Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of the ColorApp Mobile App for Health Education and Promotion for Colorectal Cancer: Quasi-Experimental Study

Background Lack of knowledge and poor attitude are barriers to colorectal cancer screening participation. Printed material, such as pamphlets and posters, have been the main approach in health education on disease prevention in Malaysia. Current information technology advancements have led to an increasing trend of the public reading from websites and mobile apps using their mobile phones. Thus, health information dissemination should also be diverted to websites and mobile apps. Increasing knowledge and awareness could increase screening participation and prevent late detection of diseases such as colorectal cancer. Objective This study aimed to assess the effectiveness of the ColorApp mobile app in improving the knowledge and attitude on colorectal cancer among users aged 50 years and older, who are the population at risk for the disease in Kedah. Methods A quasi-experimental study was conducted with 100 participants in Kedah, Malaysia. Participants from five randomly selected community empowerment programs in Kota Setar district were in the intervention group; Kuala Muda district was the control group. Participants were given a self-administered validated questionnaire on knowledge and attitudes toward colorectal cancer. A mobile app, ColorApp (Colorectal Cancer Application), was developed as a new educational tool for colorectal cancer prevention. The intervention group used the app for two weeks. The same questionnaire was redistributed to both groups after two weeks. The mean percentage scores for knowledge and attitude between groups were compared using repeated measure ANCOVA. Results There was no significant difference in age, sex, highest education level, current occupation, and diabetic status between the two groups. The number of smokers was significantly higher in the intervention group compared with the control group and was controlled for during analysis. The intervention group showed a significantly higher mean knowledge score compared with the control group with regards to time (Huynh-Feldt: F1,95=19.81, P<.001). However, there was no significant difference in mean attitude scores between the intervention and control groups with regards to time (F1,95=0.36, P=.55). Conclusions The ColorApp mobile app may be an adjunct approach in educating the public on colorectal cancer.
International Journal of Management Sciences and Business Research, Dec-2019 ISSN (2226-8235) Vol-8, Issue 12

This paper assessed the effect of the health condition of employees on the performance of small and mediumscale firms in Nigeria. The cross-sectional survey design was utilized and a questionnaire was administered to three hundred and fifty-three (353) employees of small and medium-scale firms in three (3) states of the southeast geopolitical zone of Nigeria namely Anambra, Enugu and Imo. The data obtained from the field survey were analyzed via descriptive and inferential statistical tools. The findings of the study showed that the health condition of employees significantly contributes to the performance of small and medium-scale firms in Nigeria. Based on the findings of the study, it was recommended among other management of small and medium scale firms should consider investing in the wellbeing of employees in order to ascertain their health condition. As a matter of fact, small and medium scale firms can actualize this via the provision of health insurance schemes, mental health promotion and support for mental illness, health promotion via physical activity and nutrition programmes among others. As a matter of urgency, the management of small and medium scale firms should formulate programmes aimed at improving occupational health safety.
Synchrony Assumptions for Blockchain Systems

The prominent blockchain technology aims at implementing a public ”ledger”: a decentralized consistent history of transactions proposed by an open set of participating processes, with no static membership. This problem can be seen as an instance of fault-tolerant state-machine replication [14], prominent examples of which are the crash-tolerant Paxos protocol by Lamport [11] and the BFT (Byzantine fault-tolerant) system by Castro and Liskov [3]. These systems use instances of consensus protocols in order to ensure that users get consistent views of the system evolution. Principal downside of classical consensus protocols are lack of scalability and the need for a fixed or properly reconfigurable set of participants out of which only a bounded fraction (up to one third) can be faulty. This can be hard to ensure in an open system, where an arbitrary fraction of participants can be controlled by the adversary [5]. Prominent blockchain protocols [13,15] achieve (nondeterministic) consistency by assuming that (1) the system is synchronous, (2) participants can use asymmetric cryptography, and (3) the adversary can control at most a minority (in practice, a minor fraction) of computing power. Intuitively, these assumptions are used to overcome the folklore CAP theorem [2, 8] stating that no system can combine Consistency, Availability, and Partition-Tolerance. In particular, these protocols avoid partitioning by enforcing the proof of work (PoW) mechanism requiring that a participant must solve a time-consuming cryptographic puzzle before updating the ledger. The resulting protocols are notoriously slow and energy-demanding. More recent blockchain prototypes propose to obviate the energy demands via using proof-of-stake [1, 10], proof-of-space [6], or proof of space-time [12]. However, the proposals still resort to synchronous networks and/or impose restrictions on the fraction of honest players to ensure proper security levels. An immediate question is whether these costs and assumptions are unavoidable. In this project, we intend to characterize the model assumptions that enable strong ledger consistency in an open system. This will involve determining precise bounds on the amount of synchrony [4,7] and energy/space/time consumption for implementing a generic distributed transaction ledger. This might lead to improving the conventional “proof” mechanisms, used, e.g., in Tezos [9] and Cardano [10] platforms.
The Algorithmic

