Faculty of Information Studies in Novo mesto

Economists often highlight a gross domestic product (GDP) as a key metric in determining war outcomes, despite historical exceptions, such as the Taliban’s victories over the Soviet and U.S. armies in Afghanistan—nations with vastly superior GDPs. Two critical factors that remain underexplored are the soldier’s willingness to sacrifice for their country and a country’s willingness to risk nuclear war. To address this gap, we conducted a worldwide survey to assess the maximum acceptable level of losses respondents would tolerate in their own country for varying levels of enemy losses. The findings were surprising: respondents, on average, considered 23% casualties (with a median of 10%) as an acceptable loss if it meant 100% destruction of the enemy. To determine which nuclear power might be more inclined to initiate a nuclear war, we introduce the willingness to risk ratio, defined as the ratio between the GDP that can be destroyed in enemy countries and the GDP that could be destroyed by the enemy in one’s own country. Recognizing that conventional wars can serve as a pretext for a nuclear conflict between two nuclear powers, S and S ′, we developed a network model where bravery is defined at the micro level of individual soldiers, whereas defeatism can spread contagiously throughout the network. If due to increasing aid of the nuclear power S ′ to a weaker country W, the opposing nuclear power S suffers heavier casualties, the probability of the nuclear catastrophe P surges, prompting S and S ′ to start weighing between a nuclear-war scenario and continuation of the proxy war. In this case, the increase of P ramps up the chance that the power S ′, geographically farther to the spot of conflict, stops supporting W since it is less willing to risk nuclear war and in economic terms, S ′ may lose more than S if the war escalates.

This paper considers the generalized atom-bond sum-connectivity index ABSȷ for 1 ≤ ȷ < 2 and gives the characterization of those graphs that minimize this index on the class of all fixed-order unicyclic graphs of a given maximum degree ∆. It is also proved that the cycle graph C n uniquely minimizes the aforementioned index in the class of all fixed-order unicyclic graphs. The obtained results imply conclusions concerning the maximum values of the well-known harmonic index over the considered class of graphs. When the class of investigated graphs is limited to the class of molecular unicyclic graphs, all the obtained results are still valid.

The discrepancy between perceived and actual data quality, shaped by stakeholders’ interpretations of technical specifications, poses significant challenges in governance, impacting decision-making and stakeholder trust. To address this, we introduce an automated data quality management (DQM) framework, implemented through the NRPvalid toolkit, as a standalone solution incorporating over 100 assessment tools. This framework strengthens data quality evaluation and stakeholder collaboration by systematically bridging subjective perceptions with objective quality metrics. Unlike traditional producer–user models, it accounts for complex, multi-stakeholder interactions to improve data governance. Applied to planned land use (PLU) data, the framework significantly reduces discrepancy, as quantified by error score metrics, and directly enhances building permit issuance by streamlining interactions among administrative units, municipalities, and investors. By evaluating, refining, and seamlessly integrating spatial data into the enterprise spatial information system, this scalable, automated solution supports constant data quality improvement. The DQM and its toolkit have been widely adopted, promoting transparent, reliable, and efficient geospatial data governance.

The European Union has been trying to adjust its tourism policy in response to the challenges posed by the recent COVID-19 pandemic. The funding of the EU Cohesion Policy has been one of the primary mechanisms guaranteeing that all regions can be prepared to receive tourists and cope with the sustainability challenges the pandemic has raised. The recovery and resilience plan (PRR) is the most recent instrument created to help economic growth in most European Union countries. The implementation of the PRR in Algarve and Alentejo has been fundamental to understanding the Portuguese tourism sector, as these regions are heavily dependent on tourism sector revenue and were hit hard by the consecutive lockdowns in recent years. Therefore, this policy, in brief, critically assesses the tourism-related projects that received funding from the PRR until November 2023 (ex ante) and their potential to guarantee long-term tourism sustainability in Algarve and Alentejo. Despite not achieving the expected efficiency results due to delays in project execution and low investment, the PRR is still a positive upgrade for tourism-related policy in Algarve and Alentejo.

Traditionally, academic institutions have relied on interviews with industry representatives and alumni surveys to gauge market demand, an approach that often results in dated and limited information. In this article, we show that using real‐time data from job postings in professional sites provides more direct, rich, and timely insights regarding the current demand and skill requirements. We propose a novel machine‐learning based framework for detecting the skillsets for positions requiring Master of Business and Administration (MBA). Utilizing LinkedIn job‐posting data in the US state of Pennsylvania, our analysis reveals 20 distinct functional areas. While some of these functional areas (e.g., people management) are predictable, others (e.g., supply chain project management) were not anticipated to be high in demand in the given market. Our results also identify the most sought‐after skillsets (e.g., resource allocation). Most importantly, we observe that the top skillsets span multiple functional areas. Taken together, our results can help business school program directors update and customize curricula to meet market demand.

