Abstract
Virtual staining is a promising technique that uses deep generative models to recreate histological stains, providing a faster and more cost-effective alternative to traditional tissue chemical staining. Specifically for H&E-HER2 staining transfer, despite a rising trend in publications, the lack of sufficient public datasets has hindered progress in the topic. Additionally, it is currently unclear which model frameworks perform best for this particular task. In this paper, we introduce the HER2match dataset, the first publicly available dataset with the same breast cancer tissue sections stained with both H&E and HER2. Furthermore, we compare the performance of several Generative Adversarial Networks (GANs) and Diffusion Models (DMs), and implement a novel Brownian Bridge Diffusion Model for H&E-HER2 translation. Our findings indicate that, overall, GANs perform better than DMs, with only the BBDM achieving comparable results. Moreover, we emphasize the importance of data alignment, as all models trained on HER2match produced vastly improved visuals compared to the widely used consecutive-slide BCI dataset. This research provides a new high-quality dataset, improving both model training and evaluation. In addition, our comparison of frameworks offers valuable guidance for researchers working on the topic.

Abstract
The deep ocean is vast and challenging to observe; however, it is key to knowledge of the sea and its impact on global climate. Fixed sea observing points (such as the EMSO observing nodes) provide a limited view and are complemented by expensive oceanographic campaigns with systems demanding high logistical requirements such as deep-sea ROVs.  These costs not only limit our capability for key ocean data collection in the deep but also introduce their own environmental costs.Emerging challenges in knowledge and pressure on the exploration of the deep ocean demand new technological solutions for monitoring and safeguarding the marine ecosystem.Innovative robotic technologies such as the TURTLE robotic deep-sea landers can combine long-term permanence at the seabed with mobility and dynamic reconfigurability in spatial and temporal deep-sea observation.Robotic systems of a heterogeneous nature (from conventional gliders, AUVs, or robotic landers) can be combined with standard and new sensing systems, such as bottom-deployed sensor nodes, moored systems, and cabled points when feasible.These systems can provide underwater localization services for the different assets, energy supply and high bandwidth data transfer with robotic docking stations for other mobile elements. An example of the synergy obtained with these new systems is the possibility of using robotic landers as carriers of EGIM (EMSO Generic Instrument Module) sensor payloads, providing power and data storage and flexibility in the deployment and recovery process.This approach, partly taken in the EU-funded Trident project to develop technical solutions for cost-effective and efficient observation of environmental impacts on deep seabed environments, allows for a substantial reduction in the operational and logistic requirements for deep-sea observation, greatly reducing the need for costly oceanographic campaigns or the use of expensive (economic and logistical) deep sea ROV systems.In this work, we present some of the new developments and discuss the transition from existing technological solutions to new ones integrating these recent developments.

Abstract
[No abstract available]

Abstract
Pharmaceuticals have emerged as contaminants in aquatic ecosystems, challenging the water quality concept. These compounds enter wastewater treatment plants, where inefficient treatments pose concerns for long-term river and tap water quality, consequently impacting environmental and human health. Considering this, the present study first reports the simultaneous quantification of paracetamol, salicylic acid, and carbamazepine by electrochemistry with carbon screen-printed electrodes, as well as liquid chromatography-tandem mass spectrometry (LC-MS/MS). At pH 7.4 and by optimized DPV, LODs were 0.783, 1.53, and 0.113 mu M for paracetamol, salicylic acid, and carbamazepine, respectively. The recovery values obtained by LC-MS/MS in tap water are not satisfactory regarding the data obtained in river water with DPV electrochemical experiments. Moreover, in both analytical methods, the highest sensitivity was obtained for carbamazepine, with the lowest RSD values. These analytical data highlight the remarkable sensitivity and detection skills of DPV and LC-MS/MS analysis. Developing portable potentiostats for in situ pharmaceutical detection and monitoring outside the labs is crucial for ensuring environmental and health safety. Herein two portable approaches are tested: commercial SensitSmart (R) and homemade electrochemical Internet-of-Things (IoT)-enabled devices. The results of SensitSmart (R) are reliable but lower than those obtained with a benchtop potentiostat and require a USB connection with PCs, tablets, or smartphones. The practical application of a homemade IoT device was validated with potassium ferricyanide with an output similar to benchtop potentiostat, which represents a proof of concept. In the future, these IoT devices will operate without external components or specific software.

Abstract
For the upcoming 6G wireless networks, reconfigurable intelligent surfaces are an essential technology, enabling dynamic beamforming and signal manipulation in both reflective and transmissive modes. It is expected to utilize frequency bands in the millimeter-wave and THz, which presents unique opportunities but also significant challenges. The selection of switching technologies that can support high-frequency operation with minimal loss and high efficiency is particularly complex. In this work, we demonstrate the potential of advanced components such as Schottky diodes, memristor switches, liquid metal-based switches, phase change materials, and RF-SOI technology in RIS designs as an alternative to overcome limitations inherent in traditional technologies in D-band (110-170 GHz).

Abstract
PurposeThis study aims to assess how envy, both directly and indirectly, through negative behaviors such as ostracism, negative word-of-mouth and alignment with the negative behaviors of superiors, influences innovative behavior based on the cultural dimension of individualism versus collectivism.Design/methodology/approachThe data was collected using a survey applied to 305 individuals between October 2022 and June 2023. The model developed was analyzed and validated using partial least squares estimation with structural equation modeling (PLS-SEM) and PLS-SEM multigroup analysis techniques.FindingsThe results suggest that for individualistic individuals, negative word-of-mouth exerts a greater positive mediating influence on the relationship between envy and ostracism, and that envy exerts a greater positive influence on both alignment with the negative behaviors of superiors and on ostracism. In addition, the results indicate that negative word-of-mouth and ostracism together negatively influence the relationship between envy and innovative behavior.Practical implicationsThis research provides empirical evidence that envy triggers negative behavior in both individualistic and collectivist individuals. Thus, in practical terms, envy can be considered as something more primitive that goes beyond the accepted values of sociability, especially in the organizational environment.Originality/valueThe main contribution of this research is to understand the effects of envy on innovative behavior, based on a conceptual model that considers the mental programs that differentiate one group of individuals from another. In addition, it presents theoretical and empirical implications that provide descriptive evidence of behaviors, making it possible to broaden the psychological understanding of them. In this specific sense, this research differs from other organizational studies, whose objectives are to standardize behavior.