A class of harmonic univalent functions associated with modified q−Cătaș operator

Using the modified q−Cătaș operator, we define a class of harmonic univalent functions and obtain various properties for functions in this class.  Mathematics Subject Classification (2010): 30C45

Subclasses of p-valent meromorphic functions involving certain operator

In this paper we investigate some inclusion relationships of two new subclassses of meromorphically p-valent functions, defined by means of a linear operator. We also study some integral preserving properties and convolution properties of these classes. Mathematics Subject Classification (2010): 30C45

Određivanje donepezil hidroklorida u humanoj plazmi i ljekovitim oblicima pomoću HPLC s detekcijom fluorescencije

A sensitive, isocratic reversed-phase high performance liquid chromatographic method involving fluorescence detection was developed for the determination of donepezil hydrochloride in tablets and in human plasma. Pindolol was successfully used as an internal standard. Good chromatographic separation was achieved by using analytical column C18. The system operated at room temperature using a mobile phase consisting of methanol, phosphate buffer (0.02 mol L1) and triethyl amine (pH 3.5) (55: 45: 0.5 V/V/V) at a flow rate 0.9 mL min1. The analyte and internal standard were extracted from human plasma via liquid-liquid extraction. The proposed method was validated for selectivity, linearity, accuracy and precision. The calibration curve was linear over the range of 5-2000 ng mL1 of donepezil with detection limit of 1.5 ng mL1. Intra- and inter-day relative standard deviations were less than 2.5 %. The method was found to be suitable for the quality control of donepezil hydrochloride in bulk drug as well as in human plasma.Ovaj rad opisuje HPLC metodu određivanja donepezil hidroklorida (DP) u tabletama i u ljudskoj plazmi u nano području. Postavljena je osjetljiva metoda izokratične HPLC s fluorescencijskom detekcijom. Kao unutarnji standard upotrebljen je pindolol. Dobro kromatografsko odjeljivanje postignuto je primjenom analitičke kolone C18. Radna temperatura bila je sobna, a kao mobilna faza upotrebljena je smjesa metanola, fosfatnog pufera (0,02 mol L1) i trietilamina (pH 3,5) (55:45:0.5 V/V/V). Analit i unutarnji standard su ekstrahirani iz ljudske plazme ekstrakcijom tekuće-tekuće. Predložena metoda je validirana s obzirom na selektivnost, područje linearnosti, ispravnost i preciznost. Kalibracijska funkcija bila je linearna u području od 5-2000 ng mL1 donepezila, a granica detekcije iznosila je 2 ng mL1. Relativna standardna devijacija za repetabilnost i intermedijarnu preciznost bila je manja od 2,5 %. Metoda je primjenljliva u kontroli kvalitete ljekovitih formulacija s DP-om i u praćenju DP-a u ljudskoj plazmi

Certain sufficient conditions for close-to-convexity and starlikeness of multivalent functions

By using Jack's lemma, we derive simple sufficient conditions for analytic functions to be multivalent close-to-convex and multivalent starlike

Cooperative Navigation via Relational Graphs and State Abstraction

Abstract We consider a cooperative-navigation problem in a partially observable MADRL framework. We investigate how agents cooperate to learn a communication protocol given a very large state space while generalizing to a new environment. The proposed solution leverages the notion of structured observation and abstraction, in which the raw-pixel observations are converted into a relational graph that is then used for learning abstraction. Abstraction is performed based on compression using a relational graph autoencoder (RGAE) and a multilayer perceptron (MLP) to remove irrelevant information. The results show the effectiveness of the proposed MLP and RGAE in learning better policies with better generalization capabilities. It is also shown that communication among agents is instrumental in improving the navigation task performance.Abstract We consider a cooperative-navigation problem in a partially observable MADRL framework. We investigate how agents cooperate to learn a communication protocol given a very large state space while generalizing to a new environment. The proposed solution leverages the notion of structured observation and abstraction, in which the raw-pixel observations are converted into a relational graph that is then used for learning abstraction. Abstraction is performed based on compression using a relational graph autoencoder (RGAE) and a multilayer perceptron (MLP) to remove irrelevant information. The results show the effectiveness of the proposed MLP and RGAE in learning better policies with better generalization capabilities. It is also shown that communication among agents is instrumental in improving the navigation task performance

