Miracle-dz/UniFAR-FTUs huggingface dataset:
Tackling Addiction with Integrated Care

Addiction is recognised as one of the chronic illnesses, often leading to medical, psychological, economic, and social problems, making multi-dimensional care for these people within the existing system a challenge. This loads an already loaded primary and secondary care system. The Verslavingskoepel Kempen (VKK) is an initiative to answer this challenge as it aims to integrate the care provided by the zeroth to third-line care around addiction treatment. Zeroth refers to the care and support provided by the human context of the patients, such as family and peers. This extends the integrated care model that is suggested by the WHO. The aim of the dissertation is firstly, to identify the organizational aspects crucial for the success of the network. The Findings show that success is confirmed by participating local communities when highlighting the importance of the intramural connection between their community social services and professionals, hospitals, and experience holders. All interviewees recognize collateral and process leadership with resources emerging from different partners and without hierarchical management. The second aim considers whether success must be attributed to the instrumental organizational aspects of the collaboration or the values of the different care-givers. The Findings show that in this network the organizational aspects and the caregivers’ values are complementary. The third aim is to develop conclusions on the transferability of the network model. This is confirmed; however, the governmental policy is not accompanied by a funding system within the institutionalized remuneration and reimbursement structure. It makes the initiative dependent on the motivation of partner organizations to participate financially, raises doubt about the sustainability of the initiative, and sets conditions for as well as limitations on transferability.
Anti-Spoofing for Fingerprint Recognition Using Electric Body Pulse Response

This study presents a highly reliable approach to prevent fingerprint spoofing attacks based on electric body pulse responses (BPRs) in personal Internet of Things (IoT) gadgets. Real fingerprint pulse response (RFPR) and fake fingerprint pulse response (FFPR) data were collected from ten subjects for four weeks. The FFPR was obtained by wearing a fake fingerprint made of artificial substances, such as conductive silicone, over the finger. We analyzed different patterns of FFPR compared to RFPR using an electric circuit model of the proposed fingerprint anti-spoofing system based on BPRs. Simple features comprising ten, five, or three datapoints were selected by the minimum redundancy maximum relevance (MRMR) algorithm and led to reduction in processing complexity. We also validated its robustness to sampling offset errors caused by practical sampling operations in devices based on the evaluation of classification accuracy using machine learning algorithms, such as ${k}$ -nearest neighbor (KNN) and support vector machine (SVM). Finally, the effectiveness of the selected feature was evaluated using unsupervised anomaly detection algorithms, such as principal component analysis (PCA), one-class SVM (OC-SVM), and variational autoencoder (VAE), in a practical scenario with sampling offset errors in the training and test data. The VAE outperformed PCA and OC-SVM by achieving a detection accuracy of 99.76% using raw data under 100 datapoints and 97.60% with reduced features having only five datapoints, regardless of sampling offset errors. Therefore, the proposed anomaly detection system based on EPRs can provide promising fingerprint spoof detection in IoT devices with limited computing resources.
Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning‐based logistic regression

To investigate whether machine learning (ML)‐based algorithms, namely logistic regression (LR), random forest (RF), k‐nearest neighbor (k‐NN), and gradient‐boosting decision tree (GBDT), utilizing early post‐onset parameters can predict facial synkinesis resulting from Bell's palsy or Ramsay Hunt syndrome more accurately than the conventional statistics‐based LR.
Development and Application of an Asymptotic Level Transport Pollution Model for Luxembourg Energy Air Quality Project

The connections between air pollution and the increase of respiratory diseases, are well known. In Europe, many efforts have been carried out towards the mitigation of the pollutants’ emissions over the last decades. However the ambient levels of some air pollutants, is still worrisome and a large part of the European population is still exposed to high levels pollution. The European Union supports the implementation of structural planning measures to control air pollution. The assessment and evaluation of these air quality policies must be carried out with the help of dedicated integrated assessment models. The use of integrated assessment models, which combine models from different fields, raises the need for developing specific modelling concepts in order to provide results to support policy decisions within a practical time frame. Integrated assessment models for air policy relate technologies, the emitting sources, with air quality levels. Existing photochemical air quality models are not directly suitable for integrated approaches as they are time intensive in terms of input preparation and simulation speed. This work presents the methodology and the development of a dedicated air quality model for an integrated assessment model. This approach has been designed for the Luxembourg Energy and Air Quality, LEAQ, integrated assessment model. It combines an air quality model, AUSTAL2000-AYLTP, with a techno-economic model, ETEM, which computes ozone precursors emissions related to energy consumption. The models are coupled via an optimization engine, which minimizes the total energy cost for a given ozone level. AUSTAL2000, a Lagrangian transport model, has been adapted to receive a photochemical module, the AsYmptotic Level Transport Pollution, AYLTP. This module consists of a Look-Up Table of quasi-linear reaction rates. The AUSTAL2000 model inquires the look-up table for pre-calculated initial conditions, and it reads the correspondent rate that is then used by AUSTAL2000. The look-up table has been built using a box model by simulating a large set of possible combinations of meteorological variables and precursor concentrations. A balance has been found that gives an acceptable level of accuracy, given the reduction of computational time. The development of such methodologies is important when considering integrated assessment models. Furthermore, the results of the air quality model have been compared with measurements, and with the regional model LOTOS-EUROS. The results of the validation are considered satisfactory for this type of approach. Additionally, the air quality model has been used within the in LEAQ model. Two study cases have been simulated, one including only the national emissions
Proteinase K-pretreated ConA-based ELISA assay: a novel urine LAM detection strategy for TB diagnosis

