Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of electrochemical DNA detection methods to measure circulating tumour DNA for enhanced diagnosis and monitoring cancer

Liquid biopsies are becoming an increasingly important potential replacement for existing biopsy procedures which can be invasive, painful and compromised by tumour heterogeneity. This paper reports a simple electrochemical approach tailored towards point of care cancer detection and treatment monitoring from biofluids using a label free detection strategy. The mutations under test were the KRAS G12D and KRAS G13D mutations which are both important in the development and progression of many human cancers and whose presence correlates with poor outcomes. These common circulating tumour markers were investigated in clinical samples and amplified by standard and specialist PCR methodologies for subsequent electrochemical detection. Following pre-treatment of the sensor to give a clean surface, DNA probes developed specifically for detection of the KRAS G12D and G13D mutations were immobilized onto low cost carbon electrodes using diazonium chemistry and EDC/NHS coupling. Following functionalisation of the sensor it was possible to sensitively and specifically detect mutant KRAS G12D and G13D PCR product from cancer patients against a background of wild type KRAS DNA from the representative sample. Our findings give rise to the basis of a simple and very low cost system for measuring ctDNA biomarkers in patient samples. The current time to result of the system is 4.5 hours with considerable scope for optimisation and already compares favourably to the UK National Health Service biopsy service where patients can wait weeks for their result. The paper will report the technical developments we have made in the production of clean carbon surfaces for functionlisation, show the assay performance data for KRAS G12D and G13D with a range of PCR systems and demonstrate the potential for measuring response to treatment offered by the system.
HIDRA 1.0: Deep-Learning-Based Ensemble Sea Level Forecasting
in the Northern Adriatic

Abstract. Complex interactions between atmospheric forcing, topographic constraints to air and water flow, and resonant character of the basin, make sea level modeling in Adriatic a particularly challenging problem. In this study we present an ensemble deep-neural-network-based sea level forecasting method HIDRA, which outperforms our general ocean circulation model ensemble (NEMO v3.6) for all forecast lead times and at a minuscule fraction of the numerical cost (order of 2 × 10−6). HIDRA exhibits larger bias but lower RMSE than NEMO over most of the residual sea level bins. It introduces a trainable atmospheric spatial encoder and employs fusion of atmospheric and sea level features into a self-contained network which enables discriminative feature learning. The HIDRA architecture building blocks are experimentally analyzed in detail and compared to alternative approaches. Results show individual importance of sea level input for accurate forecast lead times below 24 h and of the atmospheric input for longer time leads. The best performance is achieved by considering the input as the total sea level, split into disjoint sets of tidal and residual signals. This enables HIDRA to optimize the prediction fidelity with respect to atmospheric forcing, while compensating for the errors in the tidal model. HIDRA is trained and analysed on a ten-year (2006–2016) timeseries of atmospheric surface fields from a single member of ECMWF atmospheric ensemble. In the testing phase, both HIDRA and NEMO ensemble systems are forced by the ECMWF atmospheric ensemble. Their performance is evaluated on a one-year (2019) hourly time series from tide gauge in Koper (Slovenia). Spectral analysis of the forecasts at semi-diurnal frequency (12 h)−1 and at ground-state basin seiche frequency (21.5 h)−1 is performed by a continuous wavelet transform. The energy at the basin seiche in the HIDRA forecast is close to the observed, while NEMO underestimates it. Analyses of the January 2015 and November 2019 storm surges indicate that HIDRA has learned to mimic timing and amplitude of resonant sea level excitations in the basin.
A Review of Video Object Detection: Datasets, Metrics and Methods

Although there are well established object detection methods based on static images, their application to video data on a frame by frame basis faces two shortcomings: (i) lack of computational efficiency due to redundancy across image frames or by not using a temporal and spatial correlation of features across image frames, and (ii) lack of robustness to real-world conditions such as motion blur and occlusion. Since the introduction of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015, a growing number of methods have appeared in the literature on video object detection, many of which have utilized deep learning models. The aim of this paper is to provide a review of these papers on video object detection. An overview of the existing datasets for video object detection together with commonly used evaluation metrics is first presented. Video object detection methods are then categorized and a description of each of them is stated. Two comparison tables are provided to see their differences in terms of both accuracy and computational efficiency. Finally, some future trends in video object detection to address the challenges involved are noted.
L 2 norm-based finite-time stability and boundedness of singular distributed parameter systems with parabolic type

