Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Link Quality Fluctuation in Mobile Wireless Sensor Networks

Wireless sensor networks accommodating the mobility of nodes will play important roles in the future. In residential, rehabilitation, and clinical settings, sensor nodes can be attached to the body of a patient for long-term and uninterrupted monitoring of vital biomedical signals. Likewise, in industrial settings, workers as well as mobile robots can carry sensor nodes to augment their perception and to seamlessly interact with their environments. Nevertheless, such applications require reliable communications as well as high throughput. Considering the primary design goals of the sensing platforms (low-power, affordable cost, large-scale deployment, longevity, operating in the ISM band), maintaining reliable links is a formidable challenge. This challenge can partially be alleviated if the nature of link quality fluctuation can be known or estimated on time. Indeed, higher-level protocols such as handover and routing protocols rely on knowledge of link quality fluctuation to seamlessly transfer communication to alternative routes when the quality of existing routes deteriorates. In this article, we present the result of extensive experimental study to characterise link quality fluctuation in mobile environments. The study focuses on slow movements (<5 km h-1) signifying the movement of people and robots and transceivers complying to the IEEE 802.15. 4 specification. Hence, we deployed mobile robots that interact with strategically placed stationary relay nodes. Our study considered different types of link quality characterisation metrics that provide complementary and useful insights. To demonstrate the usefulness of our experiments and observations, we implemented a link quality estimation technique using a Kalman Filter. To set up the model, we employed two link quality metrics along with the statistics we established during our experiments. The article will compare the performance of four proposed approaches with ours.
Cultivation of Autochthonous Microalgae for Biomass Feedstock: Growth Curves and Biomass Characterization for Their Use in Biorefinery Products

In this work, the biomass productivity for biorefinery products and growth curves of three autochthonous microalgae collected in different reservoirs (“Scenedesmus sp.” (SSP), mixture of Scenedesmus sp. , Chlorella minutissima, Chlorellas sp. and Nannochloropsis sp. named “La Orden” (LO) consortium and Chlorella minutissima named “Charca Brovales” (CB) consortium) were studied in a 5.5 L column laboratory photobioreactor. Two different culture media, Arnon culture (AM) and an agriculture fertilizer-based liquid medium (FM), have been used to evaluate the growth effect of the microalgae; it was found that the medium has a clear effect on the biomass productivity and growth rate, which ranged between 0.26–0.498 g L−1 d−1 and 0.288–0.864 d−1, respectively. In general, the elemental analysis and higher heating value of microalgae biomass for the three species were independent of the culture medium used for its growth, while their lipids and sugars content depended upon the species type and culture medium used in the cultivation. “La Orden” microalga was selected (given its best adaption to the climatic conditions) to study the biomass productivity and growth rate in two exterior photobioreactors (100 L column and 400 L flat panel), using FM as a medium, obtaining values of 0.116–0.266 g L−1 d−1 and 0.360–0.312 d−1, respectively. An automation and control system was designed to operate the exterior photobioreactors pilot plant. The lipid content of this microalga in these photobioreactors was lower than in the laboratory one, with a fatty acids profile with predominantly palmitic, oleic, linoleic and linolenic acids. Also, the fresh biomass collected from these photobioreactors was studied in a batch type digestion process for biogas production, obtaining a CH4 yield of 296 ± 23 L CH4 kgVSS−1 added with a reduction in percentage of COD and vs. of 50 ± 1% and 50 ± 1.7%, respectively.
Selective elution of mercury, silver, and gold from strong-base anion-exchange resins

" . «1 •••••••••••• Introduction . •. " . Materials, equipment, and operating procedure ••••••••••••••••••••••••••••••••••• Results and discussion •••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Elution of mercury and precious metals from resins •••••••••••••••••••••••••••• Mercury elution . Silver elution •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Gold elution . Sequential elution of mercury, silver, and gold ••••••••••••••••••••••••••••• Metal recovery . " . Conclusion . " . Ref e rences . •. •.
A study of the relationships in financial performance, organization size, business classification, and program maturity of Six Sigma systems