Objectives Lipoarabinomannan (LAM), an abundant cell wall glycolipid of mycobacteria including Mycobacterium tuberculosis (Mtb), is a promising TB diagnostic marker. The current commercially available urine LAM assays are not sufficiently sensitive, and more novel detection strategies are urgently needed to fill the current diagnostic gap. Methods A proteinase K-pretreated Concanavalin A (ConA)-based ELISA assay was developed. Diagnostic performance was assessed by several bacterial strains and clinical urine samples. Results The limit of detection (LoD) of the assay against ManLAM was 6 ng/ml. The assay reacted strongly to Mtb H37Rv and M. bovis BCG, intermediately to M. smegmatis mc2155, and weakly to four non-mycobacteria pathogens. This method could distinguish TB patients from healthy controls (HCs) and close contacts (CCs) in 71 urine samples treated with proteinase K, which increases urine LAM antibody reactiveness. In TB+HIV+ and TB+HIV− patients, the sensitivity was 43.8 and 37.5%, respectively, while the specificity was 100.0%. The areas under ROC curves (AUCs) were 0.74 and 0.82, respectively. Conclusion This study implies that ConA can be paired with antibodies to detect LAM. Proteinase K treatment could effectively enhance the sensitivity by restoring the reactiveness of antibodies to LAM.
PRIMARY SCHOOL TEACHERS’ PERCEPTIONS OF THEIR SELF-DIRECTED LEARNING SKILLS AND USE OF PRACTICES THAT PROMOTE SELF-REGULATED LEARNING

In a review of the causes and consequences of the weak outcomes of South Africa’s education, researchers reported high drop-out rates before completion of secondary school and that less than five percent of learners who start primary school obtain a university qualification. The recent outbreak of the COVID-19 pandemic that resulted an unprecedented change in teaching and learning environments heightened the necessity of self-directed teachers who develop learners’ self-regulated learning skills to empower them to manage and regulate their own behaviours. Against the background of poor performance and the central role of teachers in the development of self-regulated learning skills in primary schools, the aim of this paper is to explore teachers’ perceptions of their own self-directed learning skills and the teaching strategies they utilise to develop self-regulated learning skills in their learners. The study was located within an interpretivist philosophical orientation and a qualitative research design. We made use of purposive sampling and collected data via semi-structured individual interviews from eleven teachers. Content analysis and a thematic approach were used to analyse the data deductively and inductively. Most participants were confident with their own self-directed learning skills and abilities to develop self-regulated learning skills. Participants revealed that they are well supported in the schooling environment with the needed resources to assist in developing self-regulated learning skills. Participants make use of various strategies, which seem to be more teacher initiated than learner initiated in attempting to teach and develop self-regulated learning skills by engaging in various activities.
Changes of Soil Nitrogen Fractions and nirS-Type Denitrifier Microbial Community in Response to N Fertilizer in the Semi-Arid Area of Northeast China

