Channel Information Research Articles

Lower-mass-gap Black Holes in Dense Star Clusters

The existence of compact stellar remnants in the mass range 2–5 M ⊙ has long been debated. This so-called lower-mass gap (LMG) was initially suggested by the lack of low-mass X-ray binary observations with accretors about 2–5 M ⊙, but it has recently been called into question following newer observations, including an LMG candidate with a millisecond pulsar (MSP) companion in the dense globular cluster NGC 1851. Here, we model NGC 1851 with a grid of similar dense star clusters utilizing the state-of-the-art Monte Carlo N-body code Cluster Monte Carlo, and we specifically study the formation of LMG black holes (BHs). We demonstrate that both massive star evolution and dynamical interactions can contribute to forming LMG BHs. In general, the collapse of massive remnants formed through mergers of neutron stars (NSs) or massive white dwarfs produces the largest number of LMG BHs among all formation channels. However, in more massive clusters, supernova core collapse can contribute comparable numbers. Our NGC 1851-like models can reproduce MSP—LMG BH binaries similar to the observed system. Additionally, the LMG BHs can also become components of dynamically assembled binaries, and some will be in merging BH–NS systems similar to the recently detected gravitational wave source GW230529. However, the corresponding merger rate is probably ≲1 Gpc−3 yr−1.

State-dependent and state-independent uncertainty relations for skew information and standard deviation

In this work, we derive state-dependent uncertainty relations (uncertainty equalities) in which commutators of incompatible operators (not necessarily Hermitian) are explicitly present and state-independent uncertainty relations based on the Wigner-Yanase (-Dyson) skew information. We derive uncertainty equality based on standard deviation for incompatible operators with mixed states, a generalization of previous works in which only pure states were considered. We show that for pure states, the Wigner-Yanase skew information based state-independent uncertainty relations become standard deviation based state-independent uncertainty relations which turn out to be tighter uncertainty relations for some cases than the ones given in previous works, and we generalize the previous works for arbitrary operators. As the Wigner-Yanase skew information of a quantum channel can be considered as a measure of quantum coherence of a density operator with respect to that channel, we show that there exists a state-independent uncertainty relation for the coherence measures of the density operator with respect to a collection of different channels. We show that state-dependent and state-independent uncertainty relations based on a more general version of skew information called generalized skew information which includes the Wigner-Yanase (-Dyson) skew information and the Fisher information as special cases hold. In qubits, we derive tighter state-independent uncertainty inequalities for different form of generalized skew informations and standard deviations, and state-independent uncertainty equalities involving generalized skew informations and standard deviations of spin operators along three orthogonal directions. Finally, we provide a scheme to determine the Wigner-Yanase (-Dyson) skew information of an unknown observable which can be performed in experiment using the notion of weak values.

Primary clinics achieving regular follow-up of patients: Teamwork aspects among healthcare staff

Abstract Backgrounds Studies have shown that more temporally regular primary care visits, or visits more evenly spaced in time, are associated with improved patient outcomes. However, no study has examined what clinic staff can do to encourage greater temporal regularity (TR). We aimed to understand factors related to healthcare staff dynamics that contribute to higher TR for adults with chronic conditions. Methods The study is part of a two-stage mixed methods project. In the first stage, we ranked clinics according to their TR level. In this qualitative stage, we interviewed staff at 6 clinics with extremely high TR (regular follow-up with primary care physician, or PCP), and 6 clinics with extremely low TR. Interviews were conducted with 15 PCPs, 12 nurses, 15 administrative staff, and 4 pharmacists between July 2022- September 2023. Interviews were audiotaped, transcribed, and analyzed using Atlas qualitative analysis software. Findings Themes emerged regarding best ways to promote regular follow-up of patients with chronic conditions. Strategies included having a defined system to reach out to no-show patients, having routine staff meetings to discuss no-shows, adapting workflow to meet the needs of challenging patients, dealing with bureaucracy on patients’ behalf, informal channels of communication with patients, and consulting social workers more often. Relational aspects among staff members at high TR clinics emphasized flat as opposed to hierarchical relationships, as well as high levels of teamwork. Conclusions Teamwork between staff members in primary care settings can contribute to more proactive care delivery, with greater potential to prevent long-term complications. The findings suggest that a high-functioning multidisciplinary care team that focuses on creating the right sorts of interactions and teamwork among staff members, and uses proactive strategies to help engage patients, can contribute to more temporally regular care and thus improved outcomes. Key messages • Stronger teamwork and flat relationships between staff members in primary care settings can result in proactive medicine and developing strategies that improve patients’ regular follow-ups. • Programs focusing on how medical staff utilize their dynamics/relations to improve health outcomes and how doctors’ role align with the staff, can facilitate regular follow-ups of challenging patients.

