Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of objects and image quality on melanoma classification using deep neural networks

Background: Melanoma is a type of skin cancer with a higher mortality compared to other types of skin cancers. Early and accurate diagnosis of melanoma has critical importance on its prognosis. Recently, deep neural network based models dominated the CAD systems for classification of the potential melanoma lesions. In clinical settings, capturing impeccable skin images is not always possible. In some cases, an external object such as a ruler is required for determination of lesion size. Sometimes, the skin images can be blurry, noisy or have low contrast. The aim of this work is to investigate the effects of external objects (ruler, hair) and image quality (blur, noise, contrast) on the classification of melanoma using commonly used Convolutional Neural Network(CNN) models.Results: Performance is analyzed using accuracy, sensitivity, specificity and precision metrics over 6 different test sets. Hair set has 89.22%, ruler set has 86% and none set has 88.81% as the best accuracy with DenseNet121 architecture. Also, DenseNet has the best average accuracy with comparing the other three models in other datasets, which are noise and blur. We find that ResNet is better for contrast dataset. We can infer that DenseNet can be used for melanoma classification with image distortions and degradations. Conclusion: In this study, we investigate the effect of ruler/hair and image blur, noise and contrast on the melanoma detection performance of four commonly used CNN models: ResNet50, DenseNet121, VGG16 and AlexNet. Melanoma images can be better recognized under contrast changes unlike the benign images, we recommend ResNet model whenever there is contrast issue. Noise significantly degrades the performance on melanoma images and the recognition rates decrease with compared to benign lesions in noisy set. DenseNet121 also works well in this set. Both classes are sensitive to blur changes and best accuracy is obtained with DenseNet model. The images contain ruler has decreased the classification accuracy and ResNet has better performance if there is ruler in an image. Hairy images have the best success rate in our system since it has the maximum number of images in total dataset. DenseNet performs better for both hairy and high quality images.
Hierarchical bilinear convolutional neural network for image classification

Image classification is one of the mainstream tasks of computer vision. However, the most existing methods use labels of the same granularity level for training. This leads to ignoring the hierarchy that may help to differentiate different visual objects better. Embedding hierarchical information into the convolutional neural networks (CNNs) can effectively regulate the semantic space and thus reduce the ambiguity of prediction. To this end, a multi ‐ task learning framework, named as Hierarchical Bilinear Convolutional Neural Network (HB ‐ CNN), is developed by seamlessly integrating CNNs with multi ‐ task learning over the hierarchical visual concept structures. Specifically, the labels with a tree structure are used as the supervision to hierarchically train multiple branch networks. In this way, the model can not only learn additional information (e.g. context information) as the coarse ‐ level category features, but also focus the learned fine ‐ level category features on the object properties. To smoothly pass hierarchical conceptual information and encourage feature reuse, a connectivity pattern is proposed to connect features at different levels. Furthermore, a bilinear module is embedded to generalise various orderless texture feature descriptors so that our model can capture more discriminative features. The proposed method is extensively evaluated on the CIFAR ‐ 10, CIFAR ‐ 100, and ‘Orchid’ Plant image sets. The experimental results show the effectiveness and superiority of our method.
Rational Type Contractions in Extended b-Metric Spaces

In this paper, we establish the existence of fixed points of rational type contractions in the setting of extended b-metric spaces. Our results extend considerably several well-known results in the existing literature. We present some nontrivial examples to show the validity of our results. Furthermore, as applications, we obtain the existence of solution to a class of Fredholm integral equations.
Rhetoric, Accounting and Accountability: COVID-19 and the Case of Italy

The current dramatic context of COVID-19 has urged academics and practitioners to tackle the topic of the pandemic not only regarding its medical side but from the perspective of social sciences, accounting and accountability as well. In this sense, our paper moves from the pivotal work of Higgins and Walker (2012) and Merkl-Davies and Brennan (2017) and tries to trace the use and the extent of accounting communication by companies during the peculiar context of the pandemic. Considering the nature of the elements to be evaluated, we applied a manual content analysis, a more suitable technique than software to capture subjective and emotional elements. Among the main preliminary results of the paper, the volume and the importance of emotional content come to the surface, such as self-assessment and emotional tone. The paper confirms the important role of rhetorical analysis in understanding the quality and the meaning of the information provided by companies and contributes to the stream of Critical Discourse Analysis (CDA) studies on corporate reporting.
To What Extent Is Trust a Prerequisite for Charitable Giving? A Systematic Review and Meta-Analysis

