Miracle-dz/UniFAR-FTUs huggingface dataset:
La COVID-19 y las microempresas de comercio al por menor de la Región II Atlacomulco, Estado de México. Un acercamiento mediante análisis factorial en el primer trimestre de 2021

El presente trabajo de investigación midió el impacto de la pandemia de COVID-19 considerando factores sobre tecnologías de la información, financieros, fiscales y de responsabilidad social en las microempresas del sector comercio al por menor en los municipios de la Región II Atlacomulco, del Estado de México durante el primer trimestre del año 2021. Se elaboró un instrumento denominado FFFRSI-COVID19 constituido por 44 ítems (8 socioeconómicas y 36 divididas en 7 factores) que reportan un Alfa de Cronbach de . 892, lo cual demostró una fiabilidad aceptable. Además, el análisis factorial exploratorio, como metodología para la evaluación de los datos obtenidos, indicó una correlación significativa entre todas las variables, excepto COVID-19 que se relacionó únicamente con la variable financiamiento. Esto denota el bajo nivel de apoyo por parte de las autoridades hacia este sector, así como la capacidad que han tenido para continuar sus labores económicas a través del uso de las TIC y las diferentes plataformas de comunicación con sus clientes.
Raman spectroscopy of colloidal semiconductor nanocrystals

Raman spectroscopy is a powerful method that gives insight into the atomic structure and composition of nanomaterials, but also allows to draw conclusions about their electronic properties. It is based on the inelastic scattering of light, which is able to excite phonons in the material. In the field of semiconductor nanocrystals, Raman spectroscopy has been employed to make significant contributions to the analysis of lattice distortion, interfaces, phase mixing, and defect formation. Yet, there is no clear consensus on how the electronic and crystal structure of the material interacts with the incident light to yield the observed spectra. This review gives a brief overview over the method. It then reviews the most important findings, current developments, and discusses the efforts to formulate a consistent model that allows to establish the method as a tool for structural analysis.
Multimodal Fusion-AdaBoost Based Activity Recognition for Smart Home on WiFi Platform

With the ubiquity of commodity WiFi devices and the rapid development of Internet of Things (IoT), there are increasingly intelligent sensing applications emerging by utilizing fine-grained channel state information (CSI) from WiFi signals, which can realize contactless human-computer interaction (HCI) for smart home. However, most of CSI-based activity recognition approaches are vulnerable to random noises derived from indoor environments. In this paper, we present a multimodal fusion-AdaBoost based human activity recognition scheme on WiFi platform. For this purpose, we develop two theoretical underpinnings, including a sensing model and a recognition model. The sensing model is firstly established to investigate the impact of human activities on propagation properties of WiFi signals. To this end, Fresnel zone model is used to quantify the correlation between CSI dynamics and human activities. Moreover, the recognition model is used to exploit physical activity-induced signal changes to infer potential activity information. In the process, we firstly construct a CSI tensor through integrating all CSI information at each WiFi receiver. Then, a CANDECAMP/PARAFAC (CP) decomposition method is applied to this CSI tensor for obtaining representative features. Finally, based on the features extracted, human activities are able to be recognized by combining multimodal fusion and AdaBoost. We implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experimental results confirm that the proposed scheme can achieve average recognition accuracies of 96% and 95% in two indoor scenarios, respectively.
Voltage Differencing Buffered Amplifier-Based Novel Truly Mixed-Mode Biquadratic Universal Filter with Versatile Input/Output Features

