Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Approaches in Computational Drug Delivery System

Systematizing a state-of-the-art drug delivery (DD) system which can deliver Active Pharmaceutical Ingredient (API) in safe and efficacious way is a premium process which requires years of development. Computational Drug Delivery is a unique way which utilizes computer aided simulations of models based on the Computational Fluid Dynamics (CFD), Volume of Fluid (VOF) technique and Molecular Dynamics (MD) combined with medical imaging techniques such as Computerized Axial Tomography (CAT) Scan, Magnetic Resonance Imaging (MRI) and Ultrasound to simulate in vivo conditions for better understanding of the drug delivery systems. This review article aims at elaborating the distinct approaches of Computational Drug Delivery in nanoparticulate, Microsphere Drug Delivery, liposomal Drug Delivery, brain specific, liver specific, tumour specific targeting along with optimization in traditional methods. Computational drug delivery approaches in the pharmaceutical sciences have the main goal of rational drug discovery and development.
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets

Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training. We show on three standard datasets that when these two techniques are combined, they give a significant improvement in performance, especially when the batch size and the number of generated negative examples are low relative to the size of the dataset. We then apply our techniques to a dataset containing 2 million entities and demonstrate that our model outperforms the baseline by 2.8% absolute on hits@1.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JURUSAN DENGAN METODE MULTI FACTORY EVALUATION PROCESS (MFEP) (STUDI KASUS : SMK CIBENING)

Smk Cibening is one of the schools in Cibingbin village, Cibingbin District, Kuningan Regency. The process of this course is intended so that the students can complete the school according to their abilities before continuing to a higher level, namely college. The current process of majors has disadvantages including the unavailability of special application to support courtesy, in helping counseling teachers to determine the right direction, so that the process takes a long time and also can occur a lot of consideration. to help the teachers in determining the majors of students at SMK Cibening. This study uses the Multifactor Evaluation Process (MFEP) method. The criterias used are report cards, national examination results (SKHUN) and majors. The final results of this study found that the decision support system with the Multifactor Evaluation Process (MFEP) method is able to overcome the problems in the process of determining the majors at SMK Cibening.Keywords: decision support system, WATERFALLl, with Multifactor Evaluation Process (MFEP) Method
Recursive Residual Convolutional Neural Network- Based In-Loop Filtering for Intra Frames

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original noncompressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.
Skills in Clinical Nursing

The first Australian edition of Skills in Clinical Nursing is a definitive resource that has been designed as a practical and easy-to-navigate reference for both the classroom and clinical practice settings. This text includes 95 of the most important skills performed by nursing students and graduates, organised from simple to complex and written to reflect current evidence-based practice guidelines.
Top-down knowledge hiding and innovative work behavior (IWB): a three-way moderated-mediation analysis of self-efficacy and local/foreign status

Purpose This paper aims to examine the consequences for innovative work behavior (IWB) of top-down knowledge hiding – that is, supervisors’ knowledge hiding from supervisees (SKHS). Drawing on social learning theory, the authors test the three-way moderated-mediation model in which the direct effect of SKHS on IWB is first mediated by self-efficacy and then further moderated by supervisor and supervisee nationality (locals versus foreigners). Design/methodology/approach The authors collected multi-sourced data from 446 matched supervisor-supervisee pairs working in a diverse range of organizations operating in the Kingdom of Saudi Arabia. After initial data screening, confirmatory factor analysis was conducted to test for the factorial validity of the used measures with AMOS. The hypothesized relationships were tested in regression analysis with SPSS. Findings Results showed that SKHS had both direct and mediation effects, via the self-efficacy mediator, on supervisee IWB. The mediation effect was further moderated by supervisor and supervisee nationality (local versus foreigners), which highlighted that the effect was stronger for supervisor–supervisee pairs that were local-local or foreigner-foreigner than for pairs that were local-foreigner or foreigner-local. Originality/value This study contributes to both knowledge hiding and IWB literature and discusses the useful theoretical and practical implications of the findings.
Representational similarity analyses in simultaneous EEG-fMRI measurements reveal the spatio-temporal trajectories of reconstructed episodic memories