United States companies are facing increased competition as business continues to grow globally. This is true for large companies and small-to-medium enterprises (SMEs), as well as manufacturing and non-manufacturing organizations. To remain competitive, organizations need to improve product value, technology, customization, service, and turnaround time while reducing costs and increasing innovation. Many large manufacturers have turned to Six Sigma as a quality method to guide improvement efforts. Reported results have been significant in terms of cost reduction and the bottom line. Leaders in Six Sigma efforts have been Motorola, General Electric (GE), Allied Signal, Honeywell, and Ford. These manufacturers are all large in size. SMEs have not implemented Six Sigma to the same degree as large organizations due to limited resources and capacity to successfully deploy and sustain Six Sigma. Similarly, manufacturing led the way and nonmanufacturers are now beginning to implement Six Sigma systems. As more SMEs and nonmanufacturers launch Six Sigma efforts, new challenges are encountered. Quality authorities have found Six Sigma to have a positive impact on the bottom line financial performance of large manufacturing organizations. However, it is unknown if there is a positive impact to the financial performance of SMEs and non-manufacturers implementing Six Sigma. This study examined the relationships in financial performance, organization size, business classification, and program maturity for Six Sigma systems. A sample of 606 individuals were surveyed based on a distribution list generated with membership from the American Society of Quality (ASQ),
Adaptive backstepping speed controller design for a permanent magnet synchronous motor

In the paper, a nonlinear adaptive speed controller for a permanent magnet synchronous motor based on a newly developed adaptive backstepping approach (PMSM) is presented. The exact input-output feedback linearisation control law is first introduced without any uncertainties in the system. However, in real applications, the parameter uncertainties such as the stator resistance and viscous constant and load torque disturbance have to be considered. In this case, the exact input-output feedback linearisation approach is not very effective, because it is based on the exact cancellation of the nonlinearity. To compensate the uncertainties and the load torque disturbance, the input-output feedback linearisation approach is first used to compensate the nonlinearities in the nominal system. Then, a nonlinear adaptive backstepping control law and parameter uncertainty, and load torque disturbance adaptation laws, are derived systematically by using adaptive backstepping technique. Simulation and experimental results clearly show that the proposed adaptive scheme can track the speed reference signal generated by a reference model, successfully, and that the scheme is robust to the parameter uncertainties and load torque disturbance.
"Assessing Adherent Cell Mechanical Behavior by 6 and 12-Strut Tensegrity Model"

The cytoskeleton is the main cellular component responsible for bearing mechanical loads and supporting the cell shape in the adherent cells. Number of structures have been proposed to investigate the behavior of the cytoskeleton which most of them were based on stretch elements which only stand against tension forces. actin filaments are rope-shaped filaments that only tolerate tensile forces. However, in biological systems, microtubules have a vital role in the behavior of living cells. Microtubules, which are tube-shaped filaments, have evolved to withstand compressive forces. In this way, the need to have a structure that has tensile and compression elements together for the better approximation of cell behavior is necessary. Hence, tensegrity structures were considered as a tool for modelling and understanding the cytoskeleton behavior. Multiple experiments have proven that the tensegrity model is an appropriate structure in approximating the cytoskeleton behavior of non-suspension cells. In this study, we suggested two different tensegrity structures to examine the effect of the complexities of these structures in response to mechanical stimulus. Both 6 and 12-strut structures were simulated and subjected to theoretical atomic force microscopy loading through the software ABAQUS. Next, the relation between structure reaction forces and the amount of deflection, and also cell response to the stiffness change of the substrate were investigated. After comparing behavior of these two structures, it was concluded that it is more appropriate to apply a tensegrity model with fewer struts in stiffer cells’ behavior. The structure behavior in response to changes in substrate stiffness revealed that six-strut tensegrity is more sensitive to higher substrates stiffness.
Scientific Opinion on development needs for the allergenicity and protein safety assessment of food and feed products derived from biotechnology

Abstract This Scientific Opinion addresses the formulation of specific development needs, including research requirements for allergenicity assessment and protein safety, in general, which is urgently needed in a world that demands more sustainable food systems. Current allergenicity risk assessment strategies are based on the principles and guidelines of the Codex Alimentarius for the safety assessment of foods derived from ‘modern’ biotechnology initially published in 2003. The core approach for the safety assessment is based on a ‘weight‐of‐evidence’ approach because no single piece of information or experimental method provides sufficient evidence to predict allergenicity. Although the Codex Alimentarius and EFSA guidance documents successfully addressed allergenicity assessments of single/stacked event GM applications, experience gained and new developments in the field call for a modernisation of some key elements of the risk assessment. These should include the consideration of clinical relevance, route of exposure and potential threshold values of food allergens, the update of in silico tools used with more targeted databases and better integration and standardisation of test materials and in vitro/in vivo protocols. Furthermore, more complex future products will likely challenge the overall practical implementation of current guidelines, which were mainly targeted to assess a few newly expressed proteins. Therefore, it is timely to review and clarify the main purpose of the allergenicity risk assessment and the vital role it plays in protecting consumers' health. A roadmap to (re)define the allergenicity safety objectives and risk assessment needs will be required to inform a series of key questions for risk assessors and risk managers such as ‘what is the purpose of the allergenicity risk assessment? ’ or ‘what level of confidence is necessary for the predictions? ’.
NeuraHealthNLP: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing

Dementia related cognitive impairment (CI) affects over 55 million people worldwide and is growing rapidly at the rate of one new case every 3 seconds. It is a terrifying disease that robs a person of their identity, and eventually leads to death (100% fatality rate). With a recurring failure of clinical trials, early diagnosis is crucial, but 75% of dementia cases go undiagnosed globally with up to 90% in low-and-middle-income countries. Current diagnostic methods are notoriously complex, involving manual review of medical notes, numerous cognitive tests, expensive brain scans or spinal fluid tests. Information relevant to CI is often found in the electronic health records (EHRs) and can provide vital clues for early diagnosis, but a manual review by experts is tedious and error prone. This project develops a novel state-of-the-art automated screening pipeline for scalable and high-speed discovery of undetected CI in EHRs. To understand the linguistic context from complex language structures in EHR, a database of 8,656 sequences was constructed to train attention-based deep learning natural language processing model to classify sequences. A patient level prediction model based on logistic regression was developed using the sequence level classifier. The deep learning system was tested using 343 patient EHRs and achieved 93% accuracy and AUC = 0.98 to identify patients who had no earlier diagnosis, dementia-related diagnosis code, or dementia-related medications in their EHR. These patients would have otherwise gone undetected or detected too late. The EHR screening pipeline was deployed in NeuraHealthNLP, a web application for automated and real-time CI screening by simply uploading EHRs in a browser. NeuraHealthNLP is cheaper, faster, more accessible, and outperforms current clinical methods including text-based analytics and machine learning approaches. It makes early diagnosis viable in regions with scarce health care services but accessible internet or cellular services.
Computer Vision Used in Industrial Robots

Releasing human beings from hand working has always been an target of science and technology. With the booming of Computer Vision, how to combine it with automation Artificial Intelligence better becomes a valuable topic nowadays. This thesis is written to promote the realization of automated industrial robots by means of Computer Vision and Deep Learning. And in the area of Computer Vision, Object Detection is a core issue that cannot be circumvented. How to recognize specific targets such as small elements in the vision of robots and how to distinguish their features drove the birth of Edge Detection Algorithm, which is wildly used in the area of Computer Vision out of its ability to lessen work load by efficiently sieving out irrelevant information and accurately saving those valuable information to process with. The method for Edge Detection Algorithm to work more efficiently and better has been an study heat spot. Structured Forest is used to accelerate Edge Detection Algorithm and increase the precision of it to reduce the delay time to and satisfy the industrial standard in this thesis. Besides, graphical user interface development and design is a basic skill of a computer practitioner. In order to display the whole operation result more conveniently and apply it to all kinds of platforms, the user interface named demo system was created by the prospering development tool QT. The develop process and experience of QT will be attached to this thesis.
A Novel Privacy Paradigm for Improving Serial Data Privacy

Protecting the privacy of individuals is of utmost concern in today’s society, as inscribed and governed by the prevailing privacy laws, such as GDPR. In serial data, bits of data are continuously released, but their combined effect may result in a privacy breach in the whole serial publication. Protecting serial data is crucial for preserving them from adversaries. Previous approaches provide privacy for relational data and serial data, but many loopholes exist when dealing with multiple sensitive values. We address these problems by introducing a novel privacy approach that limits the risk of privacy disclosure in republication and gives better privacy with much lower perturbation rates. Existing techniques provide a strong privacy guarantee against attacks on data privacy; however, in serial publication, the chances of attack still exist due to the continuous addition and deletion of data. In serial data, proper countermeasures for tackling attacks such as correlation attacks have not been taken, due to which serial publication is still at risk. Moreover, protecting privacy is a significant task due to the critical absence of sensitive values while dealing with multiple sensitive values. Due to this critical absence, signatures change in every release, which is a reason for attacks. In this paper, we introduce a novel approach in order to counter the composition attack and the transitive composition attack and we prove that the proposed approach is better than the existing state-of-the-art techniques. Our paper establishes the result with a systematic examination of the republication dilemma. Finally, we evaluate our work using benchmark datasets, and the results show the efficacy of the proposed technique.
HIGH FLUX DENSITY ROTATIONAL CORE LOSS MEASUREMENTS

