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Learning-based approaches for wireless PHY layers from the perspective of conventional machine learning to foundation models: A comprehensive survey
No full textThe future of wireless communication requires low latency, ultra-reliable connectivity, and the ability to manage a large number of IoT devices in real time. Achieving these demands for quality of service (QoS) can be addressed through machine learning (ML), deep learning (DL), and foundation models integrated into wireless systems and devices. Foundation models, in particular, show promise for overcoming the limitations of conventional approaches, and improving system performance was observed to be between 9.63 % and 12.80 %, with the possibility of exceeding this range under certain conditions. This review explores how ML, DL, and foundation models can be applied at the physical (PHY) layer in wireless communications. It covers various learning algorithms such as deep, recurrent, and feedforward neural networks, explaining their design, training methods, and challenges. Key applications include channel estimation, constellation design, signal detection, and optimizing signal modulation schemes to achieve better spectral efficiency and noise resilience. The paper also discusses how the ML, DL, and foundation models can enhance MIMO systems with improved detection performance. In addition, it highlights the challenges and opportunities in adopting these models in different communication domains, including trade-offs between accuracy, complexity, and generalization. Conventional ML performs better in scenarios with small datasets, low computational complexity, and tasks requiring high interpretability, whereas DL approaches tend to outperform traditional methods in large-scale, high-dimensional wireless problems such as CSI prediction, interference classification, and spectrum sensing. This survey offers valuable insights into the evolving landscape of intelligent communication systems, guiding practitioners in implementing learning-aided strategies for the next generation of wireless technology
Renewable Heat Incentive in Northern Ireland: devolved government policy failure
No full textThe Renewable Heat Incentive (RHI) scheme in Northern Ireland (NI), 2012–17, led to a major scandal in terms of misuse of public money. The RHI was the subject of a public inquiry and the RHI was a contributory factor in the collapse of the regional government in January 2017. The RHI is a case study in government failure: illustrating how well-intentioned interventions can lead to harmful outcomes. The RHI shows how the devolved machinery of government created policies containing significant design flaws. This was partly the result of a capacity problem: a lack of resources to handle complex policies. Ironically, in the RHI case, a greater level of accountability helped to skew policy in a harmful direction by giving undue influence to vested interest groups. The authors’ focus is on how the civil servants designed policies with significant flaws but with attention given to the role of consultants, special advisers and politicians.<br/
A lightweight learning-based approach for online edge-to-cloud service placement
No full textThe integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales)
Folate-functionalized polymeric nanoparticles for 5-fluorouracil delivery to prostate cancer: physicochemical and in vitro/in vivo characterization
No full textProstate cancer is the second most common cancer in men worldwide, highlighting the urgent need for effective and targeted chemotherapeutic approaches. This study reports the development and optimization of 5-fluorouracil (5-FU)–loaded poly(lactic-co-glycolic acid)–polyethylene glycol–folic acid (PLGA–PEG–FOL) nanoparticles designed for folate receptor–mediated targeted therapy. The PLGA–PEG–FOL conjugate was synthesized via a stepwise carbodiimide coupling reaction and confirmed by FT-IR analysis. Nanoparticles were formulated via a modified emulsification–solvent evaporation method and optimized through a Box–Behnken design. The optimized formulation demonstrated a particle size of 178.47 ± 3.26 nm, a narrow polydispersity index (0.119 ± 0.008), a zeta potential of −23.4 ± 0.35 mV, a high entrapment efficiency (78.93 ± 1.05%), and sustained release of 5-FU for up to 72 h. In vitro cytotoxicity assays in PC-3 prostate cancer cells revealed a 1.6-fold reduction in the IC50 value compared with that of free 5-FU, indicating enhanced therapeutic potency. In vivo efficacy was evaluated in testosterone-induced prostate cancer in male Wistar rats. Compared with the control, treatment with 5-FU-loaded PLGA–PEG–FOL nanoparticles significantly reduced the prostate index and produced a 2.2-fold decrease in serum PSA levels and a 1.9-fold decrease in serum testosterone levels. Histopathological examination confirmed the attenuation of hyperplastic and dysplastic lesions in the nanoparticle-treated group. These findings suggest that PLGA–PEG–FOL nanoparticles are a promising targeted delivery platform for enhancing the therapeutic efficacy of 5-FU in prostate cancer treatment.<br/
Adoption and special guardianship
No full textAdoption and special guardianship are two options to provide secure family homes for children who have been made subject to care orders and cannot return to their parents. The legal processes for adoption are complex and designed to meet children’s welfare needs while respecting the rights of adults. However, adoption is controversial, especially when courts override birth parents’ objections to adoption taking place. Children often require ongoing support because of their experiences in early life. Potential issues of post-placement contact with birth families may arise and the chapter considers how fixed boundaries between state intervention and private family life in adoption can no longer be assumed
Alteration in gene expression patterns and increased drug resistance in MCF7 breast cancer cells cultured on 3D collagen-based scaffolds
