Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of ZnO, Al2O3, AlZnO, and Al2O3-Doped ZnO Sensing Membrane Applied in Electrolyte-Insulator-Semiconductor Structures

In this study, ZnO, AlZnO, Al2O3, and Al2O3-doped ZnO-sensing membranes were fabricated in electrolyte–insulator–semiconductor (EIS) structures. Multiple material analyses indicate that annealing at an appropriate temperature of 500 °C could enhance crystallizations, passivate defects, and facilitate grainizations. Owing to their material properties, both the pH-sensing capability and overall reliability were optimized for these four types of membranes. The results also revealed that higher Al amounts increased the surface roughness values and enhanced larger crystals and grains. Higher Al compositions resulted in higher sensitivity, linearity, and stability in the membrane.
Editorial: Established and Novel Roles of Platelets in Health and Disease

1 Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada, Department of Pharmacology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada, Cardiovascular Research Centre, University of Alberta, Edmonton, AB, Canada, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada, Department of Haematology Research, Murdoch Children’s Research Institute, Melbourne, VIC, Australia, Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia, 7 Research Center, Montreal Heart Institute, Montreal, QC, Canada, 8 Faculty of Pharmacy, Université de Montréal, Montreal, QC, Canada
Interactive Deep Learning for Exploratory Sorting of PlantImages by Visual Phenotypes

This paper proposes an interactive system called Andromeda 1 that enables users to interact with machine learning models to allow for exploratory sorting of images through a cognitive approach that uses a reduced dimension plot. In our system, a dimension reduction algorithm projects the images into a 2D space representing similarities between the images based on visual features extracted by a deep neural network. With Andromeda, users can alter the projection by dragging a subset of the images into groups according to their domain expertise. The underlying machine learning model learns the new projection by optimizing a weighted distance function in the feature space, and the model re-projects the images accordingly. The users can explore multiple custom projections to learn about the visual support for di ↵ erent groupings based on explainable-AI feedback. Our approach incorporates user preferences into machine learning model construction and allows transfer learning from pre-trained image processing models to accomplish new tasks based on user inputs. Using edamame pod images as an example, we interactively re-project the images into di ↵ erent groupings based on maturity and disease, and identify important visual features from the pixels highlighted by the model.
Competing through coopetition: A strategy for success in hotel marketing consortia

Coopetition is a strategy for a company to achieve a competitive advantage in the market environment. However, little is known about how coopetition works in the hotel sector and which organizational departments perform better in terms of coopetitive behaviour. In this research, coopetition is analysed in the context of international hotel marketing consortia (HMC) operating in Portugal. Exploratory interviews and a survey were conducted to consortia members to analyze managers’ perceptions on coopetitive behaviour and the organizational departments engaged in coopetition. The results show that there is higher cooperation than competition in the marketing and research & investigation departments. The research findings support consortia managers to make an informed decision to augment or reduce coopetition inside and outside companies based on two dimensions: internal and external. Internally by enhancing cooperation and reducing competition behaviour according to relationships observed between members; externally, by formulating strategies for the future, thereby increasing the intensity of coopetition according to expansion goals. Finally, theoretical and managerial contributions are presented, followed by recommendations for further research.
PIMCloud: QoS-Aware Resource Management of Latency-Critical Applications in Clouds with Processing-in-Memory

The slowdown of Moore’s Law, combined with advances in 3D stacking of logic and memory, have pushed architects to revisit the concept of processing-in-memory (PIM) to overcome the memory wall bottleneck. This PIM renaissance finds itself in a very different computing landscape from the one twenty years ago, as more and more computation shifts to the cloud. Most PIM architecture papers still focus on best-effort applications, while PIM’s impact on latency-critical cloud applications is not well understood.This paper explores how datacenters can exploit PIM architectures in the context of latency-critical applications. We adopt a general-purpose cloud server with HBM-based, 3D-stacked logic+memory modules, and study the impact of PIM on six diverse interactive cloud applications. We reveal the previously neglected opportunity that PIM presents to these services, and show the importance of properly managing PIM-related resources to meet the QoS targets of interactive services and maximize resource efficiency. Then, we present PIMCloud, a QoS-aware resource manager designed for cloud systems with PIM allowing colocation of multiple latency-critical and best-effort applications. We show that PIMCloud efficiently manages PIM resources: it (1) improves effective machine utilization by up to 70% and 85% (average 24% and 33%) under 2-app and 3-app mixes, compared to the best state-of-the-art manager; (2) helps latency-critical applications meet QoS; and (3) adapts to varying load patterns.
Energy based control of flexible optical fiber using the port Hamiltonian framework

