Miracle-dz/UniFAR-FTUs huggingface dataset:
Biosynthesis of CuO NPs and its anticancer activity on human colon cancer cell lines (HT-29)

The environmentally friendly syntheses of the nanoparticles through the green way from the extracts of the plants has proven itself in the field of modern sciences, with improved drug efficiency and less toxicity. The study groups in the have bio-synthesized cost-effective and stable copper oxide nano-particles (CuONPs) from plant leaves (i.e. the Turmeric). A variety of the analytical approaches, like the UV-Visible Spectroscopy (UV-Vis), Fourier-Transformation Infra-red Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD), have been utilized for the confirmation of the syntheses of the crystalline CuONPs from the extract of the turmeric leaves (SEM), Transmission Electron Microscopy (TEM) and Selected Area Electron Diffraction (SAED) pattern. Synthesized CuONPs have been tested as well for the anti-cancer activity with the use of the MTT (3- (4,5dimethyl-2tiazolyl)-2,5-diphenyl-2tetrazolium bromide) assay on the cell lines of the human colon cancer (HT29). The results have shown that the synthesized CuONPs had a high anti-cancer cytotoxicity on the cell lines of the human colon cancer (HT-29) with an IC-50 value of 40 g mL-1, which was briefly addressed in the present paper.
The Effect of Multidimensional Factors on Organizational Adoption of Enterprise Architecture: The Moderating Role of Organization Type

This study explains the effects of organization type on the relationship between multidimensional factors and enterprise architecture (EA) adoption from the perspective of the organization. This study employs the Technology-Organization-Environment (TOE) framework to develop and test the model to explain EA adoption intention. Despite the importance of EA adoption in the public sector, very few empirical studies examined the key factors of EA adoption by public sector organizations and there remains a paucity of evidence on the moderating effect of organization type in the adoption of EA. This study underpinned the technology adoption model of TOE framework. A questionnaire was operationalized to measure the perceptions of Malaysian public sector organizations towards factors influencing EA adoption intention. Data collected through a survey covering 255 validated responses were analyzed using SMART-PLS tool. The results of the survey suggest that good governance, expected benefits and moderating effect have significant effects on EA adoption intention. In addition, organization type in the Malaysian public sector plays different roles on the relationships of good governance and expected benefit towards organizations’ EA adoption. Interestingly, adequate ICT infrastructure, external support and top management support were found as insignificant factors in this study. The study includes implications for academic researchers, decision-makers and practitioners to understand the influence of multidimensional factors and effectively conclude on the adoption process. The decision-makers can use the EA adoption model as a baseline for boosting EA adoption among MPS organizations.
A Multi-Tier Trust-Based Security Model (MTTS) for Resource-Constrained IoT Devices to Detect Blackhole Devices and ensure Authentication

In the Internet of Things based military environment, to achieve an assigned task without compromising security concern is a challenging task. Resource-constrained devices, Self-organized, disseminated nature of sensor nodes, unstable topology environment, open wireless medium, and shared network environment make IoT differ from other networks. All together leads to security threats to the IoT environment. The success of any IoT based application depends on effective communication among the devices that can be achieved by routing protocols. Typically, they designed to cope with fruitful communication but in effect, they might be targeted and affected by mischievous devices. Blackhole devices are such devices that aim to disturb the normal routing operations. But the identification of trusted devices that means authentication needs to be provided for successful task completion. At present, trust management plays an important part in prediction construction and appropriate for resource-strained devices. In this article, a multi-dimensional trust-based security model is proposed to guarantee authentication by avoiding blockhole devices based on direct, indirect, sense-making, social, and stereotypes. In addition, the q-learning mechanism is used to enhance the proposed method. The simulation study represents the applicability of the projected model.
A Trust-Based Security Model to Detect Misbehaving Nodes in Internet of Things (IoT) Environment using Logistic Regression

Ensuring authentication in the Internet of Things (IoT) environment is a crucial task because of its unique characteristics which include sensing, intelligence, large scale, selfconfiguring, connectivity, heterogeneity, open and dynamic environment. Besides, every object in the IoT environment should trust other devices with no recommendation or prior knowledge for any network operations. Hence, those characteristics and blindness in communication make security violations in the form of various attacks. Therefore, a trustbased solution is necessary for ensuring security in the IoT environment. Trust is considered as a computational measure represented through a relationship between trustor and trustee, explained in a particular context valued through trust metrics and evaluated by a trust mechanism. The proposed logistic regression-based trust model provides an efficient way to identify and isolate the misbehaving nodes in the RPL (Routing Protocol for Low Power Lossy Networks) based IoT network. It is one of the popularly used routing protocols in IoT, that builds a path especially for the constrained nodes in IoT environments. However, it is vulnerable to many attacks. The proposed model classifies and predicts the node’s behavior (trusted or malicious). This model uses the logistic regression model to predict the node’s behavior based on the integrated trust value which is computed from the direct trust, reputation score, and experience trust. It is primarily designed to address the black hole attack in the IoT environment. The mathematical analysis shows the possibility of the proposed work and the simulation results show the proposed model is better than the existing similar work.
Biorenewable Tough Blends of Polylactide and Acrylated Epoxidized Soybean Oil Compatibilized by a Polylactide Star Polymer