UTJECAJ DULJINE IGS BAZE NA TOČNOST POZICIONIRANJA GNSS-A

Since the establishment of the International GNSS Service (IGS) stations, they have been used as control stations for assigning the Precise point positioning (PPP) positions using one Global Navigation Satellite System (GNSS) receiver, which has increased from day-to-day. There are some factors affecting the accuracy of PPP positioning. This research aims to investigate the relation between the IGS distance and observed field points as well as to attempt to describe that relation mathematically/statically. For the realization of that aim, two field points are fixed inside the Assiut University campus and observed successively for a session of 24 hour observation. The position of each field point is assigned with the help of each one of the available IGS station products. It must be known that these products are found after observations in three files (IGU, IGR, and final IGS), whereas IGU is used directly as real-time data (ultra-rapid), IGR (rapid) is used through (17-41 hours) after observation, and (final IGS) used after 12 – 18 days. Coordinates and point errors of each field points are computed and represented. It has been found that the errors have a positive relation with the available IGS stations distances. The relation between these distances and point positioning errors have been represented and described according to a model. The accuracy of the presented model is (R ≅ .98, x2 ≅ 2.5 × 10-3).Od uspostave postaja Međunarodnoga GNSS servisa (IGS) iz dana u dan povećava se korištenje kontrolnih stanica za dodjelu položaja precizne točke (PPP) pomoću jednoga prijamnika Globalnoga satelitskog navigacijskog sustava (GNSS). Postoje neki čimbenici koji utječu na točnost PPP pozicioniranja. Cilj je ovoga istraživanja istražiti odnos između IGS udaljenosti i promatranih točaka polja te opisati taj odnos matematički i statički. Za realizaciju toga cilja dvije terenske točke fiksirane su unutar kampusa Sveučilišta Assiut i promatrane sukcesivno tijekom sesije promatranja od 24 sata. Položaj svake točke polja dodjeljuje se uz pomoć svakoga od dostupnih proizvoda IGS stanica. Bitno je napomenuti da se ti produkti nalaze u tri datoteke (IGU, IGR i konačni IGS) nakon promatranja, dok se IGU koristi izravno kao podatci u stvarnome vremenu (ultra-rapid), IGR (rapid) kroz 17 – 41 sat nakon promatranja, a konačni IGS nakon 12 – 18 dana. Koordinate i pogreške točaka svake točke polja izračunane su i prikazane. Utvrđeno je da su pogreške u pozitivnom odnosu s dostupnim udaljenostima IGS postaja. Odnos između tih udaljenosti i pogrešaka pozicioniranja točke prikazan je i opisan prema modelu. Točnost je prikazanoga modela R ≅ .98, x2 ≅ 2.5 × 10-3

Effect of Climate Change on Productive Traits and Genetic Parameters for Friesian Cows in Egypt

The database was obtained from 4560 records multiple records were collected from 2000 to 2022 For a total of 2560 records for Sakha station and 2000 records for El-Qardah station of the Ministry Agriculture and land reclamation in Egypt, meteorological data were obtained from the central laboratory of Agricultural climate. The data was divided into two periods, the period from 2000 to 2010 (P1) and the period from 2011 to 2022 (P2). Thermal Humidity Index (THI) and season effect on productive traits (LTMY, TMY, LP and DP) were between significant (P<0.05) and high significant (P<0.01) For both groups, except the effect of THI on the DMY in (P2) and season effect on DMY in (P1), which were non-significant. Climate change in the period from (2011 to 2022) witnessed a rise in temperature (AT) and was followed by an increase in relative humidity (RH) and atmospheric pressure (AP), as well as an increase in the value of THI especially the summer season, which is hotter than the rest of the seasons this had a negative effect on the productive traits. Estimates of permanent environmental variance for the second period for most traits were lower than additive genetic variance it may indicate that sensitivity to heat stress is not specific to the cow but is hereditary. Heritability estimates are low to moderate for most milk production traits show the possibility of improving genetically by selecting the most valuable cows for Heritability or genetic selection of cows and improving herd management

Intent Profiling and Translation Through Emergent Communication

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline

Unveiling the global hijab discourse on Instagram: A multi-layered analysis of narratives, communities and sentiments

This article presents a comprehensive analysis of the global discourse on the hijab on Instagram, a key platform for cultural and fashion expressions. Employing a mixed-methods approach, it examines a dataset of 100,000 Instagram posts to explore representations and discussions of the hijab in online communities. The study includes temporal analysis of discourse evolution, text classification of narratives using advanced natural language processing (NLP) techniques like topic modelling and sentiment analysis, and network analysis of community interactions. Key findings reveal the multifaceted nature of the hijab discourse, encompassing themes of fashion, religion and community. The temporal analysis uncovers peaks in hijab-related posts from October 2021 onwards and between May and July 2022, coinciding with Islamic events and the rise of modest fashion. Sentiment analysis indicates a generally positive and neutral perception of the hijab, while emotion analysis highlights joy, anticipation and trust as dominant emotions. Text classification identifies five main topics: hijab styles and fashion, sizing and shipping, colours and product types, religion and spirituality, and product orders. Network analysis visualizes the interconnected nature of these themes and communities. The study makes original contributions by shedding light on the ‘hijabista’ phenomenon, representing Muslim women who blend fashion with modesty on Instagram, and by demonstrating Instagram’s role in shaping contemporary hijab discourse related to identity, empowerment and cultural representation. The findings enhance understanding of social media’s impact on cultural discourses and offer valuable insights into the social and cultural implications of these online narratives for scholars, businesses and policy-makers.<br/