Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Determinants of Diabetic Retinopathy Among Type 2 Diabetic Patients in Saudi Arabia: A Systematic Review

Diabetic retinopathy (DR) is a preventable complication of diabetes; however, it is a serious one if not early recognized and properly managed as it can lead to visual impairment. This review aimed to summarize the prevalence and determinants of DR among type 2 diabetic patients in Saudi Arabia. Eligible articles for this systematic review were quantitative observational studies that were English-published between 2015 and 2021, peer-reviewed, and conducted on patients with type 2 diabetes mellitus (DM). The studies were obtained by searching PubMed/Medline, ScienceDirect, Cochrane Library Database, and Google Scholar. The risk of bias was assessed using the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies. Out of 16 preliminary studies, 12 were eligible for inclusion in this systematic review. One study was a chart review, one was a prospective study, and the remaining were cross-sectional studies. Regarding the study tool, retinopathy was diagnosed by an ophthalmologist using fundus photography and/or slit-lamp examination in most of the studies (n=7). However, some studies reported obtaining data from patient interviews and medical files (n=4). Concerning the outcome, an overall high prevalence of DR (ranging between 6.25% and 88.1%) and some significant associated risk factors were determined, including longer duration of diabetes, older age, poor blood pressure control, poor glycemic control, and physical inactivity. Most studies showed moderate overall quality. In conclusion, DR is a common complication of type 2 diabetes in Saudi Arabia. Some avoidable risk factors are identified, through which the doctors can identify patients at high risk of DR through early screening and can, thus, initiate prompt treatment to reduce the risk of vision deterioration.
Cellular dynamics of bovine aortic smooth muscle cells measured using MEMS force sensors

Adhesive cells perceive the mechanical properties of the substrates to which they adhere, adjusting their cellular mechanical forces according to their biological characteristics. This mechanical interaction subsequently affects the growth, locomotion, and differentiation of the cell. However, little is known about the detailed mechanism that underlies this interaction between adherent cells and substrates because dynamically measuring mechanical phenomena is difficult. Here, we utilize microelectromechamical systems force sensors that can measure cellular traction forces with high temporal resolution (~2.5 µs) over long periods (~3 h). We found that the cellular dynamics reflected physical phenomena with time scales from milliseconds to hours, which contradicts the idea that cellular motion is slow. A single focal adhesion (FA) generates an average force of 7 nN, which disappears in ms via the action of trypsin-ethylenediaminetetraacetic acid. The force-changing rate obtained from our measurements suggests that the time required for an FA to decompose was nearly proportional to the force acting on the FA.
Multi-Antenna Information Fusion-Based Indoor Localization Using Signals Reflections

With the ubiquitous of WiFi infrastructure, wireless signals-based indoor localization is attracting intensive attentions and fostered a range of intelligent applications. However, most of existing schemes fail to capture the full scope of the relationship between WiFi signals and physical locations. To tackle this issue, we present a framework for device-free localization by estimating the channel state information (CSI) of WiFi signals from multiple access points (APs). The main insight is that an object standing at a certain location can be simultaneously observed and recorded by different APs, generating distinct CSI measurements. Based on this, we first calculate a shared latent representation using the CSI measurement from all the APs. This representation describes the locations more comprehensively than single view individually. Then, we calculate key features using the shared representation, such as kurtosis, skewness and sample entropy. Finally, we build a classification model by the Softmax regression for achieving the purpose of accurate and robust localization. We design and test a localization prototype on commodity WiFi, and the experimental results show that our scheme can estimate locations with a high accuracy, and also outperforms existing ones.
A Fast-Transient Boost Converter With Peak Passive Ripple Mode Control and AC Couple

