Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.
Estudio de la legislación del Internet de las cosas en México

El internet de las cosas actualmente se ha proliferado de manera extraordinaria en el mundo. En México se han hecho esfuerzos por cubrir los aspectos legales que implica el uso de tan importante tecnología, tratando de solventar los vacíos existentes, ya que el avance de la tecnología ha rebasado el tiempo de legislación de las mismas. El objetivo de esta investigación es identificar el alcance de la legislación en el internet de las cosas partiendo de las generalidades, entorno, servicios y modelos de implementación en las organizaciones actuales. Se trata de una investigación documental y descriptiva, su principal contribución prevalece en la recopilación, interpretación de los aspectos legales del internet de las cosas aplicados a un caso real.
Design of highly compact self‐diplexing Y‐shaped slot antenna employing quarter‐mode substrate integrated waveguide

This communication reports the design of highly compact cavity‐backed self‐diplexing Y‐shaped slot antenna for high‐isolation employing quarter‐mode substrate integrated waveguide (QMSIW). The proposed antenna is built by using QMSIW, Y‐shaped slot and two 50 Ω feed lines. Two unequal radiating apertures are developed by inserting an Y‐shaped slot on the top side of the rectangular SIW for radiation at 3.9 and 4.9 GHz for long term evolution (LTE) and public safety band (PSB) applications, respectively. By keeping another band unaltered, the proposed antenna allows any frequency band to be tuned independently. The size of antenna is highly compact due to the use of QMSIW cavity. The isolation between two ports of the antenna is greater than 34 dB. The antenna achieves 5.43 and 5.85 dBi peak gains at 3.9 and 4.9 GHz, respectively. On both the frequency bands, the antenna efficiency is greater than 85%. The antenna has a front‐to‐back‐ratio of more than 20.6 dB and a separation from co‐to‐cross polarization level of more than 21 dB. Through manufacture and measurement, the designed antenna is validated.
Option for optimal extraction to indicate recognition of gestures using the self-improvement of the micro genetic algorithm

The hearing-impaired community uses gestures to communicate. Gestures can also be used in interactions between man and computer. However, gestures become increasingly complicated in a comparatively complex environment. A recognition algorithm with a choice of function based on the improved genetic algorithm is proposed to improve the ability to identify gestures. The recognition process includes retailing, extraction, and feeding functions before classifying the neural network. After learning gestures, the proposed method is compared with traditional methods that use the classic genetic algorithm. The proposed method demonstrates the effect of optimization and sensitivity of the function.
The Need for Basic, Translational, and Clinical Research in the Field of Hypertrophic Scars

Hypertrophic scar (HTS) is a fibrotic skin disorder that is marked by excessive inflammation and extracellular matrix deposition in response to cutaneous traumatic injuries such as burns, lacerations, incisions, and abrasions. HTS has various risk factors, available treatments, and treatment effectiveness. Research at the basic, translational, and clinical levels are in their infancy compared to fibrotic diseases in other organ systems. This chapter will review current in vitro and in vivo modeling, and highlight research needs to address gaps in the study of HTS. The following topics will be discussed in the chapter: a. Basic Science Research i. Seminal findings ii. Limitations to these models iii. Suggestions for topics of future research b. Translational Science Research i. Seminal findings ii. Limitations to these models iii. Suggestions for topics of future research c. Clinical Research i. Seminal findings ii. Limitations to these models iii. Suggestions for topics of future research.
Modification to L-H Kinetics Model and Its Application in the Investigation on Photodegradation of Gaseous Benzene by Nitrogen-Doped TiO2

In this paper, the Langmuir-Hinshelwood (L-H) model has been used to investigate the kinetics of photodegradation of gaseous benzene by nitrogen-doped TiO2 (N-TiO2) at 25 °C under visible light irradiation. Experimental results show that the photoreaction coefficient kpm increased from 3.992 × 10−6 mol·kg−1·s−1 to 11.55 × 10−6 mol·kg−1·s−1 along with increasing illumination intensity. However, the adsorption equilibrium constant KL decreased from 1139 to 597 m3·mol−1 when the illumination intensity increased from 36.7 × 104 lx to 75.1 × 104 lx, whereas it was 2761 m3·mol−1 in the absence of light. This is contrary to the fact that KL should be a constant if the temperature was fixed. This phenomenon can be attributed to the breaking of the adsorption-desorption equilibrium by photocatalytically decomposition. To compensate for the disequilibrium of the adsorption-desorption process, photoreaction coefficient kpm was introduced to the expression of KL and the compensation form was denoted as Km. KL is an indicator of the adsorption capacity of TiO2 while Km is only an indicator of the coverage ratio of TiO2 surface. The modified L-H model has been experimentally verified so it is expected to be used to predict the kinetics of the photocatalytic degradation of gaseous benzene.
Shopping addiction: A brief review.

