An intercomparison study of four different techniques for measuring the chemical composition of nanoparticles

Currently, the complete chemical characterization of nanoparticles (< 100 nm) represents an analytical challenge, since these particles are abundant in number but have negligible mass. Several methods for particle-phase characterization have been recently developed to better detect and infer more accurately the sources and fates of sub-100 nm particles, but a detailed comparison of different approaches is missing. Here we report on the chemical composition of secondary organic aerosol (SOA) nanoparticles from experimental studies of α-pinene ozonolysis at −50, −30, and −10 ∘C and intercompare the results measured by different techniques. The experiments were performed at the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). The chemical composition was measured simultaneously by four different techniques: (1) thermal desorption–differential mobility analyzer (TD–DMA) coupled to a NO$^-_3$ chemical ionization–atmospheric-pressure-interface–time-of-flight (CI–APi–TOF) mass spectrometer, (2) filter inlet for gases and aerosols (FIGAERO) coupled to an I$^−$ high-resolution time-of-flight chemical ionization mass spectrometer (HRToF-CIMS), (3) extractive electrospray Na$^+$ ionization time-of-flight mass spectrometer (EESI-TOF), and (4) offline analysis of filters (FILTER) using ultra-high-performance liquid chromatography (UHPLC) and heated electrospray ionization (HESI) coupled to an Orbitrap high-resolution mass spectrometer (HRMS). Intercomparison was performed by contrasting the observed chemical composition as a function of oxidation state and carbon number, by estimating the volatility and comparing the fraction of volatility classes, and by comparing the thermal desorption behavior (for the thermal desorption techniques: TD–DMA and FIGAERO) and performing positive matrix factorization (PMF) analysis for the thermograms. We found that the methods generally agree on the most important compounds that are found in the nanoparticles. However, they do see different parts of the organic spectrum. We suggest potential explanations for these differences: thermal decomposition, aging, sampling artifacts, etc. We applied PMF analysis and found insights of thermal decomposition in the TD–DMA and the FIGAERO

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

Extension Implications of Skill Gaps among Cassava farmers in the Niger Delta Region of Nigeria

The study evaluated the skill gap among cassava farmers in the Niger Delta region of Nigeria. A multi-stage sampling technique was used to obtain data from 270 farmers using structured questionnaire and interview schedule. Data collected were analysed using frequency counts, percentages, mean score, Chi square and Pearson Product Moment Correlation. The study revealed that there were more male (55.9%) cassava farmers in the study area with farmer’s mean age approximately 48 years and majority (75.2%) were educated.  Mean farming experience was 24 years and mean farm size 1.8 hectares. Skill gap analysis indicated 16 areas including Packaging of cassava products (MWDS = 7.61), Soil Management strategies (MWDS = 6.70) and Chemical application (MWDS =3.93) among others. The Chi square test shows that there is a significant relationship between training needs and the marital status (c2 = 18.46, p < 0.05) and the variety of cassava planted (c2= 6.397, p < 0.05) by respondents. The Pearson Products Moment Correlation also revealed that Age (r = 0.181), Farm experience (r = 0.199) and Household size (r = 0.125) had significant relationship with farmers training needs. The study concluded that there are obvious skill and competency gaps among the cassava farmers in the region who are mainly small scale. It recommended that for improved productivity, farmers training should concentrate on the critically expressed areas of skill and competency gaps and that periodic training needs assessment be done to ensure that efforts and training resources are appropriately channeled

Extension Implications of Skill Gaps among Cassava farmers in the Niger Delta Region of Nigeria

The study evaluated the skill gap among cassava farmers in the Niger Delta region of Nigeria. A multi-stage sampling technique was used to obtain data from 270 farmers using structured questionnaire and interview schedule. Data collected were analysed using frequency counts, percentages, mean score, Chi square and Pearson Product Moment Correlation. The study revealed that there were more male (55.9%) cassava farmers in the study area with farmer’s mean age approximately 48 years and majority (75.2%) were educated.  Mean farming experience was 24 years and mean farm size 1.8 hectares. Skill gap analysis indicated 16 areas including Packaging of cassava products (MWDS = 7.61), Soil Management strategies (MWDS = 6.70) and Chemical application (MWDS =3.93) among others. The Chi square test shows that there is a significant relationship between training needs and the marital status (c2 = 18.46, p < 0.05) and the variety of cassava planted (c2= 6.397, p < 0.05) by respondents. The Pearson Products Moment Correlation also revealed that Age (r = 0.181), Farm experience (r = 0.199) and Household size (r = 0.125) had significant relationship with farmers training needs. The study concluded that there are obvious skill and competency gaps among the cassava farmers in the region who are mainly small scale. It recommended that for improved productivity, farmers training should concentrate on the critically expressed areas of skill and competency gaps and that periodic training needs assessment be done to ensure that efforts and training resources are appropriately channeled