The AS2M department of FEMTO-ST Institute, specialized in microrobotics and micro-mechatronics investigate since more than 10 years on the design, modeling, fabrication, and control of microsystems and microrobots. Various systems have been achieved for micro-assembly and biomedical applications with smart actuators, sensors, and in a small volume. In addition, different methods were developed for improving the performances of microrobotics systems: feedforwards, closed-loop control through H infinity, predictive control, sliding mode, energy shaping, etc. In the framework of computer-assisted surgery, the continuum robots have shown their ability to reach some areas difficult to access. They can be categorized in terms of their structural design and actuation strategy [1]. In this project, we have developed a piezoelectric actuated optical fiber for the scanning tasks [2]. The main challenge of this project is the boundary control problem of the optical fiber with high flexibility, strong nonlinear behaviors and the multi physical fact of the interconnection with the piezoelectric tube actuator.
Assessing the clinician’s role in diabetic foot ulcers: from pre-ulceration through post-healing

Authors Gary M. Rothenberg is Clinical Assistant Professor, The University of Michigan Medical School, Ann Arbor, Michigan USA ; Sari J. Priesand is Clinical Assistant Professor, The University of Michigan Medical School, Ann Arbor, Michigan USA; Crystal M, Holmes is Podiatry Program Director & Clinical Associate Professor, The University of Michigan Medical School, Ann Arbor, Michigan, USA; Brian M. Schmidt is Clinical Assistant Professor, The University of Michigan Medical School, Ann Arbor, Michigan USA Diabetic foot ulcers (DFU) are a devastating complication of diabetes mellitus. The importance of appropriate management of diabetic foot ulcers cannot be understated and this includes prevention of future complications. Understanding the role of the provider at each stage of diabetic foot complications is essential to limit the moribund complications associated with this condition. Too often, patients are treated for a wound and then are lost to follow up and true longitudinal care is lacking. The result can be reulceration, re-amputation and potentially unnecessary hospitalisations. The hallmarks of each stage (pre-ulcerative, ulcerative and postulcerative) are highlighted. The authors provide a framework to develop and implement comprehensive diabetic foot care, no matter the stage of presentation for an individual patient. In doing so, this systematic approach aids providers in optimal management and will mitigate the risk of diabetic foot ulceration.
A Missing Link between Fidelity and Realism: an ex- perts’ assessment about an advanced motion-based driv- ing simulator

A major concern about advanced motion-based simulators is their level of fidelity i.e., how close the motion sensation in a simulator is to the one perceived in a real vehicle. In this study, we collect the assessment from an exceptional sample composed by n=33 automotive industry experts who were asked to evaluate the fidelity in terms of steering, braking, and speed. Given the subjective nature of our measure, we propose a censored-data Tobit regression model that accounts for this issue, thus providing more accurate estimations. Our results show that, on average, experts evaluated the steering actions close to the maximum level of fidelity. However, braking and speed were evaluated lower in realism, and in fact, both diminished the overall fidelity judgement up to 50%. Moreover, coefficients indicate that steering contributes more to the judgment on fidelity than braking and speed actions. Heterogeneity in the experts’ responses and general implications are discussed.
Aldosterone regulation of active sodium chloride transport in the lizard colon (<Emphasis Type="Italic">Gallotia galloti</Emphasis>)

Bioelectrical parameters and unidirectional sodium and chloride fluxes were measured under voltageclamp conditions in groups of lizards submitted to single or chronic aldosterone treatment. Both acute (AT) and chronic (CT) treatment induced significant increases in the short-circuit current (/~o), as well as in the mucosa-toserosa (J~"-s) and net sodium flux (J~,~). In AT tissues, aldosterone did not change net chloride flux (~ l 0 but did so in CT tissues. Amiloride reduced the aldosteroneincreased I~o in AT and CT tissues, inhibited J ~ in AT tissues and abolished it in CT colons. ~ t was also reduced by the diuretic in the group of AT colons, whereas no changes were observed in the CT tissues. Addition of luminal DIDS reduced Na + absorption and totally inhibited C1absorption in the AT tissues, but did not change I~. However, in CT tissues neither Na + nor C1transport were affected by DIDS. A good relationship between Isc and JNm~_ s was apparent after DIDS treatment in AT tissues. In this group, simultaneous addition of DIDS and amiloride totally abolished j~a and reduced Iso to untreated control values. Addition of serosal ouabain abolished I~c and Na + absorption in AT and CT colons, but C1absorption was only altered in AT tissues. These results support the hypothesis that aldosterone induces an electrogenic, amiloride-sensitive sodium absorption, and in a dose-dependent fashion suppresses electroneutral NaC1 absorption in the lizard colon.
PERSONALITY IDENTIFICATION BASED ON MBTI DIMENSIONS USING NATURAL LANGUAGE PROCESSING

