Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data.

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992-2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

Bioactive Streptomycetes: A Powerful Tool to Synthesize Diverse Nanoparticles With Multifarious Properties.

Nanobiotechnology has gained significant attention due to its capacity to generate substantial benefits through the integration of microbial biotechnology and nanotechnology. Among microbial organisms, Actinomycetes, particularly the prominent genus Streptomycetes, have garnered attention for their prolific production of antibiotics. Streptomycetes have emerged as pivotal contributors to the discovery of a substantial number of antibiotics and play a dominant role in combating infectious diseases on a global scale. Despite the noteworthy progress achieved through the development and utilization of antibiotics to combat infectious pathogens, the prevalence of infectious diseases remains a prominent cause of mortality worldwide, particularly among the elderly and children. The emergence of antibiotic resistance among pathogens has diminished the efficacy of antibiotics in recent decades. Nevertheless, Streptomycetes continue to demonstrate their potential by producing bioactive metabolites for the synthesis of nanoparticles. Streptomycetes are instrumental in producing nanoparticles with diverse bioactive characteristics, including antiviral, antibacterial, antifungal, antioxidant, and antitumor properties. Biologically synthesized nanoparticles have exhibited a meaningful reduction in the impact of antibiotic resistance, providing resources for the development of new and effective drugs. This review succinctly outlines the significant applications of Streptomycetes as a crucial element in nanoparticle synthesis, showcasing their potential for diverse and enhanced beneficial applications.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables.

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann-Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (- 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (- 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

The Quest for Two-Dimensional MBenes: From Structural Evolution to Solar-Driven Hybrid Systems for Water-Fuel-Energy Generation and Phototherapy.

The field of 2D materials has advanced significantly with the emergence of MBenes, a new material derived from the MAX phases family, a novel class of materials that originates from the MAX phases family. Herein, this article explores the unique characteristics and morphological variations of MBenes, offering a comprehensive overview of their structural evolution. First, the discussion explores the evolutionary period of 2D MBenes associated with the several techniques for synthesizing, modifying, and characterizing MBenes to tailor their structure and enhance their functionality. The focus then shifts to the defect chemistry of MBenes, electronic, catalytic, and photothermal properties which play a crucial role in designing multifunctional solar-driven hybrid systems. Second, the recent advancements and potentials of 2D MBenes in solar-driven hybrid systems e.g. photo-electro catalysis, hybrid solar evaporators for freshwater and thermoelectric generators, and phototherapy, emphasizing their crucial significance in tackling energy and environmental issues, are explored. The study further explores the fundamental principles that regulate the improved photocatalytic and photothermal characteristics of MBenes, highlighting their promise for effective utilization of solar energy and remediation of the environment. The study also thoroughly assesses MBenes' scalability, stability, and cost effectiveness in solar-driven systems. Current insights and future directions allow researchers to utilize MBenes for sustainable and varied applications. This review regarding MBenes will be valuable to early researchers intrigued with synthesizing and utilizing 2D materials for solar-powered water-energy-fuel and phototherapy systems.

Hierarchical Ti3C2/BiVO4 microcapsules for enhanced solar-driven water evaporation and photocatalytic H2 evolution.

Desalination processes frequently require a lot of energy to generate freshwater and energy, which depletes resources. Their reliance on each other creates tension between these two vital resources. Herein, hierarchical MXene nanosheets and bismuth vanadate (Ti3C2/BiVO4)-derived microcapsules were synthesized for a photothermal-induced photoredox reaction for twofold applications, namely, solar-driven water evaporation and hydrogen (H2) production. For this purpose, flexible aerogels were fabricated by introducing Ti3C2/BiVO4 microcapsules in the polymeric network of natural rubber latex (NRL-Ti3C2/BiVO4), and a high evaporation rate of 2.01 kg m-2 h-1 was achieved under 1-kW m-2 solar intensity. The excellent performance is attributed to the presence of Ti3C2/BiVO4 microcapsules in the polymeric network, which provides balanced hydrophilicity and broadband sun absorption (96 %) and is aimed at plasmonic heating with microscale thermal confinement tailored by heat transfer simulations. Notably, localized plasmonic heating at the catalyst active sites of the Ti3C2/BiVO4 heterostructure promotes enhanced photocatalytic H2 production evolved after 4 h of reaction is 9.39 µmol, which is highly efficient than pure BiVO4 and Ti3C2. This method turns the issue of water-fuel crisis into a collaborative connection, presenting avenues to collectively address the anticipated demand rather than fostering competition.

