Machine learning based metal recovery from the waste printed circuit boards of mobile phones for circular economy and sustainable environment

Traditional building automation controllers are having low performance in dealing with non-linear phenomena. In recent years, model predictive control (MPC) has become a notable control algorithm for building automation system capable of handling non-linear processes. Performance of model-based controllers, such as MPC, is depending on reasonably accurate process models. For a building using baseboard radiator heater, a non-linear model is a more reliable representation of heat distribution system. Therefore, this study aims to present a non-linear gray-box model for a residential building connected to the local district heating network that is equipped with radiator heat emitters. The model is supposed to forecast the indoor air temperature as well as the radiator secondary return temperature. The model is validated using measurements collected from a building in Västerås, Sweden. In addition to a better accuracy, another motivation behind using a non-linear heating circuit model is to enhance its generalization performance. With the added benefits of accuracy and generalization, this model is expected to extend practical MPC implementation for such buildings

Fixed Point Theorems for Nonexpansive Type Mappings in Banach Spaces

In this paper, we present some fixed point results for a class of nonexpansive type and α-Krasnosel’skiĭ mappings. Moreover, we present some convergence results for one parameter nonexpansive type semigroups. Some non-trivial examples have been presented to illustrate facts.The authors thanks the Basque Government for its support through Grant IT1207-19

Stillbirth at Patan Hospital, Nepal

Introductions:  Stillbirth (SB) is one of the most common adverse outcomes of pregnancy.  The aim of this study was to determine the SB rate and to identify the likely causes contributing to SB. Methods: This cross-sectional study was conducted at Patan Hospital from 15th June 2014 to 14th June 2017 for all the cases of SBs, at or after 22 weeks, birth weight of 500 gm or more. The perinatal outcome, demographic profile, fetal characteristics, causes and contributing factors were analyzed. Results: There were 262 SB out of total 23069 deliveries, (11.24 per 1000) and 119 (46.12%) had antenatal check-up (ANC) at Patan Hospital. The 214 (82.95%) SB were among 20-34 years mothers, 133 (51.55%) being multigravida. Antepartum SB were 234 (89.31%), macerated 213 (81.30%), birth weight <1000gm 86 (32.82%) and male 156 (59.54%).  The intrauterine growth restriction (IUGR) was present in 60 (22.90%), unexplained casue in 43 (16.41%), prematurity 28 (10.69%), congenital anomalies 26 (9.92%), pre-eclampsia 19 (7.25%), gestational diabetes, and abruptio placenta each 13 (4.96%). Delay in seeking care in 202 (78.30%) was a potential contributing factor. Conclusions:  The SB was 11.24/1000 births. The causes in descending order were IUGR, unexplained, prematurity, congenital anomalies, pre-eclampsia, gestational diabetes and abruptio placenta. Delay in seeking care was found as a potential contributing factor. Keywords:  antenatal check-up (ANC), birth weight, intrauterine growth restriction (IUGR), stillbirt

Towards circular economy of wasted printed circuit boards of mobile phones fuelled by machine learning and robust mathematical optimization framework

Estimating the operating conditions using conventional process analysis techniques for the maximum metal extraction from the wasted printed circuit boards (WPCB) can provide sub-optimal solutions leading to the low yield of the process. In this paper, we present a closed-loop methodological framework built on machine learning and robust mathematical optimization technique, that offers the mathematical rigour, to determine the optimum operating conditions for the maximum Cu and Ni recovery from the WPCB. Alkali leaching based novel metals recovery process from the WPCB is designed, and the experiments are conducted to collect the data on the percentage recovery of Cu and Ni against the operating levels of the process input variables (ammonia concentration (NH3 conc. (g/L)), ammonium sulfate concentration ((NH4)2SO4 conc. (g/L)), H2O2 concentration (H2O2 conc. (M)), time (h), liquid to solid ratio (L/S ratio, (mL/g)), temperature (Temp. (°C)), and stirring speed (rpm)). The experimental data is deployed to construct the functional mapping between the nonlinear output variables of metals recovery process with the hyperdimensional input space through artificial neural network (ANN) based modelling algorithm – a powerful universal function approximator. Well-predictive ANN models for Cu and Ni recovery are developed having co-efficient of determination (R2) value more than 0.90. Partial derivative-based sensitivity analysis is then carried out to establish the order of the significance of the input variables that is backed by the domain knowledge, thus promotes the interpretability of the trained ANN models. The hybridization of ANN with NLP (nonlinear programming) framework is implemented for the determination of optimized operating conditions to extract maximum Cu and Ni under separate and combined model of metal extraction. The robustness of the determined solutions is verified, the determined optimized solutions for the metal recovery are validated in the lab, and the maximum metal recovery, i.e., 100 % Cu and 90 % Ni is extracted from the WPCB. This research demonstrates the effective utilization of ANN model-based robust optimization approach for the metal recovery from the WPCB that supports the circular economy for the metal extraction industry

