A worldwide quantitative review of the iSchools' archival education

PurposeArchival studies have long been a critical part of information education around the world. This paper attempts to provide a worldwide overview of archival education among main information schools worldwide and find out their similarity and differences to suggest measures for the development of archival education.Design/methodology/approachQuantitative research is conducted including ten elements of the iSchools' archival education which are (1) geographical distribution, (2) names of degrees, (3) names of concentration/specialization, (4) names of academic units offering the programs, (5) levels of academic units offering the programs, (6) study mode, (7) credit requirement for program completion, (8) percentage of required credits, (9) capstone requirements and (10) other accreditations. Programs among different regions are compared.FindingsThe study found that 43 out of 96 iSchool members from 13 countries/regions offer a total of 45 master's level archival education, and most of them are from North America. Both similarities and differences among the schools are identified and discussed.Practical implicationsThis study’s findings suggest that iSchools may explore the possibility of organizing more conferences and forums to exchange ideas on archival studies and education issues. The iSchool community could contribute to this traditional field by attracting more members worldwide and cooperating with other accreditation organizations of archival education.Originality/valueMost research on archival education focuses on just regional or country-based issues, and scant research explores a global view.

Another Perspective of the Miller Forensic Assessment of Symptoms Test – Part II: A Quantitative Review

Reference [1] presented pooled data for the specificity of the M-FAST cut-off, but ignored or excluded data based on poor justifications and used questionable analytic methods. The analyses here corrected the problems associated with [1]. No moderator substantively influenced sensitivity values. Therefore, sensitivity values were pooled across all studies (k = 25) to provide an overall estimate. Overall, the average sensitivity of the M-FAST cut-off was estimated to be 0.87, 95% CI [0.80, 0.91], and 80% of true sensitivity values were estimated to range from 0.63 to 0.96. Thus, there could be methodological scenarios when the M-FAST cut-off may not operate efficiently. Average specificity values for the M-FAST cut-off were moderated by one variable: the comparison group. On average, specificity values for clinical comparison (k = 15) groups (i.e., 0.80, 95% CI [0.73, 0.85]) were lower than specificity values for non-clinical comparison (k = 11) groups (i.e., 0.96, 95% CI [0.89, 0.99]). Unlike the CIs, the estimated distributions of true specificity values for the two subgroups overlapped, which suggests there could be scenarios when these subgroups share the same true specificity value. The M-FAST was designed to be a screener to detect potential feigning of psychiatric symptoms. An examinee is never to be designating as feigning or malingering psychiatric symptoms based on only a positive M-FAST result. As a screening instrument, the results here show that the M-FAST cut-off is operating adequately overall and negate the conclusions of [1].

A Quantitative Review of Irrigation Development in the Yazoo–Mississippi Delta from 1991 to 2020

The Yazoo–Mississippi Delta is one of the regions within the Lower Mississippi River Basin where substantial irrigation development and consequent groundwater depletion have occurred over the past three decades. To describe this irrigation development, a study was conducted to analyze existing geospatial datasets and to synthesize the results with those of past government surveys. The effort produced a quantitative review characterizing three aspects of irrigation development from 1991 to 2020. First, the expansion of irrigated area was tracked in terms of absolute area and in terms of fraction relative to total land or cropland area. Second, trends in irrigated land cover were traced in terms of irrigated crop mix, irrigated fractions of main crops, and comparisons with non-irrigated land. Third, changes in irrigation systems were examined in terms of water sources, energy sources, and application methods. Original findings of this study for the end of 2020 included moderate positive spatial autocorrelation in the density of irrigated areas; a higher irrigated crop preference for soybean and rice over cotton and corn in highly hydric soils; and 91% and 3% of permitted areas studied being respectively under groundwater withdrawal permits exclusively and under surface water diversion permits exclusively. By compiling such information, this paper can serve as a convenient reference on the recent history and status of irrigation development in the Yazoo–Mississippi Delta.

Determinants and Cross-National Moderators of Wearable Health Tracker Adoption: A Meta-Analysis

Wearable health trackers improve people’s health management and thus are beneficial for social sustainability. Many prior studies have contributed to the knowledge on the determinants of wearable health tracker adoption. However, these studies vary remarkably in focal determinants and countries of data collection, leading to a call for a structured and quantitative review on what determinants are generally important, and whether and how their effects on adoption vary across countries. Therefore, this study performed the first meta-analysis on the determinants and cross-national moderators of wearable health tracker adoption. This meta-analysis accumulated 319 correlations between nine determinants and adoption from 59 prior studies in 18 countries/areas. The meta-analytic average effects of the determinants revealed the generalized effect and the relative importance of each determinant. For example, technological characteristics generally had stronger positive correlations with adoption than consumer characteristics, except for privacy risk. Second, drawing on institutional theory, it was observed that cross-national characteristics regarding socioeconomic status, regulative systems, and cultures could moderate the effects of the determinants on adoption. For instance, the growth rate of gross domestic product decreased the effect of innovativeness on adoption, while regulatory quality and control of corruption could increase this effect.

LED Illumination for High-Quality High-Yield Crop Growth in Protected Cropping Environments

Vegetables and herbs play a central role in the human diet due to their low fat and calory content and essential antioxidant, phytochemicals, and fiber. It is well known that the manipulation of light wavelengths illuminating the crops can enhance their growth rate and nutrient contents. To date, it has not been easy to generalize the effects of LED illumination because of the differences in the plant species investigated, the measured traits, the way wavelengths have been manipulated, and the plants’ growing environments. In order to address this gap, we undertook a quantitative review of LED manipulation in relation to plant traits, focusing on vegetables and herbs. Here, we use standardized measurements of biomass, antioxidant, and other quantitative characteristics together with the whole range of the photosynthetic photon flux density (PPFD). Overall, our review revealed support for the claims that the red and blue LED illumination is more reliable and efficient than full spectrum illumination and increases the plant’s biomass and nutritional value by enhancing the photosynthetic activity, antioxidant properties, phenolic, and flavonoids contents. Although LED illumination provides an efficient way to improve yield and modify plant properties, this study also highlights the broad range of responses among species, varieties traits, and the age of plant material.

Study on Significant Drift in the Domain of Explainable Artificial Intelligence

Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.