In deaf students, there is no contextual understanding and recognition of word types through linguistic competence testing compared to the hearing population, because 67.10% do not understand, and 10.00% of deaf children partially understand the contextual application of word types in a written text task. The aim of the study is to determine the distance of language discourse between deaf and hearing respondents and to establish a discrimination factor that can be used for practical purposes in a classification sense, with the aim of determining the priority of linguistic education and language elements of deaf children in relation to their lagging behind hearing children. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, the control group of 70 hearing students, of the same chronological age and gender. The measuring instrument “Test of comprehension of the written form of expression” was applied. The collected data were processed using the discriminant analysis method. The results of the study showed that the discrimination factor is in the sequence of the use of words, adjectives, exclamations and adverbs. Nouns and prepositions have a negative correlation, which points to the fact that these four types of words are in direct implication with nouns and prepositions, and represents information that these four types of words must be more represented in the educational materials of written expression of deaf children. The results of the study also open up a series of questions focused on the quality of the educational processes of deaf children, as well as the level of lag in written communication compared to hearing children. The results of the study can influence the raising of general rehabilitation procedures to a higher level of responsibility in education centers where deaf children are educated.

The primary objective of this study is to examine the statistically homogeneous clustering in the hierarchical arrangement of the use of adverbial clauses for location, recognition, and comprehension of words presented in three-dimensional, rebus, and distorted forms. The study was conducted on a sample of 140 participants. The first subset of participants constituted the experimental group, consisting of 70 deaf students, while the second subset formed the control group, comprising 70 hearing students of the same chronological age. A battery of tests was utilized as a measurement instrument, including the “Test of Writing and Comprehending Adverbial Clauses for Location,” “Test of Reading, Writing, and Comprehending Words Presented in Three-Dimensional Form,” “Test of Reading, Writing, and Comprehending Words Presented in Rebus Form,” and “Test of Reading, Writing, and Comprehending Words Presented in Distorted Form.” In the descriptive analysis, frequencies of the total words achieved by both deaf and hearing participants were computed for the different types of measurement instruments employed. To identify the most robust homogeneity of participants concerning the applied variables, non-hierarchical and hierarchical Cluster Analyses were employed. The research results indicate a significant lag in the use of adverbial clauses for location, writing, reading, and understanding words presented in three-dimensional, rebus, and distorted forms among deaf children in comparison to their hearing peers. The Cluster Analysis revealed the most significant linkage between the variable “Number of used adverbial clauses for location” and the variable “Number of recognized words presented in three-dimensional form.” This link was clustered with the variable “Number of written words presented in rebus form” and the variable “Number of written words presented in three-dimensional form.” An analysis of variance for isolated clusters identified a statistically significant difference in the entire measurement space of adverbial clauses for location, three-dimensional, rebus, and distorted forms of words, with a level of statistical significance at p=0.00.

Syringocystadenoma papilliferum is a benign cutaneous adnexal tumor of eccrine and apocrine glands, with a warty appearance that is usually found on the scalp, neck and face, much less frequently appears in the chest or abdomen and extremely rarely on the female genital organs, i.e. the vulva. We present a case of Syringocystadenoma papilliferum on the vulva of a 64-year-old woman. This case illustrates the atypical location of this rare disease and adds to the differential diagnosis of lesions on the vulva.

Honey and honeydew are natural foods with a very complex composition that contain both, organic and inorganic ingredients. Regardless of the progress of the industry, it can't be replaced by some production process.The quality of honey varies from year to year, and bees can never produce the same honey and honeydew. Weather conditions, grazing, treatment of bees, proximity to industry, roads, etc., greatly affect the quality of the obtained honey. Although minerals and heavy metals are minor constituents of honey, they play a vital role in determining its quality.The goal of the research is to assess the qualitative status of honey based on the content of contaminants, heavy metals from the area of the Tuzla Canton.The research was conducted on 30 (thirty) honey samples. The samples were collected in the period September/October 2022 and constitute the grazing of the specified year.In the samples that were the subject of research, As and Cd did not exceed the limit of quantification (LOQ = 0.009 mg/kg). Current regulations does not define MRL’s for these two metals. As for the quantified amount of lead (Pb), it was the same in 12 samples and in 11 samples there was an evident deviation from the MRL. The measured lead (Pb), values range from 0.06 to 5.34 mg/kg.The quality of bee products from the aspect of contamination with heavy metals can serve as bioindicator of environmental pollution, that is, as an indicator of the level of good beekeeping practices. KEYWORDS:honey; honeydew; heavy metals; Tuzla canton

COVID-19 offers many valuable lessons, many of which could be found in unique societies like Hong Kong. The metropolis is special for its drastically varying—good and bad—COVID-19 performances. Hong Kong was widely considered a pandemic control and containment success for maintaining a remarkably low number of COVID-19 infections and deaths, until it was not. In March 2022, for instance, Hong Kong had the world’s highest COVID-19 infection rates. As Hong Kong shares many similarities with other metropolises around the world, it is important to learn the hard-earned lessons from its failure to control infections. Drawing insights from the literature and our own research, this analysis aims to identify key lessons societies could gain from Hong Kong’s COVID-19 responses to ensure better preparation for future pandemics.

The study investigated the extraction yield of defatted Silybum marianum seed samples using maceration as the sole extraction technique. Different solvent types (methanol, ethanol, and water) and extraction durations were tested. Prior to extraction, the samples were ground and defatted with n-hexane. For each combinationofsolvent type, and extraction duration, the extracted mass (g of extract/g of defatted sample) was determined. The impact of each parameter on the yield was analyzed, revealing significant effects.Results showed that water-based maceration for 4 hours yielded the highest average mass of dry extract, followed by shorter durations at 2 hours. Ethanol occasionally outperformed methanol, particularly at the 2-hour mark, but methanol consistently produced lower yields across longer extraction durations. These findings emphasize the need for careful optimization of solvent type and extraction duration to maximize extraction yield.Subsequent analysis using Tukey's HSD test revealed significant differences in dry extract mass among solvents. Water yielded the highest at 2 and 4 hours, ethanol at 4 hours, and methanol at 4 hours as well. KEYWORDS:Silybum marianum;maceration;solvent types;plant extraction,yield analysis

Background Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) had been in force for 25 years, in 2017 the new MD Regulation (MDR) was introduced. One of the more stringent requirement is a need for better control of MD safety and performance post-market surveillance mechanisms. Objective To address this, we have developed an automated system for management of MDs, based on their safety and performance measurement parameters, that use machine learning algorithm as a core of its functioning. Methods In total, 1997 samples were collected during the inspection process of defibrillator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. This paper presents solution developed for defibrillators, but proposed system is scalable to any other type of MDs, both diagnostic and therapeutic. Results Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR). In addition, random forest regressor and XG Boost algorithms were tested for their predictive capabilities in the field of defibrillator output error prediction. These algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The highest accuracy achieved on this dataset was 94.8% using the Naive Bayes algorithm. The XGBoost Regressor with its r2 of 0.99 emerged as a powerful tool, showcasing exceptional predictive accuracy and the ability to capture a substantial portion of the dataset's variability. Conclusion The results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automatization of defibrillator maintenance scheduling in terms of increased safety and treatment of patients, on one side, and cost optimization in MD management departments, on the other side.

