Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wide Band Adaptive All Digital Phase Locked Loop With Self Jitter Measurement And Calibration

The expanding growth of mobile products and services has led to various wireless communication standards that employ different spectrum bands and protocols to provide data, voice or video communication services. Software defined radio and cognitive radio are emerging techniques that can dynamically integrate various standards to provide seamless global coverage, including global roaming across geographical regions, and interfacing with different wireless networks. In software defined radio and cognitive radio, one of the most critical RF blocks that need to exhibit frequency agility is the phase lock loop (PLL) frequency synthesizer. In order to access various standards, the frequency synthesizer needs to have wide frequency tuning range, fast tuning speed, and low phase noise and frequency spur. The traditional analog charge pump frequency synthesizer circuit design is becoming difficult due to the continuous down-scalings of transistor feature size and power supply voltage. The goal of this project was to develop an all digital phase locked loop (ADPLL) as the alternative solution technique in RF transceivers by taking advantage of digital circuitry’s characteristic features of good scalability, robustness against process variation and high noise margin. The targeted frequency bands for our ADPLL design included 880MHz960MHz, 1.92GHz-2.17GHz, 2.3GHz-2.7GHz, 3.3GHz-3.8GHz and 5.15GHz-5.85GHz that are used by wireless communication standards such as GSM, UMTS, bluetooth, WiMAX and Wi-Fi etc. This project started with the system level model development for characterizing ADPLL phase noise, fractional spur and locking speed. Then an on-chip jitter detector and parameter adapter was designed for ADPLL to perform self-tuning and self-calibration to accomplish high frequency purity and fast frequency locking in each frequency band. A novel wide band DCO is presented for multi-band wireless application. The proposed wide band adaptive ADPLL was implemented in the IBM 0. 13μm CMOS technology. The phase noise performance, the frequency locking speed as well as the tuning range of the digitally controlled oscillator was assessed and agrees well with the theoretical analysis.
E2EK: End-to-End Regression Network Based on Keypoint for 6D Pose Estimation

The methods based on deep learning are the mainstream of 6D object pose estimation, which mainly include direct regression and two-stage pipelines. The former are keen by many scholars at first due to their simplicity and differentiability to poses, but they usually lack in accuracy when compared with the latter that estimate the intermediate variables relating to geometries such as object keypoints or 2D-3D correspondence before PnP/RANSAC algorithm. However, the loss function of the two-stage method is non-differentiable to the 6D pose, which is hard to apply in the tasks requiring the differentiable poses. To overcome the disadvantages of the above methods, we propose an end-to-end regression network based on keypoints for 6D pose estimation. Specifically, we supervise the point-wise keypoint offsets that help the network to learn the geometric information and directly regress the 6D pose through aggregating keypoints to achieve differentiability to the pose. Furthermore, we improve the sampling method by sampling points around objects that benefits the small object and design a unit loss function that helps the learning of the keypoints. Experimental results show that our approach outperforms most methods on LM, LM-O and YCB-V datasets.
Reducing Muscular Fatigue in Trail Running - Mechanisms and Strategies

The aim of this thesis was to analyse strategies to reduce muscular fatigue in trail running and potentially draw conclusions on the underlying mechanisms. Trail running is a new and upcoming sport that induces a combination of fatigue and muscle damage in the main locomotor muscles. To obtain conclusive evidence on the effect of intervention studies a preliminary descriptive study was undertaken to characterise typical fatigue and damage. Subsequently a model was developed and validated that would allow the investigation of interventions in an applied field setting. A popular current strategy in trail running is the use of compression garments; therefore the effect of these on performance was studied as an intervention. Furthermore, prior heating is anecdotally considered beneficial and recent research has suggested a potential mechanism to link this with reduced muscle damage. Therefore a controlled laboratory study was conducted, examining the effects of passive heating on functional consequences of downhill running in an untrained population. In synopsis, the research conducted for this thesis provides descriptive evidence and a validated terrain model to further investigate fatigue reduction strategies in trail running. Additionally it adds to the current literature in disproving a positive effect of compression garments on performance and demonstrating the functional link between heating and eccentric-induced muscle damage reduction.
Transition in social risk factors and adolescent motherhood in low- income and middle- income countries: Evidence from Demographic and Health Survey data, 1996–2018

