Miracle-dz/UniFAR-FTUs huggingface dataset:
PREPARATION AND CHARACTERIZATION OF BENZOPHENONE-3 LOADED POLYMERIC NANOPARTICLES OF LACTIDE-CO- Ɛ - CAPROLACTONE AS DRUG CARRIERS

Polymeric nanoparticles (PNPs) have been developed as carriers for sunscreens being widely used for protecting the skin from adverse effects of UV radiations. The aim of this study is to formulate PNPs using Lactide Co-Ɛ- Caprolactone (PLCL) polymer containing Benzophenone-3 (Bp-3) as chemical sunscreening agent aiming to decrease the possibility of transdermal permeation, and adverse systemic side effects, as well as protecting it against photodegradation to prevent toxic and allergic degradation products. In this study, the effects of stabilizer type Tween 80 (T80) & poloxamer 188 (P188), stabilizer concentration (1% and 5% w/v) in the external aqueous phase and drug to polymer ratio (D/P) (1:1 and 2:1 w/w) were studied on entrapment efficiency percent (EE%), and particle size. Incorporation of the prepared Bp-3 PNPs into emulgels (EGs) as vehicles with highly favorable cosmetic properties were further evaluated regarding rheological properties, spreadability test, in vitro SPF, and EUVA-PF, further evaluation involving in vitro release and in vitro permeation tests were also carried out. Finally, an in vitro microbiological assay was adopted to compare the survival percent of E.coli against UV exposure for 200 minutes which is the decimal reduction time (DRT) before and after application of the selected formula. Results show that F6T80 has the highest EE% of 98.80%±0.04. F6T80EG showing convenient spreadability with pseudoplastic thixotropic flow, higher SPF and EUVA-PF values, with lower amount of Bp-3 permeated through the skin. In addition the survival percent of E.coli against UV exposure reached 31% ± 2.92 after the application of the selected formula F6T80. This study illustrated the potential use of PNPs to improve the UV blocking activity and SPF of Bp3 compared to traditional EGs containing free unencapsulated drug.
Modeling and inference for mixtures of simple symmetric exponential families of p ‐dimensional distributions for vectors with binary coordinates

We propose tractable symmetric exponential families of distributions for multivariate vectors of 0's and 1's in p dimensions, or what are referred to in this paper as binary vectors, that allow for nontrivial amounts of variation around some central value μ∈{0,1}p . We note that more or less standard asymptotics provides likelihood‐based inference in the one‐sample problem. We then consider mixture models where component distributions are of this form. Bayes analysis based on Dirichlet processes and Jeffreys priors for the exponential family parameters prove tractable and informative in problems where relevant distributions for a vector of binary variables are clearly not symmetric. We also extend our proposed Bayesian mixture model analysis to datasets with missing entries. Performance is illustrated through simulation studies and application to real datasets.
A Survey on Intrusion Detection Systems (IDS)

In this digital age, billions of people have access to internet. With growth in internet users, there is subsequent increase in the number of network attacks. User device and data in the networks are under risk. The act of gaining access to a system without permission is called intrusion. Therein comes Intrusion Detection System (IDS), a technology that monitors the network to avoid any such attacks. IDS can monitor the system activities for potential breach of security policies. In this paper, we present a review on Intrusion Detection Systems.
Energy Investment Decision-Making Under Uncertainty: The Influence of Behavioral and Social Effects

Governments throughout the world are seeking to generate more of their domestic power from renewable energy sources with the common goal of decreasing both carbon emissions and the dependence on limited fossil fuels. Making the transition to a clean and renewable energy system will require not only public engagement, but also significant financial investment from the private sector. For various reasons, including the volatile price of oil, ongoing technological developments, and uncertainty related to public policy, there is considerable uncertainty surrounding these investments. Evidence from the industry and research shows that a better understanding of the preferences and behavior of investors under conditions of increased uncertainty is needed to increase private funding. This doctoral thesis thus considers the perspectives of investors and investigates behavioral and social effects in clean energy investment decision-making, focusing on three different financial market contexts and corresponding types of investors: venture capital (first paper), public equity markets (second paper), and project finance (third paper). The results show that investors across all three financial market sectors are prone to the influence of behavioral (e.g. corporate brands in stock markets) or social effects (e.g. actions of industry peers in venture capital and wind energy “megaprojects”) in the course of their investmentrelated decisions in this context of high uncertainty. Each of the three papers reveals what mechanisms – in addition to purely rational risk-return considerations – those who seek funding for renewable energy endeavors must take into account to increase their chance of acquiring (new) capital. Gaining insight into investors’ decisionmaking processes is also essential for policymakers who are seeking to develop measures that fit the requirements and needs of the relevant capital market sector and investor. Overall, stakeholders can use the findings of this thesis to help close the gap between the supply and demand of capital in the clean energy domain and thus to aid in and accelerate the global transition to a sustainable energy system.
Review on Comparative Study between Helical Coil and Straight Tube Heat Exchanger

The purpose of this study is to determine the relative advantage of using a helically coiled heat exchanger against a straight tube heat exchanger. It is found that the heat transfer in helical circular tubes is higher as compared to Straight tube due to their shape. Helical coils offer advantageous over straight tubes due to their compactness and increased heat transfer coefficient. The increased heat transfer coefficients are a consequence of the curvature of the coil, which induces centrifugal forces to act on the moving fluid, resulting in the development of secondary flow. The curvature of the coil governs the centrifugal force while the pitch (or helix angle) influences the torsion to which the fluid is subjected to. The centrifugal force results in the development of secondary flow. Due to the curvature effect, the fluid streams in the outer side of the pipe moves faster than the fluid streams in the inner side of the pipe. The difference in velocity sets-in secondary flows, whose pattern changes with the Dean number of the flow. In current work the fluid to fluid heat exchange is taken into consideration, Most of the investigations on heat transfer coefficients are for constant wall temperature or constant heat flux. The effectiveness, overall heat transfer coefficient, effect of coldwater flow rate on effectiveness of heat exchanger when hot water mass flow rate is kept constant and effect of hot water flow rate on effectiveness when cold water flow rate kept constant studied and compared for parallel flow, counter flow arrangement of Helical coil and Straight tube heat exchangers. The inner heat transfer coefficient calculated from Wilson plot method. Then Nusselt no and correlation obtained on the basis of inner heat transfer coefficient. All readings were taken at steady state condition of heat exchanger. The result shows that the heat transfer coefficient is affected by the geometry of the heat exchanger. Helical coil heat exchanger are superior in all aspect studied here.
Animal Incursion Detection System

1,2,3Student, Computer Science and Engineering Department, ABES Engineering College, Ghaziabad, India 4Asst. Professor, Computer Science and Engineering Department, ABES Engineering College, Ghaziabad, India ---------------------------------------------------------------------***-------------------------------------------------------------------Abstract Real time object detection as the name suggests is an art of detecting various objects at that particular time. Object detection has always been a daring task. For this purpose, faster computation power is required in the identification of an object. However, any system working in actual time generates data which is unlabeled and which has a requirement of huge set of labeled data for potent training purposes. In this work,there is a presentation of a developed application for detecting specific objects (i.e. animals) based on OpenCV libraries. Agile detection methods have been proposed via this paper for the purpose of real time object detection based on Faster R-CNN, R-CNN and TenserFlow API [7]. Faster R-CNN and SSD-MobileNet are used to train the model. We also examined the comparison between SSDMobileNet and R-CNN which helped us to conclude that in comparison SSD, Faster R-CNN is superior in terms of efficiency, time taken to train the model and accuracy [5].
Diffusion of 14C-Flurprimidol in Various Carriers Through Excised Silver Maple Bark