Microwave spin resonance investigation on the effect of the post-processing annealing of CoFe2O4 nanoparticles

A novel investigation on the finite-size effects on the spin resonance properties of cobalt ferrite (CoFe2O4) nanoparticles has been performed using a room temperature ferromagnetic resonance (FMR) technique. A single broad spectrum was obtained for the CoFe2O4 nanoparticle samples, which indicated that all the samples were showing ferromagnetic characteristics. An asymmetric FMR line shape with a hefty trailing section was obtained due to the high magneto-crystalline anisotropy in CoFe2O4 nanoparticles, which changed with the size distribution. The resonance field for the samples shifted to a higher value due to the increase in the magneto-crystalline anisotropy in the CoFe2O4 nanoparticles with an increase in size. A systematic change in the resonance field and line width was observed with the change in the size distribution of the particles. Initially, it decreased with an increase in the size of the particles and increased after the critical size range. The critical size range is the imprint of the shift of the magnetic domain from a single domain to multi domain. The line width increased at higher annealing temperatures due to the enhancement in the dipole-dipole interaction, which led to a higher spin concentration as well as magneto-crystalline anisotropy. Furthermore, the saturation magnetization (M-s) as well as 'M-r/M-s' increased from 37.7 to 71.4 emu g(-1) and 0.06 to 0.31, respectively. The highest coercivity (750.9 Oe) and anisotropy constant (4.62 x 10(4) erg cm(-3)) were found for the sample annealed at 700 degrees C, which can be corroborated by the literature as the critical annealing temperature at which CoFe2O4 nanoparticles shift from single domain nanoparticles to multi-domain nanoparticles. Post-processing annealing is critical in advanced processing techniques and spin dynamics plays a vital role in various interdisciplinary areas of applications

Structure-based virtual screening methods for the identification of novel phytochemical inhibitors targeting furin protease for the management of COVID-19

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is a highly contagious respiratory disease with widespread societal impact. The symptoms range from cough, fever, and pneumonia to complications affecting various organs, including the heart, kidneys, and nervous system. Despite various ongoing efforts, no effective drug has been developed to stop the spread of the virus. Although various types of medications used to treat bacterial and viral diseases have previously been employed to treat COVID-19 patients, their side effects have also been observed. The way SARS-CoV-2 infects the human body is very specific, as its spike protein plays an important role. The S subunit of virus spike protein cleaved by human proteases, such as furin protein, is an initial and important step for its internalization into a human host. Keeping this context, we attempted to inhibit the furin using phytochemicals that could produce minimal side effects. For this, we screened 408 natural phytochemicals from various plants having antiviral properties, against furin protein, and molecular docking and dynamics simulations were performed. Based on the binding score, the top three compounds (robustaflavone, withanolide, and amentoflavone) were selected for further validation. MM/GBSA energy calculations revealed that withanolide has the lowest binding energy of −57.2 kcal/mol followed by robustaflavone and amentoflavone with a binding energy of −45.2 kcal/mol and −39.68 kcal/mol, respectively. Additionally, ADME analysis showed drug-like properties for these three lead compounds. Hence, these natural compounds robustaflavone, withanolide, and amentoflavone, may have therapeutic potential for the management of SARS-CoV-2 by targeting furin

Particles, air quality, policy and health

The diversity of ambient particle size and chemical composition considerably complicates pinpointing the specific causal associations between exposure to particles and adverse human health effects, the contribution of different sources to ambient particles at different locations, and the consequent formulation of policy action to most cost-effectively reduce harm caused by airborne particles. Nevertheless, the coupling of increasingly sophisticated measurements and models of particle composition and epidemiology continue to demonstrate associations between particle components and sources (and at lower concentrations) and a wide range of adverse health outcomes. This article reviews the current approaches to source apportionment of ambient particles and the latest evidence for their health effects, and describes the current metrics, policies and legislation for the protection of public health from ambient particles. A particular focus is placed on particles in the ultrafine fraction. The review concludes with an extended evaluation of emerging challenges and future requirements in methods, metrics and policy for understanding and abating adverse health outcomes from ambient particles

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio