Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework.

Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to their original shape when exposed to a stimulus, while also healing damage they have sustained. However, achieving both high healing/recycling efficiency and a high glass transition temperature (Tg) in SMVs has been challenging, due to the conflicting requirements between molecular chain mobility and the formation and reaction of dynamic covalent bond exchange. Based on the understanding of chemo-physical properties, this study first leverages machine learning (ML), involving supervised and unsupervised learning approaches, to navigate this complex design space of SMVs. Furthermore, we elaborated the basic mathematical frameworks of ML approaches and comprehensively compared their performances. Based on the best performing model, we designed four types of thermally robust shape memory vitrimers (TRSMVs), which boast high recycling efficiency, elevated Tg, and exemplary shape memory effects, overcoming conventional barriers. One of the discovered samples exhibited outstanding performance with a Tg of 233.5 °C, a recycling efficiency of 84.1%, and a recovery stress of 33 MPa in experiments. It aligns well with ML predictions, showcasing the potential of our ML framework in driving innovative materials design and advancing the field of smart polymers.

Von Willebrand factor assays in patients with acquired immune thrombotic thrombocytopenia purpura treated with caplacizumab.

Acquired immune thrombotic thrombocytopenic purpura (iTTP) is a rare disease with a poor prognosis if undiagnosed. It is caused by autoantibody production to the von Willebrand factor (VWF) cleaving protease, A disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS13). Caplacizumab, an immunoglobulin directed to the platelet glycoprotein Ibα receptor of VWF, has been reported to induce quicker resolution of iTTP compared to placebo. The laboratory measurement of VWF activity was significantly reduced in clinical trials of caplacizumab. Several VWF assays are available in the UK and this study investigated whether differences in VWF parameters were present in 11 patients diagnosed with iTTP and treated with daily caplacizumab. Chromogenic factor VIII activity, VWF antigen, collagen binding activity, VWF multimers and six VWF activity assays were measured prior to caplacizumab therapy and on several occasions during treatment. VWF antigen and collagen binding activity levels were normal or borderline normal in all patients. Ultra-large molecular weight multimers were present in all patients following treatment. VWF activity assays were normal or reduced during treatment, but this was reagent and patient dependant. In the unusual scenario of a caplacizumab-treated patient requiring measurement of VWF activity, it is important that laboratories understand how their local reagents perform as results cannot be predicted.

Managing Preoperative Hemostasis in Patients with Inherited and Acquired Bleeding Disorders.

Inherited and acquired bleeding disorders pose significant hemostatic challenges for surgery. Patients at particular risk of bleeding include those with inherited bleeding disorders such as hemophilia, von Willebrand disease, and platelet function defects; those on antiplatelet agents or anticoagulants; and those with acquired conditions such as immune thrombocytopenic purpura, liver disease, or renal impairment. Each has its own specific challenges and close collaboration between the anesthetic, surgical, and hematology teams is crucial. Optimizing surgical hemostasis for patients at risk involves attention to detail, with careful preoperative planning, meticulous surgical technique, prompt identification of complications and judicious use of hemostatic agents and blood components. This article gives an overview of the bleeding risks involved and therapeutic options to overcome them.

Influenza A (H1N1)pdm09 outbreak of unknown source in a Ghanaian senior high school.

BACKGROUND: Influenza is an acute viral respiratory tract infection caused by influenza virus and transmitted from person to person. Though usually seasonal in temperate climates, influenza occurs throughout the year in the tropics with outbreaks occurring at irregular intervals. On February 6, 2018, a number of students from a Senior High School (SHS) in Accra reported to a district hospital with cough, fever and other respiratory symptoms. An influenza-like illness (ILI) outbreak was suspected. We investigated to determine the magnitude and source of the outbreak and implement control and preventive measures. METHODS: We interviewed health workers, staff and students of the school as well as case-patients and reviewed health records to collect data on demographic characteristics, signs and symptoms, date of illness onset and outcome. We defined ILI case as "any person in the SHS with fever (measured axillary temperature of ≥ 37.5 °C or history of fever) and cough with or without sore throat or runny nose from January 21 to February 26, 2018". We conducted active case search to identify more cases and took oropharyngeal samples for laboratory testing. We performed descriptive and inferential analysis by calculating attack rate ratios (ARR) and their exact 95% confidence intervals (CI). RESULTS: Of the 3160 students, 104 case-patients were recorded from January 25, 2018 to February 13, 2018 (overall attack rate of 3.3%). Mean age of case-patients was 16.1 (±2.3) years with males constituting 71.2% (74/104). Sex specific attack rates were 5.6% (74/1331) and 1.6% (30/1829) for males and females respectively. Compared to females, males were 3.4 times as likely to be ill [ARR =3.4, 95%CI = (2.23-5.15)]. Nine oropharyngeal samples from 17 suspected case-patients tested positive for influenza A (H1N1)pdm09. CONCLUSION: Outbreak of influenza A (H1N1)pdm09 occurred in a SHS in Accra from January to February, 2018. Even though source of the outbreak could not be determined, prompt case management and health education on hand and personal hygiene as non-pharmacological factors probably contributed to the outbreak control. The outbreak ended with a scheduled mid-term break. This underscores the need for more evidence on the effect of school closure in influenza outbreak control.

