Search Results (798)

With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions—time, space, frequency, and power—with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

This paper explores responses to online coverage of an avian tracking project. Researchers attached novel trackers to a small group of wild magpies (Gymnorhina tibicen). These were subsequently removed by conspecifics, an example of ‘rescue behavior’ that was recounted in several media outlets. Online comments on three articles, from across the political spectrum (the Conversation, UK Guardian, and UK Daily Mail), were selected for thematic analysis. The resulting 680 comments were analyzed qualitatively and quantitatively to uncover predominant themes and the overall balance of positive and negative sentiments expressed about this tagging project or wildlife tagging generally. Topics occurring most frequently were themed into three interrelated areas: (1) sharing personal feelings and experiences, (2) comparing the merits of different species, and (3) sharing knowledge and opinion. Twenty-one percent (21%) of respondents expressed an opinion on the ethics of wildlife tagging. In the Daily Mail and Guardian, this opinion was more likely to be negative towards the use of tags. Opinion was more balanced for readers of the Conversation’s article. Willingness to comment on online news is low, and readers of this story were not asked directly for their opinion. Nevertheless, the data here illustrate some public perceptions of wildlife tagging, and there was a clear negative reaction from many responders. Widening the means through which people can engage with animal science has the potential to advance discussions around research ethics and animal welfare. Reactions to this story expose important questions for scientists seeking to engage with, and convince, the public of the merits of their work. Full article

Body stalk anomaly (BSA) is a rare and usually fatal congenital disorder involving severe malformations of the body wall, limbs, spine, and internal organs. This study presents the first documented cases of BSA in seven dogs, offering new insights into how the disorder manifests in animals. The affected fetuses consistently exhibited major anomalies, including large abdominal wall defects, structural spinal abnormalities, and a variety of limb malformations ranging from partial agenesis and meromelia to phocomelia and complete amelia. Structural urogenital anomalies and orofacial clefts were also observed, aligning with similar findings in BSA cases reported in pigs and cats. These findings support the hypothesis of a multifactorial etiology involving early embryonic disruptions, such as abnormal folding of the embryo, rupture of the amniotic membrane, and vascular compromise. The frequent occurrence of abdominal wall defects alongside umbilical cord abnormalities further suggests a shared developmental pathway. This study also highlights the value of veterinary cases in comparative embryology and the need to assess congenital anomalies as part of a broader malformation complex. By expanding the phenotypic spectrum of BSA in domestic animals, this work contributes to a deeper understanding of its pathogenesis and emphasizes the importance of further research into the genetic and environmental factors involved. Such efforts could lead to improved classification and diagnosis of complex congenital malformations, as well as facilitate cross-species comparisons. Full article

This paper explores the experiences of parents of children with autistic spectrum disorder (ASD) who have participated in parenting programmes in the UK. The parents attended generic programmes before they were able to access more specific programmes geared towards the needs of children with ASD. The parents found that the generic programmes were detrimental to the needs of their children and disruptive of family life, resulting in parental relationship breakdown in some instances. Whilst the ASD-specific programmes did provide some level of support, the most decisive factor was the sharing of experiences amongst those parents as well as the ongoing support that this fostered. The conclusion of this study is that the neoliberal responsibilisation of parents via parenting programmes is undermining the support networks of parents of children with ASD, and an asset-based community development approach would be more beneficial. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition frequently associated with gastrointestinal symptoms, gut dysbiosis, and metabolic dysfunctions such as insulin resistance (IR). Recent evidence suggests that the gut microbiota may influence both metabolic and neurological processes through the gut–brain–metabolic axis. This review explores the molecular mechanisms linking dysbiosis, IR, and ASD, focusing on pathways such as TLR/NF-κB activation, PI3K/Akt/mTOR disruption, and the action of microbial metabolites, like short-chain fatty acids (SCFAs), lipopolysaccharide (LPS), and γ-aminobutyric acid (GABA). We discuss how dysbiosis may contribute to increased intestinal permeability, systemic inflammation, and neuroimmune activation, ultimately affecting brain development and behavior. Common microbial alterations in ASD and IR—including increased Clostridium, Desulfovibrio, and Alistipes, and reduced Bifidobacterium and butyrate-producing genera—suggest a shared pathophysiology. We also highlight potential therapeutic strategies, such as microbiota modulation, insulin-like growth factor 1 (IGF-1) treatment, and dietary interventions. Understanding these interconnected mechanisms may support the development of microbiota-targeted approaches for individuals with ASD metabolic comorbidities. Full article

