ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Enhancing crop yields through improvements in the efficiency of photosynthesis and respiration

The rate with which crop yields per hectare increase each year is plateauing at the same time that human population growth and other factors increase food demand. Increasing yield potential (  Y p   ) of crops is vital to address these challenges. In this review, we explore a component of  Y p   that has yet to be optimised - that being improvements in the efficiency with which light energy is converted into biomass (  ε c   ) via modifications to CO2 fixed per unit quantum of light (α), efficiency of respiratory ATP production (  ε prod   ) and efficiency of ATP use (  ε use   ). For α, targets include changes in photoprotective machinery, ribulose bisphosphate carboxylase/oxygenase kinetics and photorespiratory pathways. There is also potential for  ε prod   to be increased via targeted changes to the expression of the alternative oxidase and mitochondrial uncoupling pathways. Similarly, there are possibilities to improve  ε use   via changes to the ATP costs of phloem loading, nutrient uptake, futile cycles and/or protein/membrane turnover. Recently developed high-throughput measurements of respiration can serve as a proxy for the cumulative energy cost of these processes. There are thus exciting opportunities to use our growing knowledge of factors influencing the efficiency of photosynthesis and respiration to create a step-change in yield potential of globally important crops.
LSV-ANet: Deep Learning on Local Structure Visualization for Feature Matching

Feature matching is a fundamental and important task in many applications of remote sensing and photogrammetry. Remote sensing images often involve complex spatial relationships due to the ground relief variations and imaging viewpoint changes. Therefore, using a pre-defined geometrical model will probably lead to inferior matching accuracy. In order to find good correspondences, we propose a simple yet efficient deep learning network, which we term the x201C;local structure visualization-attentionx201D; network (LSV-ANet). Our main aim is to transform outlier detection into a dynamic visual similarity evaluation. Specifically, we first map the local spatial distribution into a regular grid as descriptor LSV, and then customized a spatial SCale Attention (SCA) module and a spatial STructure Attention (STA) module, which explicitly allows structure manipulation and scale selection of LSV within the network. Finally, the embedded SCA and STA deduce optimal LSV for solving feature matching task by training the LSV-ANet end-to-end. In order to demonstrate the robustness and universality of our LSV-ANet, extensive experiments on various real image pairs for general feature matching are conducted and compared against eight state-of-the-art methods. The experiment results demonstrate the superiority of our method over state of the art.
State-of-the-art in product-service systems

A Product-Service System (PSS) is an integrated combination of products and services. This Western concept embraces a service-led competitive strategy, environmental sustainability, and the basis to differentiate from competitors who simply offer lower priced products. This paper aims to report the state-of-the-art of PSS research by presenting a clinical review of literature currently available on this topic. The literature is classified and the major outcomes of each study are addressed and analysed. On this basis, this paper defines the PSS concept, reports on its origin and features, gives examples of applications along with potential benefits and barriers to adoption, summarizes available tools and methodologies, and identifies future research challenges.
A Deep Neural Architecture for Real-Time Access Point Scheduling in Uplink Cell-Free Massive MIMO

In this paper, a novel hybrid architecture is proposed combining expert knowledge for optimal power allocation and deep artificial neural networks (ANN) to address the access-point scheduling problem in cell-free massive multiple-input multiple-output (MIMO) communication systems with a serial bandwidth-limited fronthaul architecture. The scheduling task is formulated as an image segmentation problem for which a supervised encoder-decoder like ANN is proposed. It consists of serially concatenated contraction and expansion layers to maximize the (regularized) cross-entropy, followed by a binary projection to undo the relaxation problem. Besides the robustness to scenarios with a time-varying system load and fronthaul bandwidth, the proposed architecture provides a complexity-efficient solution that fulfills the fronthaul bandwidth constraints and satisfies the real-time considerations. Our experimental results verify the competitive performance of the proposed solution with respect to both nonlinear solvers and state-of-art convex algorithms while the time efficiency of the ANN model outperforms the state of the art, especially, in scenarios with a large number of users.
A new neighbourhood structure for job shop scheduling problems