Effects of Low-Temperature Heat Treatment on Mechanical and Thermophysical Properties of Cu-10Sn Alloys Fabricated by Laser Powder Bed Fusion.

This study investigated the impact of low-temperature heat treatments on the mechanical and thermophysical properties of Cu-10Sn alloys fabricated by a laser powder bed fusion (LPBF) additive manufacturing (AM) process. The microstructure, phase structure, and mechanical and thermal properties of the LPBF Cu-10Sn samples were comparatively investigated under both the as-fabricated (AF) condition and after low-temperature heat treatments at 140, 180, 220, 260, and 300 °C. The results showed that the low-temperature heat treatments did not significantly affect the phase and grain structures of the Cu-10Sn alloys. Both pre- and post-treatment samples displayed consistent grain sizes, with no obvious X-ray diffraction angle shift for the α phase, indicating that atom diffusion of the Sn element is beyond the detection resolution of X-ray diffractometers (XRD). However, the 180 °C heat-treated sample exhibited the highest hardness, while the AF samples had the lowest hardness, which was most likely due to the generation of precipitates according to thermodynamics modeling. Heat-treated samples also displayed higher thermal diffusivity values than their AF counterpart. The AF sample had the longest lifetime of ~0.19 nanoseconds (ns) in the positron annihilation lifetime spectroscopy (PALS) test, indicating the presence of the most atomic-level defects.

TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition.

Convolutional Neural Networks (CNNs) are frequently used algorithms because of their propensity to learn relevant and hierarchical features through their feature extraction technique. However, the availability of enormous volumes of data in various variations is crucial for their performance. Capsule networks (CapsNets) perform well on a small amount of data but perform poorly on complex images. To address this, we proposed a new Capsule Network architecture called Tri Texton-Dense CapsNet (TTDCapsNet) for better complex and medical image classification. The TTDCapsNet is made up of three hierarchic blocks of Texton-Dense CapsNet (TDCapsNet) models. A single TDCapsNet is a CapsNet architecture composed of a texton detection layer to extract essential features, which are passed onto an eight-layered block of dense convolution that further extracts features, and then the output feature map is given as input to a Primary Capsule (PC), and then to a Class Capsule (CC) layer for classification. The resulting feature map from the first PC serves as input into the second-level TDCapsNet, and that from the second PC serves as input into the third-level TDCapsNet. The routing algorithm receives feature maps from each PC for the various CCs. Routing the concatenation of the three PCs creates an additional CC layer. All these four feature maps combined, help to achieve better classification. On fashion-MNIST, CIFAR-10, Breast Cancer, and Brain Tumor datasets, the proposed model is evaluated and achieved validation accuracies of 94.90%, 89.09%, 95.01%, and 97.71% respectively. Findings from this work indicate that TTDCapsNet outperforms the baseline and performs comparatively well with the state-of-the-art CapsNet models using different performance metrics. This work clarifies the viability of using Capsule Network on complex tasks in the real world. Thus, the proposed model can be used as an intelligent system, to help oncologists in diagnosing cancerous diseases and administering treatment required.

Thermophysical Properties of Laser Powder Bed Fused Ti-6Al-4V and AlSi10Mg Alloys Made with Varying Laser Parameters.

This study investigated the influence of diverse laser processing parameters on the thermophysical properties of Ti-6Al-4V and AlSi10Mg alloys manufactured via laser powder bed fusion. During fabrication, the laser power (50 W, 75 W, 100 W) and laser scanning speed (0.2 m/s, 0.4 m/s, 0.6 m/s) were adjusted while keeping other processing parameters constant. Besides laser processing parameters, this study also explored the impact of test temperatures on the thermophysical properties of the alloys. It was found that the thermophysical properties of L-PBF Ti-6Al-4V alloy samples were sensitive to laser processing parameters, while L-PBF AlSi10Mg alloy showed less sensitivity. In general, for the L-PBF Ti-6Al-4V alloy, as the laser power increased and laser scan speed decreased, both thermal diffusivity and conductivity increased. Both L-PBF Ti-6Al-4V and L-PBF AlSi10Mg alloys demonstrated similar dependence on test temperatures, with thermal diffusivity and conductivity increasing as the test temperature rose. The CALPHAD software Thermo-Calc (2023b), applied in Scheil Solidification Mode, was utilized to calculate the quantity of solution atoms, thus enhancing our understanding of observed thermal conductivity variations. A detailed analysis revealed how variations in laser processing parameters and test temperatures significantly influence the alloy's resulting density, specific heat, thermal diffusivity, and thermal conductivity. This research not only highlights the importance of processing parameters but also enriches comprehension of the mechanisms influencing these effects in the domain of laser powder bed fusion.