Shopping is, and has long been, an important part of everyday life. It can easily take on the characteristics and symptoms of a behavioral addiction, such as preoccupation, mental appropriation as well as compulsiveness and loss of control. Thus, shopping addiction is becoming an increasingly important topic in research, especially in the context of the current pandemic (COVID-19). The pandemic has resulted in an increase in perceived risk factors. There is a plausible relationship between high perceived stress level along with perceived social isolation and extensive leisure time in shifting toward a pathological shopping behavior. While that does not only have a negative impact on functional but also on social and financial aspects, findings suggest that the threshold is also becoming lower due to changing social values, including changing self-conception and progressing digitalization that make online shopping more accessible. Although different reasons for an increasing prevalence rate are being discussed, the diagnosis conceptualization and classification is still unclear, leading to different and unstandardized therapeutic approaches. In addition to its impact on daily life, shopping addiction can indicate other mental illnesses. As it turned out, it is often comorbid in anxiety disorders, impulsive behaviors and substance abuse. An early and targeted treatment, as well as rising awareness, seem to be crucial and call for further investigation. While young people seem to be more at risk of developing critical shopping behavior, findings show no clear gender differences in frequency but differences in terms of buying motivation. Based on published research papers, the present narrative review therefore describes and critically discusses the phenomenon of shopping addiction and the current state of research in a broad variety of topics, considering types of shopping behavior, including Internet-based, offline, mood, impulse and leisure shopping, and diagnostic and therapeutic options in the context of influencing factors mentioned above. The topics were selected at the discretion of the authors, whereby no claim is made to be exhaustive. (PsycInfo Database Record (c) 2021 APA, all rights reserved) Impact Statement Shopping is and has long been an important part of everyday life. In the context of the current pandemic (COVID-19), shopping addiction is becoming an increasingly important topic in research. Its different forms make the topic so pervasive. The focus of this work is on shopping addiction as a behavioral addiction. This brief review focuses on diagnosis, different types, therapeutic approaches, and empirical studies of shopping addiction. This review provides a brief overview of various aspects of shopping addiction. It is aimed at experts who want to find out more about this topic. In addition, the work could also be of interest to relatives of those affected. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
IMPLICATIONS AND POTENTIAL ROLE OF HUMAN TELOMERASE REVERSE TRANSCRIPTASE (HTERT) IN BLADDER CARCINOGENESIS

Bladder cancer is a heterogeneous disease and ranks as 10th most common cancer worldwide. Urothelial carcinoma (UC) is the most common histologic type of BC and majority constitute of papillary tumors that are well-differentiated (low-grade). Several genetic changes may occur in bladder cancer, but hTERT promoter mutations and its expression has been detected in most cases of transitional cell carcinoma. Numerous researches have led to the findings which suggest that the hTERT promoter mutations in conjunction with the common polymorphism and hTERT expression have potential of being used as clinical biomarkers in bladder cancer. Further studies need to explore the potential use of hTERT gene in bladder cancer detection, diagnosis and prognosis. This review focuses on the role of hTERT in bladder tumors in the backdrop of various studies published.
A road extraction method for intelligent dispatching based on MD-LinkNeSt network in open-pit mine

ABSTRACT Road extraction in open-pit mine is one of the important tasks of intelligent dispatching and manless driving. However, traditional methods for extracting open-pit roads cannot capture detailed information about roads, such as shape and surrounding environment. An MD-LinkNeSt (Multi Dilation-LinkNeSt) network is proposed to extract the open-pit road. First, the classification of UAV remote sensing image in mining area was completed based on mine road features. Second, ResNeSt and LinkNet are used for the encoding and decoding of MD-LinkNeSt network. The MD module includes encoding and decoding to form MD-LinkNeSt network.MD-LinkNeSt, a network framework for road extraction from open-pit mines, is constructed. Third, to enhance the extraction effect of MD-LinkNeSt network on open-pit road, the road loss function is added to MD-LinkNeSt network. Compared with the networks of Unet, DUnet, LinkNet and D-LinkNet, the network in this paper has higher IOU (Intersection over Union) and better continuity. Finally, the road network model is generated by using the open-pit mine road extraction method. The results show that this method can not only extract the road details better than the traditional method, but also meet the intelligent extraction accuracy requirements of the main road network in the open-pit mine.
Conversion Therapy of Unresectable Pancreatic Cancer: A Retrospective Study of the Real World

