Centerforce

Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) is one of the most promising remote sensing techniques for global atmospheric sounding. RO is a limb-sounding technique that uses GNSS signals, refracted during their propagation through the Earth’s atmosphere to a receiver on a low-Earth orbit (LEO) satellite. RO data have been proven to be of enormous value for data assimilation in numerical weather prediction (NWP) as well as in climate science over the two last decades. However, retrieving products such as temperature or humidity from RO observations is not straightforward and dedicated retrieval algorithms still have limitations, such as the need for external meteorological data. On the other hand, various new RO missions are now producing over 10,000 globally distributed profiles daily. This makes the technique interesting for the application of Artificial Intelligence (AI) models to different steps of the RO retrieval chain.This study compares an existing retrieval method entitled AROMA (Advancing the GNSS-RO retrieval of atmospheric profiles using MAchine-learning), which is based on a multi-layer perceptron (MLP), with more sophisticated deep learning (DL) architectures such as Transformers and one-dimensional convolutional neural networks (1D-CNNs). All these models are trained on multiple years of data from different RO missions, using vertical profiles of bending angle and other RO parameters as input features and operational results from a standard retrieval algorithm as targets. Validation results using both a separate test data set as well as external data will be presented, aiming to give a recommendation on the most promising type of architecture to use for the RO wet retrieval problem.

It is estimated that over 5.8 million people live with diabetes in the UK. General practice nurses are well placed to support people with diabetes and play a key role in the multidisciplinary team. This article provides an overview of what should be covered in a diabetes consultation, including important considerations for the individual's overall health and ways to empower them to practice effective self-management.

Summary: Local health services can be quickly overwhelmed in the aftermath of a disaster. As a veteran-led disaster relief charity founded in 2022, Taskforce Kiwi identified an ethical imperative to not be a burden on disaster-affected health services while it assists that community in recovering. This requires the volunteers to have the appropriate first aid skills to treat likely injuries within the team. Taskforce Kiwi identified a skill gap between basic first aid and Defense Force/Emergency Services Medic training. With no appropriate courses available, Taskforce Kiwi developed a bespoke peer-led solution that does not require complex or extensive training equipment and can thus be easily implemented by other voluntary organizations in low-resource communities. The curriculum requirements were researched and are evidence-based, focusing on common injuries encountered by disaster relief volunteers, such as wounds prone to infection and soft tissue injuries. Delivery is a one-day session covering patient assessment, hemorrhage control, wound care, including delayed closure, heat stress, coached rehydration, scripted telehealth consultation, incident roles, and the use of standing orders for medication, including inhaled methoxyflurane analgesia. A public first aid certificate is a prerequisite, and a pre-course webinar enables the peer-led training to be delivered to volunteers in a timely and cost effective manner. Post-course surveys of the Disaster First Aid course indicate an average 28% increase in perceived confidence across numerous skills. Over three sessions, Disaster First Aider capacity was increased by 24 people. The participant’s most valuable session was wound cleaning, which involved using fresh dead pigskin, soil, simulated blood, steri-strips, and dressings. The Disaster First Aid course fills the training gap between basic first aid and trained medics, building capacity within Taskforce Kiwi. When funding and equipment are scarce, the course provides an effective solution to disaster volunteer organizations.

Demand for home health is growing. Registered and licensed practical/vocational nurses (RNs and LPN/LVNs) are integral to the provision of home health services, but a limited body of evidence suggests that the workforce is unstable and possibly shrinking. We assessed RNs' and LPN/LVNs' transitions into and out of the home health sector using data from the Current Population Survey monthly files from January 2003 to June 2023. Specifically, we compared turnover from home health versus the hospital sector and assessed entry into home health from other health care and non-health care sectors. Turnover was consistently and significantly higher in the home health sector compared with the hospital sector. The predicted probability of nurses entering home health from any other sector hovered around or below 1%. High turnover combined with low home health entry raises concerns about home health nursing services nationwide.

With the upgrading of consumption and the increasing uncertainty of the external environment, e-commerce platforms urgently need to build a supply chain system that is both efficient and resilient. For Chinese consumers, their needs have changed from simply "being able to buy" to "buying well and buying fast". "Same-day delivery" or even "one-hour delivery" is no longer an extra value-added service, but has become a key factor in platform competition. Based on JD.com, a representative Chinese e-commerce company that has built its own logistics system, this paper studies the evolution path of its e-commerce supply chain under the background of same-day delivery service and the effects it brings. This paper focuses on the integration path of the three paradigms of "lean, agile and resilient". Under the macro background of deepening reforms and strengthening the resilience of industrial and supply chains, exploring the applicability of this e-commerce supply chain evolution in China has important practical significance.

