[This corrects the article DOI: 10.1021/acsomega.3c08932.].

Fokus rada je na motivaciji kao jednom od glavnih čimbenika u poučavanju i učenju stranih jezika, kao i na načinu na koji se učenici motiviraju za njihovo učenje. Kao potpora empirijskom istraživanju testirani su aspekti motivacije (intrinzične i ekstrinzične) kako bi se ispitao njihov doprinos tijekom online nastave izazvane Covidom-19 i nakon povratka u škole. U prilog dobivenim rezultatima dane su preporuke koje bi mogle poslužiti budućoj organizaciji online nastave, ali i poslužiti pedagoško-psihološkim i didaktičko-metodičkim kompetencijama nastavnika. Istraživanje je potvrdilo povećanje i intrinzične i ekstrinzične motivacije nakon povratka u školu, za razliku od razdoblja provedenoga u online nastavi s izuzetkom aspekta učenja njemačkoga jezika. Prisutna intrinzična motivacija za učenje njemačkoga jezika u predikciji učenja njemačkoga jezika daje bolje rezultate iz razdoblja 2019. u odnosu na razdoblje 2021. Zanimljivo je da su učenici 2019. više uživali u samostalnom učenju tijekom online nastave nego danas u učionicama. Utjecaj analiziranih aspekata ekstrinzične motivacije proveden je uz pomoć korištenih metoda i nastavnih oblika rada. Utvrđene vrijednosti pokazuju veću snagu u 2021. godini u odnosu na 2019. godinu. Implikacije nedostatka digitalnih kompetencija ozbiljno zahtijevaju restrukturiranje obrazovnoga rada učitelja.

Data curation is an essential component of large-scale pretraining. In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring the joint learnability of a batch. We derive a simple and tractable algorithm for selecting such batches, which significantly accelerate training beyond individually-prioritized data points. As performance improves by selecting from larger super-batches, we also leverage recent advances in model approximation to reduce the associated computational overhead. As a result, our approach--multimodal contrastive learning with joint example selection (JEST)--surpasses state-of-the-art models with up to 13$\times$ fewer iterations and 10$\times$ less computation. Essential to the performance of JEST is the ability to steer the data selection process towards the distribution of smaller, well-curated datasets via pretrained reference models, exposing the level of data curation as a new dimension for neural scaling laws.

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

Calocucullia celsiae (Herrich-Schäffer, [1850]) is an easily recognizable noctuid species, differing from all other similar species in its subfamily. Within this survey, it was recorded at two localities in Bosnia and Herzegovina. Two specimens were collected near Hutovo village in the southern Herzegovina region in April 2023, and a single specimen was collected near Zoranovići village in the central part of the country in May 2023. These are the first records of this species for Bosnia and Herzegovina and the westernmost known data on the presence of this species on the Balkan Peninsula.

Electromagnetic levitation represents a contemporary line of technology with a wide spectrum of applications in several areas of engineering. Regarding the increased demands for this technology in-depth research into its dynamics and the influence of different material characteristics used in these systems is needed. This paper presents simulation results of electrodynamic levitation system regarding different types of materials used for levitating disc. The main intention is to provide a comparison of electrodynamic levitation systems with different materials used for levitating disc in order to improve the system itself. Therefore, the impact of different materials used for the levitating disc of the electrodynamic levitation system would be investigated through some parameters of interest such as an analysis of the electromagnetic force, disk displacement, and the time required to achieve a stable disk position.

The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as automated personal assistants that adapt to the user's unique data and demands. Two key desiderata for such assistants are personalization-in the sense that the assistant should reflect the user's own style-and privacy-in the sense that users may prefer to always store their personal data locally, on their own computing device. We present a new design for such an automated assistant, for the specific use case of personal assistant for email generation, which we call Panza. Specifically, Panza can be both trained and inferenced locally on commodity hardware, and is personalized to the user's writing style. Panza's personalization features are based on a new technique called data playback, which allows us to fine-tune an LLM to better reflect a user's writing style using limited data. We show that, by combining efficient fine-tuning and inference methods, Panza can be executed entirely locally using limited resources-specifically, it can be executed within the same resources as a free Google Colab instance. Finally, our key methodological contribution is a careful study of evaluation metrics, and of how different choices of system components (e.g. the use of Retrieval-Augmented Generation or different fine-tuning approaches) impact the system's performance.

