Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of workplace exercise on workers’ cognitive performance

Introduction Occupational tasks require physical and cognitive efforts. Within this context, workplace exercise seems to be a promising intervention to improve physical capacity. However, little is known about the influence of workplace exercise on cognitive performance. Objectives This study aimed to evaluate the influence of workplace exercise on cognitive performance in administrative office workers. Methods This cross-sectional study included 16 workers who performed workplace exercise training and 14 workers who did not (control group). The assessments were conducted after 3 months of workplace exercise training (stretching exercises, two to three times/week, 10-15 minutes/day). Physical activity level was assessed with the short form of International Physical Activity Questionnaire, while cognitive performance was assessed using computerized versions of Stroop color-word test and Corsi block-tapping test. Results There was no significant difference between the groups in any Stroop test phases or in Stroop interference (349.3 ± 103.52 vs. 416.0 ± 129.7 ms; 5.37 ± 2.11 vs. 10.12 ± 6.55 %error; p > 0.05). No difference was found in Corsi test sequence of blocks (5.50 ± 0.82 vs. 5.57 ± 0.76 blocks) or in the total score (45.19 ± 15.96 vs. 46.93 ± 15.93 points; p > 0.77). Conclusions The results of this study suggest that 12 weeks of workplace exercise training does not improve the cognitive performance of office workers.
On the efficiency of hydrogen peroxide use in ECF bleaching

Le stade de blanchiment au peroxyde (P) peut reduire l'efficacite du stade subsequent au bioxyde de chlore. Cet effet est attribuable a la degradation des unites d'hydroxyle phenolique de la lignine residuelle que l'on considere comme etant les constituants attaques par le bioxyde de chlore. Cette insuffisance peut etre corrigee en utilisant des additifs dans le stade P. Nous avons mesure les teneurs en groupes d'hydroxyle phenolique, d'hydroxyle aliphatique, et d'acide carboxylique des lignines residuelles que nous avions separees de certaines pâtes kraft. L'addition de dicyandiamide dans le stade P augmentait la concentration des groupes d'hydroxyle phenolique dans la pâte, tout en permettant d'accelerer le blanchiment au peroxyde, sans par ailleurs ameliorer l'efficacite utilitaire du peroxyde et sans predisposer les pâtes aux reactions avec le bioxyde de chlore. Ces resultats demontrent que l'accroissement de la teneur en groupes d'hydroxyle phenolique n'ameliore pas l'aptitude au blanchiment des pâtes en presence du bioxyde de chlore. Ils demontrent aussi que l'emploi d'un stade P, plutot apres qu'avant le stade D, demeure une meilleure strategie de blanchiment.
Broadband Lens-Integrated CMOS Camera-Type THz Compact Antenna Test Range

This article presents the concept of a lens-integrated CMOS camera-type terahertz (THz) compact antenna test range (CATR) to determine the far-field characteristics of transmitters (TX), including the radiation pattern, directivity, half-power beam-width (HPBW), and radiation power. For concept validation, the frequency-dependent far-field radiation pattern of a 0.6–1.1 THz TX equipped with a 23.7–28.2 dBi standard gain horn antenna was measured using THz CATR. Directivity, HPBW, and radiation power have been extracted from THz CATR measurements. The first two parameters are compared with values obtained from calculations, full-wave simulations, and reference measurements performed at a conventional far-field range. In contrast, the third parameter is compared with a reference measurement performed with an absolute power meter. The following performance was achieved employing super-resolution imaging. Frequency-dependent directivity and HPBW were determined within a root mean square (RMS) accuracy of 0.85 dB and 1.16$^\circ$, respectively, related to reference measurements performed at the conventional far-field range. The frequency-dependent radiation power was determined within an RMS accuracy of 0.7 µW related to reference measurements with the absolute power meter. Camera-related limitations concerning the THz CATR application are discussed in detail, such as field-of-view limitations, angular resolution limits, scan loss, pixel-to-pixel variation, signal-to-noise ratio limitations, and the interaction between TX and camera lens.
Private Enterprise and the Sustainable Community Economic Development in Bangladesh A Case Study on the PRAN-RFL Group