Polylactide (PLA), a commercially available thermoplastic derived from plant sugars, finds applications in consumer products, disposable packaging, and textiles, among others. The widespread application of this material is limited by its brittleness, as evidenced by low tensile elongation at break, impact strength, and fracture toughness. Herein, a multifunctional vegetable oil, acrylated epoxidized soybean oil (AESO), was investigated as a biodegradable, renewable additive to improve the toughness of PLA. AESO was found to be a highly reactive oil, providing a dispersed phase with tunable properties in which the acrylate groups underwent cross-linking at the elevated temperatures required for processing the blends. Additionally, the presence of hydroxyl groups on AESO provided two routes for compatibilization of PLA/AESO blends: (1) reactive compatibilization through the transesterification of AESO and PLA and (2) synthesis of a PLA star polymer with an AESO core. The morphological, thermal, and mechanic.
The 3 D Marching Lines Algorithm and itsApplication to Crest Lines

This paper presents a powerful and general purpose tool designed to extract characteristic lines from 3D images 1. The algorithm, called Marching Lines, is inspired from the Marching Cubes algorithm, which is used to extract iso-value surfaces from 3D images. The Marching Lines algorithm extracts with sub-pixel accuracy the 3D lines corresponding to the intersection of two iso-surfaces from two diierent 3D Images. We show how to implement this algorithm to ensure that the reconstructed 3D lines have good topological properties, mainly that they are continuous and closed. We also present a new method to compute the diierential characteristics of iso-surfaces, and show an application to the extraction of crest lines in 3D Images. We explain that a crest line can be locally deened as the intersection of two surfaces , one corresponding to an iso-value in the image, and the other one to a crest surface which we deene in this paper, and whose implicit equation can be directly computed from the voxel values of the 3D image. Finally, experimental results for 3D images of the skull are presented, where crest lines are extracted, and used to compute automatically the geometric transform between two 3D scanner images of the same subject, taken in two diierent positions. 1 This process is part of a patent taken out by the INRIA 1 L'algorithme du \Marching Lines" et son application a l'extraction des lignes de cr^ ete. Cet article pr esente un outil nouveau et puissant, conn cu pour extraire des lignes caract eristiques a partir d'images tri-dimmensionnelles 2. Cet algorithme, appel e Marching Lines, est inspir e de l'algorithme du Marching Cubes qui sert pour sa part a extraire des surfaces d'iso-valeurs d'images 3D. Le Marching Lines permet d'extraire, avec une pr ecision sup erieure au pixel, les lignes 3D correspondant a l'intersection de deux iso-surfaces, obtenues a partir de deux images 3D dii erentes. Nous montrons comment impl ementer l'algorithme de mani ere a garantir que les lignes 3D reconstruites ont de bonnes propri et es topologiques; principalement que ces derni eres sont continues et ferm ees. Nous pr esentons egalement dans le m^ eme article une faa con nouvelle de calculer les caract eristiques dii erentielles des iso-surfaces, directe-ment a partir de l'image 3D, et nous en montrons l'application a l'extraction des lignes de cr^ ete. Nous expliquons qu'une ligne de cr^ ete peut ^ etre d eenie lo-calement …
A computer-modelling study of CdCO3-CaCO3solid solutions

Abstract We have applied atomistic simulation techniques to model the continuous CdCO3-CaCO3 solid solution and to investigate the thermodynamic properties. All inequivalent substitutional configurations were considered for the single unit cell and the (2 × 1 × 1) supercell for the full range of Cd concentrations, as well as doping between 0and 16.7 mol. % Cd for the (2 × 2 × 1) supercell. Our calculations show that segregation of Cd2+ and Ca2+ is energetically favourable when compared with any other cation distribution. No cation ordering is expected at high temperature and the system behaves as an ideal solid solution above 200 K, due to the configurational entropy. The lattice parameters change with the Cd concentration, where they are predicted to follow a linear relationship, due only to cation substitution but not cation ordering.
Predicting Game Difficulty and Engagement Using AI Players

