Abstract
This study aimed to investigate the effect of perceived social support on perceived employability and whether this relationship is mediated by well-being. Another objective is to study the moderating effect of perceived self-efficacy on the relationship between well-being and perceived employability. The sample comprises 316 participants, all studying at universities in Portugal. The results show that social support is positively and significantly associated with perceived employability and well-being. Well-being has a positive and significant association with perceived employability. As for the mediating effect, well-being was found to have a total mediating effect on the relationship between social support and perceived employability. Perceived self-efficacy has a positive and significant association with perceived employability. Contrary to expectations, perceived self-efficacy does not moderate the relationship between well-being and perceived employability. These results allow us to conclude that social support and well-being are the survival kits for the jungle of work. As for the practical implications, it is recommended that universities take care of the social support given to students, increasing their well-being so that their perceived employability is high.

Abstract
Ensuring reliable and high-quality electricity service is critical for consumers and Distribution System Operators (DSO). The DSO's Plan for Development and Investment in the Distribution Network (PDIDN) plays a pivotal role in enhancing network reliability and resilience while balancing technical and financial aspects. This study proposes a novel probabilistic approach for quality-of-service (QoS) estimation in distribution systems, addressing the limitations of traditional deterministic methods. Leveraging Bayesian regression, specifically the Spike and Slab technique, the model incorporates prior knowledge to improve the prediction of key QoS indicators such as SAIDI, SAIFI, and TIEPI. Using historical network data, the model demonstrates superior predictive accuracy and robustness, offering realistic confidence intervals for strategic planning. This method enables informed investments, enhances regulatory compliance, and supports renewable integration. The findings underline the potential of probabilistic modeling in advancing QoS forecasting, encouraging its application in other areas of electric network management.

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.