Shaking table tests of a one-quarter scale model of concrete hollow block masonry houses retrofitted with fiber-reinforced paint.

Unreinforced masonry (URM) buildings are prone to significant damage when subjected to ground motion. Some strengthening methods have been proposed to increase the seismic capacity. However, the widespread adoption of these methods faces various challenges, including economic constraints experienced by common people in developing countries, the complexity of implementation, efficiency, and seismic safety of each technique. This paper introduces a new retrofitting method of fiber-reinforced paint using fiberglass as the primary reinforcing material. The advantage of this technique lies in its simplicity and ease of application, with the added benefit of using the paint to improve the appearance of the house. Two 1:4 scale concrete hollow block (CHB) masonry houses were constructed to represent unreinforced masonry and retrofitted masonry structures using fiber-reinforced paint (FR-Paint). The shaking table test results indicate that the retrofitted house model showed improvements of up to 18 times in deformation capacity and up to 13 times in energy dissipation compared to the non-retrofitted house model. FR-Paint has a robust performance even in high input motion at a seismic intensity JMA of 7 (Japan Meteorological Agency). This confirms that this retrofitting method has a high earthquake-resistant performance.

A Case Report of Caudal Migration of a Lumbar Epidural Catheter Confirmed by Doppler Ultrasound: Ultrasound to the Rescue!

Combined spinal-epidural anaesthesia is an excellent technique for providing intraoperative and postoperative analgesia in patients undergoing total knee arthroplasty. Epidural catheters threaded through a Tuohy needle with a cephalad needle bevel orientation follow a winding pattern within the epidural space. Caudal or downward migration of an epidural catheter may lead to unsatisfactory anaesthesia and epidural failure. Colour flow Doppler sonography is emerging as an effective technique to determine the epidural catheter tip position. We report an interesting case of caudal migration of a lumbar epidural catheter confirmed by colour flow Doppler ultrasound.

The Comparison of the Anti-inflammatory Efficacy of Phytochemical Extracts in Punica granatum and Lawsonia inermis Among Patients Diagnosed With Chronic Periodontitis.

Introduction The oral cavity is the gateway to the human body. Periodontitis is a common inflammatory condition affecting the oral cavity and a known etiological cause of tissue destruction, discomfort, and halitosis. Pomegranate (Punica granatum) and henna (Lawsonia inermis) are herbs known to mankind from time immemorial whose extracts are proven to fight inflammation. The current study was done to evaluate the phytochemical anti-inflammatory efficacy of Punica and Lawsonia in patients with chronic periodontitis and test the potency of herbal mouthwashes in fighting the inflammatory condition affecting the oral cavity using distilled water as a control group. Materials and methods A double-blinded randomized control trial was conducted on 60 patients who were recruited and divided into three groups, in which 20 patients were prescribed with pomegranate (Punica: n=20) mouthwash and 20 patients with henna (Lawsonia: n=20) mouthwash along with distilled water (n=20). All patients were randomly allocated using the coin toss method and advised to use the prescribed mouthwash for a period of two weeks. Unstimulated saliva was collected before using the mouthwash, and salivary enzymes such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH) and their levels were assessed spectrometrically using the infrared spectrophotoscopy (IFSC) method. Each patient was assigned a mouthwash and recalled after two weeks. Unstimulated saliva was again collected, and salivary activity levels of enzymes AST, ALT, and LDH were analyzed after using mouthwash in a similar method as done before. Later on, the salivary levels of enzymes AST, ALT, and LDH were compared before and after the usage of mouthwashes. Statistical significance was seen in the salivary enzymatic activity of AST, ALT, and LDH before and after using Punica and Lawsonia mouthwashes due to their potent phytochemical action in fighting inflammation. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) 22 (IBM SPSS Statistics, Armonk, NY). The Shapiro-Wilk test was used to determine the normality and significance; intragroup comparison was done using the Wilcoxon signed-rank test and Mann-Whitney U test. Intergroup comparison was done using the Kruskal-Wallis test. Results Punica patients had much lower levels of salivary AST and ALT (p<0.001) and a decrease in LDH (p=0.002) after the usage of mouthwash for a period of two weeks. Also, patients using Lawsonia as herbal mouthwash had reduction in the values of AST (p=0.001) and LDH (p=0.003) and prominent reduction in ALT (p<0.001) after a period of two weeks. But in the case of patients using distilled water, there was an increase in the salivary enzymatic activity of AST and ALT, which was statistically significant (p<0.001), and LDH (p=0.006) depicting the disease progression even after using mouthwash for the given time period of two weeks. Conclusion This study demonstrated that both Punica and Lawsonia were effective in reducing the inflammation in patients diagnosed with chronic periodontitis. However, when intergroup comparison was done, the anti-inflammatory efficacy was superior in Punica with significant reduction in the parameters such as of AST, ALT, and LDH when compared to Lawsonia owing to its potent phytochemical constituency in cutting down the inflammation. Hence, Punica can be used as an implicated effective anti-inflammatory herbal mouthwash.