In this study, the problem of finite-time stability and boundedness for parabolic singular distributed parameter systems in the sense of L 2 norm is investigated. First, two new results on L 2 norm-based finite-time stability and finite-time boundedness for above-mentioned systems, inspired by the light of partial differential equations theory and Lyapunov functional method, are presented. Then, some sufficient conditions of L 2 norm-based finite-time stability and boundedness are established by virtue of differential inequalities and linear matrix inequalities. Furthermore, the distributed state feedback controllers are constructed to guarantee the L 2 norm-based finite-time stable and bounded of the closed-loop singular distributed parameter systems. Finally, numerical simulations on a specific numerical example and the building temperature control system equipped with air conditioning are given to demonstrate the validity of the proposed methods.
Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading

Design and simulations of an integrated photonic device that can optically detect the magnetization direction of its ultra-thin (∼12 nm) metal cladding, thus ‘reading’ the stored magnetic memory, are presented. The device is an unbalanced Mach Zehnder Interferometer (MZI) based on InP Membrane on Silicon (IMOS) platform. The MZI consists of a ferromagnetic thin-film cladding and a delay line in one branch, and a polarization converter in the other. It quantitatively measures the non-reciprocal phase shift caused by the Magneto-Optic Kerr Effect in the guided mode which depends on the memory bit’s magnetization direction. The current design is an analytical tool for research exploration of all-optical magnetic memory reading. It has been shown that the device is able to read a nanoscale memory bit (400 × 50 × 12 nm) by using a Kerr rotation as small as 0.2∘, in the presence of a noise ∼10 dB in terms of signal-to-noise ratio. The device is shown to tolerate performance reductions that can arise during the fabrication.
Reduced alpha diversity of the oral microbiome correlates with short progression‐free survival in patients with relapsed/refractory multiple myeloma treated with ixazomib‐based therapy (AGMT MM 1, phase II trial)

Alterations in the human microbiome have been linked to several malignant diseases. Here, we investigated the oral microbiome of 79 patients with relapsed/refractory multiple myeloma (MM) treated with ixazomib‐thalidomide‐dexamethasone. Increased alpha diversity (Shannon index) at the phylum level was associated with longer progression‐free survival (PFS) (10.2 vs 8.5 months, P = .04), particularly in patients with very long (>75% quartile) PFS . Additionally, alpha diversity was lower in patients with progressive disease (P < .05). These findings suggest an interrelationship between the oral microbiome and outcome in patients with MM and encourage a novel direction for diagnostic and/or therapeutic strategies.
From physical to virtual store: factors that spur Bangladeshi manufacturers/retailers to introduce multi-channel retailing

Purpose Multi-channel business operations are standard practice in most business contexts today. The popularity of multi-channel adoption among developing countries is growing fast. In Bangladesh, the retailers who adopted multi-channel retailing have been getting a very good response from the consumers. This study aims to understand the factors that influence manufacturers’ decisions to adopt multiple channels in Bangladesh. The authors applied an extended technology acceptance model with three variables: business innovation, business competition and consumer satisfaction. Design/methodology/approach Partial least squares-structural equation modeling was applied to test the data collected from 157 companies and relevant hypotheses. Findings The study findings show that Bangladeshi manufacturers are positive about, and expect benefits from, applying the new channel. The study also revealed that customer satisfaction considerably affects multi-channel adoption in Bangladesh. Similarly, business innovation and business competition play a significant role in introducing multiple business channels. Research limitations/implications This research was conducted in Bangladesh, and data are collected from Dhaka and Chittagong that may limit the generalizability of findings. Practical implications The research goal was to understand a manufacturer’s perception to adopt multi-channel in business. The proposed research model was able to address the major factors that drive a manufacturer to introduce multiple business channels, especially in Bangladesh. Originality/value Many research and case studies have been done the past couple of decades, but most of them are consumer oriented. Little research has been done to investigate a manufacturer’s point of view adapting innovation in business. Though some research articles are available online, most of them from developed countries. So, the study’s goal was to study developing countries scenario; thus, the authors choose Bangladesh.
Who Knocks on the Door of Portfolio Performance Heaven: Sinner or Saint Investors?

