The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF) from the field of filtering. A finite set of statistics called sigma points, sampled deterministically, provides a more informative and lower-variance posterior representation than the ubiquitous noise-scaling of the reparameterization trick, while ensuring higher-quality reconstruction. We further boost the performance by replacing the Kullback-Leibler (KL) divergence with the Wasserstein distribution metric that allows for a sharper posterior. Inspired by the two components, we derive a novel, deterministic-sampling flavor of the VAE, the Unscented Autoencoder (UAE), trained purely with regularization-like terms on the per-sample posterior. We empirically show competitive performance in Fr\'echet Inception Distance (FID) scores over closely-related models, in addition to a lower training variance than the VAE.

OBJECTIVES Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication, which could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled lab study, we compare how well English spectrographic voice features identify alcohol intoxication. METHODS 18 participants (72% male, aged 21-62 y) read a different randomly-assigned tongue twister prior to drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1 second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration [BrAC] >0.08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and present ensemble examine accuracy with 95% confidence intervals (CIs). RESULTS Alcohol intoxication was predicted with an accuracy of 98% (95% CI 97.1 to 98.6); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS In this small controlled lab study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.

BACKGROUND The efficacy of stromal vascular fraction (SVF) treatment, ie, stem cells, directly depends on the SVF cell count and their viability. The SVF cell count and viability are in direct correlation with adipose tissue harvesting site which yields SVF cells, thus making contribution to developing Tissue Guidance. OBJECTIVES To investigate the importance of harvesting subcutaneous adipose tissue-derived SVF cells on the concentration and viability of SVF. METHODS Adipose tissue was collected by vibration assisted liposuction, from the regions of the upper and lower abdomen, lumbar region and inner thigh region. Using the semi-automatic UNISTATION 2nd Version system (NeoGenesis, Seoul, South Korea), the obtained fat was chemically processed (collagenase enzyme) and a concentrate of SVF cells was obtained by centrifugation. These samples were then analyzed using the Luna-Stem Counter device (Logos Biosystems; Gyeonggi-do, South Korea) to measure the number and viability of SVF cells. RESULTS The highest concentration of SVF, comparing the regions of the upper abdomen, lower abdomen, lumbar, and inner thigh, was found in the lumbar region, specifically at 97498.00 per 1.0 ml of concentrate. The lowest concentration was found in the upper abdomen region. By ranking the viability values, the highest cell viability of SVF was observed in the lumbar region, measuring 36.6200%. The lowest viability was found in the upper abdomen region, measuring 24.4967%. CONCLUSIONS By comparing the upper and lower abdomen regions, lumbar and upper thigh regions, the authors have come to the conclusion that, on average, the largest number of cells and their viability was obtained from the lumbar region.

Over the last 14 years, ichthyological and ecological parameters have been monitored in the Labudovo okno Ramsar site. This area is important for its biodiversity as it is home to many rare and endangered plants and animal species. A total of 3861 fish specimens were sampled and measured at six sampling sites four times during the sampling period. An analysis of biodiversity indexes, relative biomass (kg/ha), and relative annual production (kg/ha) was carried out to assess the effectiveness of existing conservation measures. The results obtained show a trend decline in biodiversity, relative biomass, and relative annual production. This indicates a biodiversity conservation problem that should be addressed through other mechanisms in addition to the principles of the Ramsar Convention.

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations.

Climate change is recognized as a global threat that negatively impacts biodiversity and forest resources. The use of existing indicators for sustainable forest management (SFM) related to biodiversity and climate change, as well as the development of new indicators, will help assess how forest management practices impact biodiversity enhancement and climate change mitigation. A Pan-European set of criteria and indicators has been developed as a policy instrument for monitoring, evaluating, and reporting on the progress in implementing SFM. In Bosnia and Herzegovina and Western Balkans in general, the Pan-European set of criteria and indicators is an insufficiently researched topic and there is a lack of scientific research conducted regarding their development and implementation. Through the analysis of the current situation in forestry of the Federation of Bosnia and Herzegovina (FBiH), regarding the compliance and importance of the Pan-European criteria for SFM, this paper aims to explain how the international process of development and application of the Pan-European criteria for SFM can contribute to the improvement of the situation in forestry and the creation of a consistent forest policy in FBiH. The survey among forestry professionals (n=360), from the public forest administration and public forest companies in FBiH, included the sets of questions related to socio-demographic characteristics, assessment of compliance and importance of six criteria of SFM. Research results revealed that forestry professionals are mainly males, on average 41 years old, with 13 years of working experience. The majority of forestry professionals in FBiH are not familiar with Pan-European criteria for SFM, and have a low level of their understanding. On average, forestry professionals indicated that the Pan-European criteria for SFM were of high importance, while compliance with current forest management activities were rated lower on average. The large differences between responses regarding the average rating of compliance and importance of the Pan-European criteria for SFM indicate their low level of implementation in FBiH forest management activities. Accordingly, the results indicate that there is a need to organize educational lifelong learning programs in FBiH forestry sector, involving forestry professionals and other interested parties, to generate knowledge related to the Pan-European criteria for SFM and the concept of SFM in general.

Different industries are observing the positive impact of 360 video on the user experience. However, the performance of VR systems continues to fall short of customer expectations. Therefore, more research into various design elements for VR streaming systems is required. This study introduces a SW tool that offers straight-forward encoding platforms to simplify the encoding of DASH VR videos. In addition, we developed a dataset composed of 9 VR videos encoded with seven tiling configurations, four segment durations, and up to four different bitrates. A corresponding tile size dataset is also provided, which can be utilised to power network simulations or trace-driven emulations. We analysed the traffic load of various films and encoding setups using the dataset that was presented. Our research indicates that, while smaller tile sizes reduce traffic load, video decoding may require more computational power.

