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Two copper(II) complexes of 4-chloro- and 4-dimethylaminobenzaldehyde nicotinic acid hydrazones were prepared and characterized by elemental analysis, mass spectrometry, infrared and electron spectroscopy and conductometry. These rare examples of bis(hydrazonato)copper(II) complexes are neutral complex species with copper(II) center coordinated with two monoanionic bidentate O,N-donor hydrazone ligands coordinated in enol-imine form. The interaction of hydrazone ligands and corresponding copper(II) complexes with CT DNA and BSA was investigated. Copper(II) complexes are slightly effective in binding the DNA than pristine hydrazones. The results indicate groove binding or moderate intercalation which are not significantly affected by the nature of substituent at hydrazone ligands. On contrary, affinities of two copper(II) complexes toward BSA significantly differs and depends on the nature of the substituent, however in absence of thermodynamic data difference in nature of binding forces cannot be excluded. The complex bearing electron-withdrawing 4-chloro substituent has larger affinity toward BSA compared to 4-dimethyamino analogue. These findings were theoretically supported by molecular docking study.

This collective discussion brings together six women scholars of and from the post-Yugoslav space, who, using personal experiences, analyze the dynamics of knowledge production in international relations (IR), especially regarding the post-Yugoslav space. Working in Global North academia but with lived experiences in the region we study, our research is often subjected to a particular gaze, seeped in assumptions about “ulterior” motives and expectations about writing and representation. Can those expected to be objects of knowledge ever become epistemic subjects? We argue that the rendering of the post-Yugoslav space as conflict-prone and as Europe's liminal semi-periphery in the discipline of IR cannot be decoupled from the rendering of the region and those seen as related to it as unable to produce knowledge that, in mainstream discussions, is seen as valuable and “objective.” The post-Yugoslav region and those seen as related to it being simultaneously postcolonial, postsocialist, and postwar, and characterized by marginalization, complicity, and privilege in global racialized hierarchies at the same time, can make visible specific forms of multiple colonialities, potentially creating space for anti- and/or decolonial alternatives. We further make the case for embracing a radical reflexivity that is active, collaborative, and rooted in feminist epistemologies and political commitments.

Basketball is one of the popular sports in the world, and physical performance is becoming increasingly important in basketball as the game evolves. The aim of the study was to investigate the effects of a 3-week modified complex training on athletic performance of women's national basketball players. An experimental study involved the participation of 12 highly trained female basketball players (national team of Bosnia and Herzegovina). Observed variables before and after 3-weeks of modified complex training were 300 yards test, 20-yards test, lane agility and beep test. Means and standard deviations for each of the variables were calculated, and differences pre-to-post performance changes were examined using a paired sample t-test. Three weeks of specific complex training sessions show a statistically significant increase in all tested variables, 300 yards (p≤.001); 20 yards (p≤.001); Lane agility (p≤.001) and beep test (p=.028). It can be concluded that applied complex training program has significantly improved studied parameters of condition preparation of elite female basketball players.

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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.

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.

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.