Dataset of in-situ coaxial monitoring and print's cross-section images by Direct Energy Deposition fabrication.

Coaxial monitoring of the Direct Energy Deposition (DED) machines enables a real-time material deposition study. Coaxial-images contain substantial melt-pool information and incorporate situational information including the sparks' intensity, numbers, etc. Recent studies have shown that melt-pool observations correlate directly with machine parameters and artifact properties. Therefore, the melt-pool information not only can assist in measuring the machine's working condition and determining machine operation parameters' reliability but also facilitates the deposition characteristics studies like print's regime and dimensions. This information is gathered during the fabrication and can be expanded to perform various process studies and fault registration. This paper utilizes the Optomec DED machine to fabricate single-track prints with multiple process parameters, while a coaxial camera records the deposition. Each deposited track is then cut perpendicular to the print's direction to facilitate process parameters correlation study with actual geometrical deposition measured using a microscope. The coaxial images taken during fabrication, along with their process parameters, cross-cut measurements, and a developed image-processing toolbox, are presented alongside this paper to empower future research directions.

A bird's-eye view of pathologist over diagnostic confusion of oral cavity lesions.

Introduction: Oral cavity can be host to multitude of neoplastic, premalignant or non neoplastic pathological lesions. Diagnosis of lesions of oral cavity is always of interest to clinician and pathologist and rely on clinical appearance of lesions. There can be variation in diagnosis of clinical lesion with histopathology. Many oral carcinomas arise within the sites that previously had premalignant lesion. Incidence of oral cancers in population has increased among younger generations related to habits and lifestyle. These lesions during clinical presentation are misleading and create diagnostic dilemma owing to age, sex and distribution of lesions. Understanding distribution of oral mucosal lesions helps to diagnose lesions of oral cavity. Purpose of this study is to observe the variation in clinical diagnosis with histopathological diagnosis in patients with inflammatory, premalignant, benign and malignant lesions of oral cavity and oropharynx and also clinical distribution of lesions of oral cavity and oropharynx lesions by histopathology. Observations: Out of total 105 lesions, ulcer in oral cavity seen in 58 (55.23%) of patients, followed by swelling or feeling of lump in oral cavity in 36 (34.29%) of patients and foreign body sensation in 23 (21.90%) of patients with tongue as most frequent site for most of lesions of oral cavity accounting in 33 (31.43%) of cases, and less frequently lesions were seen in retro molar trigone area in 2 (1.90%) patients. Histopathological diagnosis of premalignant, non neoplastic and inflammatory lesions was made in 24 (22.85%) cases, benign tumours were diagnosed in 14 (13.33%) cases and rest of 67 (63.81%) lesions were malignant. Mucocoel were seen in five (4.76%) cases, radicular cyst was seen in one (0.95%) case of female patient and four cases of Leukoplakia with one case showing mild dysplasia. Among benign tumours 11 (10.47%) patients presented with gingivitis turned out to be squamous papillomas were seen in five (4.76%) cases, fibroma was diagnosed in four (3.80%) cases, pyogenic granuloma was diagnosed in four (3.80%) cases most commonly seen over gingiva and myoepithelioma of minor salivary gland was observed in one (0.95%) case over soft palate. Out of 67 cases of malignant lesions squamous cell carcinomas were seen in 59 (88.05%) cases followed by verrucous carcinoma in 3 (4.47%) cases, 2 (2.99%) cases were basaloid squamous cell carcinomas, mucoepidermoid carcinoma was seen in 2 (2.99%) cases and 1 (1.49%) case of adenoid cystic carcinoma was seen. Majority of squamous cell carcinomas cases in study were well differentiated in 49 (73.13%) cases followed by moderately differentiated in 16 (23.88%) cases and poorly differentiated in 2 (2.99%) cases. Malignant transformation of tonsil tissue post operatively was observed in 1 (0.95%) patients on histopathology. One (2.5%) case of myoepithelioma was seen in 60 years male over soft palate. Conclusion: Of all oral biopsies reported in study, increasing trend of malignancies in lower age groups of population making it an emerging threat to community and highlighting need to take effective measures to increase public awareness about risk factors and consequences of this condition. Screening programmes targeted to population over 25 years are recommended to overcome this.

Color Stability of Pediatric Restorative Material Over Pediatric Drug Formulation.

AIM:  To investigate the color stability of light-cured (LC) restorative material in different pediatric drug formulations. METHOD:  Two distinct restorative materials, specifically LC resin and LC glass ionomer cement (GIC), were employed to create 88 disc-shaped specimens. These comprised 44 specimens fabricated from each material. Each specimen had a diameter of 5 mm and a height of 3 mm. To conduct the experiment, specimens were randomly allocated into four experimental groups, each containing 11 specimens made of each material. This division was accomplished through the use of a stratified random sampling method. The five experimental groups and their respective liquid medications were as follows: Group 1 - montelukast sodium and levocetirizine dihydrochloride syrup, Group 2 - cefixime, Group 3 - sodium valproate, and Group 4 - metronidazole. To ensure thorough exposure to the medications, all samples underwent a two-minute agitation cycle, which was repeated every 12 h over the course of one week. Following the immersion period, the color stability of all specimens was assessed using a spectrophotometer. The data obtained from the color stability measurements were subjected to statistical analysis using one-way analysis of variance (ANOVA), followed by a post hoc test. The aim was to determine whether significant differences in color stability were observed among the groups studied. RESULTS:  The mean values and standard deviations of ΔE were calculated. The highest values of ΔE were observed in Group 3 (4.70 ± 1.89), followed by Group 4 (4.04 ± 2.10). Conversely, the lowest ΔE values were observed in Group 2 (3.23 ± 2.02) and Group 1 (3.24 ± 2.31). The calculated p-value was 0.298, and the F-value was 1.269. CONCLUSION:  This study concludes that both restorative materials, LC composite and LC GIC, are susceptible to discoloration. Sodium valproate exhibited the greatest staining effect on both materials. Conversely, cefixime had the least impact on the color of the LC composite, whereas montelukast had the least effect on the color of LC GIC.

Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.

Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.

Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learning-based approaches address this limitation by exploiting unlabeled data along with limited annotated data. Recent self-supervised learning methods use contrastive loss to learn good global level representations from unlabeled images and achieve high performance in classification tasks on popular natural image datasets like ImageNet. In pixel-level prediction tasks such as segmentation, it is crucial to also learn good local level representations along with global representations to achieve better accuracy. However, the impact of the existing local contrastive loss-based methods remains limited for learning good local representations because similar and dissimilar local regions are defined based on random augmentations and spatial proximity; not based on the semantic label of local regions due to lack of large-scale expert annotations in the semi/self-supervised setting. In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images with ground truth (GT) labels. In particular, we define the proposed contrastive loss to encourage similar representations for the pixels that have the same pseudo-label/GT label while being dissimilar to the representation of pixels with different pseudo-label/GT label in the dataset. We perform pseudo-label based self-training and train the network by jointly optimizing the proposed contrastive loss on both labeled and unlabeled sets and segmentation loss on only the limited labeled set. We evaluated the proposed approach on three public medical datasets of cardiac and prostate anatomies, and obtain high segmentation performance with a limited labeled set of one or two 3D volumes. Extensive comparisons with the state-of-the-art semi-supervised and data augmentation methods and concurrent contrastive learning methods demonstrate the substantial improvement achieved by the proposed method. The code is made publicly available at https://github.com/krishnabits001/pseudo_label_contrastive_training.

