Miracle-dz/UniFAR-FTUs huggingface dataset:
Chitin-Hyaluronan Block Copolymeric Nanoparticles for Innovative Cosmeceuticals

Chitin (C8H13O5N)n is a low-cost available unbranched polysaccharide widely distributed in nature as supporting structure of the cell wall of fungi and exoskeleton of arthropod and insects. Chitin nanofibrils are easily metabolized by the body’s endogenous enzymes and thus used in cosmetic dermatology and biotextiles. Hyaluronan is anionic, nonsulfated glycosaminoglycan which, as major component of the Extracellular Matrix (ECM) plays several biological roles, showing expensive age-related changes. A successful model of an innovative block-polymer nanoparticle (BPN) based on phosphatidylcholine, hyaluronan, and chitin nanofibrils entrapping amino acids, vitamins, and melatonin has been formulated. Both the in vitro and in vivo results obtained demonstrate the efficacy of the injected block-polymer nanoparticles in reducing skin wrinkling and ameliorating the signs of aging. Chitin nanofibrils, protecting both corneocytes and intracorneal lamellae, help to maintain cutaneous homeostasis, neutralize the activity of radicals and trap them in their structure, regularizing the correct cell turnover. The BPN seems to be useful for improving the activity of permanent fillers, rendering it useful as an anti-aging remedy for the plastic surgery armamentarium. Raymond Reed and Albert Kligman brought about the concept of “cosmeceutical” more than 50 years ago [1,2]. According to these authors, cosmeceuticals are topical Cosmetic-Pharmaceutical hybrids intended to enhance the health and beauty of skin, being based on the use of biologically active ingredients with medicinal or drug-like benefits. At this purpose, many substances, either chemically synthesized or extracted from plants or animals, are used as functional ingredients. Nowadays, many cosmetic products with biologically active ingredients have been developed and marketed, though there are discrepancies in relation to their regulations and approvals by the government [3–5]. However, a number of topical cosmeceutical treatments for conditions such as photoaging, hyperpigmentation, wrinkles, and hair damage have come into widespread use. In the cosmeceutical arena, nanotechnology has played an important role. Using new techniques to manipulate matter at an atomic or molecular level, they have been at the root of numerous innovations, opening up new perspectives for the future of the cosmeceutical industry.
The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for profit maximization. This paper presents a design solution that centers the well-being of users. It presents the theory and practice of designing a diversity-aware platform for social relations. In this approach, the diversity of users is leveraged in a way that allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity. The end users of the envisioned platform are students, who participate in the design process. Diversity-aware platform design involves numerous steps, of which two are highlighted in this paper: 1) defining a framework and operationalizing the "diversity" of students, 2) collecting "diversity" data to build diversity-aware algorithms. The paper further reflects on the ethical challenges encountered during the design of a diversity-aware platform.
First in, First out: Econometric Modelling of UK Annual CO2 Emissions, 1860–2017

The United Kingdom was the first country into the Industrial Revolution in the mid-18th Century. 250 years later, real income levels in the UK are about 7-10 fold higher per capita, even greater elsewhere, many killer diseases have been tamed, and longevity has approximately doubled. However, such beneficial developments have led to a global explosion in anthropogenic emissions of greenhouse gases. Following the Climate Change Act of 2008, the UK is now one of the first countries out, with annual CO2 emissions per capita below 1860’s levels. We develop an econometric model of its highly non-stationary emissions process over the last 150 years, confirming the key roles of reduced coal use and of the capital stock, which embodies the vintage of technology at its construction. Major shifts and outliers must be handled to develop a viable model, and the advantages of doing so are detecting the impacts of important policies and improved forecasts. Large reductions in all CO2 sources will be required to meet the 2050 target of an 80% reduction from 1970 levels, and their near elimination for a net-zero level. JEL classifications: C51, Q54.
Solving the CMBR Map Making Problem with a Stochastic Algorithm

The aim of this work is to study the feasibility of stochastic algorithms to produce Cosmic Microwave Background Radiation Radiation (CMBR) Anisotropy maps. In order to evaluate the proposed method, simulations for time-ordered data (TOD) with up to O(10) and O(10) measurements were performed. In this method, initial constraints were not imposed and the initial guess for the candidate solution was taken from an average of one hour data acquisition, instead of the standard approach of using a random initial guess. It is shown that the use of a simulated annealing algorithm can produce CMBR anisotropy maps in feasible processing time using a distributed memory parallel and a code implemented with calls to the communication library Message Passing Interface (MPI).
CPSC 547 Project Proposal UCoD - Simplifying Supply Chain Structures in the Browser

