Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregation of alkane and fluoroalkane clusters: molecular dynamics simulation results

Molecular dynamics (MD) calculations of clusters with up to 184 molecules of decane (DC/184), eicosane (ES/128) and the perfluorinated (PFDC/184, PFES/128) and semi-fluorinated (SFES/128) analogs have been performed using the AMBER 5 program. The aim was to investigate the different behaviour of alkanes and fluorinated alkanes in the process of molecular aggregation. Moreover, mixed clusters of decane and perfluorodecane (DC/92 + PFDC/92) with different starting configurations have been studied. The flexibility of the chains and the influence of structure formation in the clusters as well as the tendency for microphase separation in the mixtures have been analysed using snapshots, radial atom pair distribution functions, orientational correlation functions and diffusion coefficients.
Dynamic mechanical analysis of confined glass-forming liquids

Acoustic spectroscopy is a valuable tool, when the degrees of freedom of a material couple to strain. Since this is the case for molecular glass-forming liquids the dynamic mechanical response is a quantity of primary interest here. Dynamic mechanical analysis measurements (0.1–100 Hz) have been performed for salol, toluene and ortho-terphenyl confined in mesoporous (2.5–10 nm) silica matrices. Important parameters (relaxation times τ of the slow α-relaxation, change of T g with pore size, number and size of dynamically correlated molecules, etc.) characterizing the glass freezing in the three glass-forming liquids have been extracted from the measurements for various confining pore sizes. A comparison with literature data shows excellent agreement and demonstrates that mechanical measurements provide a very sensitive tool to probe the dynamics of molecular glass forming liquids in confined systems.
DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging

Osteoarthritis (OA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. Although primarily identified via hyaline cartilage change based on medical images, technical bottlenecks like noise, artifacts, and modality impose an enormous challenge on high-precision, objective, and efficient early quantification of OA. Owing to recent advancements, approaches based on neural networks (DNNs) have shown outstanding success in this application domain. However, due to nested non-linear and complex structures, DNNs are mostly opaque and perceived as black-box methods, which raises numerous legal and ethical concerns. Moreover, these approaches do not have the ability to provide the reasoning behind diagnosis decisions in the way humans would do, which poses an additional risk in the clinical setting. In this paper, we propose a novel explainable method for knee OA diagnosis based on radiographs and magnetic resonance imaging (MRI), which we called DeepKneeExplainer. First, we comprehensively preprocess MRIs and radiographs through the deep-stacked transformation technique against possible noises and artifacts that could contain unseen images for domain generalization. Then, we extract the region of interests (ROIs) by employing U-Net architecture with ResNet backbone. To classify the cohorts, we train DenseNet and VGG architectures on the extracted ROIs. Finally, we highlight class-discriminating regions using gradient-guided class activation maps (Grad-CAM++) and layer-wise relevance propagation (LRP), followed by providing human-interpretable explanations of the predictions. Comprehensive experiments based on the multicenter osteoarthritis study (MOST) cohorts, our approach yields up to 91% classification accuracy, outperforming comparable state-of-the-art approaches. We hope that our results will encourage medical researchers and developers to adopt explainable methods and DNN-based analytic pipelines towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice for improved knee OA diagnoses.
Combined use of UV‐C irradiation and heat treatment to improve postharvest life of strawberry fruit

The effects of UV-C (4.1kJm −2 ) and heat treatment (45 ◦ C, 3h in air) either separately or combined on the quality of strawberries (Fragaria × ananassa cv Seascape) at the 75 or 50% surface red ripening stage were assessed, the latter stage being used only in the firmness test. In addition, the development of surface fungal infections was followed and in vitro germination assays on conidia of Botrytis cinerea and Rhizopus stolonifer performed. Both heat and combined treatments, decreased hue and delayed changes of the L ∗ parameter. All treatments reduced the accumulation of anthocyanins. Control fruit softened most while fruit treated by the combined method were the firmest. The combined treatment reduced fungal infections and delayed in vitro germination of Botrytis cinerea conidia. After 2days at 20 ◦ C, treated fruit had lower amount of phenolics than the control. Neither the heat nor UV-C irradiation modified the total sugar content, although the combined treatment decreased it slightly relative to the control. Titratable acidity increased through storage at 20 ◦ C in all fruit, but no differences between control and treated fruit were detected. The combination of UV-C and heat treatments enhanced the benefits of applying each treatment separately, and could be useful to improve and extend strawberry fruit postharvest life.  2004 Society of Chemical Industry
Business Continuity Implementation for Data Center Environments