New accurate, precise, and sensitive spectrophotometric method were developed for the assay of L-ascorbic acid in pharmaceutical preparations. The determination of L-ascorbic acid was based on its oxidation by potassium peroxydisulfate in the presence of Ag(I) as a catalyst. The molar absorptivity of the proposed method was found to be 8.61 · 103 L mol-1 cm-1 at 248 nm. Beer's law was obeyed in the concentration range of 0.46–20.0 μg mL–1. Other compounds commonly found in vitamin C and multivitamin products did not interfere with the determination of L-ascorbic acid. The proposed method was successfully applied for the determination of L-ascorbic acid in pharmaceutical formulations. The results obtained with the proposed method showed good agreement with those given by the titrimetric method using iodine.

The results of orthodontic therapy largely depend, among other factors, on the preparation of the tooth enamel itself and the choice of material used to bond orthodontic brackets. The aim of this in vitro study was to determine the shear bond strength (SBS) and adhesive remnant index (ARI) score of thermo-cured glass–ionomers on different pretreated enamel, in comparison with the commonly used composite cement. Three commercially available nano-ionomer or highly viscous glass–ionomer cements (EQUIA Forte® Fil, EQUIA Fil, Ketac Universal) and two types of compo-sites (Heliosit Orthodontic, ConTec Go!) were investigated in this study. The research involved two hundred human premolars. The teeth were cleaned and polished, then randomly divided into five groups according to the enamel preparation method and the type of material. The enamel was treated in three different ways: polyacrylic acid, phosphoric acid, 5% NaOCl + etching with phosphoric acid, and a control group without treatment. Glass–ionomer cement was thermo-cured with heat from a polymerization unit during setting. Statistical analysis was performed using a Chi-square test and one-way ANOVA for independent samples. Spearman’s Rho correlation coefficient was used to examine the relationship. Regardless of the material type, the results indicated that the weakest bond between the bracket and tooth enamel was found in samples without enamel pretreatment. The majority of the materials stayed on the brackets in samples without enamel preparation, according to ARI scores. The study’s findings demonstrated that the strength of the adhesion between the bracket and enamel is greatly influenced by enamel etching and glass–ionomer thermo-curing. Clinical investigations would be required to validate the outcomes.

PURPOSE To fully characterize the orientation dependence of magnetization transfer (MT) and inhomogeneous MT (ihMT) measures in the whole white matter (WM), for both single-fiber and crossing-fiber voxels. METHODS A characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution. This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, the orientation dependence inside 31 different WM bundles was characterized to evaluate the homogeneity of the effect. Variation of the results within and between-subject was assessed from a 12-subject dataset. RESULTS Previous results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Heterogeneity of the orientation dependence across bundles was observed, but homogeneity within similar bundles was also highlighted. Differences in behavior between MT and ihMT measures, as well as the ratio and saturation versions of these, were noted. CONCLUSION Orientation dependence characterization was proven possible over the entirety of WM. The vast range of effects and subtleties of the orientation dependence on MT measures showed the need for, but also the challenges of, a correction method.

The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as automated personal assistants that adapt to the user's unique data and demands. Two key desiderata for such assistants are personalization-in the sense that the assistant should reflect the user's own style-and privacy-in the sense that users may prefer to always store their personal data locally, on their own computing device. We present a new design for such an automated assistant, for the specific use case of personal assistant for email generation, which we call Panza. Specifically, Panza can be both trained and inferenced locally on commodity hardware, and is personalized to the user's writing style. Panza's personalization features are based on a new technique called data playback, which allows us to fine-tune an LLM to better reflect a user's writing style using limited data. We show that, by combining efficient fine-tuning and inference methods, Panza can be executed entirely locally using limited resources-specifically, it can be executed within the same resources as a free Google Colab instance. Finally, our key methodological contribution is a careful study of evaluation metrics, and of how different choices of system components (e.g. the use of Retrieval-Augmented Generation or different fine-tuning approaches) impact the system's performance.