Assessing Organic Farming Adoption in Selected Districts of Tamil Nadu: Challenges, Practices, and Pathways for Growth

This study investigates the specific challenges and opportunities faced by organic farmers in Tamil Nadu, focusing on how region-specific practices influence the adoption and success of organic farming. It draws insights from a survey of 300 organic growers across five districts: Coimbatore, Dindigul, Erode, Tirupur, and Villupuram. This study is particularly relevant because there is a lack of comprehensive research on the specific challenges and opportunities faced by organic farmers in Tamil Nadu, with limited data on the long-term impact of government initiatives and region-specific management options, especially for smallholder farmers. Additionally, gaps exist in understanding the economic viability, market demand, and farmer perceptions of organic farming together with challenges like knowledge gaps, pest control, managing risks, and lower initial yields during the transition from conventional farming to organic farming. The research evaluated the factors affecting organic farming, including crop yield, the use of different organic formulations, the role of different information services, opportunities and challenges, and the impact on the perceived effectiveness of various agricultural outcomes. Various statistical analyses were performed, including K-means clustering, Poisson regression, and chi-square test. The results revealed significant variations in the adoption of various organic inputs and regionally practiced organic formulations. Crop residues (82.67%), Jeevamrith (78.33%), Amudhakaraisal (77.00%), and 3G-Extracts (74.00%) have high adoption rates, reflecting their perceived effectiveness and accessibility among farmers. However, the adoption of microbial bio-inputs such as Trichoderma viride (1.00%), Acetobacter spp. (19.00%), Azophos (consortium of phosphorus fixing microbes) (26.33%), and Azospirillum spp. (28.67%) was notably poor despite their known benefits for soil health and crop yield. The chi-square test also highlighted key challenges faced by farmers in adopting organic farming practices. The Poisson regression analysis showed significant positive impacts on crop growth and yield, disease resistance, insect control, overall plant health, and input efficiency due to organic farming. The study’s insights are essential for developing targeted strategies to overcome barriers, supporting the growth and sustainability of organic farming. Addressing these challenges and promoting effective information channels will better equip stakeholders to support organic growers, contributing to more resilient and productive agricultural systems in the region.

Assessing the role of non-state actors in health service delivery and health system resilience in Myanmar

BackgroundDue to the weaknesses of the public health system and its low reach, especially in border areas, provision of health services by non-state actors (NSAs) has historically played an important role in Myanmar. NSAs include local and international NGOs and civil society organisations (CSOs), but also Ethnic Health Organisations (EHOs) in the border areas, as well as the private (for profit) sector. This study aims to understand the changing role of NSAs in the shifting political environment of Myanmar between 2010 and 2022, and to explore their contribution to health system resilience.MethodsOur study includes three main components: a documentary review (n = 22), key informant interviews (KIIs) at central level (n = 14) and two township-level case studies (13 KIIs, 4 FGDs). Mostly qualitative data were collected in 2022 and synthesized, using a health system resilience framework to structure the analysis.ResultsDuring the transition period (2010–2014) and the new political era (2015–2020), while the country gradually transitioned to a democratic system, the government increasingly recognized NSAs. Initially, engagement with NSAs remained focused on disease-specific activities and government oversight was limited, but later it expanded to health system strengthening, including the start of a “convergence” with ethnic health systems. Progress was relatively slow, but defined by a clear vision and plans. The military coup of February 2021 brought a halt to this progress. Collaboration between government and NSAs was interrupted, and NSAs restored previous practices and parallel systems. Initially, most health service provision stopped, but with time coping strategies emerged, which showed the capacity of NSAs to absorb the shocks (focusing on basic services; using informal communication channels; maintaining buffer stocks of supplies) and adapt (changing modes of delivery and supply chains, and adjusting HRH training).ConclusionsThe study highlights the role of NSAs during crises, and provides insights on how the resilience capacities built over time by NSAs to provide services in adverse circumstances have informed the response to the latest crisis. While strategies of absorption and adaptation are noted in the study, we did not identify any transformation strategy – which might indicate the difficulty of NSAs to introduce radical changes when subjected to multiple shocks and a hostile political environment.