Irregularity measures of graphs serve as crucial tools for optimizing networks, understanding biological interactions, analyzing social dynamics, enhancing cybersecurity, and assessing market stability, offering valuable insights across diverse fields. In this paper, we introduce a family of irregularity measures for graphs with non-increasing degree sequences, termed degree scaling irregularities, defined as $\mathcal{I}(G,r)=\sum_{i=1}^n d_i r_i$, where $d=(d_1,d_2,$ $\ldots,d_n)$ is a non-increasing degree sequence of a graph G and $r = (r_1, r_2, \ldots, r_n)$ is a non-increasing n-tuple of real numbers such that $r_1+r_2+ \ldots+ r_n=0$. This family provides a versatile framework for analyzing graph irregularity. Various choices for r are explored, including using eigenvalues of matrices related to G or orientations of G. It has been proven that if G maximizes $\mathcal{I}$-irregularity among all connected graphs of order n for a given r, then G is a split graph, a graph comprised of a clique and an independent set. Additionally, we investigate the properties of graphs that maximize $\mathcal{I}$-irregularity and explore computational aspects when r is based on matrix eigenvalues.

The reasons behind the slow pace of corruption suppression within democratic systems are not well understood. We suggest that it relates to a societal inequity, precisely an insufficient parliamentary representation of the interests of private-sector workers. Our analysis of data from European Economic Area countries reveals a positive correlation between the proportion of Members of Parliament who have exclusively worked in the public sector and the level of corruption in their respective countries. Further, we find a negative correlation between a country’s level of corruption representing a form of in-group cooperation and the percentage of its population in cooperatives, which serves as an indicator of universal cooperation. Finally, the emergence of breakpoints in the analysis of corruption data motivates us to propose a network model where the economy is an evolving complex system characterized by a tipping point. We argue that, particularly in more corrupt European countries, private-sector workers should be better represented by parliamentarians with private-sector work experience to successfully combat corruption and thus promote equity and good governance.

This work examines the similarities and differences between twenty-two European countries by using the computational model Information Dynamics of Music (IDyOM) to analyze various musical elements in folk songs, children’s folk songs, and children’s songs. The examination of the (dis)similarities between 22 European countries tests two hypotheses. First, it examines whether there are significant differences in the use of musical elements between European countries that are considered to have a common musical style. Secondly, it explores whether the musical elements used in the representative music of a particular country are more similar in countries with similar cultural, political, historical and economic backgrounds and geographical proximity. The results of the research, which compared the three genres across 22 European countries, revealed significant differences that highlight the unique ways in which these genres manifest themselves and how musical elements are integrated into the musical structure, suggesting that European countries do not possess a single musical style. Furthermore, some geographically distant countries have exhibited similarities, while other geographically close countries showed dissimilarities. This implies that either there is no shared musical foundation across different countries, or that the unique variations in musical expression within certain countries have had a significant influence on the overall population.

We propose a formal model of a knowledge graph (abbr. KG) that classifies the ground triples into sets that correspond to the triple types. The triple types are partially ordered by the sub-type relation. Consequently, the sets of ground triples that are the interpretations of triple types are partially ordered by the subsumption relation. The types of triple patterns restrict the sets of ground triples, which need to be addressed in the evaluation of triple patterns, to the interpretation of the types of triple patterns. Therefore, a schema graph of a KG should include all triple types that are likely to be determined as the types of triple patterns. The stored schema graph consists of the selected triple types that are stored in a KG and the complete schema graph includes all valid triple types of KG. We propose choosing the schema graph, which consists of the triple types from a strip around the stored schema graph, i.e., the triple types from the stored schema graph and some adjacent levels of triple types with respect to the sub-type relation. Given a selected schema graph, the statistics are updated for each ground triple t from a KG. First, we determine the set of triple types stt from the schema graph that are affected by adding a triple t to an RDF store. Finally, the statistics of triple types from the set stt are updated.

The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Why does coffee wake us up? Is it because it contains caffeine, or because we are used to it waking us up after drinking it? To answer this question, we recruited twenty habitual coffee drinkers who received either caffeinated or decaffeinated coffee (placebo) in a double-blind, randomized fashion. The two substances were identical except for the presence of caffeine. We measured cognitive performance, cardiovascular responses, and whole-head EEG during rest and during an auditory-oddball task. The same measurements were done before and after ingestion. We expected to find significant differences between caffeine and placebo groups across the outcome measures. However, except for the resting-state alpha power, changes due to ingestion in physiological responses and in cognitive functioning were not significantly different between the two groups. Actually, only one of the three cognitive measures was found to be significantly altered by the ingestion. These findings suggest that regular coffee consumers respond to coffee-like beverages independently of the presence of caffeine.

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones.

