Tekst predstavlja prilagođeno izlaganje sa naučne konferencije ZAVNOBiH u retrospektivi: evaluacija historijske važnosti i savremene relevantnosti za društvo i državu koja je organizirana povodom 80. godišnjice od Prvog zasjedanja ZAVNOBiH-a. Konferencija je održana 20. novembra 2023. godine na Univerzitetu u Sarajevu - Fakultetu političkih nauka. The text represents an adapted presentation delivered at the scientific conference ZAVNOBiH in Retrospect – Evaluating Its Historical Importance and Contemporary Relevance for Society and the State, organized on the occasion of marking the 80th Anniversary of the First Session of ZAVNOBiH. The conference was held at the University of Sarajevo - Faculty of Political Sciences, on 20 November 2023.

This study aims to analyze the opinions of Bosnia and Herzegovina (BiH) citizens regarding mandatory pension insurance and the possibility of incorporating private insurance in future reforms. The research involves evaluating the satisfaction of BiH residents with the current pension system, understanding their perception of the pension fund’s risks, and identifying their attitudes towards possible pension system reforms, including the potential involvement of private insurance. The study also seeks to highlight any differences in attitudes towards socio-demographic characteristics, such as gender, employment, length of service, professional qualification, and monthly income. A survey of 812 BiH adults (representative but potentially not fully capturing the entire population) explored these aspects. While acknowledging limitations, the study reveals significant differences in attitudes based on demographics. For example, men are more optimistic about future pensions, while employed individuals are more inclined towards reform. The findings suggest general public support for pension system reform and openness to private insurance. However, the study highlights the need to consider these varying attitudes across different population groups when designing future reforms. This research provides the first quantitative data on BiH residents’ views on private insurance reform, contributing to public discourse and informing future policy changes.

La cría de caracoles, también conocida como helicicultura, es un tipo de ganadería que implica la reproducción y cultivo de caracoles para consumo humano. Aunque la cría de caracoles sigue siendo relativamente rara en muchas partes del mundo, es una industria en crecimiento que ofrece una serie de beneficios, incluido el impacto ambiental pequeño, alto potencial de beneficios y ventajas nutricionales únicas. Entre varias especies comestibles, los caracoles como Cornu aspersum y Helix pomatia han demostrado un tremendo potencial como fuentes alternativas de proteínas en las dietas animales. Tienen un perfil nutricional favorable, que contiene aminoácidos esenciales, minerales y vitaminas, lo que los convierte en valiosos suplementos o sustitutos de fuentes de proteínas tradicionales. Las técnicas y prácticas de cría de caracoles han avanzado significativamente en los últimos años, lo que ha resultado en un aumento de la productividad y mejoras en los rasgos genéticos en las poblaciones de caracoles. Además, los esfuerzos de investigación en curso están explorando el desarrollo de protocolos de cría de caracoles adaptados a requisitos veterinarios específicos. La optimización de los métodos de cría, incluida la selección artificial, el apareamiento controlado y la mejora genética, tiene un enorme potencial para mejorar los rasgos deseables en las poblaciones de caracoles. Este progreso puede contribuir al desarrollo de razas de caracoles superiores con perfiles nutricionales mejorados, resistencia a enfermedades y propiedades terapéuticas. Además, la cría de caracoles presenta una oportunidad para abordar problemas ecológicos asociados con la producción tradicional de ganado. El uso eficiente de recursos, los requisitos mínimos de espacio y el bajo impacto ambiental hacen que la cría de caracoles sea una alternativa ecológica. Los caracoles también son menos susceptibles a ciertas enfermedades infecciosas que comúnmente afectan a los animales criados tradicionalmente, lo que reduce la necesidad de antimicrobianos y promueve prácticas sostenibles. La educación continua y la investigación sobre técnicas de cría de caracoles, perfiles nutricionales y aplicaciones terapéuticas allanan el camino para prácticas veterinarias innovadoras que priorizan la sostenibilidad, el bienestar animal y mejores resultados de salud. Una mayor investigación en este campo representa una nueva oportunidad para que veterinarios y agricultores contribuyan a la salud y el bienestar de los animales al tiempo que promueven prácticas sostenibles en la industria ganadera. Este artículo de revisión tiene como objetivo proporcionar una visión general de la literatura científica sobre la cría adecuada de caracoles comestibles y los posibles desafíos de los impactos antropogénicos y climáticos negativos como un desafío potencial para los criadores.