Currently, humanity is facing two existential problems: the constant reduction of fossil fuel supplies, primarily crude oil, and global climate change, which is a direct consequence of the increasing use of fossil fuels both in industry and in the transport sector[1,2].One of the possible solutions for these problems arebiofuels, fuels obtained from renewable raw materials, as it isbiodiesel [2], which attracted attention due to characteristics such as high degradability, non-toxicity and low emission of carbon monoxide, particulate matter and unburned hydrocarbons, as well as the possibility of being used either in a mixture with fossil with diesel or independently as 100% biodiesel fuel[3,4,5,6].Heterogeneous catalysts in transesterification processes, i.e. biodiesel production, have been an area of significant and extensive research for many years.It is noticeable that there are significantly fewer works in which the application of Ca(OH)2, was investigated, and the published works show conflicting results, both in terms of its catalytic activity and in terms of the achieved yield of fatty acid methyl esters (FAME).The main goal of this work was to analyze the physico-chemical, chemical, mineralogical, morphological and surface characteristics of hydrated lime produced by Stamal Ltd.Kreševo, with the aim of examining the possibility of its application as a catalyst in the process of transesterification of vegetable oils.The obtained results unequivocally show that by using this hydrated lime as a catalyst in the transesterification process of rapeseed oil, it is possible to achieve a yield of methyl esters that meets the minimum limit of 96.5% prescribed by the European standard for biodiesel, EN 14214. KEYWORDS:biodiesel, heterogeneous catalysts, hydrated lime

This study describes a method for measuring flexion and extension motions in the hand, which is crucial for healing and restoring a complete range of motion following accidents. Precise control is essential because of the human hand's intricate anatomy, which consists of various bones and joints. The method replicates the trajectories of the five fingers, emphasizing their position, speed, acceleration, and torque at four crucial structural points: four fingers that stretch to 90 degrees and then return to 0 degrees, with the thumb creating a 20-degree angle, a crucial initial position for successful recovery. The ability to simulate these trajectories is essential for correctly tracking patients' progress and customizing rehabilitation treatments, both of which improve patient care and rehabilitation outcomes.

Increased integration of photovoltaic (PV) production creates new challenges and opportunities for the Distribution System Operators (DSO). It is crucial to establish comprehensive standards for the maximum integration of PV systems without disrupting normal operations. This paper explores the hosting capacity of concentrated and dispersed PV systems connected to LV network. The aim is to determine the maximum amount of PV generation for the low-voltage network considering voltage, line loads and transformer thermal constraints. Simulations are performed on a model of real power systems in DigSilent Power Factory software. This paper encompasses a wide range of activities vital for the successful integration of PV systems into low voltage distribution network that has been converted into micro-grid. Goal was to ensure stability, security, and efficiency of the grid while exploring best possible ways of connecting new renewable energy sources.

Frequency-locked loops are essential elements for the power converters' synchronization as they are used for parameter estimation. However, the fundamental Frequency-Locked Loop structure shows sensitivity to the presence of DC offset and harmonics in the input signal. Those disturbances are causing oscillations in the estimated grid parameters making this technique unusable in those scenarios. The enhanced Frequency-Locked Loop called DC-FLL, solved DC offset sensitivity by introducing a new loop for its estimation and rejection. This paper presents a further modification of the DC-FLL, which is resistant to the presence of harmonics, by applying a Cascade Delay Signal Cancellation. This modification is able to maintain immunity to DC offset, the heritage of the DC-FLL, and also gain immunity to harmonics due to the Cascade Delay Signal Cancellation. To evaluate performance, the experimental setup was prepared and conducted. In the experiment, a few tests were verified by using an acquisition card and the MATLAB/Simulink environment.

The increasing demand for high-quality and efficient Channel Estimation (CE) in 5G New Radio (5G-NR) systems has prompted the exploration of advanced Deep Learning (DL) techniques. While traditional methods, such as Linear Interpolation (LI) and Least Squares (LS), provide reasonable accuracy and are practical for real-time physical layer processing, recent DL-based CE approaches have primarily focused on accuracy, often without evidence of real-time capabilities. In this paper, we present a comprehensive evaluation of DL-based Super-resolution (SR) methods for CE, comparing models like Super Resolution Convolutional Neural Network (SRCNN), ChannelNet, and Enhanced Deep Super-Resolution (EDSR) in both 1D and 2D convolutional architectures. We optimize these models using NVIDIA TensorRT to reduce computational complexity and latency. Our results show that the optimized 1D-EDSR model achieves the best performance with a Mean Squared Error (MSE) of 0.0126, outperforming all other models in terms of accuracy. However, the optimized 1D-EDSR model fails to meet real-time constraints due to additional computational overhead (0.6798 ms/sample). In contrast, the 1D-SRCNN model offers a balanced trade-off between MSE (0.01738) and inference time (0.0866ms/sample), achieving 40% higher accuracy than LS (0.0288) while maintaining the best energy efficiency (1.48 mJ/sample).

