Environmental sustainability assessment of wooden furniture produced in Pakistan

Life cycle assessment was carried out for a conventional wooden furniture set produced in Mardan division of the Khyber Pakhtunkhwa province of Pakistan during 2018-19. Primary data regarding inputs and outputs were collected through questionnaire surveys from 100 conventional wooden furniture set manufacturers, 50 in district Mardan and 50 in district Swabi. In the present study, cradle-to-gate life cycle assessment approach was applied for a functional unit of one conventional wooden furniture set. Production weighted average data were modelled in the environmental impacts modelling software i.e., SimaPro v.8.5. The results showed that textile used in sofa set, wood preservative for polishing and preventing insects attack and petrol used in generator had the highest contribution to all the environmental impact categories evaluated. Total cumulative energy demand for wooden furniture set manufactured was 30,005 MJ with most of the energy acquired from non-renewable fossil fuel resources.

Drought, Low Nitrogen Stress, and Ultraviolet-B Radiation Effects on Growth, Development, and Physiology of Sweetpotato Cultivars during Early Season

Drought, ultraviolet-B (UV-B), and nitrogen stress are significant constraints for sweetpotato productivity. Their impact on plant growth and development can be acute, resulting in low productivity. Identifying phenotypes that govern stress tolerance in sweetpotatoes is highly desirable to develop elite cultivars with better yield. Ten sweetpotato cultivars were grown under nonstress (100% replacement of evapotranspiration (ET)), drought-stress (50% replacement of ET), UV-B (10 kJ), and low-nitrogen (20% LN) conditions. Various shoot and root morphological, physiological, and gas-exchange traits were measured at the early stage of the crop growth to assess its performance and association with the storage root number. All three stress factors caused significant changes in the physiological and root- and shoot-related traits. Drought stress reduced most shoot developmental traits (29%) to maintain root growth. UV-B stress increased the accumulation of plant pigments and decreased the photosynthetic rate. Low-nitrogen treatment decreased shoot growth (11%) and increased the root traits (18%). The highly stable and productive cultivars under all four treatments were identified using multitrait stability index analysis and weighted average of absolute scores (WAASB) analyses. Further, based on the total stress response indices, ‘Evangeline’, ‘O’Henry’, and ‘Beauregard B-14’ were identified as vigorous under drought; ‘Evangeline’, ‘Orleans’, and ‘Covington’ under UV-B; and ‘Bonita’, ‘Orleans’, and ‘Beauregard B-14’ cultivars showed greater tolerance to low nitrogen. The cultivars ‘Vardaman’ and ‘NC05-198’ recorded a low tolerance index across stress treatments. This information could help determine which plant phenotypes are desirable under stress treatment for better productivity. The cultivars identified as tolerant, sensitive, and well-adapted within and across stress treatments can be used as source materials for abiotic stress tolerance breeding programs.

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Cost of integrating assisted partner services in HIV testing services in Kisumu and Homa Bay counties, Kenya: a microcosting study