Health literacy demand and attitudes toward COVID-19 prevention measures among Korean American older adults and their caregivers

BackgroundHealth literacy has been linked to positive attitudes toward COVID-19 preventive measures among adolescents and young- or middle-aged adult populations. This study examined the relationship between health literacy and attitudes toward COVID-19 preventive measures among non-English speaking Korean American older adults and their caregivers. The study additionally investigated how sociodemographic characteristics were associated with attitudes.MethodsCOVID-19 survey data was collected from potential participants for an ongoing randomized controlled trial involving both Korean American older adults and their caregivers in the Baltimore-Washington and the New York Metropolitan areas (ClinicalTrials.gov Identifier: NCT03909347). Korean American older adults with normal cognition and their caregivers were allowed to participate in the survey. We used latent profile analysis to find unique clusters of participants with a similar pattern of responses to attitudes toward COVID-19 preventive measures. Based on the analysis, we employed multinomial logistic regression to investigate how health literacy and sociodemographic characteristics were associated with the clusters.ResultsWe found three clusters based on participant responses to COVID-19 preventive measures—Positive, Negative, or Mixed. Health literacy was not associated with COVID-19 related attitudes in the study sample. Men were 2.37 times more likely to be categorized as Mixed than having Positive Attitudes compared to women. The odds of a person living in the New York metropolitan area being categorized as having Mixed Attitudes compared to Positive Attitudes were also 2.67 times more than for a person living in the Baltimore-Washington area.ConclusionsDifferences in attitudes toward COVID-19 preventive measures were found among sociodemographic variables but not health literacy. Investigating what information channels or methods drive perception of public health information such as COVID-19 may help identify effective dissemination strategies for non-English speaking Korean older adults.

Organizational Strategies for Providing Foreign Language Education Services to Adults Over 45: International Practices and Approaches

Organizing educational services for people over the age of 45 is essential for their personal development, economic participation, social inclusion, and mental well-being. International and local legislation supports these initiatives, aiming to provide equitable and inclusive educational opportunities for all ages and to use the benefits of these people being fruitfully involved in the economic and social life of a country. The article aims to examine the strategies for providing educational services to adult learners (citizens over the age of 45) in different countries (European countries, the USA, Australia, China, Ukraine, Brazil, and Japan), with a focus on whether this process is integrated into social services and formal and informal education systems within the framework of the concept of lifelong learning. The methods included literature review on databases such as Google Scholar, JSTOR, and ERIC; comparative analysis of educational policy documents; case studies and reports from different nations; meta-analysis; field observations; and others typical to the field of comparative education and general education. They were utilized to provide a comprehensive and well-rounded analysis of how different countries organize and implement foreign language education services for adults over the age of 45. The findings reveal that the approaches to providing foreign language education for adults over 45 varies widely across different countries, reflecting each nation's unique historical traditions, cultural values, and current societal needs. This diversity in practice can be categorized into four main groups: services provided by social services; formal education systems; informal education systems; and self-organized services to present the prevailing approach to providing foreign language education for individuals over 45 in each country. The study concludes that the provision of foreign language learning services for individuals over 45 varies significantly across countries, reflecting diverse historical traditions, cultural attitudes, and current needs. Whether through social services, formal education systems, informal educational channels, or self-organization, each country’s approach highlights the importance of adapting educational services to meet the needs of an adult population seeking continued personal and professional growth.

Small Target Detection Algorithm Based on Improved YOLOv5

Small targets exist in large numbers in various fields. They are broadly used in aerospace, video monitoring, and industrial detection. However, because of its tiny dimensions and modest resolution, the precision of small-target detection is low, and the erroneous detection rate is high. Therefore, based on YOLOv5, an improved small-target detection model is proposed. First, in order to improve the number of tiny targets detected while enhancing small-target detection performance, an additional detection head is added. Second, involution is used between the backbone and neck to increase the channel information of feature mapping. Third, the model introduces the BiFormer, wherein both the global and local feature information are captured simultaneously by means of its double-layer routing attention mechanism. Finally, a context augmentation module (CAM) is inserted into the neck in order to maximize the structure of feature fusion. In addition, in order to consider among the required real frame as well as the prediction frame simultaneously, YOLOv5’s original loss function is exchanged. The experimental results using the public dataset VisDrone2019 show that the proposed model has P increased by 13.43%, R increased by 11.28%, and mAP@.5 and mAP@[.5:.95] increased by 13.88% and 9.01%, respectively.