To sin, or not to sin: that has been the question for many people for a long time, and nowadays that question has moved to the financial markets. The existence of studies that show that investing in vice sectors such as the alcohol, tobacco, and gambling industries, collectively known as the “triumvirate of Sin”, is profitable has created some uncertainty for investors who wonder whether or not to be socially responsible. We show that by implementing an investment strategy based on the Fama–French five-factor model, “saint” investors obtain better portfolio performance, even when transaction costs are taken into consideration, and therefore they are the ones chosen to knock on the door of portfolio performance heaven.
Sustainable Triple‐A Supply Chains

In his 2004 article, Professor Hau Lee argues that the best supply chains are not only fast and cost-effective but also agile, adaptable, and aligned. The concept of triple-A supply chains has been extensively studied in academic and trade publications and integrated into numerous operations and supply chain management curricula. It has also influenced the management approach of leaders around the world. Yet since the triple-A concept was first developed, supply chains have become increasingly global, connected, and interdependent. The increased complexity of global supply chains has reduced much-needed visibility, further complicating their management, while the growing connectivity and interdependence among different stakeholders have led to many unforeseen environmental and social issues. As a result, Professor Lee’s emphasis on triple-A supply chains is even more relevant today. In light of these new challenges and demands, we revisit the original triple-A definitions of agile, adaptable, and aligned, expanding these concepts for a more socially and environmentally conscientious world. We also discuss potential enablers of and barriers to sustainable triple-A supply chains.
Consumers’ Willingness to Pay for Fish with a Low Allergenic Potential

Abstract This study explores the demand-side perspective on the factors influencing the willingness to pay (WTP) premium price for fish with a low allergenic potential (FWLAP). A survey to assess consumers’ behavior and preferences for fish products was implemented in a representative sample of the Portuguese general population (n = 640). The contingent valuation method was applied to elicit consumers’ WTP. Results indicated that only half of the consumers were willing to pay higher prices for FWLAP. Econometric results suggested that WTP for FWLAP is explained by the presence of fish-allergy pathologies in the household, and the fish unique characteristics and quality.
Event History and Topological Data Analysis

Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well known Nelson-Aalen cumulative hazard estimator for the comparison of topological features of random fields and for testing parametric assumptions. We suggest a Cox proportional hazards approach for the analysis of embedded metric trees. The Nelson-Aalen method is illustrated on globally distributed climate data and on neutral hydrogen distribution in the Milky Way. The Cox method is use to compare vascular patterns in fundus images of the eyes of healthy and diabetic retinopathy patients.
Identifying Loneliness and Social Isolation in Care Home Residents with Sight Loss: Lessons from Using the De Jong Gierveld Scale

Context: Experience of loneliness amongst care home residents with sight loss is associated with limitations in activities of daily living, poor self-reported health, and increased rates of depression. Care homes are encouraged to use screening tools to identify those at risk of loneliness. Objectives: The study aimed to describe the findings and experience of applying a validated, multi-item scale to identify loneliness and isolation in care home residents with sight loss in England, UK. Methods: The six-item De Jong Gierveld Loneliness Scale was administered to residents residing in long-term care homes with sight loss. Participants were aged 65+ years old with vision impairment that could not be corrected by glasses. Descriptive analysis of loneliness scale data was undertaken supplemented with observational field notes of implementation challenges. Findings: Only 42 applications of the De Jong Gierveld Loneliness Scale were possible. The mean sub-scale scores for emotional loneliness, social loneliness and the mean overall loneliness score were 1.36 (sd = 1.16), 1.19 (sd = 1.04) and 2.55 (sd = 1.9) respectively. Challenges observed in scale administration and understanding of scale items by residents might preclude it as a loneliness case-identification tool in busy care home environments. Limitations: The study reports on the challenges implementing a questionnaire which achieved a low rate of data collection. Implications: For case-identification of loneliness, care homes may wish to consider use of a single-item loneliness question rather than multi-item scales due to variable length of administration and resident comprehension.
Weed Pixel Level Classification Based on Evolving Feature Selection on Local Binary Pattern With Shallow Network Classifier

This paper proposes an evolving feature selection on a rotation-invariant Local Binary Pattern (LBP) with genetic algorithm (GA) and a non-linear classifier to perform pixel-wise classification on biomass pixels. Early true leaf growth of carrots and weeds consists of 60 images provided by a public benchmark Crop/Weed Field Image Dataset (CWFID) [1] was used. The GA encodes LBP feature parameters generated from normalized difference vegetation index (NDVI) image slices as genome consisting of number of neighbours, radius, size of image slice, number of LBP combinations. LBP is a lightweight rotation invariant texture feature descriptor which encodes discriminative local texture information between crops and weeds. The study evaluated multiple ensemble models evolved by GA, where GA evolves the LBP feature parameter selection, and the number of LBP features as input variables used. The classifier can handle crop and weed overlaps, which are often present in commercial fields. Weeds often grow in close proximity and overlap crops and are similar in size, which adds complexity in discriminating them. Our experiments have shown that combinations with a back-propagation neural network with a symmetrical two hidden-layer network achieved the best classification accuracy when compared to other non-linear classifiers. By utilizing a single type of feature (LBP texture feature), our resulting Artificial Neural Network (ANN) ensemble classifier is able to achieve a classification accuracy of 83.5 %.
A large-area planar helicon plasma source with a multi-ring antenna on Linear Experimental Advanced Device (LEAD)

