Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Electrochemical Sensors for the Detection of Glycated Hemoglobin

Glycated hemoglobin (HbA1c) is the gold standard for measuring glucose levels in the diagnosis of diabetes due to the excellent stability and reliability of this biomarker. HbA1c is a stable glycated protein formed by the reaction of glucose with hemoglobin (Hb) in red blood cells, which reflects average glucose levels over a period of two to three months without suffering from the disturbance of the outside environment. A number of simple, high-efficiency, and sensitive electrochemical sensors have been developed for the detection of HbA1c. This review aims to highlight current methods and trends in electrochemistry for HbA1c monitoring. The target analytes of electrochemical HbA1c sensors are usually HbA1c or fructosyl valine/fructosyl valine histidine (FV/FVH, the hydrolyzed product of HbA1c). When HbA1c is the target analyte, a sensor works to selectively bind to specific HbA1c regions and then determines the concentration of HbA1c through the quantitative transformation of weak electrical signals such as current, potential, and impedance. When FV/FVH is the target analyte, a sensor is used to indirectly determine HbA1c by detecting FV/FVH when it is hydrolyzed by fructosyl amino acid oxidase (FAO), fructosyl peptide oxidase (FPOX), or a molecularly imprinted catalyst (MIC). Then, a current proportional to the concentration of HbA1c can be produced. In this paper, we review a variety of representative electrochemical HbA1c sensors developed in recent years and elaborate on their operational principles, performance, and promising future clinical applications.
THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN SOLID AND STRUCTURAL MECHANICS

Metal cutting is today one of the leading forming processes in the manufacturing industry. The metal cutting industry houses several actors providing machine tools and cutting tools with a fierce competition as a consequence. Extensive efforts are made to improve the performance of both machine tools and cutting tools. Performance improvements are not solely restricted to produce stronger and more durable machine tools and cutting tools. They also include knowledge about how the machine tools and cutting tools should be used to perform at an optimum of their combined capacity. Information about the dynamic properties of the machine tool cutting tool assembly is one of the aspects that carries the most potential in terms of productivity increase and process reliability. This work presents a methodology to synthesise the dynamic behaviour of a machine tool and cutting tool assembly based on component models of the machine tool and the cutting tool. The system is treated as an assembly of subcomponents in order to reduce measurement effort. The target is to get the receptance at the tip of the machine tool/cutting tool which is a prerequisite for process analysis and optimisation. This methodology is compared with today’s state-of-theart methodology which require experimental testing for each cutting tool of interests mounted in the machine tool. Comparisons are also made with previous attempts to utilize component synthesis in this matter. The subcomponent approach presented here limits the experimental tests to the machine tool component. The machine tool component model is connected to a model representation, based on a finite element model of the cutting tool. The subcomponent models are obtained and coupled on state-space form, a technique that is new to the application of component synthesis of machine tool/cutting tool structures. Proposed procedures for measurements, system identification, enforcement of physical properties on state-space models and parameter influences on coupled results are presented, implemented and validated. This methodology opens windows not only to cutting process optimisation of an existing cutting tool but it also permits tailored cutting tool solutions for existing machining operations with fixed process parameters.
Sphere Decoding in Multi-user Multiple Input Multiple Output with reduced complexity

Multiple input multiple output (MIMO) supports greater data rate and higher reliability in wireless communication. MIMO technique uses the multiple antennas to achieve high transmission rate. A low complexity decoding technique is essential for Multi user- Multiple Input Multiple Output (MU-MIMO) systems. In this paper the Sphere Decoder (SD) techniques are discussed to be used in the Multi user- MIMO scenario where the available antennas are spread over a multitude of independent access points and independent radio terminals - each having one or multiple antennas and will emphasize on reducing the Bit Error Rate (BER) in Sphere Decoder (SD).
Development of a compact radiation monitor for space application

