Опрема за одржување на ладниот ланец при снабдување со вакцини

Tерминот ладен синџир или ладен ланец означува низа активности и опрема што се применуваат за одржување на производот во одреден опсег на ниски температури од производство до потрошувачка. Непрекинат ладен ланец е непрекината серија активности за производство, складирање и дистрибуција во ладилник, заедно со придружната опрема и логистика, кои одржуваат посакуван опсег на ниски температури. Ладниот ланец се состои од опрема и правила за да се обезбеди постојана температура за термолабилни производи од нивното производство до времето за употреба. Различни нивоа во рамките на националниот систем за ладнен ланец бараат различни видови опрема за транспорт и складирање на вакцини и дилуенси во потребниот опсег на температура

Identification of the DNA methylation signature of Mowat-Wilson syndrome

Mowat-Wilson syndrome (MOWS) is a rare congenital disease caused by haploinsufficiency of ZEB2, encoding a transcription factor required for neurodevelopment. MOWS is characterized by intellectual disability, epilepsy, typical facial phenotype and other anomalies, such as short stature, Hirschsprung disease, brain and heart defects. Despite some recognizable features, MOWS rarity and phenotypic variability may complicate its diagnosis, particularly in the neonatal period. In order to define a novel diagnostic biomarker for MOWS, we determined the genome-wide DNA methylation profile of DNA samples from 29 individuals with confirmed clinical and molecular diagnosis. Through multidimensional scaling and hierarchical clustering analysis, we identified and validated a DNA methylation signature involving 296 differentially methylated probes as part of the broader MOWS DNA methylation profile. The prevalence of hypomethylated CpG sites agrees with the main role of ZEB2 as a transcriptional repressor, while differential methylation within the ZEB2 locus supports the previously proposed autoregulation ability. Correlation studies compared the MOWS cohort with 56 previously described DNA methylation profiles of other neurodevelopmental disorders, further validating the specificity of this biomarker. In conclusion, MOWS DNA methylation signature is highly sensitive and reproducible, providing a useful tool to facilitate diagnosis

Identification of the DNA methylation signature of Mowat-Wilson syndrome

Mowat-Wilson syndrome (MOWS) is a rare congenital disease caused by haploinsufficiency of ZEB2, encoding a transcription factor required for neurodevelopment. MOWS is characterized by intellectual disability, epilepsy, typical facial phenotype and other anomalies, such as short stature, Hirschsprung disease, brain and heart defects. Despite some recognizable features, MOWS rarity and phenotypic variability may complicate its diagnosis, particularly in the neonatal period. In order to define a novel diagnostic biomarker for MOWS, we determined the genome-wide DNA methylation profile of DNA samples from 29 individuals with confirmed clinical and molecular diagnosis. Through multidimensional scaling and hierarchical clustering analysis, we identified and validated a DNA methylation signature involving 296 differentially methylated probes as part of the broader MOWS DNA methylation profile. The prevalence of hypomethylated CpG sites agrees with the main role of ZEB2 as a transcriptional repressor, while differential methylation within the ZEB2 locus supports the previously proposed autoregulation ability. Correlation studies compared the MOWS cohort with 56 previously described DNA methylation profiles of other neurodevelopmental disorders, further validating the specificity of this biomarker. In conclusion, MOWS DNA methylation signature is highly sensitive and reproducible, providing a useful tool to facilitate diagnosis

Klasifikacija dvojezičnih poizvedb z uporabo medjezikovnih vektorskih vložitev besed

