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Abstract Infiltration regulates the movement and storage of water at the soil–atmosphere interface and is, therefore, a key component of many related physical and biogeochemical processes. Numerous studies have examined infiltration over the past two centuries. These efforts have resulted in the development of numerous models that capture the effects of specific soil properties and initial and boundary conditions. This proliferation of models has advanced our collective ability to understand infiltration processes but has also made it challenging for researchers to select appropriate approaches for analyzing experimental infiltration data or for conducting basic research on soil parameters like saturated hydraulic conductivity or sorptivity. Here, we aimed to reduce this uncertainty by developing a comprehensive literature review of published infiltration models, including their underlying philosophies and evolution over the years. Through this effort, we compiled and examined 138 unique infiltration models. We grouped models into two major categories, empirical and conceptual, noting that boundaries between those two categories are at times debatable. After classifying and providing a full historical retrospective of these models, we examined specific model parameters and how their usage has changed with time. We also reviewed different methods applied to estimate infiltration parameters, as well as the challenges that arise when using such methods. Finally, we proposed a framework for identifying suitable models depending on field conditions, experimental plan, and data availability.

Background For the time being, tafamidis is the only approved treatment for wild‐type transthyretin cardiac amyloidosis. However, benefits on all‐cause death only emerge after ≈18 months. The current available staging systems are unable to specifically discriminate patients at high risk of death within 18 months from diagnosis, and the selection of patients who are expected to benefit from tafamidis is left to the clinical judgment of treating physicians, being often based primarily (and sometimes only) on age. We searched baseline variables able to discriminate patients at risk of death within 18 months. Methods and Results We randomly divided our prospectively maintained database in 2 cohorts, a testing (two thirds) and a validating (one third) set, respectively. We used the Eastern Cooperative Oncology Group–Performance Status as a simple scoring of frailty. We analyzed the clinical, laboratory and instrumental features of 691 consecutive wild‐type transthyretin cardiac amyloidosis patients diagnosed between 2006 and 2021. Median follow‐up was 43 months, and 367 patients (53%) died. Eighteen‐month death was predicted by NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide; cutoff 4200 ng/L), cardiac troponin I (cutoff 92 ng/L), and age (cutoff 80 years). At multivariable analysis, Eastern Cooperative Oncology Group–Performance Status >1 along with the selected variables were independent prognostic determinants and outperformed New York Heart Association functional class. The combination of these variables identified standard‐ and high‐risk patients (all variables above the cutoffs) with a median survival of 57 and 17 months, respectively (P