The continuous evolution of technologies, combined with an increasingly widespread presence of broadband in the area, has led to a massive digitization of data. A big push was given above all in public administration and health sectors so as to allow citizens to use the services and retrieve their documents in a simpler and faster way. Obviously, for these areas it is essential to offer various levels of data maintenance and operational continuity, depending on their criticality. Various scenarios could be implemented in this specific case in which data and services are managed through different Data Centers connected to each other.
Oxygen-Transporting Parylene-HT Mesh for Cell Transplantation to Reduce Hypoxia

Cell transplantation is a way of treatment in which lost cellular functions can be recovered by transplanted cells. However, hypoxic conditions on the transplanted site hinder the survival of the cells. This paper presents an oxygenation device for cell transplantations to improve the engraftment and transplanted cell survival. The flexible and thin polymeric hollow mesh working as gas capillaries can effectively transport oxygen between microvessels and transplanted cells, maintaining adequate oxygen tension for cell survival and functionality. Devices are designed, fabricated, and characterized in this study. Both in silico and in vivo models demonstrate a 4-5 fold increase in cell viability compared to the control group without oxygen-transporting mesh. The results showed that a broad spectrum of cell transplantations could benefit from such MEMS oxygen-transporting device.
Secure MISO Broadcast Channel: An Interplay Between CSIT and Network Topology

We study the problem of secure transmission over a Gaussian two-user multi-input single-output (MISO) broadcast channel (BC) under the assumption that links connecting the transmitter to the two receivers may have unequal strength statistically. In addition to this, the state of the channel to each receiver is conveyed in a delayed manner to the transmitter. We focus on a two state topological setting of strong v.s. weak links. Under these assumptions, we first consider the MISO broadcast channel and establish inner and outer bounds on secure generalized degrees of freedom (SGDoF) region with different topology states. The encoding scheme sheds light on the usage of both resources, i.e., topology of the model and delayed channel state information at the transmitter (CSIT); and, allows digitization and multicasting of overheard side information, while transmitting confidential message over the stronger link. Furthermore, for a special class of channels, we show that the established bounds agree and so we characterize the sum SGDoF. Next, we extended this model to the Gaussian MISO two receiver channel with an external eavesdropper, under the assumption that the state of the channel which is available to each receiver is conveyed either perfectly (P) or with delay (D) to the transmitter equal fractions of time, while only delayed CSI is available from the eavesdropper to the transmitter. For this case, we first establish an outer bound on the SGDoF region. Next, we develop an achievability scheme that shows that as opposed to coding independently over different states, joint encoding across the alternating CSIT and topological states enables strictly better secure rates.
Use of Information Technology in Online Learning Process Applied by the Lecturers Based on the Education and Rank Level

The existence of the Covid-19 pandemic has influenced interaction of relations between humans, including in the learning process. This study aims to describe the types of online learning models applied by lecturers related to their educational background and rank. The study was conducted at State University of Malang. The research sample was taken by 104 lecturers. Data were collected through a questionnaire technique, and analyzed using descriptive statistics and ANOVA. The results showed that the lecturers used a variety of types of information technology which were varied in the online learning process. Of the 26 types of applications, the most dominant ones used by lecturers are WhatsApp, Email, Google Meet, SIPEJAR -Assignment, Google Classroom, Zoom, SIPEJAR - Chat, Quizizz, Google Form, and YouTube. There are differences in the variation of information technology used by lecturers in terms of education level, but there is no differences based on the rank.
Bearing Fault Feature Enhancement and Diagnosis Based on Statistical Filtering and 1.5-Dimensional Symmetric Difference Analytic Energy Spectrum

Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault feature enhancement and diagnosis based on the statistical filtering and the 1.5-dimensional symmetric difference analytic energy operator (1.5D-SDAEO) to solve the problem. 1). The statistical filtering is used to filter the background noise under the standard distinction index. 2). The 1.5D-SDAEO is used to enhance the signal impulse, suppress the residual noise, and improve the SNR. 3). The dominant frequency in the energy spectrum is compared with the rolling bearing fault characteristic frequency to the fault diagnosis. The feasibility and the superiority of the presented method are verified by the simulation, engineering, and comparison experiments. All results show that the presented method can effectively enhance the fault feature and accurately diagnose the rolling bearing fault.
Ontology-Based Fault Tree Analysis Algorithms in a Fuzzy Environment for Autonomous Ships