The ability to process diverse textual content is crucial as people communicate and share information across multiple languages. Multilingual text classification allows organizations to effectively analyze and comprehend customer feedback, inquiries, and sentiments expressed in multiple languages. It provides valuable advantages for businesses looking to enhance customer service and optimize their CRM (Customer Relationship Management) systems. By leveraging this capability, enterprises can address a variety of customer needs, providing customized and efficient support in their preferred languages, and at the same time saving costs and optimizing resources. Multilingual text classification presents challenges in representing and analyzing text data from different languages, due to differences in vocabulary, grammar, and semantics. Moreover, uneven data distribution among languages makes training a model more challenging, as some languages face the problem of limited data availability. Cross-lingual word embeddings provide a solution to the aforementioned challenges, as they represent words from different languages in a shared space. With this, we can analyze and classify text in multiple languages more effectively, using a single classification model. In this study, we assess the task of automatically classifying customer inquiries into target departments, such as legal, IT, accounting, etc. We conduct this problem as a bilingual text classification task, using Slovenian and English text data. We develop a pipeline for classifying texts from either English or Slovenian source language. We focus on implementing a parallel corpus and representing both languages in a shared space. With the use of a single but bilingual feature space, we include typical text patterns from both languages. This allows us to classify any incoming text sample without needing information about its source language. The feature space is generated using a statistical feature extraction method, TF-IDF (Term Frequency-Inverse Document Frequency). Alongside TF-IDF, we evaluate a pre-trained sentence-transformers model for extracting the embeddings and provide a side-by-side comparison of the results. We provide a qualitative and quantitative comparison of two classification algorithms, Logistic Regression and Random Forest, in bilingual and monolingual experimental scenarios. Logistic Regression outperforms Random Forest in all experimental setups. In classifying the bilingual data, Logistic Regression correctly classifies 97,6% with TF-IDF embeddings and 92,7% using pre-trained embeddings. In contrast, Random Forest achieves an accuracy of 93,8% using TF-IDF embeddings and 90,6% with the pre-trained embeddings. Additionally, we suggest incorporating Large Language Models (LLMs) into the workflow to provide an immediate response. This study focuses on evaluating the bilingual text classification using a suitable dataset, so we do not conduct a performance evaluation of the LLM itself. However, we propose utilizing the capabilities of LLM in Q&A tasks. In this context, key information needed to generate the response is provided to the LLM. We use the training documents as a search database for defining the prompt context. Using cosine similarity, we obtain the most similar documents from the database based on their bilingual embeddings. The findings demonstrate that the bilingual embeddings group the documents with a clear distinction between the languages. Consequently, the retrieved relevant documents are in the same language as the input sample, making this process language-invariant.Zagotavljanje nemotene pisne komunikacije med podjetji in strankami je ključnega pomena za uspešno poslovanje. Sestavni del tega je tudi učinkovito upravljanje elektronskih sporočil. Vseeno pa se porablja veliko naporov v velikih podjetjih, da dokumenti najdejo pravega naslovnika oz. pravi oddelek. V zadnjem času se z uvajanjem strojnega učenja lahko tudi to nalogo delno avtomatizira, saj je področje jezikovnih tehnologij tako napredovalo, da lahko tudi iz nestrukturiranega besedila izluščijo bistvo sporočil. Avtomatizacija takšnih nalog s pomočjo klasifikacije besedil lahko pomaga pri organiziranju velikih količin besedilnih podatkov. Klasifikacija besedila je podpodročje obdelava naravnega jezika (angl. Natural language processing), katerega namen je določiti najverjetnejšo kategorijo, ki ji vzorec besedila pripada na podlagi njegove vsebine. Na primer, članke po spletu bi lahko razdelili v naslednje kategorije: šport, politika, glasba, izobraževanje itd. Ta metoda precej poenostavi postopek iskanja in luščenja uporabnih informacij iz besedilnih podatkov. Klasifikacija besedila ima številne primere uporabe, kot so zaznavanje neželene pošte, klasifikacija e-pošte, razvrščanje dokumentov itd. S svojimi tehnikami se lahko uporablja na različnih področjih, kot so varnostne analize, napovedovanje prihodnjih trendov, izboljšanje uporabniške izkušnje ipd.. Večjezikovna klasifikacija besedil razširja osnovne koncepte enojezikovne klasifikacije besedil. Uporablja se lahko za različne primere uporabe z možnostjo obdelave podatkov v več jezikih. Večjezikovna klasifikacija besedil je na primer uporabna za izvajanje tržnih raziskav na globalni ravni, s čimer se izboljša proces odločanja pri trženju, razvoju izdelkov itd. S samodejnim organiziranjem in kategoriziranjem vsebin, komentarjev itd. se lahko analizirajo nastajajoči trendi in javno mnenje po vsem svetu. Razvrstitev večjezičnih besedil je lahko omejena z razlikami v slovnici, sintaksi in semantiki med jeziki. Poleg tega usposabljanje večjezikovnega modela zahteva veliko količino podatkov v različnih