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Background Changes in Doppler flow patterns of hepatic veins (HV), portal vein (PV) and intra-renal veins (RV) reflect right atrial pressure and venous congestion; the feasibility of obtaining these assessments and the clinical relevance of the findings is unknown in a general ICU population. This study compares the morphology of HV, PV and RV waveform abnormalities in prediction of major adverse kidney events at 30 days (MAKE30) in critically ill patients. Study design and methods We conducted a prospective observational study enrolling adult patients within 24 h of admission to the ICU. Patients underwent an ultrasound evaluation of the HV, PV and RV. We compared the rate of MAKE-30 events in patients with and without venous flow abnormalities in the hepatic, portal and intra-renal veins. The HV was considered abnormal if S to D wave reversal was present. The PV was considered abnormal if the portal pulsatility index (PPI) was greater than 30%. We also examined PPI as a continuous variable to assess whether small changes in portal vein flow was a clinically important marker of venous congestion. Results From January 2019 to June 2019, we enrolled 114 patients. HV abnormalities demonstrate an odds ratio of 4.0 (95% CI 1.4–11.2). PV as a dichotomous outcome is associated with an increased odds ratio of MAKE-30 but fails to reach statistical significance (OR 2.3 95% CI 0.87–5.96), but when examined as a continuous variable it demonstrates an odds ratio of 1.03 (95% CI 1.00–1.06). RV Doppler flow abnormalities are not associated with an increase in the rate of MAKE-30 Interpretation Obtaining hepatic, portal and renal venous Doppler assessments in critically ill ICU patients are feasible. Abnormalities in hepatic and portal venous Doppler are associated with an increase in MAKE-30. Further research is needed to determine if venous Doppler assessments can be useful measures in assessing right-sided venous congestion in critically ill patients.

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model’s high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO 2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a ‘heavy’ model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature ( τ ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO 2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter ( τ ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%—however, the stochastic approach reduces the energy consumption and CO 2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO 2 emission each by a factor of 18.5.

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

Ulcerative colitis (UC) is a chronic inflammatory disorder necessitating precise severity stratification to facilitate optimal therapeutic interventions. This study harnesses a triple-pronged deep learning methodology—including multimodal inference pipelines that eliminate domain-specific training, few-shot meta-learning, and Vision Transformer (ViT)-based ensembling—to classify UC severity within the HyperKvasir dataset. We systematically evaluate multiple vision transformer architectures, discovering that a Swin-Base model achieves an accuracy of 90%, while a soft-voting ensemble of diverse ViT backbones boosts performance to 93%. In parallel, we leverage multimodal pre-trained frameworks (e.g., CLIP, BLIP, FLAVA) integrated with conventional machine learning algorithms, yielding an accuracy of 83%. To address limited annotated data, we deploy few-shot meta-learning approaches (e.g., Matching Networks), attaining 83% accuracy in a 5-shot context. Furthermore, interpretability is enhanced via SHapley Additive exPlanations (SHAP), which interpret both local and global model behaviors, thereby fostering clinical trust in the model’s inferences. These findings underscore the potential of contemporary representation learning and ensemble strategies for robust UC severity classification, highlighting the pivotal role of model transparency in facilitating medical image analysis.

The emergence of biometric-based authentication using modern sensors on electronic devices has led to an escalated use of face recognition technologies. While these technologies may seem intriguing, they are accompanied by numerous implicit drawbacks. In this paper, we look into the problem of face anti-spoofing (FAS) on a frame level in an attempt to ameliorate the risks of face-spoofed attacks in biometric authentication processes. We employed a bi-directional feature pyramid network (BiFPN) that is used for convolutional multi-scaled feature extraction on the EfficientDet detection architecture, which is novel to the task of FAS. We further use these convolutional multi-scaled features in order to perform deep pixel-wise supervision. For all of our experiments, we performed evaluations across all major datasets and attained competitive results for the majority of the cases. Additionally, we showed that introducing an auxiliary self-supervision branch tasked with reconstructing the inputs in the frequency domain demonstrates an average classification error rate (ACER) of 2.92% on Protocol IV of the OULU-NPU dataset, which is significantly better than the currently available published works on pixel-wise face anti-spoofing. Moreover, following the procedures of prior works, we performed inter-dataset testing, which further consolidated the generalizability of the proposed models, as they showed optimum results across various sensors without any fine-tuning procedures.

