Search Results (11)

Background/Objectives: Diabetes management technologies—including continuous glucose monitors (CGM), insulin pumps (CSII), advanced hybrid closed-loop (AHCL) systems, and mobile health applications—have transformed diabetes care, yet gender-specific differences in ad-herence, clinical outcomes, and patient-reported outcomes remain inadequately char-acterised. Methods: We conducted a systematic literature review following PRISMA 2020 guide-lines, searching SciSpace, Google Scholar, and PubMed databases. From 852 identified records, 500 underwent title/abstract screening after duplicate removal; 16 studies were ultimately included in the qualitative synthesis. Results: Included studies examined insu-lin pumps (n = 7), CGM (n = 6), AHCL systems (n = 4), remote monitoring programmes (n = 2), and mobile health applications (n = 1); sample sizes ranged from 72 to 22,697 participants. In predominantly paediatric evidence, females demonstrated higher insu-lin pump discontinuation rates (discontinuation groups: 75% vs. 46%, p = 0.001), driv-en by body image concerns and device visibility (data from a single small predomi-nantly paediatric cohort; group sex compositions, not absolute discontinuation rates), whereas in a single small adult cohort (n = 72), males showed poorer adherence to in-termittently scanned CGM (approximately four fewer scans/day, p = 0.011). Glycaemic outcomes were modestly but consistently different: males achieved slightly higher time-in-range while females exhibited lower glycaemic variability; menstrual cycle effects on glycaemic control were partially mitigated by AHCL systems. Females con-sistently reported greater diabetes-related distress and a more negative perception of glycaemic control despite similar or better objective metrics. Conclusions: In conclusion, sex-based and gender-related differences are apparent across technology type, adherence, gly-caemic control, and psychosocial outcomes—albeit from a limited evidence base re-quiring cautious interpretation; sex-sensitive prescription, education, and technology design are warranted. Note: most included studies reported biological sex (male/female) rather than self-identified gender; conclusions should be interpreted primarily as sex-based differences. Full article

Insulin resistance, stem cell dysfunction, and muscle fiber dystrophy are all age-related events in skeletal muscle (SKM). However, age-related changes in insulin isoforms and insulin receptors in myogenic progenitor satellite cells have not been studied. Since SKM is an extra-pancreatic tissue that does not express mature insulin, we investigated the levels of insulin receptors (INSRs) and a novel human insulin upstream open reading frame (INSU) at the mRNA, protein, and anatomical levels in Baltimore Longitudinal Study of Aging (BLSA) biopsied SKM samples of 27–89-year-old (yrs) participants. Using RT-qPCR and the MS-based selected reaction monitoring (SRM) assay, we found that the levels of INSR and INSU mRNAs and the proteins were positively correlated with the age of human SKM biopsies. We applied RNAscope fluorescence in situ hybridization (FISH) and immunofluorescence (IF) to SKM cryosections and found that INSR and INSU were co-localized with PAX7-labeled satellite cells, with enhanced expression in SKM sections from an 89 yrs old compared to a 27 yrs old. We hypothesized that the SKM aging process might induce compensatory upregulation of INSR and re-expression of INSU, which might be beneficial in early embryogenesis and have deleterious effects on proliferative and myogenic satellite cells with advanced age. Full article

To enhance user experiences of reaching destinations in large, complex buildings, we have developed a indoor navigation system using Unity and a smartphone called INSUS. It can reset the user location using a quick response (QR) code to reduce the loss of direction of the user during navigation. However, this approach needs a number of QR code sheets to be prepared in the field, causing extra loads at implementation. In this paper, we propose another reset method to reduce loads by recognizing information of naturally installed signs in the field using object detection and Optical Character Recognition (OCR) technologies. A lot of signs exist in a building, containing texts such as room numbers, room names, and floor numbers. In the proposal, the Sign Image is taken with a smartphone, the sign is detected by YOLOv8, the text inside the sign is recognized by PaddleOCR, and it is compared with each record in the Room Database using Levenshtein distance. For evaluations, we applied the proposal in two buildings in Okayama University, Japan. The results show that YOLOv8 achieved mAP@0.5 $0.995$ and mAP@0.5:0.95 $0.978$, and PaddleOCR could extract text in the sign image accurately with an averaged CER% lower than 10%. The combination of both YOLOv8 and PaddleOCR decreases the execution time by $6.71s$ compared to the previous method. The results confirmed the effectiveness of the proposal. Full article

To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects. Full article

Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between ${60}^{\circ }$ and ${100}^{\circ }$ achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with ${90}^{\circ }$ to ${100}^{\circ }$ tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system. Full article