Abstract “Preventing Child Abuse: Family-Based Programs to Improve Resiliencies and Address Substance Use” introduces the evidence, theories, concepts, essential components, and practice issues for family-based prevention programs (FBPs) for families dealing with substance use problems and who have experienced cumulative adverse experiences. FBPs are interventions designed to prevent or minimize later problems, which involve the family in program practices that are trauma-informed, skills-oriented, and strengths-based. FBPs are guided by multiple theoretical perspectives. They target many outcomes for individuals and families, under the assumption that improving parenting capacity, reducing harmful substance use, and improving family relationships will translate into a reduction of both violence in the home and neglect of children’s needs. The book uses examples from existing FBPs to illustrate the concepts as well as an in-depth case study of one FBP specifically developed to address caregivers’ substance use. The book also includes chapters on child welfare outcomes research, adverse and compensatory factors that are the foundation of FBPs’ practices, prominent theories for FBPs, cultural adaptation, and evaluation methods. Since there are several worthwhile FBPs at various stages of evidence support, the authors recommend that funders and policy makers sustain programs that are beneficial to the community and show promise of preliminary efficacy while they work toward implementing increasingly robust evaluation research studies.

This study focuses on evaluating the electrochemical behavior of the ferri/ferrocyanide redox couple [Fe(CN)₆<sup>3-</sup>/Fe(CN)₆<sup>4-</sup>] on EDTA-modified carbon paste electrodes, with the aim of developing a sensitive electrochemical sensor for the detection of trivalent and hexavalent chromium Cr(III,VI). EDTA, a complexing agent well known for its strong affinity for metal ions, was incorporated at a concentration of 10% in the carbon paste to enhance the electrode's ion recognition properties. Electrochemical characterization was performed by cyclic voltammetry in the presence of the ferri/ferrocyanide redox couple, used as a probe to assess electron transfer properties. Electrochemical parameters, including the peak potential difference (ΔEp), the anodic and cathodic peak currents (Ipa, Ipc), and their ratio, demonstrated the improved redox behavior and sensitivity of the modified electrode. The observed improvement is likely due to the complexing functional groups of EDTA, which facilitate stronger interactions between the modified electrode surface and the redox species in solution. Electrochemical impedance spectroscopy supports the voltammetric findings, confirming enhanced charge transfer kinetics at the surface of the modified electrode. The results confirm the potential of this configuration as an effective platform for developing reliable and selective electrochemical sensors for detecting Cr(III,VI) in aqueous media. By studying the electrical quantities ΔEp, the formal standard potential E°, and the Ipa/Ipc current ratio, the developed electrochemical sensors can be optimized.

Recent developments in machine learning (ML) techniques enable users to extract, transmit, and reconstruct information semantics at the semantic level through ML-based semantic communication (SemCom). This significantly increases network spectral efficiency and transmission robustness. The semantic codecs among various users and modalities, based on ML, however, inevitably experience semantic drift and necessitate collaborative updating to preserve transmission quality. The various heterogeneous characteristics of most networks, in turn, introduce emerging but unique challenges for semantic codec updating that are different from other general ML model updating. In this article, we propose a heterogeneity-aware semantic codec updating scheme to achieve efficient and reliable updating in heterogeneous networks. We begin with the introduction of the core components of SemCom and then highlight key issues in semantic codec updating under network heterogeneity, discussing several potential methods. Furthermore, the scheme is provided with performance metrics. Future research directions for advancing SemCom in complex, multi-modal environments are also discussed.

Purpose The primary aim of this paper is to examine the role of asymmetry in the relationship between FDI shocks and real GDP across seven selected Asian countries, contributing to a deeper understanding of how FDI influences economic growth in the region. Design/methodology/approach This study employs two advanced methodologies: the nonlinear autoregressive distributed lag (NARDL) model and the generalized impulse response function (GIRF). The NARDL model is used to explore both the long- and short-term asymmetric effects of FDI on real GDP, while the GIRF is employed to analyze the responses of GDP to both positive and negative FDI shocks. Findings The results reveal that FDI asymmetrically affects real GDP in the long run in five of the countries under consideration: Indonesia, Malaysia, the Philippines, Singapore and Thailand. Short-term asymmetry is also confirmed for Malaysia, Thailand and Japan. Additionally, the GIRF analysis demonstrates that the responses of GDP to positive and negative FDI shocks differ significantly and are not mirror images. Practical implications The findings have important policy implications, suggesting that policymakers should consider the asymmetrical effects of FDI when designing economic strategies and responses to foreign investment shocks. These insights can enhance empirical modeling and contribute to more effective economic planning in the region. Originality/value This paper is unique in its explicit focus on asymmetry in the relationship between FDI shocks and real GDP, applying advanced econometric techniques to provide a more nuanced understanding of this dynamic. The study’s results offer valuable contributions to both policy analysis and empirical modeling in the context of Asian economies.