Supply chain intelligence: benefits, techniques and future trends

Supply Chain Management is a philosophy to manage logistical processes in complex systems, that are very difficult to integrate and analyze. Such systems can be effectively analysed by the use of Business Intelligence applications. The capability to make the right decision at the right time in collaboration with the right partners is the definition of the successful use of BI. This paper explains the need for Supply Chain Business Intelligence and introduces the driving forces for it’s implementation. New technologies such as data mining, and their role in BI systems are also discussed. Finally, key BI trends and technologies that will influence future systems are described.supply chain, business intelligence, data mining

Flora, Vegetation Pattern and Ecological Conditions in the Dolines of the Mecsek Mountains

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Irodalmi figyelő / Literature reviews

Erdős László (2015): Zöld hősök. A környezeti mozgalmak kiemelkedő alakjai Assisi Szent Ferenctől Arnold Schwarzeneggerig. – CSER Kiadó, Budapest, 263 pp. Bartha Dénes et al. (2015): Magyarország edényes növényfajainak elterjedési atlasza. – Nyugat-magyarországi Egyetem Kiadó, Sopron, 330 pp

CRM rendszerek implementálása

A CRM rendszerek olyan, a vállalati információs rendszerek közé sorolható speciális megoldások, amelyek elsõdleges célja, a hosszú távon is megbízható és jövedelmezõ ügyfélkapcsolatok kialakítása. Ezen különleges cél a CRM rendszerek vállalati implementálásának folyamatát is számos módon megkülönbözteti más információs rendszerek bevezetésétõl. Cikkünk célja a CRM rendszerek életciklus modelljének olyan módon történõ bemutatása, amely rámutat ezen rendszerek implementálásának sajátosságaira.

Adatok Magyarország flórájához és vegetációjához II.

45 taxon előfordulási adatait közöljük, melyeket nagyrészt a 2013–2016 közötti florisztikai, vegetációtani és ökológiai kutatásaink során találtunk. Adataink a  Duna–Tisza köze, a Tiszántúl és a Dél-Dunántúl különböző élőhelyeiről származnak; a felsorolt fajok között  számos szerepel a magyarországi edényes flóra veszélyeztetett fajainak listáján (pl. Astragalus dasyanthus, Erodium ciconium és Lotus angustissimus). Munkánkban törekedtünk a ritka (pl. Apium repens, Epipactis bugacensis és Trifolium strictum), a váratlan helyen megjelenő (pl. Medicago arabica, Polypodium vulgare és Polystichum setiferum), valamint a növényföldrajzi szempontból érdekesebb (pl. Piptatherum virescens, Ranunculus psilostachys és Tamus communis) taxonok adatainak közlésére.</jats:p

Interplay between phenotypic resistance to relevant antibiotics in Gram-negative urinary pathogens: a data-driven analysis of 10 years’ worth of antibiogram data

The global emergence of antimicrobial resistance (AMR) has become a critical issue for clinicians, as it puts the decades of developments in the medical field in jeopardy, by severely limiting the useful therapeutic arsenal of drugs, both in nosocomial and community-acquired infections. In the present study, a secondary analysis of taxonomic and resistance data was performed, corresponding to urinary tract infections (UTIs) caused by Gram-negative bacteria, detected between 1 January 2008 to 31 December 2017 at the Albert Szent-Gyorgyi Health Center, University of Szeged. The following were identifiable from the data collected: year of isolation; outpatient (OP)/inpatient (IP) origin of the isolate; taxonomy; and susceptibility/resistance to selected indicator antibiotics. Principal component analysis (PCA) and a correlation matrix were used to determine the association between the presences of resistance against indicator antibiotics in each taxonomic group. Overall, data from n = 16,240 outpatient and n = 13,964 inpatient Gram-negative UTI isolates were included in the data analyses. In E. coli, strong positive correlations were seen between resistance to ciprofloxacin (CIP) and gentamicin (GEN) resistance (OP: r = 0.6342, p = 0.049; IP: r = 0.9602, p < 0.001), whereas strong negative correlations were shown for fosfomycin (FOS) and nitrofurantoin (NIT) resistance (OP: r = -0.7183, p = 0.019; IP: r = -0.7437; p = 0.014). For Klebsiella spp. isolates, CIP resistance showed strong positive correlation with resistance to third-generation cephalosporins (3GC) and GEN (r = 0.7976, p = 0.006 and r = 0.7428, p = 0.014, respectively) in OP isolates, and with resistance to trimethoprim-sulfamethoxazole (SXT) and FOS (r = 0.8144, p = 0.004 and r = 0.7758, p < 0.001, respectively) in IP isolates. For members of the Citrobacter-Enterobacter-Serratia group, the resistance among indicator antibiotics showed a strong positive correlation, with the exception of FOS resistance. In the Proteus-Providencia-Morganella group, the strongest association was noted between CIP and SXT resistance (OP: r = 0.9251, p < 0.001; IP: r = 0.8007; p = 0.005). In the case of OP Acinetobacter spp., CIP showed strong and significant positive correlations with most indicator antibiotics, whereas for IP isolates, strong negative correlations arose among imipenem (IMI) resistance and resistance to other drugs. For Pseudomonas spp., strong and positive correlations were noted among resistance to beta-lactam antibiotics and aminoglycosides, with the exception of ceftazidime (CEFT), showing strong, but negative correlations. Though molecular tests and sequencing-based platforms are now considered as the gold-standard for AMR surveillance, standardized collection of phenotypic resistance data and the introduction of Big Data analytic methods may be a viable alternative for molecular surveillance, especially in low-resource settings