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Ends of the first complementary series of generalized principal series
No full textWe determine all irreducible non-tempered composition factors of induced representations appearing at the ends of the first complementary series of generalized principal series representation of either symplectic, special odd-orthogonal, or unitary group over a non-archimedean local field
Abstinence as an Outcome in Patients with Alcohol Use Disorder: A Prospective Study
No full textAim: Alcohol use disorder (AUD) has a high relapse rate post treatment with first 3 months being the most vulnerable. This study estimated the proportion of abstinent subjects 3 months after treatment and predicted the abstinence status based on clinical and sociodemographic variables. Subjects and Methods: All patients with AUD who attended the deaddiction clinic during the study period and met the inclusion and exclusion criteria were recruited into the study and followed up for 3 months after detoxification. Severity of substance use was assessed by ASSIST. Craving was assessed by Penn Alcohol Craving Scale and self-efficacy by General Self Efficacy Scale. Both parametric and non-parametric analysis were undertaken to address the research questions. Results: Out of 102 subjects, 54.9 % were abstinent for 3 months. 29.4 % relapsed, 7.85 % had a lapse and 7.85 % dropped out from the study. Abstinence status could be predicted by lower baseline craving scores, absence of comorbid psychiatric disorder and other substance use. Significant reduction in craving scores were seen in abstinent subjects at 3 months. Difference in alcohol use severity and craving scores were significant between two groups at baseline. Conclusion: Relapse should be expected in treatment for AUD with multiple factors contributing. This study findings shows the role of craving, comorbidities of psychiatric disorders and other substance use in relapse. A routine screening for comorbid psychiatry diagnosis and addressing comorbid substance use is warranted
Procjena učinkovitosti nanofiltracije i reverzne osmoze zauklanjanjeatrazina iz vode
No full textPesticide contamination from agricultural activities has become a growing environmental concern since pesticides can migrate across environmental compartments and accumulate on undesirable surfaces and in water bodies. Given their high toxicity to living organisms and resistance to degradation, developing effective removal strategies is essential. This study investigates the removal of pesticide atrazine from a binary solution using commercially available nanofiltration (NF) and reverse osmosis (RO) membranes, with molecular weight cut-offs of 150–300Da and 100–200Da, respectively. The experimental study was conducted in a laboratory-scale RO/NF system with six cells connected in parallel over a duration of 3h. Removal efficiency was determined by analysing all samples (feed and permeate) using liquid chromatography-tandem mass spectrometry. The results showed that the atrazine removal efficiency ranged from 16.0 to 84.9% with NF membranes, and from 64.0 to 93.1% with RO membranes, indicating that size exclusion was the main removal mechanism.Povećanje poljoprivrednih aktivnosti dovodi do sve veće zabrinutosti oko otpuštanja pesticida u okoliš, zbog njihove mogućnosti putovanja kroz različite dijelove okoliša i akumulacije na neželjenim površinama i u vodenim tijelima. Zbog visoke toksičnosti pesticida prema živim bićima, kao i otpornosti na razgradnju, važno je istražiti učinkovite metode za njihovo uklanjanje iz vode. U ovom istraživanju, ispitano je uklanjanje pesticida atrazina iz binarne otopine uporabom komercijalno dostupnih nanofiltracijskih (NF) i reverzno osmotskih (RO) membrana s vrijednostima granične molekulske mase od 150 do 300Da odnosno od 100 do 200Da. Eksperiment je proveden na laboratorijskom RO/NF sustavu sa šest usporedno povezanih ćelija kroz razdoblje od 3h. Da bi se ispitala učinkovitost uklanjanja, svi uzorci ulazne otopine i permeata analizirani su tekućinskom kromatografijom spregnutom s tandemskom masenom spektrometrijom. Rezultati su pokazali da je učinkovitost uklanjanja atrazina 16,0–84,9% za NF, a 64,0–93,1% za RO, što ukazuje na to da je glavni mehanizam uklanjanja efekt prosijavanja
New distribution data of the broad–bordered bee hawk moth Hemaris fuciformis (Linnaeus, 1758) in Croatia
No full textBased on the known available records of Croatia’s hawk moths, including more recent observations, this short communication provides an updated contribution to the known distribution of the broad-bordered bee hawk-moth in Croatia. Individuals of the species were recorded in the areas of Strmendolac and Vinine, Split-Dalmatia County. These observations confirm additional southern localities for the species in Croatia, beyond Makarska. Given the limited number of observations, a wider distribution of the species can be expected in the southern parts of the Mediterranean biogeographical region of Croatia
Image Generation of Aesthetic Massage types Generative AI: Applying the Khizer Abbas Framework
No full textAI image generation tools are transforming the beauty industry by offering new possibilities for visualizing skincare massage types through realistic image generation from text-based prompts. This study aims to explore this potential by selecting six massage types based on their practicality and visual representation potential. Three skincare professionals with over 10 years of experience identified experts Swedish Massage, Foot Massage, Bamboo Therapy, Aromatherapy, Thai Massage, and Stone Therapy, and images were generated using DALL·E, an AI-based image model. A total of 24 images (4 per framework) were generated for analysis. Images were created by applying Khizer Abbas framework types, R-T-F(Role-Task-Format), T-A-G(Task-Action-Goal), C-A-R-E(Context-Action-Result-Example), and R-I-S-E(Role-Input-Steps-Expectation). The application of these frameworks resulted in distinct image generation focuses, with each framework emphasizing different key elements. RTF highlighted the therapist’s role and treatment environment, while TAG effectively depicted massage techniques and relaxation states. CARE prioritized the treatment setting through example-based visuals, creating a harmonious representation of the environment and session, whereas RISE focused on step-by-step procedures and professional expertise, ensuring a structured and comprehensive visual portrayal of massage treatments. AI models faced limitations in depicting physical contact and specialized roles, but reconfiguring settings and tools improved image generation. Each framework emphasized specific elements, such as the therapist’s role, techniques, environment, and effects, allowing for a comparative analysis of visual representations across massage types. The main contribution of this study is the comparative analysis of four visual prompt frameworks applied to AI-generated skincare massage images. Further research on effective prompt-writing standards and methodologies for the beauty industry could expand AI’s applicability in beauty, education, and design fields
Electropolymerized poly(methylene blue)-modified graphite electrode for phosphate detection
No full textA graphite electrode modified with an electropolymerized poly(methylene blue) (PMB) film was prepared and evaluated as an electroanalytical platform for phosphate detection in aqueous media. The PMB layer was deposited on graphite by potentiodynamic electro-polymerization via cyclic voltammetry and characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy, scanning electron microscopy and Fourier-transform infrared spectroscopy. Compared to the bare graphite electrode, the modified surface exhibited improved interfacial electrochemical properties and an increased electroactive surface area. The electroanalytical response toward phosphate was investigated using differential pulse voltammetry (DPV), chronoamperometry and CV, revealing a linear response in the concentration range from 50 to 475 mM, as determined from DPV and chronoamperometric measurements. The modified electrode showed good reproducibility, low interference from common inorganic ions, and satisfactory performance in real water samples, with recovery values close to 100 %. These results demonstrate that the PMB-modified graphite electrode constitutes a simple and reliable electroanalytical approach for phosphate determination in environmental samples
Poly(asparagine)-modified duplex stainless steel composite carbon paste electrode for selective electrochemical detection of dopamine
No full textDopamine (DA) is a vital neurotransmitter used in clinical diagnostics and neurochemical studies. In this article, a robust, accurate, and highly sensitive method for determining DA using a duplex stainless steel (DSS)-modified carbon paste electrode (MCPE) and cyclic voltammetry is reported. To improve the sensitivity of the DSS-MCPE, the electrode was polymerized via 10 potential cycles using asparagine, a non-essential amino acid, thereby forming poly(asparagine) on the electrode surface. This polymerized electrode surface acts as a selective barrier to DA and enables the DSS-MCPE to detect DA accurately, even in the presence of interfering molecules such as ascorbic and uric acids, which have overlapping oxidation potentials. Compared with the bare carbon paste electrode (BCPE) and DSS-MCPE, poly(asp)-DSS-MCPE exhibits excellent electrochemical behaviour, a higher oxidation peak current, and improved electron transfer kinetics. The effects of variations in scan rate, pH, and DA concentration on the electrocatalytic behaviour of poly(asp)-DSS-MCPE were investigated. Also, the electrode active surface area was calculated, and the limit of detection, limit of quantification, and number of electrons and protons involved in electrochemical reactions were determined. The ease of fabrication, cost-effectiveness, and robust performance of poly(asp)-DSS-MCPE make it a promising electrocatalyst for detecting DA and other bioactive molecules, without interference from interfering molecules
Recent advances in nanomaterials-based electrochemical sensors for herbicide detection