This study deals with fault tree analysis algorithms based on an ontology-based approach in a fuzzy environment. We extend fuzzy fault tree analysis by embedding ontology-based fault tree structures. The ontology-based approach allows conducting advanced analyses grounded on rich domain knowledge. Two approaches as the conventional approach and rule-based method are applied for the calculations of failure probability and analysis of the fault tree. Collision and grounding of an autonomous ship are investigated in two scenarios. The structural design, probability calculations, and sensitivity analyses prove that the proposed system is applicable, robust, accurate and reliable. Thus, the propounded ontology-based fault tree algorithms fill a gap in safety engineering by its capability of comprehensiveness.
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial Fibrillation

In this work we propose a semi-supervised framework to visually assess the progression of time series. To this end, we present a recurrent version of the VAE to exploit the generative properties that lead it to learn in an unsupervised way a continuous compressed representation of the data. We introduce a classifier in the VAE training process to control the regulation of the latent space, allowing the network to learn latent variables that set the basis for creating an explainable evaluation of the data. We use the proposed framework to address the diagnosis of Atrial Fibrillation (AF) first validating it with simulated data with known properties and subsequently testing it with intracardiac data obtained from pacemakers and defibrillator systems.
Examining a Covid-19 Twitter Hashtag Conversation in Indonesia: A Social Network Analysis Approach

Twitter becomes one of the most adopted social media platforms globally, and Indonesia is a country with a rapid growth of Twitter user number in recent years. This paper discusses about the examination of conversation network of Twitter hashtag related to Covid-19 in Indonesia by using a network perspective. During this pandemic situation, Twitter has been increasingly adopted as a medium of conversational interaction amongst people to express their opinion and feeling about the situation, or share information, among others. At the same time, the Indonesian Government has established an official hashtag (#) to coordinate and organize conversations related to a specific topic of Covid-19, namely #BersatuLawanCovid19. In this way, the Government would be able to reach the public interest due to the capability of the hashtag to become a trending topic. This study examines how the Twitter conversations emerged and developed within the Twitter community by using Social Network Analysis approach. We have collected 793 Twitter users and 4441 Twitter chats from the hashtag #BersatuLawanCovid19. We then visualized the relationship network and examined the community using Social Network Analysis metrics with NodeXL. This study found that there is no a mutual engagement amongst the community members in terms of conversational practices. Interestingly, although some members of the community received a high number of engagement efforts from others, they do not actively respond to the initiatives. This suggests that the official account of government who is in-charge of managing the conversation need to enhance their communication strategy to improve the conversation within the community.
Abnormality Detection in Musculoskeletal Radiographs using EfficientNets

Abnormality detection in musculoskeletal radiographs, a regular task for radiologists, requires both experiences and efforts. To increase the number of radiographs interpreted each day, this paper presents cost-efficient deep learning models based on ensembles of EfficientNet architectures to help automate the detection process. We investigate the transfer learning performance of ImageNet pre-trained checkpoints on the musculoskeletal radiograph (MURA) dataset which is very different from the ImageNet dataset. The experimental results show that, the ImageNet pre-trained checkpoints have to be retrained on the entire MURA training set, before being trained on a specific study type. The performance of the EfficientNet-based models is shown to be superior to three baseline models. In particular, EfficientNet-B3 not only achieved the overall Cohen’s Kappa score of 0.717, compared to the scores 0.680, 0.688, and 0.712 for MobileNetV2, DenseNet-169, and Xception, respectively, but also being better in term of efficiency.
Audio-Visual Deep Neural Network for Robust Person Verification