The targeted use of social robots for the family demands a better understanding of multiple stakeholders’ privacy concerns, including those of parents and children. Through a co-learning workshop which introduced families to the functions and hypothetical use of social robots in the home, we present preliminary evidence from 6 families that exhibits how parents and children have different comfort levels with robots collecting and sharing information across different use contexts. Conversations and booklet answers reveal that parents adopted their child’s decision in scenarios where they expect children to have more agency, such as in cases of homework completion or cleaning up toys, and when children proposed what their parents found to be acceptable reasoning for their decisions. Families expressed relief when they shared the same reasoning when coming to conclusive decisions, signifying an agreement of boundary management between the robot and the family. In cases where parents and children did not agree, they rejected a binary, either-or decision and opted for a third type of response, reflecting skepticism, uncertainty and/or compromise. Our work highlights the benefits of involving parents and children in child- and family-centered research, including parental abilities to provide cognitive scaffolding and personalize hypothetical scenarios for their children.

Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide design insights. Nevertheless, few available NNs can handle solution prediction on varying 3D shapes. We present a novel deep operator network (DeepONet) variant called Geom-DeepONet, which encodes parameterized 3D geometries and predicts full-field solutions on an arbitrary number of nodes. To the best of the authors' knowledge, this is the first attempt in the literature and is our primary novelty. In addition to expressing shapes using mesh coordinates, the signed distance function for each node is evaluated and used to augment the inputs to the trunk network of the Geom-DeepONet, thereby capturing both explicit and implicit representations of the 3D shapes. The powerful geometric encoding capability of a sinusoidal representation network (SIREN) is also exploited by replacing the classical feedforward neural networks in the trunk with SIREN. Additional data fusion between the branch and trunk networks is introduced by an element-wise product. A numerical benchmark was conducted to compare Geom-DeepONet to PointNet and vanilla DeepONet, where results show that our architecture trains fast with a small memory footprint and yields the most accurate results among the three with less than 2 MPa stress error. Results show a much lower generalization error of our architecture on unseen dissimilar designs than vanilla DeepONet. Once trained, the model can predict vector solutions, and speed can be over $10^5$ times faster than implicit finite element simulations for large meshes.

Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning framework, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to complete solution fields. In this paper, two newly devised DeepONet formulations with sequential learning and Residual U-Net (ResUNet) architectures are trained for the first time to simultaneously predict complete thermal and mechanical solution fields under variable loading, loading histories, process parameters, and even variable geometries. Two real-world applications are demonstrated: 1- coupled thermo-mechanical analysis of steel continuous casting with multiple visco-plastic constitutive laws and 2- sequentially coupled direct energy deposition for additive manufacturing. Despite highly challenging spatially variable target stress distributions, DeepONets can infer reasonably accurate full-field temperature and stress solutions several orders of magnitude faster than traditional and highly optimized finite-element analysis (FEA), even when FEA simulations are run on the latest high-performance computing platforms. The proposed DeepONet model's ability to provide field predictions almost instantly for unseen input parameters opens the door for future preliminary evaluation and design optimization of these vital industrial processes.

This paper explores the legal regulations on the termination of pregnancy in comparative law, a sensitive topic that, although it does represent the exclusive domain of state regulation, encroaches into human rights as well. The basic research question is how selected modern democratic states legally regulate the issue of the termination of pregnancy. Hence, the research goal is to prove that the trend of modern democratic states is to allow the termination of pregnancy even on request, but also to determine the existence of recent retrograde trends in this area. In this paper and research, except for the comparative method, the analytic, dogmatic, normative, and axiological methods are utilized. Although the core of the research is comparative legal, the historic and international legal segments are presented in short in this paper. In researching the following selected states, BiH, Serbia, Croatia, Germany, USA and Ireland, it is determined that the termination of pregnancy is currently largely allowed even on the request of a pregnant woman, especially for justified reasons, with regards to a specific legal regime (Germany), a sudden shift in complete liberalization (Ireland), and even for retrograde changes towards absolute prohibition (USA). In the argument section, the right of the state to ban a medical procedure out of arbitrary reasons (at least in modern discourse) is considered (even disputed). The conclusion is, considering the practice and development of democratic states, the trend of allowing the termination of pregnancy in early stages on demand of a pregnant woman without a reason, and in later stages with a reason, is evident. Concerning the region, the situation is relative satisfactory, although in greater parts of Bosnia and Herzegovina as well as Croatia the outdated legislation needs innovations, as well as certain improvements, which at this point is inevitable.