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. We have previously demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.
Neuroleptic Malignant Syndrome in Patients With HIV in the Emergency Department

s: Articles should not include an abstract. Please note that we will consider articles outside of the theme. The Residents’ Journal accepts manuscripts authored by medical students, resident physicians, and fellows; manuscripts authored by members of faculty cannot be accepted. To submit a manuscript, please visit http://mc.manuscriptcentral. com/appi-ajp, and select “Residents” in the manuscript type field. Upcoming Issue Themes
BERBERINE AND COPTISINE IN LIQUID AMMONIA

In liquid ammonia, quaternary protoberberine alkaloids berberine and coptisine afforded unstable adducts, 8-amino-8H-berberine and 8-amino-8H-coptisine, respectively. Their structures were supported by X-ray analysis and mass spectra.
Regulable switching from p- to n-type behavior of ordered nanoporous Pt-SnO2 thin films with enhanced room temperature toluene sensing performance

Gas sensing with ordered nanoporous materials is achieving much attention because of its promising capability of detecting toxic gases at room temperature. In this work, an ordered nanoporous Pt-SnO2 sensing film is fabricated in situ on a sensing device using a block polymer template and is applied as a chemiresistive toluene gas sensor. Post synthetic hydrothermal treatment was developed for the synthesis of the ordered Pt-SnO2 sensing films with homogeneous and controlled film thickness and controllable pore size. The structure, crystallinity and composition of the ordered nanoporous Pt-SnO2 thin films are characterized by HRTEM, FESEM, SAED, and STEM. Interestingly, the Pt-SnO2 nanoporous film, an n-type material, presents tunable sensing behaviour with switching from p- to n-type toluene sensing performance as a function of the platinum content and calcination temperature. The ordered nanoporous sensing film is capable of detecting low levels of toluene, as low as 10 ppm, at room temperature, and shows good stability. Furthermore, a transition diagram has been created on the basis of the toluene sensing response, which can be used to design the p–n transitions.
Effects of 6-Benzylaminopurine on soybean sprouts growth and determination of its residue

【Objective】The effects of different concentrations of 6-Benzylaminopurine(6-BA) treatments on soybean sprouts growth were evaluated and the safety was measured by detecting the residues of 6-BA in soybean sprouts. 【Method】Spraying and soaking methods were respectively used to cultivate soybean sprouts, and fresh weight, axis length, axis diameter, germination rate and yield were measured after cultivation. 6-BA residues were determined with reference to DB S22/003-2012 in bean sprouts and nutrient solution of 6-BA. 【Result】The results showed that the best yield was 6.96 for 1.0 mg/L group in spraying method and the best yield in soaking method was 7.15 for 5.0 mg/L group. No 6-BA residue was detected in each group of bean sprouts. 6-BA was in downtrend in 3.0 mg/L group nutrient solution and no 6-BA residue was detected since the third day. 【Conclusion】Suitable concentration of 6-BA has an positive effect on soybean sprouts growth. 6-BA could be degraded and eliminated in nurturing process of soybean sprouts, hence 6-BA is proved to be of high safety.
Quantitative Salmonella enterica serovar Enteritidis risk assessment from
consumption of hard-boiled eggs, half-boiled eggs and raw eggs among
Malaysians

Quantitative Salmonella enterica serovar Enteritidis risk assessment from consumption of hard-boiled eggs, half-boiled eggs and raw eggs among Malaysians Wong, J.X. , Kuan, C.H. , Saw, S.H. , Chen, S.N. , Tan, C.W. , Yeo, S.K. , Kuan, C.S. , Phuah, E.T. , Thung, T.Y. , Son, R. and New, C.Y. Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia Department of Agricultural and Food Science, Faculty of Science, Universiti Tunku Abdul Rahman (UTAR) Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia Department of Food Science with Nutrition, Faculty of Applied Sciences, UCSI University, Kuala Lumpur, Malaysia Department of Allied Health Sciences, Faculty of Science, Universiti Tunku Abdul Rahman, 31900 Kampar, Perak, Malaysia Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia School of Biosciences, Faculty of Health and Medical Sciences, Taylor’s University Lakeside Campus, 47500 Subang Jaya, Selangor, Malaysia Neogenix Laboratoire Sdn Bhd, C707, Level 7, Block C, Kelana Square, Kelana Jaya, 47301, Petaling Jaya, Selangor, Malaysia Food Safety and Food Integrity, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Go Plus Services Sdn Bhd, 97A, Jalan BP 6/3, Bandar Bukit Puchong, 47120 Puchong, Selangor, Malaysia
Hydrogen Solubility of Magnetron Co-Sputtered FCC and BCC PdCuAu Thin Films

