Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and security solution for school bus through RFID and GSM technologies

All children throughout the world aged 4 to 17 are going to schools every weekdays. The most common transport used by children is school bus. In many countries accross the globe, most children uses school bus services to go to school and return back home especially to working parents. Although safety of their children is always the main concern of all parents especially the young ones, they have to rely on the bus services due to time constraints during working hours. Sometimes parents need to call the bus driver to ensure their children has reached home or school. This will create inconvenience to bus driver and may lead to other unwanted consequences. Realizing the root of this problem, a school bus safety and security system has been proposed. The school bus safety and security system is a solution based on Short Messaging System that notifies parents if their children have safely arrived at home or school. RFID and GSM technologies are the main technique proposed in this project. RFID is used for several purposes; to identify the children and parents’ contact number, and attendance monitoring through head count system. GSM is used as a commucation platform to inform parents’ on their children’s movement via SMS. Several tests have been conducted to analyse the overall performance of the developed hardware prototype. From the results, it can be concluded that the developed project is successfully identify the children based on their unique ID, send a text message through SMS to parents with required information; and additional feature of attendance checker. The hardware prototype was successfully tested for children’s identification, attendance and SMS notifications to parents. As a consequence, this project could increase the safety and security solution for children travelling with school bus transportation and give parents peace of mind.
Moving Toward Continuous Therapy in Multiple Myeloma

The introduction of novel agents, characterized by favorable toxicity profiles and higher manageability compared to conventional drugs employed in the past, has considerably changed the treatment paradigm for multiple myeloma. Continuous therapy currently represents the standard approach for myeloma patients both at diagnosis and at relapse. In younger patients, long-term maintenance after autologous transplantation significantly improved progression-free survival and overall survival compared to observation. Also in transplant-ineligible patients, continuous treatment with combinations of newer agents and maintenance treatment following a more intense induction phase proved to be superior as compared to fixed-duration therapy. Maintenance and continuous therapy at diagnosis have shown to deepen responses and suppress minimal residual disease. At relapse, continuous therapy allowed better disease control over time. This review covers the main evidence supporting the use of continuous therapy in multiple myeloma as well as the open issues, such as the optimal agents to be used and the optimal candidates for receiving them.
Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit

ABSTRACT This paper is a case study of utilizing machine learning for developing a decision-making system for auditors before initializing the audit fieldwork of public firms. Annual data of 777 firms from 14 different sectors are collected and a MCTOPE (Multi criteria ToPsis based Ensemble) framework is implemented to build an ensemble classifier. MCTOPE framework optimizes the performance of classification during ensemble building using the TOPSIS multi-criteria decision-making algorithm. Ensemble machine learning is used for optimizing the prediction performance of suspicious firm predictor in the previous work available at https://www.tandfonline. com/doi/full/10.1080/08839514.2018. 1451032. After achieving an accuracy of 94.6% and AUC (area under the curve) value of 0.98, this ensemble classifier is employed in a web application developed for auditors using Python and R script for the prediction of suspicious firm before planning an external audit. The performance of an ensemble classifier is validated using K-fold cross validation technique and is found to be better than the state-of-the-art classifiers.
ECG based person authentication using empirical mode decomposition and discriminant analysis

Person identification or authentication through biometric features has been widely applied for basic access and high-level security. But conventional biometrics such as fingerprints and irises tend to be easily faked or duplicated. Therefore a new biometric modality is needed to overcome that problem. In this paper, we simulate a new model of biometric systems using physical signals of the body. The proposed biometric system is based on ECG signals as a characteristic of each subject. A total of 110 raw ECG signals with a duration of 5 seconds from 11 participants were demonstrated in the proposed system. Empirical mode decomposition (EMD) and statistical analysis are used for feature extraction. Discriminant analysis with cross-validation was applied to test the performance of the proposed method. In this research, the highest accuracy of 93.6% was obtained using subspace discriminant in the scenario of all feature attributes as predictors.
Capturing the Sparsity and Tracking the Channels for Massive MIMO Networks