Nanofluidic Membrane with Spent Battery Residues for Efficient Energy Generation.

Harvesting energy in the ambient environment has become a dominant energy source and is promising toward a sustainable perspective. However, developing an energy generator with readily available raw materials and continuous output performance remains a challenge. Herein, we have demonstrated that a nanofluidic membrane with an asymmetric structure device (ASD) design by using available raw materials spent battery residues can achieve continuous power generation. The ASD can generate a voltage of ∼0.5 V by dropping 0.1 mL of water. Detailed experimental results reveal that ions in water play a synergistic role in enhancing output performance. By dropping 0.1 mL of seawater, the power output of ∼46.67 mW m-2 can be readily derived with voltage of ∼0.7 V. The work opens a viable way to generate electric power by environmental energy with facile structure design and easy-to-access materials. The output energy will greatly promote practical applications such as the power supply of low-power devices.

Multi-Walled CNTs/BiVO4 Heterostructures for Solar-Driven Evaporation System and Efficient Photocatalytic Activity against Oxytetracycline.

Developing a sustainable environment requires addressing primitive water scarcity and water contamination. Antibiotics such as oxytetracycline (OTC) may accumulate in the environment and in the human body, increasing the risks to the ecosystem. The treatment of polluted water and the production of potable water can be achieved in a variety of ways, including photodegradation, solar distillation, and filtration. Freshwater supplies can be increased by implementing energy-efficient technologies for the production of clean water. Solar water evaporation combined with photocatalytic degradation and sterilization offers a promising avenue for integration into the clean water and energy production fields. The present study reports the synthesis of a 3D solar steam generator comprised of BiVO4 and carbon nanotubes (CNT) nanocomposite decorated over a cigarette filter as the light-to-heat conversion layer for solar steam generation. The BiVO4@CNT-based 3D solar evaporator over the hydrophilic cellulosic fibers of the cigarette filter endowed excellent evaporation rates (2.36 kg m-2 h-1) under 1 kW m-2 solar irradiation, owing to its superior hydrophilicity and broadband solar absorption (96%) equipped with localized heating at microscale thermal confinement optimized by the minimum thermal conductivity of the overall system. Furthermore, the BiVO4@CNT composite exhibited a heightened photo activity up to 83% of the photodegradation of oxytetracycline (OTC) antibiotic due to the inhibition of charge recombination from the industrial effluents. This approach transforms the water-energy nexus into a synergistic bond that offers opportunities to meet expected demand, rather than being competitive.

Optimizing oxygen vacancies and electrochemical performance of CeO2-δ nanosheets through the combination of di- and tri-valent doping.

Ceramic fuel cells presently hold an important position in the future of sustainable energy. However, new concepts and designs are vital for each individual cell's component materials to improve the overall power output and stability. The limited ionic conductivity of the electrolyte component is one major challenge among these. In the present work, we developed nanosheets with a cubic fluoride structure of CeO2 and introduced the di- and tri-valent doping of La and Sr to study their impact on oxygen vacancies and its ionic transport, keeping in mind the fact that CeO2 is reduced when exposed to a reducing atmosphere. The attained La- and Sr-doped fluorite structures of CeO2 exhibited good ionic conductivity of >0.05 S cm-1 at low temperature, and their use in a fuel cell resulted in achieving a power output of >900 mW cm-2 while operating at 550 °C. Therefore, we have found that laterally combining di- and tri-valent doping could be textured to give a highly oxygen-deficient CeO2 structure with high ionic transport. Furthermore, various microscopic and spectroscopic analyses, such as HR-TEM, XPS, Raman, UV-visible, EIS, and density functional theory, were applied to investigate the change in structural properties and mechanism of the ionic transport of the synthesized La and Sr co-doped CeO2 electrolyte. This work provides some new insights for designing high-ionic-conductivity electrolytes from low-cost semiconductor oxides for energy storage and conversion devices.