High unemployment rates, inaccessible housing markets, and funding challenges create barriers to finding suitable housing for adults with Autism Spectrum Disorder (ASD) who have less obvious support needs, also known as autistic adults. While public and community housing services in Aotearoa New Zealand (AoNZ) may be an option, a lack of accessible designs leaves families uncertain about future care options. This paper, part of the MBIE-funded Public Housing and Urban Regeneration: Maximising Wellbeing research programme in partnership with registered Community Housing Provider, Te Toi Mahana (TTM), takes an exploratory approach to ask how public and community housing can support and help enable semi-independent living for autistic adults. It investigates how design elements—such as dwelling layouts, material choices, colour schemes, lighting, acoustics, shared and community spaces, and external environments—impact the wellbeing of autistic adults. By extension, insights may also inform private housing design. The study focuses on autistic adults who may be considered ‘mid-to-high’ functioning or those who have been previously diagnosed with Asperger’s Syndrome, whose housing needs are often overlooked. It develops guiding principles and detailed guidance points for public and community housing, informed by the literature, case studies, and data from a photo elicitation study and interviews undertaken with autistic adults in AoNZ. These guiding principles are tested through the speculative redesign of a large TTM site in Newtown, Wellington, AoNZ. Findings should be of interest to government agencies, housing providers, architects, stakeholders, and others involved in shaping the built environment, as well as autistic adults and their supporters, both in AoNZ and internationally. Full article

Mangrove forests support coastal resilience, biodiversity, and significant carbon sequestration, yet they face escalating threats from climate change, urban expansion, and land-use change. Traditional remote sensing workflows often struggle with large data volumes, complex preprocessing, and limited computational resources. Google Earth Engine (GEE) addresses these challenges through scalable, cloud-based computation, extensive, preprocessed imagery catalogs, built-in algorithms for rapid feature engineering, and collaborative script sharing that improves reproducibility. To evaluate how the potential of GEE has been harnessed for mangrove research, we systematically reviewed peer-reviewed articles published between 2017 and 2022. We examined the spectrum of GEE-based tasks, the extent to which studies incorporated mangrove-specific preprocessing, and the challenges encountered. Our analysis reveals a noteworthy yearly increase in GEE-driven mangrove studies but also identifies geographic imbalances, with several high-mangrove-density countries remaining underrepresented. Although most studies leveraged streamlined preprocessing and basic classification workflows, relatively few employed advanced automated methods. Persistent barriers include limited coding expertise, platform quotas, and sparse high-resolution data in certain regions. We outline a generalized workflow that includes automated tidal filtering, dynamic image composite generation, and advanced classification pipelines to address these gaps. By synthesizing achievements and ongoing limitations, this review offers guidance for future GEE-based mangrove studies and conservation efforts and aims to improve methodological rigor and maximize the potential of GEE. Full article

Functional magnetic resonance imaging (fMRI) has become instrumental in researching the functioning of the brain. One application of fMRI is investigating the brains of people with autism spectrum disorder (ASD). The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing initiative. While ABIDE offers raw data and data preprocessed with various atlases, independent component analysis (ICA) for dimensionality reduction remains underutilized. ICA is a data-driven way to reduce dimensionality without prior assumptions on delineations. Additionally, ICA separates the noise from the signal, and the signal components correspond well to functional brain networks called resting-state networks (RSNs). Currently, no large, readily available dataset preprocessed with ICA exists. Here, we address this gap by presenting ABIDE’s data preprocessed to extract ICA-based resting-state networks, which are publicly available. These RSNs unveil neural activation clusters without atlas constraints, offering a perspective on ASD analyses that complements the predominantly atlas-based literature. This contribution provides a resource for further research into ASD, benchmarking between methodologies, and the development of new analytical approaches. Full article

