Kocarev, LJupcho

Preferred name

Kocarev, LJupcho

Official Name

Kocarev, LJupcho

Alternative Name

Kocarev, Ljupco

Main Affiliation

ljupcho.kocarev@finki.ukim.mk

Now showing 1 - 9 of 9

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Inferring Cuisine - Drug Interactions Using the Linked Data Approach
(Springer Nature, 2015-03-20)
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Food - drug interactions are well studied, however much less is known about cuisine - drug interactions. Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in the world. Here we address the problem of how known negative food - drug interactions are spread in different cuisines. We show that different drug categories have different distribution of the negative effects in different parts of the world. The effects certain ingredients have on different drug categories and in different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine - drug interactions for patients which are being administered drugs with known negative food interactions. A patient being under a treatment with one such drug should be advised not only about the possible negative food - drug interactions, but also about the cuisines that could be avoided from the patient's diet.
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Bridging Online and Offline Social Networks: Multiplex Analysis
(Elsevier BV, 2016-05-06)
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Andrej Gajduk
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Tamara Dimitrova
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We show that three basic actor characteristics, namely normalized reciprocity, three cycles, and triplets, can be expressed using an unified framework that is based on computing the similarity index between two sets associated with the actor: the set of her/his friends and the set of those considering her/him as a friend. These metrics are extended to multiplex networks and then computed for two friendship networks generated by collecting data from two groups of undergraduate students. We found that in offline communication strong and weak ties are (almost) equally presented, while in online communication weak ties are dominant. Moreover, weak ties are much less reciprocal than strong ties. However, across different layers of the multiplex network reciprocities are preserved, while triads (measured with normalized three cycles and triplets) are not significant.
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Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter
(Elsevier BV, 2019-10)
Janjic, Predrag
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Petrovski, Kristijan
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Dolgoski, Blagoja
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Smiley, John
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Background: Standard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe automated segmentation and measurement of each myelinated axon and its sheath in EMs of arbitrarily oriented human white matter from autopsies. New methods: Preliminary segmentation of myelin, axons and background by machine learning, using selected filters, precedes automated correction of systematic errors. Final segmentation is done by a deep neural network (DNN). Automated measurement of each putative fiber rejects measures encountering pre-defined artifacts and excludes fibers failing to satisfy pre-defined conditions. Results: Improved segmentation of three sets of 30 annotated images each (two sets from human prefrontal white matter and one from human optic nerve) is achieved with a DNN trained only with a subset of the first set from prefrontal white matter. Total number of myelinated axons identified by the DNN differed from expert segmentation by 0.2%, 2.9%, and -5.1%, respectively. G-ratios differed by 2.96%, 0.74% and 2.83%. Intraclass correlation coefficients between DNN and annotated segmentation were mostly>0.9, indicating nearly interchangeable performance. Comparison with existing method(s): Measurement-oriented studies of arbitrarily oriented fibers from central white matter are rare. Published methods are typically applied to cross-sections of fascicles and measure aggregated areas of myelin sheaths and axons, allowing estimation only of average g-ratio. Conclusions: Automated segmentation and measurement of axons and myelin is complex. We report a feasible approach that has so far proven comparable to manual segmentation.
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A first passage under resetting approach to income dynamics
(Elsevier BV, 2023-10)
Jolakoski, Petar
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Pal, Arnab
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Sandev, Trifce
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Metzler, Ralf
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Non-Markovian SIR epidemic spreading model
(Arxiv repository, 2021-07-15)
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Igor Tomovski
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Trifce Sandev
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We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete- and continuous-time versions. The incubation period, delayed infectiousness and the distribution of the recovery period are modeled with general functions. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, in the spring, 2020.
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Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: evidence from Bayesian model averaging
(Scientific Reports, 2022-05-02)
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Jolakoski, Petar
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Utkovski, Zoran
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The COVID-19 pandemic resulted in great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influenced the extent to which these discrepancies appeared. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate whether 28 variables, which describe a diverse set of health and socio-economic characteristics, correlate with the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only a few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.
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Aligning economic feasibility, investor perceptions, and media narratives in renewable energy transitions: A socio-economic systems perspective
(Elsevier BV, 2025-12)
Dedinec, Aleksandar
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Dedinec, Aleksandra
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Prodanova, Jana
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Kulebanova, Stefani
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Janevski, Darko
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Smart Energy Siting to Guide Renewable Energy Transition
(Springer Science and Business Media LLC, 2025-10-30)
Dedinec, Aleksandar
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Dedinec, Aleksandra
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Nakev, Slave
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Dimitriev, Dejan
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Mihajloska, Emilja
<title>Abstract</title> <p>The rapid expansion of renewable energy demands strategic planning to ensure environmentally and socially responsible vision that reduces conflicts and facilitates the deployment of low carbon energy. Here we advance a methodology for energy planning through the spatial prioritization of barren lands and brownfields for photovoltaic and wind power development, demonstrated via a case study in Macedonia. The study incorporates environmental constraints (e.g., slope, protected areas, biodiversity), technical factors (e.g., solar irradiation, wind speed, proximity to grid and roads), and socio-economic indicators (e.g., available workforce, settlement proximity), utilizing a multi-criteria decision analysis framework integrated with the Analytic Hierarchy Process (AHP). GIS mapping tools were used to evaluate multiple scenarios over 450,000 hectares of land, generating high-resolution suitability maps. A questionnaire was administered to 93 stakeholders from public institutions, the private sector, and academia to determine AHP weighting. The integration of national cadaster data with ecosystem classifications further validated the spatial accuracy and credibility of the analysis. The results reveal substantial land areas suitable for photovoltaic (up to 50 GW) and wind (up to 457 MW) installations, even under strict environmental constraints. Sensitivity analyses underscore the spatial and technical trade-offs when additional exclusions such as Important Bird Areas and Important Plant Areas are considered. This framework offers a replicable and transparent approach for governments seeking to balance energy security, land use efficiency, and ecological preservation. The case study illustrates how data-driven, participatory planning tools can guide equitable and sustainable renewable energy expansion aligned with national energy and climate goals.</p>
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AI-driven landscape values mapping
(AIP Publishing, 2026-02-01)
Jovanovikj, David
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Stojcheva, Marija
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Domazetoski, Viktor
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Nakev, Slave
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Dedinec, Aleksandra

Kocarev, LJupcho

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