ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Secure Integrated Circuit with Physical Attack Detection based on Reconfigurable Top Metal Shield

Invasive physical attacks on integrated circuits (ICs), such as de-packaging, focused ion beam (FIB) chip editing, and micro-probing attempts, constitute security threats for chips with potentially valuable information, such as smart cards. Using a state-of-the-art circuit-editing technique, an attacker can remove an IC's top metal layer, leaving its secure information exposed to micro-probing attacks. Security ICs can be seriously threatened by such attacks and thus require on-chip countermeasures. Conventional active shields, however, have difficulty coping with physical attacks based on FIB chip editing (i.e., bypassing the top metal shield). This study presents a novel countermeasure against physical attacks based on the use of a reconfigurable metal shield for both top metal removal and micro-probing attack detection. This shield consists of two circuits: an FIB chip editing detection circuit consisting of a random number generator and a micro-probing attempt detection circuit consisting of two conditionally synchronized ring oscillators. Both circuits share a randomly reconfigured top metal shield, which represents a promising solution for security against state-of-the-art invasive attacks.
Review of 3-D Endoscopic Surface Imaging Techniques

This paper provides an overview of state-of-the-art 3-D endoscopic imaging technologies. Physical objects in the world are 3-D, yet traditional endoscopes can only acquire 2-D images that lack depth information. This fundamental restriction greatly limits our ability to perceive and understand the complexity of real world objects. Lack of 3-D information also hinders our ability to quantitatively measure 3-D objects. In both medical imaging and industrial inspection applications, 3-D surface imaging capability would add one more dimension, literally and figuratively, to the existing imaging technologies. Over the past decades, tremendous new technologies and methods emerged in the 3-D surface imaging field. In this paper, we first provide a classification of these technologies. We then describe each category in detail, with representative designs and examples. This overview would be useful to researchers in the field since it provides a snapshot of the current state-of-the-art, from which subsequent research in meaningful directions is encouraged. This overview also contributes to the efficiency of research by preventing unnecessary duplication of already performed research.
Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification

Choosing appropriate scales for remotely sensed image classification is extremely important yet still an open question in relation to deep convolutional neural networks (CNN), due to the impact of spatial scale (i.e., input patch size) on the recognition of ground objects. Currently, the optimal scale selection processes are extremely cumbersome and time-consuming requiring repetitive experiments involving trial-and-error procedures, which significantly reduce the practical utility of the corresponding classification methods. This issue is crucial when trying to classify large-scale land use (LU) and land cover (LC) jointly (Zhang et al., 2019). In this paper, a simple and parsimonious Scale Sequence Joint Deep Learning (SS-JDL) method is proposed for joint LU and LC classification, in which a sequence of scales is embedded in the iterative process of fitting the joint distribution implicit in the joint deep learning (JDL) method, thus, replacing the previous paradigm of scale selection. The sequence of scales, derived autonomously and used to define the CNN input patch sizes, provides consecutive information transmission from small-scale features to large-scale representations, and from simple LC states to complex LU characterisations. The effectiveness of the novel SS-JDL method was tested on aerial digital photography of three complex and heterogeneous landscapes, two in Southern England (Bournemouth and Southampton) and one in North West England (Manchester). Benchmark comparisons were provided in the form of a range of LU and LC methods, including the state-of-the-art joint deep learning (JDL) method. The experimental results demonstrated that the SS-JDL consistently outperformed all of the state-of-the-art baselines in terms of both LU and LC classification accuracies, as well as computational efficiency. The proposed SS-JDL method, therefore, represents a fast and effective implementation of the state-of-the-art JDL method. By creating a single, unifying joint distribution framework for classifying higher order feature representations, including LU, the SS-JDL method has the potential to transform the classification paradigm in remote sensing, and in machine learning more generally.
Overexpressing NeuroD1 reprograms Müller cells into various types of retinal neurons