Job shop scheduling problem (JSP) is a widely studied NP-complete combinatorial optimisation problem. Neighbourhood structures play a critical role in solving JSP. At present, there are three state-of-the-art neighbourhood structures, i.e. N5, N6, and N7. Improving the upper bounds of some famous benchmarks is inseparable from the role of these neighbourhood structures. However, these existing neighbourhood structures only consider the movement of critical operations within a critical block. According to our experiments, it is also possible to improve the makespan of a scheduling scheme by moving a critical operation outside its critical block. According to the above finding, this paper proposes a new N8 neighbourhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block. Besides, a neighbourhood clipping method is designed to avoid invalid movement, discarding non-improving moves. Tabu search (TS) is a commonly used algorithm framework combined with neighbourhood structures. This paper uses this framework to compare the N8 neighbourhood structure with N5, N6, and N7 neighbourhood structures on four famous benchmarks. The experimental results verify that the N8 neighbourhood structure is more effective and efficient in solving JSP than the other state-of-the-art neighbourhood structures.
Towards optimal VLAD for human action recognition from still images

Human action recognition from still image has recently drawn increasing attention in human behavior analysis and also poses great challenges due to the huge inter ambiguity and intra variability. Vector of locally aggregated descriptors (VLAD) has achieved state-of-the-art performance in many image classification tasks based on local features. The great success of VLAD is largely due to its high descriptive ability and computational efficiency. In this paper, towards optimal VLAD representations for human action recognition from still images, we improve VLAD by tackling three important issues including empty cavity, ambiguity and pooling strategies. The empty cavity limits the performance of VLAD and has long been overlooked. We investigate the empty cavity and provide an effective solution to deal with it, which improves the performance of VLAD; we enhance the codewords with middle level of assignments which are more reliable and can provide more useful information for realistic activity; we propose incorporating the generalized max pooling to replace sum pooling in VLAD, which is more reliable for the final representation. We have conducted extensive experiments on four widely-used benchmarks to validate the proposed method for human action recognition from still images. Our method produces competitive performance with state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.
Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections

Random projection is a popular method for dimensionality reduction due to its simplicity and efficiency. In the past few years, random projection and fuzzy c-means based cluster ensemble approaches have been developed for high-dimensional data clustering. However, they require large amounts of space for storing a big affinity matrix, and incur large computation time while clustering in this affinity matrix. In this paper, we propose a new random projection, fuzzy c-means based cluster ensemble framework for high-dimensional data. Our framework uses cumulative agreement to aggregate fuzzy partitions. Fuzzy partitions of random projections are ranked using external and internal cluster validity indices. The best partition in the ranked queue is the core (or base) partition. Remaining partitions then provide cumulative inputs to the core, thus, arriving at a consensus best overall partition built from the ensemble. Experimental results with Gaussian mixture datasets and a variety of real datasets demonstrate that our approach outperforms three state-of-the-art methods in terms of accuracy and space-time complexity. Our algorithm runs one to two orders of magnitude faster than other state-of-the-arts algorithms.
Power Line Communications: State of the art in research, development and application

Power Line Communications (PLC) is currently well-known technology. In the last 10-15 years its development and improvements became considerable so that the mass deployment takes place. In this paper we describe the main trends of PLC development. (C) 2014 Elsevier GmbH. All rights reserved.
A concise review of the bioactivity and pharmacological properties of the genus Codium (Bryopsidales, Chlorophyta)

The genus Codium is one of the most important genera of marine green macroalgae. Its distribution is widespread worldwide and it has a high degree of diversity in species and characteristics. This genus plays an important ecological role in marine ecosystems as it is a primary producer. However, some species in the genus Codium are invasive species and may disturb the functioning of the ecosystem. Economically, Codium has promising potential as a source of diverse nutritional and pharmacological compounds. Codium is edible, has a high nutrient value, and is rich in bioactive compounds. Hence, some species of Codium have been consumed as food and used as herbal medicines in some Asian countries. In recent decades, studies of the bioactivity and pharmacological properties of the genus Codium have attracted the attention of scientists. This review aims to identify gaps in studies analyzing Codium that have been conducted in the past three decades by assessing published research articles on its bioactivity and pharmacological properties. Compounds obtained from Codium have demonstrated significant biological activities, such as immunostimulatory, anticoagulant, anticancer, anti-inflammatory, antioxidant, antiviral, antibacterial, antifungal, antitumor, anti-angiogenic, osteoprotective, and anti-obesity activities. This review provides information that can be used as a future guideline for sustainably utilizing the genus Codium.
A Smart CMOS Assay SoC for Rapid Blood Screening Test of Risk Prediction