In this work a compact radiation monitor prototype has been developed. The present device and its technology will directly contribute towards the CPT-SCOPE project, a NorwegianGerman student project developing a radiation monitor for space application. The present design utilises an application specific integrated circuit (ASIC) which provides a low-power and small-form factor solution. Such system is required for small satellites and CubeSats. The development of the device has been carried out in multiple design steps. A functioning radiation monitor prototype has been developed, implemented and tested. First measurements of cosmic ray background, beta particle and low-energetic gamma ray sources verify its functionality. This marks a major milestone towards a compact radiation monitor for nanosatellites. While this work deals with a technology demonstrator, it directly contributes to the CPTSCOPE project. The finished CPT-SCOPE instrument and its flight aboard BEXUS 20 in October 2015 will be the next milestone. In the future this should lead towards CPT-SCOPE 2.0, a CubeSat-ready compact radiation monitor. The project demonstrated the usability of COTS components in hybrid with specialised, radiation-hard integrated circuits for radiation detection. This approach allows for rapid and inexpensive developments for small satellite and CubeSat instrumentation.
A Systematic Review of the Transthoracic Impedance during Cardiac Defibrillation

For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected articles was used to review and assess human TTI and the influencing factors found. The human TTI extended from 12 to 212 Ω in the literature selected. Excluding outliers and pediatric measurements, the mean TTI recordings ranged from 51 to 112 Ω with an average TTI of 76.7 Ω under normal distribution. The wide range of human impedance can be attributed to 12 different influencing factors, including shock waveforms and protocols, coupling devices, electrode size and pressure, electrode position, patient age, gender, body dimensions, respiration and lung volume, blood hemoglobin saturation and different pathologies. The coupling device, electrode size and electrode pressure have the greatest influence on TTI.
Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective is the modification or hybridization of a metaheuristic algorithm on the text FS problem? , and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.
Data Collecting and Energy Charging Oriented Mobile Path Design for Rechargeable Wireless Sensor Networks

Energy efficiency is one of the most important concerns in wireless sensor networks (WSNs). As far as we know, almost all energy efficiency researches of WSNs focus on energy conservation in some respects such as wireless data transmission and minimal data collection. Recently, wireless energy transfer has been a promising technology to prolong the lifetime of microsensor nodes, and so the traditional WSNs can be extended to rechargeable WSNs. Rechargeable WSNs is a new type of wireless sensor networks, where each sensor node can replenish energy through wireless charging. For rechargeable WSNs, it is powered by reusable energy or harvested energy, so the energy efficiency problem can be completely solved. Furthermore, mobile data collection has been well recognized to have significant advantages over sensory data collection manner using static sinks. In this paper, by employing one or multiple recharging sinks to replenish energy for sensor nodes and collect sensory data concurrently, we propose a novel wireless charging and mobile data collecting method based on self-organizing map (SOM) unsupervised learning for rechargeable WSNs. In other words, the sink mobility and energy replenishment are jointly considered in this paper. Finally, we evaluate the performance of the proposed algorithms through software simulation. Extensive results verify that the performance of the proposed algorithm can reduce the travel cost of mobile sink and improve the residual energy level for sensor nodes. As a results, it is very promising in the field of data acquisition in wireless sensor networks.
Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.
A non-linear 3D printed electromagnetic vibration energy harvester

This paper describes a novel electromagnetic energy harvester that exploits the low flexural modulus of ABS and comprises of a nonlinear mechanism to enhance the generated power and bandwidth. The device is printed using desktop additive manufacturing techniques (3D printing) that use thermoplastics. It has a ‘V’ spring topology and exhibits a softening spring non-linearity introduced through the magnetic arrangement, which introduces a monostable potential well. A model is presented and measurements correspond favourably. The produced prototype generates a peak power of approximately 2.5mW at a frame acceleration of 1g and has a power bandwidth of approximately 1.2→1.5Hz and 3.5→3.9Hz during up and down sweeps respectively. The device has a power density of 0.4mW/cm3 at a frame acceleration of 1g and a density of 0.04mW/cm3 from a generated power of 25μW at 0.1g.
Diagnosis of Systemic AL-Type Amyloidosis Revealed by Severe Restrictive Cardiomyopathy: Case Report and Literature Review

