Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaffold microarchitecture regulates angiogenesis and the regeneration of large bone defects

Emerging 3D printing technologies can provide exquisite control over the external shape and internal architecture of scaffolds and tissue engineering (TE) constructs, enabling systematic studies to explore how geometric design features influence the regenerative process. Here we used fused deposition modelling (FDM) and melt electrowriting (MEW) to investigate how scaffold microarchitecture influences the healing of large bone defects. FDM was used to fabricate scaffolds with relatively large fibre diameters and low porosities, while MEW was used to fabricate scaffolds with smaller fibre diameters and higher porosities, with both scaffolds being designed to have comparable surface areas. Scaffold microarchitecture significantly influenced the healing response following implantation into critically sized femoral defects in rats, with the FDM scaffolds supporting the formation of larger bone spicules through its pores, while the MEW scaffolds supported the formation of a more round bone front during healing. After 12 weeks in vivo, both MEW and FDM scaffolds supported significantly higher levels of defect vascularisation compared to empty controls, while the MEW scaffolds supported higher levels of new bone formation. Somewhat surprisingly, this superior healing in the MEW group did not correlate with higher levels of angiogenesis, with the FDM scaffold supporting greater total vessel formation and the formation of larger vessels, while the MEW scaffold promoted the formation of a dense microvasculature with minimal evidence of larger vessels infiltrating the defect region. To conclude, the small fibre diameter, high porosity and high specific surface area of the MEW scaffold proved beneficial for osteogenesis and bone regeneration, demonstrating that changes in scaffold architecture enabled by this additive manufacturing technique can dramatically modulate angiogenesis and tissue regeneration without the need for complex exogenous growth factors. These results provide a valuable insight into the importance of 3D printed scaffold architecture when developing new bone TE strategies.
Influence of transurethral enucleation with bipolar of the prostate on erectile function: Prospective analysis of 51 patients at 12-month follow-up

Background Transurethral enucleation with bipolar (TUEB) is a safe and effective surgery for benign prostatic obstruction (BPO). However, few data exist concerning the influence of TUEB on erectile function (EF) in patients with BPO. Objective To evaluate the influence of TUEB on EF in patients with BPO at 3- and 12-month follow-up. Material and methods We prospectively enrolled 51 patients who underwent TUEB from June 2016 to April 2020. We evaluated maximum urinary flow rate (Qmax), postvoid residual urine (PVR), International Prostate Symptom Score (IPSS), quality of life (QoL), and International Index of Erectile Function-5 (IIEF-5) preoperatively and at 3- and 12-month follow-up. We classified the patients according to their preoperative IIEF-5 score into group 1 (IIEF-5 ≥10; n = 24) and group 2 (IIEF-5 <10; n = 27), and for further evaluation of EF, into subgroups a: severe (IIEF-5 5–7), b: moderate (8–11), c: mild to moderate (12–16), d: mild (17–21), and e: no erectile dysfunction (22–25). Data are displayed as median or median (interquartile range). Results The study comprised 51 patients with a median age of 75 (70.5–79.5) years. Median prostate and transition zone volumes were 69.5 (46.5–78.8) mL and 30.5 (19–43) mL, respectively. Urinary function improved significantly when comparing respective preoperative, 3-month, and 12-month follow-up values: Qmax (7.6, 12.9, 15.2 mL/s), PVR (50, 0, 0 mL), IPSS (20.5, 9, 6), and QoL (5, 2, 2), respectively. There was no significant change in IIEF-5 score across the three time points: 9, 7, 8. The IIEF-5 score slightly but significantly increased between the preoperative and 12-month follow-up values in group 2 (5, 5, 6) and subgroup a (5, 5, 5). Conclusion TUEB was effective and safe surgery for patients with BPO and showed no significant influence on EF at 12-month follow-up after TUEB in patients with BPO.
Voluntary physical activity in early life attenuates markers of fatty liver disease in adult male rats fed a high-fat diet

Abstract Paediatric fatty liver disease (FLD) can develop into steatohepatitis, cirrhosis and hepatocellular carcinoma in adulthood. We assessed if early life physical exercise reduced the effects of high-fat (HF) diet-induced steatosis. Male HF-fed rats with access to a running wheel from weaning until day (D)60 (early exercise) or from D67 to D120 (late exercise) were compared with control HF- or chow-fed groups with no wheel. At D63 and D120, liver histopathology (Kleiner score), type I collagen and plasma enzymes were assessed. At D63, early life activity significantly reduced histopathology scores (total, portal inflammation, steatosis, ballooning, but not lobular inflammation or fibrosis) and the number of rats affected. At D120, HF control scores were higher than in chow-fed controls, but the effect of activity was selective: early exercise reduced portal inflammation, steatosis, ballooning and fibrosis, but late activity affected only portal inflammation and ballooning. The chow-fed portal inflammation score was significantly less than all HF groups, but lobular inflammation was lower in the HF control group only. The fibrosis score in the HF early exercise and control chow group were lower than in the late exercise and sedentary HF groups, indicating that early life exercise was more effective than when activity was introduced later in life. Plasma biomarkers showed minor between-group differences. The retained effect on liver histopathology rat at D120 after only early life exposure activity suggests that timing of introduction of exercise is critical in reducing FLD scores and prevalence in children, young adults and possibly into adulthood.
Second major redesign of the National Crime Victimization Survey (NCVS)