The denitrification process is one of the important processes in the soil nitrogen (N) cycle, and is closely related to the loss of soil N fertilizer. Five treatments were included in this study: N0 (control, no N application); N90 (N application rate 90 kg ha−1); N150 (N application rate 150 kg ha−1); N210 (N application rate 210 kg ha−1); and N270 (N application rate 270 kg ha−1), to study the effect of different N application rates on the soil nirS-type denitrifying bacterial community structure, the influence of key enzyme activities during the denitrification process, and the main environmental factors affecting the variation of the denitrifying bacterial community in maize field soil under the mulched fertigation system in the semi-arid region of Northeast China. The results showed that increasing N fertilizer application significantly increased the contents of soil inorganic N and acid-hydrolyzable organic N, but significantly decreased pH. N fertilizer significantly increased nitrate reductase (NAR) activity and nitrite reductase (NIR) activity. Excessive application of N fertilizer significantly increased the nirS gene copy numbers, and, at the same time, significantly decreased the diversity of nirS-type soil denitrifying bacteria. Proteobacteria was the dominant denitrifying phylum in all treatments, and N application promoted the growth of Bradyrhizobium belonging to this phylum. The application of N fertilizer significantly changed the community structure of nirS denitrifying bacteria, and the NO3−-N content was the most important factor for this observation. Soil organic matter (SOM) and non-hydrolyzable N (NHN) indirectly affected the denitrifying bacterial community structure through NAR activity and NIR activity, while soil total N (TN) and nitrate N (NO3−-N) indirectly affected yield through denitrifying bacterial community structure. Although increasing N fertilizer was beneficial in increasing soil nutrients, the community structure of nirS-type denitrifying bacteria changed significantly. This is attributed to the increase in soil NO3−-N accumulation caused by a large amount of N application. The results of this research provide an important scientific basis for further research on the response mechanism of farmland soil denitrifying microorganisms to different N fertilizer managements under the mulched fertigation system in the semi-arid region of Northeast China.
Hubungan Motivasi dengan Prestasi Belajar Siswa Kelas X SMK Negeri 1 Ratahan

Penelitian ini memiliki tujuan untuk mengetahui motivasi belajar siswa kelas X SMK Negeri 1 Ratahan, hasil belajar siswa kelas X di SMK Negeri 1 Ratahan dan hubungan motivasi dengan hasil belajar siswa kelas X SMK Negeri 1 Ratahan. Penelitian ini menggunakan metode kuantitaif. Adapun jenis penelitiannya yaitu korelasional. Penelitian ini merupakan penelitian yang melibatkan 45 orang siswa. Data dikumpulkan dengan menggunakan instrument penelitian seperti kuesioner. Dari 44 butir soal yang diuji coba, terdapat 43 soal yang valid dengan nilai reliabilitas sebesar 0,953. Teknik analsis data yang digunakan pada penelitian ini yaitu teknik kolerasi product moment. Hasil penelitian ini menunjukkan bahwa rata-rata motivasi belajar siswa kelas X SMK Negeri 1 Ratahan adalah 51,6 atau termasuk dalam kategori sedang, rata-rata hasil belajar siswa kelas X SMK Negeri 1 Ratahan adalah 64,5 atau dalam kategori sedang, dan terdapat hubungan positf dan signifikan antara motivasi dengan hasil belajar siswa kelas X SMK Negeri 1 Ratahan.
Scaling behaviour of corrugated sandwich panels under impact load

The majority of the experimental studies investigating the dynamic response of sandwich panels are conducted using small-scale models that cannot be applied directly in the marine industry. In this study, a velocity–stress–mass (VSG) method based on the Johnson–Cook equation (VSG–JC) is derived to determine the relationship between a full-size structure (prototype) and its scaled-down model. The developed methodology is verified using theoretical and numerical solutions for an impact-loaded beam and plate, respectively. Theoretically, the model response can explicitly predict the behavior of a full-size structure using the VSG–JC method, whereas the strain rate cannot be obtained precisely via numerical simulation; hence, the corrected results for the model deviate slightly from the prototype results. Additionally, this method is compared with the VSG method based on the Cowper–Symonds equation (VSG–CS) in the numerical simulation of sandwich panels. The comparison results indicates that the VSG–JC method is more accurate than the VSG–CS method. The dynamic response of the scaled models predicted using the VSG–JC method coincides with that of the prototype, thus demonstrating that the VSG–JC method is valid for evaluating the scaling behavior of sandwich structures subjected to impact loads.
Automated generation and correction of diagram‐based exercises for Moodle

One of the most time‐consuming task for teachers is creating and correcting exercises to evaluate students. This is normally performed by hand, which incurs high time costs and is error‐prone. A way to alleviate this problem is to provide an assistant tool that automates such tasks. In the case of exercises based on diagrams, they can be represented as models to enable their automated model‐based generation for any target environment, like web or mobile applications, or learning platforms like Moodle. In this paper, we propose an automated process for synthesizing five types of diagram‐based exercises for the Moodle platform. Being model‐based, our solution is domain‐agnostic (i.e., it can be applied to arbitrary domains like automata, electronics, or software design). We report on its use within a university course on automata theory, as well as evaluations of generality, effectiveness and efficiency, illustrating the benefits of our approach.
Efficacy of various herbicides for management of weed flora in citrus orchards.