Amyloidosis refers to a group of systemic diseases whose common feature is the extracellular accumulation of insoluble fibrillar proteins in tissues (heart, reins, digestive tract). Several types of amyloidosis have been described; the primary (AL) and the secondary (AA) forms are by far the most frequent. AL amyloidosis is rare but not exceptional disease, with a prevalence of 500new cases per year in France. It is related to be precipitation in tissues of monoclonal light chains of immunoglobulin in the form of fibrils. Its diagnosis is histological based on the biopsy analysis of an affected organ or on non-invasive biopsies (subcutaneous fat, accessory salivary glands, etc) with the detection of amorphous deposits stained by congo red with dichroism and birefringence in polarised light, which is the reference examination. But the diagnostic approach can also be done by thioflavin staining which is very sensitive but not specific for amyloidosis and always requieres a diagnostic confirmation by congo red staining. We report the case of a patient with severe restrictive cardiomyopahy and presenting a set of paraclinical arguments specific for amyloidosis whose diagnosis was made by thioflavin staining.
Comparative Genomic Analysis of a Panton–Valentine Leukocidin-Positive ST22 Community-Acquired Methicillin-Resistant Staphylococcus aureus from Pakistan

Staphylococcus aureus (S. aureus) ST22 is considered a clinically important clone because an epidemic strain EMRSA-15 belongs to ST22, and several outbreaks of this clone have been documented worldwide. We performed genomic analysis of an S. aureus strain Lr2 ST22 from Pakistan and determined comparative analysis with other ST22 strains. The genomic data show that Lr2 belongs to spa-type t2986 and harbors staphylococcal cassette chromosome mec (SCCmec) type IVa(2B), one complete plasmid, and seven prophages or prophage-like elements. The strain harbors several prophage-associated virulence factors, including Panton–Valentine leukocidin (PVL) and toxic shock syndrome toxin (TSST). The single nucleotide polymorphism (SNPs)-based phylogenetic relationship inferred from whole genome and core genome revealed that strain Lr2 exhibits the nearest identities to a South African community-acquired methicillin-resistant S. aureus (CA-MRSA) ST22 strain and makes a separate clade with an Indian CA-MRSA ST22 strain. Although most ST22 strains carry blaZ, mecA, and mutations in gyrA, the Lr2 strain does not have the blaZ gene but, unlike other ST22 strains, carries the antibiotic resistance genes erm(C) and aac(6′)-Ie-aph(2″)-Ia. Among ST22 strains analyzed, only the strain Lr2 possesses both PVL and TSST genes. The functional annotation of genes unique to Lr2 revealed that mobilome is the third-largest Cluster of Orthologous Genes (COGs) category, which encodes genes associated with prophages and transposons. This possibly makes methicillin-resistant S. aureus (MRSA) Lr2 ST22 strain highly virulent, and this study would improve the knowledge of MRSA ST22 strains in Pakistan. However, further studies are needed on a large collection of MRSA to comprehend the genomic epidemiology and evolution of this clone in Pakistan.
Survival of Hydrops Fetalis with and without Fetal Intervention

Objectives: To investigate the survival rate of hydrops fetalis after fetal interventions and neonatal intensive care. Methods: We reviewed the medical records of patients diagnosed with hydrops fetalis from January 2009 to December 2019 at Changhua Christian Children’s Hospital. All cases had abnormal fluid accumulation in at least two body compartments during pre- and postnatal examination. The primary outcome measure was the mortality rate. We also collected information regarding disease etiology, duration of hospital stay, Apgar score, gestational age at birth, initial hydrops fetalis diagnosis, fetal intervention, first albumin and pH levels, and maternal history. Results: Of the 42 cases enrolled, 30 survived and 12 died; the mortality rate was 28.6%. Furthermore, 22 cases received fetal intervention, while 20 cases did not; there was no significant difference in their survival rates (75% and 68%, respectively). Survival rate was associated with gestational age at birth, initial diagnosis time, birthweight, Apgar score, initial albumin and pH levels, and gestational hypertension. Only one case was immune-mediated. Among the nonimmune-mediated cases, the three most common etiologies were lymphatic dysplasia (12/42), idiopathic disorders (10/42), and cardiovascular disorders (5/42). Conclusions: Overall, hydrops fetalis was diagnosed early, and fetal intervention was performed in a timely manner. Preterm births were more frequent, and birthweight was lower in the cases that underwent fetal intervention than in those that did not, but there was no significant between-group difference in mortality. The initial diagnosis time, gestational age at birth, birthweight, Apgar score, and first albumin and pH levels were independently associated with mortality.
Lightweight adaptive personalised e-advertising