: Personality means prevailing characteristics, habits, and behaviors that encourage a person to react, communicate, and think in a unique direction. It dictates, how a person acts, communicates, responds, and establishes his/her desires. Personality analysis makes a person unique in terms of his/her personality traits. Personality analysis can be beneficial in several real-life scenarios such as good or service and course recommendations, candidate screening, advertising, health advice, relationship counseling, and much more. In this research work, our main objective is to create a model that can analyze personality attributes of an individual which is based on named entity identification and a combination of feature engineering techniques including TF-IDF count vectorization and word2vec embedding. The most popular and open source Myers-Briggs Type Indicator (MBTI) dataset consisting of more than 8500 user-written posts has been used for evaluation purposes. We have implemented the most widely used and well-known ensemble learning models for experimentation consisting of Bagging, Boosting, and Stacking. A comparative analysis of the predictions made by the Ensemble Learning Algorithms has been carried out to determine the best overall performance. The results demonstrate that the stacking model delivers the best performance with an accuracy of 97%.
PHYSICAL REVIEW E 104, 034130 (2021) Stationary solution and H theorem for a generalized Fokker-Planck equation

Max Jauregui,1,* Anna L. F. Lucchi ,2,3 Jean H. Y. Passos,2,3 and Renio S. Mendes2,3 1Departamento de Matemática, Universidade Estadual de Maringá, Av. Colombo, 5790 CEP 87020-900 Maringá PR Brazil 2Departamento de Física, Universidade Estadual de Maringá, Av. Colombo, 5790 CEP 87020-900 Maringá PR Brazil 3National Institute of Science and Technology for Complex Systems, Rua Xavier Sigaud 150, 22290-180 Rio de Janeiro, Brazil
Review on Performance Evaluation of Green Innovation in Enterprise Logistics

Facing the pressure of energy conservation and emission reduction under the global climate change and the environmental pollution and energy constraints under the rapid development of domestic economy, the road of low-carbon, green and ecological development and the realization of sustainable development have become the strategic guidance of China's economic transformation and development reform. As the "13th Five -Year" economic development mode transformation battle is about to start, enterprises have to integrate "green" elements in the process of formulating strategies to meet the increasingly fierce competition in the global market. As an important means of enterprise logistics performance management, the construction of logistics performance evaluation index system adapted to the new requirements of green development has become an urgent issue. Green innovation in logistics reflects the integration of green development and innovation-driven development, and plays an important role in economic development and environmental protection. Green development is an inevitable choice for transformation and upgrading in the new situation and enhancing international competitiveness. Establishing a correct and feasible evaluation index system of logistics green innovation plays an important role in assessing and mastering the development of enterprises, industries and regions. On the basis of sorting out the relevant concepts of green innovation, this paper reviews the research status of green innovation performance from the aspects of index system, research methods and influencing factors. Literature review found that: (1) The green innovation performance of existing literature is mostly concentrated in China's industry, manufacturing industry or enterprises whose energy consumption has a large impact on the environment, and there are few studies on logistics, service industry, emerging industries and modern agriculture. (2) There is no targeted evaluation index system in each specific industry. (3) There are more external factors influencing the development of green innovation in logistics than internal factors, and the interaction between each factor is not specified.
Laboratory of Economics for the Assessment of Environmental Impact Caused by Industrial Activities—Implementation of the WTP-WTA Approach