We will follow the broad proof outline from last lecture but, in order to allow for a simpler proof, we will use an object that is quite different from a fingerprinting code. (See below for an explanation of the relationship between the two objects.) We will consider a universe {1, . , N} from which the mechanism receives a multiset S ⊂ [N ] of size n, sampled with replacement from the uniform distribution on [N ]. The mechanism answers statistical queries q, . , q posed adaptively by an analyst, and aims to return answers a close to the population mean q(UN ) = 1 N ∑N i=1 q (i). The crucial difference with the model considered previously is that the mechanism does not see a complete description of the queries. Instead, as with fingerprinting codes, we assume that the analyst can “name” possible users of being in the mechanism’s sample. In each round j, the mechanism sees only the values {q(i)}i∈Sj corresponding to the set S ⊆ S of currently unnamed sample points. We summarize the game concisely here:
Morphological study of defects in laminated joints of composite materials using microCT

Computed microtomography (microCT) has been used to inspect composite materials of the most varied types. From a volumetric analysis it is possible to characterize the phases (matrix and reinforcement) present in the materials, as well as to determine the percentage of defects that may exist. In laminated joints of pipe in polymeric composite material reinforced by fiberglass, delaminations and voids have been observed that may compromise the strength of these materials. A basic analysis with microCT allows to verify the presence of these occurrences, but to differentiate them a more accurate analysis is necessary. The objective of this study was to analyze computed microtomography of laminated composites. The results quantitative of the matrix and reinforcement were provided and a study of the sphericity of the defects was performed. The results demonstrated that more spherical defects tend to be small voids, whereas less spherical defects correspond to delaminations.
Stochastic analysis of average-based distributed algorithms

Abstract We analyze average-based distributed algorithms relying on simple and pairwise random interactions among a large and unknown number of anonymous agents. This allows the characterization of global properties emerging from these local interactions. Agents start with an initial integer value, and at each interaction keep the average integer part of both values as their new value. The convergence occurs when, with high probability, all the agents possess the same value, which means that they all know a property of the global system. Using a well-chosen stochastic coupling, we improve upon existing results by providing explicit and tight bounds on the convergence time. We apply these general results to both the proportion problem and the system size problem.
Detecting trace methane levels with plasma optical emission spectroscopy and supervised machine learning

Trace methane detection in the parts per million range is reported using a novel detection scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing spectra with a complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach based on partial least squares discriminant analysis was implemented for CH4 concentrations up to 100 ppm in He, to provide binary classification of samples above or below a threshold of 2 ppm. With a low-resolution spectrometer and a custom spectral alignment procedure, a prediction accuracy of 98% was achieved, demonstrating the power of machine learning with otherwise prohibitively complex spectral analysis. This work establishes proof of principle for low cost and high-resolution trace gas detection with the potential for field deployment and autonomous remote monitoring.
Quantitative Descriptive Analysis and Acceptance Testing of Yogurt with no Added Sugar

This study aimed to evaluate the effect of sucralose added in Greek Yogurt flavored with araticum (Annona crassiflora) and mangaba (Hancornia speciosa) through sensory characterization. The Greek yogurt was prepared with skimmed milk, inoculated starter cultures, filtered and it was supplemented with the appropriate amount of sucralose, added sweetened and pasteurized fruit pulp (araticum or mangaba). The total phenolic compounds and texture were performed and sensory analyses were carried out by Quantitative descriptive analysis (QDA) and acceptance test in storage for 7 and 28 days. The QDA results showed that the main attributes were color, lightness, creaminess, presence of particles, fullness, and aroma. Eighty percent and 85% of the panelists were said they would buy the araticum Greek yogurt and the mangaba Greek yogurt after 7 days of storage, respectively. Additionally, 71% and 77% were said they would buy the araticum Greek yogurt and the mangaba Greek yogurt after storage for 28 days at 4oC, respectively. The sensory profile and acceptance test results of the Greek yogurts developed indicated no perceptions caused by adding sucralose to the yogurt after storage for different times. The highest concentration of phenolic compounds in the araticum Greek yogurt was perceived by the panelists in aroma and flavor attributes. Changing sucrose to sucralose was not imperceptible under the storage and consumption conditions.
Training Process Automation for Computer Vision