A network of European organisations is coordinating a workshop in New York (USA) on September 26, 2024, as part of the Science Summit 2024 at the 79th Session of the UN General Assembly (UNGA79). This network represents active communities from the fields of biodiversity, ecology, and engineering. It aims to strengthen science, technology, and innovation efforts to achieve the UN Sustainable Development Goals (SDGs).These communities, through European initiatives like the European Research Infrastructures, the European Open Science Cloud (EOSC), and Digital Twin projects, have selected the Kunming-Montreal Global Biodiversity Framework (K-M GBF) as a testbed for contributing to the SDGs. Their collective focus is on the network shared impact rather than individual projects. By examining a common approach to the K-M GBF, they aim to enhance their contributions to the framework's strategic goals, particularly its 2030 and 2050 targets.In this direction, the network: Recognises the contribution and rights of indigenous peoples and local communities as custodians of biodiversity and holders of traditional knowledge for the conservation, restoration, and sustainable use of ecosystems. By adhering to the Open Science principles of “Findable Accessible Interoperable Reusable” (FAIR) and “Collective Benefit, Authority to Control, Responsibility, Ethics” (CARE), and by being consistent with the practices adopted by the scientific community, the members of the network promote traceability of their work and of the materials they use, including those provided by indigenous peoples.Implements a variety of approaches to improve biodiversity monitoring, management, and protection. It promotes multi- and cross-disciplinary, integrative approaches to enhance its contribution to many of the Framework objectives. Its members support research on biodiversity at all levels of the biological organisation, from single-celled organisms, through collections and specimen data and up to the scale of ecosystems, as well as on how biodiversity responds to climate change. A key role in this process is already being played by the biodiversity Research Infrastructures, both in the EU and globally, through bi- and multi-directional linking and an increased interoperability of their data holdings, the provision of advanced access to semantically structured FAIR data, the provision of single points of access to federated data discovery from different data domains, thus supporting multi-disciplinary research addressing questions of high complexity and importance to society.All organisations in the network are committed to the three principal objectives of the Convention on the Biological Diversity, namely conservation, sustainable use, and fair sharing of benefits derived from the utilisation of natural resources. They contribute significantly to the three above principal objectives of the CBD, by: (a) making biodiversity information readily available and developing systems to support decision making and conservation efforts that directly contribute to our ability to live sustainably with nature, as concerns the first of the principal objectives above; (b) identifying priorities and targets and raising awareness of the need to streamline efforts among scientific and societal actors, are critical elements towards the second objective; (c) developing technologies to enable the sharing of data, services and other products related to genetic resources, which are used in combination with any other type of resource or product (e.g. taxonomic, literature, environmental, etc.), are included among the activities to achieve the third principal objective. Contributes to the achievement of the K-M GBF objectives through science, technology and innovation, based on scientific evidence, traditional knowledge, and innovative practices. This support is translated into activities such as: (a) providing solutions for research, data sharing and management, and scientific computing solutions to researchers, learners, policy makers, public administrations and businesses; (b) developing standard operating procedures, implementing standards, and promoting open science principles to enhance research integrity, accuracy and accountability in science; (c) providing federated research services, resources, and other research products to promote multidisciplinary knowledge and innovation; (d) creating models (e.g. of climate and human activity related land-use changes in biodiversity dynamics and ecosystem services), automated data flows (from sensors to data systems) and integration (e.g. biodiversity data flows combined with environmental and human activity variables; (e) building digital twins for informed decision making, such as the European Digital Twin of the Ocean (European DTO), with assured connectivity to newly collected high quality environmental and biodiversity data; (f) providing training and capacity building services for innovative tools.Supports the consideration of the ecosystem approach principle in the implementation of the K-M GBF, with a number of activities being developed by the network: (a) providing virtual representations of the ocean and land, integrating observations, modelling, and digital infrastructures, and creating digital twins that allow the scientific community to simulate and study “what if” scenarios; (b) developing and implementing technologies that enable a cross-domain, multidisciplinary approach to the study of biodiversity and ecosystems; (c) promoting ecosystem-based approaches to biodiversity management and habitat conservation in innovative publications venues (e.g. Nature Conservation, Biodiversity Data Journal, One Ecosystem, etc.).Promotes collaboration and synergies between the Convention on Biological Diversity and its Protocols, as well as with other biodiversity-related conventions, relevant multilateral agreements and international organisations and processes, as this will facilitate the implementation of the K-M GBF. The network is developing a variety of work, including: (a) collaborating with bodies and organisations responsible for the implementation of the CBD and its Protocols (e.g. IUCN, IPBES, European Commission) to co-design and co-develop research resources and products to support their mandates; (b) establishing strong links with policy actors such as the European Commission and the European Parliament, the JRC and others. Participate in social, scientific and technical initiatives in the European arena, such as the European Green Deal, the EU Knowledge Centre for Biodiversity and its Science Service for Biodiversity, the Biodiversity Knowledge Hub, the EU Pollinators Initiative and the EOSC. Developing links with the private sector through the Science/Business initiative, cooperation with the European Environmental Bureau (EEB), and the EOSC Digital Innovation Hub (EOSC DIH); (c) integrating and sharing of computational resources and expertise will not only advance the frontiers of scientific knowledge, but also ensure that data-driven research initiatives around the world are well supported.Contributes to the understanding and researching of the links between biodiversity and health. Particular emphasis will be placed on the following activities: (a) participating in initiatives and projects such as the EOSC Health Cluster, a platform for interdisciplinary research, EC projects such as B4Life, B-Cubed and BioAgora by publishing research that investigates how biodiversity affects human health; (b) using data from multiple sources to numerically demonstrate the links between human and environmental health, in the context of the One Health concept; (c) using digital twins to create Virtual Research Environments (VREs) that generate knowledge on how biodiversity patterns derived from taxa and habitats interact with patterns derived from data and information on their health; (d) publishing the results of the research, such as studies on zoonotic diseases, biodiversity and mental health, and the benefits of ecosystem services for public health. During the workshop, the participants will present their collective contribution to the implementation of the K-M GBF and invite international and regional stakeholders to present their expectations on the above topic. Based on stakeholder input, the network will publish a white paper outlining its approach.Finally, these communities will issue an open call to forge an international alliance to further integrate biodiversity conservation into the priorities of the UN Summit of the Future agenda priorities and the post-SDG agenda.