Community Economic Development (CED) practice in Bangladesh is vital in ensuring the sustainability of the community and economy of Bangladesh and it contributes to the creation and maintenance of economic, social, ecological, human, political, and cultural capitals of communities in a sustainable and progressive manner. Existing literature in Bangladesh perspective mostly focuses on NGO contributions. However, local large conglomerates have contributions in this aspect and the study would like to tap on the research gap by conducting an intensive case study on PRAN-RFL Group (PRG), one of the largest conglomerates in Bangladesh and South East Asia. The case study provided a theoretical background of the business profile of the PRG. Researchers collected data from the organizations about their CED contributions to different areas ofBangladesh even in the ongoing COVID19 time. PRG’s contributions to those remote areas were found significant and valuable to make the society sustainable and resilient. For this reason, the partnership between the PRG and the community (individual entrepreneur) was remarkable. The study concluded that PRG’s contribution to the CED practices set examples of their competitors of Bangladesh in various CED practices. The study recommended further quantitative analysis in assessing community farmers and reverse migration in this aspect.
Comprehensive use of IT solutions to monitor the state of construction sites

The work examines new IT - solutions for monitoring the state of construction projects. Two areas are investigated: the first - monitoring of high-rise buildings and other large-scale objects, the second - monitoring of construction sites located in remote, hard-to-reach areas. The possibilities of integrated use of the following information technologies are considered: Machine Vision, Wireless Sensor Network WSN, Edge Computing. Other new IT solutions are also in the field of view of the study: Cloud Computing, the Internet of Things, etc. To improve the quality of monitoring, it is proposed to use advances in other subject areas - in the field of unmanned aerial vehicles (drones) and in the space area (geostationary satellite communications). The idea of using drones and artificial earth satellites to monitor objects (particularly buildings) is not new in itself, but the use of these devices as part of the proposed set of IT technologies allows to bring solutions in the construction industry to a qualitatively higher level.
Customer Engagement as the Basis for Technology Decisions in a Smart City

This article examines the involvement of citizens in the process of making technological decisions aimed at developing a smart environment, from smart home to urban infrastructure. The study is based on an approach that takes into account the dual behavior of the consumer of smart services - passive and active behavior. Highlighted the contradictions that create obstacles to the effective functioning of the smart city: the selective multidirectionality of the smart city development strategy and sustainable development goals; developing smart solutions for citizens without taking into account their opinions; polar approach of smart city residents to involvement in the development and implementation of smart solutions. The risks associated with the likelihood of incomplete return from the use of smart solutions are identified: consumer distrust of data security and privacy; insufficient literacy of residents regarding understanding of smart technology management processes; significant financial costs and the orientation of smart solutions to wealthy citizens; lack of sufficient evidence of the effectiveness of using a number of smart solutions; the difficulty of implementing an integrated approach to ensuring the growth of the quality of life of citizens. In accordance with the concept of the study, two directions for mitigating or eliminating risks are proposed - depending on the significant or insignificant involvement of citizens. Specific measures have been developed to help mitigate and eliminate risks. The experience of the creation of the People’s Institute of the Voronezh State Technical University was used.
Noncontact Vital Sign Monitoring System with Dual Infrared Imaging for Discriminating Respiration Mode

Oral respiration causes constriction of the upper airway in the retropalatal and retroglossal regions, thereby increasing the risk of sleep disorder. One of the best methods to detect early signs of obstructive sleep apnea syndrome (OSAS) is daily monitoring of the respiration rate and mode of respiration during sleep. The vital signs are measured by a noncontact method in order to avoid burdening the subject and to allow differentiation between the various respiratory modes. In this study, we developed a system to measure the respiration rate and mode using farand near-infrared cameras, and assessed the effectiveness of the proposed system and algorithm. A near-infrared camera detected the positions of the nostril and mouth, which are the pathways of expired and inspired air, respectively; while the far-infrared camera measured temperature changes in the nostril and mouth to derive the respiration rate and mode for detecting apnea. We enrolled 10 participants and measured their respiration rates using the aforementioned system under three states: nasal respiration, oral respiration, and apnea. The root-mean-square error for the respiration rate was 0.27 bpm, indicating that the system measured respiration without error in 92% of the trials. There was no error in discriminating between nasal and oral respiration. Additionally, this system detected apnea quite satisfactorily. The results of the experiment con rm that the system we developed effectively measures respiration in a noncontact manner.
DeepSuggest: Using Neural Networks to Suggest Related Keywords for a Comprehensive Search of Clinical Notes

