Due to its ecological and financial benefits, earth building has gained global attention, with earth bricks being extensively used. Shrinkage and crack development have a considerable impact on the performance and quality of earth bricks. This study employs laboratory experiments to examine the shrinkage behavior of earth bricks reinforced with wheat and barley straw. In addition to this, the impact of cement and gypsum additives is examined. The obtained results indicate that increased fiber content reduces crack formation effectively. However, higher levels of cohesive soil have been shown to have a negative influence on shrinkage behavior. In general, higher fiber contents contribute to the improvement of earth brick performance. These findings offer useful insights for improving the composition and characteristics of reinforced earth bricks, resulting in enhanced performance and quality in sustainable construction practices.

Climate change adaptation is paramount, but increasing evidence suggests that adaptation action is subject to a range of constraints. For a realistic assessment of future adaptation prospects, it is crucial to understand the timescales needed to overcome these constraints. Here, we combine data on documented adaptation from the Global Adaptation Mapping Initiative with national macro indicators and assess future changes in adaptation constraints alongside the Shared Socioeconomic Pathways, spanning a wide range of future socio-economic development scenarios. We find that even in the most optimistic scenario, it will take until well after 2050 to overcome key constraints, which will limit adaptation for decades to come particularly in vulnerable countries. The persistence of adaptation constraints calls for stringent mitigation, improved adaptation along with dedicated finance and increasing efforts to address loss and damage. Our approach allows to ground truth indicators that can be further used in climate modelling efforts, improving the representation of adaptation and its risk reduction potential.

Over the last decades, new mobility offers have emerged to enlarge the coverage and the accessibility of public transportation systems. In many areas, public transit now incorporates on-demand transport lines, that can be activated at user need. In this paper, we propose to integrate lines without predefined schedules but with predefined stop sequences into a state-of-the-art trip planning algorithm for public transit, the Trip-Based Public Transit Routing algorithm [33]. We extend this algorithm to non-scheduled lines and explain how to model other modes of transportation, such as bike sharing, with this approach. The resulting algorithm is exact and optimizes two criteria: the earliest arrival time and the minimal number of transfers. Experiments on two large datasets show the interest of the proposed method over a baseline modelling.

Purpose This paper aims to reveal the role of two theories that impact seniors’ entrepreneurial intentions. Both the socioemotional selectivity theory (SST) and the self-determination theory (SDT) re-shape seniors’ intentions to create entrepreneurial opportunities and activities after they retire. Design/methodology/approach This paper uses secondary data resources for developing the research concept, which might leverage seniors’ ultimate goal of creating entrepreneurial activities. A comprehensive past-paper analysis was performed. One hundred papers were initially considered for inclusion in this research. However, after a rigorous synthesisation process, 80 publications were selected for further analysis. Findings This paper presents an investigation of seniors’ entrepreneurship, with a specific emphasis on the SST and the SDT. It suggests potential models that could gauge senior entrepreneurs’ propensity to engage in entrepreneurial endeavours to support the socioeconomic advancement of society. Furthermore, this research discussed the limitations of the enlightening concepts presented to scholars and decision-makers. Originality/value The originality of this paper is illuminated by its idea of integrating two theories (the SST and the SDT), suggesting that these theories can possibly better observe senior entrepreneurs’ intentions in creating an entrepreneurial venture after they retire.

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-ideruification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.

BACKGROUND: Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE: The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS: A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms “artificial intelligence in obstetrics”, “maternal-fetal medicine”, and “machine learning” combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS: According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION: The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

We analyze the signatures of new physics scenarios featuring third-family quark-lepton unification at the TeV scale in lepton-quark fusion at hadron colliders. Working with complete UV dynamics based on the SU(4) gauge symmetry in the third-family fermions, we simulate the resonant production of a vector leptoquark at the next-to-leading order, including its decay and matching to the parton showers. The precise theoretical control over this production channel allows us to set robust bounds on the vector leptoquark parameter space which are complementary to the other production channels at colliders. We emphasize the importance of the resonant channel in future searches and discuss the impact of variations in the model space depending on the flavor structure of the vector leptoquark couplings.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Ten questions to guide reflection and assessment of the “good” in robotics projects are suggested. Ten questions to guide reflection and assessment of the “good” in robotics projects are suggested.

Wireless sensor networks play a key role in various applications such as environmental monitoring, smart cities and medical monitoring. Latency and reliability are two basic aspects that affect the efficiency and quality of service of these networks. In this research, delay and reliability analyzes of WSN were conducted through the application of mathematical models. Different parameters were analyzed such as distance, number of devices, and bandwidth to gain a deeper understanding of their impact on network performance. Mathematical models have been developed that take into account random variables and changing factors in order to conduct a more precise analysis. Through simulations and numerical experiments, Matlab codes will be used to simulate and analyze the actual process, all with the aim of achieving minimum delay and maximum reliability. This research provides a deeper insight into the characteristics of WSN and provides guidelines for the design and optimization of these networks.

The use of augmented reality (AR) allows for the integration of digital information onto our perception of the physical world. In this article, we present a comprehensive review of previously published literature on the implementation of AR in physics education, at the school and the university level. Our review includes an analysis of 96 papers from the Scopus and Eric databases, all of which were published between 1st January 2012 and 1st January 2023. We evaluated how AR has been used for facilitating learning about physics. Potential AR-based learning activities for different physics topics have been summarized and opportunities, as well as challenges associated with AR-based learning of physics have been reported. It has been shown that AR technologies may facilitate physics learning by providing complementary visualizations, optimizing cognitive load, allowing for haptic learning, reducing task completion time and promoting collaborative inquiry. The potential disadvantages of using AR in physics teaching are mainly related to the shortcomings of software and hardware technologies (e.g. camera freeze, visualization delay) and extraneous cognitive load (e.g. paying more attention to secondary details than to constructing target knowledge).

In this paper, we introduce and provide insight into the two innovative applications designed to enhance the lives of persons with Down syndrome, focusing on seamless integration between the two. The first is a mobile application that helps users manage their daily routines by monitoring and predicting activity durations, considering their unique challenges. The second is a web application for parents/teachers/other adults to streamline activity scheduling, progress tracking, and reminders.

Webpage layout presentation failures can negatively affect the usability of a web application as well as the end-to-end user experience. The need for automated methods of visual inspection becomes obvious in complex web applications. However, visual inspection still heavily relies on manual inspection because the tools currently available are not yet advanced enough. This paper compares the performance results of three visual testing tools: Galen, AyeSpy, and Percy, and focuses on opportunities for their enhancement.