Policy Implications This article describes recent and ongoing efforts to redesign (a) the NCVS survey instruments to reflect the changing demographics of victims, emerging crime types, and timely issues related to victimization risk and victim responses to crime and (b) the NCVS sample to generate state and local estimates of crime. The implications of these improvements for researchers and policy makers are discussed.
TIPSY: predicting where traffic will ingress a WAN

In addition to consumer workloads, public cloud providers host enterprise workloads such as video conferencing and AI+ML pipelines. Enterprise workloads can, at times, overwhelm the available ingress capacity on individual peering links. Traditional techniques to address this problem in the consumer setting do not always apply here, such as use of CDN caches in eyeball networks. Ingress congestion events necessitate shifting traffic to other peering links at short timescales. While content providers use such techniques in the egress direction, ingress is inherently a different and more challenging problem. Once a packet leaves an enterprise network, it is subject to opaque routing policies that influence the path to the cloud provider. We present TIPSY, a statistical-classification-based system for predicting the peering link through which a flow will enter a WAN. TIPSY's predictions are used to safely operate a congestion mitigation system that injects BGP withdrawal messages to redirect traffic away from congested peering links. We train TIPSY on traffic data from the Azure WAN, and we demonstrate 76% accuracy in predicting through which 3 peering links (out of thousands) a flow will enter the network after BGP withdrawals.
A Survey on Personality Prediction

Personality is an important psychological structure that is responsible for individual differences between people. Social networking site has become the most universally used communication and interaction platform for all people over the past few years. By predicting the personality of a person could be useful in several areas like personalized recommendations, strategies of marketing, business intelligence, sociology, human resources management, and mental diagnosis. Predicting personality with the help of social media data is an encouraging approach as this method does not require any questionnaires to be filled by users. The Big Five test is an approach where asks some questions and predicts the personality of humans of the following categories neuroticism, extraversion, conscientiousness, agreeableness, and openness to experience. According to the reply of persons, it can easily generalize into 5 personality categories. The outcome of this paper is to show a comparative study of various strategies and algorithms used in this field. We will inspect ways to use the new technology to future development in this area also understand human personality and contribute to the development of personality theory.
A Proposed Design of a Lecture Material to Reduce Learning Complexity

Cognitive Load Theory (CLT) and Human-Computer Interaction (HCI) concepts need to be combined to understand better how students learn in more complex environments. For this reason, this paper combines Cognitive Load Theory (CLT) and Human-Computer Interaction (HCI). The primary purpose of this paper was to reduce the student’s cognitive load through a lecture note. With the help of HCI design-centered principles on a lecture note, we have implemented the lecture note to lessen students’ cognitive load. The lecture note could be an example of how HCI can be designed in a lecture note. This paper has discussed the research questions with several learning theories that measured students’ cognitive load. Based on learning theories and HCI design principles, we have developed two lecture notes; the first is HCI design-based, and the second is in the absence of HCI design principles. The reason for making two lecture notes is to observe which lecture note is reducing or increasing students’ cognitive load when we may conduct the survey1. The survey was performed among some undergraduate students. When studied, the cognitive load was lowered among the student in lecture one, where the HCI design concepts were applied, and the average cognitive efficiency was 1.52 from 143 participants. And the lecture note two, where HCI design principles did not apply, has increased students’ cognitive load. The cognitive efficiency of lecture note two was 0.60 from 112 participants, which illustrates the importance of HCI in lecture notes.
Collaborators and hecklers: Performative pedagogy and interruptive processes

