Miracle-dz/UniFAR-FTUs huggingface dataset:
Two male patients from an extended seven generation Turkish family diagnosed with Renpenning syndrome: identifying the causative mutation and review of the literature

Intellectual disability (ID) is a lifelong condition that begins during the developmental period, and characterized by significant limitations in intellectual functioning and adaptive behavior including social, conceptual and practical skills. In these case series, we aimed to identify the genetic etiopathogenesis of two male patients with ID from a seven-generation large-Turkish family. Two affected boys with syndromic ID were evaluated. Genome-wide auto zygosity mapping was performed on affected individuals and other available healthy family members for identifying shared chromosomal segments between affected individuals. Critical region co-segregating with the disease was confirmed and narrowed down by short tandem repeat polymorphism markers. Whole exome sequencing was performed to identify the responsible genes and mutations. Sanger sequencing was performed for segregation analysis. We performed a comprehensive genetic analysis to reveal the underlying genetic aetiology of the patients and identified a mutation on PQBP1 gene (NM_005710.2:c.459-462delAGAG) that is associated with Renpenning syndrome. Considering the clinical findings and genetic data of the affected probands, the patients were diagnosed with Renpenning syndrome and this is the first report for Renpenning syndrome with attention deficit and hyperactivity disorder comorbidity.
TRANSFORMATIVE LEADERSHIP IN DOCTORAL STUDENTS’ PROFESSIONAL DEVELOPMENT SITUATED IN STUDENT ORGANIZATIONS

The purpose of this self-study is to examine how transformative leadership in student organizations contributes to doctoral students’ professional development in higher education. Drawing from Mezirow’s (1997) notion of transformative learning and Bass’s (1990) theory of transformational leadership, the researchers discuss how an academic student organization, Alpha Upsilon Alpha, provided opportunities for transformative leadership in scholarship and service thus crafted academic identities and re-envisioned student organizations as spaces of transformative professional development.
ARLIF-IDS - Attention augmented Real-Time Isolation Forest Intrusion Detection System

Distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt the normal traffic of a targeted server, service or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic. Emerging technologies such as the Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks. Previous literature demonstrates the utility of lower number of significant features for intrusion detection. Thus, it is essential to have a fast and effective security identification model based on low number of features. In this work, a novel Attention-based Isolation Forest Intrusion Detection System is proposed. The model considerably reduces training time and memory consumption of the generated model. For performance assessment, the model is assessed over two benchmark datasets, the NSL-KDD dataset&the KDDCUP'99 dataset. Experimental results demonstrate that the proposed attention augmented model achieves a significant reduction in execution time, by 91.78%, and an average detection F1-Score of 0.93 on the NSL-KDD and KDDCUP'99 dataset. The results of performance evaluation show that the proposed methodology has low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.
Boosting Transferable Meta-learning via Unsupervised Visual Representation

Deep neural network have been widely used in image classiﬁcation, but it requires a lot of labeled data to train to obtain a higher accuracy rate. However, extensive data acquisition and manual label annotation are expensive. Therefore, few-shot classiﬁcation is of crucial signiﬁcance which aims to recognize novel categories with only few labeled data. Many existing few-shot classiﬁcation algorithms predict categories by comparing the feature embeddings of query images which are not good enough with those from a few labeled images (support examples) or us-ing full connected layer to classify. In this paper, we propose a learning method to obtain better features by ﬁne-tuning Deep neural network using transferable meta-learning. In addition, we exploit the complementarity of few-shot learning and self-supervision learning and use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. We conduct extensive experiments using two challenging few-shot learning benchmarks: Mini-Imagenet and Fewshot-CIFAR100. Experimental results have proved the effectiveness of our method and perfect performance on benchmarks.
Does Personal Financial Distress Affect Workers’ Productivity? Evidence from Real Estate Agents