Waste water in the galvanic process contains high concentrations of heavy metals that pose a direct danger to humans and the environment. Conventional methods for their removal are quite expensive and generate a large amount of waste. The development of new and improvement of existing methods for the removal of heavy metals from galvanic wastewater are the subject of many studies. Compared to other purification methods, the adsorption is becoming an increasingly popular method of wastewater purification, especially if the adsorbent is cheap, easily available and does not require any other treatment before use. Therefore, the aim of the work was to investigate the possibility of using natural bentonite for the removal of heavy metal ions from multi-component water systems of the galvanic industry. For this purpose, the physico-chemical characterization of natural bentonite was performed, and then the influence of pH value, time and temperature on the adsorption efficiency was examined. The results of adsorption showed that natural bentonite can be used as an adsorbent for the removal of heavy metal ions from waste galvanic waters, and that at pH 5 it achieves the maximum removal efficiency for Cu(II):Cr(III):Ni(II) ions in the percentage ratio 100 : 99.990 : 99.998. The results showed that the highest removal efficiency for Cu (II) ions was achieved in the first 10 minutes, and 20 minutes for Cr (III) and Ni (II) ions. The maximum efficiency of Cu (II) removal was achieved at all temperatures, while for Cr (III) 99.99% and Ni (II) 100% maximum efficiency was achieved at 35°C, which indicates that the adsorption process is endothermic. The experimental results of the adsorption of Cu (II) metal ions are in good agreement with the Langmuir and Freundlich theoretical models, while for Cr (III) and Ni (II) ions they are in better agreement with the Langmuir adsorption model.

Next-generation mobile networks, such as 5G/6G, have envisioned the possibility of direct communication between user devices, known as Device-to-Device (D2D) communication. Given that in D2D networks, traffic is transmitted ad-hoc from device to device, the range and quality of service are directly dependent on the number of nodes forming the D2D network. Therefore, we need to incentivize users to participate in the network operation through appropriate compensation for the provided resources and work done. A D2D network formed this way is inherently decentralized, making blockchain the primary choice as a technology. In this paper, we propose a new blockchain-based protocol for active tracing of IP traffic via in-band network telemetry. The experiment demonstrates that the proposed protocol can record all nodes participating in traffic forwarding in the D2D network through active traffic monitoring. Blockchain-based microtransactions can use participation records provided by our protocol to incentivize users to expand and strengthen the D2D network.

Embedded real-time clock systems have a large number of applications in practice. The main issue is the accuracy of time they show, which is why performing time synchronization is very important for their usability and reliability. This paper proposes an embedded real-time analogue clock that uses an AdaFruit NeoPixel LED ring for visualizing current time. Three different colors are used for showing hour, minute and second values, whereas different levels of brightness are used for describing accurate values of time to the level of milliseconds. An Ethernet LAN module is used for performing time synchronization via a remote NTP server. Dynamic synchronization interval change is used for removing the effect of the microcontroller clock error on the accuracy of the shown time. After being put to use, the system was able to perform multiple functions successfully, including the conveying of information to the user when the clock is out of sync.

Software development is implemented in several key phases, one of which is software testing. Software testing consists of selecting techniques for the purpose of finding software defects and bugs in the process of writing code. There are several ways and approaches that lead us to that purpose, with the goal of selecting the most adequate method in terms of cost, complexity, and efficiency. In this paper, we will take a deeper dive into mutation testing techniques. Mutation testing techniques are fault-based and focus more on test structures than the input data, which is considered the testing start point. The basic concept of mutation testing consists of a few steps, which will be covered in this paper, and metrics that measure how effective the tests really are. With a few code examples, we will show why code coverage, which is mostly taken as a measure while testing, is sometimes not the most reliable source and does not give a full picture when talking about the quality of written tests.

The applications presented in this conference paper focus on the development of a mobile and web application serving as a planner with a focus on tracking persons with Down syndrome. These innovative technological solutions contribute to the development of independence and functionality for persons with Down syndrome while emphasizing the importance of inclusivity in society. In addition to focusing on organizing activities, the mobile and web applications provide support and facilitate daily tasks. The web application allows parents/guardians/teachers to add new activities to the planner and track the progress of these activities. On the other hand, the mobile application enables persons with Down syndrome to record their activities within the application, considering their specific challenges, and customizing the user interface to their needs.

With the growing requirements to keep the security of supply higher than ever the room for failures is getting smaller in today's power systems, while the increased integration of distributed renewable energy sources is additionally complicating fault detection. By using big data that is collected in modern power systems, artificial intelligence algorithms can significantly improve the capabilities of traditional protection schemes. However, the choice of the artificial intelligence algorithm can significantly impact the scheme accuracy. This paper analyses a novel approach for power system fault detection and classification by using automated machine learning procedure that iterates over different data transformations, machine learning algorithms, and hyperparameters to select the best model. By simulating and testing tens of thousands of fault scenarios on a realistic test system, the suggested approach resulted with robustness and high accuracy.