2D MXenes Embedded Perovskite Hydrogels for Efficient and Stable Solar Evaporation.

Solar evaporation is a facile and promising technology to efficiently utilize renewable energy for freshwater production and seawater desalination. Here, the fabrication of self-regenerating hydrogel composed of 2D-MXenes nanosheets embedded in perovskite La 0.6Sr 0.4Co 0.2Fe 0.8O3- δ (LSCF)/polyvinyl alcohol hydrogels for efficient solar-driven evaporation and seawater desalination is reported. The mixed dimensional LSCF/Ti3C2 composite features a localized surface plasmonic resonance effect in the polymeric network of polyvinyl alcohol endows excellent evaporation rates (1.98 kg m-2 h-1) under 1 k Wm-2 or one sun solar irradiation ascribed by hydrophilicity and broadband solar absorption (96%). Furthermore, the long-term performance reveals smooth mass change (13.33 kg m-2) during 8 h under one sun. The composite hydrogel prompts the dilution of concentrated brines and redissolves it back to water (1.2 g NaCl/270 min) without impeding the evaporation rate without any salt-accumulation. The present research offers a substantial opportunity for solar-driven evaporation without any salt accumulation in real-life applications.

Recent Progress and Perspective of an Evolving Carbon Family From 0D to 3D: Synthesis, Biomedical Applications, and Potential Challenges.

A variety of imaging techniques are available for detecting biological processes with sufficient penetration depth and temporal resolution. However, inflammation, cardiovascular, and cancer-related disorders might be difficult to diagnose with typical bioimaging methods because of the lack of resolution in the imaging of deep tissues. Therefore, nanomaterials are the most promising candidate to overcome this hurdle. This review is on the utilization of carbon-based nanomaterials (CNMs), ranging from zero-dimension (0D) to three-dimension (3D), in the development of fluorescence (FL) imaging, photoacoustic imaging (PAI), and biosensing for the early detection of cancer. Nanoengineered CNMs, such as graphene, carbon nanotubes (CNTs), and functional carbon quantum dots (QDs), are being further studied for multimodal biometrics and targeted therapy. CNMs have many advantages over conventional dyes in FL sensing and imaging, including clear emission spectra, long photostability, low cost, and high FL intensity. Nanoprobe production, mechanical illustrations, and diagnostic therapeutic applications are the key areas of focus. The bioimaging technique has facilitated a greater understanding of the biochemical events underlying multiple disease etiologies, consequently facilitating disease diagnosis, evaluation of therapeutic efficacy, and drug development. This review may lead to the development of interdisciplinary research in bioimaging and sensing as well as possible future concerns for researchers and medical physicians.

Strontium-Cobaltite-Based Perovskite (SrCoO3) for Solar-Driven Interfacial Evaporation Systems for Clean Water Generation.

