Miracle-dz/UniFAR-FTUs huggingface dataset:
Rational Discovery of Antiviral Whey Protein-Derived Small Peptides Targeting the SARS-CoV-2 Main Protease

In the present work, and for the first time, three whey protein-derived peptides (IAEK, IPAVF, MHI), endowed with ACE inhibitory activity, were examined for their antiviral activity against the SARS-CoV-2 3C-like protease (3CLpro) and Human Rhinovirus 3C protease (3Cpro) by employing molecular docking. Computational studies showed reliable binding poses within 3CLpro for the three investigated small peptides, considering docking scores as well as the binding free energy values. Validation by in vitro experiments confirmed these results. In particular, IPAVF exhibited the highest inhibitory activity by returning an IC50 equal to 1.21 μM; it was followed by IAEK, which registered an IC50 of 154.40 μM, whereas MHI was less active with an IC50 equal to 2700.62 μM. On the other hand, none of the assayed peptides registered inhibitory activity against 3Cpro. Based on these results, the herein presented small peptides are introduced as promising molecules to be exploited in the development of “target-specific antiviral” agents against SARS-CoV-2.
Using a Weekly Patient-Reported Outcome Questionnaire to Track Acute Toxicity in Patients Undergoing Pelvic Radiotherapy for Gynecologic Cancers

There are limited patient-reported outcome (PRO) data tracking changes in toxicity in patients actively undergoing radiotherapy. Between 2015–2019, acute toxicity was prospectively measured in 698 patients undergoing a 5-week course of pelvic radiotherapy for gynecologic cancers using a weekly PRO questionnaire. Our questionnaire was able detect a pattern of onset and resolution of acute gastrointestinal (GI) and genitourinary (GU) toxicity in 27 out of 32 questions. Logistic regression analysis showed that increasing GI and GU toxicity at week 2 could predict for severe toxicity at week 5. However, due to a low number of severe events, univariate results could not be productively added to a multivariate model. We observed a >70% response rate for all sections of the questionnaire, except for questions on sexual and vaginal health, which had a 13% average response rate. By demonstrating that PRO data can be used to track acute toxicity during radiotherapy, there is a need to further examine how this tool may be implemented in the clinic to provide complex, adaptive care, such as early side effect management, and modifying radiation delivery in real-time.
A Value Framework for Technology Potentials - Business Adoption of Emotion Detection Capabilities

This paper proposes a modified approach for analyzing and presenting the value generation, capture, and distribution from organizational adoption of new technologies. The focal technology used is emotion detection, and technical approaches and possibilities, as well as some of the inherent challenges and limitations are briefly described. For this technology potential, a novel framework for value generation, capture, and distribution is developed using a design science approach. The framework allows organizations to select appropriate adoption areas and derive methods for risk and problem mitigation. This framework is then demonstrated within the selected setting of emotion detection capabilities.
Corporate Diversification and Firm Performance: Theoretical Boundaries and Proposed Research Agenda

By applying a meta-narrative review approach and thematic analysis, 202 articles over the period 2000-2020 were evaluated to identify and collate the literature on corporate diversification and firm performance into six theoretical perspectives: industrial economics, resource-based view, agency theory, financial perspective, organizational learning and institutional perspective. A novel research agenda is proposed that suggests two alternative explanations: the geopolitical and historical perspectives. It is concluded that these should be part to the study of corporate diversification and firm performance.
Unsupervised Learning and Online Anomaly Detection: An On-Condition Log-Based Maintenance System

The Large Hadron Collider (LHC) demands a huge amount of computing resources to deal with petabytes of data generated from High Energy Physics (HEP) experiments and user logs, which report user activity within the supporting Worldwide LHC Computing Grid (WLCG). An outburst of data and information is expected due to the scheduled LHC upgrade, viz., the workload of the WLCG should increase by 10 times in the near future. Autonomous system maintenance by means of log mining and machine learning algorithms is of utmost importance to keep the computing grid functional. The aim is to detect software faults, bugs, threats, and infrastructural problems. This paper describes a general-purpose solution to anomaly detection in computer grids using unstructured, textual, and unsupervised data. The solution consists in recognizing periods of anomalous activity based on content and information extracted from user log events. This study has particularly compared One-class SVM, Isolation Forest (IF), and Local Outlier Factor (LOF). IF provides the best fault detection accuracy, 69.5%.
Oil-Immersed Power Transformer Condition Monitoring Methodologies: A Review