Does Personal Financial Distress Affect Workers’ Productivity? Evidence from Real Estate Agents BY Liuming Yang April 14, 2022 Committee Chair: Dr. Vincent Yao Major Academic Unit: Department of Real Estate The paper studies the direct effect of personal financial distress on workers’ productivity and its spillover effects on their customers. Using the real estate brokerage industry as a setting and agents’ bankruptcy filings as a proxy for personal financial distress, we first construct an empirical model that predicts the likelihood of personal financial distress at the individual by year level. After validating the model, we apply the constructed ex ante “financial distress score” to study its effect on agents’ productivity. We find that both listing and sale prices are significantly lower and time on market is much shorter as the financial distress score increases while controlling for agents’ fixed effects and other market and property attributes. These results suggest that financially distressed agents are more motivated to close the deal quickly at a lower price. In addition, we find that the less desirable sales outcomes induced by agents’ financial distress spill over to homeowners’ future buying activities. These owners subsequently buy smaller houses with much higher loan-to-value ratios. Does Personal Financial Distress Affect Workers’ Productivity? Evidence from Real Estate Agents Liuming Yang ∗
A Convolutional Deep Neural Network Approach for Skin Cancer Detection Using Skin Lesion Images

—Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a signiﬁcant improvement over previous work, achieving an accuracy of 97%.
MANDIBULAR IMPLANT-SUPPORTED PRECISION ATTACHMENT-RETAINED FIXED PROSTHESIS: CASE REPORT

Background: A common clinical problem confronting prosthodontists is the design and maintenance of edentulous mandibular by the fixed implant-supported prosthesis. Purpose: Aim of the paper is to study and describes the rehabilitation of a patient with edentulous mandible via precision attachment-retained implant-supported fixed prosthesis to compensate for the effect of mandibular deformation. Materials and Methods: In one patient scheduled for the implant-supported prosthetic substructure was divided into three parts, the interforaminal region, and the posterior segments were not connected rigidly, but by a precision, attachment reducing the tension that can occur around the implant by increasing the precision and passive fit. Results: Two years after the delivery of the prosthesis to the patient, the fit of the substructure and, the bone adaptation around the implant were observed not to have deteriorated in the radiograph. Conclusions: Precision attachment-retained mandibular implant-supported fixed prosthesis used in an edentulous patient is a good alternative to reduce mandibular deformation and provide good stabilization and retention.
Title : Which parameter is the most effective in predicting poor outcomes in sepsis : C-reactive protein , albumin , or C-reactive protein / albumin ratio ? Running Title : Sepsis and C-reactive protein / albumin ratio

Objective: Albumin and C-reactive protein (CRP) are connected with adverse clinical outcomes in sepsis. The purpose of this study was to investigate the diagnostic value of CRP / albumin ratio in patients with sepsis in the intensive care unit (ICU). Materials and Methods: Our retrospective study worked out patients admitted to the ICU for sepsis. The patients’ acute physiology and chronic health evaluation (APACHE) II scores, sex, age, CRP, albumin levels, white blood cell, and sepsis-related organ failure assessment (SOFA) scores at ICU admission, mechanical ventilation (MV) stay, ICU stay, bacteremia, and mortality were enrolled. Results: A total of 849 patients diagnosed with sepsis were involved in our study. The in-ICU mortality rate was 55% (467/849). The mortality group had remarkably higher APACHE II scores, duration of MV duration, ICU stay, SOFA scores, CRP level, and CRP/albumin ratio and lower albumin level (p<0.05). In the analysis of receiver operating characteristics for mortality prediction, the area under rotation and cut-off values were 0.820 and> 95 mg / L for CRP, 0.813 and ≤2.6 g/dL for albumin, and 0.843 and >53.7 for CRP/albumin ratio. Conclusion: In this study, it was found that the CRP / albumin ratio was a more effective parameter than CRP or albumin alone in predicting mortality in sepsis patients.
New aspects of a direct torque controlled induction motor drive

Direct torque control (DTC) is a competitive alternative to the field oriented control nowadays in practical application. By DTC, the flux and the torque of the induction machine can be controlled directly. This paper presents a simulation working under the DTC concept, but for practical implementation some new aspects should be investigated: (1) the current is controlled indirectly, requiring the implementation of the current limitation mainly during starting operation; (2) the low speed operation is a special case of this drive. The possible unstable operating should be avoided; and (3) the dynamic limits of the power switches must be considered. It contains the switching frequency, the minimal pulse width and the dead-time of the switches in the same inverter leg. Simulation and experimental results are given.
Some approaches to real time evaluation for HF data communications

A project is currently being undertaken in which techniques to evaluate the HF ionospheric channel in real time, with particular reference to the requirements of communications systems employing modern flexible modems (for modulation selection), are being investigated. Four approaches to the measurement of the channel dispersion characteristics have so far been considered and tests conducted on short range (approximately 300 km) paths within the UK. These techniques are described in this paper together with some results from the experimental work. So far in this project, consideration has only been given to measurement of propagation delay and frequency dispersion parameters.
Inaccurate load compensation force during fast goal-directed elbow flexion