In this project, we use multivariate analysis techniques on simultaneously acquired EEG and fMRI data to investigate the spatio-temporal dynamics of memory retrieval. We specifically explore how the neural representations of a retrieved object differ from the representations of the originally perceived object. Participants studied novel associations between objects and verbs in an encoding phase, and subsequently recalled the objects upon presentation of the corresponding verb cue. Multivariate pattern classifiers were first used to decode perceptual and conceptual processing from EEG and fMRI brain activation patterns separately. At the hemodynamic level, we found that conceptual features were generally dominant during memory retrieval, and represented at later processing stages than perceptual features. At the electrophysiological level, the conceptual dominance was reflected in an earlier representation of conceptual than perceptual information during recall, showing a reversed order relative to initial encoding (perception). Finally, representational similarity analyses allowed us to map the EEG time courses onto spatial fMRI patterns, demonstrating again that it is primarily the later stages of visual processing that are recapitulated during memory recall. Together, the results shed light onto the nature of mental representations during the reconstruction of visual objects from memory.
Visualizing the Stability of 2D Point Sets from Dimensionality Reduction Techniques

We use k‐order Voronoi diagrams to assess the stability of k‐neighbourhoods in ensembles of 2D point sets, and apply it to analyse the robustness of a dimensionality reduction technique to variations in its input configurations. To measure the stability of k‐neighbourhoods over the ensemble, we use cells in the k‐order Voronoi diagrams, and consider the smallest coverings of corresponding points in all point sets to identify coherent point subsets with similar neighbourhood relations. We further introduce a pairwise similarity measure for point sets, which is used to select a subset of representative ensemble members via the PageRank algorithm as an indicator of an individual member's value. The stability information is embedded into the k‐order Voronoi diagrams of the representative ensemble members to emphasize coherent point subsets and simultaneously indicate how stable they lie together in all point sets. We use the proposed technique for visualizing the robustness of t‐distributed stochastic neighbour embedding and multi‐dimensional scaling applied to high‐dimensional data in neural network layers and multi‐parameter cloud simulations.
Shared visual illusions between humans and artificial neural networks

Any information processing system should allocate resources where it matters: it should process frequent variable values with higher accuracy than less frequent ones. While this strategy minimizes average error, it also introduces an estimation bias. For example, human subjects perceive local visual orientation with a bias away from the orientations that occur most frequently in the natural world. Here, using an information theoretic measure, we show that pretrained neural networks, like humans, have internal representations that overrepresent frequent variable values at the expense of certainty for less common values. Furthermore, we demonstrate that optimized readouts of local visual orientation from these networks’ internal representations show similar orientation biases and geometric illusions as human subjects. This surprising similarity illustrates that when performing the same perceptual task, similar characteristic illusions and biases emerge for any optimal information processing system that is resource limited.
Automatic Ship Detection on Inland Waters: Problems and a Preliminary Solution

Marine ship traffic is usually monitored using a combination of Automatic Identification System (AIS) and radar networks. Video monitoring is often used to control vessel traffic on rivers, in ports, and other restricted areas. Despite the many means used, the task of automatic detection and identification of vessels on inland waters is not trivial. In this paper, the problem of automatic ship detection using the video surveillance system is analyzed and discussed primarily in the context of systems' performance. In the proposed solution, we assume that detection uses only video streams from the existing monitoring system without any additional hardware or special configuration or placement of cameras. In addition, the detection must work in real time and the system must detect all moving vessels, including small boats and kayaks. The main contributions of this work consist in presenting the results from several performance tests using different background subtraction algorithms as well as discussing problems that make moving vessels difficult to
Use of Logistic Regression for Understanding and Prediction of Customer Churn in Telecommunications

Customer churn, loss of customers due to switch to another service provider or non-renewal of commitment, is very common in highly competitive and saturated markets such as telecommunications. Predictive models need to be implemented to identify customers who are at risk of churning and also to discover the key drivers of churn. The aim of this paper is to use demographic and service usage variables to estimate logistic regression model to predict customer churn in European Telecommunications provider and to find the factors influencing customer churn. An interesting findings came out of the estimated model – younger customers who are shorter time with company, who use mobile data and sms more than traditional calls, having occasional problem with paying bills, with students account and ending contract in the near future are typical representatives of customers who tend to leave the company. An interaction terms added as explanatory variables showed that effect of usage of data and voice vary depending on the year of birth. The quality of the logistic regression model was assessed by Hosmer-Lemeshow test and pseudo R squared measures. An independent testing data set was further used to evaluate the predictive ability of the model by computation of performance metrics such as the area under the ROC curve (AUC), sensitivity and precision. The resulting model was able to catch 94.8% of customers who in fact left the company. Quality of the model was confirmed also by high value of AUC metric equal to 0.9759. Logistic regression represents a very useful tool in prediction of customer churn not only thanks to its interpretability, but also for its predictive power.
Columbia-Presbyterian Medical Center Integrated Academic Information Management System (IAIMS) Outpatient Clinical Information System Implemented in a Faculty General Medicine Practice.