Design and Development of a Website for Village Services and Information in Watesnegoro Village Government, Ngoro District, Mojokerto Regency

The development of information technology is now experiencing rapid progress. Therefore, these technological developments reflect the transformation of society towards a deeper engagement with digital tools, especially through internet access and the increasing popularity of website usage. In Watesnegoro Village, the creation of a village website acts as an up-to-date information channel that appeals to internet users. This research aims to help digitize services and also promote the potential of the village through the information system on the website. The methods used in this research are observation and interview. The result of this research is the creation of the Watesnegoro village website consists of observation, data collection, website creation, testing, training. However, the process of digitizing services at the Watesnegoro village office is still not perfect. This is due to the low skills of village officials who are not familiar with online services. In addition, the socialization of the website has not been carried out evenly in the community

Rating the Surrounding Vehicle-to-Everything Field, Based on Channel Utilization and Information Influence

<div>Automated vehicles (AVs) can get additional information from infrastructure and other vehicles via vehicle-to-everything (V2X) communication. However, how can an AV decide if the surrounding V2X field can reliably provide qualitative, relevant, and trustworthy information? Related research analyzes V2X performance from various angles. However, not only are there identified open gaps in the analysis of loaded channels, but there has also not yet been an effort to design a lightweight metric for rating the quality of the surrounding V2X field. Hence, this work aims to close this existing performance measurement gap and develop a metric for rating the quality of the surrounding V2X field. This article first highlights the gaps identified in performance analysis before closing them with a dedicated measurement campaign. Next, it combines these findings with related research to design a straightforward V2X field rating metric. The resulting V2X field rating metric is a starting point for the AD system to decide if sensor information from the V2X field should be directly incorporated or handled with care.</div>

Compact ellipticals in the Antlia cluster of galaxies

Compact ellipticals (cEs) are rare objects in the local Universe, but relevant for the evolution of galaxies. Their origin is not completely understood, with evidence equally suggesting that they are relics of early epochs, and remnants of tidal disruptions. This article aims to characterise two cEs, members of the nearby Antlia cluster, providing insights into the formation channels of cEs residing in high-density environments. This dataset consists of archival ACS/HST images and Gemini/GMOS slit spectra. The isophotal analysis of the cEs was compared with their kinematics and stellar population synthesis. The results were also compared with a sample of cEs and `relic' galaxies from the literature, to put them into context. Their characterisations highlight clear differences between the two cEs. Although the stellar populations for both cEs are mainly old, with mass-weighted ages above 10\,Gyr, FS90\,110, the most massive one, has a minor contribution from an intermediate-age population. Besides, it presents rotational evidence, a remarkably bright nucleus for its luminosity, and a clear dichotomy between the inner and outer components. It is probably the remnant of a low-mass disc galaxy that has been tidally disrupted by the close massive elliptical NGC\,3258. On the contrary, FS90\,192 does not present evidence for either rotation or discy isophotes, and its population synthesis leads to a stellar population with ages above 12\,Gyr. It is probably a low-mass analogue to `relic' galaxies.

Internet usage time and trust in doctors: evidence from China

BackgroundThe growth of internet use has greatly impacted many societal sectors, including healthcare. However, current evidence about the relationship between internet usage time and public trust on doctors is insufficient.ObjectiveTo examine the relationship between the time spent on the internet and the level of trust in doctors, and explore its mechanism.MethodsMultiple regression models are employed, utilizing a sample of 19,193 observations from the China Family Panel Studies (CFPS) in 2020. First, we regress the relative trust in doctors compared to trust in strangers on internet usage time. Subsample results across distinct population groups are explored. Second, we examine the mechanism of using the internet as an information channel through the mediation method, and further investigate the role of the internet as a communication platform by analyzing the moderation effects of socializing activity as well as social media engagement.ResultsWe find a significant negative relationship between increased internet usage time and trust in doctors. This correlation remains robust across different model specifications and variable measurements. The negative correlation is more pronounced among population groups who are elderly, male, married, with low educational levels, insured and residing in urban areas. The role of the internet as a source of information is validated as a mechanism producing the negative influence. The role of the internet as a communication platform tends to mitigate the negative consequences of internet usage, as engaging in online socializing activity can have a positive moderation effect. However, for social media engagement, its overall effect on trust in doctors is negative, potentially due to social media’s dual function as a communication platform and an information source.ConclusionsAlthough individual-focused literature tends to demonstrate a positive impact of the internet on trust in doctors in China, our study, which focuses on the general public, reveals contrasting results. Our findings imply that the primary manner in which internet usage, encompassing social media usage, influences public trust in doctors is by molding a collective perception of them. Consequently, the medical community may need to take a more proactive approach in expanding online communication channels for interacting with patients and the wider public.