Abstract Objective A large amount of clinical data are stored in clinical notes that frequently contain spelling variations, typos, local practice-generated acronyms, synonyms, and informal words. Instead of relying on established but infrequently updated ontologies with keywords limited to formal language, we developed an artificial intelligence (AI) assistant (named “DeepSuggest”) that interactively offers suggestions to expand or pivot queries to help overcome these challenges. Methods We applied an unsupervised neural network (Word2Vec) to the clinical notes to build keyword contextual similarity matrix. With a user's input query, DeepSuggest generates a list of relevant keywords, including word variations (e.g., formal or informal forms, synonyms, abbreviations, and misspellings) and other relevant words (e.g., related diagnosis, medications, and procedures). Human intelligence is then used to further refine or pivot their query. Results DeepSuggest learns the semantic and linguistic relationships between the words from a large collection of local notes. Although DeepSuggest is only able to recall 0.54 of Systematized Nomenclature of Medicine (SNOMED) synonyms on average among the top 60 suggested terms, it covers the semantic relationship in our corpus for a larger number of raw concepts (6.3 million) than SNOMED ontology (24,921) and is able to retrieve terms that are not stored in existing ontologies. The precision for the top 60 suggested words averages at 0.72. Usability test resulted that DeepSuggest is able to achieve almost twice the recall on clinical notes compared with Epic (average of 5.6 notes retrieved by DeepSuggest compared with 2.6 by Epic). Conclusion DeepSuggest showed the ability to improve retrieval of relevant clinical notes when implemented on a local corpus by suggesting spelling variations, acronyms, and semantically related words. It is a promising tool in helping users to achieve a higher recall rate for clinical note searches and thus boosting productivity in clinical practice and research. DeepSuggest can supplement established ontologies for query expansion.
Optimizing the modified conjugate gradient algorithm

In this paper, an efficient GV1-CG is developed to optimizing the modified conjugate gradient algorithm by using a new conjugate property. This is to to increase the speed of the convergence and retain the characteristic mass convergence using the conjugate property. This used property is proposed to public functions as it is not necessary to be a quadratic and convex function. The descent sharp property and comprehensive convergence for the proposed improved algorithm have been proved. Numerical results on some test function indicate that the new CG-method outperforms many of the similar methods in this field.
Prevalence and correlates of cervical squamous intraepithelial lesions among HIV-infected and uninfected women in Central Kenya

Introduction cervical intraepithelial neoplasia the precursor of cervical cancer occurs with increased frequency in women infected with human immunodeficiency virus (HIV). This study aimed at determining the prevalence and correlates of abnormal cervical cytology among HIV-infected women and compare to the uninfected women. Methods a cross-sectional study conducted among HIV-infected and uninfected women enrolled in a HIV study in Central Kenya. All women had baseline Pap smear examination assessed using Bethesda system. Bivariate and multivariate logistic regression methods were employed to assess the correlates of cervical squamous epithelial lesions (CSIL). Results a total 480 women had an acceptable baseline smear, 373 (78%) were HIV-infected. Median age was 30.2 years [IQR 25.4-35.5]. Overall prevalence of CSIL was 37% (176/480) with the prevalence of low grade squamous intraepithelial lesion (LSIL), atypical squamous cells undetermined significance (ASCUS), high grade squamous intraepithelial lesions (HSIL) and atypical glandular cells (AGC) were 17%, 14%, 4% and 2% respectively. HIV-infected women had a higher prevalence of CSIL at 42% as compared to HIV-uninfected women at 19%. HIV infection was the predictor associated with development of CSIL at multivariate analysis and specifically, HIV-infected women were 3 times (AOR 3.1, 95% CI: 1.8 - 5.4, p<0.005) more likely to have CSIL than HIV-uninfected women. The age 35 - 44 years was protective to developing CSIL (AOR 0.45, 95% CI: 0.24 - 0.87, p=0.018). Conclusion cervical squamous epithelial lesions is a major problem among Kenyan women. HIV infection confers a higher risk to development of CSIL. Cervical cancer screening should be an established practice in HIV programs.
Design and accomplishment of immersive virtual reality in safety training of offshore oil platform power system