The Five Great Mantras (Odae chinŏn) is one of the most widely circulated collections of Buddhist dhāraṇīs in premodern Korea, having been published or existing in several variant editions during the Chosŏn period (1392–1910). The title refers to the following dhāraṇīs: (1) “The Forty-Two Mantras of the Bodhisattva Avalokiteśvara,” (2) Nīlakaṇṭha-dhāraṇī, (3) Mahāpratisarā-dhāraṇī, (4) Buddhoṣṇīṣa-dhāraṇī, and (5) Uṣṇīṣavijaya-dhāraṇī. Another spell, “The Basic Dhāraṇī of the Bodhisattva Avalokiteśvara,” was also added, so there are a total of six dhāraṇīs contained in the book. Although most scholarship has hitherto understood the Five Great Mantras to date from the late fifteenth century, when editions with transcriptions of the dhāraṇīs in the Korean script appeared in trilingual format along with Siddhaṃ and Sinitic transliterations, due to the patronage of Queen Insu (1437–1508) and the linguistic ability of the monk Hakcho (fl. 1464–1520), some evidence has come to light suggesting that the Five Great Mantras was initially published as early as the mid-fourteenth century in the late Koryŏ period (918–1392). This essay provides a detailed analysis of the components that appear in the Five Great Mantras by analyzing six variant editions of the text dating from the Chosŏn period, including Brief Transcriptions of Efficacious Resonance (Yŏnghŏm yakch’o) in Sinitic and Korean vernacular translation. The Five Great Mantras demonstrates the significance of non-canonical materials in the Korean Buddhist tradition and suggests a fruitful avenue for study of similar woodblock prints and manuscripts in the Sinitic Buddhist tradition. Full article

As the pre-part of tasks such as fault detection and line inspection, insulator detection is a crucial task. However, considering the complex environment of high-voltage transmission lines, the traditional insulator detection accuracy is unsatisfactory, and manual inspection is dangerous and inefficient. To improve this situation, this paper proposes an insulator detection model Siamese ID-YOLO based on a deep neural network. The model achieves the best balance between speed and accuracy compared with traditional detection methods. In order to achieve the purpose of image enhancement, this paper adopts the canny-based edge detection operator to highlight the edges of insulators to obtain more semantic information. In this paper, based on the Darknet53 network and Siamese network, the insulator original image and the edge image are jointly input into the model. Siamese IN-YOLO model achieves more fine-grained extraction of insulators through weight sharing between Siamese networks, thereby improving the detection accuracy of insulators. This paper uses statistical clustering analysis on the area and aspect ratio of the insulator data set, then pre-set and adjusts the hyperparameters of the model anchor box to make it more suitable for the insulator detection task. In addition, this paper makes an insulator dataset named InsuDaSet based on UAV(Unmanned Aerial Vehicle) shoot insulator images for model training. The experiments show that the insulator detection can reach 92.72% detection accuracy and 84FPS detection speed, which can fully meet the online insulator detection requirements. Full article

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease. Full article

Insulator defect detection is an important task in inspecting overhead transmission lines. However, the surrounding environment is complex, and the detection accuracy of traditional image processing algorithms is low. Therefore, insulator defect detection is still mainly performed manually. In order to improve this situation, we proposed an insulator defect detection method called INSU-YOLO based on deep neural networks. Overexposure points in the image will interfere with insulator detection, so we used image augment to reduce noise and extract the edge information of the insulator. Based on an attention mechanism, we introduced a structure called attention-block where the backbone extracts the feature map, and this aims to improve the ability of our method to detect insulators. Insulators have a variety of specifications, and the location and granularity of defects are also different. Therefore, we proposed an adaptive threat estimation method based on the area ratio between the entire insulator and the defect area. In addition, in order to solve the problem of data shortage, we established a dataset called InsuDetSet for model training. Experiments on the InsuDetSet dataset demonstrated that our model outperforms existing state-of-the-art models regarding both the detection box and speed. Full article

Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images. Full article

Young workers are in particular need of occupational safety and health (OSH) services, but it is unclear whether they have the necessary access to such services. We compared young with older workers in terms of the access to and awareness of OSH services, and examined if differences in employment conditions accounted for age-differences. We used survey data from Italy (INSuLA 1, 2014), with a sample of 8000 employed men and women aged 19 to 65 years, including 732 young workers aged under 30 years. Six questions measured access to services, and five questions assessed awareness of different OSH issues. Several employment conditions were included. Analyses revealed that young workers had less access and a lower awareness of OSH issues compared with older workers. For instance, odds ratios (OR) suggest that young workers had a 1.44 times higher likelihood [95%—confidence interval 1.21–1.70] of having no access to an occupational physician, and were more likely (2.22 [1.39–3.38]) to be unaware of legal OSH frameworks. Adjustment for selected employment conditions (company size, temporary contract) substantially reduced OR’s, indicating that these conditions contribute to differences between older and younger workers. We conclude that OSH management should pay particular attention to young workers in general and, to young workers in precarious employment, and working in small companies in particular. Full article