Peak current mode (PCM) control is often used nowadays in boost converters to improve the stability and control bandwidth because of its single pole characteristic. However, the slope of inductor current causes large output voltage deviation and settling time during load-transient. This article proposes a peak passive ripple mode (PPRM) control with ac couple to solve the aforementioned load-transient issue. PPRM control generates a passive ripple for modulation and couples output voltage signal by the passive RC circuit to achieve a fast transient response. Thus, the current sensor and active circuits can be eliminated. Small signal models are proposed for proposed controls. The experiment and simulation results verified the superior load-transient response of the proposed control with the best figure-of-merit among the recent papers. Compare PPRM control with ac couple to PCM control, the output voltage undershoot/overshoot during load step-up/down reduces to only 42% and 38%, respectively. The proposed small signal models also match the experimental results.
The application to aircraft recognition of pattern descriptions based on geometrical parsing and descriptions of the image boundary

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Delivery of Immunostimulatory Cargos in Nanocarriers Enhances Anti-Tumoral Nanovaccine Efficacy

Finding a long-term cure for tumor patients still represents a major challenge. Immunotherapies offer promising therapy options, since they are designed to specifically prime the immune system against the tumor and modulate the immunosuppressive tumor microenvironment. Using nucleic-acid-based vaccines or cellular vaccines often does not achieve sufficient activation of the immune system in clinical trials. Additionally, the rapid degradation of drugs and their non-specific uptake into tissues and cells as well as their severe side effects pose a challenge. The encapsulation of immunomodulatory molecules into nanocarriers provides the opportunity of protected cargo transport and targeted uptake by antigen-presenting cells. In addition, different immunomodulatory cargos can be co-delivered, which enables versatile stimulation of the immune system, enhances anti-tumor immune responses and improves the toxicity profile of conventional chemotherapeutic agents.
Hierarchical feature fusion network with receptive field block for fabric defect detection

Defect detection as a part of industrial production is essential to monitor product quality. In the last decade, convolutional neural networks have been widely used in industrial defect detection, but existing CNNS-based fabric defect detection models suffer from the problems of not fully utilizing contextual information and inadequate characterization of the underlying features. To address the issues above, we propose a novel hierarchical feature fusion network with receptive field block is proposed for fabric defect detection. The low-level features are efficiently characterized by designing the context-aware feature extraction module (CAFEM) with a short connection. Then the novel receptive field block with five branches (RFB-5) can integrate different scale high-level feature maps, and the holistic attention module (HAM) is adopted to focus on the significant information. Moreover, the low-level features are fused with the high-level features to better represent the fabric texture information. Finally, a joint loss with a boundary IoU loss and a cross entropy loss is adopted to guide the network to learn more detailed information. Experimental results conducted on our built fabric image dataset demonstrate that the proposed method makes it possible to locate the defective areas in the fabric, which is superior to the seven existing methods.
The dynamic of discriminatory reform: how does discretionary pricing neutralize the productivity gains of energy subsidy reform in Iran?

ABSTRACT A distortion caused by previous policies could distort the results of reforms. This may explain why policies to reduce industrial fuel subsidies have not necessarily resulted in increases in aggregate productivity in countries with mandated pricing. To identify and measure these distortions’ effects, we estimate a structural dynamic firm model with endogenous technology adaptation using data from the manufacturing firms in Iran. By connecting two price distortions and their results on the real sector, results suggest significant room for a distributional policy. We estimated dispersion elasticity comparable to the price elasticity of energy consumption in the manufacturing sector. Results suggest that the intensive margin is the primary driver of energy price elasticity, whereas the other channels mostly offset it. Moreover, total factor productivity slightly improves in light of a reduction in energy consumption if, at the same time, the redistribution policy boosts the aggregate demands.
A Theranostic Approach to Imaging and Treating Melanoma with 203Pb/212Pb-Labeled Antibody Targeting Melanin