This paper provides a comprehensive overview of the latest Field Programmable Gate Array (FPGA) technologies that are being used to enhance smart and sustainable agriculture practices. The review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) instructions as a report standard and different inclusion and exclusion criteria were utilized. To ensure data accuracy, reduce bias, and manage references, the Zotero tool was used. The agriculture industry is facing numerous challenges, such as water scarcity, soil degradation, and climate change, which can be mitigated through the integration of modern technologies. FPGAs have emerged as a promising solution in agriculture due to their high performance, flexibility, and low power consumption. In this paper, we discuss various FPGA-based solutions that are being developed to address key agricultural challenges, including precision farming, crop monitoring, livestock management, and environmental sensing. Authors also review FPGA architectures and programming tools that are specifically designed for agricultural applications. The use of FPGA-based systems in precision agriculture has resulted in numerous benefits, including reducing energy consumption, water usage, and temperature while expanding the range of applications. Additionally, the combination of IoT and FPGA is proving to be a promising solution for livestock management, and FPGA technology has also shown promise in energy efficiency and plant disease diagnosis. Overall, FPGA technology has proven to be a valuable tool in various aspects of agriculture, with its use expected to increase in the future.

For many years now MATLAB has been considered the academia standard when it comes to technical computing and simulation. Many university and college courses rely on multiple tool-boxes and ad-dons that MATLAB provides. With its relatively simple syntax, and large user community it has been, for so many years, a logical choice for academia. However, more often than not, students fresh out of university have been facing a new software that has very quickly become an industry standard in many areas of electrical engineering. On a simple example of DC motor control, this paper aims to showcase advantages of early adoption and using LabViewfor programming and simulation purposes in academia.

Purpose This study aims to investigate the relationship between Islamic governance and the social performance of Islamic banks, pioneering a new aspect in terms of the impact of the National Shariah Board (NSB) on the social performance of Islamic banks. The essential body in the Islamic banks in charge of Islamic governance is the Shariah Supervisory Board (SSB). Therefore, in this study, the authors explore how the characteristics of the Shariah board and Islamic governance mechanisms influence the social performance of Islamic banks. Design/methodology/approach Panel data methods are applied to the annual data of 43 banks from 14 countries over the period 2012–2018 to explore the impact of Islamic governance on Islamic banks’ social performance. The authors have used all available bank annual reports in the given period. Social performance is measured by Maqasid al-Shariah (in terms of the goals of the Islamic moral economy) index using a comprehensive evaluation framework. Islamic governance is represented by the improved Islamic Governance Score (IG-Score) index, which measures the quality of Islamic governance in Islamic banks. In the research, the authors also introduce the frequency of SSB meetings in IG-Score. Findings The findings suggest a strong link between Islamic governance and the social performance of Islamic banks, illustrating the importance of the Shariah board in achieving maqasid. On the other hand, the research discovered that NSBs are inefficient and the existence of NSB can jeopardize the social performance of Islamic banks. The results of this research imply valuable recommendations for Islamic banks that are keen to improve their social performance. Originality/value Besides investigating the impact of SSB governance on the social performance of Islamic banks by using an improved IG score index, to the best of the authors’ knowledge, this is the first study that investigates the impact of NSBs on the social performance of Islamic banks.

Abstract: The degradation of the environment is one of the most urgent challenges today. Since the industrial revolution, we have only known the model of linear economy that deals with the relationship between growth and consumption with the creation of large amounts of waste. As an alternative, a new the concept of the mod-ern economy, the circular economy. The underlying assumptions of such a system are characterised by a ten-dency towards efficient use, and recycling and re-use of resources asit would limit the negative environmental impacts of the economy, while reducing costs in economic activities with the aim of economic growth. Our goal in this paper is to highlight the role and significance of the Circular Economy and natural resources in the process of creation of competitive advantages in a globaly connected world as well as in Bosnia and Herzegovina. Our companies have preferred the mass production method of material wealth based on the mass consumption of natural resources as the main economic development method while pursuing high economic growth and maximum economic profit. These days, this economic development method faces various limitations. Many problems, such as mass generation of wastes exceeding the natural purification capacity, enormous damage environment, deepening of natural disasters and global warming, various disputes surrounding natural resources. This analysis highlights that the use of circular economy tools can help economic policy makers and researchers to take into account the impact on the environment during strategic planning activities and projections of economic growth in BiH.

A search is presented for a heavy resonance $Y$ decaying into a Standard Model Higgs boson $H$ and a new particle $X$ in a fully hadronic final state. The full Large Hadron Collider Run 2 dataset of proton-proton collisions at $\sqrt{s}= 13$ TeV collected by the ATLAS detector from 2015 to 2018 is used, and corresponds to an integrated luminosity of 139 fb$^{-1}$. The search targets the high $Y$-mass region, where the $H$ and $X$ have a significant Lorentz boost in the laboratory frame. A novel signal region is implemented using anomaly detection, where events are selected solely because of their incompatibility with a learned background-only model. It is defined using a jet-level tagger for signal-model-independent selection of the boosted $X$ particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark $X$ decay into two quarks, covering topologies where the $X$ is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into $b\bar{b}$, and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section $\sigma(pp \rightarrow Y \rightarrow XH \rightarrow q\bar{q}b\bar{b}$) for signals with $m_Y$ between 1.5 and 6 TeV and $m_X$ between 65 and 3000 GeV.