Understanding the dynamics of social risk factors in the occurrence of adolescent motherhood is vital in designing more appropriate prevention initiatives in low-income and middle-income countries (LMICs). We aimed this study to examine the transition of social risk factors and their association with adolescent motherhood in LMICs since the initiation of the MDGs. We analysed 119967 adolescent girls (15–19 years) from 40-nationally representative Demographic Health Surveys in 20 LMICs that had at least two surveys: a survey in 1996-2003(baseline, near MDGs started) and another in 2014-2018(endline). Adolescent motherhood (having a live birth or being pregnant before age 20) was the outcome of interest, whereas social risk factors including household wealth, girls’ level of education, and area of residence were the exposures. The association between adolescent motherhood and the social risk factors, as well as changes in the strength of the association over time were observed using multilevel logistic regression analysis. On an average, the proportion of adolescent mothers without education decreased by -15·61% (95% CI: -16·84, -14·38), whereas the poorest adolescent mother increased by 5·87% (95% CI: 4·74, 7·00). The national prevalence of adolescent motherhood remained unchanged or increased in 55·00% (11/20) of the studied countries. Comparing baseline to endline, the overall adjusted odds ratio (AOR) of adolescent motherhood increased for both poorest (AOR = 1·42, 95% CI: 1·28, 1·59) and rural residences (AOR = 1·09, 95% CI: 1·01, 1·17), and decreased, but not statistically significant for the low level of education (AOR = 0·92, 95% CI: 0·84, 1·01 for no education). Our study concludes that social risk factors of the adolescent mother had shifted in different directions during MDGs and SDGs eras, and adolescent mothers remained more disadvantaged than non-mothers in LMICs. Efforts need to be enhanced to improve adolescent girls’ education. Intervention should be prioritised in disadvantaged communities to delay adolescent first birth and prevent adolescent motherhood in LMICs.
The Analytical Solutions of the Stochastic Fractional Kuramoto–Sivashinsky Equation by Using the Riccati Equation Method

In this work, we consider the stochastic fractional-space Kuramoto–Sivashinsky equation using conformable derivative. The Riccati equation method is used to get the analytical solutions to the space-fractional stochastic Kuramoto–Sivashinsky equation. Because this equation has never been examined with space-fractional and multiplicative noise at the same time, we generalize some previous results. Moreover, we display how the multiplicative noise influences on the stability of obtained solutions of the space-fractional stochastic Kuramoto–Sivashinsky equation.
Tenecteplase in Ischemic Stroke: Challenge and Opportunity

Purpose of Review Intravenous thrombolysis is the first-line therapy for ischemic stroke, and alteplase has been used as an intravenous thrombolysis drug for over 20 years. However, considering its low rate of recanalization and risk of intracerebral hemorrhage, alteplase may not be the optimal thrombolytic drug of choice for ischemic stroke. Tenecteplase (TNK) is a genetically engineered, mutant, tissue plasminogen activator that is a potential substitute to alteplase in ischemic stroke. The pharmacokinetic advantages of TNK include greater fibrin selectivity than alteplase and prolonged half-life time. In this review, we have summarized the clinical trials of TNK in ischemic stroke. Recent Findings Clinical trials showed a higher recanalization rate of TNK over alteplase without increasing the rate of intracerebral hemorrhage. However, not all clinical trials showed superiority of TNK over alteplase in functional outcomes and early neurological improvement. TNK was superior to alteplase in terms of recanalization in patients who fulfilled the imaging mismatch criteria and in those planning to undergo mechanical thrombectomy. Summary TNK has the potential to substitute alteplase for ischemic stroke therapy. Future TNK clinical trials that target functional outcomes are warranted.
Flowchart for Clustered-Based Channel Allocation Management Scheme

In the GSM system, several Base Transceiver Station (BTS) are controlled by a single Base Station Controller (BSC). In this paper, the BTSs in a given BSC is referred to as a cluster. In this paper, flowchart for clustered-based channel allocation management scheme is presented. The mechanism is meant to reduce call blocking/dropping to the barest minimum. Usually, traffic intensity value varies from one BTS to another, as it is directly proportional to the number of GSM users that make or receive calls within a particular period of time. In order to maintain a reliable GSM network that will provide minimum call blocking/dropping, the paper presents the flowchart for network resource management scheme that will adopt sharing of traffic loads among BTSs that belong to a particular network cluster. The scheme also considers the following; available channels, mobility factor, offered traffic, number of nearby BTSs, new call arrival rate, handoff call arrival rate and mean call duration. With limited available channels per BTS, instead of blocking a new call or dropping a handoff call whenever a particular BTS reaches its maximum allowable capacity, this newly proposed scheme however checks for a nearby BTS within that cluster that has free channels, and then performs a routine operation that will transfer some percentage of the traffic load to the free nearby BTS. This routine operation therefore allows the BTS to admit more calls.
Integrating Informal Learning into Deployment Planning and Project Scheduling