Continuous Comprehensive Monitoring of Melt Pool Morphology Under Realistic Printing Scenarios with Laser Powder Bed Fusion.

In laser powder bed fusion (LPBF) additive manufacturing, the melt pool (MP) characteristics are key indicators for process and part defects. For example, the laser scan location on the build plate can slightly change the MP size and shape due to the f-θ optics of the printer. The laser scan parameters can cause variations in MP signatures that may indicate lack-of-fusion and keyhole regimes. However, the effects of these process parameters on MP monitoring (MPM) signatures and part properties are not yet fully understood, especially during a multilayer big-part printing. In this study, our objective is to comprehensively evaluate the dynamical changes of MP signatures (location, intensity, size, and shape) under realistic printing scenarios-printing multilayer objects at different build plate locations with various print process settings. To accomplish this, we developed a coaxial high-speed camera-based MPM system for a commercial LPBF printer (EOS M290), to capture MP images continuously throughout a multilayer part. From our experimental data and results, we find that the MP image position on the camera sensor is not stationary as reported in the literature and is partly subjected to scan location. Its correlations to process deviation or part defect need to be determined. Also, the MP image profile can significantly reflect the changes in print process conditions. The developed system and analysis method can be used to establish a comprehensive profile of MP image signatures for online process diagnosis and part properties prediction, thus ensuring quality assurance and control in LPBF.

Whole-body Composition Profiling Using a Deep Learning Algorithm: Influence of Different Acquisition Parameters on Algorithm Performance and Robustness.

OBJECTIVES: To develop, test, and validate a body composition profiling algorithm for automated segmentation of body compartments in whole-body magnetic resonance imaging (wbMRI) and to investigate the influence of different acquisition parameters on performance and robustness. MATERIALS AND METHODS: A segmentation algorithm for subcutaneous and visceral adipose tissue (SCAT and VAT) and total muscle mass (TMM) was designed using a deep learning U-net architecture convolutional neuronal network. Twenty clinical wbMRI scans were manually segmented and used as training, validation, and test datasets. Segmentation performance was then tested on different data, including different magnetic resonance imaging protocols and scanners with and without use of contrast media. Test-retest reliability on 2 consecutive scans of 16 healthy volunteers each as well as impact of parameters slice thickness, matrix resolution, and different coil settings were investigated. Sorensen-Dice coefficient (DSC) was used to measure the algorithms' performance with manual segmentations as reference standards. Test-retest reliability and parameter effects were investigated comparing respective compartment volumes. Abdominal volumes were compared with published normative values. RESULTS: Algorithm performance measured by DSC was 0.93 (SCAT) to 0.77 (VAT) using the test dataset. Dependent from the respective compartment, similar or slightly reduced performance was seen for other scanners and scan protocols (DSC ranging from 0.69-0.72 for VAT to 0.83-0.91 for SCAT). No significant differences in body composition profiling was seen on repetitive volunteer scans (P = 0.88-1) or after variation of protocol parameters (P = 0.07-1). CONCLUSIONS: Body composition profiling from wbMRI by using a deep learning-based convolutional neuronal network algorithm for automated segmentation of body compartments is generally possible. First results indicate that robust and reproducible segmentations equally accurate to a manual expert may be expected also for a range of different acquisition parameters.

Cross-Reactive SARS-CoV-2 Neutralizing Antibodies From Deep Mining of Early Patient Responses.

Passive immunization using monoclonal antibodies will play a vital role in the fight against COVID-19. The recent emergence of viral variants with reduced sensitivity to some current antibodies and vaccines highlights the importance of broad cross-reactivity. This study describes deep-mining of the antibody repertoires of hospitalized COVID-19 patients using phage display technology and B cell receptor (BCR) repertoire sequencing to isolate neutralizing antibodies and gain insights into the early antibody response. This comprehensive discovery approach has yielded a panel of potent neutralizing antibodies which bind distinct viral epitopes including epitopes conserved in SARS-CoV-1. Structural determination of a non-ACE2 receptor blocking antibody reveals a previously undescribed binding epitope, which is unlikely to be affected by the mutations in any of the recently reported major viral variants including B.1.1.7 (from the UK), B.1.351 (from South Africa) and B.1.1.28 (from Brazil). Finally, by combining sequences of the RBD binding and neutralizing antibodies with the B cell receptor repertoire sequencing, we also describe a highly convergent early antibody response. Similar IgM-derived sequences occur within this study group and also within patient responses described by multiple independent studies published previously.

