Explanatory Variables of Dropout in Colombian Public Education: Evolution Limited to Coronavirus Disease

<p style="text-align: justify;">Student dropout, defined as the temporary or definitive suspension of the exercise of the right to education, is attributable to multiple variables classified into individual, academic, institutional, and socioeconomic determinants which may be exacerbated in the context of the Coronavirus disease (COVID-19) pandemic. Consequently, this work aims to synthesize, from the available evidence, the behaviour and influence of the explanatory variables of school dropout in infant school, primary school and, high school in Colombia for the period 2014-2019 compared to the period 2020-2021 under the COVID-19 pandemic conditions. The research methodology consisted of a systematic review of 125 indexed articles for 2014-2019 and 32 reports related to dropout in Colombian Basic education for the 2020-2021 period. The systematic review of the 157 articles revealed that dropout was studied and explained in both time periods, mainly from the academic determinant whose most cited explanatory variables were: ‘teachers’, ‘curriculum’ and ‘methodologies used’. Moreover, it could be perceived that in the period 2014-2019, the socioeconomic variable was the second dropout determinant, considering ‘family income” as the most important indicator, while in 2020-2021 the “infrastructure” and the ‘political environment’ remained as the most dominant. Lastly, in 2020-2021, the variable ‘teachers’ was highly cited showing that their practice made students maintain their interest despite the physical distance.</p>

Surgical management of continuous and mixed type of OPLL in cervical spine: Experience in tertiary level hospital.

Background: Ossification of the posterior longitudinal ligament (OPLL) is a chronically progressive disease of ectopic enchondral and membranous ossification of posterior longitudinal ligament (PLL). Controversy still persists over the superiority of various surgical approaches for cervical OPLL management. Purpose: To see the efficacy of expansive laminoplasty for the management of continuous and mixed type of cervical OPLL retrospectively. Methods: Records of 20 male and 8 female aged 36-72 years (mean, 56.64 years), who underwent surgical treatment posteriorly for continuous and mixed type OPLL by laminoplasty were obtained from the year 2004 - 2020. Clinical features along with imaging studies, which included X -ray/CT /MRI, were done for the diagnosis of OPLL. Multiple variables were studied, including demographics, surgical parameters, complications and functional outcomes. Results: They were followed on an average of 59.86 ± 20.95 months (range, 24 -108 months). The average operative duration was 95 ± 15.52 min (range: 70 - 140), and the intraoperative blood loss was 199.29 ± 33.55 ml. The cervical curvature index reduced to 8.81 ± 1.96 from 11.00 ± 2.49 and the VAS score decreased from 4.25 ± 0.75 to 2.43 ± 1.40. mJOA score improved from 8.64 ± 1.03 to 13.96 ± 1.26 on the last follow-up after surgery (p < 0.01), with average recovery rate of 65.5 %. Conclusions: The management for cervical myelopathy with multilevel stenosis due to continuous and mixed type of OPLL by Laminoplasty is safe and effective.

Honey bee (Apis mellifera ligustica) acetylcholinesterase enzyme activity and aversive conditioning following aluminum trichloride exposure

Background Aluminum is the third most prevalent element in the earth’s crust. In most conditions, it is tightly bound to form inaccessible compounds, however in low soil pH, the ionized form of aluminum can be taken up by plant roots and distributed throughout the plant tissue. Following this uptake, nectar and pollen concentrations in low soil pH regions can reach nearly 300 mg/kg. Inhibition of acetylcholinesterase (AChE) has been demonstrated following aluminum exposure in mammal and aquatic invertebrate species. In honey bees, behaviors consistent with AChE inhibition have been previously recorded; however, the physiological mechanism has not been tested, nor has aversive conditioning. Results This article presents results of ingested aqueous aluminum chloride exposure on AChE as well as acute exposure effects on aversive conditioning in an Apis mellifera ligustica hive. Contrary to previous findings, AChE activity significantly increased as compared to controls following exposure to 300 mg/L Al3+. In aversive conditioning studies, using an automated shuttlebox, there were time and dose-dependent effects on learning and reduced movement following 75 and 300 mg/L exposures. Conclusions These findings, in comparison to previous studies, suggest that aluminum toxicity in honey bees may depend on exposure period, subspecies, and study metrics. Further studies are encouraged at the moderate-high exposure concentrations as there may be multiple variables that affect toxicity which should be teased apart further.

Dryland irrigation increases accumulation rates of pedogenic carbonate and releases soil abiotic CO2

Agricultural fields in drylands are challenged globally by limited freshwater resources for irrigation and also by elevated soil salinity and sodicity. It is well known that pedogenic carbonate is less soluble than evaporate salts and commonly forms in natural drylands. However, few studies have evaluated how irrigation loads dissolved calcium and bicarbonate to agricultural fields, accelerating formation rates of secondary calcite and simultaneously releasing abiotic CO2 to the atmosphere. This study reports one of the first geochemical and isotopic studies of such “anthropogenic” pedogenic carbonates and CO2 from irrigated drylands of southwestern United States. A pecan orchard and an alfalfa field, where flood-irrigation using the Rio Grande river is a common practice, were compared to a nearby natural dryland site. Strontium and carbon isotope ratios show that bulk pedogenic carbonates in irrigated soils at the pecan orchard primarily formed due to flood-irrigation, and that approximately 20–50% of soil CO2 in these irrigated soils is calcite-derived abiotic CO2 instead of soil-respired or atmospheric origins. Multiple variables that control the salt buildup in this region are identified and impact the crop production and soil sustainability regionally and globally. Irrigation intensity and water chemistry (irrigation water quantity and quality) dictate salt loading, and soil texture governs water infiltration and salt leaching. In the study area, agricultural soils have accumulated up to 10 wt% of calcite after just about 100 years of cultivation. These rates will likely increase in the future due to the combined effects of climate variability (reduced rainfall and more intense evaporation), use of more brackish groundwater for irrigation, and reduced porosity in soils. The enhanced accumulation rates of pedogenic carbonate are accompanied by release of large amounts of abiotic CO2 from irrigated drylands to atmosphere. Extensive field studies and modelling approaches are needed to further quantify these effluxes at local, regional and global scales.

