Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing information security metrics and measures for risk assessment of an organization

Abstract The absence of public safety normalization bodies can antagonistically affect the reception of worldwide security guidelines and best practices. This research is done for the benefit of people, process and technology for any organization. This paper presents a novel pragmatic network protection evaluation structure that is custom-made to the ISO 2700x standard necessities for the improvement of Information Security Management System (ISMS). This model can be utilized for both self-appraisal and examining/scoring instruments by public network protection specialists. Utilizing this model, associations can consider their current data security the broad frameworks in contrast to nearby and global guidelines by using worked in pre-review instruments. All things considered, the model will assist associations with assessing and improving their status for developing dangers and dangers. In this system, an original numerical model was likewise planned and carried out for the scoring/rating instrument, specifically, the public digital protection list (aeNCI). The aeNCI utilizes various boundaries to decide the development of existing network safety programs at public associations and produce a classiﬁcation and correlation reports. The outcomes empowered the partner to confirm the security conﬁguration of their frameworks and recognize possible attacks/hazard vectors.
Research on Determinants and Potential of Goods Trade between China and ASEAN Countries under “the Belt and Road” Initiative

The trade between China and ASEAN countries has been very close since ancient times. Taking China and ASEAN countries as research objects, this paper analyzes the current situation and changing trend of China's and ASEAN countries' trade in goods based on the import and export data of both sides from 2001 to 2018. By using the trade gravity model, this paper empirically analyzes the determinants of trade in goods between China and ASEAN countries, and further calculates the trade potential of goods between China and ASEAN countries. The empirical results show that the GDP of China and ASEAN countries has a positive impact on bilateral trade, while the geographical distance between trading countries has a negative impact on bilateral trade. In terms of trade potential, China's trade in goods with more than half of the ASEAN countries still has a huge potential for expansion and a broad prospect for trade in goods.
A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral–spatial features of HSI. We proposed a new neighborhood grouping normalized matched filter (NGNMF) for BS, which can reduce the data dimension while preserving the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intraclass variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral–spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods.
Uncertainty modeling, propagation, and quantification techniques with applications in engineering dynamics

In the field of engineering dynamics, three of the main challenges associated with stochasticity relate to a) uncertainty modeling, b) uncertainty quantification, and c) uncertainty propagation. Addressing challenge a) relates to the development of methodologies for the interpretation/analysis of measured/available data, as well as for subsequent estimation of pertinent stochastic models, i.e. quantification of the underlying stochastic process/field statistics, while challenge b) relates to quantifying the error of those estimates in a priori, if possible, manner. However, in several engineering applications large amounts of data can be difficult to acquire for several reasons, such as cost, data loss or corruption, as well as limited bandwidth/storage capacity. Furthermore, available data can often be highly limited and irregularly sampled, and thus, standard techniques for spectral estimation, (e.g. Fourier decomposition), can demonstrate poor performance. Further, addressing challenge c) relates to the development of methodologies for determining complex system response/reliability statistics, i.e. development of analytical/numerical methodologies for solving nonlinear high-dimensional stochastic (partial) differential equations efficiently. In this regard, the Monte Carlo simulation (MCS) has been perhaps the most versatile tool. Nevertheless, there are cases, especially for large-scale systems, where the MCS can be computationally prohibitive. Thus, there is a need for developing efficient approximate analytical and/or numerical approaches. In this thesis, techniques are developed for addressing selected aspects of challenges a, b), and c). First, a general Lp norm (0 < p ≤ 1) minimization approach is proposed for estimating stochastic process power spectra subject to realizations with incomplete/missing data. Specifically, relying on the assumption that the recorded incomplete data exhibit a significant degree of sparsity in a given domain, employing appropriate Fourier and wavelet bases, and focusing on the L1 and L1/2 norms, it is shown that the approach can satisfactorily estimate the spectral content of the underlying process. Finally, the effect of the chosen norm on the power spectrum estimation error is investigated, and it is shown that theL1/2 norm provides almost always a sparser solution than the L1 norm. Numerical examples consider several stationary, non-stationary, and multi-dimensional processes for demonstrating the accuracy and robustness of the approach, even in cases of up to 80% missing data. Second, the challenge of quantifying the uncertainty in stochastic process spectral estimates based on realizations with missing data is addressed. Specifically, relying on relatively relaxed assumptions for the missing data and on a Kriging modeling scheme, utilizing fundamental concepts
A dramatic course after the resection of an SMARCA4‐deficient undifferentiated tumour

Thoracic SMARCA4‐deficient undifferentiated tumours are ordinarily found as a huge mass with systemic metastasis, and the prognosis is poor. The potential of immunotherapy for these unresectable tumours has been reported. An asymptomatic 68‐year‐old man with a smoking history had a left lung mass without distant metastasis and underwent complete resection. Two months after surgery, with no adjuvant therapy, he developed multiple distant metastases with aphasia and died 4 months after surgery. Adjuvant treatment may be necessary with immune checkpoint inhibitors, with a closer follow‐up to detect recurrence without symptoms.
Safe Static Type Checking With Systems of Mutually Recursive Classes and Inheritance