Palladium–copper–gold (PdCuAu) ternary alloy membranes are promising alternatives to pure palladium for the purpose of hydrogen separation processes. In the current work, PdCuAu ternary alloys were prepared by magnetron cosputtering deposition. All as-deposited samples showed face-centered cubic (fcc) structure in the composition range of study: 30 ≤ [Pd] ≤ 100, 0 ≤ [Cu] ≤ 66, and 0 ≤ [Au] ≤ 6 (in at%). The body-centered cubic (bcc) phase was prepared from the fcc samples by annealing for 4 h at 400 °C under Ar and was identified in alloys with Pd content between 40 and 46 at%. The hydrogen solubility was evaluated for both fcc and bcc phases using an electrochemical method and an electrochemical in situ X-ray diffraction method, respectively. The alloys’ hydrogen solubility increases with the lattice parameter and palladium content, and decreases with crystallite size and phase transition to bcc. In the same range of composition, the hydrogen solubility of the fcc and bcc phases vary in the same way with.
Assessment of Cyanide Concentrations in Cassava Peels obtained at different levels of Processing for Resource Reuse

1Department of Environmental Health Sciences, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria 2Environmental Management Unit, College of Agriculture, Engineering and Science, Bowen University, Iwo, Osun State, Nigeria 3Department of Biochemistry, College of Natural and Applied Sciences, Chrisland University, Owode, Km 5 Ajebo Road, Abeokuta, Ogun State, Nigeria
Employee Work Productivity: In Terms of Leadership and Work Motivation

Leadership and work motivation were some aspects that can increase the work productivity of PT. Ramayana Lestari Sentosa. Human resources had a very important role in managing the retail business, where they should have skill knowledges in both soft skills and hard skills. Moreover, the success of a company must be supported by the employee productivity. Hence, the leader of a company should provide positive encouragements to their employees. Furthermore, a reliable leadership held the key point in the organizational development. The efforts to improve leadership and work motivation among the employees were already done effectively, but the work productivity was still not yet reach the target. The sampling technique used in this study was purposive sampling method. The selected respondents were the employees of PT Ramayana Lestari Sentosa with working period of more than 5 years. In this case, the researcher took a sample of 100 respondents. The hypothesis of this study were tested using the technique of Multiple Linear Analysis. The results of this study indicate that leadership had no direct effect on the employee productivity, while work motivation was able to significantly intercede between the influence of leadership and the employee productivity. The implication of this study can contribute to management in making policies to increase employee productivity, where leaders who have managerial competence and responsibility for completing tasks followed by employee achievement motivation will have an effect on the progress of the company. Keywords—leadership, work motivation, work productivity
Rem – Rapid Eye Mount. a Fast Slewing Robotized Infrared Telescope

The main goal of the REM project is the observation of prompt afterglow of Gamma–Ray Burst (GRB) events. Such observations at Near InfraRed (NIR) wavelengths are even very promising, since they allow to monitor high–z Ly-α absorbed bursts as well as events occurring in dusty star–forming regions. In addition to GRB science, a large amount of time (∼ 40%) will be available for different scientific targets: among these the study of variability of stellar objects open exciting new perspectives.
1 . 1 The Models of Private Information Retrieval Information-Theoretic

Private Information Retrieval (PIR) protocols allow a client to retrieve a data item from a database while hiding the identity of the item being retrieved. In information-theoretic k-server PIR protocols the database is replicated among k servers, and each server learns absolutely nothing about the item the client retrieves. In computational PIR protocols the database is stored on one server, and a computationallybounded server learns nothing about the item the client retrieves. The important challenge in this area is to design protocols in which the communication complexity of retrieving one out of n bits of data is sub-linear. This primer discusses the PIR problem, surveys the known results on PIR, describes the basic construction of information-theoretic and computational PIR protocols, and presents applications of PIR protocols. The primer is intended for readers that do not necessary have background in cryptography. In particular, we supply the background from cryptography, combinatorics, and number theory that is needed to understand the results.
AN EFFICACIOUS INTRUSION DETECTION SYSTEM FOR NETWORKS EXPLOITING RECURRENT NEURAL NETWORKS

-Intrusion detection in networks is an increasingly growing concern of information security because of its significance in modern IT infrastructure. A Network Intrusion Detection System (NIDS) aids system administrators in detecting gaps in security of networks thus preventing malignant intrusions. Anyway, several issues occur when generating an adaptable and systematic NIDS for the attacks which cannot be predicted. In this paper, a Recurrent Neural Network (RNN) depending on Intrusion Detection System (IDS) is introduced and analysed. The deep learning based RNN is utilized for classification as well as detects the count of neurons and various impacts of the rate of learning on accuracy. The NSLKDD dataset is utilized for the training of the proposed system and is tested for evaluating the performance in detecting intrusions. The comparisons of the obtained accuracy of the RNN are performed with existing classifiers and the experimental results showed promising outputs for real world application in intrusion detection systems.