Miracle-dz/UniFAR-FTUs huggingface dataset:
SECURITY IN SENSOR NETWORKS AND NECESSITY FOR SELF DESTRUCTION

The main objective of this paper is security in sensor networks. In general sensor networks are launched at high sensitive areas such as war fares where apart from the performance of the network, the security constraint is to be seriously considered. Though there are some security protocols like μ TESLA, SNEPdeveloped with concern to the sensor nodes that possess limited resources like memory, computation power, battery, and network bandwidth, these security protocols only provide authentication and confidentiality to the messages passed by the sensor nodes but the real toughest of the attacks that is hard to deal. Hence, node capturing effect where an enemy acquires full control over sensor nodes through direct physical contact and can obtain confidential information such as the network topology, encryption passwords and may also use the captured node to mislead the other nodes as well as the server node. Since we cannot practically demand an efficient access control to thousands of nodes distributed in a large territory and as it is very difficult to assure tamper-resistance requirements as the sensor nodes frequently need to be inexpensive to justify their use, there is more chance for the enemies to attack using this technique. In order to handle this type of issue, we introduce a self destruction mechanism to the sensor nodes where if a node is identified to be under this attack, self destructs itself and protects the valuable information from getting into the hands of enemies.
CONTEXT CENTERED RESPONSE FOR ENABLING PERVASIVE ENVIRONMENT USING DATA CENTRIC WSNS

Dense distributed heterogeneous wireless sensor nodes (WSN) intend to communicate and realize of wide pervasive environment using contexts involved in and around. Pervasiveness needs the exchanged data to be regularly available. Making instant available of priority based among data centric is miserable task in pervasive environment. To gather large volume of accurate data, we develop a mechanism for congestion free and absolutely available link for smart devices communication at low cost. Proposing rich context architecture to assist the communication of cooperative tiny nodes and processed data as information with computational node using autonomous coordination. Every region is assigned with gathered heterogeneous sensor nodes which controls and detects contexts/events occurrence for generating raw data. All these information are extended to the external world through widgets and applications, normally a smart device. Using user, physical and environmental contexts, an event occurrence is confirmed for further decision making for large scale exploration where continuous human access is not necessary.
Tumor-to-liver standard uptake ratio using fluorine-18 fluorodeoxyglucose positron emission tomography computed tomography effectively predict occult lymph node metastasis of non–small cell lung cancer patients

Objectives We aimed to investigate predictive factors of occult lymph node metastasis and to explore the diagnostic value of various standardized uptake value (SUV) parameters using fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET/CT) in predicting occult lymph node metastasis of clinical N0 non–small cell lung cancer patients. Methods We retrospectively analyzed PET/computed tomography parameters of tumor and clinical data of 124 clinical N0 non–small cell lung cancer patients who underwent both preoperative 18F-FDG PET/computed tomography and anatomical pulmonary resection with systematic lymph node dissections. The SUVmax, SUVmean, metabolic total volume, and total lesion glycolysis of the primary tumor was automatically measured on the PET/computed tomography workstation. Standardized uptake ratio (SUR) were derived from tumor standardized uptake value divided by blood SUVmean (B-SUR) or liver SUVmean (L-SUR), respectively. Results According to postoperative pathology, 19 (15%) were diagnosed as occult lymph node metastasis among 124 clinical N0 non–small cell lung cancer patients. On univariate analysis, carcinoembryonic antigen, cytokeratin 19 fragment, lobulation, and all PET parameters were associated with occult lymph node metastasis. The area under the receiver operating characteristic curve, sensitivity, and negative predictive value of L-SURmax were the highest among all PET parameters (0.778, 94.7%, and 98.4%, respectively). On multivariate analysis, carcinoembryonic antigen, cytokeratin 19 fragment, and L-SURmax were independent risk factors for predicting occult lymph node metastasis. Compared to L-SURmax alone and the combination of carcinoembryonic antigen and cytokeratin 19 fragment, the model consisting of three independent risk factors achieved a greater area under the receiver operating characteristic curve (0.901 vs. 0.778 vs. 0.780, P = 0.021 and 0.0141). Conclusions L-SURmax showed the most powerful predictive performance than the other PET parameters in predicting occult lymph node metastasis. The combination of three independent risk factors (carcinoembryonic antigen, cytokeratin 19 fragment, and L-SURmax) can effectively predict occult lymph node metastasis in clinical N0 non–small cell lung cancer patients.
Supplemental Material for “Anomalous protein kinetics on low-fouling surfaces”

Mohammadhasan Hedayati, Matt J. Kipper, 2, 3 and Diego Krapf 3, 4 Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA School of Advanced Materials Discovery, Colorado State University, Fort Collins, CO 80523, USA Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
Douleur en néphrologie

