Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Cost IEEE 802.15.7 Communication System Based on Organic Photodetection for Device-to-Device Connections

In this article, we compare two different kinds of commercial light-emitting diodes (LEDs) in transmission and organic photodetectors based on poly(3-hexylthiophene) (P3HT) and a phenyl-C61-butyric acid methyl ester (PCBM) blend used as active layer in reception. Photovoltaic cells based on massive heterojunctions of semiconductor polymers have focused the attention of researchers due to their several potential advantages over their inorganic counterparts, such as their simplicity, low cost, and ability to process large area devices, even on flexible substrates. Furthermore, in logistics, storage management systems require the implementation of technological solutions that allow the control of merchandise in real time by means of light-emitting diode signals that send information about the product. However, the slow response time of these organic photodetectors should not be critical for this application, where the light intensity changes are very slow, which limits the speed of data transmission compared to inorganic based systems that use wireless optical communications. Finally, we show a low-cost visible light communication system based on organic photodetectors with a frame based on on-off keying with Manchester encoding to support device-to-device connections.
Trends in diabetes self‐management education: where are we coming from and where are we going? A narrative review

To summarize the history, development and efficacy of diabetes self‐management education on glycaemic control and mental health in adults and children or adolescents with type 1 diabetes and people with type 2 diabetes. A further aim was to review the status of implementation of diabetes self‐management education into routine care and outline current gaps in implementation and research.
Ammonia Toxicity Degrades Animal Health, Growth

Ammonia nitrogen consisting of un-ionized ammonia (NH3) and ammonium ion (NH4 +) occurs in waters of aquaculture production systems as a waste product of protein metabolism by aquatic animals and degradation of organic matter by bacteria and other microorganisms. Ammonia nitrogen also reaches ponds in nitrogen fertilizers such as ammonium sulfate, ammonium phosphate and urea that hydrolyze to produce ammonia nitrogen. The proportion of ammonia nitrogen existing as NH3 increases as water temperature and especially pH increase (Table 1). Salinity decreases the proportion of NH3 at a given pH and temperature, but the effect is not great. For example, at pH 8 and 25° C, the contributions of un-ionized ammonia nitrogen (NH3N) to ammonia nitrogen at different salinities are: freshwater, 4.90%; 5 ppt salinity, 4.93%; 10 ppt, 4.78%; 15 ppt, 4.63%; 20 ppt, 4.48%; 25 ppt, 4.34%; 30 ppt, 4.20%; 35 ppt, 4.07%. Most of the waste nitrogen in aquatic animals is transported in the blood to the gills, where it diffuses into the water as NH3. When NH3 concentration is low in the surrounding water, there is a high concentration gradient to facilitate loss of ammonia from animal blood to the water. An increase of NH3 in the water decreases the gradient, resulting in a higher concentration of NH3 in the blood and leading to adverse physiological consequences that can be lethal if the NH3 concentration becomes excessive.
ON THE INFINITE DIVISIBILITY OF SCALE MIXTURES

The paper contains a new and elementary proof of the fact that if α ∈ (0, 1] then every scale mixture of a symmetric α-stable probability measure is infinitely divisible. This property is known to be a consequence of Kelker’s result for the Cauchy distribution and some nontrivial properties of completely monotone functions. It is known that this property does not hold for α = 2. The problem discussed in the paper is still open for α ∈ (1, 2).
Experimental investigation of dynamic contact angles on adaptive surfaces

Dynamic wetting phenomena appear in many industrial and natural processes and their understanding is important for the improvement of material design in many fields. When a liquid wets a surface, it is possible that the contact changes the wetting properties of the surface e.g. by polymer swelling or molecular reorganisation. A theory for the dynamic contact angle hysteresis (CAH) on adaptive surfaces was proposed by Butt et al. In the present work, a high-speed goniometer was built to measure dynamic contact angles at velocities from less than 1 mm/s to more than 20 cm/s. In the future, this setup can be used in combination with other methods to verify if the adaptive wetting theory is correct. The setup consists of an adjustable tilted plate, a high-speed camera and a backlight illumination. Two modes of adaptation can be investigated on the setup: velocity dependent contact angles for fast adaptation time scales and drop-wise adaptation for slow adaptation time scales. The functionality of the setup was verified by measuring the dynamic CAH on two testing materials, namely fluorosilane and poly(dimethylsiloxane)(PDMS) brushes. The measurements were compared to the theories of adaptive and non-adaptive wetting. The molecular kinetic theory and adaptive wetting theory have shown good fitting performance. Additionally, drop-wise adaptation was investigated by measuring velocity and contact angles over a series of drops on PDMS. The velocity was found to increase or decrease over the drop number. The increasing velocity can be explained by a significant increase of contact angles that was observed. Exposing the surface to air for a few minutes or the use of an ionising air blower can reverse the observed changes in velocity to a great extent, but not completely. Additional experiments proved that drop charging occurs and also shows drop-wise dependence. It is not clear if this effect is strong enough to explain the change in contact angle, but it is a promising starting point for future investigations.
Rethinking Semantic Segmentation for Table Structure Recognition in Documents