On the threshold of a new technological era, Sixth Generation (6G) networks promise to revolutionize global connectivity, bringing mobile communications to data speeds in the terabits per second range and ultra-low latency. These networks will enhance the user experience enable a wide range of advanced applications and emerging services. Artificial Intelligence (AI)-powered network functions and services, also known as Network Intelligence Functions (NIF) and Network Intelligence Service (NIS), are essential to achieve this vision. In this study, we present the design and development of an end-to-end framework for orchestrating AI-based functions. Utilizing Kubernetes (K8s) and Prefect, we showcase its implementation through an AI-driven Traffic Classification (TC) use case. Our results confirm the feasibility of the proposed framework, offering valuable insights in the lifecycle management design, such as data collection, decision-making, and critical performance metrics, including deployment time and model performance in terms of accuracy and inference times among three different Machine Learning (ML)-based TC models.

Image-based high-throughput plant phenotyping utilises various imaging techniques to automatically and non-invasively understand the growth of different plant species. These innovative imaging infrastructures are implemented to monitor plant development over time in indoor or outdoor environments. However, understanding the relationship between genotype and phenotype interactions under different environments remains challenging. This research study demonstrates superior extraction of leaf morphological features of different Arabidopsis thaliana ecotypes by analysing leaf geometry using a sequence of RGB images. Upon successful extraction of anatomical features, leaf length and area are converted into physical coordinates. Furthermore, considering these leaf features as 1D signals, the Fourier Spectrum is analysed, and most descriptive features are selected using PCA. Finally, leaf shape classification is established by training and testing five distinct ML models. A thorough evaluation of selected models demonstrates superiority in classifying two common leaf shapes of Arabidopsis plants.

ABSTRACT Background Inherited kidney diseases (IKDs) and congenital anomalies of the kidney and urinary tract (CAKUT) are causes of kidney failure requiring kidney replacement therapy (KRT) that major renal registries usually amalgamate into the primary renal disease(PRD) category ‘miscellaneous’ or in the glomerulonephritis or pyelonephritis categories. This makes IKDs invisible (except for polycystic kidney disease) and may negatively influence the use of genetic testing, which may identify a cause for IKDs and some CAKUT. Methods We re-examined the aetiology of KRT by composing a separate IKD and CAKUT PRD group using data from the European Renal Association (ERA) Registry. Results In 2019, IKD-CAKUT was the fourth most common cause of kidney failure among incident KRT patients, accounting for 8.9% of cases [IKD 7.4% (including 5.0% autosomal dominant polycystic kidney disease), CAKUT 1.5%], behind diabetes (23.0%), hypertension (14.4%) and glomerulonephritis (10.6%). IKD-CAKUT was the most common cause of kidney failure among patients <20 years of age (41.0% of cases), but their incidence rate was highest among those ages 45–74 years (22.5 per million age-related population). Among prevalent KRT patients, IKD-CAKUT (18.5%) and glomerulonephritis (18.7%) were the two most common causes of kidney failure overall, while IKD-CAKUT was the most common cause in women (21.6%) and in patients <45 years of age (29.1%). Conclusion IKD and CAKUT are common causes of kidney failure among KRT patients. Distinct categorization of IKD and CAKUT better characterizes the epidemiology of the causes of chronic kidney disease (CKD) and highlights the importance of genetic testing in the diagnostic workup of CKD.