In this paper, a first-of-a-kind mixed-mode universal filter employing three VDBAs and three passive components, is proposed. The filter operates in all four modes and provides all five filter responses, namely voltage-mode (VM), current-mode (CM), trans-impedance-mode (TIM), or trans-admittance-mode (TAM). Additionally, the same filter topology can also work as a CM single-input-multi-output (SIMO) filter. A state-of-the-art comparison of various ‘voltage differencing’ variants of the voltage differencing buffered amplifier (VDBA)-based SIMO/MISO (single-input-multi-output/multi-input-single-output)-type biquad filters further highlight the significance of the presented research. In the proposed no passive component matching is required for generating the filter responses. The filter circuit also provides inbuilt tunability of the quality factor independent of the pole frequency. The non-ideal frequency dependent gain and component sensitivity analyses of the filter were also performed. The Silterra Malaysia 0. 18μm process design kit (PDK) is employed to design and validated the proposed VDBA-based filter using the Cadence design software. The simulation results closely follow the theoretical predictions. To further verify the practical feasibility of the proposed filter, an experimental evaluation is also completed. The VDBA-based filter is implemented using off-the-shelf operational transconductance amplifiers Intersil CA3080, Texas Instruments LF356 op-amp, and Analog Devices AD844s. The filter is designed for a characteristic frequency of 100 kHz. The time and frequency domain measurement results indicate the proper functioning of the filter.
Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
When Do Work-Family Policies Work? Unpacking the Effects of Stigma and Financial Costs for Men and Women

Work-family policies—such as parental leave and flextime—can help to facilitate gender equality in workplaces and in families. But policy use is typically low, varies significantly from one workplace to another, and is often more prevalent among women than men. Extant research suggests that flexibility stigma—workplace norms that penalize workers for utilizing policies that facilitate non-work demands—as well as the financial costs associated with policy use, contribute to this pattern. However, previous studies have been largely correlational in nature, and have had difficulty assessing how these factors may interact with one another to shape gendered patterns of policy use. In this study, we offer novel causal traction on this set of issues. Using an original, population-based survey experiment, we examine how the salience of flexibility stigma and financial costs affect men's and women's intentions to use work-family policies. We find that these factors exert a large direct effect on men's and women's intentions to use work-family policies. Moreover, the gender gap in parental leave use intentions is large in workplace contexts with high flexibility stigma and high financial costs, but this gap narrows significantly under more favorable conditions. Findings point to the importance of organizational contexts and policy design in shaping work-family policy use and, in turn, gender inequality.
Complete idiot's guide™ to meeting and event planning, the

If you are given the responsibility of planning a meeting for your business, you need to make your company look good. This guide gives you suggestions for planning the budget, negotiating a space to meet, choosing a caterer, setting up the room, and planning programs. You will be able to produce an event that is organized and within your budget.
Why Do Insects Close Their Spiracles? A Meta-Analytic Evaluation of the Adaptive Hypothesis of Discontinuous Gas Exchange in Insects

Simple Summary Insects breathe with the aid of thin capillary tubes that open out to the exterior of their body as spiracles. These spiracles are often modulated in a rhythmic gas pattern known as the discontinuous gas exchange cycle. During this cycle, spiracles are either firmly shut to allow no gaseous exchange or slightly open/fully open to allow for gaseous exchange. Two explanations are put forward to rationalize this process, namely, the rhythmic pattern is to (1) reduce water loss or (2) facilitate gaseous exchange in environments with high carbon dioxide and low oxygen. Interestingly, certain insects (such as some desert insects) do not use this rhythmic pattern where it would have been most beneficial and logical. Such an observation has led to the questioning of the explanations of the discontinuous gas exchange cycle. Consequently, we attempt to resolve this controversy by conducting a meta-analysis by synthesizing apposite data from across all insects where a discontinuous gas exchange cycle has been reported. A meta-analysis allows for a shift from viewing data through the lens of a single species to an order view. Thus, our goal is to use this holistic view of data to examine the explanations of the discontinuous gas exchange cycle across multiple groups of insects. Abstract The earliest description of the discontinuous gas exchange cycle (DGC) in lepidopterous insects supported the hypothesis that the DGC serves to reduce water loss (hygric hypothesis) and facilitate gaseous exchange in hyperoxia/hypoxia (chthonic hypothesis). With technological advances, other insect orders were investigated, and both hypotheses were questioned. Thus, we conducted a meta-analysis to evaluate the merit of both hypotheses. This included 46 insect species in 24 families across nine orders. We also quantified the percent change in metabolic rates per °C change of temperature during the DGC. The DGC reduced water loss (−3.27 ± 0.88; estimate ± 95% confidence limits [95% CI]; p < 0.0001) in insects. However, the DGC does not favor gaseous exchange in hyperoxia (0.21 ± 0.25 [estimate ± 95% CI]; p = 0.12) nor hypoxia, but did favor gaseous exchange in normoxia (0.27 ± 0.26 [estimate ± 95% CI]; p = 0.04). After accounting for variation associated with order, family, and species, a phylogenetic model reflected that metabolic rate exhibited a significant, non-zero increase of 8.13% (± 3.48 95% CI; p < 0.0001) per °C increase in temperature. These data represent the first meta-analytic attempt to resolve the controversies surrounding the merit of adaptive hypotheses in insects.
An Integrated Linkage Map of Three Recombinant Inbred Populations of Pea (Pisum sativum L.)

