Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular screening programmes for precision medicine: lessons learned from personalized medicine trials

ABSTRACT Introduction: The efficacy of targeted therapies in patient populations selected on the basis of the molecular features of their tumours has gained tremendous attention and is shifting the current focus of treatment to biomarker-driven clinical trials. Molecularly-selected patients are the best-case-scenario for proof-of-concept in the early development of new drugs and a potential path for registration. Developing targeted therapies in biomarker-selected patient populations can also face additional challenges. Areas covered: Different approaches in order to select patients for specific treatments are possible, using modern and relatively inexpensive genotyping platforms, but quite often logistics are their bottleneck. Main stakeholders may have different points of view on the co-development of drug-test (Pharma, diagnostic companies, clinical investigators, patients, Regulatory Agencies), which will influence how clinical research is implemented, and ultimately its feasibility. Expert commentary: Molecular prescreening programs vary in their focus of attention, with two clearly differentiated tactics: finding patients for trials or finding trials for patients. Sample management, type of assays and platforms used, logistics, turn-around time, and cost will all depend on the scope of the prescreening program and will define its returns. In conclusion, to optimize benefit for individual patients a biomarker-driven setting for clinical trials is a rational approach.
SURVEY ON SIGNATURE BASED INTRUCTION DETECTION SYSTEM USING MULTITHREADING

The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective. Many intrusion detection techniques have been developed on fixed wired networks but have been turned to be inapplicable in this new environment. We need to search for new architecture and mechanisms to protect computer networks. Signature-based Intrusion Detection System matches network packets against a pre-configured set of intrusion signatures. Current implementations of IDS employ only a single thread of execution and as a consequence benefit very little from multi-processor hardware platforms. A multi-threaded technique would allow more efficient and scalable exploitation of these multi-processor machines.
An FPGA Implementation of Hashed Key-Value Store Using Bloom Filter

: In distributed applications of database management lengthy memory access greatly reduces the performance. To overcome the limitation network processors utilize different techniques. Key-Value store is the key intrinsic component for many network applications just as online retailing and cloud computing. Such storage usually supports for operation on key value pairs. So, it can be stored in memory and be used for data-retrieval hierarchy, speed responses for queries. Till now, existing methods are deterministic. Providing such accuracy and storage comes at a cost of CPU time and memory. On contrary, in this paper we propose a new architecture for Key-Value pairs storage which is more compact. Its design is based on probabilistic space efficient data structure called bloom filter to test for membership in a set. In this project, we open up the concept of bloom filter to support key value storage operation with reduced false positive rate. The size of bloom filter need not be significantly large to minimize false positives. The bloom filter is widely used in various applications because of its storage efficiency and operational ability. Bloom filter is suitable for local functions such as logging, storage, filtering and reduces unnecessary memory transactions. Storing the inputs in the hashes as calculated and verifying their presence using bloom filter is coded in VHDL and simulated using Xilinx ISE, Implemented on Virtex 5. Simulation results shows false positive of proposed methods converges to zero and hash collisions of bloom filter has significantly reduced to zero and number of elements is inserted is increased. Finally we increase the accuracy of existence of elements by slicing bloom filter. Hence the overall false positive rate becomes zero.
A Novel Approach to Trust Based Recommender Systems Leveraged by Context Attributes

—Internet has undergone many fold expansion in last couple of decades or so, the pitfall of that is the data overloading problem which has become extremely intricate for retrieving useful information from internet. Users searching for intended contents have endless number of Web pages to navigate and require enormous efforts, requires judgmental aptitude and intuitiveness to extract meaningful information from almost unlimited number of pages and huge content. Recommender systems are meant to be an important solution to the data overload and skewed information problem that persists today in World Wide Web. Very recent research trend in Recommender systems encourages towards consideration of Context awareness along with trust based filtering.One of the major challenges in the context aware recommender system is the selection of relevant contexts and appropriately weighting the relevant contexts for prediction calculations. Also dynamic nature of trust puts practical challenges in using trust based recommender system. The selection of a few most relevant contexts and using them with proper importance factors incorporates aspects of dynamic behaviour of trust and are vital for enhanced accuracy in the recommender output, as irrelevant and inappropriate contexts assimilation decreases the accuracy of recommender output, creates data sparseness and also increases the computational complexity. There are various ways to infuse the weightages of relevant contexts in recommender systems. While doing the neighbourhood formation, trust propagation and predictions, context weighting plays a pivotal role towards increasing the accuracy of Recommender Systems. In this paper, we propose an approach that incorporates the relative weightages of relevant contexts in trust calculations and neighbourhood formation. Trust network thus formed is leveraged by the context attributes. This approach is advantageous in terms of increased recommender accuracy and also overcome data sparseness of hard context filtering methods.
Image Based Detection and Inspection of Cracks on Bridge Surface Using an Autonomous Robot [Review]

