Miracle-dz/UniFAR-FTUs huggingface dataset:
In silico approach for designing a novel recombinant fusion protein as a Candidate Vaccine against HPV

Human papillomavirus (HPV) is the main biological agent causing sexually transmitted diseases (STDs), including precancerous lesions and several types of prevalent cancers. To date, numerous types of vaccines are designed to prevent high-risk HPV. However, their prophylactic effect is not the same and does not clear previous infections. Therefore, there is an urgent need for developing therapeutic vaccines that trigger cell-mediated immune responses for the treatment of HPV. The HPV16 E6 and E7 proteins are ideal targets for vaccine therapy against HPV. Fusion protein vaccines, which include both immunogenic interest protein and an adjuvant for augmenting the immunogenicity effects, are theoretically capable of guarantee the power of the immune system against HPV. A vaccine construct, including HPV16 E6/E7 proteins along with a heat shock protein GP96 (E6/E7-NTGP96 construct), was designed using in silico methods. By the aid of the SWISS-MODEL server, the optimal 3D model of the designed vaccine was selected, followed by physicochemical and molecular parameters were performed using bioinformatics tools. Docking studies were done to evaluate the binding interaction of the vaccine. Allergenicity, immunogenicity, B, and T cell epitopes of the designed construct were predicted. Immunological and structural computational results illustrated that our designed construct is potentially proper for stimulation of cellular and humoral immune responses against HPV. Computational studies showed that the E6/E7-NTGP96 construct is a promising candidate vaccine that needs further in vitro and in vivo evaluations.
Energy Efficient Resource Orchestration in Fog-Enabled Connected Vehicle Networks using Deep Reinforcement Learning

Increase in technological advancements has led to a revolution in vehicular networks. Fog computing is integrated into the existing vehicular networks to form Fog-Enabled Connected Vehicle Networks (FCVN) to increase the latency and throughput. Orchestration schemes are proposed to solve load balancing issues with normal device association schemes. In particular, graph-based orchestration schemes have shown a remarkable increase in service capability. However, solving the orchestration problem with multiple objectives using iterative approaches is difficult and takes time to converge. To solve these problems, we propose an improvement to the existing graph-based orchestration schemes using Reinforcement Learning (RL). A robust reward function is also proposed to tune the importance given to different objectives. The maximum resource reduction possible between every pair of fog node (FN) is computed using RL and is considered as the weight of the link. Then, maximum weight matching is performed on the resultant graph to find optimal pairs of FNs for service migrations. To demonstrate the algorithm, two objectives, energy consumption and service capability is considered. The results reveal our algorithm simultaneously improves both the objectives when compared to the existing graph-based orchestration schemes.
EEG-based depression classification using harmonized datasets

In this work harmonization technique was used to combine multiple depression-related datasets with EEG data into one. A simple CNN model was trained on each individual dataset and combined one to solve the same binary classification problem. Comparison of obtained metrics allowed to back up the necessity to combine data from different sources. Additionally, the inadequacy of employing regular shuffle split instead of per-patient split to the problem under consideration was demonstrated.
Smartphone-based Skin Cancer Detection using Image Processing and Convolutional Neural Network

Cancer is one of the deadliest diseases in the world. Among all the cancers, skin cancer is often unnoticed and as a result, the death rates are rising day by day. If skin cancer is detected early the survival rate increases, but most people are deprived of it as the detection takes time, money, and pain. A smartphone-based skin cancer detection system can serve a lot of people in this regard. Image processing has brought many revolutionary changes in recent times. Moreover, CNN (Convolutional Neural Network) has performed well these days in terms of image pattern analysis and classification. In this study, the corroboration of image processing has been established with CNN in a smartphone-based skin cancer detection system to detect skin cancer from skin lesion images. The proposed method has achieved 85% of accuracy with an F1-score of 86% on test data with Cohen's kappa coefficient of 0.70 and Matthews correlation coefficient of 0.71.
LIP Reading for Specially Abled Persons

Lip Reading is also called Visual Speech Recognition, is a technique that utilizes only the visual scene of speech recognition. The proposed framework deals with three main steps: Detecting human faces and lips, feature extraction, and classification. The novel method used in this work is the detection of salience in images. To deal with the temporal data's the image frames were concatenated. The proposed work uses a public database called MIRACL-VCl that includes different words and phrases. The work focuses on the English word recognition based on the Convolutional Neural Network (CNN) with and without salient features. Both networks used the VGG 19 pre-trained network and fine-tuned the layers to make it suitable for the dataset mentioned. Salient detection of image frames improves the accuracy of the system than the normal flow.
Water Body Identification from the Satellite Images using Color Component Analysis with Morphological Operations