Any improper work operation may lead to electrical safety accidents such as equipment failure and life safety. Design and accomplishment of immersive virtual reality (IVR) in safety training of oil platform power system was put forward in this paper. The purpose of power system safety training based on IVR technology is essentially to improve the interface ability between employees and relevant information of power system. On the basis of having a set of hardware equipment based on IVR technology, users can immerse themselves in the virtual world through this set of IVR equipment, and interact with the software platform through matching input and output devices to achieve the purpose of safety training. After full investigation, from micro to macro, from model to scene, a virtual power station scene and its safety training story line are constructed by using 3ds MAX software and Unity 3D software. On this basis, the operation function of the student module, the interaction function of the teacher module, and the registration and assessment management function of the system are developed. The installation of power system safety training based on virtual reality technology was used in a power station, which can significantly enhance the safety awareness and operation level of power employees.
Real-time Performance-Focused Localisation Techniques for Autonomous Vehicle: A Review

— Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about 𝟏𝟏𝟏𝟏 𝟕𝟕 times, whilst accuracy varies by about 100 times. Vision-and data fusion-based localisation techniques have about 2–5 times potential for improving accuracy compared with lidar-based localisation. Lidar-and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar-and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-to-everything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs’ localisation based on the quantitative comparison results are discussed.
Scenario Planning to Assess Ethical Culture

Scenario planning has presented itself as a useful tool for organizations to cope with high levels of contextual uncertainty, either to support decision-making, reframe strategy, or to improve stakeholder relationships. Limited attention has been given to how scenario planning may be used in practice to assess an organization's ethical system. Four industrial cases serve as practical examples of the scenario planning development across three different phases—orientation and exploration; building; and validation and implementation. This article provides lessons derived from the industrial cases for companies and practitioners to visualize scenario planning as a useful complementary tool for ethical culture assessment. We rely on complexity science and systems thinking for scenario planning to support the analysis of an organization's ethical system.
Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

: Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3 + with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3 + with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.
Low-Dimensional-Networked Metal Halide Perovskites: The Next Big Thing

Low-dimensional-networked (low-DN) perovskite derivatives are bulk quantum materials in which charge carriers are localized within ordered metal halide sheets, rods, or clusters that are separated by cationic lattices. After two decades of hibernation, this class of semiconductors reemerged in the past two years, largely catalyzed by the interest in alternative, more stable absorbers to CH3NH3PbI3-type perovskites in photovoltaics. Whether low-DN perovskites will surpass other photovoltaic technologies remains to be seen, but their impressively high photo- and electroluminescence yields have already set new benchmarks in light emission applications. Here we offer our perspective on the most exciting advances in materials design of low-DN perovskites for energy- and optoelectronic-related applications. The next few years will usher in an explosive growth in this tribe of quantum materials, as only a few members have been synthesized, while the potential library of compositions and structures is believed to.
Designing Multi-Task Convolutional Variational Autoencoder for Radio Tomographic Imaging

Radio tomographic imaging (RTI) emerges to model the environment and detect the passive targets by a wireless network. In this work, the received signal strength (RSS) measurements are collected from an uncalibrated network, and a multi-task convolutional variational autoencoder model is proposed to realize RTI. The presented model is trained end-to-end to denoise the RSS measurements, reconstruct the static tomographic images, estimate the parameters of the wireless network, and classify the measurement noise level, simultaneously. The multi-task variational learning strategy is able to improve the generalization of the model. Numerical experiments demonstrate the efficacy of our RTI method.
Prediction of On-Street Parking Level of Service Based on Random Undersampling Decision Trees

Effective on-street parking is key to reduce urban traffic and pollution in densely populated cities. Thus, researchers have focused on forecasting future occupancy values depending on factors like time, space, or weather. This approach shows high average performances, but fails in predicting congested scenarios, actually the most critical. This work proposes a data-driven parking level of service (LOS) predictor that outperforms traditional methods, solving its inherent class imbalance issue by means of Random Undersampling Boost classifiers. We trained and validated the LOS classifiers using 13 months of data collected from the smart parking system in the city of Madrid, Spain. Results display average recall values above 0.94 and 0.87 at prediction horizons up to 10 and 60 minutes respectively. We compare these results with traditional regression-based occupancy predictors showing that our classifier clearly outperforms the formers predicting the minority classes, which carry the most significant information for drivers and parking managers. We further analyze the impact on performance of temporal and spatial features, revealing mid-term temporal data as the most relevant forecasting information, and low correlations between parking behaviors in bordering neighborhoods. In the light of these results, we believe that the proposed data-driven parking LOS classification has the potential to open a novel perspective on the parking occupancy forecasting field.