- Crack inspection is an important task in the maintenance of bridge and it is closely related to structural health of bridge. Currently it is done through a very manual procedure, an experienced human inspector monitors the whole bridge surface visually and try to detect cracks on the bridge and marks the location of crack. But this manual approach having some limitations such limited accuracy. Proposed research focuses on implementing a system having a robot, equipped with a camera to collect images of surface and a global map is created locating position of cracks. The robot will navigate on the bridge surface to collect the surface image data at predetermined locations. An image processing technique is used for processing the image and creating map of cracks. We will use different algorithm in image processing which can find out the cracks and non-cracks in an image. In this research we will create 2D-map which is essential and main aim of this work.
Spectrum Utilization in an Efficient Manner Using Overlay and Underlay of Cognitive Radio

: Spectrum utilization is improved significantly by allowing a secondary user to utilize a licensed band when the primary user (PU) is absent. Cognitive radio (CR), as an agile radio technology, has been proposed to promote the efficient use of the spectrum. Interest in Cognitive Radio (CR) remains strong as the communications community strives to solve the spectrum congestion problem. This paper proposed a method of using the overlay waveforms that exploit unused (white) spectrum holes and underlay waveforms that leads to a modest increase of the noise floor for these primary users.
The Effects of Sustainable Innovation in Management Strategies on E-Purchase Intention: An Empirical Study using SEM Statistical Analyses

This paper was intended to determine what factors affect online shoppers’ purchase intention in the e-business environment and to verify how organizations’ internal and external dynamics may underlie the success of e-commerce companies. A set of quantitative surveys and in-depth interviews with the senior managers working with e-commerce companies, a conceptual model and a number of hypotheses were proposed. Both were instrumental to a comparative analysis between two typical e-business companies, Alibaba and Amazon. The factor analysis and structural equation modeling (SEM) were adopted for statistical and empirical analyses.The results showed positive correlations among the identified factors indicating a great influence of innovative performance in different areas of management strategies on e-purchase intention; they also demonstrated the great impact from the awareness of sustainability development in e-commerce companies. These results may provide some insight on how e-business companies can improve their performance and win in fierce competition.
Development and Psychometric Study of the Academic Life Satisfaction Scale (ALSS) in a Higher Education Students Sample

This study aimed to develop and test the psychometric properties of the self-administered academic life satisfaction scale (ALSS) in a Portuguese sample of undergraduate students. An instrument development design process and psychometric study was performed. The data were collected between October and December 2016 in Lisbon campuses. The content and face validity analysis were performed by using a focus group. The ALSS ́s psychometric study was performed on a 500-undergraduate student’s convenience sample. The participants were invited to fill out an online questionnaire: sociodemographic data; one self-perception question about academic performance; and the ALSS. The Principal Component Analysis (PCA) and Cronbach’s α internal consistency was performed. The reliability, verified through internal consistency, showed to be adequate (Cronbach’s α =0.80). A two-dimensions structure construct validity was established by PCA, explaining 42.90% of total variance. The criterion validity was demonstrated by positive significative correlations between ALSS academic self-perception performance. The ALSS is a valid, reliable and very brief selfresponse instrument that is now available to measure academic life satisfaction among Portuguese students. It can be used to support more accurate health promotion programs in the campuses to improve student ́s academic success, wellbeing and mental health.
A min-max Approach for Improving the Accuracy of Effort Estimation of COCOMO

— This study proposed to extend the Constructive Cost Model (COCOMO) by incorporating the concept of min-max approach to estimation. Formal effort estimation models like Constructive Cost Model (COCOMO) are limited by their inability to manage uncertainties and imprecision surrounding software projects early in the development life cycle. A min-max approach is suggested to rectify data uncertainties and modeling errors. The proposed method of min-max is used to improve the accuracy of effort estimation of COCOMO and its result have been compared with the gradient descent, robust fuzzy clustering, k-mean clustering methods of estimation. It has been observed that the proposed method have lowest and steady state absolute estimate error AE(k) and mean absolute estimate error MAE(k) for different value of k(time series) and different step-size s.
GCS Decolour Based Binarization of Historical Manuscripts