Abstract Environmental stressors (such as ammonia) in aquaculture could increase the risk of pathogenicity, posing a more severe threat to farmed fish. The aim of this study was to investigate the effects of ammonia stress on the pathogenicity of Shewanella spp. in Oreochromis niloticus. First, a 96-hour static test was used to determine the median lethal concentration (LC50) of unionized ammonia to Nile tilapia. After 96 h of exposure, the Un-ionized ammonia (UIA) LC50 was estimated to be 4.26 mg/L. Second, an experiment was conducted to test the effect of unionized ammonia stress on the pathogenicity of Shewanella spp. in O. niloticus for 30 days. A study involved 180 fish divided into six groups, with the first group serving as a control. The second group (AMN1/10) and the third group (AMN1/20) were not challenged and were exposed to 1/10 (0.42 mg/L) and 1/20 (0.21 mg/L) of the 96-hour LC50 of UIA, respectively. Then 0.2 mL (0.14 × 105) of Shewanella spp. was intraperitoneally injected into the fourth (SH), fifth (SH + AMN1/10), and sixth (SH + AMN1/20) groups, which were subjected to 0, 1/10 (0.42 mg/L), and 1/20 (0.21 mg/L) of the 96-hour LC50 of UIA, respectively. The survival rate, hematological indices, immunological parameters, and antioxidant activity of the fish significantly decreased when they were exposed to ammonia and Shewanella infection separately or together. Histopathological changes were also observed in the kidney and liver. Furthermore, both individual and combined exposures significantly altered renal and hepatic function, with notable increases in glucose and cortisol levels, as well as in the expression of proinflammatory cytokine genes (TNF-α and IL-1ß). However, the detrimental effects of co-exposure to ammonia stress and Shewanella infection were greater than those of separate exposures. As a result, we may say that increased ammonia concentrations enhance the infection of Shewanella spp. These findings could contribute to a better understanding of Shewanella infection in Nile tilapia.

Abstract Background Mild hypoxic ischemic encephalopathy is associated with sub optimal cognition and learning difficulties at school age. Although whole-body hypothermia reduces death and disability after moderate or severe encephalopathy in high-income countries, the safety and efficacy of hypothermia in mild encephalopathy is not known. The cooling in mild encephalopathy (COMET) trial will examine if whole-body hypothermia improves cognitive development of neonates with mild encephalopathy. Methods The COMET trial is a phase III multicentre open label two-arm randomised controlled trial with masked outcome assessments. A total of 426 neonates with mild encephalopathy will be recruited from 50 to 60 NHS hospitals over 2 ½ years following parental consent. The neonates will be randomised to 72 h of whole-body hypothermia (33.5 ± 0.5 C) or normothermia (37.0 ± 0.5 C) within six hours or age. Prior to the recruitment front line clinical staff will be trained and certified on expanded modified Sarnat staging for encephalopathy. The neurological assessment of all screened and recruited cases will be video recorded and centrally assessed for quality assurance. If recruitment occurs at a non-cooling centre, neonates in both arms will be transferred to a cooling centre for continued care, after randomisation. All neonates will have continuous amplitude integrated electroencephalography (aEEG) at least for the first 48 h to monitor for seizures. Predefined safety outcomes will be documented, and data collected to assess resource utilization of health care. A central team masked to trial group allocation will assess neurodevelopmental outcomes at 2 years of age. The primary outcome is mean difference in composite cognitive scores on Bayley scales of Infant and Toddler development 4th Edition. Discussion The COMET trial will establish the safety and efficacy of whole-body hypothermia for mild hypoxic ischaemic encephalopathy and inform national and international guidelines in high income countries. It will also provide an economic assessment of whole-body hypothermia therapy for mild encephalopathy in the NHS on cost-effectiveness grounds. Trial registration number NCT05889507 June 5, 2023.