Trust is assumed to be important for charitable giving. However, disparate associations have been found, and recent theoretical approaches emphasize motives for giving that do not rely on trust. To resolve this tension, we conducted a systematic review of evidence generated between 1988 and 2020. A meta-analysis of 69 effect sizes from 42 studies sampling 81,604 people in 31 countries confirmed a positive association between trust and giving across diverse measures, r = . 22. Meta-regressions showed that organizational (r = .35) and sectoral trust (r = .27) were more strongly associated with giving than were generalized (r = .11) or institutional trust (r = .14). The relationship was also stronger in non-western (vs Western) countries and in nonrepresentative (vs nationally representative) samples. All evidence was correlational, and few studies measured actual behavior. We discuss implications for theories of trust and for fundraising practice, and highlight critical gaps in evidence.
Lubrication Behavior of Water Molecules Confined in TiO2 Nanoslits: A Molecular Dynamics Study

Titanium (Ti) metal has been widely used in orthopedic implants, such as knee replacements and fracture fixation devices, where water is the base fluid of the lubricant. In this work, a series of nonequilibrium molecular dynamics have been carried out to investigate the microstructure and lubrication of water molecules confined in TiO2 nanoslits under shearing. The effects of varying slit gap widths (0.8, 1.2, 1.6, and 2.0 nm) and shear velocities (200, 100, 50, and 10 m/s) on the friction coefficients between TiO2 and water molecules were evaluated to shed light on the role of the confined water molecules on lubrication. Simulation results showed that the friction coefficient decreased as the slit width increased. Detailed analysis of water molecules microstructure revealed that water molecules confined in the slits were layered. Typically, all the water molecules in Layer 1 and some water molecules in Layer 2 could reach the sliding velocity of the wall, which were in agreement with the reported mobilit.
Corrosion Investigation of Chromium Nitride and Chromium Carbide Coatings for PEM Fuel Cell Bipolar Plates in Simulated Cathode Condition

Transition metals nitrides and carbides are used as coatings on bipolar plates for proton exchange membrane fuel cells (PEMFCs) due to their suitable electrical conductivity and corrosion resistance. Chromium electroplated AISI 316L stainless steel bipolar plates were treated by plasma nitriding and solid carburizing to form chromium nitride and chromium carbide, respectively. The presence of CrN/Cr2N and Cr7C3/Cr23C6 was verified by X‐ray diffractometry (XRD) in chromium nitride and chromium carbide coatings, respectively. The corrosion behavior of coatings was investigated by potentiodynamic polarization, potentiostatic polarization and electrochemical impedance spectroscopy (EIS) in simulated cathode condition. Coated samples showed better corrosion behavior than untreated bare sample. EIS results indicated decrease in corrosion current density after 500 hours, however coatings acted as barrier against solution access to substrate. Corrosion current densities of coatings were close to targets of United Stated department of energy (DOE).
Powerful and Tunable THz Emitters Based on the Fe/Pt Magnetic Heterostructure

In this work, we report our study on the THz emission in Fe/Pt magnetic heterostructures. We have carried out a comprehensive investigation of THz emission from Fe/Pt magnetic heterostructures, employing time-domain THz spectroscopy. We reveal that by properly tuning the thickness of Fe or Pt layer, THz emission can be greatly improved in this type of heterostructure. We further demonstrate that the THz field strength emitted from a newly designed multilayer (Pt/Fe/MgO)$_n$ with n=3 can reach a value of ~1.6 kV/cm, which is comparable to the values from the conventional GaAs antenna with a bias of 4 kV/cm, and the nonlinear crystals, e.g., 100 micrometer GaP and 2 mm ZnTe. For the first time, the intensity and spectrum of THz wave is demonstrated to be tunable by the magnetic field applied on the patterned magnetic Fe/Pt heterostructures. These findings thus promise novel approaches to fabricate powerful and tunable THz emitters based on magnetic heterostructure.
Projections of Local Knowledge-Based Adaptation Strategies of Mexican Coffee Farmers

Local knowledge can be a strategy for coping with extreme events and adapting to climate change. In Mexico, extreme events and climate change projections suggest the urgency of promoting local adaptation policies and strategies. This paper provides an assessment of adaptation actions based on the local knowledge of coffee farmers in southern Mexico. The strategies include collective and individual adaptation actions that farmers have established. To determine their viability and impacts, carbon stocks and fluxes in the system’s aboveground biomass were projected, along with water balance variables. Stored carbon contents are projected to increase by more than 90%, while maintaining agroforestry systems will also help serve to protect against extreme hydrological events. Finally, the integration of local knowledge into national climate change adaptation plans is discussed and suggested with a local focus. We conclude that local knowledge can be successful in conserving agroecological coffee production systems.
Liquidity commonality-economic cycle nexus in emerging Asian economies: An ARDL approach