Many countries including India are frequently affected by the natural disasters like floods. In general, predicting natural disasters accurately is very difficult, but advanced technologies can be utilized to come out of such difficulties or to reduce the impact of natural disasters. Satellite image processing is one of the efficient ways to detect water bodies in earth regions which may help the agriculture industry or to identify the flooded regions. In this paper, we propose a scheme to identify the water bodies from the satellite images which will be useful for various applications. During our research, we have created a set of water body images by cropping satellite images. The properties of the water body regions analyzed using an algorithm and computed a set of possible threshold values for the pixels representing the water bodies. The threshold values obtained from the analysis of ‘water body images' are used in the proposed algorithm to identify water bodies in any given image. A sequence of morphological operations is introduced to refine the results that are obtained through pixel color component analysis. The result analysis is carried out on a set of satellite images and it achieved good results.
Effects of Heatwaves on the Failure of Power Distribution Grids: a Fault Prediction System Based on Machine Learning

Nowadays, a power system failure can drastically affect the reliability and normal operation of power distribution grids. The preparation for these failure events is currently approached with post-event analysis to identify the area of the system that requires the most resources in order to prevent future failures. Nevertheless, the forecasting of such events can be useful to anticipate the failure and possibly avoid it. In this work, we employ several machine learning approaches to analyze historical failure data and predict power grid outages based on operational and meteorological data. The approach is tested with real failure data of a power distribution network in the South of Italy, demonstrating advantageous results also to determine areas requiring particular attention.
Tunable visible emission and white light generation by Ag nanoclusters in Tm3+/Yb3+ doped GeO2-PbO glasses

GeO2-PbO glasses demonstrated to be adequate hosts for incorporation of rare-earth ions and nucleation of metallic nanoparticles. However, Ag nanoclusters were not explored in GeO2-PbO glasses. The luminescence of Ag nanoclusters in glasses has several applications such as UV-driven flexible color monitors, mercury-free white light generation under UV pumping, tunable light sources/lasers, and solar cells. The present investigation shows white light generation by Ag nanocluster in Tm3+/Yb3+ doped GeO2-PbO glasses. Moreover, we also demonstrate tunable visible emission provided by Ag nanoclusters. Glasses were produced by melt-quenching technique followed by annealing to reduce internal stress. Different emission colors ranging from the blue to the green-yellowish were obtained for GeO2-PbO glasses doped only with AgNO3, under different excitation wavelengths. Doping GeO2-PbO glasses with AgNO3 and Yb3+/Tm3+ ions resulted in a blue emission for excitation at 405nm, whereas white light generation was observed for excitation with a commercial 365 nm UV lamp. The photoluminescence (PL) was attributed to the combination of emission bands, from Tm3+ ions and Ag nanoclusters. The results reported herein show the potential of GeO2-PbO glasses for white light emitting devices such as light phosphors for UV light driven W-LEDs.
Modelling Public Security Operations: Evaluation of the Holistic Security Ecosystem (HSE) Proof-of-Concept

Abstract : More so than engineered systems, human factors, and specifically having humans-in-the-loop, can lead to unforeseen behaviours resulting in unexpected organizational failures. In the world of emergency response, these failures may be related not only to response activities, but also to information processing and sharing that consequently undermine the organizational ecosystems' situational awareness of unfolding events. The TIF project, Modelling Public Security Operations, has the goal of accounting for the human factor by more fully exploring its inherent complexity through experiments and simulations. This work reviews the development of a new capability in this area using computer simulation, as well as the lessons learned from this effort. It represents a detailed proof-of-concept into how human factors can be explored through multiagent systems. The modeling and simulation challenge remains that more research on human factors must be conducted, including work to support improved validation methods for computer human-factor models.
An Attention on Sentiment Analysis of Child Abusive Public Comments Towards Bangla Text and ML