Peu de donnees existent sur la prevalence et l'intensite de la douleur en milieu nephrologique, en particulier dans les phases avancees de l'insuffisance renale. Cependant, de par les pathologies qui y sont suivies, les repercussions de l'atteinte renale ainsi que par les methodes de traitement, il est vraisemblable qu'un nombre non negligeable de patients souffre de douleurs aigues et/ou chroniques, avec eventuellement des repercussions au niveau psychologique et sur la qualite de la vie. Des techniques de prise en charge existent qui meritent d'etre optimisees dans ce contexte.
Anti-platelet aggregation activity of melaleuca bracteata var.revolution gold derived betulinic acid and its derivatives

VAR.REVOLUTION GOLD DERIVED BETULINIC ACID AND ITS DERIVATIVES BY OSUNSANMI FOLUSO OLUWAGBEMIGA (Msc UNILAG) 201200147 A DISSERTATION SUBMITTED IN FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF DOCTORATE OF SCIENCE IN THE DEPARTMENT OF BIOCHEMISTRY AND MICROBIOLOGY, FACULTY OF SCIENCE AND AGRICULTURE, UNIVERSITY OF ZULULAND, KWADLANGEZWA, SOUTH AFRICA SUPERVISOR: PROF A.R. OPOKU CO-SUPERVISOR: PROF F.O SHODE
Personalized Taste and Cuisine Preference Modeling via Images

With the exponential growth in the usage of social media to share live updates about life, taking pictures has become an unavoidable phenomenon. Individuals unknowingly create a unique knowledge base with these images. The food images, in particular, are of interest as they contain a plethora of information. From the image metadata and using computer vision tools, we can extract distinct insights for each user to build a personal profile. Using the underlying connection between cuisines and their inherent tastes, we attempt to develop such a profile for an individual based solely on the images of his food. Our study provides insights about an individual's inclination towards particular cuisines. Interpreting these insights can lead to the development of a more precise recommendation system. Such a system would avoid the generic approach in favor of a personalized recommendation system.
AUTONOMIC CLEVER SEARCH

An approach to enhance search engine with information about word senses available in WordNet is tried out in this paper. It is an autonomic search which manipulates it’s the idea of the user by getting the input and provides a best result. It is a wrapper for WordNet based Internet search engines. Most of the pharmacy world is in need of medicine search where in the images collaborate to find the words. This autonomic WordNet is the means to provide many solutions to pharmacy world. In the wrapper for search engines presented, WordNet information is used to specify a user’s request or to classify the results of a publicly available web search engine, like Google, Yahoo etc. It also deals with, categorizing things (here it deals with users) that exist or may exist in some domain.
Competency Test in Cricket Defensive Drive using Double Pendulum Dynamics

The level of competency of playing cricket defensive drive was measured by using double pendulum model. Three defensive drives by three right-handed batters were recorded in the bat and wrist mounted inertial sensors. The sensor recorded acceleration profiles were tried to match with those obtained by modeling the drives with double pendulum dynamics imposing the initial conditions at start of each drives. In the analysis, the movement of the bat was modeled as the lower pendulum and the wrist as the upper pendulum of the double pendulum model. Phase portrait derivative method was used to analyze each drives in the pendulum dynamics to quantify the competency of the action for the drives. The method showed that the better competency of two batters (error 2.5% and 7.4%) compared to third one (error 13.9%) can easily be identified observing the statistical features (i. e. standard deviation of the bat velocity, ratio of major to minor axis of the phase portrait curve, null hypothesis between sensor data and data from the double pendulum model) extracted from the sensor and double pendulum model. Competency test can be a useful tool for cricket coaching environment; furthermore, incorporation of mathematics in cricket batting might add a new dimension in batting research.
Follow the Sound of Children’s Heart: A Deep-Learning-Based Computer-Aided Pediatric CHDs Diagnosis System

Auscultation of heart sounds is a noninvasive and less costly way for congenital heart disease (CHD) diagnosis, especially for pediatric individuals. The deep-learning-based computer-aided heart sound analysis has been widely studied and developed in recent years. In this article, we develop a deep-learning-based computer-aided system for pediatric CHDs diagnosis using two novel lightweight convolution neural networks (CNNs). One key issue of most existing deep-learning-based systems is the scarcity of large-scale data sets for CNN learning. To this end, we collect heart sounds from newborns and children with physicians’ annotations to construct a pediatric heart sound data set that contains 528 high-quality recordings (nearly 4 h in total) from 137 subjects. With the constructed data set, deep CNN models can be easily trained as classifiers in computer-aided CHDs diagnosis systems. The experimental results demonstrate the superiority of our proposed methods in terms of diagnosis performance and parameter consumption in the application of Internet of Things.
Estimation of PWM-Induced Iron Loss in IPM Machines Incorporating the Impact of Flux Ripple Waveshape and Nonlinear Magnetic Characteristics