Due to its enhanced capacity and energy efficiency, massive MIMO has become a critical technology for the 5 G and beyond mobile networks. However, fully achieving its benefits requires the accurate estimation of channel. In this article, we propose a low-overhead characteristic learning, tracking, and monitoring mechanism for the time-varying massive MIMO channels. Specially, we exploit the common spatial sparsity and temporal correlation of the channels. Firstly, using the virtual channel representation and modeling the temporal correlation as an autoregressive process, we formulate the time-varying massive MIMO channel as a sparse signal model. Then, a sparse Bayesian learning (SBL) scheme based on the expectation maximization (EM) is proposed to determine the model parameters of the channel. To achieve the posteriors of different model parameters, the approximate message passing (AMP) is used in the expectation step. Furthermore, the Kalman filtering (KF) with a reduced dimension is used to track the downlink (DL) channel. To observe the change of model parameters and start the relearning process, a monitoring scheme based on the Bayesian Cramér-Rao bound (BCRB) is also developed. Finally, numerical results are provided to demonstrate the performance of our proposed scheme.
High‐performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks

This research paper shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high‐level features from convolutional Neural Network (CNN) system. By recognize inherent deep feature representations in image patches for morphological changes in medicinal imaging information discovery. Various performance measurements, High Frequency Error Norm (HFEN), Mean Squared Error MSE, peak Signal‐to‐noise‐ratio (PSNR), Structural Similarity Index (SSI), are utilized to inspect different dataset. As presented in the paper outcomes, the proposed method can accomplish predominant performance compared with other cutting‐edge techniques with either low or high‐level features in terms of the synthesis and reconstruction rate. The proposed deep learning algorithm has a training time of 5 seconds with 0.97 SSI and 0.15 HFEN. In all investigations, the outcome shows that the proposed image synthesis and reconstruction framework reliably exhibited progressively precise outcomes when contrasted with best in class.
Staple Fixation of Lisfranc Injuries: A New Description of a New Technique and Early Clinical Results

The treatment of Lisfranc injuries continues to evolve as there is now a growing awareness of the need to obtain internal fixation along with the preservation of the articular cartilage. The use of staples offers a low profile means of maintaining internal fixation without the disruption of the articular cartilage inherent with the use of screw or pin fixation. This paper will outline the indications and technique of staple fixation for the treatment of Lisfranc injuries.
Performance Analysis of Mobile Ad Hoc Network Routing Protocols Using ns-3 Simulations

Mobile ad hoc networks (MANETs) consist of mobile nodes that can communicate with each other through wireless links without reliance on any infrastructure. The dynamic topology of MANETs poses a significant challenge for the design of routing protocols. Many routing protocols have been developed to discover routes in MANETs through various mechanisms such as source, distance vector, and link state routing. In this thesis, we present a comprehensive performance comparison of several prominent MANET routing protocols. The protocols studied are Destination-Sequenced Distance-Vector (DSDV), Optimized Link State Routing (OLSR), Ad Hoc On-Demand Distance Vector protocol (AODV), and Dynamic Source Routing (DSR). We consider a range of network dynamicity and node density, model three mobility models: Steady-State Random Waypoint (SS-RWP), Gauss-Markov (G-M), and Lévy Walk, and use ns-3 to evaluate their performance on metrics such as packet delivery ratio, end-to-end delay, and routing overhead. We believe this study will be helpful for the understanding of mobile routing dynamics, the improvement of current MANET routing protocols, and the development of new protocols.
PATELLA ALTA AND RECURRENT DISLOCATION OF THE PATELLA

Fifteen knees were treated with a surgical technique designed for recurrent patellar dislocations when associated with patella alta. The average number of patellar dislocations preoperatively was 12, and the determination of patella alta was made using the technique described by Insall and Salvati, which is a ratio of the length of the patellar tendon to the longest diagonal length of the patella. The normal value for this ratio is 1.02 plus or minus 20%. The average preoperative ratio in this study was 1.58 (range, 1.2-2.1), which changed to 1.08 (range, 0.99-1.14) by the time of follow-up examination. The surgical technique used involves transposing the patellar tendon insertion distally without any medialization or recessing and allows for good bone contact for healing, secure fixation, and immediate postoperative motion. There were no recurrences of patellar dislocation postoperatively and few complications. Only one patient complained of anterior knee pain in the follow-up period. This technique is thought to give good results when it is used specifically for recurrent patellar dislocations associated with patella alta.
The Experience of Pregnancy for Women with Bipolar Disorder: An Exploratory Study