The transportation system has the most considerable negative impacts on the environment due to greenhouse gas (GHG) emissions, so various governments seek to reduce GHG emissions by using the best alternative vehicle technologies in the transportation system. This paper presents a framework for selecting the best alternative vehicle technology in Egypt to reduce the emission of GHG toward sustainability. Multi-criteria decision-making (MCDM) is used to deal with various conflicting criteria. This paper uses two MCDM methods: stepwise weight assessment ratio analysis (SWARA) and root assessment method (RAM). This study extends the SWARA and RAM methods under single-valued neutrosophic sets (SVNSs). The SVN-SWARA method is used to compute the criteria weights, and the SVN-RAM method is used to rank the alternatives. This study used seven main criteria, 40 sub-criteria, and 12 alternative vehicle technologies. The results in weights of criteria show that social criterion has the highest importance, and the market criterion has the lowest importance. The results in the rank of alternatives show the Electric vehicles (alternative) are the best choice for GHG emission reduction and for achieving sustainability in the transportation system in Egypt. The sensitivity and comparative analysis showed that the rank of alternatives is stable under different scenarios and that the proposed methodology is effective.

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods.

Soil structure is a key attribute of soil quality and health that significantly impacts water infiltration. Structure can be significantly altered by natural or anthropogenic drivers including soil management practices and can in turn impact soil infiltration. Those changes in soil structure are often complex to quantify and can lead to conflicting impacts on water infiltration into soils. Here, we present a narrative systematic review (SR) of the impacts of soil structure on water infiltration. Based on inclusion and exclusion criteria, as well as defined methods for literature search and data extraction, our systematic review led to a total of 153 papers divided into two sets: experimental (131) and theoretical (22) papers. That implied a significant number of in-situ and field experiments that were conducted to assess the impacts of soil structure on water infiltration under the influence of different land uses and soil practices. Analysis of the metadata extracted from the collected papers revealed significant impacts of soil structure on water infiltration. Those effects were further attributed to land use and management, where we demonstrate the impact of three unique categories: soil amendments, crop management and tillage. Furthermore, significant correlations were established between infiltration rate and soil structural properties, with R2 values ranging from 0.51 to 0.80 and for saturated hydraulic conductivity and soil structural properties, with R2 values ranging from 0.21 to 0.78. Finally, our review highlighted the significant absence of and the need for theoretical frameworks studying the impacts of soil structure on water infiltration.

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex networks, including sensors, actuators, appliances, and cyber services. The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks, especially privacy-related attacks such as inference and data poisoning ones. Federated Learning (FL) has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications. Even though the significance of developing privacy-preserving FL has drawn as a great research interest, the current research only concentrates on FL with independent identically distributed (i.i.d) data and few studies have addressed the non-i. i.d setting. FL is known to be vulnerable to Generative Adversarial Network (GAN) attacks, where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors. This paper proposes an innovative Privacy Protection-based Federated Deep Learning (PP-FDL) framework, which accomplishes data protection against privacy-related GAN attacks, along with high classification rates from non-i. i.d data. PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets. The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8% as accuracy improvements.