No full textIn recent decades, herbicides have been extensively used to preserve the quantity and quality of crops, thereby meeting the growing demand for food production worldwide. Environmental pollution resulting from the excessive utilization of pesticides, particularly the over-application of herbicides to safeguard desirable crops from weeds, poses a significant threat to both human health and the ecological system. It is essential to detect these pollutants at low concentrations, particularly in water and soil samples. While commonly accepted analytical procedures (chromatography and spectroscopy methods) are available, these highly sensitive and time-consuming methods are hindered by their high costs, the requirement for bulky equipment, the need for user training, and the necessity for sample pre-treatment. Electrochemical sensors address the limitations of traditional detection methods and offer significant potential for the efficient, sensitive, and cost-effective detection of herbicides. The development of nanomaterial-based electrochemical sensors for detecting herbicides has attracted considerable attention because of their benefits, including high selectivity, sensitivity, real-time monitoring capabilities, and user-friendliness. This review provides a thorough overview of the recent advancements in nanomaterial-based electrochemical herbicide sensors. The review begins with a general introduction, followed by a discussion on electrochemical sensors and the significance of incorporating nanomaterials into electrochemical sensors. Additionally, the review highlights recent advancements in electrochemical sensors that utilize various nanomaterials, including carbon-based nanomaterials, metal and metal oxide nanoparticles, metal-organic frameworks and transition metal chalcogenides, for the quantitative determination of herbicides. Finally, the review outlines the perspectives associated with the practical application of nanomaterial-based electrochemical herbicide sensors
Usporedba generativnih suparničkih mreža za sintezu slika listova vinove loze
No full textOvaj rad istražuje primjenu generativnih suparničkih mreža za generiranje slika listova vinove loze te provodi usporednu analizu triju arhitektura baziranih na generativnim suparničkim mrežama, a to su osnovni model generativnih suparničkih mreža, duboke konvolucijske suparničke mreže i Wasserstein generativne suparničke mreže s gradijentnim penalom. Eksperimentalni dio rada s generativnim suparničkim mrežama temelji se na skupu stvarnih slika listova vinove loze koje su prethodno normalizirane i pripremljene za treniranje modela. Svaki od promatranih modela treniran je zasebno, nakon čega su generirane sintetičke slike korištene za kvantitativnu evaluaciju kvalitete generiranja. Za procjenu sličnosti između stvarnih i generiranih slika korištene su standardne metrike: Frechet Inception Distance, Kernel Inception Distance i Inception Score, koje se često primjenjuju u analizi generativnih modela. Dobiveni rezultati pokazuju da duboka konvolucijska generativna suparnička mreža ostvaruje najbolje performanse među analiziranim modelima, s najnižom vrijednošću Frechet Inception Distance i Kernel Inception Distance metrike te najvišim Inception Score rezultatom, što upućuje na stabilnije treniranje i bolju kvalitetu generiranih slika. Wasserstein generativna suparnička mreža s gradijentnim penalom postiže srednje rezultate, dok osnovni model generativne suparničke mreže pokazuje najslabije performanse, što je u skladu s nalazima sličnih istraživanja u literaturi. Zaključno, rezultati potvrđuju da naprednije arhitekture generativnih suparničkih mreža pružaju značajne prednosti u generiranju realističnih slika listova vinove loze te imaju potencijal za daljnju primjenu u poljoprivrednim i računalno-vidnim sustavima
Framework Domestication of AI Tools in the Everyday Lives of Croatian Citizens
No full textThis article analyses how generative artificial intelligence (AI) can be domesticated within everyday life by revisiting and extending classic theories of technological integration. Drawing on Silverstone’s domestication model, the analysis outlines how technologies acquire meaning through appropriation, objectification, incorporation, and conversion within the moral economy of the household. To address the limits of a framework originally developed for television, the article integrates insights from the Actor-Network Theory, and the Technology Acceptance Model.
Empirical results show that 54,5% of Croatian citizens use AI tools, while 45,5% do not. Usage varies significantly by age and region, with 27,8% using AI occasionally, 26,7% frequently, and 23,6% not at all. Non-use is primarily explained by insufficient knowledge, lack of interest, and distrust. Familiarity and perceived competence strongly shape attitudes and incorporation into daily routines, indicating that domestication often breaks down at the appropriation stage. Overall, the findings highlight AI’s ambivalent role as both intermediary and mediator in everyday practices