Background : Bipolar disorder often emerges in an individual’s late teens and early twenties, thus women with bipolar disorder are impacted for the majority of their childbearing years. Pregnancy brings a unique set of challenges to this population, including risk of relapse, teratogenicity of medications, and increased risk of postpartum psychosis, yet no research exists on this population’s experience of pregnancy. Design: This study utilized semi-structured telephone interviews to gather preliminary qualitative data on the experience of pregnancy for women with bipolar disorder with the long-term goal of developing patient education materials and improving nursing-led interventions to help manage illness during pregnancy and postpartum. Method: A purposive sample of four English-speaking women, age 29-39, with a confirmed diagnosis of bipolar disorder I or II and who had given birth within the last eighteen months, was recruited nationally via the electronic and mass media, and postings on bulletin boards in a range of educational and related settings. One open-ended focused telephone interview was held with each client, and conducted by the first author. She was also responsible for doing the audiotaping, transcription, and coding of the data using a descriptive qualitative design. Results: Women with bipolar disorder described a unique pregnancy experience, influenced by the intersection of mental illness, pregnancy, and pharmacotherapy. Recurrent themes included hopefulness, patient and healthcare provider as advocates, the importance of interdisciplinary care, education, community support and the mixed impact of medication. Discussion & Conclusion : This research contributes to initiatives designed to improve healthcare for pregnant women with bipolar disorder, augment patient education materials, and inform the design of nurse-led interventions. Empirical research findings become even more valuable tools for improving patient care when paired with the voices of patients themselves. As pregnant women are a historically under-studied population, this study also contributes to the larger body of scientific research on this population.
Mobilität, Lokomotion, posturale Kontrolle nach Schädel-Hirn-Trauma

Zusammenfassung Ein wichtiges Ziel der stationären Rehabilitation von Patienten mit Schädel-Hirn-Traumata (SHT) ist die Verbesserung der Mobilität, insbesondere die Verbesserung der posturalen Kontrolle und der selbstständigen Gehleistung. Darauf ist die Physiotherapie spezialisiert. Durch Einschränkungen der Balance und der Gehfähigkeit sind zahlreiche Alltagsleistungen wie selbstständige Toilettengänge, die Essenszubereitung sowie das Aufstehen und Hinsetzen nur mit viel Unterstützung von Hilfspersonen möglich. Dem Wiederherstellen der Mobilität kommt innerhalb der neurologischen Rehabilitation somit eine erhebliche Bedeutung zu.
Communication Model and Capacity Limits of Covert Channels Created by Software Activities

It has been shown that digital and/or analog characteristics of electronic devices during executing programs can create a side-channel which an attacker can exploit to extract sensitive information such as cryptographic keys. When the attacker modifies the software application to exfiltrate sensitive information through a channel, this channel is called a covert channel. In this paper, we model this covert channel as a communication channel and derive upper and lower capacity bounds. Because the covert channels are not designed to transmit information, they are exposed not only to the errors created by the transmission, but also by varying the execution time of computer activities, and/or by insertions from other activities such as interrupts, stalls, etc. Combining all of these effects, we propose to model the covert channel as an insertion channel where the transmitted sequence is a pulse amplitude modulated signal with random pulse positions. Utilizing this model, we derive capacity bounds of the covert channel with random insertion and substitution due to the noise and jitter errors, and propose a receiver design that can correctly detect the computer-activity-created signals. To illustrate the severity of leakages, we perform experiments with high clock speed devices at some distance. Further, the theoretical derivations are compared to empirical results, and show good agreement.
The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients

By gleaning insights from the data, fuzzy clustering capable to learn from data, identify patterns and make decision with minimum human intervention. However, it cannot simply study in detail regarding the quality of data, particularly knowledge of human being. Since the data are collected through decision-makers, the quality and human knowledge of the particular data are crucial factors to be considered. Compared to classical fuzzy numbers, z-numbers has ability to describe the human knowledge because it has both restraint and reliability part in its definition. Consequently, the implementation of z-numbers in fuzzy clustering algorithm is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in uncertain information development. Thus, there are two objectives of this paper; (i) to propose a reliable fuzzy clustering algorithm using z-numbers and; (ii) to cluster the Chronic Kidney Disease (CKD) patients based on the selected indicators to identify which cluster the patients belongs to (Cluster 0, Cluster 1, Cluster 2, Cluster 3 or Cluster 4) based on the membership functions defined. A case study of the CKD patients with the selected indicators is considered to demonstrate the capability of z-numbers to handle the knowledge of human being and uncertain information and also will present the idea in developing a robust and reliable fuzzy clustering algorithm particularly in dealing with knowledge of human being using z-numbers.
Performance Analysis of Full-Duplex Relay-Aided NOMA Systems Using Partial Relay Selection