CCMT: Dataset for crop pest and disease detection.

Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others. In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test sets. The latter consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.

A Thermoset Shape Memory Polymer-Based Syntactic Foam with Flame Retardancy and 3D Printability.

Here we report a thermoset shape memory polymer-based syntactic foam inherently integrated with flame retardancy, good mechanical properties, excellent shape memory effect, and 3D printability. The syntactic foam is fabricated by incorporating a high-temperature shape memory polymer (HTSMP) as the matrix, with 40 vol % hollow glass microspheres (HGM) K20, K15, and K1 as fillers. Compressive behavior, strain-controlled programming followed by free recovery, stress recovery, and flame retardancy of these three syntactic foams were studied. Dynamic mechanical analysis and thermal characterization validate their high glass transition temperature (T g = ∼250 °C) and excellent thermal stability. Our results suggest that the foam consisting of K20 HGM exhibits high compressive strength (81.8 MPa), high recovery stress (6.8 MPa), and excellent flame retardancy. Furthermore, this syntactic foam was used for three-dimensional (3D) printing by an extruder developed in our lab. Honeycomb, sinusoidal shapes, and free-standing helical spring were printed for demonstration. This high-temperature photopolymer-based syntactic foam integrated with high T g, flame retardancy, high recovery stress, and 3D printability can be beneficial in different sectors such as aerospace, construction, oil and gas, automotive, and electronic industries.

Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application.

In order to expand the output of solar power systems for efficient integration into the national grid, solar energy resource assessment at site is required. A major impediment however, is the widespread scarcity of radiometric measurements, which can be augmented by satellite observation. This paper assessed the suitability of satellite-based solar radiation resource retrieved from the NASA-POWER archives at [Formula: see text] spatial resolution over Ghana-West Africa, to develop a long-term source reference. The assessment is based on the criteria of comparison with estimations from sunshine duration measurement for 22 synoptic stations. Overall, the satellite-based data compared well with ground-based estimations by r = 0.6-0.94 ± 0.1. Spatiotemporally, the agreement is strongest over the northern half Savannah-type climate during March-May, and weakest over the southern half Forest-type climate during June-August. The assessment provides empirical framework to support solar energy utilization in the sub-region.

Uncertainty Estimation Using Variational Mixture of Gaussians Capsule Network for Health Image Classification.

Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with limited ability to express uncertainty. Many of them tend to be overconfident on out-of-distribution data, making them less trustworthy and hence reducing their suitability for practical adoption in safety-critical areas such as health and self-driving cars. In this work, we propose a capsule network based on a variational mixture of Gaussians to train distributions of network weights as opposed to a single set of weights and enable the model to express its predictive uncertainty on out-of-distribution data. Training distributions of weights have the added advantage of avoiding overfitting on smaller datasets which are common in health and other fields. Although Bayesian neural networks are known to exhibit slow training and convergence, experimental results show that the proposed model can retrieve only relevant features, converge faster, is less computationally complex, can effectively express its predictive uncertainties, and achieve performance values that are comparable to the state-of-the-art models. This is an indication that CapsNets can exhibit the transparency, credibility, reliability, and interpretability required for practical adoption.

GC3558: An open-source annotated dataset of Ghana currency images for classification modeling.

The field of deep learning has led to remarkable advancements in many areas, including banking. Identifying currency denomination type and model is challenging due to intraclass variation and different illumination conditions. Although, in this domain, many datasets regarding currency denomination type and model, e.g., Indian Currency, Thai Currency, Chinese Currency, U.K. currency, etc., have already been experimented with by different researchers. More datasets are needed from a variety of currencies, especially Ghana currency (cedi). This article presents the Ghana Currency image dataset (GC3558) of 3558 color images in 13 classes created from a high-resolution camera. The dataset is comprised of only genuine currency. The class consists of coin and paper notes: 10 pesewas coin, 20 pesewas coin, 50 pesewas coin, 1 cedi coin, 2 cedis coin, 1 cedi note, 2 cedis note, 5 cedis note, 10 cedis note, 20 cedis note, 50 cedis note, 100 cedis note and 200 cedis note. All images are de-identified, validated, and freely available for download to A.I. researchers. The dataset will help researchers evaluate their machine learning models on real-world data.