Based on the recent advancements in the domain of semantic segmentation, Fully-Convolutional Networks (FCN) have been successfully applied for the task of table structure recognition in the past. We analyze the efficacy of semantic segmentation networks for this purpose and simplify the problem by proposing prediction tiling based on the consistency assumption which holds for tabular structures. For an image of dimensions H × W, we predict a single column for the rows (ŷ_row ∊ H) and a predict a single row for the columns (ŷ_row ∊ W). We use a dual-headed architecture where initial feature maps (from the encoder-decoder model) are shared while the last two layers generate class specific (row/column) predictions. This allows us to generate predictions using a single model for both rows and columns simultaneously, where previous methods relied on two separate models for inference. With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13 image-based table structure recognition dataset with an average F-Measure of 92.39% (91.90% and 92.88% F-Measure for rows and columns respectively). With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13. The obtained results advocate that constraining the problem space in the case of FCN by imposing valid constraints can lead to significant performance gains.
A Method for Generation of Random Lattice Structure for Additive Manufacturing

Additive manufacturing technologies provide us new opportunities to fabricate a product with much more complex geometry and architecture that conventional manufacturing ones such as turning, milling, grinding, casting and molding do not have the ability to implement. Hence, lattice structures are becoming more popular and used extensively in the industrial product design to minimize the usage of materials due to the additive manufacture technologies. Lattice structures are formed by the cellular unit of structure repeated regularly or irregularly in a design area. However, the creation and generation of these structures for additive manufacturing in the current CAD environment have many difficulties. Therefore, the paper will propose a new approach to design the lattice structure, especially the random one. This approach allows helping product designers to design and generate lattice structure randomly based on the Voronoi diagrams.
Generating Synthetic Handwritten Mathematical Expressions from a LaTeX Sequence or a MathML Script

Collecting handwritten mathematical expressions (HMEs) generally requires a lot of time and effort for data preparation, data collection, annotation and so on. In this paper, we present a method for generating realistic HMEs in a wide variety of structures and styles from a LaTeX sequence or a MathML script using online isolated symbol patterns. Our method firstly positions all symbols in a symbol relation tree constructed from the input LaTeX or MathML script. Then, it places normalized online symbol patterns into the corresponding locations. A questionnaire-based experiment shows that the synthetic patterns are as clear and natural as the real patterns. Therefore, we can use the generated synthetic HME patterns for research on HME recognition and clustering.
Instance Aware Document Image Segmentation using Label Pyramid Networks and Deep Watershed Transformation

Segmentation of complex document images remains a challenge due to the large variability of layout and image degradation. In this paper, we propose a method to segment complex document images based on Label Pyramid Network (LPN) and Deep Watershed Transform (DWT). The method can segment document images into instance aware regions including text lines, text regions, figures, tables, etc. The backbone of LPN can be any type of Fully Convolutional Networks (FCN), and in training, label map pyramids on training images are provided to exploit the hierarchical boundary information of regions efficiently through multi-task learning. The label map pyramid is transformed from region class label map by distance transformation and multi-level thresholding. In segmentation, the outputs of multiple tasks of LPN are summed into one single probability map, on which watershed transformation is carried out to segment the document image into instance aware regions. In experiments on four public databases, our method is demonstrated effective and superior, yielding state of the art performance for text line segmentation, baseline detection and region segmentation.
Deep Network with Pixel-Level Rectification and Robust Training for Handwriting Recognition

Offline handwriting recognition is a well-known challenging task in the optical character recognition field due to the difficulty caused by various unconstrained handwriting styles and limited training data. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN- and RNN-based recognizer. We also propose an adjacent output mixup method for RNN layer’s training to improve the generalization ability of the recognizer, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model to significantly expand the training data scale, and further analyze their contributions to the improvement in the model performance. The proposed method performs well on four public offline handwriting benchmarks, including the IAM, Rimes, IFN/ENIT and CASIA-HWDB datasets.
Analysis of Retailer's Order Decision with the Allowance of ACC Payment Based on Supply Chain Financing