 Abstract —One of the most important areas of artificial intelligence is computer vision-based objects recognition and perception of the environment in general. It is known that the artificial neural networks (ANN) training process is a rather complicated, delicate and time-consuming process. Collecting the data needed for training and giving it such form that will be required for a given moment makes this process even more complicated. The achievement of the desired goal requires a large amount of work and, accordingly, takes much time. Since the tasks related to the training process in the majority of cases must be performed by a human, and in the case of process automation, human intervention is to some extent still required. For this reason, we suggest a new approach implementing the software which makes possible to detect unknown object independently by computer vision, and then automatically carry out the steps necessary for the training process. In other words, by means of this algorithm, detection of unknown object in the frame occurs and then automatic search of materials necessary for training via the Internet (or elsewhere) and after collecting the desired amount of information, the training of artificial neural networks starts. As a result, we get a perfect file of Haar Cascade type format and the given object is added to the list of the known objects. In our opinion, this approach is convenient and gives artificial intelligence a greater degree of autonomy provided by computer vision.
An empirical study of transboundary air pollution of the Beijing-Tianjin region

In this paper, we present a stochastic differential game to model the transboundary air pollution problems of the Beijing-Tianjin region with emission permits trading. By using stochastic optimal control theory, we obtain the Hamilton-Jacobi-Bellman equation satisfied by the value function for the cooperative games. Next, we solve the Hamilton-Jacobi-Bellman equation by using a fitted finite volume method. Finally, the efficiency and the usefulness of the fitted finite volume method are illustrated by an empirical study.
Research on Dependences of Speech Pitch Parameters on Pulse and Heartbeat Signals

In this paper, we consider the inﬂuence of the cardiovascular system to the process of speech production. We propose the mathematical model of the impact of cardiovascular system elements on the process of speech pitch synthesis. This model takes into account the eﬀect of the functioning of the heart and the pulsations of the large vessels of the thorax on the intensity of the air ﬂow from the lungs during breathing, as well as the eﬀect of the pulsations of the blood vessels of the vocal cords on the speech synthesis process. We made the initial check of approximate properties of the model in practice. In the future, the proposed model allows to construct the precision model of speech signal forming. The solution of the direct synthesis problem allows to increase the speech quality in the synthesis, recognition and coding processes. The inverse problem aims to increase the eﬃciency of physiologic and psycho-emotional state estimation of a person. In conclusion, we discussed further research directions that may improve the quality of the estimates obtained on the basis of the formulated model.
Poster: Securing Distributed System Conﬁguration through Optimization and Reasoning on Graphs

—Complex distributed systems are inherently difﬁcult to secure due to the many interdependencies amongst their components, vulnerabilities, and conﬁguration parameters. To address this problem, we present an approach for improving the security posture of distributed systems by examining the security impact of conﬁguration changes across their interdependent components. We construct a graph-based model of the system and its vulnerabilities and use it to analyze the attack surface and the impact of attacks. We show how the model can be optimized using SMT solvers to derive conﬁgurations that minimize the impact of attacks while preserving system functionality.
Wearable Devices for Digital Health: The SPHERE Wearable 3

This paper presents a novel wearable device: the SPHERE Wearable 3. The Wearable 3 is a wrist-worn sensor node tailored for low maintenance residential health and behaviour monitoring. It features multiple inertial measurement sensors, as well as a heart-rate sensor, capacitive button, and OLED screen. The Wearable 3 builds on our expe-rience with previous generations of wearable sensing nodes while adding novel features, including interactive elements. We present a novel two-way communication based on a cus-tomized implementation of the Bluetooth Low Energy (BLE) protocol to connect the Wearable 3 with a gateway device, and describe various optimizations of energy consumption. The Wearable 3 is currently being deployed in residential homes in Bristol, UK, as part of a project aiming to collect diagnostic data from early-stage Alzheimer’s patients.
Development and evaluation of analytical techniques for antibodies and antibody-drug conjugates From verification of conjugation to stability testing