Abstract We describe a clinical information system for hospital-based ambulatory care implemented in the context of the institution's IAIMS Phase III effort. Key features of this application are physician data entry to maintain summary clinical profiles that include medication lists, problem lists, and preventive care, and integration with other components of the Clinical Information System at the levels of the database, the user interface, and data sharing. A goal of this application is to provide coded data as a substrate for computer-based decision support.
The Lec Rat: A New Model for Hepatitis and Liver Cancer

This work on liver cancer, one of the most common and increasing malignancies, which often occurs in association with hepatitis, considers the possible aetiological roles of hepatitis in the development of liver cancer by studying the characteristics of an animal model (the LEC rat). The LEC rat is a new mutant strain which suffers from hepatitis and liver cancer in extremely high incidence. The characteristics of hepatitis, preneoplastic stages and liver cancer of the LEC rat are all discussed from the viewpoints of biochemistry, immunology, genetics, oncology and pathology.
Lifetime Enhancement of a Circulated Cooling Perovskite Quantum Dots Colloidal Solution System for Laser Illuminations

Perovskite quantum dots (QDs) attracted a lot of attention recently due to their high light conversion efficiency, and high color purity. However, perovskite materials are sensitive to heat. High temperature will cause serious quenching problem of the perovskite QDs. In order to solve the QDs thermal quenching problem without destroying their optical characters. A circulating quantum dots colloidal solution converter (CQD-converter) was developed. A closed loop pump tube coated with Teflon was utilized to prevent perovskite QDs from the external environment. A peristaltic pump was utilized to drive the QDs colloidal solution through a Peltier cooler for dissipating the heat of the QDs colloidal solution. The lifetime of the perovskite QDs can be significantly improved under a 450nm laser excitation. The effects of temperature of the Peltier cooler and flow velocity on the optical properties of QDs were also investigated. Compared to the static quantum dots colloidal solution (SQD-converter), which quenched after 7 min, the optical properties were maintained after 600 min for the CQD-converter.
An Examination of Life Stressors Experienced by Families of Children with Asthma In Low-Income Communities of Color.

Objectives: Disparities in childhood asthma are compounded by residence in urban underserved areas, where many of the stressors that contribute to asthma exacerbation are present. Community health workers (CHWs) have been shown to be effective in assisting caregivers of children with asthma in low-income communities of color; yet, studies have not fully investigated the experiences of CHWs to explore life stressors, especially those more tangential to asthma, and how they might impede asthma management and care. Open-ended documentation made by CHWs during a household environmental intervention study provided the basis for exploring life stressors faced by caregivers of children with asthma in Detroit, Michigan, and strategies CHWs used to reduce or mitigate the effect of those stressors. Methods: Data collected by CHWs as part of an intervention study for childhood asthma was analyzed for this study. Over a two-year period, CHWs conducted repeat home visits to 115 intervention group caregivers of children with asthma. Following each contact, CHWs documented through handwritten notes the content of their discussions, provision of education and supplies, non-asthma related activities (e.g., food referrals), subsequent actions to be taken, and general observations made. Using qualitative data analysis methods, asthma-related and other life stressors experienced by caregivers were identified, as were strategies implemented to assist in addressing these stressors. Results: CHWs documented important life stressors faced by families of children with asthma, as well as strategies utilized by CHWs to provide assistance. Among the life
Adaptive Power Allocation for Distortion Minimization in Generalized Polar Optical Wireless Communications