Biparental recombinant inbred line (RIL) populations are sets of genetically stable lines and have a simple population structure that facilitates the dissection of the genetics of interesting traits. On the other hand, populations derived from multiparent intercrosses combine both greater diversity and higher numbers of recombination events than RILs. Here, we describe a simple population structure: a three-way recombinant inbred population combination. This structure was easy to produce and was a compromise between biparental and multiparent populations. We show that this structure had advantages when analyzing cultivar crosses, and could achieve a mapping resolution of a few genes.
Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.
A Hybrid A*/Automaton Approach to On-Line Path Planning with Obstacle Avoidance

Autonomous path planning capability is a critical requirement for future unmanned vehicle operations. This paper presents a novel path planning approach for rapid construction of feasible, yet agile, trajectories. The approach relies upon separating the planning task into an on-line and an o-line component. The o-line component consists of building a library of motion primitives using nonlinear simulation, the primitives are then sequenced on-line using an A* search. The main benefit of this separation is that it shifts the computationally intensive work to the o-line component and leaves the on-line task relatively light. A major benefit of using A* is that the search algorithm is unaected by the changes to the library of trim primitives. Thus in the event that a UAVs capabilities are degraded, e.g. due to damage, any unavailable motion primitives are simply removed from the library and A* will not included them in the path construction. To speed up the on-line search time, a sub-optimal modification is made to the standard A* algorithm that delivers a feasible sub-optimal path quickly with relatively small increase in total path cost. Examples of waypoint planning and obstacle avoidance are given to illustrate the path planning approach.
Leadership Dimensions for Engineering Project Success

Business dynamics are fast changing in the face of globalization, offering a plethora of opportunities. The unique nature of engineering projects with their set budgets, time frames coupled with other constraints poses severe challenges in view of the complexities involved. Project failures affect the business prospects of organizations whose core operations involve execution of engineering projects. Project stakeholders have varied interests in the successful implementation of engineering projects. Frequently, the project manager is not equipped adequately to meet the environmental challenges and the expectations of the project stakeholders. The project manager does not have the luxury to select the team of his choice. With team members new to the project environment, the project manager faces additional challenges that are not purely transaction-oriented to address the issues of meeting project milestones. Organizations need strong managers to lead their staff toward accomplishing business goals. In this study, desired dimensions of leadership have been identified to aid the project manager to avoid the pitfalls of project failures and to steer the projects to greater success.
Leveraging an Implementation Science Framework to Measure the Impact of Efforts to Scale Out a Total Worker Health® Intervention to Employers

The role of dissemination and implementation (D&I) science is critical to the translation of Total Worker Health® into practice and to the success of interventions in addressing current and future implications for worker safety, health, and well-being. D&I frameworks can guide researchers to design Total Worker Health (“TWH”) delivery approaches that use flexible implementation strategies to implement the core components of programs for employers with varying contextual factors, including small/mid/large-sized businesses and different industry types. To date, there have been very few examples of applying implementation frameworks for the translation and delivery of interventions into organizational settings that require adoption and implementation at the business level to benefit the working individuals. We present a TWH case study, Health Links™, to illustrate an approach to applying an existing implementation framework, RE-AIM, to plan, design, build, and then evaluate TWH implementation strategies. Our case study also highlights key concepts for scaling-out TWH evidence-based interventions where they are implemented in new workplace settings, new delivery systems, or both. Our example provides strong support of key implementation planning constructs including early and consistent stakeholder engagement, tailored messaging and marketing, flexibility, and adaptations in implementation strategies to maximize adoption, implementation, and maintenance among participating businesses.
Facial Expression Recognition Based on the Fusion of Infrared and Visible Image