Visualizing supply chain networks is difficult but important. Analyzing supply chains as node-link graphs can reveal topological information about the dependencies that exist between parts and the resources those parts depend on. In previous work, we developed a heuristic algorithm that detects underutilized components in a supply chain. In this work, we present UCoD: the Underutilized COnstraint Detector, a tool that highlights the output of this heuristic algorithm, and allows supply chain planners to visualize the structure of their supply chain, and help them identify underutilization and anomalous patterns and trends in their network. UCoD consists of a juxtaposed view of a node-link diagram and an adjacency matrix of the supply chain graph, and a superimposed line chart that allows supply chain planners to inspect time-series of utilization for the nodes in their network. This functionality allows supply chain planners to verify the output of our heuristic algorithm and simplify their product structures.
User-oriented Dialogue Response Generation Using Multi-task Learning with Personality Prediction

Modeling the personality of users helps chatbots to generate more personalized responses that are more speciﬁc and ﬁt to the target users. Since existing approaches require conversation histories of the target users to train a model for generating responses personalized to them, it is diﬃcult to generate responses personalized to users unseen in the training data. In this study, we propose a method that learns to instantly capture users’ personalities from their dialogue contexts and generates personalized responses. We ﬁrst create a proﬁle-augmented dialogue dataset using conversational logs and the associated users’ proﬁle descriptions collected from Twitter, and then train our model to respond each utterance using multi-task learning with personality estimation. In inference, unseen users can be successfully processed without their proﬁles. We conduct both automatic evaluation and human evaluation to show the eﬀectiveness of our approach.
A NEW MODEL FOR DIURNAL FLUCTUATION IN SOIL TEMPERATURE: THE DAMPING WAVE EQUATION

Using an XQC-300 electronic temperature recorder, a continuous automatic record of the in situ diurnal changes in soil temperature under different weather conditions over several years was collected in a valley of the Sichuan Basin in China. A vast amount of experiment data was obtained. Analysis of the measurements of diurnal fluctuation in soil temperature showed that the fluctuation could be described by a damping wave equation. A comparison was made between the damping wave equation and the sine wave equation to describe the diurnal fluctuation under different weather conditions and in different seasons. The results showed that the damping wave equation was more accurate. Using given boundary conditions, a damping wave equation describing the diurnal fluctuation at different depths of the soil body was derived from the heat flow equation. The calculated result and the measured data at different depths in an upland and a paddy soil were found to be in good agreement. These results show that the damping wave equation is much better than the sine wave equation in describing the diurnal changes of soil temperature and that the sine wave equation is a special case of the damping wave equation when the damping coefficient equals zero.
Ablastic Improvement of Retinoblastoma Eyes Enucleation

Purpose: Increasing the ablasticity of retinoblastoma (RB) eyes enucleation by the new surgical technologies using high-frequency electric welding of biological tissues (HEWBT). Materials and Methods: Primary enucleation was performed in 72 children (72 eyes) aged 2 84 mo (average 34.21 ± 20.11 mo) with monolateral RB T3-T4 stage. The surgery was carried out using the EK-300M1 and EKV Z-300 PATONMED devices and special instruments. Results: Two methods of enucleation in advanced stage RB have been elaborated using the high-frequency electric welding (HEWBT) for optic nerve (ON) and its orbital part resection using “cutting” and “cutting + welding” modes. No bleeding, tissue edema and other complications were observed during the operation, which made possible to perform ON resection on maximal distance from the eye ball with perineural spaces welding, exclude the orbit tamponade and significantly reduced the surgery time. Intraorbital polytetrafluoroethylene implant of various sizes was implanted simultaneously after enucleation. Histopathology revealed RB with optic nerve invasion in 22 eyes (30.5%), including: intralaminar 15 (20.8%), retrolaminar 5 (6.9%), surgical margin 2 (2.8%), while in one case RB cells were also detected in the removed orbital ON fragment. Remote observations from 12 to 82 mo (ave 46.04 ± 18.07 mo) were followed in 47 children. There were no cases of orbital relapse, invasion into the brain and distant metastases, no deformities and cicatricial changes of the conjunctival cavity. All children demonstrated good cosmetic effect with regular prosthesis replacement. Conclusion: The elaborated new methods of RB eyes enucleation using various modes of HEWBT for optic nerve cutting make the operation in cases of advanced RB safer, less traumatic and the most important, increased its ablasticity. The absence of any serious complications in children in the remote terms after enucleation (orbital relapse, cranial cavity spreading, distant metastasis) indicates the high efficiency of the applied methods of enucleation with the HEWBT impact on the ON.
Detection of Alzheimer's Disease (AD) in MRI Images using Deep Learning