Arguing for the positive disruptive nature of interruption, this paper concentrates on my current performative and pedagogic usage of interruption within my teaching as the means to achieve three aims: 1) develop aspects of practice discussed in my doctoral thesis ‘Tactics of Interruption: Provoking Participation in Performance Art’ (Campbell 2016) related to the focused usage of interruptive processes in contemporary art practice (Arlander 2009: 2) provide students with direct experience of how interruption may command immediate reaction and force collaborative means of working, i.e. collective survival tactics to deal with interruption; and 3) theorise, articulate and demonstrate how interruption relates to critical reflection (on the part of both student and teacher), extending the ideas of Maggi Savin-Baden (2007) to propose interruption as reflection. To achieve these aims, the paper discusses how I have implemented interruption into learning activity design and evidences how I have created activities that aim to help students understand collaborative learning in cross-disciplinary projects through an effective use of realia (interruption is part of real life). I discuss one first year teaching seminar at Loughborough University in March 2015 (and subsequent related iterations) combining performance, fine art and collaboration methodologies where students directly engaged in a range of activities not displaced from their own life experiences; there was heavy student engagement in digital technologies, and interruption. The main outcomes of the teaching session support and go beyond the aims by relating to: a) experiential learning related to the interplay between ‘collaboration’ and ‘interruption’; b) performative pedagogy and inclusion; c) the interplay between teaching, liveness and interruption; and d) performative pedagogy and the exchange of power relation.
Simulation of High Intensity Focused Ultrasound Device in Healthcare Application for Non-Invasive Heat Induced Tissue Ablation

Since the last decade, High Intensity Focused Ultrasound (HIFU) has been actively used in medical care for the treatment of various cancers. HIFU is a technique that employs a fixed ultrasonic transducer with a focusing lens, allowing the transmitted signal to reach higher intensity levels within a specific focal zone of relevance. Mechanical and thermal impacts are the main steps of HIFU ablation. In this study, experiments and simulations on tissue ablation with HIFU were carried out to see how multiple tissue ablation worked and how to improve tumor ablation while avoiding damage to surrounding healthy tissue by adjusting the ideal intensity and lens radius of curvature of the transducer. The analysis employs clinical applications to evaluate the optimum properties of the proposed model. For this experiment, several soft and hard tissues were selected from the human body. Each tissue's temperature was determined to be 310.15-degree Kelvin. At a specified acoustic power and exposure time, the tissues' optimal frequency (1.6 MHz, 2.25 MHz, 3.4 MHz, and 3.5 MHz) and power (10 W, 17 W and 20 W) were identified. By using a focal length of 60 mm, we have completed all of the computations. Numerous cancers, including the brain, heart, skull, liver, kidney and bone, have all shown positive results. This finding looks promising for HIFU tumor ablation surgery.
Sentiment Analysis on COVID-19 Vaccine Twitter Data using Neural Network Models

Nowadays, social media is crucial for informing people about global issues such as the current COVID-19 pandemic. People have expressed different perspectives on the COVID-19 vaccine, and Twitter has proven to be an excellent medium for sharing these. The purpose of this research is to propose a text vectorized neural network (NN) model and compare it with long short-term memory (LSTM), and bidirectional long short-term Memory (BiLSTM) to analyse sentiment on COVID-19 vaccine twitter data. The raw Twitter data is collected and three different raters annotate the data as positive or negative. The label is finalized using the kappa value. Then, Natural Language Processing (NLP) methods are used to process the twitter data. The study concludes that the proposed text vectorized NN model outperforms other models in terms of accuracy, as it achieves 81% in the test dataset, while LSTM and BiLSTM obtain 75% and 74%, respectively. The text vectorized NN model receives a 51% score in Matthews’ correlation coefficient (MCC), while LSTM and BiLSTM acquire 37% and 39% scores. Other performance metrics, such as precision, recall, f1-score, and confusion matrix were also used to validate the models more effectively.
Augmented Paths and Reodesics for Topologically-Stable Matching

We propose a fully-automatic method that computes from scratch point-to-point dense correspondences between isometric shapes under topological noise. While relying on pairwise distance preservation constraints is common and generally sufficient to handle isometric deformations, presence of topological noise needs further actions that we present as our main contributions. First, instead of comparing distances over two paths on two input surfaces, we cast fuzzy votes at the path endpoints based on topologically-robust heat diffusion from path vertices. Second, we make the matching even more stable to topological noise by introducing the so-called reodesics, which are locally shortest geodesics that go through robust matches. In addition to the five standard datasets for isometric shape correspondence with and without topological noise, we employ and release a sixth one geared specifically towards topological noise evaluation with ground-truth information. We demonstrate our qualitative and quantitative advantages over seven recent state-of-the-art methods on these six datasets.
Smart Trash Distribution, and Recycling Processes Using IoT Sensing & Mobile Application

The waste processing system is the most important portion of renewable energy and the industrial revolution. The majority of developing countries lack a garbage collection and processing mechanism. It causes many dangerous diseases and damages the beauty of lives due to its bad odors. They have few resources but a lot of raw materials. Therefore, they need to modify them so they may utilize them once more when needed. This research goal is to detect different types of waste, such as metal, plastic, liquid, and non-metal, and recycle them in their own way. This waste will be indicated by the inductive proximity sensor, a capacitive proximity sensor, and a water level indicator sensor. We have designed a prototype, a mobile application, and performed simulations to verify the device's stability level. We may check the garbage count and if the bin is full or not using the mobile application.
A Traffic Flow Steering Algorithm for Hybrid Terrestrial-Satellite Backhaul Network

