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Triply periodic minimal surface (TPMS) metamaterials characterized by mathematically-controlled topologies exhibit better mechanical properties compared to uniform structures. The unit cell topology of such metamaterials can be further optimized to improve a desired mechanical property for a specific application. However, such inverse design involves multiple costly 3D finite element analyses in topology optimization and hence has not been attempted. Data-driven models have recently gained popularity as surrogate models in the geometrical design of metamaterials. Gyroid-like unit cells are designed using a novel voxel algorithm, a homogenization-based topology optimization, and a Heaviside filter to attain optimized densities of 0-1 configuration. Few optimization data are used as input-output for supervised learning of the topology optimization process from a 3D CNN model. These models could then be used to instantaneously predict the optimized unit cell geometry for any topology parameters, thus alleviating the need to run any topology optimization for future design. The high accuracy of the model was demonstrated by a low mean square error metric and a high dice coefficient metric. This accelerated design of 3D metamaterials opens the possibility of designing any computationally costly problems involving complex geometry of metamaterials with multi-objective properties or multi-scale applications.

Glioblastomas presenting topographically at the cerebellopontine angle (CPA) are exceedingly rare. Given the specific anatomical considerations and their rarity, overall survival (OS) and management are not discussed in detail. The authors performed an integrative survival analysis of CPA glioblastomas. A literature search of PubMed, Scopus, and Web of Science databases was performed per PRISMA guidelines. Patient data including demographics, clinical features, neuroimaging, management, follow-up, and OS were extracted. The mean age was 39 ± 26.2 years. The mean OS was 8.9 months. Kaplan–Meier log-rank test and univariate Cox proportional-hazards model identified hydrocephalus (log-rank, p = 0.034; HR 0.34; 95% CI 0.12–0.94; p = 0.038), chemotherapy (log-rank, p < 0.005; HR 5.66; 95% CI 1.53–20.88; p = 0.009), and radiotherapy (log-rank, p < 0.0001; HR 12.01; 95% CI 3.44–41.89; p < 0.001) as factors influencing OS. Hydrocephalus (HR 3.57; 95% CI 1.07–11.1; p = 0.038) and no adjuvant radiotherapy (HR 0.12; 95% CI 0.02–0.59; p < 0.01) remained prognostic on multivariable analysis with fourfold and twofold higher risk for the time-related onset of death, respectively. This should be considered when assessing the risk-to-benefit ratio for patients undergoing surgery for CPA glioblastoma.

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.

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.

The protection and preservation of the privacy of personal data are one of the main requirements when it comes to an application dealing with the processing of such data. It is no different when it comes to information systems that store and process data about students in higher education systems. The public presentation of such data represents a serious threat to the safety of students as well as their status within the higher education system. For this reason, it is necessary to use the possibility of advanced technologies in order to raise data security to the highest level. One such technology that is able to provide transparency, security and data protection at a high level is blockchain technology. In this work, the Hyperledger Fabric distributed ledger private blockchain network was analyzed and its usability in terms of user rights management in higher education system was evaluated. Experimental analysis showed that such a platform has the ability to take advantage of private blockchain technologies in terms of user rights management and to provide security, flexibility and scalability of the system.

Maintaining and establishing transparency, security and privacy, when the data that should be included as part of documents that should serve as public educational documents in the labor market, are a challenging task, especially nowadays when we have more frequent cyber-attacks on public institutions. Setting up the security mechanisms of information systems that should store, process and show this type of data can be a very demanding job. For this reason, the introduction of new technologies in this area, such as blockchain technology, leads to considerable system and implementation relief. In this paper, private blockchain platforms are analyzed from the point of view of processing digital certificates or diplomas in the higher education system. An overview of the most popular platforms of this type is given. The most appropriate solution for these needs are discussed and proposed.

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.