Semi-supervised task-driven data augmentation for medical image segmentation.

Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of annotated samples from experts is time-consuming and expensive. Consequently, numerous methods have been proposed in the literature for learning with limited annotated examples. Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield high accuracy. In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task. The generator of the proposed method models intensity and shape variations using two sets of transformations, as additive intensity transformations and deformation fields. Both transformations are optimized using labeled as well as unlabeled examples in a semi-supervised framework. Our experiments on three medical datasets, namely cardiac, prostate and pancreas, show that the proposed approach significantly outperforms standard augmentation and semi-supervised approaches for image segmentation in the limited annotation setting. The code is made publicly available at https://github.com/krishnabits001/task_driven_data_augmentation.

Indian Society for Assisted Reproduction Consensus Guidelines on Preimplantation Genetic Testing in In vitro Fertilization Clinics.

STUDY QUESTION: What are the good practice guidelines for Pre implantation genetic testing applicable in INDIA? WHAT IS ALREADY KNOWN: Pre-Implantation Genetic Testing (PGT) is not new in India. It is used to identify euploid embryos for transfer, thus enabling couples to achieve a healthy pregnancy. There has been a lot of controversy surrounding PGT in the international forums; most of these debates have failed to reach a consensus on whether PGT should be offered or its concerns be validated more. STUDY DESIGN SIZE DURATION: This is the report of a 2-day consensus meeting where two moderators were assigned to a group of experts to collate information on Pre implantation genetic testing and embryo biopsy practices in INDIA. This meeting utilised surveys, available scientific evidence and personal laboratory experience into various presentations by experts on pre-decided specific topics. PARTICIPANTS/MATERIALS SETTING METHODS: Expert professionals from ISAR representing clinical, embryological and genetic fields. MAIN RESULTS AND THE ROLE OF CHANCE: The report is divided into various components defining the terminologies, the various requirements, qualifications, recommendations on PGT -A,M,SR, and quality management: the report and recommendations of the expert panel reflect the discussion on each of the topics and try to lay down good practice points for labs to follow. LIMITATIONS REASONS FOR CAUTION: The recommendations are solely based on expert opinion. Future availability of data may warrant an update of the same. WIDER IMPLICATIONS OF THE FINDINGS: These guidelines can help labs across the country to standardise their PGT services and improve clinical outcomes. STUDY FUNDING/COMPETING INTERESTS: The consensus meeting and writing of the paper was supported by funds from CooperSurgical India.

Test-time adaptable neural networks for robust medical image segmentation.

Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol. Remarkable performance degradation of CNNs in this scenario is well documented in the literature. To address this problem, we design the segmentation CNN as a concatenation of two sub-networks: a relatively shallow image normalization CNN, followed by a deep CNN that segments the normalized image. We train both these sub-networks using a training dataset, consisting of annotated images from a particular scanner and protocol setting. Now, at test time, we adapt the image normalization sub-network for each test image, guided by an implicit prior on the predicted segmentation labels. We employ an independently trained denoising autoencoder (DAE) in order to model such an implicit prior on plausible anatomical segmentation labels. We validate the proposed idea on multi-center Magnetic Resonance imaging datasets of three anatomies: brain, heart and prostate. The proposed test-time adaptation consistently provides performance improvement, demonstrating the promise and generality of the approach. Being agnostic to the architecture of the deep CNN, the second sub-network, the proposed design can be utilized with any segmentation network to increase robustness to variations in imaging scanners and protocols. Our code is available at: https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization.

Variability of manual segmentation of the prostate in axial T2-weighted MRI: A multi-reader study.