Sentiment analysis is a process of detecting sentiment through texts. It has become a very popular research work in natural language processing. Maximum researchers work with social media texts, tweets, and reviews. In this paper, we are working with Bangla text for child sentiment analysis. Child sentiment means a child's emotion which can be positive, negative, and neutral. Child sentiment analysis is needed for future development in psychology which will help researchers to detect a child's sentiment. This research paper will help to detect a child's positive, negative and neutral sentiments. To build this model we apply many machine learning and deep learning algorithms. The BERT model gives the best accuracy for our data set which is a deep learning model and MNB gives the highest accuracy from machine learning algorithms.
Aerospace Sensor Component and Subsystem Investigation and Innovation-2 Component Exploration and Development (ASCII-2 CED). Delivery Order 0005: Reconfigurable Processing With Bio-Inspired Embedded Systems On A Chip

Abstract : The idea of exploring the multidimensional characteristics biocomputing deals with involves a multilevel protein logic interaction with hybrid analog and digital systems (bio/digital/analog systems). In the protein biocomputing world, all models are nonlinear exponential in nature. These nonlinear systems are linearized by boundary limited frequency, time response and dimensional ranges. The protein model and its correct development is probably the most difficult aspect for biocomputing issues. These future computing devices will be chemical sensors, fluidic computer systems, optical switches, nanoactuators, self-repairing computing systems, nanorobots and reusable analog components. Noise reduction techniques for these systems involve spiking neural network techniques for data extraction. Reusable Analog Integrated Circuit Components are needed for hardware signal processing and for System-on-A-Chip (SoC) Integration. These include: Microwave Operational Amplifier(s); Switched Capacitor Filters; and Voltage References. During this research period, the analog design efforts have been focused on re-configurable microwave Op-Amp. A prototype 0.18 CMOS SOI design structure fort RF high-gain op-amp was submitted for prototype implementation.
Family Power: Its Impact on Unit Readiness

Abstract : The purpose of this study is to determine the degree of impact the Army family has on readiness, evaluate efforts being made to minimize the impact and make recommendations to improve current Army Family Support Program policy and enhance readiness. The obvious question arises as to what extent improvements can be made and still maintain a combat-ready force capable of accomplishing the primary mission of defending the nation. Old and new policies must be evaluated as to their effect on the family, and conversely, the family's ability to affect, positively or negatively, Army readiness.
Vision 2020+ - strategies for integrating smart renewable power parks with super grid

In the journey towards 22nd century, world will be facing severe energy crisis. Supergrid will be required to feed the increasing electricity demand. Supergrid offers the potential to increase penetration of renewable energy resources. This paper provides overview of supergrid and smart renewable energy power parks. The paper will highlight the various technical challenges involved in the integration of renewable power parks with supergrid. The paper will further identify and briefly describe how smart technologies can smooth the integration challenges. Future improvement areas will also be highlighted. (6 pages)
A METHOD FOR COMPUTING ROTARY WING AIRLOAD DISTRIBUTION IN FORWARD FLIGHT

Abstract : An investigation was made to obtain a solution to the airloads problem for a rotating wing in steady-state translational flight. A sufficient amount of the wake detail was retained to enable accurate computation of the wake-induced velocities at the blade. The method of solution developed is numerical, utilizes high-speed digital computation, and is relatively simple to use. Each blade was represented by a segmented lifting line and the shed and trailing vorticity distributions are represented by a continuous mesh of segmented vortex filaments originating at the instantaneous position of the blade. The wake of each blade of the rotor was different; these wakes change with the azimuth position of the blades to correspond to the instantaneous wake configuration for each azimuth position. The method developed was used to compute the rotor airload distributions for the Bell HU-1A, the Sikorsky H-34, and an NASA model rotor in forward flight. The computed airloads were harmonically analyzed and the radial distribution of each harmonic compared with the measured distributions. In general, encouraging agreement is obtained in these comparisons. Effects of changing some of the parameters of the computation were investigated.
Crew Endurance Management Practices: A Guide for Maritime Operations

Abstract : This guide presents a formal program of practices (the Crew Endurance Management System) for controlling risk factors that affect crewmember performance and shipboard safety in the commercial maritime industry. The CEMS program is overviewed; a real-world example is provided; techniques for managing crewmember energy and performance levels are provided; operational risk factors affecting crewmember energy and performance levels are addressed; procedures for implementing a CEMS program are described; and supplementary materials are provided.
The 2008 Global Financial Crisis and COVID-19 Pandemic: How Safe are the Safe Haven Assets?