The onset of retinal degenerative disease is often associated with neuronal loss. Therefore, how to regenerate new neurons to restore vision is an important issue. NeuroD1 is a neural transcription factor with the ability to reprogram brain astrocytes into neurons in vivo. Here, we demonstrate that in adult mice, NeuroD1 can reprogram Müller cells, the principal glial cell type in the retina, to become retinal neurons. Most strikingly, ectopic expression of NeuroD1 using two different viral vectors converted Müller cells into different cell types. Specifically, AAV7m8 GFAP681::GFP-ND1 converted Müller cells into inner retinal neurons, including amacrine cells and ganglion cells. In contrast, AAV9 GFAP104::ND1-GFP converted Müller cells into outer retinal neurons such as photoreceptors and horizontal cells, with higher conversion efficiency. Furthermore, we demonstrate that Müller cell conversion induced by AAV9 GFAP104::ND1-GFP displayed clear dose- and time-dependence. These results indicate that Müller cells in adult mice are highly plastic and can be reprogrammed into various subtypes of retinal neurons.
A secured internet of robotic things (IoRT) for long-term care services in a smart building

Long-term care refers to any support, both medical and non-medical, provided to the elderly with a chronic illness or disability due to physical or mental conditions. Since the cost of long-term care insurance is not inexpensive, low-cost devices and sensors can be used to create medical assistance systems to reduce human maintenance costs. The requirement of security and privacy under healthcare information protection is a critical issue for internet of medical things (IoMT) data transmission. In this paper, we designed an IoMT security robot for a long-term care system. The goal of this IoMT security robot is to provide secure transmission of the residents' private information. It is composed of three layers, namely, collection, encryption, and transmission. The function of the IoMT security robot is to first collect data from the patient or the elderly, then provide efficient data encryption, and deliver secured data transmission mechanisms to send the valuable data to the cloud. This IoMT security robot also has a server authentication mechanism, and a support IoT and IoMT devices inspection function. Our evaluation results showed that even when we utilized a low power consumption device like Raspberry Pi, AES algorithm achieved an encrypt and decrypt of 100-100 K bytes under 9 ms, which is a lot better than ECC, which takes about 104 ms. Further, we found that the AES only takes 0.00015 s to decrypt 100 Bytes data, which is way faster than the ECC algorithm, which takes 0.09 s.
Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images

Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.
Application Analysis of Traditional Cultural Elements in the Environmental Art Design of Coastal Cities

In the design of urban modern environmental art, traditional cultural elements are widely used, and many environmental art designers can integrate traditional cultural elements with modern environmental art perfectly, so that traditional cultural elements can be better inherited and carried forward. This paper summarizes the connotation of traditional cultural elements and modern environmental art design, briefly analyzes the role of traditional cultural elements in the design of modern environmental art in coastal cities and the current situation of modern environmental art design, and focuses on how to strengthen the use of traditional cultural elements in the modern environmental art design in coastal cities.
Molecular transformation of dissolved organic matter and formation pathway of humic substances in dredged sludge under aerobic composting

Using Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) and molecular reaction network analysis, this study investigated molecular transformation of dissolved organic matter (DOM) and formation pathway of humic substances (HS) in dredged sludge during aerobic composting. The results showed that macromolecular N-containing compounds in dredged sludge are abundantly transformed into unsaturated and aromatic oxygenated compounds, exhibiting physicochemical properties similar to those of humus. Especially, N-containing compounds with one nitrogen atom are susceptible to oxidative deamination. Furthermore, assemblages of reactive fragments (e.g., -C7H8O2, -C10H12O2, -C2H2O2, and -C4H6O2) were identified as potential precursors to HS formed by the following reactions: starting with protein deamination and desulfurization, lignin delignification cascaded, finally decarbonylation occurred. This work provides novel insight for optimizing the process of stabilization and humification of dredged sludge.
On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration

Primal-dual interior-point method (PDIPM) is the most efficient technique for solving sparse linear programming (LP) problems. Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs) have the potential to meet this requirement. However, their peculiar architecture entails a positive relationship between problem density and speedup, conversely implying a limited affinity of GPUs for problem sparsity. To overcome this difficulty, the state-of-the-art hybrid (CPU-GPU) implementation of PDIPM exploits presence of supernodes in sparse matrices during factorization. Supernodes are groups of similar columns that can be treated as dense submatrices. Factorization method used in the state-of-the-art solver performs only selected operations related to large supernodes on GPU. This method is known to underutilize GPU's computational power while increasing CPU-GPU communication overhead. These shortcomings encouraged us to adapt another factorization method, which processes sets of related supernodes on GPU, and introduce it to the PDIPM implementation of a popular open-source solver. Our adaptation enabled the factorization method to better mitigate the effects of round-off errors accumulated over multiple iterations of PDIPM. To augment performance gains, we also used an efficient CPU-based matrix multiplication method. When tested for a set of well-known sparse problems, the adapted solver showed average speed-ups of approximately 55X, 1.14X and 1.05X over the open-source solver's original version, the state-of-the-art solver, and a highly optimized proprietary solver known as CPLEX, respectively. These results strongly indicate that our proposed hybrid approach can lead to significant performance gains for solving large sparse problems.
Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbines

Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ice, thus ensuring turbine safety. This article presents a wavelet-driven multiscale graph convolutional network (MWGCN), which is a supervised deep learning model for blade icing detection. The proposed model first uses wavelet decomposition to capture multivariate information in the time and frequency domains, and then employs a temporal graph convolutional network (GCN) to model the intervariable correlations of the decomposed multiscale wavelets and their temporal dynamics. In addition, this article introduces scale attention to the MWGCN for a further improvement of the model and proposes the method to address the class imbalance problem of the training data sets. Finally, the article conducts comprehensive experiments to evaluate the proposed model, and the results demonstrate the effectiveness of the model in blade icing detection and its better performance over eight state-of-the-art algorithms, with 17.2% and 11.3% higher F1 scores over the best state-of-the-art baseline on the labeled datasets.
Improving the robustness of variational optical flow through tensor voting

Differential optical flow methods allow the estimation of optical flow fields based on the first-order and even higher-order spatio-temporal derivatives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due to poor illumination conditions, the use of partial derivatives will amplify that noise and thus end up affecting the accuracy of the computed flow fields. The typical approach in order to reduce that noise consists of smoothing the required gradient images with Gaussian filters, for instance by applying structure tensors. However, that filtering is isotropic and tends to blur the discontinuities that may be present in the original images, thus likely leading to an undesired loss of accuracy in the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian filtering, and shows that the discontinuity preserving capabilities of the former yield more robust and accurate results. In particular, a state-of-the-art variational optical flow method has been adapted in order to utilize a tensor voting filtering approach. The proposed technique has been tested upon different datasets of both synthetic and real image sequences, and compared to both well known and state-of-the-art differential optical flow methods. (C) 2012 Elsevier Inc. All rights reserved.
Alteration of Upstream Autophagy-Related Proteins (ULK1, ULK2, Beclin1, VPS34 and AMBRA1) in Lewy Body Disease

Autophagy is associated with the pathogenesis of Lewy body disease, including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). It is known that several downstream autophagosomal proteins are incorporated into Lewy bodies (LBs). We performed immunostaining and Western blot analysis using a cellular model of PD and human brain samples to investigate the involvement of upstream autophagosomal proteins (ULK1, ULK2, Beclin1, VPS34 and AMBRA1), which initiate autophagy and form autophagosomes. Time course analysis of cultured cells transfected with flag-α-synuclein and synphilin-1 revealed upregulation of these upstream proteins with accumulation of LB-like inclusions. In human specimens, only mature LBs were positive for upstream autophagosomal proteins. Western blotting of fractionated brain lysates showed that upstream autophagosomal proteins were detected in the soluble and insoluble fraction in DLB, corresponding to the bands of phosphorylated α-synuclein. However, Western blot analysis of total brain lysates in PD and DLB showed that the increase of upstream autophagosomal proteins was only partial. The quantitative, qualitative and locational alteration of upstream autophagosomal proteins in the present study indicates their involvement in the pathogenesis of LB disease. Our data also suggest that misinduction or impairment of upstream autophagy might occur in the disease process of LB disease.
Advances in microbial electrochemistry-enhanced constructed wetlands

Constructed wetland (CW) is an effective ecological technology to treat water pollution and has the significant advantages of high impact resistance, simple construction process, and low maintenance cost. However, under extreme conditions such as low temperature, high salt concentration, and multiple types of pollutants, some bottlenecks exist, including the difficulty in improving operating efficiency and the low pollutant removal rate. Microbial electrochemical technology is an emerging clean energy technology and has the similar structure and pollutant removal mechanism to CW. Microbial electrochemistry combined with CW can improve the overall removal effect of pollutants in wetlands. This review summarizes characterization methods of microbial electrochemistry-enhanced constructed wetland systems, construction methods of different composite systems, mechanisms of single and composite systems, and removal effects of composite systems on different pollutants in water bodies. Based on the shortcomings of existing studies, the potential breakthroughs in microbial electrochemistry-enhanced constructed wetlands are proposed for developing the optimization solution of constructed wetlands.
Disruption of proteostasis causes IRE1 mediated reprogramming of alveolar epithelial cells