Mitochondrial dysfunction is a key driver of neurological disorders due to the brain’s high energy demands and reliance on mitochondrial homeostasis. Despite advances in genetic characterization, the heterogeneity of mitochondrial diseases complicates diagnosis and treatment. Mitochondrial dysfunction spans a broad clinical spectrum, from early-onset encephalopathies to adult neurodegeneration, with phenotypic and genetic variability necessitating integrated models of mitochondrial neuropathology. Mutations in nuclear or mitochondrial DNA disrupt energy production, induce oxidative stress, impair mitophagy and biogenesis, and lead to neuronal degeneration and apoptosis. This narrative review provides a structured synthesis of current knowledge by classifying mitochondrial-related neurological disorders according to disrupted biochemical pathways, in order to clarify links between genetic mutations, metabolic impairments, and clinical phenotypes. More specifically, a pathway-oriented framework was adopted that organizes disorders based on the primary mitochondrial processes affected: oxidative phosphorylation (OXPHOS), pyruvate metabolism, fatty acid β-oxidation, amino acid metabolism, phospholipid remodeling, multi-system interactions, and neurodegeneration with brain iron accumulation. Genetic, clinical and molecular data were analyzed to elucidate shared and distinct pathophysiological features. A comprehensive table synthesizes genetic causes, inheritance patterns, and neurological manifestations across disorders. This approach offers a conceptual framework that connects molecular findings to clinical practice, supporting more precise diagnostic strategies and the development of targeted therapies. Advances in whole-exome sequencing, pharmacogenomic profiling, mitochondrial gene editing, metabolic reprogramming, and replacement therapy—promise individualized therapeutic approaches, although hurdles including heteroplasmy, tissue specificity, and delivery challenges must be overcome. Ongoing molecular research is essential for translating these advances into improved patient care and quality of life. Full article

Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements while maintaining competitive accuracy. We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data. Experiments across benchmark datasets demonstrate that: (i) our weakly supervised methods achieve performance comparable to fully supervised approaches while significantly reducing supervision requirements; (ii) the proposed multi-task framework enhances performance through knowledge sharing between related tasks; (iii) our weakly self-supervised approach demonstrates remarkable efficiency with just 10% of labeled data. These results not only highlight the complementary strengths of different learning paradigms, offering insights into tailoring HAR solutions based on the availability of labeled data, but also establish that our novel weakly self-supervised framework offers a promising solution for practical HAR applications where labeled data are limited. Full article

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are linked by shared genetic mutations and overlapping clinical features, forming a clinical spectrum. This systematic review and meta-analysis analysed 97 studies, including 3212 patients with key ALS/FTD gene mutations, to identify gene-specific behavioural profiles. Chromosome 9 open reading frame 72 (C9orf72) mutations were strongly associated with psychotic symptoms and aggression, while superoxide dismutase 1 (SOD1) mutations had minimal cognitive effects. Progranulin (PGRN) mutations correlated with apathy and hallucinations, microtubule-associated protein tau (MAPT) mutations with disinhibition, and charged multivesicular body protein 2B (CHMP2B) with social impairments. Fused in sarcoma (FUS) mutations caused early sleep disturbances, TANK-binding kinase 1 (TBK1) led to disinhibition, and presenilin 1 and 2 (PSEN1/2) was linked to severe aggression. Prodromal cognitive changes in PGRN, MAPT, and CHMP2B mutations suggested early disease onset. Despite overlapping symptoms and clinical heterogeneity, understanding gene-specific patterns could inform tailored care strategies to enhance the quality of life for ALS and FTD patients. This study calls for refined guidelines integrating genetic behavioural profiles to improve patient and family support. Full article

Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder with a significant impact on public health. ASD diagnosis is based on clinical observation and typically occurs around three years of age. The identification of reliable ASD markers could facilitate early diagnosis and help pinpoint therapeutic targets for effective interventions. Long non-coding RNAs (lncRNAs), particularly those derived from blood, have been recently proposed as potential biomarkers in many pathological conditions, including neurological diseases. This manuscript summarizes original studies examining human dysregulated blood-derived lncRNAs as potential ASD biomarkers. LncRNAs are described by grouping them according to the selection strategy used by the authors: (i) lncRNAs involved in biological processes impaired in ASD or in pathological conditions sharing the disrupted signaling pathways of ASD; and (ii) lncRNAs identified through high-throughput analysis. The study highlights key priorities for future research: assessing the ability of lncRNAs to distinguish ASD from other neurological disorders, extending analyses to larger and younger cohorts to validate candidate biomarkers in early life, and integrating multiple data sources to establish validated biomarker networks for clinical application. This review indicates that research on blood-derived lncRNAs in ASD is still in its early stages. Full article

TXS 0506+056 is a blazar associated with neutrino events. The study on its variation mechanics and periodicity analysis is meaningful to understand other BL Lac objects. The local cross-correlation function (LCCF) analysis presents a 3$\sigma$ correlation in both the $\gamma$-ray versus optical and optical versus radio light curves. The time lag analysis suggests that the optical and $\gamma$-ray band share the same emission region, located upstream of the radio band in the jet. We use both the weighted wavelet Z-transform and generalized Lomb–Scargle methods to analyze the periodicity. We find two plausible quasi-periodic oscillations (QPOs) at ${506}_{-56}^{+133}$ days and ${175}_{-7}^{+15}$ days for the light curve of the optical band. For the $\gamma$-ray band, we find that the spectrum varies with the softer when brighter (SWB) trend, which could be explained naturally if a stable very high energy component exists. For the optical band, TXS 0506+056 exhibits a harder when brighter (HWB) trend. We discover a trend transition from HWB to SWB in the X-ray band, which could be modeled by the shift in peak frequency assuming that the X-ray emission is composed of the synchrotron and the inverse Compton (IC) components. The flux correlations of $\gamma$-ray and optical bands behave anomalously during the period of neutrino events, indicating that there are possible other hadronic components associated with neutrino. Full article

The adoption of Electronic Lab Notebooks (ELNs) significantly enhances research operations by enabling the streamlined capture, storage, and dissemination of data. This promotes collaboration and ensures organised and efficient access to critical research information. Microsoft SharePoint^® (SP) is an established, widely used, web-based platform with advanced collaboration capabilities. This study investigates whether SP can meet the needs of engineering research projects, particularly in a collaborative environment. The paper outlines the process of adapting SP into an ELN tool and evaluates its effectiveness compared to established ELN systems. The evaluation considers several categories related to data management, ranging from data collection to publication. Six distinct application scenarios are analysed, representing a spectrum of collaborative research projects, ranging from small-scale initiatives with minimal processes and data to large-scale, complex projects with extensive data requirements. The results indicate that SP is competitive in relation with established ELN tools, ranking second among the six alternatives evaluated. The adapted version of SP proves particularly effective for managing data in engineering research projects involving both academic and industrial partners, accommodating datasets for around 1000 samples. The practical implementation of SP is demonstrated through a collaborative engineering research project, showing its use in everyday research tasks such as data documentation, workflow automation, and data export. The study highlights the benefits and usability of the adapted SP version, including its support for regulatory compliance and reproducibility in research workflows. In addition, limitations and lessons learned are discussed, providing insights into the potential and challenges of using SP as an ELN tool in collaborative research projects. Full article