A power transformer is one of the most critical and expensive assets in electric power systems. Failure of a power transformer would not only result in a downtime to the entire transmission and distribution networks but may also cause personnel and environmental hazards due to oil leak and fire. Hence, to enhance a transformer’s reliability and extend its lifespan, a cost-effective and reliable condition monitoring technique should be adopted from day one of its installation. This will help detect incipient faults, extend a transformer’s operational life, and avoid potential consequences. With the global trend to establish digital substation automation systems, transformer online condition monitoring has been given much attention by utilities and researchers alike. Several online and offline condition monitoring techniques have been recently proposed for oil-immersed power transformers. This paper is aimed at providing a state-of-the-art review for the various condition monitoring technologies used for oil-immersed power transformers. Concept of measurements and analysis of the results along with the future trend of condition monitoring techniques are presented.
Reinforced Concrete Beams

• IN MODERN BUILDING CONSTRUCTION large open­ ings in beams are used to provide passage for utility ducts and pipes. The dead space in false ceilings is thus put to economical use. Unless proper provision is made in the design office for these openings, there is a risk that each of the subtrades will knock out holes to suit their in­ stallations and possibly weaken the structural frame of the building to a critical degree. There­ fore, the designer is often faced with the necessity of providing adequate holes in the structural frame without reducing its carrying capacity.
Routing and charging locations for electric vehicles for intercity trips

ABSTRACT This study addresses two problems in the context of battery electric vehicles (EVs) for intercity trips: the EV routing problem and the EV optimal charging station location problem (CSLP). The paper shows that EV routing on the shortest path subject to range feasibility for one origin–destination (O–D) pair, called the shortest walk problem (SWP), as well as a stronger version of the problem – the p-stop limited SWP – can be reduced to solving the shortest path problem on an auxiliary network. The paper then addresses optimal CSLPs in which EVs are range feasible with and without p-stops. We formulate the models as mixed-integer multi-commodity flow problems on the same auxiliary network without path and relay pattern enumeration. Benders decomposition is used to propose an exact solution approach. Numerical experiments are conducted using the Indiana state network.
Clinical and radiologic outcomes of pleomorphic adenoma and adenoid cystic carcinoma of the lacrimal gland.

PURPOSE To compare the radiologic and clinical features of primary lacrimal gland pleomorphic adenoma and adenoid cystic carcinoma. METHODS This study retrospectively reviewed imaging findings and medical records of lacrimal gland pleomorphic adenoma and adenoid cystic carcinoma. RESULTS Eleven patients with pleomorphic adenoma and 16 patients with adenoid cystic carcinoma were evaluated. There were no statistically significant differences between groups regarding age or sex. Proptosis was the most common presenting symptom in both groups. Adenoid cystic carcinomas were more likely to present with a palpable mass, diplopia, pain, and sensory loss than pleomorphic adenomas, although the differences were not statistically significant between groups. Furthermore, there were no significant differences in terms of homogeneity and globe indentation between lacrimal gland pleomorphic adenoma and adenoid cystic carcinoma on computed tomography (CT)(all p>0.05). The rates of bone invasion, tumor calcification, and wedge sign were significantly higher in adenoid cystic carcinomas, and bone remodeling was statistically significantly higher in pleomorphic adenomas, on CT(all p<0.05). Pleomorphic adenomas were significantly more likely to show well-defined margins, lobulated contours, heterogeneous contrast enhancement, and hyperintensity on T2-weighted magnetic resonance images (all p<0.05). CONCLUSION When differentiating between lacrimal gland pleomorphic adenoma and adenoid cystic carcinoma, evaluation of radiologic features along with clinical features is of great importance. Lobulated contours may be a significant distinguishing radiologic feature suggesting pleomorphic adenoma.
A Cyborg Insect Reveals a Function of a Muscle in Free Flight

While engineers put lots of effort, resources, and time in building insect scale micro aerial vehicles (MAVs) that fly like insects, insects themselves are the real masters of flight. What if we would use living insect as platform for MAV instead? Here, we reported a flight control via electrical stimulation of a flight muscle of an insect-computer hybrid robot, which is the interface of a mountable wireless backpack controller and a living beetle. The beetle uses indirect flight muscles to drive wing flapping and three major direct flight muscles (basalar, subalar, and third axilliary (3Ax) muscles) to control the kinematics of the wings for flight maneuver. While turning control was already achieved by stimulating basalar and 3Ax muscles, electrical stimulation of subalar muscles resulted in braking and elevation control in flight. We also demonstrated around 20 degrees of contralateral yaw and roll by stimulating individual subalar muscle. Stimulating both subalar muscles lead to an increase of 20 degrees in pitch and decelerate the flight by 1.5 m/s2 as well as an induce in elevation of 2 m/s2.
Research on Intelligent Bodybuilding System Based on Machine Learning