IntroductionHospital quality improvement and hospital performance are commonly evaluated using parameters such as average length of stay (LOS), patient safety measures and rates of hospital readmission. Thirty-day readmission (30-DR) rates are widely used as a quality indicator and a quantifiable metric for hospitals since patients are often readmitted for the exacerbation of conditions from index admission. The quality of patient education and postdischarge care can influence readmission rates. We report the 30-DR rates of patients with asthma using a national dataset for the year 2013.ObjectivesThe aim of our study was to assess the 30- day readmission (30-DR) rate as well as, the causes and predictors of readmissions.Study designs/methodsUsing the Nationwide Readmission Database (NRD) (2013), we identified primary discharge diagnoses of asthma by using International Classification of Diseases, Ninth Revision, Clinical Modification code ‘493’. Categorical and continuous variables were assessed by a χ2 test and a Student’s t-test, respectively. The independent predictors of unplanned 30-DR were detected by multivariate analysis. We used sampling weights, which are provided in the NRD, to generate the national estimates.ResultsThere were 130 490 (weighted N=311 173) inpatient asthma admissions during 2013. The overall 30-DR for asthma was 11.9%. The associated factors for 30-DR were age 45–84 years (40.32% vs 29.05%; p<0.001), enrolment in Medicare (49.33% vs 30.61% p<0.001), extended LOS (mean, 4.40±0.06 vs 3.25±0.04 days; p<0.001), higher mean cost (US$8593.91 vs US$6741.31; p<0.001) and higher disposition against medical advice (DAMA) (4.14% vs 1.51%; p<0.001). The factors that increased the chance of 30-DR were advanced age (≥45–64 vs ≤17 years; OR 4.61, 95% CI 4.04 to 5.27, p<0.0001), male sex (OR 1.19, 95% CI 1.13 to 1.26, p<0.0001), a higher Charlson Comorbidity Index (CCI) (OR 1.16, 95% CI 1.14 to 1.18, p<0.0001), DAMA (OR 2.32, 95% CI 2.08 to 2.59, p<0.0001), non-compliance with medication (OR 1.34, 95% CI 1.24 to 1.46, p<0.0001), post-traumatic stress disorder (OR 1.48, 95% CI 1.22 to 1.79, p<0.0001), alcohol use (OR 1.45, 95% CI 1.27 to 1.65, p<0.0001), gastro-oesophageal reflux disease (OR 1.20, 95% CI 1.14 to 1.27, p<0.0001), obstructive sleep apnoea (OR 1.11, 95% CI 1.03 to 1.18, p<0.0042) and hypertension (OR 1.11, 95% CI 1.06 to 1.17, p<0.0001).ConclusionsWe found that the overall 30-DR rate for asthma was 11.9% all-cause readmission. Major causes of 30-DR were asthma exacerbation (36.74%), chronic obstructive pulmonary disease (11.47%), respiratory failure (6.46%), non-specific pneumonia (6.19%), septicaemia (3.61%) and congestive heart failure (3.32%). One-fourth of the revisits occurred in the first week, while half of the revisits took place in the first 2 weeks. Education regarding illness and the importance of medicine compliance could play a significant role in preventing asthma-related readmission.

