This article examines the recent trends in whistleblowing regulation, analysing the issue of financial rewards as one of the key distinctions between the legislative solutions on the matter in the United States as compared to European jurisdictions. Using the lens of corruption theories, the article concludes that the usage of financial rewards increases the overall regulatory capacity of the state to reduce corruption and fraud and reduce the emerging, largely anonymous digital whistleblowing. The financial rewards are also, due to the peculiar nature of both corruption and whistleblowing, an adequate tool to help to quantify the effects of whistleblowing. The article argues that the introduction of financial rewards should not be viewed as dependent on the differences in the legal traditions or culture but on the quality of the institutions and their ability to assess the reports of the whistleblowers. The article offers considerations concerning the conditions for the introduction of financial rewards.
The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.
Introduction: Antimicrobial resistance and the rapid spread of multiresistant bacteria represent one of the main public health problem in limited resources countries. This issue is significantly worsening since the COVID-19 pandemic due to the unreasonably increased antibiotics prescription to patients with confirmed SARS-CoV-2 infection. The aim of this study was to examine whether COVID-19 pandemic (2020, 2021) was associated with increased antibiotic consumption in inpatient and outpatient settings in the middle size urban region (Republic of Srpska/Bosnia and Herzegovina) in comparison to period before the pandemic (2019). Additionally, we aimed to determine antimicrobial resistance and the presence of multiresistant bacteria in the regional hospital (“Saint Apostol Luka” Hospital Doboj) in 2021. Methodology: The consumption of antibiotics in inpatient was calculated as Defined Daily Dose per one hundred of patient-days. The consumption of antibiotics in outpatient was calculated as Defined Daily Dose per thousand inhabitants per day. Resistance of bacteria to antibiotics is expressed as a rates and density for each observed antibiotic. The rate of resistance was calculated as a percentage in relation to the total number of isolates of individual bacteria. The density of resistance of isolated bacteria against a specific antibiotic was expressed as the number of resistant pathogens/1000 patient days. Results: Antibiotic consumption in hospital setting registered during 2019, 2020 and 2021 was as follows: carbapenems (meropenem: 0.28; 1.91; 2.33 DDD/100 patient-days, respectively), glycopeptides (vancomycin: 0.14; 1.09, 1.54 DDD/100 patient-days, respectively), cephalosporins (ceftriaxone: 6.69; 14.7; 14.0 DDD/100 patient-days, respectively) and polymyxins (colistin: 0.04; 0.25; 0.35 DDD/100 bed-days, respectively). Consumption of azithromycin increased drastically in 2020, and dropped significantly in 2021 (0.48; 5.61; 0.93 DDD/100 patient-days). In outpatient setting, an increase in the consumption of oral forms of azithromycin, levofloxacin and cefixime, as well as parenteral forms of amoxicillin-clavulanic acid, ciprofloxacin and ceftriaxone, was recorded. In 2021, antimicrobial resistance to reserve antibiotics in hospital setting was as follows: Acinetobacter baumanii to meropenem 66.0%, Klebsiella spp to cefotaxime 67.14%, Pseudomonas to meropenem 25.7%. Conclusion: Recent COVID-19 pandemic was associated with increased antibiotic consumption in inpatient and outpatient settings, with characteristic change of pattern of azithromycin consumption. Also, high levels of antimicrobial resistance to reserve antibiotics were registered in hospital setting with low prevalence of identified pathogen-directed antimicrobial prescription. Strategies toward combat antimicrobial resistance in the Doboj region are urgently needed.
Paper covers image classification using the Keras API in TensorFlow. The dataset used is a set of labelled images consisting of characters from the Pokémon media franchise. In order to artificially generate additional data, the process of data augmentation has been applied on the initial dataset to reduce overfitting. A comparison between DenseNet-121, DenseNet-169 and DenseNet-201 has been made to observe which of the models scores a greater accuracy. A Graphics Processing Unit (GPU) has been set up to work with TensorFlow in order to efficiently train the model.
This paper presents the use of different prediction algorithms in order to recognise the popularity of a song. That recognition gives features that are directly affecting popularity of a song. For this research, data from several hundreds of the most popular songs were used in combination with songs that often appear on different playlists from different musicians. The reason for this mixing of songs is done to ensure that the model works as efficiently as possible by comparing popular songs features with those of that are no longer trending. The processing of the collected data gave an excellent insight into the importance of certain factors on the popularity of a certain song. As a result of research, month of release, acoustics and tempo were represented as features that are mostly correlated with popularity. Through the processing and analysis of a large amount of data, four models were created using different algorithms. Algorithms that were used are Decision Tree, Nearest Neighbour Classifier, Random Forest and Support Vector Classifier algorithms. The best results were achieved by training the model with the Decision Tree algorithm and accuracy of 100%.
Predictive modelling and AI have become a ubiquitous part of many modern industries and provide promising opportunities for more accurate analysis, better decision-making, reducing risk and improving profitability. One of the most promising applications for these technologies is in the financial sector as these could be influential for fraud detection, credit risk, creditworthiness and payment analysis. By using machine learning algorithms for analysing larger datasets, financial institutions could identify patterns and anomalies that could indicate fraudulent activity, allowing them to take action in real-time and minimize losses. This paper aims to explore the application of predictive models for assessing customer worthiness, identify the benefits and risks involved with this approach and compare their results in order to provide insights into which model performs best in the given context.