: Facial expression recognition is a research hot spot in the fields of computer vision and pattern recognition. However, the existing facial expression recognition models are mainly concentrated in the visible light environment. They have insufficient generalization ability and low recognition accuracy, and are vulnerable to environmental changes such as illumination and distance. In order to solve these problems, we combine the advantages of the infrared and visible images captured simultaneously by array equipment our developed with two infrared and two visible lens, so that the fused image not only has the texture information of visible image, but also has the contrast information of infrared image. On the other hand, we improved the WGAN by adding SSIM and LBP loss functions to ensure the structural similarity between the fused image and infrared image, and also the texture similarity between the fused image and visible image respectively. Finally, a facial expression recognition model Pyconv-SE18 with pyramid convolution and attention mechanism module is designed to extract the important feature information of facial expression in multiple scales. We add cosine distance loss function to reduce the feature difference within the class. Experiment results show that the robustness of expression recognition algorithm to illumination is improved based on the fused images. The accuracy of this model on FER2013 and CK+ public data sets are 69.3% and 94.6%, respectively.
Using Machine Learning for Pharmacovigilance: A Systematic Review

Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.
HPV vaccination among seropositive, DNA negative cohorts: a systematic review & meta-analysis

Objective Vaccine efficacy among previously exposed, but currently uninfected women, i.e., those who have serological evidence of a prior human papillomavirus (HPV) infection without corresponding detectable HPV DNA, remains incompletely defined. This meta-analysis assessed the serotype-specific efficacy of prophylactic HPV vaccination against HPV16/18 persistent infection (PI) and cervical intraepithelial neoplasia (CIN) among seropositive, DNA negative (SPDN) women enrolled to randomized controlled trials (RCTs) of HPV L1-based vaccines. Methods Searches were conducted on 08/16/20 on MEDLINE, Embase, Scopus and CENTRAL. RCTs of L1-based prophylactic bivalent or quadrivalent HPV vaccines, reporting serotype-specific clinical efficacy endpoints in the HPV16/18 seropositive, DNA-negative populations were included. Relative risks (RRs) of 6-month PI (6mPI), 12-month PI (12mPI), CIN1+ and CIN2+ were pooled using a random-effects model. Results A total of 1,727 citations were reviewed. 8 studies, with a total of 9,569 SPDN participants, met all eligibility criteria. The RR of 6mPI (RR=0.22; 95% confidence interval [CI]=0.08–0.61; p=0.018), 12mPI (RR=0.20; 95% CI=0.05–0.80; p=0.035), CIN1+ (RR=0.13; 95% CI=0.05–0.30; p=0.003) and CIN2+ (RR=0.15; 95% CI=0.04–0.59; p=0.022) was significantly reduced in the vaccinated compared to the unvaccinated group. Conclusion Our findings suggest high serotype-specific efficacy for HPV vaccination among cohorts of women with evidence of prior HPV16/18 infections, including 87% efficacy (95% CI=70%–95%; p=0.003) against HPV16/18 cervical dysplasia. HPV vaccination is highly effective among uninfected women, regardless of prior exposure history. Trial Registration PROSPERO Identifier: CRD42020206888
EVALUATION OF CRITICAL SUCCESS/FAILURE FACTORS IN EPC PROJECTS OF PT.IKPT

EPC project stands for Engineering, Procurement and Construction. Each stage has a group of activities that is limited by time, cost and resources according to contract that has been agreed. Like other projects, the success or failure of EPC project is usually determined by criteria mentioned above: cost, time and performance. Apparently, there is an ambiguity in determining whether the project is a success or a failure. One of the reasons is lists of success or failure factors vary in various studies in the literature. Furthermore, many of these factors do not, in practice, directly affect project success or failure. To address those problems, Belassi and Tukel (1996) have done a research and gathering information about critical success or failure factors. The factors were illustrated in a framework that can be used to determine the critical success or failure factors in projects. This research was performed in order to identify the critical factors affecting the success/failure of EPC project. This research is held by evaluating the monthly reports and minutes of meeting. Some questionnaires were also distributed to support the information that has been gathered. The result of this research is a framework that can be used to determine the critical factors affecting the execution of EPC project.