Supply chain financing is an efficient and effective channel for small and medium-sized enterprises to obtain the short-term capital and keep the sustainability of the supply chain. This study focuses on the optimal ordering strategy of the retailer with limited capital and random demand of customers, and combines the ACC payment model with supply contracts. The numerical analysis supported the feasibility of the model. Finally this work provides some possible issues for future research.
Optimal Control of Blank Holder Force Based on Deep Reinforcement Learning

In deep drawing, reasonable control of blank holder force is the key to the quality of finished products. Traditional blank holder force control methods often need to model the highly non-linear deep drawing process. Because it is difficult to obtain an accurate system dynamics model, the results of traditional methods deviate from the actual situation. In this paper, a blank holder force control model based on the integration of deep reinforcement learning and finite element analysis is proposed. The blank holder force control policy is optimized by combining the perception ability of deep neural network with the decision-making ability of reinforcement learning, which avoiding the fitting of system dynamics. Firstly, an algorithm of blank holder force optimization based on deep reinforcement learning is proposed. The deep neural network is used to deal with the large state space. Secondly, by using a new network structure to construct the policy network, the blank holder force policy is divided into global part and local part, which effectively improves the control effect of policy. Experiments show that the proposed control model can effectively optimize blank holder force control policy and improve product quality compared with the traditional deep reinforcement learning algorithm.
Retrospective study of acute spinal cord injury between 2005 and 2007 in Pudong New Area,Shanghai

Objective To understand the incidence,medical process and specialized management of acute spinal cord injury(ASCI) in Pudong New Area,Shanghai.Methods All the medical records of ASCI between 2005 and 2007 in the first-class and second-class hospitals in Pudong New Area were retrospectively studied.Results A total of 200 cases were included in the study.The annual incidence of ASCI in Pudong New Area was 25 per million;and the majority of patients were middle-aged construction workers,hence it affected the labor force.The highest hospital expense was 300 thousand RMB,and the total expense added up to 9.67 million RMB.The average daily cost was 2,026 RMB,and the per capita cost was 48,300 RMB.Among the 117 cases who reached hospitals in 8 hours after ASCI occurred,only 32 of them were treated with Methylprednisolone at once.Totally,114 cases underwent surgeries,in which the mean operation time was 3.2 hours,the intraoperative blood loss averaged 538 ml(200~3 000 ml) and the mean blood transfusion was 731 ml(100~2 600 ml). However,the assessment of efficacy in the original medical records was not clear.Conclusion The incidence of ASCI has a tendency to increase,and it is of great importance for the treatment of ASCI patients to enhance the correct pre-hospital care at the medical emergency centers and to improve the management process of hospital emergency staff.In addition,the diagnosis,assessment and information recording of ASCI patients remain to be further upgraded.
A Comparison Between SEIADR versus SEIR Discrete Epidemic Models *

This paper proposes, studies and discusses in some detail discrete epidemic models of SEIADR (susceptible-exposed-symptomatic infectious- asymptomatic infectious-recovered, or immune- dead- infectious-recovered) and SEIR (susceptible-exposed-symptomatic infectious-recovered) continuous in time models. The first one incorporates the asymptomatic infectious and the lying infective bodies as infectious extra populations on the standard populations of SEIR type models. Several controls are proposed in the general case as, for instance, vaccination treatment and the removal of the lying infective corpses. The control rules can optionally include feedback information.
Solving Deficit Funding Issues in Indonesian Health Insurance Systems

Indonesian National Health Insurance System (INHIS) is a government program that aims to provide health insurance to citizens by guaranteeing their health services. However, INHIS finances continued to experience a deficit since 2014. The purpose of this study was to find a solution so that the stock of funds remained stable. This is done by changing the patient's referral mechanism in INHIS. This is one solution so that the total health care costs do not increase. In this case the dynamic system simulation model is needed to find the relationship pattern between variables so that it can estimate the condition of INHIS funds. The decision variables in this study are the total health care costs and the stock of funds. From the simulation process, the results show that the proposed model can reduce the total health care costs by 44% and be able to maintain a stable supply of funds up to 40% of the sudden increase.
Preventing foot ulcers in patients with diabetes