Dynamic organizations constantly search for new ways to improve the quality of their processes. A core element of these efforts is the integration of informal learning into daily workflows. Interdisciplinary research is required to accomplish this challenging task and make informal learning manageable. In this study, we propose a novel combination of organizational theory and operational research using a quantitative project scheduling approach to create efficient workflows, including systematized informal learning. The concept of integrating the informal learning activities of knowledge sharing, reflection, and self-organization is presented based on a multi-objective, multi-resource constrained project scheduling problem with limited renewable resources, activity splitting, and preemption. Additional simulation studies demonstrate the calculation of informal learning ratios, together with further discussion of strategic management options.
VAE-Based Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis

Video-level sentiment analysis is a challenging task and requires systems to obtain discriminative multimodal representations that can capture difference in sentiments across various modalities. However, due to diverse distributions of various modalities and the unified multimodal labels are not always adaptable to unimodal learning, the distance difference between unimodal representations increases, and prevents systems from learning discriminative multimodal representations. In this paper, to obtain more discriminative multimodal representations that can further improve systems’ performance, we propose a VAE-based adversarial multimodal domain transfer (VAE-AMDT) and jointly train it with a multi-attention module to reduce the distance difference between unimodal representations. We first perform variational autoencoder (VAE) to make visual, linguistic and acoustic representations follow a common distribution, and then introduce adversarial training to transfer all unimodal representations to a joint embedding space. As a result, we fuse various modalities on this joint embedding space via the multi-attention module, which consists of self-attention, cross-attention and triple-attention for highlighting important sentimental representations over time and modality. Our method improves F1-score of the state-of-the-art by 3.6% on MOSI and 2.9% on MOSEI datasets, and prove its efficacy in obtaining discriminative multimodal representations for video-level sentiment analysis.
Extrusion 3D bioprinting of functional self-supporting neural constructs using a photoclickable gelatin bioink

Many in vitro models of neural physiology utilize neuronal networks established on two-dimensional substrates. Despite the simplicity of these 2D neuronal networks, substrate stiffness may influence cell morphology, network interactions and how neurons communicate and function. With this perspective, three-dimensional (3D) gel encapsulation is a powerful to recapitulating aspects of in vivo features, yet such an approach is often limited in terms of the level of resolution and feature size relevant for modelling aspects of brain architecture. Here, we report 3D bioplotting of rat primary cortical neural cells using a hydrogel system comprising gelatin norbornene (GelNB) and poly (ethylene glycol) dithiol (PEGdiSH). This bioink benefits from a rapid photo-click chemistry, yielding eight-layer crosshatch neural scaffolds and a filament width of 350 µm. The printability of this system depends on hydrogel concentration, printing temperature, extrusion pressure and speed. These parameters were studied via quantitative comparison between rheology and filament dimensions to determine the optimal printing conditions. Under optimal conditions, cell viability of bioprinted primary cortical neurons at day 1 (68 ± 2%) and at day 7 (68 ± 1%) were comparable to the 2D control group (72 ± 7%). The present study relates material rheology and filament dimensions to generate compliant free-standing neural constructs through bioplotting of low-concentration GelNB-PEGdiSH, which may provide a step forward to study 3D neuronal function and network formation.
Oversampling With Reliably Expanding Minority Class Regions for Imbalanced Data Learning

This paper proposes a simple interpolation Oversampling method with the purpose of Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate minority region around each original minority sample, then exploits this region to further identify those clean subregions without distributing any majority sample. The synthetic samples are only allowed to generate in the clean subregions, so that the regions of the minority class can be broadened reliably. Given that the learning from multiclass imbalanced data is more challenging as compared to two-class scenarios, we also extend OREM to handle multiclass imbalance problems by leveraging an iteration procedure of generating synthetic samples, consequently leading to a multiclass oversampling algorithm OREM-M. The key peculiarity of OREM-M is to reduce the class overlapping not only between the synthetic minority and original samples, but also from the synthetic samples of different minority classes. In this way, OREM-M ensures that the data of each class after oversampling can be modeled well. In addition, we embed OREM into boosting framework to develop a new ensemble method OREMBoost addressing class imbalance problems. Extensive experiments demonstrate the effectiveness of the proposed OREM, OREM-M, and OREMBoost.
Clinicians’ Perspective on Factors Affecting Pain Management among Patients with Terminal Illness Admitted at Longisa County Referral Hospital, Kenya.