Introduction: Besides mechanical cleaning, mouthwashes, and denture cleansers are typical supplements for oral care. Many chemical mouthwashes and denture cleansers provide positive effects, but finding safe and natural alternatives should be the priority. Aim of study: This study aims to investigate the effectiveness of natural materials as mouthwash and denture cleansers. Material and method: In examining the antimicrobial efficacy of mouthwashes, the minimum inhibitory concentration values were determined for five groups: distilled water (negative control), 0.2% chlorhexidine, chitosan solution, Saussurea costus solution, and chitosan-Saussurea solution. As for denture cleansers, the efficacy was evaluated after immersing polymethylmethacrylate (PMMA) disks in the previous solutions. The cell viability of Human Gingival Fibroblast (hGFs) after treatment with tested solutions was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. Data was analyzed using ANOVA with Bonferroni post hoc tests. Result: The prepared solutions showed comparable antimicrobial efficacy and cell viability as chlorohexidine. The chitosan-Saussurea costus solution demonstrated a higher antimicrobial effect and better prevention of microorganisms’ adherence to PMMA disks and cell viability. It also did not affect the roughness of PMMA in the short term. Conclusion: The new denture cleaning and mouthwash agent, which contains the natural constituents of chitosan and Saussurea costus, demonstrates significant antibacterial effectiveness and serves as a viable eco-friendly alternative to commercial mouthwash, as well as exerting minimal influence on denture roughness. Clinical implications: Chitosan and Saussurea costus have antibacterial and anti-inflammatory properties that can potentially enhance oral health. These materials present a viable path toward the production of safe, efficient, and sustainable oral care products due to their natural and biocompatible substitutes.

Split manufacturing was introduced as a countermeasure against hardware-level security threats such as IP piracy, overbuilding, and insertion of hardware Trojans. However, the security promise of split manufacturing has been challenged by various attacks which exploit the well-known working principles of design tools to infer the missing back-end-of-line (BEOL) interconnects. In this work, we define the security of split manufacturing formally and provide the associated proof, and we advocate accordingly for a novel, formally secure paradigm. Inspired by the notion of logic locking, we protect the front-end-of-line (FEOL) layout by embedding secret keys which are implemented through the BEOL in such a way that they become indecipherable to foundry-based attacks. At the same time, our technique is competitive with prior art in terms of layout overhead, especially for large-scale designs (ITC’99 benchmarks). Furthermore, another concern for split manufacturing is its practicality (despite successful prototyping). Therefore, we promote an alternative implementation strategy, based on package-level routing, which enables formally secure IP protection without splitting at all, and thus, without the need for a dedicated BEOL facility. We refer to this as “poor man’s split manufacturing” and we study the practicality of this approach by means of physical-design exploration.

In this work, three different polyurethane (PU) foams were prepared by mixing commonly used isocyanate and polyol with different isocyanate indices (1.0:0.8, 1.0:1.0, 1.0:1.1). Then, the prepared polyurethane foam samples were coated by dip-coating with a fixed ratio of nitrogen-doped, bamboo-shaped carbon nanotubes (N-BCNTs) to obtain pressure sensor systems. The effect of the isocyanate index on the initial resistance, pressure sensitivity, gauge factor (GF), and repeatability of the N-BCNT/PU pressure sensor systems was studied. The pore volume was crucial in finetuning the PU-foam-based sensors ability to detect large strain. Furthermore, large pore volume provides suitable spatial pores for elastic deformation. Sensors with large pore volume can detect pressure of less than 3 kPa, which could be related to their sensitivity in the high range. Moreover, by increasing the pore volume, the electrical percolation threshold can be achieved with a minimal addition of nanofillers. On the other hand, PU with a smaller pore volume is more suitable to detect pressure above 3 kPa. The developed sensors have been successfully applied in many applications, such as motion monitoring and vibration detection.

Speaker identification is gaining popularity, with notable applications in security, automation, and authentication. For speaker identification, deep-convolutional-network-based approaches, such as SincNet, are used as an alternative to i-vectors. Convolution performed by parameterized sinc functions in SincNet demonstrated superior results in this area. This system optimizes softmax loss, which is integrated in the classification layer that is responsible for making predictions. Since the nature of this loss is only to increase interclass distance, it is not always an optimal design choice for biometric-authentication tasks such as face and speaker recognition. To overcome the aforementioned issues, this study proposes a family of models that improve upon the state-of-the-art SincNet model. Proposed models AF-SincNet, Ensemble-SincNet, and ALL-SincNet serve as a potential successor to the successful SincNet model. The proposed models are compared on a number of speaker-recognition datasets, such as TIMIT and LibriSpeech, with their own unique challenges. Performance improvements are demonstrated compared to competitive baselines. In interdataset evaluation, the best reported model not only consistently outperformed the baselines and current prior models, but also generalized well on unseen and diverse tasks such as Bengali speaker recognition.