: Pre-processing is an essential step in the binarization of historical manuscripts in order to preserve the information, especially in the case of low contrast images. This paper addresses the problem of contrast preservation of the historical manuscripts using GCS decolorization based binarization process. The paper also involves a comparison with various other algorithms which is the nick, Sauvola, Otsu, with respect to standard color to gray conversion and GCS decolorization helping us understand the advantages that this algorithm presents. Promising experimental results obtained on the DIBCO-2010 dataset, and this shows the validness of the proposed framework against conventional color to gray conversion based contemporary binarization methods.
A Review on Virtual Grid Based Dynamic Routes Adjustment in WSN

— In wireless sensor network, using the sink mobility has been considered as a decent procedure to adjust the hubs or nodes energy dissipation. In spite of its various favorable circumstances, the information scattering to the portable sink is a testing assignment for the asset obliged sensor hubs because of the element system topology created by the sink portability. For effective information conveyance, hubs need to reproduce their courses toward the most recent area of the versatile sink, which undermines the vitality protection objective. As examined already a virtual grid based dynamic routes adjustment (VGDRA) scheme that aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sink. We propose a set of communication rules that governs the routes reconstruction process thereby requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sink. Simulation results demonstrate reduced routes reconstruction cost and improved network lifetime of the VGDRA scheme when compared with existing work. In this review, we analyze the some latest techniques for life time improvement in WSN.
A Comparative Study of Decision Tree and Naive Bayes Machine Learning Model for Crime Category Prediction in Chicago

Crimes have a massive effect on individuals regards their minds, concern, and psyche, along with their indisputable impact on society. As a result, law enforcement keeps monitoring their control areas so they would notice any suspicious activity, which makes them more cautious and raises their ability to prevent potential criminal activity. This study aims to create a machine learning model that will be able to predict the potential crime category in a particular geographic area, by exploring and analyzing the existing repeated incidents data. Decision Tree and Naive Bayes are applied on a dataset, which was extracted from the Chicago Police Department's CLEAR. By applying these techniques, law enforcement agencies will highly benefit as they will be able to predict the potential crimes, which will increase the ability to prevent them. We have applied the two algorithms on the selected top 9 features from the chosen dataset. However, comparing the two algorithms, Decision Tree performed better than Naïve Bayes algorithm.
Preserving Privacy and Deduplication on Cloud with Attribute-Based Encryption and AES

: “Cloud Computing” is a general term for the delivery of hosted services over the internet. Cloud computing is moving increasingly to a destination with no return: the consolidation as an essential tool for the future existence of the internet world.Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. Firstly, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Secondly, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext. But generation of ciphertext is a heavy computation in Attribute-Based Encryption (ABE) for large files. To improve the system’s performance we are using the Symmetric Encryption algorithm, such as AES. The procedure of Encryption is performed by the data owner himself/herself first chooses a random number K as the symmetric key and encrypts the plaintext message M using K with the symmetric encryption algorithm. The encrypted data can be denoted as E K (M). Then the owner encrypts the symmetric key K using CP-ABE under the access policy defined by him/her.
Diterpenes From the Marine Brown Algae of the Genus Dilophus

Brown algae of the genus Dilophus contain plenty of biologically active secondary metabolites with diverse structures. Excellent progress has been made in the discovery of diterpenes with extensive chemical defense activity from this genus. Most of these diterpenes exhibit significant biological activities, such as antifungal, cytotoxic, and feeding-deterrent activities. In the present review, we summarized diterpenes isolated from the brown algae of the genus Dilophus.
An Efficient Technique for White Blood Cells Nuclei Automatic Segmentation

— Now days, blood testing is one of the most important clinical examinations. The characteristics (quantity, shape and color) of the white blood cell (WBC) can give vital information about a patient’s health. But, the manual inspection is time-consuming and requires adequate technical knowledge. Therefore, automatic medical diagnosis systems are necessary to help physicians to diagnose diseases in a fast and competent way. The main aim of blood cell segmentation is to extract the cells from complicated background and to segment every cell into morphological components such as nucleus, cytoplasm, and some others. Accuracy of earlier algorithms depends momentously on the initial contrast of the image. This limitation leads to capturing of all objects with gray-levels close to that of the WBCs. To overcome this disadvantage we propose to use the nucleus minimum segment size as a constraint to eliminate the non-nucleus objects. The proposed algorithm used, reduces noise effect and enhances accuracy of segmentation. All previous methods use different techniques for segmentation which gives less efficiency compared to proposed method for nucleus and cytoplasm segmentation. In this paper, we propose a new method based on gray scale contrast enhancement and filtering. For removal of false objects minimum segment size is implemented. Near about 365 blood images will be tested for this technique. Each of the five normal white blood cell types can be evaluated to compare separate performance.
Seroepidemiology of Hepatitis B, C & HIV among Blood Donors in Jos,North-Central Nigeria