Simple Summary Metastatic melanoma is a deadly disease that claims thousands of lives each year despite the introduction of several new drugs into the clinic over the past decade, inspiring the need for novel therapeutics. We investigate targeting melanin pigment, which causes melanoma, with protein molecules called antibodies, which carry a radioactive payload to visualize or treat melanoma tumors. In this study, we imaged and treated melanoma in mice using a c8C3 antibody to melanin and two radioisotopes of lead—Lead-203 for imaging and Lead-212 for therapy. Imaging with Lead-203-bound antibodies allowed for visualization of the tumors in mice, while treatment with Lead-212-bound antibodies slowed down the growth of these aggressive tumors. The treatment was not toxic to mice. We concluded that the melanin-targeting Lead-203/Lead-212-bound c8C3 antibody is a promising agent for imaging and therapy of metastatic melanoma (so-called theranostic), which warrants further investigation. Abstract Metastatic melanoma is a deadly disease that claims thousands of lives each year despite the introduction of several immunotherapeutic agents into the clinic over the past decade, inspiring the development of novel therapeutics and the exploration of combination therapies. Our investigations target melanin pigment with melanin-specific radiolabeled antibodies as a strategy to treat metastatic melanoma. In this study, a theranostic approach was applied by first labeling a chimeric antibody targeting melanin, c8C3, with the SPECT radionuclide 203Pb for microSPECT/CT imaging of C57Bl6 mice bearing B16-F10 melanoma tumors. Imaging was followed by radioimmunotherapy (RIT), whereby the c8C3 antibody is radiolabeled with a 212Pb/212Bi “in vivo generator”, which emits cytotoxic alpha particles. Using microSPECT/CT, we collected sequential images of B16-F10 murine tumors to investigate antibody biodistribution. Treatment with the 212Pb/212Bi-labeled c8C3 antibody demonstrated a dose-response in tumor growth rate in the 5–10 µCi dose range when compared to the untreated and radiolabeled control antibody and a significant prolongation in survival. No hematologic or systemic toxicity of the treatment was observed. However, administration of higher doses resulted in a biphasic tumor dose response, with the efficacy of treatment decreasing when the administered doses exceeded 10 µCi. These results underline the need for more pre-clinical investigation of targeting melanin with 212Pb-labeled antibodies before the clinical utility of such an approach can be assessed.
Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals.
Target thrust measurement for applied-field magnetoplasmadynamic thruster

In this paper, we present a flat target thrust stand which is designed to measure the thrust of a steady-state applied-field magnetoplasmadynamic thruster (AF-MPDT). In our experiments we varied target-thruster distances and target size to analyze their influence on the target thrust measurement results. The obtained thrust-distance curves increase to local maximum and then decreases with the increasing distance, which means that the plume of the AF-MPDT can still accelerate outside the thruster exit. The peak positions are related to the target sizes: larger targets can make the peak positions further from the thruster and decrease the measurement errors. To further improve the reliability of measurement results, a thermal equilibrium assumption combined with Knudsen’s cosine law is adapted to analyze the error caused by the back stream of plume particles. Under the assumption, the error caused by particle backflow is no more than 3.6% and the largest difference between the measured thrust and the theoretical thrust is 14%. Moreover, it was verified that target thrust measurement can disturb the working of the AF-MPD thruster, and the influence on the thrust measurement result is no more than 1% in our experiment.
INFRARED OBSERVATIONS OF DUST AROUND HELIUM NOVA V445 PUPPIS

We detected bright mid- to far-infrared emission from the helium nova V445 Puppis in the AKARI all-sky survey data taken in 2006. Assuming an optically thin condition, we decomposed the spectral energy distribution (SED) of V445 Puppis in October 2006 by model ﬁtting and found that the SED can be explained by a combination of cold amorphous carbon (125 K and the mass of 4 . 5 +6 . 6 − 2 . 7 × 10 − 4 M (cid:12) ) and warm amorphous carbon (250 K and the mass of 1 . 8 +1 . 0 − 0 . 5 × 10 − 5 M (cid:12) ). Assuming that the former is pre-existing dust formed in the past nova outbursts and the latter is newly formed dust in December 2000’s nova wind, this result suggests that the amount of dust formed around V445 Puppis in a single outburst is larger than 10 − 5 M (cid:12) , which is larger than those in any other classical novae ever reported.
Curvature Analysis and Visualization of Dental Mesh Models

In order to extract the morphology characteristic of teeth surface, a curvature estimation and visualization method for dental mesh models is investigated in this paper. By using a local cubic surface fitting method the model discrete curvatures are approximated and then improved through histogram equalization algorithm in order to get better views of curvature. The experiment results also demonstrate that the method performs good calculation and visualization performance, which make the model concave and convex characteristics appear more clearly and thus lay a good foundation for subsequent teeth feature extraction.
T-2 and HT-2 Toxins: Toxicity, Occurrence and Analysis: A Review