Voice and face are two most popular biometrics for person verification, usually used in speaker verification and face verification tasks. It has already been observed that simply combining the information from these two modalities can lead to a more powerful and robust person verification system. In this article, to fully explore the multi-modal learning strategies for person verification, we proposed three types of audio-visual deep neural network (AVN), including feature level AVN (AVN-F), embedding level AVN (AVN-E), and embedding level combination with joint learning AVN (AVN-J). To further enhance the system robustness in real noisy conditions where not both modalities can be accessed with high-quality, we proposed several data augmentation strategies for each proposed AVN: A feature-level multi-modal data augmentation is proposed for AVN-F and an embedding-level data augmentation with novel noise distribution matching is designed for AVN-E. For AVN-J, both the feature and embedding level multi-modal data augmentation methods can be applied. All the proposed models are trained on the VoxCeleb2 dev dataset and evaluated on the standard VoxCeleb1 dataset, and the best system achieves 0.558, 0.441% and 0.793% EER on the three official trial lists of VoxCeleb1, which is to our knowledge the best published single system results on this corpus for person verification. To validate the robustness of the proposed approaches, a noisy evaluation set based on the VoxCeleb1 is constructed, and experimental results show that the proposed system can significantly boost the system robustness and still show promising performance under this noisy scenario.
Hierarchical Decomposed-Objective Model Predictive Control for Autonomous Casualty Extraction

In recent years, several robots have been developed and deployed to perform casualty extraction tasks. However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty. Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner. There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location. In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty. We implement this controller on ResQbot — a proof-of-concept mobile rescue robot we previously developed — capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure. HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure. At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision. We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method’s performance, and compare it with baseline approaches. The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.
A Self-Paced BCI With a Collaborative Controller for Highly Reliable Wheelchair Driving: Experimental Tests With Physically Disabled Individuals

Brain-controlled wheelchairs (BCWs) are a promising solution for people with severe motor disabilities, who cannot use conventional interfaces. However, the low reliability of electroencephalographic signal decoding and the high user's workload imposed by continuous control of a wheelchair requires effective approaches. In this article, we propose a self-paced P300-based brain–computer interface (BCI) combined with dynamic time-window commands and a collaborative controller. The self-paced approach allows users to switch between control and noncontrol states without requiring any additional task or mental strategy, while the dynamic time-window commands allow balancing the reliability and speed of the BCI. The collaborative controller, combining user's intentions and navigation information, offers the possibility to navigate in complex environments and to improve the overall system reliability. The feasibility of the proposed approach and the impact of each system component (self-paced, dynamic time window, and collaborative controller) are systematically validated in a set of experiments conducted with seven able-bodied participants and six physically disabled participants steering a robotic wheelchair in real-office-like environments. These two groups controlled the BCW with a final driving accuracy greater than 99%. Quantitative and subjective results, assessed through questionnaires, attest to the effectiveness of the proposed approach. Altogether, these findings contribute to improving the usability of BCWs and, hence, the potential for their use by target users in home settings.
A Reliable Energy and Spectral Efficient Spectrum Sensing Approach for Cognitive Radio Based IoT Networks

The Internet of Things (IoT) enables the connectivity of disparate devices and the exchange of real-time data among those devices. It requires large amounts of bandwidth to ensure the quality of service (QoS), but sufficient bandwidth is not always available due to the limited frequency spectrum allocated for wireless data communication. Cognitive radio (CR) is a promising technology that enhances the utilization of the spectrum by allowing unlicensed users/secondary users (SU) access to the licensed primary users (PU) spectrum under certain conditions. The CR based IoT (CR-IoT) network can overcome the spectrum scarcity problem in a conventional IoT network. In a CR-IoT network, energy efficiency must be considered for avoiding interference between the PU and the SU, as the conventional energy detection (ED) technologies consume significant energy for CR operations. To mitigate this problem, we propose a novel energy efficient sequential ED spectrum sensing technique which enhances the sensing duration of each unlicensed CR-IoT user/SU by utilizing the reporting time slot when compared to the conventional non-sequential ED spectrum sensing scheme. In addition, each unlicensed CR-IoT user calculates the weight factor based on the Kullback Liebler Divergence score, which enhances the detection performance. Thereafter, each CR-IoT user in the CR-IoT network sequentially passes on both the local sensing result and the weight factor to the corresponding fusion center (FC) via the allocated reporting channel, which extends the sensing time duration of the CR-IoT user. The FC uses the local sensing result and the weight factor of each CR-IoT user to make a global decision by using the soft fusion rule. The results obtained through simulations show that the proposed sequential ED spectrum sensing scheme achieves a better sensing performance, an enhanced sum rate, an enhanced energy and spectral efficiency when compared to the conventional non-sequential ED spectrum sensing scheme with interference constraints.