Explore the vast range of titles available.

In Pakistan, reinforced concrete frame houses are the most widely used and common construction technology. In a country that experiences extreme hot and cold seasons throughout the year, buildings need to be adaptable to the climate to improve the thermal comfort of the inhabitants. Therefore, the aim of this study was to improve thermal comfort in reinforced concrete frame houses using passive design and energy efficiency measures in Quetta, Pakistan. Thermal comfort of a representative house was investigated using a building performance simulation. The building model created in EnergyPlus was validated by comparing it with on-site monitored data in both summer and winter seasons. The model was calibrated using statistical methods. Then, the calibrated model was used to perform a whole year simulation in which various orientations, ventilation, passive design, and energy efficiency strategies were applied to perform parametric analysis for the improvement of thermal comfort. The best fit-to-context thermal comfort model was selected, and the potential of bioclimatic design strategies was quantified. The results indicate that by adopting passive design strategies comfort hours can be increased from 43% to 59%. The results of the study revealed many findings which could be useful for architects and building engineers to set a future direction for improvement of indoor comfort in Quetta as well as in many other areas of Balochistan Province in Pakistan.

Recent literature has reported that radiological features of coronavirus disease (COVID-19) patients are influenced by computed tomography. This study aimed to assess the characteristic chest X-ray features of COVID-19 and correlate them with clinical outcomes of patients. This retrospective study included 120 COVID-19 patients. Baseline chest X-rays and serial chest X-rays were reviewed. A severity index in the form of maximum radiological assessment of lung edema (RALE) score was calculated for each lung, and scores of both the lungs were summed to obtain a final score. The mean ± standard deviation (SD) and frequency (%) were determined, and an unpaired t test, Spearman’s rank correlation coefficient, and logistic regression analyses were performed for statistical analyses. Among 120 COVID-19 patients, 74 (61.67%) and 46 (38.33%) were males and females, respectively; 64 patients (53.33%) had ground-glass opacities (GGO), 55 (45.83%) had consolidation, and 38 (31.67%) had reticular-nodular opacities, with lower zone distribution (50%) and peripheral distribution (41.67%). Baseline chest X-ray showed a sensitivity of 63.3% in diagnosing typical findings of SARS-CoV-2 pneumonia. The maximum RALE score was 2.13 ± 1.9 in hospitalized patients and 0.57 ± 0.77 in discharged patients (p value <0.0001). Spearman’s rank correlation coefficient between maximum RALE score and clinical outcome parameters was as follows: age, 0.721 (p value <0.00001); >10 days of hospital stay, 0.5478 (p value <0.05); ≤10 days of hospital stay, 0.5384 (p value <0.0001); discharged patients, 0.5433 (p value <0.0001); and death, 0.6182 (p value = 0.0568). The logistic regression analysis revealed that maximum RALE scores (0.0932 [0.024–0.367]), (10.730 [2.727–42.206]), (1.258 [0.990–1.598]), and (0.794 [0.625–1.009]) predicted discharge, death, >10 days of hospital stay, and ≤10 days of hospital stay, respectively. The study findings suggested that the RALE score can quantify the extent of COVID-19 and can predict the prognosis of patients.

Begomoviruses have emerged as destructive pathogens of crops, particularly in the tropics and subtropics, causing enormous economic losses and threatening food security. Epidemics caused by begomoviruses have even spread in regions and crops that were previously free from these viruses. The most seriously affected crops include cassava; cotton; grain legumes; and cucurbitaceous, malvaceous, and solanaceous vegetables. Alphasatellites, betasatellites, and deltasatellites are associated with the diseases caused by begomoviruses, but begomovirus–betasatellite complexes have played significant roles in the evolution of begomoviruses, causing widespread epidemics in many economically important crops throughout the world. This article provides an overview of the evolution, distribution, and approaches used by betasatellites in the suppression of host plant defense responses and increasing disease severity.

The study deals with a three-storey log house located in the suburbs of Ljubljana, Slovenia (temperate climate). Firstly, the calibrated thermal model of the log house was defined. The calibrated model had an hourly NMBE between −2.12 % and 1.84 % and a CV(RMSE) between 3.16 % and 3.57 %. Then, the adaptive thermal comfort during the warmer part of the year was assessed according to EN 16798-1 and future climate (SRES A2 scenario). Additionally, various building-related and organisational measures for overheating prevention were evaluated. It was found that the most effective measures to prevent overheating are the organisational measures of shading activation and night ventilation. It was demonstrated that the efficiency of night ventilation would even improve over time. Thus, at the end of the 21 st century, discomfort hours could be reduced by 67 % compared to the baseline. In contrast, building-related measures have a significant effect only when combined with organisational measures. Overall, in 2071–2100 adaptive thermal comfort was improved most when the measure of increased thermal insulation was coupled with shading and night ventilation, resulting in 1053 discomfort hours less than the baseline case.

The main focus of this work is on the systematic examination of the dynamical fluctuation detectable by two-particle rapidity correlations σc2 in the forward–backward (FB) pseudo-rapidity windows by analyzing the experimental data obtained from the interactivity of 84Kr nuclei with AgBr target nuclei of nuclear emulsion detector (NED) at 1 A GeV. The FB multiplicity fluctuation of created pions in NED has been also conducted using the highly intense quantity technique ΣFB. The results are compared with other available experimental observations and found to be consistence. When compared to the higher energy data that have been previously analyzed, the results on very intensive fluctuation measures at a lower energy show substantially more variations.

Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry’s small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.

The integration of multiple technical, economic, environmental, and social criteria establishes Multi-Criteria Decision Analysis (MCDA) as a dependable decision-making tool in the context of interdisciplinary research. This study employs a literature-based methodology to illustrate how MCDA, particularly utilizing the Analytical Hierarchy Process (AHP) and TOPSIS models, has been utilized to tackle intricate decision-making issues. It also highlights the noteworthy discoveries derived from real-world applications, drawing upon previous research and case studies. This study explores the methodologies employed in the commonly utilized AHP and TOPSIS approaches, highlighting their broad applicability across various industries from 2000 to 2023. Additionally, a comprehensive examination of the applications of MCDA has been organized into five distinct sectors, namely supply chain, healthcare, business, resource management, and engineering & manufacturing.

Objective: This study was conducted to assess the level of pregnancy-associated glycoprotein (PAG) in whole and skim milk samples, and its suitability for early pregnancy diagnosis in goats.Methods: A two-step sandwich enzyme-linked immunosorbent assay (ELISA) system for estimation of milk PAG was developed and validated, which employed caprine-PAG specific polyclonal antisera. Whole and skim milk samples (n = 210 each) from fifteen multiparous goats were collected on alternate days from d 10 to d 30, and thereafter weekly till d 51 postmating. PAG levels in milk samples were estimated by ELISA and the pregnancies were confirmed at d40 post-mating by transrectal ultrasonography (TRUS).Results: The level of PAG in whole and skim milk samples of both pregnant and nonpregnant goats remained below the threshold values until d 24 after mating. Thereafter, PAG concentration in whole and skim milk increased steadily in pregnant goats, whereas it continued below the threshold in non-pregnant does. The PAG profiles in whole and skim milk of pregnant goats were almost similar and exhibited strong positive relationship (r = 0.891; p<0.001). Day 26 post-mating was identified as the first time-point for significantly (p<0.05) higher milk PAG concentration in pregnant goats than to non-pregnant goats. When compared to TRUS examination for pregnancy diagnosis, the accuracy and specificity of PAG ELISA using whole and skim milk samples were 94.5% and 95.4%; and 95.3% and 100%, respectively. The high values of area-under-curve (0.904 [whole milk] and 0.922 [skim milk]), demonstrate outstanding discrimination ability of the milk assays. Among the sampling dates chosen, d 37 post-mating was identified as the best suitable time point for collection of milk samples to detect pregnancy in goats.Conclusion: The PAG concentration in whole and skim milk of goats collected between days 26 and 51 post-breeding can be used for the accurate prediction of pregnancy and may be useful for assisting management decisions in goat flocks.

Selecting alternatives wisely is essential in any multi-attribute decision-making (MADM) process, and it requires the participation of multiple experts. Initial decision data and the contributing experts’ reliability affect the preciseness of the decision outcomes. However, no expert possesses exhaustive knowledge, and their estimations may exhibit varying preferences. Many times, experts hesitate to give contradictory/ conflicting information. Hence, the information gathered from them includes the hesitancy of the expert and conflict. To account for expert influence while preserving the integrity of the original data during aggregation, this paper proposes a hesitant bi-fuzzy information fusion (HBFIF) method that integrates expert reliability (ER) into the decision-making framework in the conflicting framework. Initially, the impression of expert reliability in the decision-making process is examined, and a reliability metric is constructed based on the degree of similarity among expert opinions. The power average (PA) operator is then used with expert reliability to aggregate expert preferences while preserving as much of the original data as feasible. Subsequently, inspired by the TOPSIS method, the hesitant bi-fuzzy information fusion approach is developed to maintain experts’ original opinions and risk preferences depending on their assessed reliability. Finally, the proposed HBFIF method is applied to the multi-attribute decision-making framework in selecting an efficient hydrogen storage method in automobiles to confirm its effectiveness and applicability, underscoring its practical relevance and implementation.

Postinfectious glomerulonephritis (PIGN) is primarily a disease of childhood. It occurs after upper respiratory tract infection or skin infections. Streptococcus is the most common causative agent, but in the elderly, staphylococcus is the main culprit. In adults, PIGN is more common in immunocompromised patients, particularly diabetics and alcoholics. Here, we report the case of an elderly diabetic male who presented with severe acute kidney injury with active urinary sediment after acute gastroenteritis. Additional analyses revealed a very low serum C3 level and a normal serum C4 level. Renal biopsy showed diffuse proliferative glomerulonephritis with crescents. Direct immunofluorescence showed mesangial and capillary wall staining for C3 and IgG (2+, mesangial and segmental capillary wall, granular). Renal electron microscopy showed subepithelial hump-like electron-dense deposits. The role of steroid in the treatment of PIGN is controversial and there is no standard protocol, but our patient responded very well to steroid as he did not require hemodialysis after 2 weeks of initiation of steroid therapy. We should be aware of an atypical presentation of PIGN in elderly to ensure correct diagnosis.