This study investigates the impact of liquidity commonality on the economic cycle for 7 emerging Asian economies over a period of 1997-2018, using Autoregressive Regressive Distributed Lag (ARDL) approach to Cointegration. Gross domestic investment, total consumption expenditure, net trade, and unemployment rate are studied as macro variables in the analysis. The nexus has been discussed both in the short-run and long-run. A significant relationship between economic growth and stock market liquidity commonality is found for large economies including China, India, Indonesia, and Malaysia; however, we found mixed evidence regarding the direction of the relationship for different economies. The aggregate analysis revealed that liquidity commonality has a positive impact on economic growth in the short-run and a negative association in the long-run. As a non-diversifiable risk factor, liquidity co-movement shocks spread the market wide and disrupt the overall functioning of financial markets and eventually affect the economy. For regulators and policymakers and particularly for those in emerging economies, understanding the factors affecting economic cycles and recognizing their dynamics and magnitude is important for policy coordination and market development. Further, the firms in Asian markets operate in legal and regulatory environments distinct from those of firms analyzed in the previous literature. A major knowledge gap pertaining to Asian emerging markets serves as the primary motivation for this study.
Mapping Induced Subgraph Isomorphism Problems to Ising Models and Its Evaluations by an Ising Machine

SUMMARY Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a speciﬁc graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, ﬁnding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an e ﬃ cient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.
Synergistic effects between iron–graphene and melamine salt of pentaerythritol phosphate on flame retardant thermoplastic polyurethane

A comparison of melamine salt of pentaerythritol phosphate (MPP), and a synergistic agents, iron–graphene (IG) was performed in thermoplastic polyurethane (TPU) by masterbatch-melt blending on thermal and flame retardant properties. The flame retardant properties of TPU composites were characterized by limiting oxygen index (LOI), UL 94 and cone calorimeter test (CCT). The CCT results revealed that IG can significantly enhance flame retardant properties of MPP in TPU. The peak heat release rate of neat TPU and flame retardant TPU/MPP composites decreased from 2192.6 and 226.7 to 187.2 kW/m2 compared with that of TPU containing 0.25 wt% IG. The thermal stability and thermal decomposition of TPU composites were characterized by thermogravimetric analysis (TGA) and thermogravimetric/Fourier infrared spectrum analysis (TG-IR). The results indicated IG and MPP can improve the thermal stability of TPU. The formation of thermal conductive network by IG can promote the decomposition of MPP into nonflammable melt, which can play the role of heat barrier and restrict the diffusion of fuels into combustion zone and access of oxygen to the unburned fuels. Copyright © 2016 John Wiley & Sons, Ltd.
Functional block copolymer nanocarriers for anticancer drug delivery

Polymer-based nanocarriers for anticancer drug delivery bearing “clickable”, biodegradable, pH-sensitive and subcellular targeting functions were designed and successfully obtained. Firstly, well-defined functional amphiphilic diblock copolymers were synthesized applying a multistep controlled polymerization and modification procedure. As a result, copolymers comprising alkyne-end functionalized biodegradable poly(D,L-lactide) and polycationic poly(N,N-dimethylaminoethyl methacrylate) blocks were obtained. The latter blocks were additionally functionalized with subcellular targeting triphenylphosphonium cations. The amphiphilic block copolymers self-associated in aqueous media into nanosized functional micelles that were able to incorporate the natural anticancer drug curcumin into their biodegradable cores. The in vitro cytotoxicity evaluation of block copolymer micelles indicated a low intrinsic inhibitory potential on the proliferation of different human cell lines. More importantly, the drug-loaded nanocarriers demonstrated an obvious ability to induce apoptosis and exhibited more prominent inhibition of the NF-κB transcription factor in cancer cell lines and their drug-resistant analogues, as compared to the free drug. The obtained results are optimistic for potential application of the functional block copolymers in nanomedicine.
Of robots and robotkind: Extending intergroup contact theory to social machines