Wearable robotic lifting aids compensate a proportion of the gravity during heavy lifting and can thereby reduce the burden on the operator’s musculoskeletal system. This study investigates the effect of errors in the compensation force of an inaccurate load compensation system on fast reaching movements of an operator. In an experimental study, subjects (n=18) held a 2.7 [kg] object while connected to a haptic manipulator that largely compensated the load by a constant force of 23 [N]. Subjects were are asked to perform elbow-flexion movements against gravity towards a target, either 30 or 50° away in a Fitts’ like task. The experiment consisted of two parts: In Part A the independent measure was a noise over the compensation force (a filtered Gaussian white noise with variances of either 1 or 1.5 [N], over a set of trials). In Part B an estimation error was added as a second independent measure (a sudden overestimate or underestimate of the compensation force of 5 [N] added as a step in the middle of catch trials). Dependent metrics used are the rise time, settling time, first peak error and the agonist-antagonist muscle co-activation. It was hypothesized that subjects would show increased settling times for both error types. In Part A it was further hypothesized that subjects would react to the noise by intentionally moving slower (increased rise time) and by stiffening their limb (increased agonist-antagonist muscle co-activation). As hypothesized, the results showed that the settling time significantly increased for both the added noise and the sudden estimation error. Interestingly, contrary to what was hypothesized, the results of Part A generally did not show an increase in rise time and overall a lower muscle activity with noise of both muscles of the agonist-antagonist pair. From these results, it can be concluded that noise and estimation errors lead to longer movement times during fast goal-directed elbow flexion. Contrary to what current literature suggests, a reasonable amount of noise over the compensation force even seemed to cause subjects reducing the amount of co-activation. Apparently unpredictabilities like noise not always cause an increase of co-activation, and it can be concluded that more research is needed on what exactly makes humans choose their limb-impedance strategy in practical situations.
Application of Lyapunov matrix inequality based unsymmetrical saturated control to a multi-vectored propeller airship

The problem of the design of a controller for a multi-vectored propeller airship is addressed. The controller includes anti-windup that takes into account unsymmetrical actuator constraints. First, a linear transformation is applied to transform the unsymmetrical constraints into symmetric constraints with an amplitude-bounded exogenous disturbance. Then, a stability condition based on a quadratic Lyapunov function for the saturated closed-loop system is proposed. The condition considers both amplitude-bounded and energy-bounded exogenous disturbances. Thus, the controller design problem is transformed into a convex optimization problem expressed in a bilinear matrix inequality form. Two controller design methods were applied: one-step controller and traditional anti-windup controller. The one-step method obtains the controller and the anti-windup compensator in one step while the anti-windup controller method separates this process into the linear controller design and the compensator design. Simulation results showed that both controllers enlarge the stability zone of the saturation system and have good tracking performance. It is shown that the anti-windup controller design method not only has a larger region of stability, but the demanded actuator output exceeds the constraints less and has a smaller anti-windup coefficient matrix compared to the one-step method.
A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting

In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.
An Accelerator for Sparse Convolutional Neural Networks Leveraging Systolic General Matrix-matrix Multiplication

This article proposes a novel hardware accelerator for the inference task with sparse convolutional neural networks (CNNs) by building a hardware unit to perform Image to Column (Im2Col) transformation of the input feature map coupled with a systolic-array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the Im2Col transformation with the GEMM computation to maximize parallelism. We propose a novel design for the Im2Col unit that uses a set of distributed local memories connected by a ring network, which improves energy efficiency and latency by streaming the input feature map only once. The systolic-array-based GEMM unit in the accelerator can be dynamically configured as multiple GEMM units with square-shaped systolic arrays or as a single GEMM unit with a tall systolic array. This dynamic reconfigurability enables effective pipelining of Im2Col and GEMM operations and attains high processing element utilization for a wide range of CNNs. Further, our accelerator is sparsity aware, improving performance and energy efficiency by effectively mapping the sparse feature maps and weights to the processing elements, skipping ineffectual operations and unnecessary data movements involving zeros. Our prototype, SPOTS, is on average 2.16 \( \times \) , 1.74 \( \times \) , and 1.63 \( \times \) faster than Gemmini, Eyeriss, and Sparse-PE, which are prior hardware accelerators for dense and sparse CNNs, respectively. SPOTS is also 78 \( \times \) and 12 \( \times \) more energy-efficient when compared to CPU and GPU implementations, respectively.
Optimisation of fragrance finishing on cotton by grafting of β-cyclodextrin based microcapsules: Application of the experimental design methodology