Background: Despite current efforts and availability of advanced technology such as nucleic acid amplification testing and Immunoassays, the risk of Transfusion Transmissible viral Infections (TTVIs) continues to exist. In low resource economies, where periodic large scale serosurveillance is impracticable; the use of donor seroprevalence to monitor the trends of epidemiologically significant TTVIs in adult population becomes imperative. This study therefore aimed to determine blood donor profile and seroepidemiology of HIV, HBV and HCV infection status in Jos North-Central Nigeria. Methods: In this cross sectional study, 510 prospective blood donors were recruited as they presented for donation at the National Blood Transfusion Service centre and Plateau Specialist Hospital Jos. Their blood samples were tested for HbsAg, anti-HCV, and HIV, using a third generation ELISA, by Bio-Rad. Data analyzed with SPSS version 20, associations between categorical variables tested using Chi-square (χ2) test and P<0.05 was considered significant. Results: There were 510 respondents, aged 18 to 65 years. Mean age±SD was 30.20±9.10. The overall prevalence of TTVIs was 15.5% which was significantly lower than the 22.1% found in an earlier study. The prevalence of HBV, HIV and HCV were 7.5%. 3.1% and 2.7% respectively, while co infections with HIV/HBV, HIV/HCV and HBV/HCV were 1.6%; 0.2%; and 0.4% respectively. Infection prevalence was observed to be significantly lower in donors with tertiary education. Conclusion: There was a decline in the prevalence of TTVIs in Jos, North-central Nigeria, and there was a significant association between educational level and blood donation (P<0.05). We recommend a need to sustain the present policies and efforts, donor education and implementation of stringent donor eligibility criteria to further minimize the risk.
In vivo bioluminescence tomography-guided radiation research platform for pancreatic cancer: an initial study using subcutaneous and orthotopic pancreatic tumor models

Genetically engineered mouse model(GEMM) that develops pancreatic ductal adenocarcinoma (PDAC) offers an experimental system to advance our understanding of radiotherapy (RT) for pancreatic cancer. Cone beam CT (CBCT)-guided small animal radiation research platform (SARRP) has been developed to mimic the RT used for human. However, we recognized that CBCT is inadequate to localize the PDAC growing in low image contrast environment. We innovated bioluminescence tomography (BLT) to guide SARRP irradiation for in vivo PDAC. Before working on the complex PDAC-GEMM, we first validated our BLT target localization using subcutaneous and orthotopic pancreatic tumor models. Our BLT process involves the animal transport between the BLT system and SARRP. We inserted a titanium wire into the orthotopic tumor as the fiducial marker to track the tumor location and to validate the BLT reconstruction accuracy. Our data shows that with careful animal handling, minimum disturbance for target position was introduced during our BLT imaging procedure(<0.5mm). However, from longitudinal 2D bioluminescence image (BLI) study, the day-to-day location variation for an abdominal tumor can be significant. We also showed that the 2D BLI in single projection setting cannot accurately capture the abdominal tumor location. It renders that 3D BLT with multipleprojection is needed to quantify the tumor volume and location for precise radiation research. Our initial results show the BLT can retrieve the location at 2mm accuracy for both tumor models, and the tumor volume can be delineated within 25% accuracy. The study for the subcutaneous and orthotopic models will provide us valuable knowledge for BLTguided PDAC-GEMM radiation research.
Maximum Throughput of cognitive Relay systems

One of the greatest challenges in cognitive radio systems is power allocation of the secondary users for maximizing throughput. When the primary users give up the frequency bands, the secondary users utilize the spectrum holes. Furthermore, the secondary users can simultaneously occupy the spectrum with interference-aware. In this paper we consider approach of the spectrum interference to support the maximum throughput of the network. We investigate our method under one secondary user. To show the performance of our approach, we compare it with equal power allocation between the secondary user and relay.