One of the major classes of mycotoxins posing serious hazards to humans and animals and potentially causing severe economic impact to the cereal industry are the trichothecenes, produced by many fungal genera. As such, indicative limits for the sum of T-2 and HT-2 were introduced in the European Union in 2013 and discussions are ongoing as to the establishment of maximum levels. This review provides a concise assessment of the existing understanding concerning the toxicological effects of T-2 and HT-2 in humans and animals, their biosynthetic pathways, occurrence, impact of climate change on their production and an evaluation of the analytical methods applied to their detection. This study highlights that the ecology of F. sporotrichioides and F. langsethiae as well as the influence of interacting environmental factors on their growth and activation of biosynthetic genes are still not fully understood. Predictive models of Fusarium growth and subsequent mycotoxin production would be beneficial in predicting the risk of contamination and thus aid early mitigation. With the likelihood of regulatory maximum limits being introduced, increased surveillance using rapid, on-site tests in addition to confirmatory methods will be required. allowing the industry to be proactive rather than reactive.
End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. However, there are two major research gaps in existing works: (i) they lack fusion (and synchronization) of multi-sensors, camera, LiDAR and radar; and (ii) generic scalable, and user-friendly end-to-end implementation. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as camera, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors, namely, camera, LiDAR, and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications and suitable for quick and large-scale practical deployment.
Image Encryption Using Stream Cipher Based on Nonlinear Combination Generator with Enhanced Security

The images are very largely used in our daily life; the security of their transfer became necessary. In this work a novel image encryption scheme using stream cipher algorithm based on nonlinear combination generator is developed. The main contribution of this work is to enhance the security of encrypted image. The proposed scheme is based on the use the several linear feedback shifts registers whose feedback polynomials are primitive and of degrees are all pairwise coprimes combined by resilient function whose resiliency order, algebraic degree and nonlinearity attain Siegenthaler’s and Sarkar, al.’s bounds. This proposed scheme is simple and highly efficient. In order to evaluate performance, the proposed algorithm was measured through a series of tests. These tests included visual test and histogram analysis, key space analysis, correlation coefficient analysis, image entropy, key sensitivity analysis, noise analysis, Berlekamp-Massey attack, correlation attack and algebraic attack. Experimental results demonstrate the proposed system is highly key sensitive, highly resistance to the noises and shows a good resistance against bruteforce, statistical attacks, Berlekamp-Massey attack, correlation attack, algebraic attack and a robust system which makes it a potential candidate for encryption of image. © 2012 Published by NTMSCI Selection and/or peer review under responsibility of NTMSCI Publication Society
Pixel-Parallel Three-Layer Stacked CMOS Image Sensors Using Double-Sided Hybrid Bonding of SOI Wafers

This study reports pixel-parallel three-layer stacked complementary metal–oxide–semiconductor image sensors developed for the first time. The hybrid bonding of silicon-on-insulator (SOI) wafers with Au electrodes embedded in SiO2 surfaces enabled face-to-back as well as face-to-face bonding, facilitating multilayer stacking, and pixel-parallel signal processing. A three-layered pixel, comprising a photodiode (PD), pulse generation circuit, and counters, served as analog-to-digital converters (ADCs), reducing its size in comparison to conventional two-layered sensors. In addition, wafer bonding introduced a thin Si layer as a bonding medium, thereby ensuring bonding strength and achieving alignment accuracy of less than $1~{\mu } \text{m}$ for the entire area of 8-in wafers. The developed three-layered pixel circuit demonstrated a linear response of 16-bit digital signal output. A prototype sensor with a quarter–quarter video graphics array (QQVGA) successfully captured video images. The results revealed the feasibility of multilayered sensors with high-performance characteristics, such as high resolution, high speed, and wide dynamic range, as well as multiple functionalities, including signal processing, memory, and computation. The developed 3-D integration technology can enhance sensor applications, ranging from high-quality video cameras, recognition, automotive systems, robots, and measurements to various Internet-of-Things devices.