Objective: A retrospective study of the real world was conducted to analyze whether patients with unresectable pancreatic cancer (URPC) can benefit from conversion therapy, and to screen out pancreatic cancer patients who are suitable for conversion therapy.Patients and Methods: Inquired about patients with URPC who visited Zhejiang Provincial People's Hospital from January 2015 to April 2021. We selected 25 patients with URPC who underwent conversion therapy, and 19 patients with locally advanced pancreatic cancer (LAPC) who directly underwent surgery to conducted a retrospective analysis. Results: The median overall survival (OS) of 25 patients with URPC who received conversion therapy was 28 months (95%CI: 15.46-40.54 months), and the median progression-free survival (PFS) was 12 months (95%CI: 9.26-14.74 months). The curative resection (R0) rate was 84% (22/25). Conclusions: Conversion therapy improves the R0 rate of patients with URPC, and prolongs OS and PFS.
Ion Flotation of La3+, Cd2+, and Cs+ using Monorhamnolipid Collector

Water scarcity is a global issue that is expected to continue increasing in importance in the coming decades. Reclaimed water is one important source available to meet future needs. The reclamation process for wastewaters, particularly from industrial sources, involves the need to remove low-level contaminants. Here we report the efficacy of an ion flotation process that uses the biosurfactant monorhamnolipid as a metal collector to recover Cs+, Cd2+, and La3+ from water. These elements were tested at collector-to-colligend ratios of 2, 5, and 10. The collector-to-colligend ratio and metal valence play a large role in determining flotation success with removal efficiencies varying widely. The maximum removal efficiency for the metals when floated individually were 46.2, 99.8, and 98.6% for Cs+, Cd2+, and La3+, respectively. When mixed together at near equimolar concentrations removal efficiencies were 39.4, 98.4, and 88.1%, respectively. Removal efficiency for Cs+, Cd2+, and La3+ were up to 49.9, 99.5, and 51.5% when mixed at a ratio of 200:10:1, whereas conditional stability constants predict a removal order of La3+ > Cd2+> Cs+. Future research should examine parameters, including pH and ionic strength, that may affect the flotation process as well as actual metal-contaminated waste streams to evaluate the usefulness of this technology.
Layer-Wise Geometry Aggregation Framework for Lossless LiDAR Point Cloud Compression

Point cloud compression is critical to deploy 3D applications like autonomous driving. However, LiDAR point clouds contain many disconnected regions, where redundant bits for unoccupied 3D space and weak correlations between points make it a troublesome problem to achieve efficient compression. This paper aims to aggregate LiDAR point clouds to get compact representations with full consideration of the point distribution characteristics. Specifically, we propose a novel Layer-wise Geometry Aggregation (LGA) framework for LiDAR point cloud lossless geometry compression, which adaptively partitions point clouds into three layers based on the content properties, including a ground layer, an object layer, and a noise layer. The aggregation algorithms are delicately designed for each layer. Firstly, the ground layer is fitted to a Gaussian Mixture Model, which can uniformly represent ground points using much fewer model parameters than adopting the original 3D coordinates. Then, the object layer is tightly packed to reduce the space between objects effectively, and a dense layout for points can benefit compression efficiency. Finally, in the noise layer, the difference between neighbor points is reduced by reordering using Morton Code, and the reduced residuals can help saving bit consumption. Experimental results demonstrate that the proposed LGA significantly outperforms competitive methods without prior knowledge by 12.05~23.37% compression ratio gains. Furthermore, the enhanced LGA with prior knowledge shows consistent performance gains than the latest reference software. Additional results also validate the robustness and stability of our proposed scheme with acceptable time complexity.
Big Data Adoption in Project Management: Insights From French Organizations

Big data have the potential to revolutionize the project management, but it is not clear how. Despite the growing interest, there is a paucity of exploratory research to assess the practice and the impact of big data tools and technologies on project management approaches and practices. To address this gap, in this article, we initially embrace the technological–organizational–environmental (TOE) framework as the research basis for the adoption process and conducts in-depth interviews with project managers from French organizations in different sectors. The synthesis of interviews reveals that many organizations are still in the early stage of the adoption process, mainly due to a lack of resources, especially expertise. Besides the factors of the three contexts of TOE, the project-level factors are also found to be critical for the adoption of big data in project management. Most of them adopted big data solutions to support them in the conception, definition, and execution phases of project management. Drawing on the findings, this article also provides guidelines to broaden the understanding of big data applications and their role in project management. Based on these results, we present a model with testable propositions and discuss insights that arise for organizations and project managers regarding how to apply big data tools and technologies to create value and overcome the related challenges.
Analysis of industrial x-ray computed tomography data with deep neural networks