Bibliometric structured review of mobile information systems

Abstract This study attempts to fill the identified research gap in the existing literature through a bibliometric analysis and discovered 20 highly global cited papers with 1376 citations and yielded eight (8) core categories of knowledge in MobIS: (1) Information Systems, (2) Adoption, (3) Acceptance, (4) Satisfaction, (5) Information Systems Success and (6) Information Systems continuance. The results show that the distribution of the annual papers flows along the downslope. It was a bit stable in 2016 and since then descend from 2017 to 2020. As a young discipline, there is a need for more productivity, impact, and collaboration in the field of MobIS

Teaching experiences during COVID-19 pandemic:narratives from ResearchGate

Abstract The COVID-19 pandemic has remarkably affected every sphere of our lives and education inclusive. It greatly disrupted the teaching and learning process, necessitating alternative approaches at different times to ameliorate the situation. In the new normal, various pedagogical tactics and teaching strategies are employed for content delivery. More so, different platforms are utilized to enable learning as well as assessment techniques. This study, therefore, sought to explore the teaching experiences during the pandemic. It also highlights the success factors of online teaching during the COVID-19 semester and the best assessment strategies used during the pandemic. The methodology adopted in the study is narrative and, precisely, the Naturalist narrative. The data utilized are the discussions and responses from questions posed on ResearchGate. The retrieved responses reveal both positive and negative experiences, but more of the challenges encountered dominated the reviews. Many factors account for the negative experiences such as internet issues including its cost, student’s participation rate, insufficient media instructions, lack of students preparation, and preference for face to face class. The responses further show that various platforms enable teaching to continue remotely during the COVID-19 pandemic, and the preference of tools used by individuals differs based on interest, audience, location, the content of the course, accessibility, among others. We gave a conclusion and offered a useful suggestion for future study

Nigeria social enterprise perception on social issues, profit maximization, innovation, and knowledge sharing for poverty eradication

Abstract Poverty alleviation is one of the significant problems that social enterprise wants to solve. It could beabsolute, relative, generational, situational, urban, or rural poverty in Africa. There are scanty literature and a shallowunderstanding of how technology intervenes for social entrepreneurship in a developing context. This study wasconducted to address the identified gap. Quantitative method was used to gather data from the respondents utilizingNigerian samples. This study used SPSS ver. 26 and Python for the descriptive statistics and 5 Likert Scales dataanalysis for 33 measurement items. The result shows that Nigeria’s social enterprise focused on social issues such associal vision, social need, social change, social goal, social value, social mission, and social efforts. The knowledge ofSME owners’ insights on business start-ups in Nigeria and how social entrepreneurship could facilitate business growthand renewal is essential for future pursuit of social entrepreneurship. The importance of communication, operational,social, and collaborative technology was highlighted, and the study concluded with limitations and future researchopportunities

Insight from Nigerian banking customers discussions:a study of contextual semantic search and Twitter sentiment analysis

Abstract Globally, the bank is a significant financial institution that engages customers in millions daily. This financial institution helps its customers in saving their money, with withdrawals, and money transfer electronically. Despite the inevitability of these financial institutions, they are still struggling to improve their customer’s satisfaction index scores. The intervention of the Central Bank and Federal Competition and Consumer Protection Commission are yielding positive results whenever the local bank failed, but the response time to the customer grievances is quite challenging. The literature reviewed for this study indicates the cruciality of the sentiment analysis technique. This study utilized Twitter Crawler API called the Twitter Scraper for data collection and Textblob, Vader and SentiStrength for the data analyses. The result shows the sentiments in the International Authorization Banking group as against the National Authorization group and a slight difference between the polarities of their customer’s tweets. This study gives new insights internationally and nationally to banking managers and proposes future research directions

Stigma of Covid -19 on economic activities:a case study of Shein in Mexico

Abstract During the economic crisis due to a virus called COVID-19, different countries were affected worldwide, and several micro and macro companies have been affected because they had to temporarily close their business platform or experience a decrease in their sales. The conflict has reached Mexico, causing a voluntary quarantine. Mexicans’ perception of China has worsened because of the pandemic. International companies from China are affected by their operations. This store has a decrease in sales due to a bad perception of Chinese products by Mexicans. This research embarked on quantitative methodology with descriptive statistics, correlation and ANOVA through SPSS version 26 to answer the arising research questions. The results show a mixed feeling of the buyers. Some customers wish to continue to patronize e-commerce while others wish to disassociate themselves with the e-commerce platform due to the stigma of the country of origin. This finding reveals that there is a tendency for the e-commerce platform to lose some of its customers. This study contributes to the literature of stigma theoretically and managerially. The managerial contribution will help SHEIN to regain their lost reputation and other companies that suffers from COVID-19 stigmatization