This paper presents a novel event-triggered control (ETC) design framework based on measured $\mathcal{L}_{p}$ norms. We consider a class of systems with finite $\mathcal{L}_{p}$ gain from the network-induced error to a chosen output. The $\mathcal{L}_{p}$ norms of the network-induced error and the chosen output since the last sampling time are used to formulate a class of triggering rules. Based on a small-gain condition, we derive an explicit expression for the $\mathcal{L}_{p}$ gain of the resulting closed-loop systems and present a time-regularization, which can be used to guarantee a lower bound on the inter-sampling times. The proposed framework is based on a different stability- and triggering concept compared to ETC approaches from the literature, and thus may yield new types of dynamical properties for the closed-loop system. However, for specific output choices it can lead to similar triggering rules as “standard” static and dynamic ETC approaches based on input-to-state stability and yields therefore a novel interpretation for some of the existing triggering rules. We illustrate the proposed framework with a numerical example from the literature.

Mammography is the leading methodology used to diagnose breast cancer. Effective, cheap and reliable, the mammography can be used to screen large populations, if the imagery produced can be analysed efficiently. State-of-the-art generative artificial intelligence approaches can be used to create tools able to aid in this task. Here we present a study focused on the emerging research topic of the application of generative diffusion models to the task of anomaly detection and we apply if for detecting anomalies on mammograms. Diffusion models exhibit promising results in making pixel-level predictions with image level annotations, but no such application has been published so far regarding mammography. We have, therefore, developed a novel approach utilizing U-net backbone that is able to generate mammograms with Fréchet Inception Distance (FID) of 14.62. We showed its ability to perform anomaly detection with Intersection over Union (IoU) of 0.195 which demonstrates the viability of our approach for early-stage research.

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.

This research aimed to analyze the impact of two different non-Saccharomyces yeast species on the aromatic profile of red wines made from the cv. Babić (Vitis vinifera L.) red grape variety. The grapes were obtained from two positions in the Middle and South of Dalmatia. This study compared a control treatment with the Saccharomyces cerevisiae (Sc) strain as a type of sequential inoculation treatment with Lachancea thermotolerans (Lt x Sc) and Torulaspora delbrueckii (Td x Sc). The focus was on the basic wine parameters and volatile aromatic compound concentrations determined using the SPME-Arrow-GC/MS method. The results revealed significant differences in cis-linalool oxide, geraniol, neric acid, and nerol, which contribute to the sensory profile with floral and rose-like aromas; some ethyl esters, such as ethyl furoate, ethyl hexanoate, ethyl lactate, ethyl 2-hydroxy-3-methylbutanoate, ethyl 3-hydroxy butanoate, diethyl glutarate, and diethyl succinate, contribute to the aromatic profile with fruity, buttery, overripe, or aging aromas. A sensory evaluation of wines confirmed that Td x Sc treatments exhibited particularly positive aromatic properties together with a more intense fullness, harmony, aftertaste, and overall impression.

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.

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.

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.

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.

The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as using personal data to fine-tune these models to imitate a user's unique writing style. Two key requirements for such assistants are personalization - in the sense that the assistant should recognizably reflect the user's own writing style - and privacy - users may justifiably be wary of uploading extremely personal data, such as their email archive, to a third-party service. In this paper, we present a new design and evaluation for such an automated assistant, for the specific use case of email generation, which we call Panza. Panza's personalization features are based on a combination of fine-tuning using a variant of the Reverse Instructions technique together with Retrieval-Augmented Generation (RAG). We demonstrate that this combination allows us to fine-tune an LLM to reflect a user's writing style using limited data, while executing on extremely limited resources, e.g. on a free Google Colab instance. Our key methodological contribution is the first detailed study of evaluation metrics for this personalized writing task, and of how different choices of system components--the use of RAG and of different fine-tuning approaches-impact the system's performance. Additionally, we demonstrate that very little data - under 100 email samples - are sufficient to create models that convincingly imitate humans. This finding showcases a previously-unknown attack vector in language models - that access to a small number of writing samples can allow a bad actor to cheaply create generative models that imitate a target's writing style. We are releasing the full Panza code as well as three new email datasets licensed for research use at https://github.com/IST-DASLab/PanzaMail.

We present DeepRIoT, a continuous integration and continuous deployment (CI/CD) based architecture that accelerates the learning and deployment of a Robotic-IoT system trained from deep reinforcement learning (RL). We adopted a multi-stage approach that agilely trains a multi-objective RL controller in the simulator. We then collected traces from the real robot to optimize its plant model, and used transfer learning to adapt the controller to the updated model. We automated our framework through CI/CD pipelines, and finally, with low cost, succeeded in deploying our controller in a real F1tenth car that is able to reach the goal and avoid collision from a virtual car through mixed reality.

Abstract 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. PLAIN LANGUAGE SUMMARY What is the context? Hypertension is a strong independent risk factor for development of chronic kidney disease (CKD) and progression of CKD to ESKD. Improved adherence to the guidelines in the treatment of CKD is believed to provide further reduction of cardiorenal events. European Society of Hypertension Excellence Centres (ESH-ECs) have been developed in Europe to provide excellency regarding management of patients with hypertension and implement guidelines. Numerous deficits regarding general practitioner CKD screening, use of nephroprotective drugs and referral to nephrologists prior to referral to ESH-ECs have been reported. In contrast, real-life management of these patients among ESH-ECs is unknown. Before implementation of strategies to improve guideline adherence in Europe, we aimed to investigate how patients with CKD are managed among the ESH-ECs. What is the study about? In this study, a survey was conducted in 2023 by the ESH to assess management of CKD patients referred to ESH-ECs. The questionnaire contained 64 questions asking ESH-ECs representatives to estimate how patients with CKD are managed among their centres. What are the results? RAAS blockers are initiated in 90% of ESH-ECs in CKD patients, but the initiation of MRA and SGLT2i is less frequently done. Hyperkalemia is the main barrier for initiation or adequate dosing of RAAS blockade, and its most reported management was RAAS blockers dosage reduction. These findings will be crucial to implement strategies in order to improve management of patients with CKD and guideline adherence among ESH-ECs.