in the study. The proportions of positive samples determined by the three techniques were significantly different from each other (p<0.05): Mini-FLOTAC (16%), Cornell-Wisconsin (47%), and Proudman and Edwards (70%). The Proudman and Edwards technique counted consistently higher numbers of tapeworm eggs compared to the other two techniques throughout the study [p < 0.05]. Total raw counts of tapeworm eggs across the study for each technique were 16, 88, and 410 for the Mini-FLOTAC, Cornell-Wisconsin, and Proudman and Edwards, respectively. This study demonstrated that the Proudman and Edwards technique was superior in diagnosing A. perfoliata infection. Future work needs to assess this technique's potential for Fecal Egg Count Reduction Testing (FECRT). • Mean counts of each technique did not change significantly over the study period. • Proudman and Edwards' technique had the highest percent positive counts. • 65.6% of all eggs counted were attributed to 33.3% (6/18) of the horses. • All techniques performed statistically differently from each other. • The Cornell-Wisconsin technique considerably outperformed the mini-FLOTAC technique. [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]

Research on the Implicit-Affect-Primes-Effort model (Gendolla, 2012) found that priming happiness or anger in challenging tasks results in stronger sympathetically mediated cardiovascular responses, reflecting effort, than priming sadness or fear. Recent studies on action shielding revealed that personal task choice can attenuate affective influences on action execution (e.g., Gendolla et al., 2021). The present experiment tested if this action shielding effect also applies to affect primes' influences on cardiovascular response. Participants (N = 136) worked on a cognitive task with integrated briefly flashed and backward masked facial expressions of sadness vs. happiness. Half of the participants could ostensibly choose whether they wanted to work on an attention or on a memory task, while the other half was assigned to one task. Our findings revealed effects on cardiac pre-ejection period (PEP), which align with the expected outcomes for a task of unfixed difficulty where participants establish their own performance standard. Most importantly, task choice shielded against the implicit affective influence on PEP that was evident when the task was externally assigned. Effects on systolic blood pressure (SBP) reactivity largely corresponded to those of PEP. • Participants could choose or were assigned to a cognitive task. • Briefly flashed happiness vs. sadness primes were presented during the task. • When the task was assigned, sadness primes led to stronger cardiac PEP reactivity than happiness primes. • This priming effect was no longer observed when participants could personally choose their task. [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]