Disruption of alveolar type 2 cell (AEC2) protein quality control has been implicated in chronic lung diseases, including pulmonary fibrosis (PF). We previously reported the in vivo modeling of a clinical surfactant protein C (SP-C) mutation that led to AEC2 endoplasmic reticulum (ER) stress and spontaneous lung fibrosis, providing proof of concept for disruption to proteostasis as a proximal driver of PF. Using two clinical SP-C mutation models, we have now discovered that AEC2s experiencing significant ER stress lose quintessential AEC2 features and develop a reprogrammed cell state that heretofore has been seen only as a response to lung injury. Using single-cell RNA sequencing in vivo and organoid-based modeling, we show that this state arises de novo from intrinsic AEC2 dysfunction. The cell-autonomous AEC2 reprogramming can be attenuated through inhibition of inositol-requiring enzyme 1 (IRE1α) signaling as the use of an IRE1α inhibitor reduced the development of the reprogrammed cell state and also diminished AEC2-driven recruitment of granulocytes, alveolitis, and lung injury. These findings identify AEC2 proteostasis, and specifically IRE1α signaling through its major product XBP-1, as a driver of a key AEC2 phenotypic change that has been identified in lung fibrosis.
Fuzzy knowledge evaluation model as a methodological basis for automation of pedagogical testing

Implementation of the mathematical methods and state-of-the-art information technologies into such an informal and descriptive subject as pedagogics is considered to be one of the demands of the times, resulting from the necessity to adapt educational systems to a modern, information-oriented society as fast as possible. The presented study considers a trainee's knowledge evaluation model intended for the procedure of testing. This fuzzy knowledge evaluation model is based on the methods of fuzzy algebra.
Developing a digital game for excel skills learning in higher education - a comparative study analyzing differences in learning between digital games and textbook learning

In higher education, many universities in Taiwan let college students learn excel in a self-directed way. The current axle of the Excel curriculum mainly relies on self-directed learning. In the study, we designed the digital game "Legendary Wizard Excel" and took a certified Excel textbook as the research tool. The game we designed integrated the role-play with cognitive scaffolding to help learners learn Excel skills, whereas the textbook we used was "Excel Expert" in the Microsoft Office Specialist. We compared the Learning Effectiveness, Flow Status, and Technology Acceptance Model with 187 college students between two tools, and found that: (1) The game reached a high Technology Acceptance Model; (2) Both groups of learners had significant improvements in learning effectiveness and were engaged in the activity; (3) On learning effectiveness, learners in game-based learning groups achieved higher than learners in textbook groups; (4) Learners in game-based learning groups engaged better in the activity than learners in textbook groups. Therefore, in the future, we looked forward to bringing our results to higher education levels and workplace training to enhance the Excel skills.
Long-Term Non Anesthetic Preclinical Study Available Extra-Cranial Brain Activator (ECBA) System for the Future Minimally Invasive Human Neuro Modulation

In recent years, electroceuticals have been spotlighted as an emerging treatment for various severe chronic brain diseases, owing to their intrinsic advantage of electrical interaction with the brain, which is the most electrically active organ. However, the majority of research has verified only the short-term efficacy through acute studies in laboratory tests owing to the lack of a reliable miniaturized platform for long-term animal studies. The construction of a sufficient integrated system for such a platform is extremely difficult because it requires multi-disciplinary work using state-of-the-art technologies in a wide range of fields. In this study, we propose a complete system of an implantable platform for long-term preclinical brain studies. Our proposed system, the extra-cranial brain activator (ECBA), consists of a titanium-packaged implantable module and a helmet-type base station that powers the module wirelessly. The ECBA can also be controlled by a remote handheld device. Using the ECBA, we performed a long-term non-anesthetic study with multiple canine subjects, and the resulting PET-CT scans demonstrated remarkable enhancement in brain activity relating to memory and sensory skills. Furthermore, the histological analysis and high-temperature aging test confirmed the reliability of the system for up to 31 months. Hence, the proposed ECBA system is expected to lead a new paradigm of human neuromodulation studies in the near future.