This article presents an improved estimation model to predict the additional iron loss due to pulsewidth modulation (PWM) switching in interior permanent magnet (IPM) machines. The input current including the predicted PWM-induced current ripple is fed to two-dimensional (2-D) finite-element analysis to calculate the PWM-induced flux ripple distribution in the analyzed machine. A special frequency decomposition method is proposed to separate the predicted flux ripple into 1x and 2x PWM frequency components. Combining these results with the modified dynamic Jiles–Atherton model, the PWM-induced iron loss is calculated incorporating the impact of the actual flux ripple waveshape and the lamination steel's nonlinear magnetic characteristics. Experiments have been conducted to evaluate the prediction accuracy and investigate the impact of several factors on PWM-induced core losses (i.e., both iron and magnet loss) in IPM machines. These results demonstrate that the model offers promising estimation accuracy over a wide range of machine and inverter operating conditions.
Resilence of Network Stateful Firewalls against Emerging DoS Attacks: A Case Study of the BlackNurse Attack

Traditional Distributed Denial of Service (DDoS) attacks usually flood target network servers with malicious traffic. This would generally require a set of attack hosts and large network traffic volume to be able to crash or degrade the performance of target servers causing service disruptions. Recently, new types of DDoS attacks have emerged and target specifically network security devices, mainly firewalls and intrusion prevention systems (IPS). In contrast to traditional DDoS attacks, these emerging attacks use low volume of malicious traffic. This paper is concerned solely with an emerging denial of firewalling attack, called the BlackNurse attack. This new attack uses special formatted ICMP packets to overwhelm the CPUs on targeted firewalls. This paper offers detailed insights into the understanding of the BlackNurse attack principles, practical attack generation, and its general effect on impacted firewalls and the network behind them. Performance evaluations are conducted on commercial grade Juniper NetScreen SSG 20 and Cisco ASA 5540 firewalls to measure the harmfulness of the BlackNurse attack when subjected to each of them. In addition, available attack mitigations pros and cons are discussed. OS screening features on Juniper NetScreen SSG 20 are used, as example, to test their effectiveness in thwarting the attack.
Spam SMS Filtering using Recurrent Neural Network and Long Short Term Memory

SMS is the abbreviation for Short Messaging Service which uses standard protocols for mobile devices to exchange information via short text messages. Today SMS's are an easy, inexpensive and widely accepted way to communicate rather than phone calls. Spam can be described as random unsolicited messages sent at large without any authorization from the receiver. People still deal with spammer's misusing SMS's to advertise false claims and can gain access to private information of users. Emails, social media sites, review, and even Twitter has seen spammers trying to intrude everywhere with the advent of Internet. Spam appears in many forms like comments, emails, search results and personal messages where spammers tend to gain revenues. Various Machine Learning algorithms like Neural Networks have tried to detect spam messages and normal messages or ham from SMS's. These techniques can learn high level features automatically using raw data unlike traditional ways where features are selected after analysis for classification. In this research paper, we propose a new method utilizing Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) using Keras models and Tensorflow backend to detect Spam and Ham from ‘SpamSMSCollection’ dataset available at UCI machine learning repository. Crucial preprocessing of dataset included tokenization, TF-IDF Vectorization and removal of stopwords. Overall accuracy of 98% is achieved and shows improvement from other machine learning algorithms for spam detection.
How Big Data in Health 4.0 Helps Prevent the Spread of Tuberculosis

Internet development has initiated an industry 4.0 evolution, where the production process can be arranged virtually and connected with large data services. The use of big data in all fields now also impacts on the health sector better known as health 4.0. This study analyzes the use of big data in the era of industry 4.0 in the health domain with the aim of knowing how much the benefits of big data technology and health 4.0 can help prevent the spread of infectious diseases. In this study, we took papers online and conducted an analogous review process. The results of the paper that we reviewed further provided us with an overview and proposal for a scheme for monitoring infectious diseases such as TB by utilizing big data. And open new opportunities to understand public health and the prevention of infectious diseases in the health era 4.0.
State of the Art in Bionic Hands

Prosthetic hands have made a significant influence on the quality of life of people with upper arm amputation. Research on prosthetic hands today is focused on replicating the functionalities of the biological hands. The present article provides a bibliometric survey on bionic hands, done through a compilation of a scientific publications database on the field of prosthetic hands spanning the last two decades. Through network-based information analysis, meaningful patterns are inferred, and several key questions significant to bionic prosthetic hands are answered. The article gives an insight into the growth, progress, and future trend in bionic hand prostheses, including identifying subject areas and discovering distinct research communities within the subject. The analysis clearly reveals the rapid expansion of bionic hand research over the last two decades, which is expected to rise considerably in near future.
An Incremental Kernel Extreme Learning Machine for Multi-Label Learning With Emerging New Labels