To determine the efficacy of different post-emergence herbicides against weed flora in citrus orchards, experiments during July-Aug (summer) and Oct-Nov 2009 (autumn) were laid out in a randomized complete block design with three replications. The experiments comprised of five treatments including four herbicides and a weedy control. The four herbicides were glyphosate (Title 95%SG, a candidate product of the Auriga Chemicals Enterprises), glyphosate (Round up, used as standard product), gramoxone (Remote 20%SL, a candidate product of Ali Akbar Enterprises) and gramoxone (Gramoxone 200SL, used as standard product). Statistical analyses of the data indicated that the herbicides had a significant weed control and weed canopy reduction. The herbicides, Title 95%SG, Round up, Remote 20%SL and Gramoxone 20SL decreased the total weed density to 2.6, 6.5, 23.3 and 31.4 weeds m, respectively in summer; whereas the respective values in autumn were 1.6, 1.5, 4.1 and 5.2 weeds m. Similarly the total weed canopies were 3.0, 5.8, 10.8 and 13.2% weeds m, respectively in summer; and 2.2, 1.4, 3.6 and 4.8% weeds m in autumn. However, their effect on individual weeds was statistically at par. The herbicide, Title 95%SG had a statistically similar performance in comparison with that of Round up. Similarly, Remote 20%SL performed statistically at par with Gramoxone 200SL. The highest values for weed canopy coverage were in the weedy check i.e. 87% m 2 in summer and 83% m in autumn; whereas lowest values were 3% and 2.2% recorded in Title 95%SG in summer and autumn, respectively. Both experiments disclosed that all the herbicides significantly decreased the canopies of all the weed species as compared to control treatment and that no injury was recorded on the citrus trees.
Pyroptosis and Inflammasome-Related Genes-NLRP3, NLRC4 and NLRP7 Polymorphisms Were Associated with Risk of Lung Cancer

Background Cancer development and tumor immune microenvironment remodeling are closely linked to pyroptosis and inflammasome activation. However, little information is available in single nucleotide polymorphisms (SNPs) in pyroptosis and inflammasome-related genes in patients with lung cancer. This study aims to evaluate the associations between pyroptosis-related gene (NLRP3, NLRC4, and NLRP7) polymorphisms and the risk of lung cancer. Methods The MassARRAY platform was used to genotype six SNPs of the NLRP3, NLRC4, and NLRP7 genes in 660 lung cancer cases and 660 controls. Results Individuals with rs35829419-A, rs385076-C, and rs775882-T alleles exhibited a higher risk of lung cancer (p < 0.01), while rs212704-T appears protective (p = 0.006). The rs35829419-AA, rs385076-TC/CC, and rs775882-CT/TT genotypes were associated with various degrees of elevated risk of lung cancer (p<0.02), whereas rs212704-TT was associated with a reduced risk of the disease (p=0.014). Genetic models analysis showed that rs35829419, rs385076, and rs775882 was associated with an increased risk of lung cancer, while rs212704 was related to a reduced risk in all three models (p < 0.05). The four SNPs remained significant in smoker and nonsmoker subgroups (p < 0.05). However, rs35829419 was correlated with risk of adenocarcinoma and small cell lung cancer, and rs212704 was only protective for squamous cell carcinoma. The rs385076 and rs775882 were associated with all three pathological types (p < 0.01). Conclusion Besides providing candidate markers for identification of high-risk populations and early prevention of the disease, our research also provided new insight into anti-tumor strategies targeting inflammasomes and pyroptosis.
Inventory optimization of building materials under the dual constraints of carbon emissions and stochastic demand

According to the characteristics of the demand for building materials, this paper constructs multiperiod inventory models for single materials with and without the carbon cap-and-trade (C&T) mechanism and analyses the models by means of operational research methods. Several findings are obtained through theoretical analysis and numerical experiments. The optimal order arrangement in terms of the C&T mechanism can reduce the carbon emissions from building material transportation and storage. In most cases, with an increase in carbon price, the economic order quantity and expected carbon emissions, which are not related to the carbon cap level, decrease. Strict, mild and minimal effects on emissions reduction caused, respectively, by strict, moderate and weak C&T mechanisms are observed for various combinations of carbon trading prices and carbon caps. Finally, the above results are verified by numerical simulation, providing evidence for policymakers to work out reasonable C&T mechanisms to avoid the blindness of carbon constraint policy setting.
International Conference on System-integrated Intelligence : New Challenges for Product and Production Engineering , SysInt 2016 Multiobjective Model Predictive Control of an Industrial Laundry

In a wide range of applications, it is desirable to optimally control a system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When it is not possible to compute the entire control trajectory in advance, for instance due to uncertainties or unforeseeable events, model predictive control methods can be applied to control the system during operation in real time. In this article, we present an algorithm for the solution of multiobjective model predictive control problems. In an offline scenario, it can be used to compute the entire set of optimal compromises whereas in a real time scenario, one optimal compromise is computed according to an operator’s preference. The results are illustrated using the example of an industrial laundry. A logistics model of the laundry is developed and then utilized in the optimization routine. Results are presented for an offline as well as an online scenario. c © 2016 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of SysInt 2016.