In recent years, people’s health is facing many challenges as their workload is increasing and their lives are becoming more and more stressful. In this context, healthy living has become a topic of concern and more and more people are choosing to promote their bodies through fitness. To address these existing problems in action recognition research, this paper designs and implements a machine learning-based intelligent fitness system to monitor three important parameters in physical activity: the type of action, the number of actions, and the period of action. Through the action recognition algorithm and the period calculation method, the three important parameters of action type, number of actions, and action period are calculated to generate a more comprehensive description of the limb actions. Experiments are conducted to show that the proposed deep neural network learns well on small datasets, achieving 97.61% action recognition accuracy and SVM achieving over 96% recognition accuracy.
Macro- and Microresearch on Swelling Characteristics and Deformation Mechanism of Red-Bed Mudstone in Central Sichuan, China

Exploring the development of multiscale behaviors of red-bed mudstone is beneficial for in-depth evaluation of swelling capacity and its progressive failure mechanism. In this paper, a macro-micro study was performed on rock samples in central Sichuan, China. The influence of environmental factors such as water content, degree of soaking, and gaseous moisture absorption was discussed. Spatial characterizations were finally described from pore structure and distribution. Experimental results suggested that deformation curve under water immersion had three stages of rapid growth, deceleration growth, and gradual stabilization. It can enter stable deformation within 60~80h and has short-term swelling effects, while gaseous moisture absorption presented long-term performance during 0~700 h. The difference in deformation between partial and complete immersion may be the release of swelling potential caused by humidity gradient and capillary suction. The strain-time curves during water immersion all tended to short-term swelling. Moreover, repeated drying and wetting promoted the time of shrinkage adjustment in the second cycle to be significantly shortened, resulting in a large amount of irreversible plastic deformation. Although initial water content influenced the final stable strain, it only changed the degree of deformation at initial stage. For the microscale structures, the closer to the end, the greater the porosity and the smaller the middle. The average porosity after 0, 1, and 2 dry and wet cycles reached 0.227%, 4.027%, and 6.121%, respectively. Geometric parameters such as volume and surface area of the pores enlarged with the increase of the number of cycles. Small pores gradually evolved into microcracks and large fissures. Besides, the pores developed from “tree-like” to “net-like” structures with continuous cycles.
New lessons for technology policy and climate change: investment for innovation

The direction of UK energy policy requires a renewed impetus if the goal of climate change stabilization is to be met. Cost is not the main issue: a transformation to a low-carbon energy system may be no more expensive than meeting future energy demands with fossil fuels. Institutional barriers are preventing the large-scale adoption of the necessary technologies. New institutions to promote low-carbon technologies have not yet led to investment on the necessary scale. Further changes to the operation of the UK electricity markets to create a ‘level playing field’ for small-scale and intermittent generation are necessary. UK policy can contribute to international agreements following on from the Kyoto Accord, which also need to address the institutional barriers to energy technology development and transfer.
A Lasso-based Collaborative Filtering Recommendation Model

—This paper proposes a new approach to solve the problem of lack of information in rating data due to new users or new items, or there is too little rating data of the user for items of the collaborative filtering recommendation models (CFR models). In this approach, we consider the similarity between users or items based on the lasso regression to build the CFR models. In the commonly used CFR models, the recommendation results are built only based on the feedback matrix of users. The results of our model are predicted based on two similarity calculated values: (1) the similarity calculated value based on the rating matrix; (2) the similarity calculated value based on the prediction results of the Lasso regression. The experimental results of the proposed models on two popular datasets have been processed and integrated into the recommenderlab package showed that the suggested models have higher accuracy than the commonly used CFR models. This result confirms that Lasso regression helps to deal with the lack of information in the rating data problem of the CFR models.
Multi-Exit DNN Inference Acceleration Based on Multi-Dimensional Optimization for Edge Intelligence