In this paper, we investigate adaptive power allocation for generalized polar optical wireless communications (OWC), where a general complex bipolar signal is converted into magnitude and phase signals for intensity modulation. Mean square errors (MSE) between the input complex signals and the re-constructed complex signals are derived to characterize signal distortion. Optimal power scaling factors and power allocation are investigated to minimize the distortion. Under a sole average intensity constraint, closed-form optimal power scaling factors are derived and found to be input-dependent. Specifically, they are determined by the first and second moments of the magnitude signals, the first moment of the phase signals as well as the channel state. Under both average and peak intensity constraints, the expression of MSE regarding the power scaling factors is derived but it is too complicated to find the optimal power allocation. Thus, we propose to use a small-scale numerical search for practical power allocation. As an example, we adopt the proposed power allocation to polar optical orthogonal frequency division multiplexing (P-OFDM) systems and analyze its achievable bit error rate (BER). Numerical simulations are presented to validate the analysis. It is shown that the proposed power allocation greatly improves the performance in terms of MSE and BER and the proposed power allocation-enhanced P-OFDM (EP-OFDM) outperforms existing optical OFDM schemes over various channels in the high signal-to-noise ratio (SNR) regime, especially in the systems with a peak intensity constraint.
Temporal and spatial evolution of cerebral injury in the piglet asphyxia model : a comparative study of serial magnetic resonance biomarkers and histopathology

Background: After hypoxia-ischaemia and successful resuscitation, cerebral energy metabolism transiently recovers to the normal level (latent phase); after a variable period of time this phase is followed by secondary energy failure (SEF) in those subjects with an adverse outcome. A better understanding of the regional evolution of SEF may enhance the application of future neuroprotective strategies. Aims: The aim of this thesis was to determine associations between the insult severity, regional SEF evolution, and subsequent histo-pathological brain injury using magnetic resonance biomarkers. Methods: An established piglet model of neonatal encephalopathy was used. 1. Twenty-nine piglets were studied either normothermic or hypothermic (35°C or 33°C during 2-26 hours after hypoxia-ischaemia). 31-phosphorus magnetic resonance spectroscopy (31P MRS) was serially acquired; the brain was assessed histo-pathologically after 48 hours. 2. Global 31P MRS, and maps of apparent diffusion coefficient (ADC) and transverse relaxation time (T2) were serially obtained in 3 control and 18 asphyxiated piglets. Histo-pathological brain injury and MR biomarkers were compared at time periods of 16-48 hours after hypoxia-ischaemia. Results: 1. Severe acute insult, short latent phase, severe SEF and profound histo-pathological brain injury were associated between each other. 2. Transient recovery in phosphocreatine (PCr) higher than its baseline was indicative of absent subsequent evolution of SEF, whereas sub-baseline PCr recovery was suggestive of severe SEF. 3. Global 31P MRS biomarkers and regional ADC obtained just prior to termination and up to 18-24 hours before termination predicted histo-pathological brain injury; the predictive value was optimal for global PCr/ inorganic phosphate (Pi), followed by global PCr/exchangeable high-energy phosphate pool (EPP), Pi/EPP and regional ADC. Conclusions: Although 31P MRS was more accurate, regional ADC predicted subsequent brain injury up to 18 hours in advance of termination. Regional ADC can be used as a sensitive early marker for subsequent tissue injury when 31P MRS is not available.
An Efficient Data Reduction Technique using Change Coding for Wireless Visual Sensor Network

: Wireless Visual Sensor Network is one of the most important applications of Wireless Sensor Network which provide a sensor based intelligent distributed system. Visual Sensor Networks has less complexity, identical performance and better quality of service. Visual Sensor Network contains a large number of camera nodes for providing a visual view of an object. The majority of industries use a network of cameras in order to obtain real-time data for monitoring and control. These networks are wired based in which data is sent over a wired link and power is available from wall power supplies. By using this system, the Captured Image from Camera and dataset is created . this data set is stored in local device which is raspberry pi. Raspberry pi has 1 GB RAM and 1.4 GHz frequency as well as it has unique features than other embedded system devices. This captured Dataset needs to a reduction in the Images for uploading to the server. Before this some morphological image processing techniques are applied for getting good quality of an image. Then for reduction of those images, change coding technique is applied. In change coding XOR operation of two adjacent frames is takes place. Finally, an image will be uploading to the Server.
Architecting in the Cloud - How Public Cloud Environments are Helping Software Architects

: As the new patterns in different zones are risen step by step, the new engineering for different applications is developing. There is an interest from clients for intuitive, rich and dynamic experience on different stages. These requests are fulfilled by the applications having high accessibility, versatility and simple to-execute on cloud stage. A large portion of the associations need to refresh their applications every now and again. In this paper, we discuss how these new technologies and the architecture of applications in the public cloud helps the software architects, which can support to achieve above requirements. The applications with this new architecture have multiple services which can deploy independently. These services focus on a minor part of the applications which provide scalability and agility to the applications