In this paper, we proposed a traffic flow steering algorithm for a hybrid terrestrial-satellite backhaul network to minimize network congestion and improve overall network throughput. To that end, we categorize data flows as delay tolerant or delay-sensitive based on the delay-tolerant level. Considering a predetermined threshold, if a potential overloaded backhaul link is found the delay-tolerant flows are steered through a secondary satellite link and the delay-sensitive flows are routed through the primary link. The simulation result shows that the proposed algorithm can increase the network throughput by distributing the load among the backhaul links.
Climate Change Myths Detection Using Dynamically Weighted Ensemble Based Stance Classifier

Fake news in social media is currently one of the major obstacles that stand in the way of mass-scale climate action. To identify problematic posts that agree with misconceptions regarding climate change or global warming, we developed a dynamically weighted ensemble-based stance classifier capable of classifying the stance a piece of text holds towards a specified target. Our stance classifier has proven to be extremely successful and has exceeded the accuracies of all previous stance classifiers by a large margin in classifying ’agree’ and ’disagree’ stances. The characteristic feature of the ensemble is that weights are dynamically assigned to the constituent models based on their chosen stance for the post to be categorized and the accuracy with which the base model can classify that particular stance. The model is accompanied by a large dataset comprised of the most popular myths regarding climate change as targets along with a significant number of associated posts containing the target that can have one of four possible stances towards its associated myth including: ’agree’, ’disagree’, ’discuss’, and ’unrelated’. The proposed model is capable of understanding even difficult aspects of human conversation such as humor and sarcasm. It has achieved an overall accuracy of 82.03%.
Performance Analysis of Parallel Overlapping Community Detection Algorithms in Large-scale Social Networks

In today's online social network systems, various communities comprising various unique characteristics and interests play a significant role in complex network analysis. Often such communities comprise nodes which belong to many different communities, resulting rise of parallel overlapping communities. To effectively detect such parallel overlapping communities, various algorithms have been proposed based on node clustering, edge clustering and other approaches. In this paper, these algorithms to detect parallel overlapping communities are studied thoroughly, and their performances are analyzed comparatively. Graph-based approach such as Sequencial Clique Percolation (SCP), intrinsic Longitudinal Community Detection (iLCD), link-based approach such as Top Graph Clusters (TopGC), fuzzy-based approach such as Cluster-Overlap Newman Girvan Algorithm (CONGA), agent and dynamic-based and modularity scoring based approach etc. are comparatively analyzed to evaluate their performance for overlapping community detection using dataset from Zachary karate club and Stanford large network. Analysis conducted on SCP and iLCD using the same datasets show that SCP cannot detect dynamic overlapping communities in a dense network as effectively as iLCD. It is also observed, CONGA can detect the distinctive community clusters in Zachary karate dataset. It is also noted that TopGC is suitable in detecting overlapping clusters from large networks with limited time and memory usage, making it useful for experiments in workstations with lower specs and limited processing power.
Class Imbalance Problems in Machine Learning: A Review of Methods And Future Challenges

Nowadays, class imbalance problem is one of the most important affairs among machine learning and data mining researchers. In this problem, majority of the sample data are labeled as one class and only a few samples are labeled as another class. This creates an imbalance in the dataset, and it is hard to solve because common classification algorithms are not designed to face these sorts of data. It means, the machine shows a result that is more influenced by the majority class, while the minority class is neglected. In this review paper we explore the main issues that come with the class imbalance problem, we also discuss different techniques to handle this problem. A fair knowledge about the multiclass imbalance problem is also given. In the end, we indicate some opportunities and challenges for future research in this domain.
Distributed fault detection for uncertain Lipschitz nonlinear multi‐agent systems in finite frequency domain

This article focuses on distributed fault detection for uncertain Lipschitz nonlinear multi‐agent systems (MASs). To handle parameter uncertainties, a finite‐frequency H−/H∞$$ {H}_{-}/{H}_{\infty } $$ unknown input observer (UIO) is designed to formulate the reference residual system and based on it, a robust UIO corresponding to the fault detection system is designed using an H∞$$ {H}_{\infty } $$ model matching approach. In the UIO constructed for one agent, the unknown input composed of faults in the other agents and disturbances in the whole system is partitioned to decouple part of them, which avoids the constraint of the rank condition of UIO and allows for fault detection for more than one faulty agent. The generated residual is sensitive to the corresponding fault while robust against the nondecoupled unknown input and parameter uncertainties, of which the design conditions are transformed into a set of linear matrix inequalities (LMIs). A numerical simulation is conducted to illustrate the effectiveness of the proposed method.