Context:Among persons diagnosed as having diabetes mellitus, the prevalence of foot ulcers is 4%to 10%, the annual population-based incidence is 1.0%to 4.1 %, and the lifetime incidence may be as high as 25%. These ulcers frequently b ecome infected, cause great morbidity, engender considerable financial costs, an d are the usual first step to lower extremity amputation. Objective:To systemat ically review the evidence on the efficacy of methods advocated for preventing d iabetic foot ulcers in the primary care setting. Data Sources, Study Selection, and Data Extraction:The EBSCO, MEDLINE, and the National Guideline Clearinghous e databases were searched for articles published between January 1980 and April 2004 using database-specific keywords. Bibliographies of retrieved articles wer e also searched, along with the Cochrane Library and relevant Web sites. We revi ewed the retrieved literature for pertinent information, paying particular atten tion to prospective cohort studies and randomized clinical trials. Data Synthesi s:Prevention of diabetic foot ulcers begins with screening for loss of protecti ve sensation, which is best accomplished in the primary care setting with a brie f history and the Semmes-Weinstein monofilament. Specialist clinics may quantif y neuropathy with biothesiometry, measure plantar foot pressure, and assess lowe r extremity vascular status with Doppler ultrasound and ankle-brachial blood pr essure indices. These measurements, in conjunction with other findings from the history and physical examination, enable clinicians to stratify patients based o n risk and to determine the type of intervention. Educating patients about prope r foot care and periodic foot examinations are effective interventions to preven t ulceration. Other possibly effective clinical interventions include optimizing glycemic control, smoking cessation, intensive podiatric care, debridement of c alluses, and certain types of prophylactic foot surgery. The value of various ty pes of prescription footwear for ulcer prevention is not clear. Conclusions:Sub stantial evidence supports screening all patients with diabetes to identify thos e at risk for foot ulceration. These patients might benefit from certain prophyl actic interventions, including patient education, prescription footwear, intensi ve podiatric care, and evaluation for surgical interventions.
Discourse Descriptor for Document Incremental Classification Comparison with Deep Learning

We propose both a new strategy to weight a text vector for document classification and a comparison of a deep learning approach versus an incremental classification approach, integrating our novel strategy. Bag-of-word vectors are classic approaches to describe a textual document in document classification objective. A weakness of the bag of words is to lose the organization of the discourse within the document. Inspired by some Deep Learning approaches and Natural Language Processing for text classification, we suggest a simple strategy, featuring the terms according to their relative positions within the discourse sequence. For experimentations, we apply this strategy to a recent document incremental classification approach from the state-of-the-art. And, we propose an original comparison between Incremental learning and Deep learning, by comparing the incremental system with a CCN-RNN-based approach. It demonstrates that both approaches are competitive in similar contest.
Research on Strategic Leading Mechanism of Latecomer Firms

The purpose of this paper is to analyze how the strategy of latecomer firms leads innovation and promotes the accumulation and improvement of capability in its development with an analytical framework of “strategy-innovation paradigm-capability” and tries to sum up the internal evolution logic of strategy. Based on this, it makes a longitudinal case study of Haier Group from 1984 to 2018. Through the research, it constructs the L.S. I.C model, which reveals the key role of the strategy in the innovation and capability in the catch-up process of latecomer firms: strategy leads innovation. Innovation is the basis of forming capability. Different innovation paradigm forms different enterprise capability. At the same time, the mechanism of strategic leading innovation is supported by leadership mechanism, learning mechanism and coordination mechanism. The contributions of this paper are as follows: it enriches the theoretical basis of strategic evolution, makes a pioneering analysis of the relationship between strategy, innovation and capability, and probes into the national technology policy.
Sharing Personal Failure Story in Organization: Sharing with Individual or Organization?

Learning from failure in organization is important. There are, however, few organizations which are good at it. There are difficulties to disclose your failure experience to your colleagues in organization because of stigma, guilty feelings, shame and other disadvantages. Nevertheless, there was a company in Japan, which was good at learning from failure. Authors employed the company to examine whether failure-story sharing with a particular coworker induced failure-story-sharing with anonymous members in organization and/or other particular coworker. Authors found that failure-story-sharing with a particular coworker induced directly failure-story-sharing with other particular coworker, and induced one with anonymous members in organization, partially mediated by work-value-sharing in organization. And Authors also found that the work-value-sharing in organization did not matter with your own behavior of failure-story sharing with a particular coworker.
A Generic Knowledge-based Model for Commercial Offers: Towards a Unified Model to Configure Products, Services and PSS During Calls for Tenders

Today, consumption patterns are changing: firstly, customers (private and industrial ones) want more and more products and services that can be personalized to their needs, and secondly, they are more and more willing to pay for usage of a product rather than ownership. Companies have therefore to adapt their catalog of solutions by putting on the market customizable and suitable solutions going from products to services, including all their possible combinations. The aim of this article is to propose a generic knowledge-based model, dedicated to commercial offers configuration, which can cope with all the diversity of solutions a company can deliver. Up to our knowledge, even if some works on product, service and product-service system exist, none is generic enough to support such commercial offer configuration while bidding. In this paper, after a brief state-of-the-art, the need of a generic model is set up. Then, a unified model is proposed and illustrated on use-cases coming from industrial situations.