Bark banding is one alternative to trunk injection of tree growth regulators (TGRs). The objective of this study was to compare the penetrability of various oils as carriers for a TGR applied to the bark. Five p.l of 14C-flurprimidol (225,000 dpm) and unlabeled flurprimidol dissolved in isopropyl alcohol and selected oils (Androc Basal Oil, ArborChem Basal, Arborchem Clean Cut, CWC Hy-Grade I, Exxon Aromatic 200, Exxon Orchex 796, Leffingwell Carrier #9, and N.G. Gilbert Riteway Oil) were applied to bark excised from silver maple (Acer saccharinum ). Bark discs, 1.35 cm diameter, were sealed in a diffusion chamber and the rates and amounts of 14C-flurprimidol that moved through the bark were measured by liquid scintillation spectrometry. Rate of diffusion, measured as dpm/day for nine days, was similar for each oil. The pattern of penetration was an initial flux the first 24 hours followed by a diminished rate of penetration by the end of day two and a gradual increase during the next seven days.
Performance Analysis of Group Selection in UAV Networks with Backhaul-Aware Biasing

The limited wireless backhaul capacity has become the major bottleneck for UAV communications, while in existing researches of UAV networks, the relation between cell selection and backhaul capacity has not been modeled. This paper proposes cell group selection with backhaul-aware biasing for UAV networks and analyzes system performance by deriving the rate outage probability via stochastic geometry, where the user’s maximum data rate is constrained by backhaul capacity. Specifically, cell group selection is no longer distance-based and considered with backhaul capacity bias factor, where UAVs with higher backhaul capacity will have a larger bias factor to match the backhaul variance. In addition, the dynamic UAV group is organized with the N largest bias reference signal receiving power (BRSRP), where users can utilize the diversity gain by adjusting serving UAV dynamically as the channel conditions change. Analytical results show that the outage probability is decreased by 58% when cell group size N = 3 and UAV optimal density λ u = 600 / k m 2 when UAV height h = 150 m .
Person Classification based on Spatio-Temporal Features from Kinect Skeletal Structure using Machine Learning Techniques

1-4BE student, Department of Computer Engineering, Pillai College of Engineering, Navi Mumbai, India-410206 5Assistant Professor, Department of Computer Engineering, Pillai College of Engineering, Navi Mumbai, India-410206 ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract There has been a recent demand for person classification based on their behavioral pattern. This is used for many security and analysis purposes. Gait recognition is one of the most intriguing and systematic studies of human and animal locomotion. The kinect skeletal structure or human pose key points helps in the formation of external skeletal structure. The features are then analyzed using machine learning technique to classify them based on the pose and behavioral pattern. Considering an environment, the behavior or activity of a person can be classified as suspicious or normal. Thus the model will classify activities or actions performed by a person or a group as suspicious and normal activities considering an environment.
A SURVEY FOR AUTOMATIC IDENTIFICATION AND COUNTING OF BLOOD CELLS

1Padma Mali, Dept. of electronics and communication engineering, KLS GIT, Belagavi ,India 2Vineeta P Gejji, Dept. of electronics and communication engineering, KLS GIT, Belagavi , India 3Vishwesh K Aithal, Dept. of electronics and communication engineering, KLS GIT, Belagavi ,India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract The blood consists of the RBCs, WBCs, Platelets and Plasma. The blood count defines the state of health. Blood may be a health indicator therefore segmentation and identification of blood cells is extremely important. Complete Blood Count (CBC) is counting of all the cells which determines person’s health. The RBC and WBC count are vital to diagnose various diseases like anemia, leukemia, tissue damage, etc. Earlier, in the hospital laboratories were doing manual counting of blood cells using device called Hemocytometer and microscope. But this method extremely monotonous, laborious, time consuming, and results in the wrong results thanks to human errors. Also, there are some expensive machines like Analyzer, which aren't affordable by every laboratory. This produces a picture processing-based system which will automatically detect and count the number of RBCs and WBCs within the blood sample image. Image processing algorithm involves six steps. Also, it's supported segmentation process and a transfer learning process was applied to use this system to the microscopic detection of blood cells. The experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging.
Lifestyle Changes and Increased Urinary Cadmium Levels in People with Type 2 Diabetes Mellitus: A Case Control Study in Tehran-Iran