Multi-label learning with emerging new labels is a practical problem that occurs in data streams and has become an important new research issue in the area of machine learning. However, existing models for dealing with this problem require high learning computational times, and there still exists a lack of research. Based on these issues, this paper presents an incremental kernel extreme learning machine for multi-label learning with emerging new labels, consisting of two parts: a novelty detector; and a multi-label classifier. The detector with free-user-setting threshold parameters was developed to identify instances with new labels. A new incremental multi-label classifier and its improved version were developed to predict a label set for each instance, which can add output units incrementally and update themselves in unlabeled instances. Comprehensive evaluations of the proposed method were carried out on the problems of multi-label classification with emerging new labels compared to comparative algorithms, which revealed the promising performance of the proposed method.
Self-driven Active-Clamped Forward Power Converter with Output Ripple-Cancellation for Aerospace Applications

This paper presents a novel structure of self-driven active-clamped forward power converter with output ripple-cancellation. The output ripple-cancellation structure can improve the output filter by using coupling inductor and additional DC blocking capacitor. Auxiliary winding of transformer is used to drive the active-clamped switch, and clamp circuit can make the drive voltage be stable with changing input voltage or duty cycle. The operation principle and experimental results are shown in this paper, which has the maximum efficiency of 93%.
Advances in Adjuvant Therapy for the Treatment of High-risk Melanoma

© TOUCH MEDICAL MEDIA 2013 Abstract The global incidence of melanoma is increasing, and the prognosis for patients remains poor. High-dose interferon-alpha (HD-IFN-a) and pegylated IFN are the only US Food and Drug Administration (FDA)-approved agents for adjuvant therapy for high-risk melanoma, and an improvement in relapse-free survival (RFS) has been observed consistently across nearly all published studies and meta-analyses. Some studies and meta-analyses have also supported an overall survival (OS) benefit. However, despite a number of adjuvant studies, controversy remains regarding the role of this treatment. As the benefits in OS are modest with IFN treatment, there is therefore a need for new therapeutic targets, new drugs, and optimum patient selection. Current research is investigating new adjuvant agents, either individually or in combination, which may advance the standard of care beyond HD-IFN. Additionally, identifying biomarkers of patients with greater likelihood of response may allow patient-specific therapeutic approaches. Following the recent FDA approval of ipilimumab, vemurafenib, dabrafenib, and trametenib for metastatic melanoma, ongoing adjuvant trials are now underway.
Comprehensive Review of Automatic Lung Segmentation Techniques on Pulmonary CT Images

Segmentation is the process of partitioning an image into distinctive subsets that share similar characteristics. Segmentation is an important prerequisite to semantic image analysis. Segmentation in general is useful in many different applications such as object and face detection and recognition. Particularly in medical image analysis segmentation plays a vital role in efficient processing of images. Segmentation is used to determine the volume of mass, planning of radiotherapy, and detection of artifacts in various organs. In lung cancer diagnosis, segmentation of lungs is the crucial step. Segmenting lungs from nearby structures significantly reduce the execution time of nodule detection and helps improve its efficiency. Lung segmentation is challenging and difficult task considering the heterogeneous nature of lung fields, closeness in gray level of different soft tissues, anatomical variability, and differences in scanners and scanning protocols and dose of radiation. Various automatic and semi-automatic approaches are presented for lung or nodule segmentation. The proposed study is a review of numerous techniques for lung segmentation. The present work investigated lung segmentation methods starting with conventional methods to machine learning techniques and finally the most remarkable methods of deep learning.
A Coarse-Grained Pipeline Architecture for Sequence Alignment

A coarse-grained pipelined engine for DNA sequence alignment technique is exhibited in this manuscript. A pipelined structure with an alignment co-processor are designed for rapid and highly developed DNA sequence alignment. Moreover, this architecture efficiently uses memory windows to store array values, in lieu of using GPU and CAM that use up huge power and cost, to improve the space and speed complexity of sequence alignment in real time. A pipeline scheme has been embedded in the hardware architecture for array formation operations to accomplish temporal parallelism to prepare direction array. For a large amount of sequences, this architecture consumes reasonably low memory space and uses a dedicated alignment co-processor to acquire desired aligned sequences. The entire hardware architecture is tested using a customise unit considering various length of database and query sequences. The numerical results show that the engine takes reasonably less time to align sequences than existing designs. This has been achieved by the proposed one by avoiding comparison operations during sequence alignment phase. It is also observed that the pipeline engine provides reasonably acceptable speed-up (S ∝ N) that depends on the number of nucleotides (N) of database∝ sequences.