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly implemented by model partitioning which inevitably incurs the large transmission overhead of DNN’s intermediate data. A popular solution introduces multi-exit DNNs to reduce latency by enabling early exits. However, existing work ignores the correlation between exit settings and synergistic inference, causing incoordination of device-to-edge. To address this issue, this paper first investigates the bottlenecks of executing multi-exit DNNs in edge computing and builds a novel model for inference acceleration with exit selection, model partition, and resource allocation. To tackle the intractable coupling subproblems, we propose a <underline>M</underline>ulti-exit DNN inference <underline>A</underline>cceleration framework based on <underline>M</underline>ulti-dimensional <underline>O</underline>ptimization (MAMO). In MAMO, the exit selection subproblem is first extracted from the original problem. Then, bidirectional dynamic programming is employed to determine the optimal exit setting for an arbitrary multi-exit DNN. Finally, based on the optimal exit setting, a DRL-based policy is developed to learn joint decisions of model partition and resource allocation. We deploy MAMO on a real-world testbed and evaluate its performance in various scenarios. Extensive experiments show that it can adapt to heterogeneous tasks and dynamic networks, and accelerate DNN inference by up to <inline-formula><tex-math notation="LaTeX">$13.7\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>13</mml:mn><mml:mo>. </mml:mo><mml:mn>7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3172402.gif"/></alternatives></inline-formula> compared with the state-of-the-art.
Arbuscular mycorrhizal fungi mediate how plant herbivory history influences herbivore performance

Herbivore performance is influenced by plant host quality and chemical defences, both of which are influenced by belowground symbionts of plants and herbivory history. The previous herbivory may suppress or facilitate subsequent herbivore performance through changes in plant chemistry and tissue quality. Likewise, arbuscular mycorrhizal fungi (AM fungi) can alter both plant quality and plant responses to aboveground herbivory, as mycorrhizal‐mediated alterations of the nutritional budget may alter leaf nutritional quality, chemical profiles, and available leaf surface area. Despite evidence that both herbivory history and AM fungi can independently affect herbivore performance, few studies directly test both at the same time. Here, the role of AM fungi and herbivory history on the performance of a pest insect (Spodoptera frugiperda) on a common forb (Solidago altissima) grown with different AM fungal additions (no AM fungi, single species, multispecies) was directly tested. Evidence that the effect of herbivory history on herbivore performance depended on the AM fungal environment of the plant was found. Specifically, the composition of the AM fungal additions altered the directionality of herbivore performance on plants with and without prior herbivory. Without added AM fungi, herbivores demonstrated a classically suppressed growth rate on grazed plants. With a single species AM fungi added, herbivores performed the same on host plants, regardless of herbivory history. With diverse AM fungi added, herbivore growth increased on grazed plants. Mycorrhizal‐mediated changes in plant chemical profiles did not explain herbivore performance. These results emphasise the need for direct tests of mycorrhizal diversity on plant‐insect interactions.
A novel DeepML framework for multi‐classification of breast cancer based on transfer learning

In the automated diagnosis of breast cancer (BC), microscopic images based on multi‐classification play a prominent role. Multi‐classification of BC means to differentiate among the sub‐categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub‐categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub‐category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi‐classify BC cells into 8 sub‐categories. These 8 sub‐categories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi‐classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% & 89% average accuracy for 90–10% and 80–20% train‐test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches.
Attention-Based Deep Learning Framework for Hemiplegic Gait Prediction With Smartphone Sensors

This research revealed a reliable hemiplegia gait monitoring strategy to help medical practitioners in keeping track of a patient’s status. Although numerous technologies have been utilized in the past to collect motion data from patients, the high costs and huge spaces required make them challenging to use in a home setting for rehabilitation. A telemedicine protocol requires a reliable patient monitoring technique that can automatically record and classify patient movements. To achieve this, we propose an attention-based deep learning framework for hemiplegia gait prediction with smartphone-based sensory data, i.e., accelerometer and gyroscope. Firstly, convolutional neural network long short-term memory (CNN-LSTM) architecture is proposed to automatically learn potential features from the high-frequency sensory data. Moreover, considering the effectiveness of the domain expert knowledge-based hand-engineered features for gait analysis, we combine the automatically learned features and the extracted hand-engineered features from sensory data. Secondly, an attention network is proposed to tune the significance of two different features, considering these two different sourced features may be complementary to each other. Finally, extensive experiments are carried out to establish the effectiveness of the suggested hemiplegia gait prediction method in the evaluation of $5\times $ 2 fold cross-validation and leave-one-subject-out (LOSO) cross-validation, which is more difficult and practical.