A micro-controller unit (MCU) assisted immunoassay lab-on-a-chip is realized in 0.35 mu m CMOS technology. The MCU automatically controls the detection procedure including blood filtration through a nonporous aluminum oxide membrane, bimolecular conjugation with antibodies attached to magnetic beads, electrolytic pumping, magnetic flushing and threshold detection based on Hall sensor array readout analysis. To verify the function of this chip, in-vitro Tumor necrosis factor-alpha (TNF-alpha) and N-terminal pro-brain natriuretic peptide (NT-proBNP) tests are performed by this 9 mm(2)-sized single chip. The cost, efficiency and portability are considerably improved compared to the prior art.
Online Disinhibition and Online Trolling Among Chinese College Students: The Mediation of the Dark Triad and the Moderation of Gender

The Dark Triad (Machiavellianism, psychopathy, narcissism) is associated with online disinhibition and antisocial online behaviors. However, the mediating role between online disinhibition and online trolling has never previously been investigated. We examined direct and indirect associations between online disinhibition and online trolling via the Dark Triad among 1,303 participants. The results showed that online disinhibition is positively correlated with online trolling, and their link is partly mediated by Machiavellianism and psychopathy. Furthermore, men exhibited higher levels of Dark Triad traits and were more likely to engage in online trolling than women. Moderated mediation analyses indicated that gender moderated the relationship between psychopathy and online trolling. The study provided a promising perspective for the intervention of online trolling, namely netizens should be taught to reduce impulsivity and improve empathy and self-control, especially for men.
Highly efficient neoteric histogram-entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection

Thresholding for segmentation is an important key step and necessary process in various applications. Estimating an accurate threshold value for a complex and coarse image is computationally expensive and lacks accuracy and stability. This study is aimed at developing a general histogram-entropy-based thresholding method, referred as our HEBT method, for fast and efficient automatic threshold value evaluation. In the proposed method, the probability density function and Shannon entropy derived from 1D bimodal histogram have been used to find the optimal threshold values automatically. The proposed method implemented with a three-frame differencing segmentation technique has been tested on real-time datasets - change detection 2012, change detection 2014, and Wallflower - to identify pedestrians and vehicles. The performance of our HEBT method has been compared with six state-of-the-art automatic thresholding methods. The experimental segmented image results confirmed that our HEBT method is more adaptable and better suited for real-time systems with severe challenging conditions of great variations. Further, the new HEBT method achieved the best segmentation results with highest values of several performance parameters, i.e. recall, precision, similarity, and f-measure. Interestingly, the computation time is the lowest for the proposed method than the state-of-the-art methods, promising its application for a fast and effective image segmentation.
Additive models and separable pooling, a new look at structural similarity

Objective quality metrics predict perceived quality of image signals computationally and can: (i) benchmark and monitor compression and processing algorithms and (ii) optimise their performance for a given application (content, bandwidth, packet loss...). Structural similarity, represented by the well known SSIM index is a framework for objective assessment of image quality well known for its relative simplicity and robustness. Despite its practical appeal, SSIM's performance level, measured as agreement with subjective quality scores, lags more complex state-of-the-art metrics. We present a new look into structural similarity that uses an additive model and a spatial pooling approach that decouples individual structural comparisons and utilises the quality driven aggregation paradigm. We apply this new approach to both baseline intensity SSIM and gradient SSIM (GSSIM) frameworks and show, through extensive evaluation on four publicly available subjective datasets that it provides considerably more ordered (linear) relationship between objective and subjective quality for a variety of input conditions. We demonstrate that newly formulated structural similarity metrics using this approach are capable of equal or even better performance than more complex state-of-the-art objective metrics in the process lending support to the theory that humans base their opinion on the worst sections of the observed signal. (C) 2013 Elsevier B.V. All rights reserved.
A novel neural network based approach to latent overlapped fingerprints separation

Overlapped fingerprints are often found in latent fingerprints lifted from crime scenes and in live-scan fingerprint images when the surface of fingerprint sensors contains residues of fingerprints of previous users. Such overlapped fingerprints usually cannot be processed accurately by contemporary commercial fingerprint matchers, which has led many researchers to propose methods designed to separate the overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on neural networks. Our algorithm works in a block-based fashion. After producing an initial estimation of the orientation fields present in the overlapped fingerprint image, it uses a neural network to separate the mixed orientation fields, which are then post-processed to correct remaining errors and enhanced using the global orientation field enhancement model. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy on the Tsinghua Overlapped Latent Fingerprint Database (containing real-world overlapped fingerprints obtained by forensic methods), while also showing encouraging results (second only to state-of-the-art) on the Tsinghua Simulated Overlapped Fingerprint Database (containing artificially overlapped fingerprints of a good quality).
Retrospective study of the clinicopathological characteristics and prognostic factors of gastrointestinal stromal tumors in Chinese patients