Energy conversion processes involve losses. Specifically core losses, which are a result of the magnetization process in cored electrical energy conversion and storage devices. The cores are made of soft ferromagnetic materials that are easily magnetized and demagnetized. These soft magnetic cores, allow a reduction in size, higher energy storage density, and a reduction in magnetizing current, when compared to non-cored devices. The characterization of soft ferromagnetic materials is traditionally done under unidirectional pulsating fields, which is sufficient for single-phase transformers and inductors, where the cores are under pulsating fields. However, T-joints of three phase transformers and teeth-roots of rotating machine stator cores are exposed to two-dimensional rotational fields of higher core loss. Pulsating measurements are therefore insufficient in the characterization of soft ferromagnetic materials used in rotating electrical machines or in three phase transformers. In two-dimensional fields, the magnetization direction changes with time, tracing a flux density locus. This requires the measurement of tangential magnetic field and flux density components, hence the associated loss. This study proposes a two-dimensional rotational core loss tester for high flux density measurements up to about 2 T, at 60 Hz. Its frequency measurement range is from 60 Hz to 1 kHz. The initial sizing was done analytically, then implemented in three-dimensional finite element analysis, prototyped and experiments performed to verify its capability. It was validated by testing two 0.35 mm and 0.65 mm thick samples. Very high flux densities in the range of 2 T at 60 Hz were achieved in both samples. For the thinner sample, flux densities of 1.8 T and 1.6 T were measured at 400 Hz and 1 kHz, respectively, while for the thicker one, the range reduced to 1.7 T and 1.4 T, at 400 Hz and 1 kHz, respectively. The magnetizer also reproduced non-sinusoidal flux density waveforms, for flux densities less than or equal to 1.0 T, without any waveform control. The proposed rotational core loss setup will find application in the characterization of electrical steels, and generation of pulsating and rotational core loss data. This data can then be applied in core loss models, uprating of megawatt (MW) rated machines, transient and hotspots analysis, and in the design of higher power density machines, such as high-speed machines.
Statistics for social workers

All chapters conclude with "Concluding Thoughts" and "Study Questions. " Preface. 1. Introduction to Statistical Analysis. Uses of Statistical Analysis.General Methodological Terms.Levels of Measurement.Levels of Measurement and Analysis of Data.Other Measurement Classifications.Categories of Statistical Analyses. 2. Frequency Distributions and Graphs. Frequency Distributions.Grouped Frequency Distributions.Using Frequency Distributions to Analyze Data.Misrepresentation of Data.Graphical Presentation of Data.A Common Mistake in Displaying Data. 3. Central Tendency and Variability. Central Tendency.Variability. 4. Normal Distributions. Skewed Distributions.Normal Distributions.Converting Raw Scores to Z Scores and Percentiles.Deriving Raw Scores From Percentiles. 5. Introduction to Hypothesis Testing. Alternative Explanations.Probability. Refuting Sampling Error.Research Hypotheses.Testing the Null Hypothesis.Statistical Significance.Errors in Drawing Conclusions About Relationships.Statistically Significant Relationships and Meaningful Findings. 6. Sampling Distributions and Hypothesis Testing. Sample Size and Sampling Error.Sampling Distributions and Inference.Sampling Distribution of Means.Estimating Parameters From Statistics.Other Distributions. 7. Selecting a Statistical Test. The Importance of Selecting the Correct Statistical Test.Factors to Consider When Selecting a Statistical Test.Parametric and Nonparametric Tests.Multivariate Statistical Tests.General Guidelines for Test Selection.Getting Help With Data Analyses. 8. Correlation. Uses of Correlation.Perfect Correlations.Nonperfect Correlations.Interpreting Linear Correlations.Using Correlation For Inference.Computation and Presentation of Person's R.Nonparametric Alternatives.Using Correlation With Three or More Variables.Other Multivariate Tests that Use Correlation. 9. Regression Analyses. What is Prediction? What is Simple Linear Regression? Computation of the Regression Equation.More About the Regression Line.Interpreting Results.Using Regression Analyses in Social Work Practice.Regression With Three or More Variables.Other Types of Regression Analyses. 10. Cross-Tabulation. The Chi-Square Test of Association.Using Chi-Square in Social Work Practice.Chi-Square With Three or More Variables.Special Applications of the Chi-Square Formula. 11. t Tests and Analysis of Variance. The Use of t Tests.The One-Sample t Test.The Dependent t Test.The Independent t Test.Simple Analysis of Variance (One-Way Anova). Appendix A. Using Statistics to Evaluate Practice Effectiveness. Evaluating Programs.Evaluating Individual Practitioner Effectiveness. Glossary. Index.
LBRO: Load Balancing for Resource Optimization in Edge Computing

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.