Objective Real-life management of patients with hypertension and chronic kidney disease (CKD) among European Society of Hypertension Excellence Centres (ESH-ECs) is unclear : we aimed to investigate it. Methods A survey was conducted in 2023. The questionnaire contained 64 questions asking ESH-ECs representatives to estimate how patients with CKD are managed. Results Overall, 88 ESH-ECS representatives from 27 countries participated. According to the responders, renin-angiotensin system (RAS) blockers, calcium-channel blockers and thiazides were often added when these medications were lacking in CKD patients, but physicians were more prone to initiate RAS blockers (90% [interquartile range: 70-95%]) than MRA (20% [10-30%]), SGLT2i (30% [20-50%]) or (GLP1-RA (10% [5-15%]). Despite treatment optimisation, 30% of responders indicated that hypertension remained uncontrolled (30% (15-40%) vs 18% [10%-25%]) in CKD and CKD patients, respectively). Hyperkalemia was the most frequent barrier to initiate RAS blockers, and dosage reduction was considered in 45% of responders when kalaemia was 5.5-5.9 mmol/L. Conclusions RAS blockers are initiated in most ESH-ECS in CKD patients, but MRA and SGLT2i initiations are less frequent. Hyperkalemia was the main barrier for initiation or adequate dosing of RAS blockade, and RAS blockers' dosage reduction was the usual management.

Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learning), a generalist model capable of efficiently solving multiple COPs and which can be fine-tuned to solve new COPs. GOAL consists of a single backbone plus light-weight problem-specific adapters, mostly for input and output processing. The backbone is based on a new form of mixed-attention blocks which allows to handle problems defined on graphs with arbitrary combinations of node, edge and instance-level features. Additionally, problems which involve heterogeneous nodes or edges, such as in multi-partite graphs, are handled through a novel multi-type transformer architecture, where the attention blocks are duplicated to attend only the relevant combination of types while relying on the same shared parameters. We train GOAL on a set of routing, scheduling and classic graph problems and show that it is only slightly inferior to the specialized baselines while being the first multi-task model that solves a variety of COPs. Finally, we showcase the strong transfer learning capacity of GOAL by fine-tuning or learning the adapters for new problems, with only few shots and little data.

The purpose of the study was to evaluate and identifying the level of excess weight and obesity in older students between 15 and 18 years, as important benchmarks of the level of health in order to update the recommendations regarding the promotion of an active and healthy lifestyle. A cross-sectional study was conducted on a sample of 400 subjects, (186 boys and 214 girls), aged 15 to 18. Anthropometric data including: body height, body weight, Body Mass Index (BMI). Participants' BMI was estimated using the Percentile BMI calculator for children and teenagers aged 2 to 19. Study adolescents were defined as underweight, normal (healthy) weight, overweight, and obese according to the CDC child growth characteristics for age, sex, and BMI. 350 (85.5%) subjects were healthy weight; 26 respondents (6.5%) were overweight, 17 (4.25%), were obese, while 7 (1.75%) underweight. The analysis of the individual results of male and female subjects points to increased values of the body mass of males (18.81%), compared to female pupils (3%). Out of a total of 186 male students, 10.75% were in the overweight category, and 8.06% were categorized as obese, in constrast 2.80% of the girls were overweight and (1%<), in the obese category, which is an outstanding result, where obesity practically does not exist. According to the results of this study (for both sexes), in relation to gender, there were more malnourished girls (2.33%), compared to boys (1%<). Among high school students in Bosnia and Herzegovina, the number of children with overweight and obesity is relatively low compared to data from other countries. Based on the relevant results of this study, we consider it necessary to update strategies for promoting an active and healthy lifestyle regarding physical activity and eating habits for adolescents in relation to the specifics of the countries of residence and European trends. Keywords: BMI; students; overweight; obesity; weight status category; high school.