Social Media as a Village Government Communication Channel in Improving Public Services to the Community in Minahasa Regency

Public service issues are still often the main topic in society, starting from the lack of clear information and slow service that occurs in government in various cases, both related to information, administration and implementation of development in various fields, to the lack of resources that are able to provide the best service to the community. The current era of information technology, new media, which includes social media, is certainly one of the right solutions in overcoming communication and information problems for the government, but the problem is whether social media has been used optimally in carrying out public service duties by the government, especially the government in the village. Along with the many complaints from the public regarding government services, even though communication and information technology is already present in the midst of society and the government. This is the basis of this study, to be studied and studied in more depth, using a qualitative research approach, related to how social media becomes a communication channel for village governments in improving public services. Village governments in Minahasa Regency, North Sulawesi Province have not optimally used social media as an information channel in an effort to improve public services. This is evidenced by the lack of official social media from the village government as a media/information channel in supporting public services. Not all village governments have the human resource capabilities that can manage and operate social media well, actively and intensively in providing information to the public regarding programs, policies, and government regulations. Most village governments in Minahasa Regency do not yet have official social media as a medium/channel of information to the public. There are still many village government officials who use their personal social media for the needs of conveying information to the public, even then only when necessary. Obstacles to the Village Government in utilizing social media to improve public services include limited human resources, technological infrastructure, and cultural constraints, these factors affect the effectiveness of communication through social media.

Analysis of Influencing Factors of Green View Index Based on Street View Segmentation

Abstract. The calculation of green view index in street view image has become an emerging choice for many researchers. This article proposes a PSAT-Net semantic segmentation model and specifically designs a pyramid pooling attention module. This module cleverly integrates channel and spatial information, implicitly implementing a three-dimensional attention mechanism, and significantly improving performance indicators. By performing detailed semantic segmentation on street view image, this article deeply analyzes the impact of different street view parameter configurations on green view index, and studies in detail how these parameters affect the visual perception of urban greening. The experiment found that: (1) The Green view index(GVI) of most sampling points decreases as the FOV increases. The increase in GVI is mainly affected by the crown width and pitch angle, and the high-value points of GVI are more easily affected. (2) Pitch angle usually causes a slight increase in GVI, which is mainly affected by the distribution of tall trees and low vegetation. Tall trees cause an increase in GVI, while low vegetation causes a decrease in GVI. Similarly, high value points of GVI are more likely to produce larger differences. (3) The Heading parameter has no obvious impact on the overall visual impact of GVI. The difference in independent sampling points comes from the angle between the lens acquisition direction and the road forward direction.These findings not only enrich the theoretical connotation of green view index research, but also provide valuable references for practical applications.

Unsupervised Clustering-based 3D Static Scene Construction Using LiDAR Channel and Azimuth Angle

Abstract. Cameras are typically used at road intersections to collect data to perform object detection but struggle in low-light and harsh weather. On the other hand, Light Detection and Ranging (LiDAR) is used as a key technology in 3D vision systems. It gives the 3D point cloud, which includes accurate depth information, but its resolution is cost-dependent, with higher resolutions being more expensive. Deep learning-based method requires large, labelled dataset which increases the cost, time and accuracy depending on the model trained on labelled dataset. To perform object detection in point cloud data is a challenging task due to the incomplete representations, data sparsity and unavailability of training data. To overcome this by using an unsupervised approach, it is important to identify the static scene and then detect the moving object. This work incorporated a novel approach to construct static scene using azimuth angle and laser channel information using an unsupervised clustering approach. This work incorporates two modules I) data collection using VLP-16 LiDAR, and II) static scene construction using DBSCAN (density-based spatial clustering and noise) clustering-based approach. Data is collected at a 4-legged intersection and pre-processed to extract aggregated distances corresponding to the unique pair of azimuth angle and laser channel. DBSCAN is used to perform clustering on the aggregated distances, based on the highest silhouette score and lowest intra distance between points in cluster, static points are identified, and static scene constructed. The qualitative evaluation of method demonstrates that the algorithm effectively and accurately filters out background points.

Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model

Improving wind power generation efficiency and lowering maintenance and operational costs are possible through the early and efficient diagnosis and repair of surface defects in wind turbines. To solve the lightweight deployment difficulty and insufficient accuracy issues of the traditional detection methods, this paper proposes a high-precision PC-EMA block based on YOLOv8 using partial convolution (PConv) combined with an efficient multiscale attention (EMA) channel attention mechanism, which replaces the bottleneck layer of the YOLOv8 backbone network to improve the extraction of target feature information from each layer of the network. In the feature fusion phase, GSConv, which can retain more channel information, is introduced to balance the model’s complexity and accuracy. Finally, by merging two branches and designing the PConv head with a low-latency PConv rather than a regular convolution, we are able to effectively reduce the complexity of the model while maintaining accuracy in the detection head. We use the WIoUv3 as the regression loss for the improved model, which improves the average accuracy by 5.07% and compresses the model size by 32.5% compared to the original YOLOv8 model. Deployed on Jetson Nano, the FPS increased by 11 frames/s after a TensorRT acceleration.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

Cat Caring Behaviors and Ownership Status of Residents Enrolling a Cat in a Free Sterilization Program.

Most cats entering animal shelters in Australia are less than 12 months old and emanate from low socioeconomic areas. Many are unidentified and may be either owned or semi-owned cats (cats cared for by someone who does not consider themselves the owner). In Australia, approximately 33% of cats entering shelters and pounds are euthanized annually, adversely affecting the mental health of staff involved. Our study investigated cat ownership and caregiving behaviors of cats enrolled in the Australian Pet Welfare Foundation's Community Cat Program, which included free sterilization, microchipping, and preventive healthcare. The suburbs targeted in Ipswich, Australia had a high per capita cat intake into animal shelters. We surveyed 1094 cat owners and semi-owners. Most cats were under 12 months old (79%), primarily domestic short-haired (69%), and acquired through informal channels such as friends, family, or as strays. Veterinary care was limited, with only 15% of cats having visited a veterinarian and 28% being vaccinated. Prior to hearing about the free sterilization program, 88.5% of participants identified as owners, while 11.5% were semi-owners. Before completing the survey, these semi-owners transitioned from an informal caregiving relationship to becoming owner of the cat/s. Of the semi-owners, 93% indicated that their transition to ownership was driven by feelings of responsibility, emotional attachment, and access to free sterilization services. These findings suggest that targeted interventions, such as accessible veterinary care and sterilization programs, are crucial in encouraging semi-owners to take full ownership of cats, improving cat welfare, and reducing unplanned breeding. Supporting this transition helps curb overpopulation and fosters improved caregiving, benefiting both cats and communities.

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.

Identifying the Seizure Occurrence Period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems (CADs) for Epileptic Seizure Detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on super vised learning approaches that require labeled data. To address this, our study introduces an unsupervised learning framework for ESD using a 1D-Cascaded Convolutional Autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5-second epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction error. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, preci sion, and false positive rate scores of 98.00±3.51%, 94.94±6.92%, 99.60±0.30%, 79.92±13.56% and 0.0044±0.0030/h of a typical patient in CHB-MIT datasets. Finally, the developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. This automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

UNeXt: An Efficient Network for the Semantic Segmentation of High-Resolution Remote Sensing Images

The application of deep neural networks for the semantic segmentation of remote sensing images is a significant research area within the field of the intelligent interpretation of remote sensing data. The semantic segmentation of remote sensing images holds great practical value in urban planning, disaster assessment, the estimation of carbon sinks, and other related fields. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is gradually increasing. This increase in resolution brings about challenges such as significant changes in the scale of ground objects, redundant information, and irregular shapes within remote sensing images. Current methods leverage Transformers to capture global long-range dependencies. However, the use of Transformers introduces higher computational complexity and is prone to losing local details. In this paper, we propose UNeXt (UNet+ConvNeXt+Transformer), a real-time semantic segmentation model tailored for high-resolution remote sensing images. To achieve efficient segmentation, UNeXt uses the lightweight ConvNeXt-T as the encoder and a lightweight decoder, Transnext, which combines a Transformer and CNN (Convolutional Neural Networks) to capture global information while avoiding the loss of local details. Furthermore, in order to more effectively utilize spatial and channel information, we propose a SCFB (SC Feature Fuse Block) to reduce computational complexity while enhancing the model’s recognition of complex scenes. A series of ablation experiments and comprehensive comparative experiments demonstrate that our method not only runs faster than state-of-the-art (SOTA) lightweight models but also achieves higher accuracy. Specifically, our proposed UNeXt achieves 85.2% and 82.9% mIoUs on the Vaihingen and Gaofen5 (GID5) datasets, respectively, while maintaining 97 fps for 512 × 512 inputs on a single NVIDIA GTX 4090 GPU, outperforming other SOTA methods.