Abstract The grafting reaction of β-cyclodextrin-based microcapsules containing neroline onto pure cotton jersey knitted fabric is studied. The influence of the concentrations of microcapsules, citric acid (CA) crosslinking agent and catalyst on the fabric mass gain was studied to determine the optimum operating conditions. For this purpose, an experimental design based on response surface methodology (RSM) was used leading to the development of a predictive statistical model. The optimal grafting conditions were thus determined: 107.6 g ⋅ L−1 for microcapsules concentration, 111.9 g ⋅ L−1 for CA concentration and 50 g ⋅ L−1 for disodium hydrogen phosphate (DHP) catalyst concentration (CAT). Morphology, tensile properties, air permeability and washing fastness of finished textile were studied to evaluate the effectiveness of the optimised grafting process.
A method to enrich polypeptidyl-tRNAs to capture snapshots of translation in the cell

Life depends on proteins, which all exist in nascent states when the growing polypeptide chain is covalently attached to a tRNA within the ribosome. Although the nascent chains; i.e., polypeptidyl-tRNAs (pep-tRNAs), are considered as merely transient intermediates during protein synthesis, recent advances have revealed that they are directly involved in a variety of cell functions, such as gene expression control. An increasing appreciation for fine-tuning at translational levels demands a general method to handle the pep-tRNAs on a large scale. Here, we developed a method termed peptidyl-tRNA enrichment using organic extraction and silica adsorption (PETEOS), and then identify their polypeptide moieties by mass spectrometry. As a proof-of-concept experiment using Escherichia coli, we identified ∼800 proteins derived from the pep-tRNAs, which were markedly biased towards the N-termini in the proteins, reflecting that PETEOS captured the intermediate pep-tRNA population during translation. Furthermore, we observed the changes in the pep-tRNA set in response to heat shock or antibiotic treatments. In summary, PETEOS will complement conventional methods for profiling nascent chains such as ribosome profiling. Significance Statement In the central dogma of biology, RNA and protein are usually regarded as two completely independent molecular species. However, they are combined into a single species called peptidyl-tRNA (pep-tRNA) during the translation process in the ribosome. Despite the importance of pep-tRNAs as precursors of all proteins in the cell, a general method to analyze pep-tRNAs on a large scale was lacking. Taking advantage of the properties of pep-tRNAs as RNA and protein, we developed a method to enrich the pep-tRNAs by organic solvent extraction and silica column separation. The method, termed PETEOS, not only provides a unique approach to examine the nascent state of proteins but also may be effective in capturing snapshots of translation status in the cell.
Research progress of carbon materials in the field of three-dimensional printing polymer nanocomposites

Abstract Three-dimensional (3D) printing technology is an additive manufacturing technology designed to rapidly process and manufacture complex geometrical components based on computer model design. Based on a 3D data model, materials are accumulated layer by layer through computer control, and the 3D model is finally turned into a stereoscopic object. Compared with traditional manufacturing methods, 3D printing technology has the advantages of saving man-hours, easy operation, no need for molds, and strong controllability of component geometry. With the development of this technology, according to the core materials and equipment and other elements of the printing molding technology, several types of 3D printing technologies such as fused deposition modeling, selective laser sintering, stereolithography, and solvent cast-3D printing have gradually formed. This review focuses on the principles and characteristics of several of the most representative 3D printing molding processes. And based on carbon nanomaterial (carbon fibers, graphene, and carbon nanotubes) reinforced polymer composite materials, the research progress of different 3D printing molding processes in recent years is reviewed. At the same time, the commercial application of 3D printing molding process in this field is analyzed and prospected.
Beamforming Techniques for a Quad-Mode Antenna Array

Various excitation strategies are investigated for an 2x2 array of quad-mode antennas (QMA’s), with the aim of achieving maximum gain over a full hempishere. The first determines a fixed excitation for the four ports of one QMA, achieving a gain pattern with a gain variation of only 1.7 dB across a full hemisphere. Four of these antennas are then beamformed. This is compared to a system in which all sixteen ports of a 2x2 QMA array are used for beamforming. It is shown that the first case results in a reduction of 5dB with respect to the second, but using only four beamforming channels instead of sixteen.