The study sought to explore clinicians’ perspective on factors affecting pain management on terminally ill patients admitted at Longisa County Referral Hospital in southern part of Rift Valley region in Kenya. Factors that facilitates pain management are: use of WHO guidelines, education of clinicians on pain management and incorporating pain assessment as the fifth vital sign. Conversely, barriers leading to under treatment of pain include: lack of knowledge on pain management, lack of pain assessment tools/guidelines, misconceptions of narcotics among clinicians, lack of essential medicines, understaffing, patients and family beliefs of pain, inconsistent supply of opioids and restrictive regulation of opioids. This was an exploratory qualitative study in which in-depth interviews were conducted with clinicians who were purposively sampled to include those who worked in areas with palliative care patients until thematic saturation was achieved. Data was analyzed through thematic content analysis and presented in form of narrative. The study revealed that clinician do not treat pain adequately. Additionally, significant knowledge deficiencies exist regarding current principles of pain management practices and presence of beliefs that interfere with optimal care. Significant knowledge deficiencies are evident regarding currently accepted principles of pain management practices, as well as beliefs and lack of essential drugs including opioids which hinders pain management therefore clinicians must be educated continuously especially with ongoing educational sessions with a focus on knowledge of current pain management principles.
Bridging Networks, Systems and Controls Frameworks for Cybersecurity Curriculums and Standards Development

Applied Cybersecurity practices in the US private and public industry are transitioning to an overall focus on Cyber Risk Management. It is hence necessary to align IT-Cybersecurity professional association application standards and related educational curricula with emerging applications in practice. Current standards and educational curricula seem fragmented across Networks Protocols and Network Analysis Tools Frameworks, Systems and Networks Infrastructure Frameworks, and, Risk Management & Controls Policy Frameworks. This article develops an applied framework for aligning, integrating, and, streamlining standards and curricula across the above three levels to align them with needs of applied Risk Management practice. The Cyber Risk Management framework is developed with focus on VoIP networks which have been gaining central prominence across diverse industries such as global Banking and Finance over the past decade. Despite central role technologically and economically, sparse attention has been given to critical vulnerabilities described as the ‘weakest links’ in global Banking and Finance networks as evident from Cybersecurity and Penetration Testing. This article demonstrates the contribution of the proposed Cyber Risk Management framework in addressing such critical gaps in global Banking and Finance Cybersecurity and Information Assurance practices as an example while being extendable to multiple other industries such as Healthcare.
Resilient Model Predictive Adaptive Control of Networked Z-Source Inverters Using GMDH

Power grids are increasingly evolving into more efficient smart grids thanks to advancements in information technology. However, these intelligent communication-dependant power systems are becoming more susceptible to cyberattacks than their traditional peers. Power inverters are one of the main network connected devices supporting grid stability, which can potentially be targets for cyber-threats. This article presents an attack-resilient model predictive adaptive controller for a class of inverters, called Z-source inverters, to protect them against deception attacks. The proposed control structure includes a model predictive controller equipped with an unknown input Kalman filter and an estimator for system perturbations. The group method of data handling technique is used to estimate system uncertainties and make the system robust against perturbations. The unknown input Kalman filter is also adopted to estimate the states and unknown inputs in the presence of noisy measurements and cyberattacks on the control signal. We mathematically prove that in the presence of noise and perturbations, the proposed controller guarantees the stability of the system under a deception attack, which causes a delay, less than a sampling time, in control signals. Simulation results reveal the effectiveness of the presented controller in protecting the system against pulse, scaling and random attacks in the presence of system uncertainties, source and load fluctuations and output noises.
Prevalence of depression and anxiety among patients with breast cancer: a cross-sectional study

Background: Breast cancer patients have a significant rate of depression, according to prior studies. During some phases of patient management, particularly early after surgery, these patients are at risk for depression. Objectives: to assess the prevalence of depression and anxiety among patients with breast cancer and risk factors associated with it. Methods: This cross-sectional study enrolled 74 patients diagnosed with breast cancer in the general surgery clinic at King Abdulaziz University Hospital in Jeddah, Saudi Arabia. Two questionnaires were used, Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7), filled in by patients who attended the clinic or electronically by sending the questionnaires to the patients. Significant depression was defined as PHQ-9 score 10 and significant anxiety was defined as GAD-7 score 10. Sociodemographic and medical data were also collected. This research was approved by the unit of biomedical ethics at King Abdulaziz University Faculty of Medicine. Results: From the 74 patients with breast cancer, 36% of the women were found to have depression and 24% were found to have anxiety and the prevalence of having both depression and anxiety was 23.0%. Mean PHQ-9 score was (8.9, SD: 5.8). The mean GAD-7 score was (6.7, SD: 5.2). Significant depression and significant anxiety were found to be more in Saudi nationals, those who did not go for surgical therapy, patients with metastatic disease at the time of filling in the questionnaire and married women. These results were not statistically significant. Conclusion: This study showed that the prevalence of depression and anxiety was similar to previously conducted studies; it also showed no associations between the risk factors studied and anxiety or depression in patients with breast cancer. Keywords: Breast Cancer, depression, anxiety, Saudi Arabia, PHQ-9, GAD-7