jezikih, ki pa niso vedno na voljo. To je značilno za jezike, ki imajo manj podatkov, dostopnih za razvoj sistemov umetne inteligence, kot jeziki z velikimi viri podatkov, kot je angleščina. Možna rešitev je vključitev vzporednih korpusov, ki jih je mogoče pridobiti z orodji za prevajanje. Omenjena rešitev bi lahko izboljšala težavo z dostopnostjo podatkov v primerih, ko podatki niso enakomerno razdeljeni med jezike. V bistvu bi lahko razširili obseg modela na razvrščanje besedil v katerem koli ciljnem jeziku tako, da bi ga učili na prevedenih podatkih v tem jeziku. V našem pristopu vključimo preveden korpus v slovenščini, da bi prikazali uporabo vzporednega korpusa, ki ga je mogoče posplošiti na jezike, ki imajo omenjene podatkovne vire. Za ustvarjanje učinkovitega postopka razvrščanja besedila moramo besedilne informacije pretvoriti v numerične podatke, ki jih modeli strojnega učenja lahko razumejo. Vsakemu vzorcu je dodeljen vektor vložitev, vzorci znotraj iste kategorije pa imajo ponavadi podobne vložitve. Ta podobnost omogoča modelu, da jih združi skupaj in sprejme informirane odločitve o razvrščanju. Za izračun teh vložitev implementiramo model, znan kot TF-IDF (angl. term Frequency-Inverse Document Frequency). Vsaki besedi je dodeljena vrednost na podlagi pogostosti besede v določenem dokumentu in njene skupne pogostosti v vseh dokumentih. Z uporabo TF-IDF lahko zajamemo pomen besed znotraj vsakega dokumenta in zagotovimo numerično predstavitev, ki zajame bistvo besedila. Ena beseda predstavlja eno značilko v vektorju vložitev. Poleg tega implementiramo tudi alternativni pristop k statističnemu TF-IDF pri izločanju značilk, natančneje, vnaprej naučen model BERT (angl. Bidirectional Encoder Representations from Transformers) z uporabo knjižnice sentence-transformers. S primerjavo obeh tehnik luščenje značilk ocenimo njun vpliv na celotno učinkovitost klasifikacije. Ocenjujemo izvajanje klasifikacije večjezičnih besedil v tipičnem primeru uporabe: mednarodno podjetje, ki želi izboljšati storitve za stranke. Samodejna kategorizacija in neposredno posredovanje povratnih informacij in zahtev strank v ciljni oddelek lahko skrajšata komunikacijski cevovod in precej pospešita proces komunikacije. Poleg tega so komentarji in vprašanja strank zelo pomembni za sisteme, ki skrbijo za upravljanje odnosov s strankami (angl. Customer Relationship Management). Samodejno kategoriziranje takih vprašanj omogoča podjetju, da hitro oceni, prilagodi in izboljša svoj proces odločanja ter učinkovito dodeli vire. V naši raziskavi predstavljamo cevovod za avtomatizacijo kategorizacije elektronske pošte, ki jo poenostavljamo v nalogo klasifikacije besedila. Pri tem, ocenjujemo delovanje dveh klasifikacijskih algoritmov. Logistična regresija (angl. Logistic Regression) prekaša metodo naključnih gozdov (angl. Random Forest) v vseh eksperimentalnih nastavitvah. Pri razvrščanju dvojezičnih besedil logistična regresija pravilno razvrsti 97,6% z vložitvami TF-IDF in 92,7% z uporabo vnaprej naučenih vložitev. Nasprotno pa metoda naključnih gozdov doseže natančnost 93,8% z uporabo vložitev TF-IDF in 90,6% z vnaprej naučenimi vložitvami. Naši poskusi raziskujejo vpliv uporabe različnih velikosti značilk (angl. n-grams), glede na število besed v eni značilki. S spreminjanjem števila besed v eni značilki analiziramo, kako vključitev različnih besednih kombinacij vpliva na točnost postopka klasifikacije. Ugotovitve kažejo, da so značilke, sestavljene iz ene besede (angl. uni-grams) najbolj informativne za postopek klasifikacije. Najučinkovitejša služba za stranke lahko takoj zagotovi koristne in natančne odgovore na vprašanja strank. Iz tega razloga predlagamo dodajanje jezikovnih modelov v cevovod. Uporabljajo se pri nalogah, kot so analiza sentimenta, strojno prevajanje, sistemi za vprašanja in odgovori itd. Zmožnosti jezikovnih modelov pridejo do izraza pri nalogah, povezanih s pogosto postavljenimi vprašanji in odgovori. S pomočjo jezikovnih modelov lahko ustvarimo samodejni odgovor na vhodno vprašanje, kar precej pospeši proces komunikacije. Poudariti moramo, da ne ocenjujemo uspešnosti jezikovnih modelov, saj je naš glavni cilj oceniti klasifikacijo dvojezičnih besedil z uporabo korpusa, primernega za takšno nalogo. Kljub temu pa predstavljamo zastavljeno metodologijo. Kosinusno podobnost (angl. cosine similarity) uporabljamo v procesu iskanja podobnosti, da pridobimo ustrezne dokumente. Vložitve vhodnega vzorca primerjamo z vložitvami vseh učnih vzorcev. S tem identificiramo najbolj podobne dokumente vhodnemu besedilu. Ti dokumenti so nabor informacij za jazikovni model pri ustvarjanju avtomatskih odgovorov. Jezikovni model analizira podani kontekst ter poskuša ustvariti skladen povzetek oz. odgovor, ki zajema ključne informacije, ki so relevantne za vhodno besedilo. Vendar pa obstaja potencialna omejitev jezikovnih modelov pri ustvarjanju odgovorov, zlasti če pridobivajo kontekst iz različnih jezikov. To vodi do izziva določanja ustreznega jezika za generiranje odgovora. Glede na to, da naš cevovod nima modula za detekcijo jezika in deluje z dvojezičnimi podatki, moramo zagotoviti, da se jezik odgovora ujema z jezikom vhodnega besedila. Naše ugotovitve kažejo, da za ustvarjanje odgovora ne potrebujemo eksplicitnega znanja ciljnega jezika. To je zato, ker vložitve dejansko ločijo podatke v ločene skupine, kar pomeni, da imajo tudi vzorci v istem izvornem jeziku podobne vložitve. Posledično se jezik pridobljenih podobnih dokumentov ujema z jezikom testnega dokumenta. To pomeni, da v večini primerov lahko uporabimo jezikovne modele za generiranje odgovora in bo ta odgovor v enakem jeziku, kot je bilo zastavljeno vprašanje