Solar-driven evaporation technology is often used in areas with limited access to clean water, as it provides a low-cost and sustainable method of water purification. Avoiding salt accumulation is still a substantial challenge for continuous desalination. Here, an efficient solar-driven water harvester that consists of strontium-cobaltite-based perovskite (SrCoO3) anchored on nickel foam (SrCoO3@NF) is reported. Synced waterways and thermal insulation are provided by a superhydrophilic polyurethane substrate combined with a photothermal layer. The structural photothermal properties of SrCoO3 perovskite have been extensively investigated through state-of-the-art experimental investigations. Multiple incident rays are induced inside the diffuse surface, permitting wideband solar absorption (91%) and heat localization (42.01 °C @ 1 sun). Under 1 kW m-2 solar intensity, the integrated SrCoO3@NF solar evaporator has an outstanding evaporation rate (1.45 kg/m2 h) and solar-to-vapor conversion efficiency (86.45% excluding heat losses). In addition, long-term evaporation measurements demonstrate small variance under sea water, illustrating the system's working capacity for salt rejection (1.3 g NaCl/210 min), which is excellent for an efficient solar-driven evaporation application compared to other carbon-based solar evaporators. According to the findings of this research, this system offers significant potential for producing fresh water devoid of salt accumulation for use in industrial applications.

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method.

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient's abnormal activities; based upon this information, the patient's psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model's performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

Biomass-Printed Hybrid Solar Evaporator Derived from Bio-polluted Invasive Species, a Potential Step toward Carbon Neutrality.

Biomass-based photothermal conversion is of great importance for solar energy utilization toward carbon neutrality. Herein, a hybrid solar evaporator is innovatively designed via UV-induced printing of pyrolyzed Kudzu biochar on hydrophilic cotton fabric (KB@CF) to integrate all parameters in a single evaporator, such as solar evaporation, salt collection, waste heat recovery for thermoelectricity, sieving oil emulsions, and water disinfection from microorganisms. The UV-induced printed fabric demonstrates stronger material adhesion as compared to the conventional dip-dry technique. The hybrid solar evaporator gives an enhanced evaporation rate (2.32 kg/m2 h), and the complementary waste heat recovery system generates maximum open-circuit voltage (Vout ∼ 143.9 mV) and solar to vapor conversion efficiency (92%), excluding heat losses under one sun illumination. More importantly, 99.98% of photothermal-induced bacterial killing efficiency was achieved within 20 min under 1 kW m-2 using the hyperthermia effect of Kudzu biochar. Furthermore, numerical heat-transfer simulations were performed successfully to analyze the enhanced interfacial heat accumulation (75.3 °C) and heat flux distribution of the thermoelectric generators under one sun. We firmly believe that the safe use of bio-polluted invasive species in hybrid solar-driven evaporation systems eases the environmental pressure toward carbon neutrality.

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture.

The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option.

Nanocomposite-Enhanced Efficient Evaporation System for Solar-Driven Seawater Desalination-An Optimized Design for Clean Water Production.

Solar-driven evaporation is a promising technology for desalinating seawater and wastewater without mechanical or electrical energy. The approaches to obtaining fresh water with higher evaporation efficiency are essential to address the water-scarcity issue in remote sensing areas. Herein, we report a highly efficient solar evaporator derived from the nanocomposite of anatase TiO2/activated carbon (TiO2/AC), which was coated on washable cotton fabric using the dip-dry technique for solar water evaporation. The ultra-black fabric offers enhanced solar absorption (93.03%), hydrophilic water transport, and an efficient evaporation rate of 1.65 kg/m2h under 1 kW m-2 or one sun solar intensity. More importantly, the sideways water channels and centralized thermal insulation of the designed TiO2/AC solar evaporator accumulated photothermal heat at the liquid and air interface along with an enhanced surface temperature of 40.98 °C under one sun. The fabricated solar evaporator desalinated seawater (3.5 wt%) without affecting the evaporation rates, and the collected condensed water met the standard of drinking water set by the World Health Organization (WHO). This approach eventually enabled the engineering design groups to develop the technology pathways as well as optimum conditions for low-cost, scalable, efficient, and sustainable solar-driven steam generators to cope with global water scarcity.

Interfacial Photothermal Heat Accumulation for Simultaneous Salt Rejection and Freshwater Generation; an Efficient Solar Energy Harvester.