ABSTRACT This study seeks to advance how intergroup dynamics can help us better understand the relations between humans and robots. Intergroup contact theory states that negative feelings toward an outgroup can be reduced through controlled intergroup contact. This study tests this theory by having study participants interact with either a human (member of the ingroup) or large humanoid robot (member of an outgroup) and measuring changes in social distance before and after the interaction. The findings suggest that robotkind is a distinct social group separate from humankind and as predicted by intergroup contact theory, exposure to a specific robot can override held prejudices against robots as a social group.
Identifying Infliximab- (IFX-) Responsive Blood Signatures for the Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a severe chronic pathogenic inflammatory abnormality that damages small joints. Comprehensive diagnosis and treatment procedures for RA have been established because of its severe symptoms and relatively high morbidity. Medication and surgery are the two major therapeutic approaches. Infliximab (IFX) is a novel biological agent applied for the treatment of RA. IFX improves physical functions and benefits the achievement of clinical remission even under discontinuous medication. However, not all patients react to IFX, and distinguishing IFX-sensitive and IFX-resistant patients is quite difficult. Thus, how to predict the therapeutic effects of IFX on patients with RA is one of the urgent translational medicine problems in the clinical treatment of RA. In this study, we present a novel computational method for the identification of the applicable and substantial blood gene signatures of IFX sensitivity by liquid biopsy, which may assist in the establishment of a clinical drug sensitivity test standard for RA and contribute to the revelation of unique IFX-associated pharmacological mechanisms.
Roundup causes high levels of mortality following contact exposure in bumble bees

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society Centre for Ecology, Evolution & Behaviour, Department of Biological Sciences, School for Life Sciences and the Environment, Royal Holloway University of London, Egham, UK
The epistemological privilege of fieldwork: A collective investigation in war-torn Syria

The article highlights the epistemological privilege of observation in social contexts marked by armed violence and disorder. Faced with these situations, researchers have sometimes considered that their work begins once the conflicts have stabilized, thus favouring archives (written or oral) and secondary sources over observation and interviews collected in context. Despite the difficulties it poses, however, investigation in context offers the researcher, through a sometimes-brutal confrontation with exceptional situations, the possibility of greater theoretical creativity by opening to new objects and new research questions. In return, such research requires a more reactive modality of theoretical elaboration, with a constant reciprocal interaction between hypotheses and data production. Rather than developing these themes in an abstract way, we return to the investigation we conducted on the Syrian conflict. We analyse in particular the difficulties of access, the risks of selection bias, the problems associated with remote research and subcontracting of data production, and the advantages of conducting collective fieldwork.
APPROPRIACY OF PROCESS APPROACH IN THE TEACHING OF COMPOSITION WRITING IN UPPER PRIMARY CLASSES IN KENYA

Imaginative composition writing skills (IMW) in Kenyan primary schools strengthen learners’ learning, thinking and reflect on their overall academic performance. However, achieving good composition writing is challenging especially to non-native learners of English because of its complexity and nature. Some factors attributed to learners’ writing difficulties are: the curriculum, the pedagogic approach used and teachers’ lack of ability in writing instruction. These factors are in tandem with the ICW status in Vihiga County where over (70%) of Class 8 learners’ composition score are unsatisfactory, 60 % of teachers are uncomfortable to teach ICW while 75% of learners find it boring. To address these ICW difficulties, this study assessed the process approach out of the three principal writing approaches because it’s seen as the best alternative to product approach and that genre approach is considered a newcomer. The objective of the study was to assess the effectiveness of process approach in developing composition writing. Archer’s theory of reflexivity guided the study which used qualitative exploratory research design and was conducted in Vihiga County. Data collection tools were Lesson Observation and Interview Schedules. Validity and reliability were tested through triangulation and thick description. From Class 6-8, 30 lessons in 10 purposively selected schools were observed and 30 teachers interviewed. Data were analyzed thematically through transcription, coding and identification of themes. The key finding was: ineffective utilisation of the process approach due to teachers’ knowledge gaps on the approach. The study recommended teachers to use process approach effectively in ICW and the Ministry of Education to in-service teachers on writing approaches.
Exerted force estimation using a wearable sensor during manual material handling

While evaluating the physical workload on workers, the force exerted needs to be estimated in a convenient way because biomechanical analysis for physical workload evaluation requires data on both posture and exerted force. The aim of this study was to investigate the effectiveness of exerted force estimation using a wearable device that can be easily utilized even by ordinary workers. We measured eight electromyograms (EMGs) on the forearm and the forearm posture with an armband type wearable sensor during manual material handling with varying holding weights and heights. The measurement results showed that the EMGs monotonically increased with an increase in weight. In addition, the EMGs varied with the holding height even when the same weight was held. We constructed an estimation function of the weight using multiple regression analysis. Two sets of explanatory variables were used to investigate the effectiveness of the forearm posture data: the eight EMGs (i.e., SET 1) and the eight EMGs and the two forearm angles (i.e., SET 2). Multiple regression analysis showed that the accuracy of SET 2 was better than that of SET 1. In addition, the average absolute error of the estimation function with SET 2 was 1.49 kg; thus, we concluded that the accuracy of this estimation function has sufficient accuracy for the evaluation of physical workload.