Background: Cadmium is ranked in the top ten environmental toxicants but its impact on Type 2 Diabetes Mellitus (T2DM) through lifestyle and diet is contradictory. Purpose: This study aimed to compare the urinary Cadmium levels (u Cd) in T2DM in an age and gender matched case control study in over 40 years volunteers and find the association of u Cd with diet, smoking, anthropometric factors and lifestyle in study participants. Methods: Face-to-face interviews based on structured questionnaires were conducted to obtain sociodemographic and life style of 200 female and male participants (100 cases and 100 control). Considering the exclusion criteria, u Cd of 30 T2DM and 30 normal participants were determined by ICP-mass analysis in Tehran. Results: Urine analysis indicated that individuals with type 2 diabetes have significantly higher U-Cd (0.0407 ± 0.0073 ppm vs. 0.0150 ± 0.0019 ppm, p = 0.001) but this difference was not contributed to their recent smoking habits, diet, life style, living and working address and anthropometric factors. The same results were observed in females and males separately. Long term history (> 20 years) of smoking as well as number of smoke buts/day affected the U-Cd levels in total volunteers (n = 60, p = 0.043). Conclusion: Chronic Cadmium exposure could be contributed to the incidence of T2DM. Except long term smoking history, lifestyle didn’t affect U-Cd levels in the residents of Tehran. Precautionary measure to reduce the exposure of people with this element is recommended to prevent the occurrence of diabetes.
Proteinase K

What is Proteinase K? PProteinase K (also protease K or endopeptidase K) is a broad-spectrum serine protease widely used in molecular biology. Proteinase K is able to digest native keratin (hair), hence, the name “Proteinase K”. It is commonly used because of its broad specificity, that makes it useful to clean nucleic acid complexe samples and to lyse cells. It has been used for isolation of mRNA, high molecular weight DNA and to inactivate other enzymatic activities. The enzyme was discovered in 1974 in extracts of the fungus Engyodontium album (formerly Tritirachium album).
Disturbance Rejection Trajectory Tracking Control for an Unmanned Quadrotor Based on Hybrid Controllers

The purpose of this article is to explore a dual-loop problem regarding the trajectory tracking control of a quadrotor unmanned aerial vehicle, applying a linear active disturbance rejection and conditional integrator sliding mode controller, namely, LARC-CISMC. The quadrotor system model is derived through Newton–Euler method and consists of two subsystems. The hybrid controller for position and attitude loops is constructed. An evaluation of the proposed controller is presented in comparison with the linear active disturbance rejection controller. Simulation comparisons and experimental tests illustrate that the proposed controller has a satisfied robustness and accuracy under lumped disturbances.
Detecting Crime Scenes using ML

The system that we have developed detects the crimes happening in real world. This system helps to avoid crimes and reduce them. Through face detection, object detection (gun, knife etc.), fire detection; the crimes can be detected. The deep learning paradigm has been recently applied for the first time to this task too, in the form of a 3D Convolutional Neural Network that processes the whole video sequence as input. So, basically the whole video will be an input to the system and the crimes will be detected and the alarm will be generated.
Practical Anonymity Sets in a Pseudonymous Forum Setting

Pseudonymous forums are online websites where users can post publicly visible content and participate in discussions under a pseudonym. Such forums are not perfectly private, as their privacy can be compromised to traffic analysis attacks. However, many methods of providing perfect privacy to such a system come with a heavy performance cost—whether in bandwidth or latency. We examine the practicality of anonymity sets, a defense against such attacks that can still provide a formal privacy guarantee with less performance losses, and attempt to simulate their implementation in a real-world setting using real data scraped from Reddit, a popular pseudonymous forum. We try various different methods of creating these anonymity sets, finding that K-means with some dimensionality compression yields decent results; we also propose a method of defining a common traffic budget for members of a set. We find that anonymity sets are a feasible defense against such attacks in the pseudonymous forum setting.
Failure EvolutionLawofReinforcedAnchor SystemunderPullout Load Based on DIC