Virtual classes, which were rst used in Beta MMP89], have recently been suggested by Thorup Tho97] as an extension to Java to support genericity. While virtual classes are a convenient mechanism to support simultaneous modiication of mutually recursive classes, they rely on dynamic type checking for type safety. We suggest a variant which has similar expressiveness as virtual classes, but supports safe static type checking. To ease the comparison with earlier work we express our construct as an addition to Java, though similar constructs may be added to a variety of object-oriented languages. For example, this proposal can also be seen as a statically type-safe alternative to Eiiel's \anchored types".
Temperature Effect on Thermal Imaging and Deep Learning Detection Models

Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.
Reinforcing electrochemical performance of Ni‐rich NCM cathode co‐modified by mg‐doping and Li3PO4‐coating

The structural and interfacial degradation of Ni‐rich cathode materials during the cycling continuously delays its commercialization of lithium‐ion batteries (LIBs). To overcome these drawbacks, we propose a MgHPO4‐modified LiNi0.8Co0. 1Mn0.1O2 (M‐NCM) cathode material. From electrochemical analysis, M‐NCM takes the combining advantages of Mg doping and Li3PO4 coating. Interestingly, M‐NCM shows a better discharge capacity of 203.5 mAh g−1, delivering outstanding initial coulombic efficiency of 86.1% compared to the pristine LiNi0.8Co0. 1Mn0.1O2. Additionally, M‐NCM displays advanced cycle performance with a superior capacity retention of 81.1% after 100 cycles. Furthermore, M‐NCM effectively suppresses interfacial undesirable reactions at electrode/electrolyte interface during cycling. Consequentially, this study finds that MgHPO4 surface modification can bring enhanced electrochemical performance of Ni‐rich‐layered cathode materials for high‐energy and long‐term LIBs.
Artificial Intelligence System for Emotion Recognition and Text Analytics

Harnessing the power of emotional intelligence by analyzing a person’s behavioral and linguistic skills can help humans improve their approach to social interactions. In this paper, we propose an artificial intelligence-based stand-alone system that will allow us to classify and analyze facial expressions in real-time and perform sentiment analysis by examining the body of the text (extracted from audio) to understand the opinion expressed by it. This helps us provide a deeper understanding of how humans really feel at a given time. The proposed system uses a deep neural network (DNN) for classifying eight basic emotions based on features extracted from facial expressions and uses pretrained sentiment analysis tools to quantify text (extracted from audio) based on polarity. The system is implemented by extracting the audio and visual cues from real-time scenarios and using these extracted cues to perform facial expression recognition and text sentiment analysis. The integrated system extracts video and audio simultaneously with a frame rate of 4-5 fps. The facial emotion detection system successfully detects facial expressions of faces detected in real-time video with an accuracy of about 86.75%. The speech extracted is converted to text, cleaned, and processed to determine if the attitude of the speaker in a given situation is positive, negative, or neutral.
Artificial Intelligence-Based Protection for Smart Grids

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLABTM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.
On computing viscoelastic Love numbers for general planetary models: the ALMA3 code

The computation of the Love numbers for a spherically symmetric self-gravitating viscoelastic Earth is a classical problem in global geodynamics. Here we revisit the problem of the numerical evaluation of loading and tidal Love numbers in the static limit for an incompressible planetary body, adopting a Laplace inversion scheme based upon the Post-Widder formula as an alternative to the traditional viscoelastic normal modes method. We also consider, whithin the same framework, complex-valued, frequency-dependent Love numbers that describe the response to a periodic forcing, which are paramount in the study of the tidal deformation of planets. Furthermore, we numerically obtain the time-derivatives of Love numbers, suitable for modeling geodetic signals in response to surface loads variations. A number of examples are shown, in which time and frequency-dependent Love numbers are evaluated for the Earth and planets adopting realistic rheological profiles. The numerical solution scheme is implemented in ALMA3 (the plAnetary Love nuMbers cAlculator, version 3), an upgraded open-source Fortran 90 program that computes the Love numbers for radially layered planetary bodies with a wide range of rheologies, including transient laws like Andrade or Burgers.
Temperature change and mitigation potential of Indian cement industry

Abstract Cement is one of the highest energy-consuming and emission generating industries around the world. To reduce greenhouse emissions, several mitigation measures have been proposed, and their effectiveness is estimated. However, estimates of the global temperature change potential of the cement industry have seldom been performed. Hence, in this study, we propose a new framework that estimates CO2 emissions and other seven pollutants to estimate temperature change potential from the cement industry. The underlying framework uses system dynamics, where the effectiveness of four mitigation measures, i.e., a shift in demand, newer methodologies to produce clinker, use of energy efficiency improvements, and implementation of renewable energy, are explored. The results indicate that renewable sources of energy show highest mitigation potential. The cement industry has contributed to an increase in 2 mK temperature since 1990, which is likely to grow up to 14.8 mK by 2050 if no mitigation measures are applied. Energy efficiency improvements by extensions of perform achieve and trade scheme can reduce 0.33 mK from the Indian cement industry. This paper provides a unique opportunity for estimating temperature influence of the cement industry, which can be further implemented for other countries.
Measuring Creativity in the Fermi Problem, a Type of Mathematical Modelling, Applying Information Theory

