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IRMOF-3 with a high surface area prepared by a hydrothermal method was used for deposition of Pd(OAc) 2 on IRMOF-3 particles. The final catalyst was characterized with FT-IR, nitrogen physisorption, thermogravimetry, scanning electron microscopy, transmission electron microscopy combined with energy dispersive X-ray analysis, wide angle X-ray diffraction spectroscopy and X-ray photoelectron spectroscopy. The prepared porous catalyst was effectively used in the Heck coupling reaction in the presence of an organic base. The reaction parameters such as the type of base, amounts of catalyst and solvents, temperature were optimized. The catalyst was then easily separated, washed, and reused 4 times without significant losses of catalytic activity. Graphical Abstract

Magnetic particles were prepared by a hydrothermal method and then successively covered by Metal–Organic-Frameworks MIL-101-NH 2 with a high surface area. This was followed by deposition of Pd(OAc) 2 on Fe 3 O 4 –NH 2 @MIL-101-NH 2 particles. The final catalyst was characterized with FT-IR, nitrogen physisorption, thermogravimetry (TGA), scanning electron microscopy (SEM) combined with energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), vibrating sample magnetometry (VSM), wide-angle X-ray diffraction spectroscopy (XRD) and X-ray photoelectron spectroscopy (XPS). The prepared magnetic catalyst was effectively used in the Heck coupling reaction in the presence of an inorganic base. The reaction parameters such as the base type, amounts of catalyst and solvents, temperature, and substrates ratios were optimized. The catalyst was then magnetically separated, washed, and reused 7 times without losing significantly catalytic activity. Graphic Abstract

Mesoporous SBA-15 silicate with a high surface area was prepared by a hydrothermal method, successively modified by organic melamine ligands and then used for deposition of Pd nanoparticles onto it. The synthesized materials were characterized with infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), nitrogen physisorption, scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR) and inductively coupled plasma (ICP-OES). The catalyst was effectively used in the Mizoroki–Heck coupling reaction of various reactants in the presence of an organic base giving the desired products in a short reaction time and with small catalysts loadings. The reaction parameters such as the base type, amounts of catalyst, solvents, and the temperature were optimized. The catalyst was easily recovered and reused at least seven times without significant activity losses.Graphic Abstract

A thorough experimental optimization of supported ionic liquid catalyst (SILCA) was performed in order to obtain a stable and efficient catalyst for the Heck reaction. Out of fifteen proposed structures, propyl imidazolium bromide-tetramethylguanidinium pentanoate modified SiO2 loaded with PdCl2 appeared to be the most stable and to have a good activity in the reaction between butylacrylate and iodobezene, resulting in a complete conversion in 40 min at 100 °C, in four consecutive experiments. This study elucidated on the stability of the catalytic system with an ionic liquid layer during the catalyst synthesis but also under reaction conditions. In the bis-layered catalyst, the imidazolium moiety as a part of internal layer, brought rigidity to the structure, while in external layer pentanoic acid gave sufficiently acidic carboxylic group capable to coordinate 1,1,3,3-tetramethylguanidine (TMG) and thus, allow good dispersion of Pd nanoparticles. The catalyst was characterized by means of XPS, FT-IR, TEM, ICP-OES, ζ-potential, EDX, TGA, and 13C NMR. The release and catch mechanism was observed, whereas Pd re-deposition can be hindered by catalyst poisoning and eventual loss of palladium.

Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternative is to use the known information about sequence, structure, and functional properties of genes and proteins to predict functions using statistical methods. In this paper, we describe the Multi-Source k-Nearest Neighbor (MS-kNN) algorithm for function prediction, which finds k-nearest neighbors of a query protein based on different types of similarity measures and predicts its function by weighted averaging of its neighbors' functions. Specifically, we used 3 data sources to calculate the similarity scores: sequence similarity, protein-protein interactions, and gene expressions. We report the results in the context of 2011 Critical Assessment of Function Annotation (CAFA). Prior to CAFA submission deadline, we evaluated our algorithm on 1,302 human test proteins that were represented in all 3 data sources. Using only the sequence similarity information, MS-kNN had term-based Area Under the Curve (AUC) accuracy of Gene Ontology (GO) molecular function predictions of 0.728 when 7,412 human training proteins were used, and 0.819 when 35,622 training proteins from multiple eukaryotic and prokaryotic organisms were used. By aggregating predictions from all three sources, the AUC was further improved to 0.848. Similar result was observed on prediction of GO biological processes. Testing on 595 proteins that were annotated after the CAFA submission deadline showed that overall MS-kNN accuracy was higher than that of baseline algorithms Gotcha and BLAST, which were based solely on sequence similarity information. Since only 10 of the 595 proteins were represented by all 3 data sources, and 66 by two data sources, the difference between 3-source and one-source MS-kNN was rather small. Based on our results, we have several useful insights: (1) the k-nearest neighbor algorithm is an efficient and effective model for protein function prediction; (2) it is beneficial to transfer functions across a wide range of organisms; (3) it is helpful to integrate multiple sources of protein information.

Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these methods treats every candidate signal phase as equally worth trying, discarding the rich domain knowledge that traffic theory already provides. This paper proposes fuzzy-guided exploration, in which a multi-criteria fuzzy inference system uses local traffic conditions, with phase pressure as its primary input, to assign each candidate phase a priority. These priorities define a sampling distribution used in place of the uniform draw. We evaluate the method across four MARL algorithms covering independent learning (IQL) and the centralized training with decentralized execution paradigm (VDN, QMIX, and QPLEX) on both a synthetic grid and a real-world network. Fuzzy-guided exploration consistently improves upon the baseline in all combinations, with tangible gains on the synthetic grid and substantially larger improvements on the real-world network. These findings demonstrate that exploration is an effective intervention point for domain-knowledge integration in cooperative MARL, and that pressure-based scoring provides a well-suited signal to serve that role in traffic signal control.

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

Despite numerous measures, bacterial sepsis associated with the transfusion remains a major threat. The incidence of septic events induced by platelets transfusion is approximately 10 times higher than with transfused red blood cells due to their storage temperature. This caused new Standard that implements the methods for the detection and reduction of bacteria in the platelet concentrates (PC). The aim is to consider the possibility of wider application of this tests in order to extend the shelf-life of PC. Sterility testing of PC is done once or twice per month using BacT/Alert BPA and BacT/Alert BPN bottles. If positive, all products from the initial unit were tested to confirm or deny the status. During six years period, 67236 PC units were made and 872 of them were tested. Only two were found initially positive. After testing the other products from the same initial unit, results were negative so, final results proclaimed false positive. Pretransfusion bacterial detection is an important potential method for reducing the risk of bacteriemia and transfusion-associated septic reactions. In addition to routine measures, Mirasol PRT pathogen inactivation system, could be included. This allows certain amount of PC to be inactivated during the first 32 hours. Untreated PC units would be stored in standard conditions and for given time (three days) potentially present bacteria would reach a detectable level. This way the quantity of samples for sterility testing could be reduced, taking only 2 ml of each of four units of PC. Samples would be planted at the same vial-aerobic bottle, which would also, double the capacity in BacT/Alert 3D automated system.

Background/Objectives: Dupuytren’s contracture is a chronic fibroproliferative disorder of the palmar fascia that leads to progressive flexion deformities and functional impairment. The Southampton Dupuytren’s Scoring Scheme (SDSS) is a disease-specific patient-reported outcome measure designed to quantify disability in this condition. This study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Serbian version of the SDSS. Methods: A cross-sectional study was conducted at the Institute for Orthopedic Surgery “Banjica”, Belgrade, from January 2024 to March 2025. Sixty-eight patients with Dupuytren’s contracture completed the Serbian SDSS, the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, the 12-Item Short Form Health Survey (SF-12), and a Visual Analogue Scale (VAS) for pain. Translation followed standardized forward–backward procedures. Internal consistency was assessed with Cronbach’s alpha, construct validity with confirmatory factor analysis (CFA), and convergent validity with Pearson’s correlation coefficients. Results: The Serbian SDSS demonstrated excellent internal consistency (Cronbach’s α = 0.914). CFA supported a unidimensional five-item structure with strong factor loadings (0.76–0.93) and acceptable fit indices (χ2 = 10.094, df = 5, p = 0.073; IFI = 0.979; CFI = 0.978; TLI = 0.956). Convergent validity was confirmed by strong correlations with DASH (r = 0.779) and VAS (r = 0.702) and a strong negative correlation with SF-12 PCS (r = −0.802). Conclusions: The Serbian SDSS is a valid and reliable instrument for assessing functional disability in patients with Dupuytren’s contracture and offers a robust, patient-centered measure for clinical and research use.

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.