Källsten, M. 2020. Development and evaluation of analytical techniques for antibodies and antibody-drug conjugates. From verification of conjugation to stability testing. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1901. 83 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0867-8. Antibody-drug conjugates (ADCs) are becoming increasingly important in oncology. ADCs are inherently heterogeneous analytes. For a successful drug candidate to go through to the clinic, extensive characterization and evaluation of the constructs are necessary. The drug-toantibody ratio (DAR) directly determines the toxicity and efficacy of the product and has to be closely monitored during the entire process. In this thesis, several analytical techniques suitable for DAR determination in early-stage development have been evaluated against two more readily available options. The main focus in all studies has been on cysteine-linked ADCs, due to their prevalence in successful designs and their higher demand on the analytical set-up. The comparisons showed that mass spectrometry (MS) derived DAR values did conform well with hydrophobic interaction chromatography derived values, irrespective of which MS instrument was used. For MS instruments, overall the desolvation was found to have a bigger impact on the apparent DAR values than the resolution of the instrument and low-resolution instruments, such as triple quadrupoles, can be viable options for DAR determination in earlystage development. Furthermore, it also could be concluded that MS-derived DAR values are susceptible to alterations in the sample preparation workflow. Stability testing is vital to ensure a safe product. In this thesis, different compositions of mobile phases in size exclusion chromatographic (SEC) were tested for their ability to elute ADC aggregates. It was concluded that sufficient ionic strength to elute ADC aggregates from a SEC column purely by adding ammonium acetate cannot be achieved without exceeding salt concentrations compatlible with MS sources. Finally, two new analytical workflows have been designed by applying a proteomic desalting protocol and supercharging reagents to mAb and ADC samples. Both techniques showed promise; the magnetic beads as a more flexible desalting alternative for mAbs and ADCs and the addition of selective supercharging reagents for improved sensitivity and peak shapes in MS spectra (without significant alterations of the derived DAR values). Taken together, this thesis provides guidance on many aspects of ADC analysis from DAR determination to aggregate detection. The gathered knowledge can help setting up faster or more reliable quality checks for new candidates.
Lookahead Contraction Policies for Bayesian Ranking and Selection with Pairwise Comparisons

We propose and evaluate novel sampling policies for a Bayesian ranking and selection problem with pairwise comparisons. We introduce the lookahead contraction principle and apply it to three types of value factors for lookahead policies. The resulting lookahead contraction policies are analyzed both with the minimal number of lookahead steps required for obtaining informative value factors, and with fixed number of lookahead steps. We show that lookahead contraction reduces the minimal number of required lookahead steps, and that contraction guarantees finiteness of the minimal lookahead. For minimal lookahead we demonstrate empirically that lookahead contraction never leads to worse performance, and that lookahead contraction policies based on expected value of improvement perform best. For fixed lookahead, we show that all lookahead contraction policies eventually outperform their counterparts without contraction, and that contraction results in a performance boost for policies based on predictive probability of improvement.
Semantic-based Analysis of Javadoc Comments

Developers often document their code with semi-structured comments such as Javadoc. Such comments are a form of speciﬁcation, and often document the intended behavior of a code unit, as well as its preconditions. The goal of our project is to analyze Javadoc comments to generate assertions aiming to verify that a software unit indeed behaves as expected. Existing works with this goal mainly rely on syntactic-based techniques to match natural language terms in comments to elements in the code under test. In this paper we show the limitations of syntax-based techniques, and we present our roadmap to semantically analyze Javadoc comments.
Angiographic Restenosis in Coronary Bifurcations Treatment with Regular Drug Eluting Stents and Dedicated Bifurcation Drug-Eluting BiOSS Stents: Analysis Based on Randomized POLBOS I and POLBOS II Studies

Aim The marked variation in bifurcation anatomy has brought about an ongoing search for stents specifically constructed for coronary bifurcations. This study aimed to analyze the angiographic restenosis prevalence and patterns and predictors of different patterns in dedicated bifurcation BiOSS® vs. current generation drug-eluting stents implanted in coronary bifurcation lesions based on data from two clinical trials POLBOS I and II. Methods Dedicated bifurcation BiOSS® stents were compared with drug-eluting stents (DES) in patients with stable coronary artery disease (CAD) or nonST elevation acute coronary syndrome (NSTE-ACS) (POLBOS I: paclitaxel eluting BiOSS® Expert vs. DES; POLBOS II: sirolimus eluting BiOSS® LIM vs. DES). Provisional T-stenting was the default treatment. Morphological pattern of in-stent restenosis according to the modified Mehran classification adopted for bifurcation lesions was assessed with bifurcation dedicated quantitative coronary angiographic software (CAAS 5.11, Pie Medical Imaging BV, the Netherlands). Results In total, 445 patients (222 patients in BiOSS group and 223 patients in DES group) were included into the analysis. In BiOSS group 24 cases of angiographic restenosis (10.8%) were recorded, and in DES group—17 cases (7.6%) at 12 months follow-up (angiographic control rate at follow-up—90.3%). In the BiOSS group most frequent medina classification in restenotic cases was 0.0.1 (25%), whereas in DES—0.0.1 and 0.1. 1 (23.5% each). In multivariate regression analysis proximal optimization technique was associated with the lowest chance for restenosis (OR 0.15, 95% CI 0.06–0.33), whereas diabetes on insulin was associated with the highest risk of restenosis (OR 4.21, 95% CI 1.48–11.44). Conclusions The angiographic restenosis pattern and rate was similar between BiOSS stents and DES in coronary bifurcation lesions.