The purpose of this study was to investigate the clinicopathological characteristics and prognostic factors of patients with gastrointestinal stromal tumors (GISTs) in mainland China. We retrospectively analyzed the clinicopathological characteristics and survival data of 149 patients with GISTs admitted to Shengjing Hospital of China Medical University from July 2011 to October 2017. The following details were collected from all patients: sex, age, symptoms, preoperative examination, pathology, surgical procedures, and follow-up data. Recurrence-free survival (RFS) and overall survival (OS) were used to assess survival outcomes. The Kaplan-Meier method was performed to draw survival curves and calculate the survival rate. The log-rank test was performed for univariate analysis, and the significant factors were included in multivariate analysis using a Cox proportional hazards model to determine prognostic factors. The 5-year RFS rate was 78.5 % and 5-year OS rate was 83.2 %. The univariate analysis showed that the following prognostic factors could significantly predict 5-year RFS and OS: tumor size, initial status, modified NIH classification, mitotic index, CD117 expression, Ki67 index, and surgical procedure (P < 0.05). The multivariate analysis showed that mitotic index, CD117, and Ki67 index were independent prognostic factors associated with 5-year RFS and 5-year OS. This study provides a reference for the clinicopathological characteristics and prognostic factors of patients with GISTs in mainland China, and the results suggest that focusing on immunohistochemical markers in clinical practice may be more reliable for the prediction of clinical outcomes.
MIPSGPU: Minimizing Pipeline Stalls for GPUs With Non-Blocking Execution

Improving the latency hiding ability is important for GPU performance. Although existing works, which mainly target on either improving thread level parallelism or optimizing memory hierarchy, are effective at improving GPUs' latency hiding ability, warps are still blocked after executing long latency operations, reducing the number of schedulable warps. This article revisits the recently proposed non-blocking execution for GPUs to improve the latency hiding ability of GPUs. With non-blocking execution, instructions from warps blocked by long latency operations can be pre-executed to make full use of GPU resources. However, we find that the state-of-the-art non-blocking GPU architecture gains limited performance improvement. Through in-depth analysis, we observe that the poor performance is largely due to inefficient pre-execution state management, duplicate instruction extraction, frequent early eviction and severe resource congestion. To make non-blocking execution actually useful for GPUs and minimize hardware overheads, we carefully redesign the non-blocking architecture for GPUs based on our analysis and propose MIPSGPU. Our evaluations show that MIPSGPU, relative to the state-of-the-art non-blocking GPU architecture, improves performance of memory intensive applications by 19.05 percent, and reduces memory to SM traffics by 14 percent.
A progressive lossless/near-lossless image compression algorithm

A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes.
Aberrant expression of microRNA-4443 (miR-4443) in human diseases

miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443.
The Role of Community Centre-based Arts, Leisure and Social Activities in Promoting Adult Well-being and Healthy Lifestyles

Developed countries are experiencing high levels of mental and physical illness associated with long term health conditions, unhealthy lifestyles and an ageing population. Given the limited capacity of the formal health care sector to address these public health issues, attention is turning to the role of agencies active in civil society. This paper sought to evaluate the associations between participation in community centre activities, the psycho-social wellbeing and health related behaviours. This was based on an evaluation of the South West Well-being programme involving ten organisations delivering leisure, exercise, cooking, befriending, arts and crafts activities. The evaluation consisted of a before-and-after study with 687 adults. The results showed positive changes in self-reported general health, mental health, personal and social well-being. Positive changes were associated with diet and physical activity. Some activities were different in their outcomes-especially in cases where group activities were combined with one-to-one support. The results suggest that community centre activities of this nature offer benefits that are generically supportive of health behaviour changes. Such initiatives can perform an important role in supporting the health improvement objectives of formal health care services. For commissioners and partner agencies, accessibility and participation are attractive features that are particularly pertinent to the current public health context.