Background HIV assisted partner services (aPS), or provider notification and testing for sexual and injecting partners of people diagnosed with HIV, is shown to be safe, effective, and cost-effective and was scaled up within the national HIV testing services (HTS) program in Kenya in 2016. We estimated the costs of integrating aPS into routine HTS within an ongoing aPS scale-up project in western Kenya. Methods We conducted microcosting using the payer perspective in 14 facilities offering aPS. Although aPS was offered to both males and females testing HIV-positive (index clients), we only collected data on female index clients and their male sex partners (MSP). We used activity-based costing to identify key aPS activities, inputs, resources, and estimated financial and economic costs of goods and services. We analyzed costs by start-up (August 2018), and recurrent costs one-year after aPS implementation (Kisumu: August 2019; Homa Bay: January 2020) and conducted time-and-motion observations of aPS activities. We estimated the incremental costs of aPS, average cost per MSP traced, tested, testing HIV-positive, and on antiretroviral therapy, cost shares, and costs disaggregated by facility. Results Overall, the number of MSPs traced, tested, testing HIV-positive, and on antiretroviral therapy was 1027, 869, 370, and 272 respectively. Average unit costs per MSP traced, tested, testing HIV-positive, and on antiretroviral therapy were $34.54, $42.50, $108.71 and $152.28, respectively, which varied by county and facility client volume. The weighted average incremental cost of integrating aPS was $7,485.97 per facility per year, with recurrent costs accounting for approximately 90% of costs. The largest cost drivers were personnel (49%) and transport (13%). Providers spent approximately 25% of the HTS visit obtaining MSP contact information (HIV-negative clients: 13 out of 54 min; HIV-positive clients: 20 out of 96 min), while the median time spent per MSP traced on phone and in-person was 6 min and 2.5 hours, respectively. Conclusion Average facility costs will increase when integrating aPS to HTS with incremental costs largely driven by personnel and transport. Strategies to efficiently utilize healthcare personnel will be critical for effective, affordable, and sustainable aPS.

Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial

Background: Intraoperative hypotension is associated with increased postoperative morbidity and mortality. Methods: We randomly assigned patients undergoing major general surgery to early warning system (EWS) and hemodynamic algorithm (intervention group, n = 20) or standard care (n = 20). The primary outcome was the difference in hypotension (defined as mean arterial pressure < 65 mmHg) and as secondary outcome surrogate markers of organ injury and oxidative stress. Results: The median number of hypotensive episodes was lower in the intervention group (−5.0 (95% CI: −9.0, −0.5); p < 0.001), with lower time spent in hypotension (−12.8 min (95% CI: −38.0, −2.3 min); p = 0.048), correspondent to −4.8% of total surgery time (95% CI: −12.7, 0.01%; p = 0.048).The median time-weighted average of hypotension was 0.12 mmHg (0.35) in the intervention group and 0.37 mmHg (1.11) in the control group, with a median difference of −0.25 mmHg (95% CI: −0.85, −0.01; p = 0.025). Neutrophil Gelatinase-Associated Lipocalin (NGAL) correlated with time-weighted average of hypotension (R = 0.32; p = 0.038) and S100B with number of hypotensive episodes, absolute time of hypotension, relative time of hypotension and time-weighted average of hypotension (p < 0.001 for all). The intervention group showed lower Neuronal Specific Enolase (NSE) and higher reduced glutathione when compared to the control group. Conclusions: The use of an EWS coupled with a hemodynamic algorithm resulted in reduced intraoperative hypotension, reduced NSE and oxidative stress.

The influence of methods of basic tillage and fertilizers on the content of basic nutrients in leached chernozem

Relevance. The preservation, reproduction and rational use of agricultural soil fertility is the main condition for the stable development of the agro-industrial complex. Mechanical tillage systems, the use of mineral and microbiological fertilizers are one of the main links in adaptive landscape farming systems. In the conditions of a sharp decrease in the rates of fertilizer application, an increase in the imbalance of elements of mineral nutrition of plants observed in recent years in agroecosystems, the function of improving the regimes of chernozems, preserving their fertility is designed to perform resource-saving technologies of soil cultivation in combination with effective methods of using agrochemicals that combine environmental and economic feasibility.Methods. The experiments were laid in 2017–2019 on chernozem heavy loamy soils typical for most farms in the Ulyanovsk region. The object of the study is spring wheat, the variety Ulyanovskaya 100. The subject of the study is the methods of tillage, doses of mineral fertilizers, the biological product "BisolbiFit". The following technological methods of using the biological product were studied: seed treatment before sowing, non-root treatment of vegetating plants and a combination of these methods. The experiment was carried out on three backgrounds: N0P0K0 (control); 2) N30P30K30; 3) N60P60K60.Results. It was found that the best nitrification ability was possessed by variants with fine combback and comb-back with soil-deepening treatment, in which the weighted average content of nitrate nitrogen was 3.29–3.33 mg/100 g, which is 35–36%; 26–28%; 43–44% more than with fine, conventional non-dump and dump treatment respectively. Plowing improved the conditions of phosphorus and potassium nutrition of plants by 25–37% and 6–14% compared to other treatments. When N30P30K30 and N60 P60 K60 were applied to the soil, the content of nitrate nitrogen increased by 46 and 91%, phosphorus — by 0–14% and potassium — by 6 and 21% compared to the nonfertilized background. More effective in terms of the effect on the productivity of spring wheat were comb-shaped treatments, where the average yield was 2.89–2.94 t/ha, which exceeded the usual plowing by 0.19–0.24 t/ha. The greatest increase in yield was obtained when combining the methodsseed treatment + spraying of vegetative plants with the biological preparation "BisolbiFit". On an unfertilized background, the increase in grain yield was -0.71, on the background of N30P30K30 — 1.04, on the background of N60P60K60 — 1.56 t/ha.