Capitalizing on the benefits of non-orthogonal multiple access (NOMA) and full-duplex relaying as key technologies to boost spectral efficiency in the next generation of wireless communications, herein we investigate the performance of a cooperative network in which a source communicates with two destinations via one node selected from a set of full-duplex amplify-and-forward relays. For this purpose, a power-domain NOMA scheme is used to transmit information from the source to the destinations, and partial relay selection is performed to choose the relay based on the channel state information of the first hops. The system performance is characterized in terms of both the outage probability and the ergodic capacity, for which, exact analytical expressions are derived in integral form. In addition, to reduce the computational complexity of the obtained analytical results, closed-form expressions are derived from lower-bound, approximate, and asymptotic analyses. From these analytical expressions, the impact on the system performance of the number of relays, the power allocation factor between the NOMA destinations, and the residual self-interference at full-duplex relays is assessed. The correctness of our analyses is validated by Monte Carlo simulations, and a comparison with the half-duplex relay-aided NOMA system counterpart is also provided.
Metody obróbki danych EMG

The article presents both the methods of data processing of electromyography (EMG), and EMG signal analysis using the implemented piece of software. This application is used to load the EMG signal stored in a file with the . C3D extension. The analysis was conducted in terms of the highest muscles activaton during exercise recorded with Motion Capture technique.
Essays on partially identifying models and their applications

This thesis studies partial identification in discrete outcome models and their empirical applications. Chapter 1 investigates popular count data instrumental variable (IV) models. Many methods in the literature ignore the discreteness of count outcomes and thereby suffering from undesirable misspecification problems. To address this problem, a partially identifying count data IV model is developed. The model requires neither strong separability of unobserved heterogeneity nor a triangular system. Identified sets of structural features are derived. The size of the identified set can be very small when the support of an outcome is rich or instruments are strong. The proposed approach is applied to study effects of supplemental insurance on healthcare utilisation. In Chapter 2, partial identification in competing risks models for discretely measured or interval censored durations are studied. These models are partially identifying because of 1) the unknown dependence structure between latent durations, and 2) the discrete nature of the outcome. I develop a highly tractable bounds approach for underlying distributions of latent durations by exploiting the discreteness and I investigate identifying power of restrictions on the dependence structure with no assumptions on covariate effects. Bounds are obtained from a system of nonlinear conditional moment (in)equalities. I devise a solution method that requires much less computational burden than existing methods. Asymptotic properties of bound estimators and a simple bootstrap procedure are provided. Chapter 3 applies the proposed bounds approach in Chapter 2 to reevaluate trends in cancer mortality by extending the “war on cancer” data studied in Honoré and Lleras-Muney (2006). I find substantial reduction in cancer mortality. Estimated patterns differ from the original findings. In another application, I investigate the effects of extended unemployment benefits on unemployment spells using data from Farber et al. (2015). Bound estimates support the original finding that extended benefits did not discourage active job seekers during and after the Great Recession.
A novel gate driver circuit for depletion‐mode a‐IGZO TFTs

In this paper, a novel gate driver circuit, which can achieve high reliability for depletion mode in a‐InGaZnO thin‐film transistors (TFTs), was proposed. To prevent the leakage current paths for Q node effectively, the new driving method was proposed by adopting the negative gate‐to‐source voltage (VGS) value for pull‐down units. The results showed all the VOUT voltage waveforms were maintained at VGH voltage despite depletion‐mode operation. The proposed circuit could also obtain stable VOUT voltage when the threshold voltage for all TFTs was changed from −6.5 to +11.5 V. Therefore, the circuit can achieve high reliability regardless of threshold voltage value for a‐IGZO TFTs. In addition, the output characteristics and total power consumption were shown for the alternating current (AC)–driven and direct current (DC)–driven methods based on 120‐Hz full‐HD graphics (1920 × 1080) display panel. The results showed that the AC‐driven method could achieve improved VOUT characteristics compared with DC‐driven method since the leakage current path for Q node can be completely eliminated. Although power consumption of the AC‐driven method can be slightly increased compared with the DC‐driven method for enhancement mode, consumption can be lower when the operation has depletion‐mode characteristics by preventing a leakage current path for pull‐down units. Consequently, the proposed gate driver circuit can overcome the problems caused by the characteristics of a‐IGZO TFTs.