—Alzheimer's disease is an unrepairable degenerative brain disease. Every four seconds, someone in the world is diagnosed with Alzheimer's disease. The result is fatal, as it leads to death. As a result, it's crucial to catch the disease early on. The leading cause of dementia is Alzheimer's disease. Dementia causes a reduction in reasoning abilities and interpersonal coping skills, which affects people's ability to function independently. The patient will forget recent events in the early stages. If the illness progresses, they will gradually forget whole events. It is essential to diagnose the disease as soon as possible. This paper proposes a model that takes brain MRI sample images as input and determines whether a person has mild, moderate, or no Alzheimer's disease as an output. We are using the VGG19 and DenseNet169 architectures for this classification, providing a comparative analysis of which architecture shows promising results.
Inspecting of CNN and ANN Algorithms using Digit Recognition Model

- Deep Learning is the most stimulating and mind-blowing some portion of Machine Learning. It's a system that urges PCs to do what becomes alright easily for individuals. Profound learning is getting heaps of thought recently and considering current conditions. It's achieving results that were unreasonable beforehand. In profound learning, a PC model
Impact of Social Work Characteristics on Turnover Intention of Millennials

Organizations are still grappling to understand the nuances of working of millennials so that HRM practices can be upgraded to handle their attraction and retention more effectively. There is an increased interest among practitioners and scholars to understand the impact of social work characteristics on various employee lifecycle touch points in order to make organizations more appealing to young employees. This study aims to empirically test the impact of social work characteristic on turnover intentions of millennials. The study was conducted in a manufacturing company which has various production facilities and sales branches across India with a sample size of 268 respondents. The study uses four social work characteristics – (i) Social support, (ii) Interdependence, (iii) Interaction outside the organization (iv) Feedback from others as antecedent constructs on turnover intention of millennials which is the out-come construct. The findings of the study are consistent with the results of previous researches with significant managerial implications
IMPACT OF MOTIVATION ON EFFECTIVENESS OF SALES FORCE THROUGH TRAINING: A STUDY OF TELECOMMUNICATION SECTOR

In the face of complex performance environment and the rapid expansion of basic range of products and the need to sell in new ways to new customers place such a burden on the sales force that their existing knowledge simply may be inadequate to perform the task efficiently. Improving sales performance through sales force motivation is a growing issue in telecommunication sector. Hence by adopting motivation the author conceptualized two dimensions of motivation strategy that are critical for better performance. In the model developed, motivation is an independent variable, training as mediating variable and sales force performance as dependent variable. For this 121 respondents from five telecommunication companies has been studied. Descriptive statistics is calculated such as correlation, regression, ANOVA. The results show strong relationship between the dimensions of motivational factors and training towards better performance of sales force in organizations.
Micropropagation of rambutan (Nephelium lappaceum L.)

An efficient protocol has been developed for in vitro propagation of rambutan through tissue culture. Embryonic plants as well as multiple shoots were developed from seed explants cultured on Schenk and Hildebrandt (SH) basal medium supplemented with NAA (0.5 mg l-1) and BAP (1 mg l-1). The multiple shoots were separated and subcultured on the same media. Callus initiation with somatic embryoids were generated from the excised cotyledons that were cultured on SH medium supplemented with 1 mg l -1 2,4-D whereas direct somatic plantlets were developed from the excised cotyledons cultured on SH medium supplemented with 1 mg l -1 2,4-D and 0.25 mg l -1 BAP. Regenerated plantlets with well developed shoots and roots were successfully transferred to soil. This system could be utilized for biotechnological applications and also in generating variability. This is the first report on in vitro propagation of rambutan.
Authorized King James Version

