Abstract

As climate change drives increasingly severe heatwaves, the strain on public health systems continues to grow, particularly for vulnerable populations. Our work argues for the integration of digital health technologies into heatwave action plans, drawing lessons from the COVID-19 pandemic's success in deploying such tools. It explores the potential of digital communication strategies, telemedicine, and data-driven simulations to enhance public awareness, maintain healthcare accessibility, and improve real-time crisis responses. Despite their effectiveness, digital solutions remain underutilized in existing European heat-health action plans. We emphasize the need for a proactive, systems-based approach to optimize heatwave management and ensure equitable healthcare access, particularly for at-risk communities. Integrating digital health innovations can transform heatwave response strategies, making them more flexible, efficient, and capable of saving lives.

Abstract
In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
This study develops some sustainable tourism advertising effects and consumer environmental awareness-raising and examines them by advertising certification and advertising format in a field experiment. The tourism advertising effects are analyzed by five dependent variables: trust and credibility, environmentalism, ad relevance, realism, and flow. Several ANOVA and multiple comparison tests were performed to understand whether these variables varied between groups. Experimental research findings indicate that flow and video format affect tourism advertising and consumer environmental awareness-raising. This study demonstrates the importance of understanding the concept of sustainable tourism and awareness-raising. It also points to identifying the best communication strategies to promote a sustainable destination, as different communication methods may lead to different results. In addition, it provides valuable information for marketers to consider when implementing their communication strategies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Recent advances in optical trapping have opened new opportunities for manipulating micro and nanoparticles, establishing optical tweezers (OT) as a powerful tool for single-cell analysis. Furthermore, intelligent systems have been developed to characterize these particles, as information about their size and composition can be extracted from the scattered radiation signal. In this manuscript, we aim to explore the potential of optical tweezers for the characterization of sub-micron size variations in microparticles. We devised a case study, aiming to assess the limits of the size discrimination ability of an optical tweezer system, using transparent 4.8 µm PMMA particles, functionalized with streptavidin. We focused on the heavily studied streptavidin-biotin system, with streptavidin-functionalized PMMA particles targeting biotinylated bovine serum albumin. This binding process results in an added molecular layer to the particle’s surface, increasing its radius by approximately 7 nm. An automatic OT system was used to trap the particles and acquire their forward-scattered signals. Then, the signals’ frequency components were analyzed using the power spectral density method followed by a dimensionality reduction via the Uniform Manifold Approximation and Projection algorithm. Finally, a Random Forest Classifier achieved a mean accuracy of 94% for the distinction of particles with or without the added molecular layer. Our findings demonstrate the ability of our technique to discriminate between particles that are or are not bound to the biotin protein, by detecting nanoscale changes in the size of the microparticles. This indicates the possibility of coupling shape-changing bioaffinity tools (such as APTMERS, Molecular Imprinted Polymers, or antibodies) with optical trapping systems to enable optical tweezers with analytical capability. © 2024 SPIE.

Abstract
Today, autonomous agents, the Internet of Things, and smart devices produce more and more distributed data and use them to learn models for different purposes. One challenge is that learning from local data only may lead to suboptimal models. Thus, better models are expected if agents can exchange data, leading to approaches such as federated learning. However, these approaches assume that data have no value and, thus, is exchanged for free. A machine learning data market (MLDM), a framework based on multiagent systems with a market-based perspective on data exchange, was recently proposed. In an MLDM, each agent trains its model based on both local data and data bought from other agents. Although the empirical results are interesting, several challenges are still open, including data acquisition and data valuation. The MLDM is an illustrative example of how the value of data can and should be integrated into the design of distributed ML systems.

Abstract
Energy efficiency of software is crucial in minimizing environmental impact and reducing operational costs of ICT systems. Energy efficiency is therefore a key area of contemporary software language engineering research. A recurrent discussion that excites our community is whether runtime performance is always a proxy for energy efficiency. While a generalized intuition seems to suggest this is the case, this intuition does not align with the fact that energy is the accumulation of power over time; hence, time is only one of the factors in this accumulation. We focus on the other factor, power, and the impact that capping it has on the energy efficiency of running software. We conduct an extensive investigation comparing regular and power-capped executions of 9 benchmark programs obtained from The Computer Language Benchmarks Game, across 20 distinct programming languages. Our results show that employing power caps can be used to trade running time, which is degraded, for energy efficiency, which is improved, in all the programming languages and in all benchmarks that were considered. We observe overall energy savings of almost 14% across the 20 programming languages, with notable savings of 27% in Haskell. This saving, however, comes at the cost of an overall increase of the program's execution time of 91% in average. We are also able to draw similar observations using language specific benchmarks for programming languages of different paradigms and with different execution models. This is achieved analyzing a wide range of benchmark programs from the nofib Benchmark Suite of Haskell Programs, DaCapo Benchmark Suite for Java, and the Python Performance Benchmark Suite. We observe energy savings of approximately 8% to 21% across the test suites, with execution time increases ranging from 21% to 46%. Notably, the DaCapo suite exhibits the most significant values, with 20.84% energy savings and a 45.58% increase in execution time. Our results have the potential to drive significant energy savings in the context of computational tasks for which runtime is not critical, including Batch Processing Systems, Background Data Processing and Automated Backups.