Adaptation and personalisation is aimed at improving the user experience in e-systems. Personalisation was initially applied in the fields of distance learning and web-based educational systems. Adaptation can be also used in e-advertising, to increase customer satisfaction and encourage repeat visits to websites. Several models/frameworks have been designed for adaptation, for instance AHAM, LAOS, AdRosa, and MyAds. Many systems have been developed based on these frameworks. Most previous models/frameworks were primarily designed for personalised educational experience and were aimed at standalone systems, which cannot be (easily) integrated into existing websites in a lightweight manner. In addition, some of them are used in the portal model of advertising, since they match the interests of the publisher and the advertiser. The aim of this work is to overcome the limitations and weaknesses of these models and systems to deliver adaptive advertising. This work firstly attempts to support and facilitate the integration between adaptive systems and business websites. It also introduces a method to control and adapt advertisements located and owned by businesses. This thesis further proposes a generalised model, the Layered Adaptive Advertising Integration (LAAI), as the starting point for the development of an adaptive advertisement system. In a second stage, it presents a study that assesses the effectiveness of a system (AEADS) based on this model, via a trial run of a model prototype with users (both customers and business owners). In a third stage, social networks are used as inputs for the user model of customers, to enhance the efficiency of acquiring user information, as an addition to the user registration process. Furthermore, social interactions, such as the facility to use “like”, are added to the user model, and the delivery process has the ability to apply actions based on this data. Finally, an evaluation of the whole system proposed is conducted, with business owners and Internet users alike.
A case learning tool for operations of power systems

This paper presents the development of a case learning tool for operation support (CALTOS). The authors have proposed a new framework of automatic knowledge acquisition through machine learning. The learning algorithm named HCL produces operational rules from cases in the past. The HCL has been built into CALTOS and it provides an environment for rapid and efficient construction of intelligent systems which can update its knowledge base without explicit revision. This paper argues the difficulty of intelligent systems development and proposes a solution with CALTOS. Here, two developed systems are introduced, one is for an extra-high voltage substation and the other is for a dispatching office. A dispatching office generates instruction sequences for its supervised substations or related offices, while a substation produces switching sequences of devices for each instruction given by its supervising office. These systems have been developed just by means of adding facility data and operational cases of the target substation or the dispatching office.
Supplier concentration, market power and R&D investment

ABSTRACT This paper examines the relationship between supplier concentration and R&D investment of Chinese firms using the sample of all A-share listed companies on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2008 to 2015. Empirical results present the significant negative relationship between supplier concentration and firms’ R&D expenditure. Further analysis indicates that the weaker the firm’s market power, the stronger the negative impact of supplier concentration on the firm’s R&D investment. This paper enriches corporate R&D investments, supply chains, and market power literature. As developing countries deepen their participation in global supply chain networks and transform through technological upgrading, the findings of this paper have important practical implications for developing country firms to overcome R&D resistance and for policymakers to lead industrial development.
An Experimental Study on Step-Up DC–DC Converters for Organic Photovoltaic Cells

: This work studies two circuit topologies to step-up the voltage supplied by an organic photovoltaic (OPV) cell. Comparison and validation of the proposed topologies are accomplished throughout analytical, simulation, and experimental results. Two circuit solutions were found more suitable to boost the harvested OPV cell low voltage, depending on the load condition: the classical hard-switching boost converter and a multilevel boost converter. Both experimental circuits include the drive of the MOSFET switch based on an LC oscillator at 1.2 MHz, allowing the implementation of a conversion system, supplied by voltages as low as 500 mV, with output voltages from 1.2 V up to 7 V, under solar simulator conditions. The circuit area for each converter prototype is 2.35 cm 2 , with a total area below 3.0 cm 2 for the overall energy harvesting system, including the OPV cell, which makes this proposal an extremely compact solution for ultra-low power harvesting applications.