A planar helicon plasma source with a four-ring antenna has been developed on Linear Experimental Advanced Device (LEAD) in Southwestern Institute of Physics. The diameter of the largest antenna ring is 320 mm. This source is located outside of the vacuum chamber without any vacuum interface and injects radio frequency power into the chamber through a 340-mm-diameter quartz window. A low power threshold of 150 W for electromagnetic-mode to wave-mode transfer is experimentally confirmed. A large volume plasma with a density of over 1019 m−3 and a plasma generation efficiency (total number of electrons diveded by input power) of over 30× 1013 W−1 indicate the high performance of this large-area helicon plasma source, promising LEAD a suitable device for fundamental plasma physics and plasma material interaction research.
Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review

This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic databases (Embase, MEDLINE, Scopus, PubMed, and the Cochrane Library) returned 747 unique records on 20 December 2019. Predetermined inclusion and exclusion criteria were applied to the search results, resulting in 15 highest-quality publications. A manual search through the reference lists of relevant review articles found from the database search was conducted, yielding no additional records. A validation dataset of the trained deep learning algorithms was used for creating a set of optimal properties for an ideal diabetic retinopathy classification algorithm. Potential limitations to the clinical implementation of such systems were identified as lack of generalizability, limited screening scope, and data sovereignty issues. It is concluded that deep learning algorithms in the context of diabetic retinopathy screening have reported impressive results. Despite this, the potential sources of limitations in such systems must be evaluated carefully. An ideal deep learning algorithm should be clinic-, clinician-, and camera-agnostic; complying with the local regulation for data sovereignty, storage, privacy, and reporting; whilst requiring minimum human input.
Strongly perturbed sliding mode adaptive control of vehicle active suspension system considering actuator nonlinearity

Aiming at the problem of improving driving comfort under the condition of ensuring driving safety and limited suspension travel, this paper proposes a sliding mode adaptive controller suitable for strong disturbance action. Considering the nonlinear characteristics of the hydraulic actuator, a one-half vehicle suspension model with uncertain parameters and external disturbances is established. In the sliding mode design part of the controller, the sliding mode equivalent control items of the vertical and pitch subsystems are derived, and the optimised constant velocity approach law is adopted. The sliding mode switching control item is given to ensure that the controller can effectively adjust the vehicle body posture when the system is subjected to strong external disturbance. Finally, by selecting the adaptive law, the controller can adapt to the uncertainty of the vehicle body parameters. The simulation results for the specific example considered in this work show that the control strategy can greatly improve the ride comfort under the premise of ensuring the safety of the vehicle and the stability of the steering. Compared with the backstepping adaptive control, the control strategy of this paper shows superiority when the system is subjected to strong disturbance.
A fault‐tolerant workflow management system with Quality‐of‐Service‐aware scheduling for scientific workflows in cloud computing

Cloud computing provides solutions to many scientific and business applications. Large‐scale scientific applications, which are structured as scientific workflows, are evaluated through cloud computing. In this paper, we proposed a Quality‐of‐Service‐aware fault‐tolerant workflow management system (QFWMS) for scientific workflows in cloud computing. We have considered two real‐time scientific workflows, i.e., Montage and CyberShake, for an evaluation of the proposed QFWMS. The results of the proposed QFWMS scheduling were evaluated through simulation environment WorkflowSim and compared with three well‐known heuristic scheduling policies: (a) minimum completion time (MCT), (b) Maximum‐minimum (Max‐min), and (c) Minimum‐minimum (Min‐min). By considering Montage scientific workflow, the proposed QFWMS reduces the make‐span 8.86%, 8.94%, and 5.53% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 6.19%, 3.52%, and 3.60% compared with existing three heuristic policies. Likewise, by considering CyberShake scientific workflow, the proposed QFWMS reduces the make‐span 19.54%, 21.41%, and 25.71% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 8.78%, 8.40%, and 8.61% compared with existing three heuristic policies. More so, for QFWMS, SLA is neither violated for time constraints nor for cost constraints. While for MCT, Max‐min and Min‐min scheduling policies, SLA is violated 32, 37, and 23 times, respectively. Conclusively, the proposed QFWMS scheduling and management system is one of the significant workflow management systems for execution and management of scientific workflows in cloud computing.