In primary hyperoxaluria type 1 excessive endogenous production of oxalate and glycolate leads to increased urinary excretion of these metabolites. Although genetic testing is the most definitive and preferred diagnostic method, quantification of these metabolites is important for the diagnosis and evaluation of potential therapeutic interventions. Current metabolite quantification methods use laborious, technically highly complex and expensive liquid, gas or ion chromatography tandem mass spectrometry, which are available only in selected laboratories worldwide. Incubation of ortho-aminobenzaldehyde (oABA) with glyoxylate generated from glycolate using recombinant mouse glycolate oxidase (GO) and glycine leads to the formation of a stable dihydroquinazoline double aromatic ring chromophore with specific peak absorption at 440 nm. The urinary limit of detection and estimated limit of quantification derived from eight standard curves were 14.3 and 28.7 µmol glycolate per mmol creatinine, respectively. High concentrations of oxalate, lactate and L-glycerate do not interfere in this assay format. The correlation coefficient between the absorption and an ion chromatography tandem mass spectrometry method is 93% with a p value < 0.00001. The Bland–Altmann plot indicates acceptable agreement between the two methods. The glycolate quantification method using conversion of glycolate via recombinant mouse GO and fusion of oABA and glycine with glyoxylate is fast, simple, robust and inexpensive. Furthermore this method might be readily implemented into routine clinical diagnostic laboratories for glycolate measurements in primary hyperoxaluria type 1.
Web developers utilize responsive web design principles and frameworks to develop websites that are accessible on various platforms. As consumers often access websites through laptops, tablets, mobile phones, and desktop computers, it is necessary for the website to adjust its appearance according to the device's display frame width. However, the quality assurance process for responsive web pages is typically manual, time-consuming, and error prone. This study introduces ReDeCheck, an open-source automated website layout checking tool developed by Thomas A. Walsh, Gregory M. Kapfhammer, and Phil McMinn. The tool identifies the most common types of responsive design failures by utilizing a set of display frame widths based on the presentation of the website's dynamic layout, also known as the Responsive Layout Graph. This paper verifies the tool's functionality and its underlying concepts.
Mycotoxins have become a serious issue in the animal feed industry and have also affected the aquaculture industry. Mycotoxins can create serious health problems in aquatic and terrestrial animals, and their presence in agricultural products may result in significant economic losses. To reduce the impact of mycotoxins on Nile tilapia fry, two commercially available products—Organically Modified Clinoptilolite (OMC) and multi-component mycotoxin detoxifying agent (MMDA)—were used in this study. Six diets as treatments (T1 = Control (C); T2 = Control + OMC 2 g/kg (OMC); T3 = Control + MMDA 2 g/kg (MMDA); T4 = AFB1 0.5 mg/kg (AF); T5 = AFB1 0.5 mg/kg + 2 g/kg OMC (AFOMC); T6 = AFB1 0.5 mg/kg + MMDA 2 g/kg (AFMMDA)) with similar crude protein levels (35.75 ± 0.35%) were formulated and fed to Nile tilapia fry (1.97 ± 0.1 g) for a period of 84 days. These fish were housed in 18 aquaria (100 L) at a density of 50 fish/aquarium. The results from this study showed that MMDA significantly (p < 0.05) improved the survival of fish by 16% as compared to the control group. Nevertheless, growth parameters were not affected among the treatments. These results also indicated that protein intake was significantly higher in the control and OMC diet (T2) compared to aflatoxin B1-fed tilapia. The protein efficiency ratio (PER) was significantly higher in the AFMMDA as compared to the control and MMDA. A 14-day bacterial challenge test with Aeromonas hydrophila demonstrated that diets containing MMDA or OMC improved survival when AFB1 was present in the diet. Therefore, the supplementation of feed with MMDA or OMC is recommended to ameliorate the negative effects of AFB1 in Nile Tilapia feeds.
In this article, an upgraded version of CUDA-Quicksort - an iterative implementation of the quicksort algorithm suitable for highly parallel multicore graphics processors, is described and evaluated. Three key changes which lead to improved performance are proposed. The main goal was to provide an implementation with increased scalability with the size of data sets and number of cores with modern GPU architectures, which was successfully achieved. The proposed changes also lead to significant reduction in execution time. The execution times were measured on an NVIDIA graphics card, taking into account the possible distributions of the input data.
Air pollution represents one of the most complex problems of humanity. Traffic contributes significantly to this by emitting large amounts of harmful gases. This problem is particularly pronounced at urban intersections due to frequent changes in vehicle movement dynamics. This paper primarily presents the influence of intersection geometry on pollutant emissions levels. In addition, the influence of various traffic policies promoting greater use of public transport and zero-emission vehicles is also examined. The research combines the field part of recording existing intersections in Sarajevo, Bosnia and Herzegovina with traffic microsimulation. Detailed data on vehicles’ movements were obtained by advanced video processing using the DataFromSky tool, while the PTV Vissim 2022 and Bosch ESTM (2022) software were used to simulate traffic and estimate emissions at geometrically different intersections. The results showed that, in saturated traffic conditions, signalized intersections cause up to 50% lower emissions compared with two-lane and turbo roundabouts and that the impact of the geometric change is more significant than the impact of zero-emission vehicles. In unsaturated conditions, the differences in emissions at different intersections are negligible, with the highest reductions in pollution achieved by using zero-emission vehicles.
With the progress of technology and mankind, demand for different job positions has emerged. Reports indicating various new job types in the last decade are continuously published, giving us perspective on where we were a decade ago and where we are now. Most of the jobs are created around new technologies, yet not exclusively as jobs within technology production or usage (e.g., machine learning engineers, data scientists, app developers, etc.), but also as a type of jobs built atop of new technologies (e.g., social media manager, podcast producer, content moderator, etc.). With new job types, there is a gap between qualified employees and employers demands created. Taking into consideration trends that we have seen in the last years, more and more new job types will be created, and we can predict that this gap will become larger as time passes.
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