The King James Version (KJV), also known as the King James Bible (KJB) or simply the Authorized Version (AV), is an English translation of the Christian Bible for the Church of England, begun in 1604 and completed as well as published in 1611 under the sponsorship of James VI and I. The books of the King James Version include the 39 books of the Old Testament, an intertestamental section containing 14 books of the Apocrypha, and the 27 books of the New Testament. The translation is noted for its “majesty of style”, and has been described as one of the most important books in English culture and a driving force in the shaping of the English-speaking world.
Comments to accompany a Power point file for the National Academy of Sciences Committee on assessing the taxonomic status of the red wolf and Mexican gray wolf

Researchers working on wolf genetics and taxonomy generally accept the accuracy of data (e.g., DNA sequences, morphological measurements, SNP genotypes) in published work. Perhaps there are different opinions on the best way to analyze data or if some data are more relevant than others for subspecies designations, but the primary difference of opinion is the interpretation of data and whether to name a subspecies or not.
A comparative study of semantic segmentation using omnidirectional images

The semantic segmentation of omnidirectional urban driving images is a research topic that has increasingly attracted the attention of researchers. This paper presents a thorough comparative study of different neural network models trained on four diﬀerent representations: perspective, equirectangular, spherical and ﬁsheye. We use in this study real perspective images, and synthetic perspective, ﬁsheye and equirect-angular images, as well as a test set of real ﬁsheye images. We evaluate the performance of convolution on spherical images and perspective images. The conclusions obtained by analyzing the results of this study are multiple and help understanding how diﬀerent networks learn to deal with omnidirectional distortions. Our main ﬁnding is that models trained on omnidirectional images are robust against modality changes and are able to learn a universal representation, giving good results in both perspective and omnidirectional images. The relevance of all results is examined with an analysis of quantitative measures.
Nanorods self-assembled from polyaromatic compounds: A molecular dynamics study [1]

In this work, we probed the self-assembly of polyaromatic (PA) compounds into one-dimensional (1D) nanorods, and addressed the effect of solvent and solute compositions on the self-assembly. Two kinds of solutes, a single type of PA compounds and a mixture of four types of PA compounds, were simulated in toluene, n-heptane and heptol (a mixture of toluene and n-heptane). Through analyzing the molecular geometries, the solution conditions that favor the formation of 1D nanorods were proposed.
Recurrent Venous Thrombosis and Breakthrough Throm- bosis: A Narrative Review

Venous Thromboembolism (VTE) is a vexing heterogeneous disease that, along with Myocardial Infarction (MI) and stroke, is among the top three cardiovascular killers. VTE's morbidity and mortality globally cause high social, health, and economic impacts. The modern diagnostic strategies of Deep Venous Thrombosis (DVT) and Pulmonary Embolism (PE) developed to aid physicians in diagnosing these presentations and using imaging resources effectively have shown limitations in several subgroups, including patients with recurrent VTE or those who are already on anticoagulation therapy. In the light of a previously reported breakthrough (recurrent) DVT while on rivaroxaban 20 mg PO daily in a 43-year-old Caucasian female, this review discusses the various etiologies, which contribute to anticoagulant's failure and VTE's recurrences in general to increase awareness among practicing physicians about these subgroups. The roles of imaging and D-dimer testing in these subgroups are also discussed, leading the author to cautiously conclude that diagnostic imaging plays a central role in identifying recurrence regardless of D-dimer testing and recommending baseline sonography upon completing DVT anticoagulation therapy for future references. Accurate diagnostic strategies are needed to ensure the best available care and treatment reaches the patient. Based on this review, the author constructs a hypothetical algorithm targeted to diagnose recurrent VTE or breakthrough VTE while on anticoagulants coupled with the possible causes for recurrent and breakthrough VTE. This algorithm should only be considered as hypothesis-generating for specifically designed prospective studies to assess and validate the algorithm's potential in decreasing the incidence of missed diagnosis of VTE and their underlying etiologies in these subgroups.
Exploring Machine Learning Techniques for Coronary Heart Disease Prediction

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), KNearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. Keywords—Coronary heart disease; machine learning; prediction; classification