Water scarcity has emerged as an intense global threat to humanity and needs prompt attention from the scientific community. Solar-driven interfacial evaporation and seawater desalination are promising strategies to resolve the primitive water shortage issue using renewable resources. However, the fragile solar thermal devices, complex fabricating techniques, and high cost greatly hinder extensive solar energy utilization in remote locations. Herein, we report the facile fabrication of a cost-effective solar-driven interfacial evaporator and seawater desalination system composed of carbon cloth (CC)-wrapped polyurethane foam (CC@PU). The developed solar evaporator had outstanding photo-thermal conversion efficiency (90%) with a high evaporation rate (1.71 kg m-2 h-1). The interfacial layer of black CC induced multiple incident rays on the surface allowing the excellent solar absorption (92%) and intensifying heat localization (67.37 °C) under 1 kW m-2 with spatially defined hydrophilicity to facilitate the easy vapor escape and validate the efficacious evaporation structure using extensive solar energy exploitation for practical application. More importantly, the long-term evaporation experiments with minimum discrepancy under seawater conditions endowed excellent mass change (15.24 kg m-2 in consecutive 8 h under 1 kW m-2 solar irradiations) and promoted its operational sustainability for multi-media rejection and self-dissolving potential (3.5 g NaCl rejected from CC@PU surface in 210 min). Hence, the low-cost and facile fabrication of CC@PU-based interfacial evaporation structure showcases the potential for enhanced solar-driven interfacial heat accumulation for freshwater production with simultaneous salt rejection.

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine.

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.

Erratum: Arshad, N., et al. Super Hydrophilic Activated Carbon Decorated Nanopolymer Foam for Scalable, Energy Efficient Photothermal Steam Generation, as an Effective Desalination System. Nanomaterials 2020, 10, 2510.

The authors wish to make the following corrections to this paper [...].

Nanoenabled Photothermal Materials for Clean Water Production.

Solar-powered water evaporation is a primitive technology but interest has revived in the last five years due to the use of nanoenabled photothermal absorbers. The cutting-edge nanoenabled photothermal materials can exploit a full spectrum of solar radiation with exceptionally high photothermal conversion efficiency. Additionally, photothermal design through heat management and the hierarchy of smooth water-flow channels have evolved in parallel. Indeed, the integration of all desirable functions into one photothermal layer remains an essential challenge for an effective yield of clean water in remote-sensing areas. Some nanoenabled photothermal prototypes equipped with unprecedented water evaporation rates have been reported recently for clean water production. Many barriers and difficulties remain, despite the latest scientific and practical implementation developments. This Review seeks to inspire nanoenvironmental research communities to drive onward toward real-time solar-driven clean water production.

Green thin film for stable electrical switching in a low-cost washable memory device: proof of concept.

Low-cost and washable resistive switching (RS) memory devices with stable retention and low operational voltage are important for higher speed and denser non-volatile memories. In the case of green electronics, pectin has emerged as a suitable alternative to toxic metal oxides for resistive switching applications. Herein, a pectin-based thin film was fabricated on a fluorine-doped tin oxide glass substrate for RS mechanism. The presence of sp3-C groups with low binding energy corresponds to tunable charged defects and the oxygen vacancies confirmed by the O 1s spectra that plays a decisive role in the resistive switching mechanism, as revealed by X-ray photoemission spectroscopy (XPS). The surface morphology of the pectin film shows homogeneous growth and negligible surface roughness (38.98 ± 9.09). The pectin film can dissolve in DI water (10 minutes) owing to its ionization of carboxylic groups, that meet the trends of transient electronics. The developed Ag/pectin/FTO-based memory cell exhibits stable and reproducible bipolar resistive switching behavior along with an excellent ON/OFF ratio (104) and negligible electrical degradation was observed over 30 repeated cycles. Hence, it appears to be a valuable application for green electronics. Indeed, biocompatible storage devices derived from natural pectin are promising for high-density safe applications for information storage systems, flexible electronics, and green electronics.