Sooting tendencies of diesel fuel component mixtures follow a linear mixing rule

With the growing importance of climate change, soot emissions from engines have been receiving increasing attention since black carbon is the second largest source of global warming. A sooting tendency can be used to quantify the extent of soot formation in a combustion device for a given fuel molecule, and therefore to quantify the soot reduction benefits of alternative fuels. However real fuels are complex mixtures of multiple components. In this work, we have used experimental methods to investigate how the sooting tendency of a blended fuel mixture is related to the sooting tendencies of the individual components. A test matrix was formulated that includes sixteen mixtures of six components that are representative of the main categories of hydrocarbons in diesel (eicosane (ECO) for alkanes, isocetane (ICE) for isoalkanes, butylcyclohexane (BCH) for cycloalkanes, 1-methylnaphthalene (1MN) for aromatics, tetralin for naphthoaromatics, and methyl-decanoate (MDC) for oxygenates). Most of the mixtures contain three to five components. The sooting tendency of each mixture was characterized by yield sooting index (YSI), which is based on the soot yield when a methane/air nonpremixed flame is doped with 1000 ppm of the test fuel. The YSIs were measured experimentally. The results show that the blending behavior is linear, i.e., the YSI of the mixtures is the mole-fraction-weighted average of the component YSIs. Experimental results have shown that the sooting tendency of a fuel mixture can be accurately estimated as the linear combination of the individual components. In addition, mass density of the mixtures is also measured, and a linear blending rule is applied to test whether mixing rules exist for mass density of diesel mixtures in this study. Results also have shown that the mixing rule tested in this study is valid and mass density of a mixture can be accurately estimated from the linear combination of the individual components.

Organic carbon in atmospheric precipitation in the urbanized territory of the South of Western Siberia, Russia

This paper presents the results of studying the contents of total (TOC) and dissolved (DOC) organic carbon in atmospheric precipitation and their deposition fluxes on the territory of the city of Barnaul. Samples of atmospheric precipitation (rain and snow) were collected from May 2016 to December 2020 in the city center, additionally at the end of winter 2018–2019 samples of snow cover were taken in the territory of the city and its environs. The studies showed a significant content of organic carbon (OC) in atmospheric precipitation: the weighted average concentrations for the study period were 7.2 ± 0.6 and 4.2 ± 0.4 mg/L for TOC and DOC, respectively. The annual flux of OC deposition with atmospheric precipitation on the territory of Barnaul over the past three years has varied within 2.4–3.9 t/km2 for TOC and 1.4–2.1 t/km2 for DOC. To visualize the spatial distribution of organic matter over the territory of Barnaul, simple kriging was used, implemented in the Geostatistical Analyst module (ArcGIS® Desktop). The flow of organic carbon input into the snow cover during the winter period was used as data for the geostatistical model. According to the model, the deposition of OC from the atmosphere occurs unevenly throughout the urban area and depends on the location and intensity of pollution sources.

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.