Many methods of measuring creativity have been studied - mainly in psychology. In recent years, there have been attempts to incorporate such creativity into mathematical modelling, a topic handled in mathematics education. Accordingly, some studies have been trying to assess creativity in it. However, there have been no clear criteria or formulas that can be used for any problem, since a rubric has been created for each problem and evaluated individually. In the present study, to measure creativity in the Fermi problem, a type of mathematical modelling, a formula that applies information theory used in information science is proposed and examined using Structural Equation Modeling (SEM). A survey of Japanese junior high school students (n = 364) was conducted and analyzed, and the results show that the model using the formula proposed in the present study is a good fit. In addition, a moderate positive correlation (r = .41, p < .01 ) is found between creativity in the Fermi problem and creativity in psychology measured by the Test for Creative Thinking-Drawing Production (TCT-DP).
Use of Prazosin for Post-traumatic Stress Disorder in Adolescents is Associated with Longer Inpatient Psychiatric Hospitalization

Objectives: Prazosin has been shown to be effective in reducing PTSD-related nightmares in adolescents in outpatient studies with modest sample sizes. There are no data on the effects of prazosin on length of inpatient psychiatric hospitalization. Given prazosin’s adverse effects, we hypothesize that prazosin initiation during inpatient psychiatric hospitalization is associated with prolonged length of stay. Patients and Methods: Retrospective chart review identified 255 psychiatric admissions of adolescents with PTSD from 2012-2017 at an academic medical center. The treatment group comprised 101 admissions during which prazosin was initiated. Given a lack of rating scales, number of concurrent diagnoses and discharge medications were used to approximate overall illness severity. Results: There were no statistical differences between the treatment and non-treatment (control) groups with respect to age, gender, number of diagnoses or discharge medications. The mean dose of prazosin was 1.5±0.7 mg nightly with mean duration of 7.6±6.1 days. Length of hospital stay was significantly longer for the prazosin group compared with controls (11.1±6.6 vs 9.5±5.8 days, p=0.04). There was no difference in rates of orthostatic hypotension between groups. Adverse reactions were observed in 41% of individuals taking prazosin with a discontinuation rate of 14% due to intolerable side effects, lack of efficacy or request to reduce polypharmacy. Conclusions: Treatment with prazosin during inpatient psychiatric hospitalization may be associated with increased length of stay for adolescents with PTSD. Further studies are needed to validate these results.
Antimicrobial Photodynamic Therapy for the Treatment of Peri-implantitis: A Literature Review

Aim: The aim of this article is to describe the existing scientific literature on photodynamic therapy (PDT) effectiveness for peri-implantitis treatment. Materials and Methods: A date-restricted electronic search strategy was performed in PubMed and Scopus databases. The following words were used: “peri-implant photodynamic therapy,” “photodynamic therapy in implantology,” “effectives of photodynamic therapy in peri-implantitis,” “Antimicrobial Photodynamic Therapy,” “Photodynamic Therapy for the treatment of peri-implantitis,” and “Mechanical debridement and photodynamic therapy in implantology. ”Results: Of the 15 articles, 13 articles were selected, of which 11 were prospective and experimental studies and 2 were longitudinal. The PDT effectiveness of peri-implantitis inflammation treatment was the most reported and investigated. Conclusion: There is scientific evidence that may support the use of PDT for peri-implantitis treatments. However, even more studies would still be needed to have solid evidence.
Public Health Midwives’ Perceptions, Challenges and Attitudes Towards Health Education on Vulvovaginal Discharge

Background and Objective: Public Health Midwife (PHM) is the grass root level health care provider in the Sri Lankan health care system. This study was carried out to assess perceptions, challenges and attitudes of PHMs’ in educating women on vulvovaginal discharge (VVD) and their current health education activities on hygienic practices and behaviour changes among females aged 18 to 49 years. Methods: This cross-sectional study was conducted among all volunteering PHMs who work in the Colombo district, Sri Lanka. A self-developed, content validated, pre-tested, self-administered questionnaire was used to collect data. Descriptive statistics were used to analyse data using SPSS software version 21. Results: A total of 308 PHMs participated and the mean age of the participants was 40 years (SD = 10). Thirty-six percent of the participants had work experience of more than 11 years. Majority of PHMs have performed health education activities during home visits (72.7%) and at clinics (66.2%). Most (97%) agreed that educating females on VVD is one of their primary responsibilities. Majority (70.7%) disagreed that “for most patients, health education does little to promote health-seeking behaviour” indicating positive attitudes towards health education. Nearly 17% of the participants rated their competency in providing health education on VVD as poor. Lack of available time (84.7%), teaching aids (75%) and women’s anxiety and shyness in asking questions or revealing health problems in the community (73.4%) were the major challenges faced during their health education on gynaecological health conditions. Conclusions: The majority of the PHMs had positive attitudes towards health education on VVD. It is recommended that learning opportunities should be created in the form of training programmes. This will help in updating knowledge, skills and attitudes to meet the growing demand for quality health education and referral activities at the community level.