Given two graphs G and H, their modular product $G\diamond H$ is defined to be the graph with $V(G\diamond H)=V(G)\times V(H)$ and $E(G\diamond H)=E(G\Box H)\cup E(G\times H)\cup E(\overline{G}\times \overline{H})$. A dominating set of G is any set $D\subseteq V(G)$ such that every vertex of G not contained in D has a neighbor in D. A total dominating set of G is a dominating set D of G with the additional property that all vertices of D also have a neighbor in D. The domination number $\gamma (G)$ (resp. total domination number $\gamma _{t}(G)$) of G is the cardinality of a smallest dominating set (resp. total dominating set) of G. In this work we give several upper and lower bounds for $\gamma (G\diamond H)$ in terms of $\gamma (G),$ $\gamma (H)$, $\gamma _{t}(\overline{G})$ and $\gamma _{t} (\overline{H})$, where $\overline{G}$ is the complement graph of G. Further, we fully describe graphs where $\gamma (G\diamond H)=k$ for $k\in \{1,2,3\}$. Several conditions on G and H under which $\gamma (G\diamond H)$ is at most 4 and 5 are also given. A new type of simultaneous domination $\bar{\gamma }(G)$, defined as the smallest number of vertices that dominates G and totally dominates the complement of G, emerged as useful and we believe it could be of independent interest. We conclude the paper by proposing few directions for possible further research.

We consider general atom-bond sum-connectivity indices ABSa for 1/2 ≤ a ≤ 1 and study their values over all trees on a given number of vertices with a fixed maximum degree. We obtain both the minimum and the maximum values and characterize the corresponding trees. The obtained results recover previously known results for the atom-bond sum-connectivity index and imply analogous results for the well-known harmonic index. The results remain valid when the class of considered graphs is restricted to the class of molecular trees.

This chapter explores how Slovenian sociology influenced the national identity within cultural, political, and economic contexts, using cultural political economy concepts. It emphasizes sociology’s role in post-socialist transformations from the late 1980s to the early 1990s. The authors analyze trends in two Slovenian social science peer-reviewed journals. The analysis concludes the main topics of scientific endeavors were social inequality, lifestyles, feminism, and welfare state issues. Sociological scientists thus played an influential role in shaping the social transformation within the first decades of Slovenian independence. It is also detected that the Slovenian sociological community is actively engaged and well-connected to other professional circles, especially in politics.

The redesign of the geographical boundaries in Eastern Europe triggered changes in the (newly) emerged countries. As a result, not only did authoritarian regimes transition to democracies, but there was also the need to embrace the market economy and adapt toward the new realities of the post-socialist transformations in Eastern Europe. Thus, the fall of the USSR entailed the emergence of 15 independent republics and started a transition process, not only in these newly established republics but also in the countries of the post-socialist block. This chapter will focus on analysing a practical case study concerning the role of sociology during the early stages of transition and developing a new political, social, and economic system in the Republic of Moldova. This research is the first attempt to understand the role of Moldovan sociology in the framework of the post-socialist transformation. Following the Cultural Political Economy model, the research will attempt to understand the role of the Moldovan sociology academic group during the post-socialist transition. The research focuses on analysing and comparing the perceptions of Moldovan sociologists, active or knowledgeable, of the volatile situation of Moldova in the early 1990s.

While sociology shows continued interest in its impact on the real world, there is virtually no research on its impact on one of the major recent transformations, namely the post-socialist one in Central and Eastern Europe. Aiming to fill this void, this chapter sets the conceptual and analytical framework for research on the topic. We suggest the Cultural Political Economy (CPE) as the starting point, with a view to focus on how sociologies (re) create social, political, and economic imaginaries and thus formulate a particular conception of society. CPE provides the tools to explore evolutionary mechanisms and selectivities that produce hegemonic discourses. We then present results of preliminary empirical exploration, which revealed two key dimensions shaping the impact of sociologies—status of the profession and transformative power—and on this basis, develop four ideal types of sociologies’ role in societal transformations: activators, voyeurs, fellow travellers, and marginals. We conclude with four temporal phases with respect to the challenges faced and topics covered—the nascent phase, the reflective phase, the reorientation phase, and new crises and challenges—and two modes of sociology that are changing over time: sociology as a crisis science and sociology as a reflexive science.

The author discusses the role of sociologists in a role of intellectuals in the process of post-communist transformation. His main claim is that the intellectual engagement of sociologists is necessary for successful societal transformation in former communist countries. However, it brings a threat of ideologisation and politicisation of academic space. For developmentally beneficial intellectual engagement to take place, several conditions (at both the personal and systemic levels) must be fulfilled. One can speak about intellectual imagination in terms of avoidance of stereotypes and clichés); autonomy toward the political elite and other powerholders; critical distance, especially toward one’s ideological orientations and political beliefs; dedication in terms of devoting time and energy to public causes; and pluralism, meaning the existence of different ideological strains within the intellectual circle.

In random sequential adsorption (RSA), objects are deposited on a substrate randomly, irreversibly, and sequentially. Attempts of deposition that lead to an overlap with previously deposited objects are discarded. The process continues until the system reaches a jammed state when no further additions are possible. We analyze a class of RSA models on a two-row square ladder graph in which landing on an empty site in a graph is allowed when at least b neighboring sites in the graph are unoccupied ( b∈N). In this paper we complement this typical way of studying RSA models by analyzing also the structure of the set of all jammed states in a static way, disregarding the dynamics that led to a particular jammed state. In both considered settings (dynamic and static) we provide explicit expressions for key statistics that describe the average proportion of the substrate covered by deposited objects, and then we comment on significant differences between the two settings. We illustrate all of our findings through a toy model for ensembles of trapped Rydberg atoms with blockade range b.