This paper compares the performance of safe haven assets during two stressful stock market regimes – the 2008 Global Financial Crisis (GFC) and COVID-19 pandemic. Our analysis across the ten largest economies in the world shows that the traditional choice, gold, acts as a safe haven during the GFC but fails to protect investor wealth during COVID. Our results suggest that investors might have lost trust in gold. Furthermore, silver does not serve as a safe haven during either crisis. The Swiss Franc serves as a strong safe haven during both crises; whereas, the US dollar acts more effective as a safe haven during the GFC than in COVID. The US Treasuries maintain its safe havens status during both crises even though the US has the highest death toll and infection rates of COVID in the world. The largest cryptocurrency, Bitcoin appears as a speculative asset and presents more systematic risk than investment in the stock markets during COVID. Finally, the largest stablecoin, Tether emerges as an effective global safe haven across the sample countries during COVID. Thus, our results suggest that, during a pandemic, investors should prefer liquid and stable assets rather than gold.
With people in mind : design and management of everyday nature

With People in Mind explores how to design and manage areas of "everyday nature" - parks and open spaces, corporate grounds, vacant lots and backyard gardens, fields and forests - in ways that are beneficial to and appreciated by humans. The authors introduce a new way of thinking about natural areas and present concrete and detailed recommendations for effective design and management. Enhanced by a wealth of illustrations and images, With People in Mind translates the results of many years of empirical studies into practical design and management approaches. It is a readable and flexible guide for practitioners and students in landscape architecture, planning, resource management, environmental psychology, and other fields.
Developing Policies for Indian Entrepreneurs

Now a day’s term entrepreneurship not a new term for this world, everyone has seen its magic, effect not only in India but all over the world. This paper contributes the effects which entrepreneurship (Lundstrom and Stevenson (2005) has on improving economic conditions of country and what all the fields left yet where future research can be done. It also helps in explaining meaning of entrepreneurship policy, its effect on economy and how a government should create a policy so it can help entrepreneurs policy to grow.
Smart Grid Communications and Networking: Demand-side management for smart grid: opportunities and challenges

Introduction Demand-side management (DSM) is one of the key components of the future smart grid to enable more efficient and reliable grid operation [1]. To achieve a high level of reliability and robustness in power systems, the grid is usually designed for peak demand rather than for average demand. This usually results in an under-utilized system. To remedy this problem, different programs have been proposed to shape the daily energy consumption pattern of the users in order to reduce the peak-to-average ratio in load demand and use the available generating capacity more efficiently, avoiding the installation of new generation and transmission infrastructures. However, the increasing expectations of the customers both in quantity and quality [2], emerging new types of demand such as plug-in hybrid electric vehicles (PHEVs), which can potentially double the average household energy consumption [3], the limited energy resources, and the lengthy and expensive process of exploiting new resources give rise to the need for developing some more advanced methods for DSM. Since electricity cannot be stored economically, wholesale prices (i.e., prices set by competing generators to regional electricity retailers) vary drastically between the low-demand times of day and the high-demand periods. However, these changes are usually hidden from retail users. That is, end users are usually charged with some average price. To alleviate this problem, various time-differentiated pricing methods have been proposed in the literature. Some examples include day-ahead pricing, time-of-use pricing, critical-peak-load pricing, and adaptive pricing [4–7]. By equipping users with two-way communication capabilities in smart grid systems and by adopting real-time pricing (RTP) methods, it is possible to reflect the fluctuations of wholesale prices to retail prices.
A Study on the Method of Conducting a Large Container Vessel Safely to the Newly Built Container Pier to get alongside in Busan Harbour

In this paper, the authors calculated maneuvering motions of a large container vessel approaching to the newly built container piers to get alongside to her berth in Busan harbour. The motion calculations were done by using fixed coordinate system and the object of the calculations is to check the maneuvering motions are safe or not for berthing the large vessel to her berth. The result of calculations manifested that a large container vessel can get alongside to the piers without any difficulty under normal weather conditions by using 2 Z. Peller tug boats of 4500 H.P. each and also these demonstrated it is difficult to conduct and get her alongside to the piers under rough weather conditions of wind force 16.9m/sec or more. Under rough weather conditions of 6 by Beaufort scale the average wind velocity is about 13.5m and if we add 25% increase of the normal velocity to it, the wind will becomes a gust of 16.9m/sec. So it is advisable to avoid conducting a large container vessel to the pier under the rough weather conditions of 6 or more by Beaufort scale. Also, it is better to use 3 Z. peller tug boats of 4500 H.P. each under the above mentioned rough weather in a case of unavoidable circumstances.