Концерт флејта и пијано, настап на д-р Невенка Трајкова со студент по флејта на втор циклус студии при ФМУ - УГД

Настап на д-р Невенка Трајкова, со студентката Елена Мирасчиева на втор циклус студии, во класата на Проф. м-р Златка Митева по флејта при ФМУ-УГД. Во програмата беа изведени композиции од : Е. Бартон, С.Стојков, К.Рајнике, Ч.М.Видо

Good practice in cold drug supply chain

Pharmacy could not function without detailed, efficient, flexible and secure cold distribution chains, so in the future this would mean using more sophisticated delivery techniques and technologies. The cold chain consists of equipment and rules to ensure a constant temperature for thermo-sensitive products from their production to the time of use. Aim: The aim of this study is to evaluate data from WHO, EMA and FDA, their guidelines and directions, as well as relevant data from primary, secondary and tertiary literature. Method: In this paper we will review the regulatory measures and recommendations of the WHO, EU and US regarding the cold chain and will discuss the similarities and differences in their regulation. We summarized the reviewed literary data and sorted them according to the importance of the treated problem, made a comparison of the regulatory measures in order to ensure a safe cold chain and drew appropriate conclusions. Result: The reviewed documents do not show large and substantial differences in the approach to the cold chain and its importance for product safety. Conclusion: Drugs that require storage conditions under controlled temperature must be distributed in a way to ensure that their quality is not diminished

Explaining Bad Forecasts in Global Time Series Models

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and explainable artificial intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand how value changes in the feature vector can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making

Is the Fractal Dimension of the Contour-lines a Reliable Tool for Classification of Medical Images?

When two-dimensional medical images are subject to fractal analysis, one of the methods used is to detect the contour of objects in the binary images and later to estimate the fractal dimension of the extracted contour. This scalar characteristic of the medical image should help in discrimination between normal and abnormal tissues. In this paper we expose the factors that affect the reliability of such examinations and put the fractal dimension in question as a valid criterion for description, classification and recognition in medical diagnosing

Is the Fractal Dimension of the Contour-lines a Reliable Tool for Classification of Medical Images?

When two-dimensional medical images are subject to fractal analysis, one of the methods used is to detect the contour of objects in the binary images and later to estimate the fractal dimension of the extracted contour. This scalar characteristic of the medical image should help in discrimination between normal and abnormal tissues. In this paper we expose the factors that affect the reliability of such examinations and put the fractal dimension in question as a valid criterion for description, classification and recognition in medical diagnosing