Applying Machine Learning and AI Explanations to Analyze Vaccine Hesitancy

The paper identifies and quantifies the impact of race, poverty, politics, and age on COVID-19 vaccination rates in counties across the continental US. Both traditional Ordinary Least Square (OLS) regression analysis and Random Forest machine learning algorithms are applied to quantify contributing factors for county-level vaccination hesitancy. With the machine learning model, joint effects of multiple variables (race/ethnicity, partisanship, age etc.) are considered simultaneously to capture the unique combination of what factors affect the vaccination rate. By implementing a state-of-the-art Artificial Intelligence Explanations (AIX) algorithm, it is possible to solve the black box problem with machine learning models and provide answers to the "how much" question for each measured impact factor in every county. For most counties a higher percentage vote for Republicans, a greater African American population share, and a higher poverty rate lower the vaccination rate. While a higher Asian population share increases the predicted vaccination rate. The impact on the vaccination rate from the Hispanic population proportion is positive in the OLS model, but only positive for counties with very high Hispanic population (65% and more) in the Random Forest model. Both the proportion of seniors and the one for young people in a county have a significant impact in the OLS model - positive and negative, respectively. In contrast, the impacts are ambiguous in the Random Forest model. Because results vary between geographies and since the AIX algorithm is able to quantify vaccine impacts individually for each county, this research can be tailored to local communities. This way it is a helpful tool for local health officials and other policymakers to improve vaccination rates. An interactive online mapping dashboard that identifies impact factors for individual U.S. counties is available at https://www.cpp.edu/~clange/vacmap.html. It is apparent that the influence of impact factors is not universally the same across different geographies.

Climate Change and Health Impacts in Pakistan

Conferring to the Global Risk Index, Pakistan is ranked as the 7th most susceptible country to the inexorable influence of climate change. Before this century ends, the annual mean temperature in Pakistan is expected to rise from 3°C to 5°C for a focal worldwide discharge situation. Usually, annual precipitation is not relied upon to have a critical long haul pattern. Ocean level is relied upon to ascend further by 60 centimeters. All these climatic events are likely to disrupt the economy, lives, and the socio-political aspects of human life. Pakistan has already witnessed massive loss in terms of human, infrastructural, and economic aspects. The chapter is designed to understand both the direct and indirect health risks associated with frequent climatic events like floods, drought, and heat waves in Pakistan. After analyzing the available literature, it was observed that floods and drought have direct and indirect health risks associated with them while in case of heat waves, health risks cannot be established precisely as multiple variables are involved, playing a significant role.

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential.

Optimised Reduction of Surgical Gloves Pinholes using Forward Search Method

This research investigates the factors that affect the existence of pinholes in surgical gloves during the manufacturing process. Since eight factors affect the existence of pinholes in surgical gloves, a two-level fractional factorial design 28-4 was used to study the main effects and the first-order interactions of the multiple variables. Multiple linear regressions are used to model the data. This paper also examines the presence of influential points in the data using the influential measures in linear regression such as Cook’s Distance, DFFITS, DFBETAS, Studentized Residual, Standardized Residual, Hadi's measure, and the robust forward search. The impact of influential points is further assessed through deletion of potential influential points and model selection using adjusted R2, information criterion, and stepwise selection to see whether these influential points significantly improved the existing model.

Structural Timber Connections with Dowel-Type Fasteners and Nut-Washer Fixings: Mechanical Characterization and Contribution to the Rope Effect

Dowel-type fasteners are one of the most used type of connections in timber joints. Its design follows the equations included in the Eurocode 5. The problem with these equations is that they do not adequately contemplate the resistive capacity increase of these joints, when using configurations which provoke the so-called rope effect. This effect appears when using threaded surface dowels instead of flat surface dowels, expansion kits or nut-washer fixings at the end of the dowel. The standards consider this increase through a constant value, which is a poor approximation, because it is clearly variable, depending on the joint displacement and because is much bigger, especially when using nut-washer fixings. It is also very important because of the rope effect trigger interesting mechanisms that avoids fragile failures without warning of the joints. For these reasons, it is essential to know how these configurations work, how they help the joint to resist the external loads and how much is the increase resistance capacity in relationship with the joint displacement. The methods used to address these issues consisted of a campaign of experimental tests using actual size specimens with flat surface dowels, threaded surface dowels and dowels with washer-nut fixings at their ends. The resistance capacity results obtained in all the cases has been compared with the values that will come using the equations in the standards. After the tests the specimens were cut to analyze the timber crushings, their widths, the positions and level of plasticizations suffer in the steel dowels and in the washer-nut fixings and the angle formed in the dowel plastic hinges. With all this information the failure mode suffered by the joints has been identified and compared with the ones that the standards predict. The results for the size materials and types of joints studied shows that the crush width average values go from 20 mm with flat surface dowels, to 24 mm in threaded to 32 mm in threaded with washer-nut fixings. The rope effect force/displacement goes from 100 N/m in threaded surface dowels to 500 N/m in threaded with washer-nut fixings. Finally, the load capacities are on average 290% higher those indicated in the standard. The main conclusion is that the rope effect force should be considered in the standards in more detail as a function of multiple variables, especially the displacement of the joint.