To obtain the failure evolution law, a pullout test model of the anchor system is proposed based on the digital image correlation (DIC) measurements. By the study of the displacement field, the strain field, and the force transfer law of the anchor system under the pulling load, the failure law of the anchor system is revealed. )e results show that (1) the failure mode and the ultimate bearing capacity of the anchor system are related to the thickness of the anchor agent; (2) in the anchor system, the pulling force is gradually transferred from the loading end to the free end along the steel bar, and the greater the thickness of the anchoring agent, the deeper the transfer range; (3) during the loading, the deformation of the anchoring system is mainly concentrated at the interface between the anchoring agent and the concrete and expands to the depth along the steel bar; and (4) the failure evolution rate of the anchorage system is related to the loading stage. )e failure evolution of the anchor system can be divided into the elastic phase, the plastic phase, and the deformation rebound phase.
Development and Validation of a Fecal Testosterone Biomarker in Mus musculus and Peromyscus maniculatus1

This is a report on the development and validation of an ELISA method to determine fecal testosterone levels, and on their evaluation as a biomarker for adverse effects of endocrine-disrupting chemicals on reproductive health using male rodents of the Peromyscus maniculatus and Mus musculus species as an animal model. The ELISA antibody had the highest specificity for testosterone (100%), followed by dihydrotestosterone (57.4%) and androstenediol (0.27%). Radiolabeled testosterone was injected i.p. into three mice. Fecal samples were collected, extracted, and analyzed by liquid scintillation counting. The ELISA was performed to characterize the excretion kinetics and metabolic fate of circulating testosterone. Solubilization of feces with 10% methanol overnight provided an extraction efficiency of 87% for all metabolites; an ethyl ether extraction was more selective for testosterone. The fecal excretion of the testosterone was a biphasic process with a majority of the radioactivity recovered in the first 24 hours. HPLC analysis revealed at least five testosterone metabolites in feces, with most metabolites being less polar than testosterone. This study forms the initial evaluation of what will become a field monitoring tool.
A Comparison of Graph Optimization Approaches for Pose Estimation in SLAM

Simultaneous localization and mapping (SLAM) is an important tool that enables autonomous navigation of mobile robots through unknown environments. As the name SLAM suggests, it is important to obtain a correct representation of the environment and estimate a correct trajectory of the robot poses in the map. Dominant state-of-the-art approaches solve the pose estimation problem using graph optimization techniques based on the least squares minimization method. Among the most popular approaches are libraries such as g2o, Ceres, GTSAM and SE-Sync. The aim of this paper is to describe these approaches in a unified manner and to evaluate them on an array of publicly available synthetic and real-world pose graph datasets. In the evaluation experiments, the computation time and the value of the objective function of the four optimization libraries are analyzed.
The Influence of Social Support from Parents on the Level of Physical Activity in Adolescents

A survey has been conducted on a sample of 974 randomly selected respondents from several faculties within the University “Hasan Prishtina” in Prishtina, Kosovo, to determine how social support from parents influences the level of physical activity in adolescents of both genders. Physical activity is assessed with the standardized IPAQ questionnaire (International Physical Activity Questionnaire), intended to investigate the frequency, duration, and intensity of physical activity. Social support from parents was assessed using the Parental Support Scale, which consisted of five items. The data were processed by using appropriate statistical nonparametric methods (χ2 test and Mann-Whitney U-tests). Based on the obtained results, it can be concluded that parents can have a significant influence on the physical activity in adolescents of both genders. Parents can promote physical activity for their children in various ways, such as: by the conveyance of positive attitudes and values, by organizing activities for their children, by participating in joint activities with children, and by providing transportation to places where they can be physically active. Research has shown that there are no differences in the perceived social support from parents between male students and female students; however, for girls, social support from parents is a more significant determinant that affects whether and to what extent they will engage in physical activity.