No full text2D models have been instrumental in breast cancer research and advancing our knowledge of the disease, both in terms of progression and treatment. However, they fail to replicate the three-dimensional (3D) architectural and microenvironmental properties that influence tumour behaviour in vivo and response to therapy. To overcome these limitations, 3D culture models have emerged as valuable tools in cancer research. These 3D models provide a more physiologically relevant environment by allowing cells to grow in a three-dimensional structure that better replicates the tumour microenvironment. Here we demonstrated significant alterations in gene expressions in breast cancer cells cultured in a 3D collagen-gelatin scaffold compared to 2D cultured cells. In addition, we successfully applied the scaffolds as a test bed for therapeutic agents, demonstrating a significant increase in chemoresistance upon culture in the collagen-gelatin scaffolds (2D relative IC of 0.00028 μM vs. 3D scaffold IC of 0.00045 μM, 60.7 % increase). Genes associated with extracellular matrix (ECM) modification and synthesis, glycolysis, hypoxia and tumour survival had elevated expression levels in the 3D culture of MCF7 cells, indicating a switch to a more aggressive phenotype. Furthermore, we demonstrated for the first time the importance of the adaption period to the 3D environment, with resistance to docetaxel acquired as a function of cell pre-culture time within the scaffold. Overall, the findings herein demonstrate how 3D collagen scaffolds can potentially bridge the gap between 2D culture and animal models, through more accurately modelling the gene expression profiles of cancer cells and their response to therapeutic agents
Incidence and prognosis of apparent-treatment resistant hypertension: a multi-state analysis using real world evidence
No full textBackgroundThere is limited evidence regarding the incidence and prognosis of apparent resistant hypertension (aRHT) in hypertensive patients. This study aimed to estimate the incidence of aRHT and assess the risk of cardiovascular and kidney complications in patients with aRHT compared to those without aRHT, using a multi-state analysis.MethodsThis retrospective cohort study utilized real-world data from hypertensive patients treated at Ramathibodi Hospital, Bangkok, Thailand, between January 2010 and June 2024. aRHT was defined as having uncontrolled blood pressure (BP), while using ≥ 3 antihypertensive medications or having controlled BP with using ≥ 4 antihypertensive medications. The outcomes of interest were cardiovascular and kidney complications including coronary artery disease (CAD), stroke, heart failure (HF), and chronic kidney disease (CKD), and all-cause mortality. A multi-state analysis was applied to estimate the risk of disease progression from hypertension without complications to aRHT, CAD, stroke, HF, CKD, and all-cause death. Kaplan-Meier estimates with a clock-reset approach were used to calculate transition probabilities for each progression. Multivariate Cox regression analysis was applied to assess the risk factors of aRHT and assess the prognosis of aRHT.ResultsAmong 114,364 hypertensive patients, the incidence of aRHT was 2.61 per 100 person-years (95% confidence interval [CI], 2.56–2.65). Results from multivariate Cox regression analysis found that the independent risk factors of aRHT were increasing age, males, obesity, type 2 diabetes mellitus, dyslipidemia, and having cardiovascular and kidney complications including CAD, stroke, CKD, and HF. Regarding the prognosis of aRHT, compared to non-aRHT patients, those with aRHT had significant higher risk of CAD, CKD, HF, and all-cause mortality with hazard ratios (95% CI) of 1.80 (1.56–2.08), 1.93 (1.79–2.08), 4.24 (3.54–5.08), and 2.84 (1.89–4.27), respectively.ConclusionsThe risk of aRHT was higher in hypertensive patients with cardiovascular and kidney complications compared to those without. Patients with aRHT had a worse prognosis than hypertensive patients without aRHT, as evidenced by higher risks of CAD, CKD, HF, and all-cause death
Carbon trading markets: literature review on mechanisms, accounting, and market models coupled with energy markets
No full textTo tackle the climate change, various decarbonization strategies and approaches have been adopted to achieve low-carbon transition in the energy systems. Among them, carbon pricing plays a significant role in emission reduction through the carbon trading markets. This paper establishes a unified analytical framework that connects three core components: carbon market policies and mechanisms, demand-side carbon accounting methods, and coupled energy and carbon market models. It analyzes carbon trading market policy, mechanisms, and current practices in both primary and secondary trading, which is the basis of carbon accounting and market models coupled with energy markets. Then, the carbon accounting methods in the energy systems are reviewed from the demand side to support market clearing. Based on the mechanism and accounting, the coupled energy and carbon trading market models are reviewed by market participants, scope, structure, mechanisms, and coupled operation modes, serving as the foundation of energy and carbon market coupling. By integrating insights across policy, accounting, and modeling, this paper provides a coherent basis and practical implications for designing coupled energy and carbon markets. Key research directions of carbon-energy systems are identified to support the transition toward net-zero energy systems.javascript:void(0)
Identification of important outcomes for surgical and brace treatment of adolescent idiopathic scoliosis
No full textAimsHigh-quality clinical trials in adolescent idiopathic scoliosis (AIS) are needed to guide decision-making but progress is hindered by suboptimal selection of outcome measures. Identifying meaningful outcomes for consistent measurement across clinical trials and routine practice is critical. However, there is currently no understanding of which treatment outcome domains are considered important by adolescents, their parents, and healthcare professionals (HCPs). This study is the first to address this gap internationally.MethodsThis study represents the first stage of core outcome set (COS) development, following gold-standard guidance. A cross-sectional qualitative interview study with 40 participants (adolescents with AIS, their parents, and HCPs) was conducted. Semi-structured interviews were analyzed to identify and categorize important AIS treatment outcomes. Analytical rigour was ensured through coder agreement and stakeholder consultation.ResultsA total of 91 important outcome domains were identified; 53 outcome domains applying to both bracing and surgery, with 15 additional outcome domains for bracing only, and 23 additional outcome domains for surgery only. Of the 91 outcome domains, more than three-quarters (71/91, 78%) related to life impact, with smaller proportions relating to physiological/clinical outcomes (13/91, 14%), resource use (4/91, 4%), and adverse events (1/91, 1%).ConclusionThe current study highlights treatment outcomes considered important by adolescents with AIS, their parents, and HCPs. These findings will inform outcome selection in clinical trials and routine practice, as well as facilitating an ongoing programme of research to develop a COS for evaluating treatment of AIS