PURPOSE: To evaluate the interreader variability in prostate and seminal vesicle (SV) segmentation on T2w MRI. METHODS: Six readers segmented the peripheral zone (PZ), transitional zone (TZ) and SV slice-wise on axial T2w prostate MRI examinations of n = 80 patients. Twenty different similarity scores, including dice score (DS), Hausdorff distance (HD) and volumetric similarity coefficient (VS), were computed with the VISCERAL EvaluateSegmentation software for all structures combined and separately for the whole gland (WG = PZ + TZ), TZ and SV. Differences between base, midgland and apex were evaluated with DS slice-wise. Descriptive statistics for similarity scores were computed. Wilcoxon testing to evaluate differences of DS, HD and VS was performed. RESULTS: Overall segmentation variability was good with a mean DS of 0.859 (±SD = 0.0542), HD of 36.6 (±34.9 voxels) and VS of 0.926 (±0.065). The WG showed a DS, HD and VS of 0.738 (±0.144), 36.2 (±35.6 vx) and 0.853 (±0.143), respectively. The TZ showed generally lower variability with a DS of 0.738 (±0.144), HD of 24.8 (±16 vx) and VS of 0.908 (±0.126). The lowest variability was found for the SV with DS of 0.884 (±0.0407), HD of 17 (±10.9 vx) and VS of 0.936 (±0.0509). We found a markedly lower DS of the segmentations in the apex (0.85 ±â€¯0.12) compared to the base (0.87 ±â€¯0.10, p < 0.01) and the midgland (0.89 ±â€¯0.10, p < 0.001). CONCLUSIONS: We report baseline values for interreader variability of prostate and SV segmentation on T2w MRI. Variability was highest in the apex, lower in the base, and lowest in the midgland.

The validity of RFID badges measuring face-to-face interactions.

Face-to-face interactions are important for a variety of individual behaviors and outcomes. In recent years, a number of human sensor technologies have been proposed to incorporate direct observations in behavioral studies of face-to-face interactions. One of the most promising emerging technologies is the application of active Radio Frequency Identification (RFID) badges. They are increasingly applied in behavioral studies because of their low costs, straightforward applicability, and moderate ethical concerns. However, despite the attention that RFID badges have recently received, there is a lack of systematic tests on how valid RFID badges are in measuring face-to-face interactions. With two studies, we aim to fill this gap. Study 1 (N = 11) compares how data assessed with RFID badges correspond with video data of the same interactions (construct validity) and how this fit can be improved using straightforward data processing strategies. The analyses show that the RFID badges have a sensitivity of 50%, which can be enhanced to 65% when flickering signals with gaps of less than 75 s are interpolated. The specificity is relatively less affected by this interpolation process (before interpolation 97%, after interpolation 94.7%)-resulting in an improved accuracy of the measurement. In Study 2 (N = 73) we show that self-report data of social interactions correspond highly with data gathered with the RFID badges (criterion validity).

Early detection of capping risk in pharmaceutical compacts.

Capping is a common mechanical defect in tablet manufacturing, exhibited during or after the compression process. Predicting tablet capping in terms of process variables (e.g. compaction pressure and speed) and formulation properties is essential in pharmaceutical industry. In current work, a non-destructive contact ultrasonic approach for detecting capping risk in the pharmaceutical compacts prepared under various compression forces and speeds is presented. It is shown that the extracted mechanical properties can be used as early indicators for invisible capping (prior to visible damage). Based on the analysis of X-ray cross-section images and a large set of waveform data, it is demonstrated that the mechanical properties and acoustic wave propagation characteristics is significantly modulated by the tablet's internal cracks and capping at higher compaction speeds and pressures. In addition, the experimentally extracted properties were correlated to the directly-measured porosity and tensile strength of compacts of Pearlitol®, Anhydrous Mannitol and LubriTose® Mannitol, produced at two compaction speeds and at three pressure levels. The effect compaction speed and pressure on the porosity and tensile strength of the resulting compacts is quantified, and related to the compact acoustic characteristics and mechanical properties. The detailed experimental approach and reported wave propagation data could find key applications in determining the bounds of manufacturing design spaces in the development phase, predicting capping during (continuous) tablet manufacturing, as well as online monitoring of tablet mechanical integrity and reducing batch-to-batch end-product quality variations.