Up-to-the-minute Data Policy Updates for Participatory Studies

https://uit.no/research/bin</a

On pairs of rr-primitive and kk-normal elements with prescribed traces over finite fields

Given $\mathbb{F}_{q^{n}}$, a field with $q^n$ elements, where $q$ is a prime power, $n$ is positive integer. For $r \in \mathbb{N}$, $k \in \mathbb{N} \cup \{ 0 \}$, an element $\epsilon \in \mathbb{F}_{q^n}$ is said to be $r$-primitive if its multiplicative order is $\frac{q^n -1}{r}$ and it is referred to as $k$-normal if the greatest common divisor of the polynomial $\sum_{i=0}^{n-1} \epsilon^{q^i} x^{n-1-i}$ with $x^n -1$ has degree $k$ in $\mathbb{F}_{q^n}[x]$. In this article, for $r_1,r_2 ,m_1,m_2 \in \mathbb{N}$, $k_1,k_2 \in \mathbb{N}\cup \{0\}$, a rational function $F = \frac{F_1}{F_2}$ in $\mathbb{F}_{q}[x]$ with deg($F_i$) $\leq m_i$; $i=1,2,$ satisfying some conditions, and $a,b \in \mathbb{F}_{q}$, we construct a sufficient condition on $(q,n)$ which guarantees the existence of an $r_1$-primitive, $k_1$-normal element $\epsilon \in \mathbb{F}_{q^n}$ such that $F(\epsilon)$ is $r_2$-primitive, $k_2$-normal with $\operatorname{Tr}_{\mathbb{F}_{q^n}/\mathbb{F}_q}(\epsilon) = a$ and $\operatorname{Tr}_{\mathbb{F}_{q^n}/\mathbb{F}_q}(\epsilon^{-1}) = b$. Further, for $m_1=10 , m_2=11$, we demonstrate an example showing the existence of 3-primitive, 2-normal element $\epsilon$ in $\mathbb{F}_{q^n}$ such that $F(\epsilon)$ is 2-primitive, 1-normal with $\operatorname{Tr}_{\mathbb{F}_{q^n}/\mathbb{F}_q}(\epsilon)=a$ and $\operatorname{Tr}_{\mathbb{F}_{q^n}/\mathbb{F}_q}(\epsilon^{-1})$ $=b$ for any prescribed $a,b \in \mathbb{F}_{q}$ except from possible 10 values of $(q,n)$ in field of characteristics 13

Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing

Congenital Lingual Melanotic Macule : Rare entity in infants

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Compliant Sharing of Sensitive Data with Dataverse and Lohpi

https://projects.iq.harvard.edu/dcm2021/home</a

Ectrodactyly, Ectodermal Dysplasia, Cleft Lip, and Palate (EEC Syndrome) with Tetralogy of Fallot: A Very Rare Combination

Ectrodactyly, ectodermal dysplasia, and cleft lip/palate syndrome (EEC) syndrome is a rare genetic disorder with an incidence of around 1:90,000 live births. It is known with various names which includes split hand–split foot–ectodermal dysplasia–cleft syndrome or split hand, cleft hand or lobster claw hand/foot. We report first case of EEC with associated heart disease (Tetralogy of Fallot) who was diagnosed as EEC on the basis of clinical features and EEC was confirmed with genetic analysis

Capturing Nutrition Data for Sports: Challenges and Ethical Issues

Presentation at the 29th International Conference on Multimedia Modeling, 09. - 13.01.23, Bergen, Norway: https://www.mmm2023.no/.Nutritionplaysakeyroleinanathlete’s performance, health, and mental well-being. Capturing nutrition data is crucial for analyzing those relations and performing necessary interventions. Using traditional methods to capture long-term nutritional data requires intensive labor, and is prone to errors and biases. Artificial Intelligence (AI) methods can be used to remedy such problems by using Image-Based Dietary Assessment (IBDA) methods where athletes can take pictures of their food before consuming it. However, the current state of IBDA is not perfect. In this paper, we discuss the challenges faced in employing such methods to capture nutrition data. We also discuss ethical and legal issues that must be addressed before using these methods on a large scale

Effects of Occupational Exposure on the Health of Rag Pickers Due to Fungal Contamination at Waste Dumping Sites in Gwalior (India)

We investigated fungal contamination near different waste dumping sites and assessed the health risk factors of rag pickers associated with collection of waste in Gwalior during the year 2014-15. Petri plates were exposed at waste dumping sites and were transferred to the laboratory, analysis and identification was mainly carried out by culturing the fungal colonies by following standard procedures. A pretested questionnaire was used to evaluate the health problems among the rag pickers. Results indicated that all the dumping sites are contaminated with different types of fungal pathogens like Alternaria alternate, Aspergillus flavus, A. fumigates, A. niger, Cladosporium, Fusarium, Mucor, Penicillium and Rhizopus. Our study reported higher incidence of musculoskeletal and respiratory diseases among rag pickers. There is also strong need for carrying out similar assessment studies for other cities too. This will entail generation of more precise site specific information regarding fungal species and associated health risk factor