X-ray computed tomography (XCT) is increasingly utilized industrially at material- and process development as well as in non-destructive quality control; XCT is important to many emerging manufacturing technologies, for example metal additive manufacturing. These trends lead to increased needs of safe automatic or semi-automatic data interpretation, considered an open research question for many critical high value industrial products such as within the aerospace industry. By safe, we mean that the interpretation is not allowed to unawarely or unexpectedly fail; specifically the algorithms must react sensibly to inputs dissimilar to the training data, so called out-of-distribution (OOD) inputs. In this work we explore data interpretation with deep neural networks to address: robust safe data interpretation which includes a confidence estimate with respect to OOD data, an OOD detector; generation of realistic synthetic material flaw indications for the material science and nondestructive evaluation community. We have focused on industrial XCT related challenges, addressing difficulties with spatially correlated X-ray quantum noise. Results are reported on training auto-encoders (AE) and generative adversarial networks (GAN), on a publicly available XCT dataset of additively manufactured metal. We demonstrate that adding modeled X-ray noise during training reduces artefacts in the generated imperfection indications as well as improves the OOD detector performance. In addition, we show that the OOD detector can detect real and synthetic OOD data and still model the accepted in-distribution data down to the X-ray noise levels.
Evaluation of Past and Future Climate Trends under CMIP6 Scenarios for the UBNB (Abay), Ethiopia

Climate predictions using recent and high-resolution climate models are becoming important for effective decision-making and for designing appropriate climate change adaptation and mitigation strategies. Due to highly variable climate and data scarcity of the upper Blue Nile Basin, previous studies did not detect specific unified trends. This study discusses, the past and future climate projections under CMIP6-SSPs scenarios for the basin. For the models’ validation and selection, reanalysis data were used after comparing with area-averaged ground observational data. Quantile mapping systematic bias correction and Mann–Kendall trend test were applied to evaluate the trends of selected CMIP6 models during the 21st century. Results revealed that, ERA5 for temperature and GPCC for precipitation have best agreement with the basin observational data, MRI-ESM2-0 for temperature and BCC-CSM-2MR for precipitation were selected based on their highest performance. The MRI-ESM2-0 mean annual maximum temperature for the near (long)-term period shows an increase of 1.1 (1.5) °C, 1.3 (2.2) °C, 1.2 (2.8) °C, and 1.5 (3.8) °C under the four SSPs. On the other hand, the BCC-CSM-2MR precipitation projections show slightly (statistically insignificant) increasing trend for the near (long)-term periods by 5.9 (6.1)%, 12.8 (13.7)%, 9.5 (9.1)%, and 17.1(17.7)% under four SSPs scenarios.
Effect of screw access hole on the fracture load of implant-supported monolithic zirconia single crown

Implant-supported cement–screw-retained crowns combine the advantages of screw-retained and cement-retained crowns. However, the occlusal screw access hole interrupts porcelain integrity, which may result in porcelain cracks or fractures. There is insufficient scientific evidence to prove that screw access holes affect the fracture load of implant-supported monolithic zirconia crowns. In this study, we investigated the effects of the screw access hole and its preparation technique on the fracture load of implant-supported monolithic zirconia single crown. The crowns were designed for the maxillary right first premolar. Three techniques analysed for screw access hole preparations included computer-aided designed/computer-aided manufactured (CAD/CAM) before sintering, manually prepared after sintering, and then resintering. Our findings show that the screw access holes and preparation techniques have no significant effects on the fracture load of implant-supported monolithic zirconia single crown. On the other hand, the screw access hole preparation techniques affect failure initiation in implant-supported monolithic zirconia single crown.
Optical fabrication chain design

It takes optical systems such as sophisticated car headlights, telescopes or endoscopes to apply light as a tool, for example to see in the dark, to detect rocks in outer space or stones in a human kidney. As the accuracy of the usage of light tools is increasing rapidly, the quality of optical systems has to follow accordingly. So the process of generating optical systems needs to be optimized to enable the optimum transfer of accuracy and quality to all subsequent entities in the creation process, right from the initial application idea through to final acceptance testing.