In this work, a methodological framework has been developed in the form of an algorithmic process, which includes 28 stages of activities and 6 decision nodes, for the assessment of the environmental impact caused by industrial activities. The main part of this process is a modification of the Contingent Valuation Method (CVM), which relied heavily on research based on the WTP-WTA (Willingness to Pay/Receive, respectively) assessment. The research can be carried out either in a strictly controlled environment or on site and as is (“in laboratory conditions” or “real conditions”, respectively, in the terminology of experimental economics). The application of this methodology is presented, referring to three cases of industrial pollution (in three different locations) caused by (i) a cement plant, (ii) an oil refinery complex, and (iii) a small industrial town with a heavily polluted port, in which several industrial units coexist, while all of them are located near Athens, Greece. The results are interpreted/discussed and conclusions are drawn.
2.5D-VoteNet: Depth Map based 3D Object Detection for Real-Time Applications

We address the 3D object detection task by capturing features directly on depth maps with a 2D CNN. Most existing 3D object detection methods take point clouds as input, even when each point cloud is converted from a single depth map. Although they have achieved impressive performance, point cloud based 3D detectors usually have high computational cost and complex structure, which limits their application on mobile devices and in real-time scenarios. Building on the state-of-the-art VoteNet [29], we propose 2.5D-VoteNet, a powerful and efﬁcient depth map based 3D detection pipeline. Since our models extract features directly on depth maps, most computation remains in 2D space and can be efﬁciently executed. Instead of using an off-the-shelf 2D CNN, we introduce relative depth convolution (RDConv) to learn robust local features. Our end-to-end pipeline achieves state-of-the-art results on the challenging SUN RGB-D [39] benchmark and surpasses the baseline with a clear margin on ScanNet [8] frame-level detection task. Meanwhile, our method reaches a signiﬁcantly higher inference speed than existing methods (69 FPS).
Enterprise Profit Optimization : The Next Wave in Value Creation Leading Edge 5 FULFILLMENT

Visionary executives have long dreamed of maximizing the return from their assets through the end-to-end optimization of their business. The development and continued evolution of Supply Chain Management solutions and the advent of demand-side solutions such as Pricing and Revenue Optimization have enabled the optimization of many parts of the value chain, improved their coordination, and brought end-to-end optimization tantalizingly close. It is now within reach. For the first time, there is a solution that drives profitable growth through the simultaneous optimization of the supply-side and demand-side functions – both within a company and throughout its trading network. Known as Enterprise Profit OptimizationTM (EPO), it is likely to create the next wave in enterprise management. Combining the proven power of cost reduction solutions with the revolutionary breakthroughs of emerging Pricing and Revenue Optimization tools, EPO integrates disparate parts of the value chain into a powerful engine of efficiency and profit enhancement. With corporate profits under intense pressure, such a solution couldn’t come at a more opportune time. Increased business complexity in online and off-line channels, and increased market competition pose continuing threats to profit margins. Investors are forcing eBusinesses to accelerate their plans to reach and enhance profitability. In order to improve bottom-line results in this pressurized environment, companies must drive improvements in operational efficiency and simultaneously find ways to achieve profitable revenue growth. Moving far beyond uncoordinated attempts to optimize the demand and supply sides of a business, Enterprise Profit Optimization looks across the entire enterprise to enhance profitability. When mismatches of supply and demand occur – as they always do – they should not be viewed strictly as a supply chain problem or a pricing problem, but rather as an opportunity for Enterprise Profit Optimization. By tightly integrating pricing and marketing actions on the demand side with the complex and ever changing conditions of the supply chain, EPO ensures that you make the most profitable use of all of your critical assets. Coordination becomes real synergy because functions across the organization are unified around a common goal: ensuring that the right products are offered to the right customers, through the right channels, at the right price in order to enhance profit – all at maximum efficiency.
LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.
Mapping interstellar dust with Gaussian processes

Interstellar dust corrupts nearly every stellar observation, and accounting for it is crucial to measuring physical properties of stars. We model the dust distribution as a spatially varying latent field with a Gaussian process (GP) and develop a likelihood model and inference method that scales to millions of astronomical observations. Modeling interstellar dust is complicated by two factors. The first is integrated observations. The data come from a vantage point on Earth and each observation is an integral of the unobserved function along our line of sight, resulting in a complex likelihood and a more difficult inference problem than in classical GP inference. The second complication is scale; stellar catalogs have millions of observations. To address these challenges we develop ziggy, a scalable approach to GP inference with integrated observations based on stochastic variational inference. We study ziggy on synthetic data and the Ananke dataset, a high-fidelity mechanistic model of the Milky Way with millions of stars. ziggy reliably infers the spatial dust map with well-calibrated posterior uncertainties.