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With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

Crony capitalism and self-fulfilling expectations by international creditors are often suggested as two rival explanations for currency crisis. This paper examines a possible linkage between the two that has not been explored much in the literature: corruption may affect a country's composition of capital inflows in a way that makes it more likely to experience a currency crisis that is triggered/aided by a sudden reversal of international capital flows. We find robust evidence that poor public governance is associated with a higher loan-to-FDI ratio. Such a composition of capital flows has been identified as being associated with a higher incidence of a currency crisis. We also find some weaker evidence that poor public governance is associated with a country's inability to borrow internationally in its own currency. The latter is also associated with a higher incidence of a currency crisis. To sum up, even though crony capitalism does not forecast the timing of a crisis, it can nevertheless increase its likelihood. This paper illustrates a particular channel through which this can happen.

Nanomaterial such as metals and metal oxide photocatalysts have emerged as important tools for removing contaminants from wastewater and as antibacterial agents to prevent infections; this is mainly due to their stability under different irradiation conditions. Herein, the catalytic and antimicrobial activities of nanocrystalline silver (Ag), supported on tungsten oxide (WO3) nanoparticles prepared using the deposition-precipitation synthesis technique, are studied. The synthesized material was characterized as XRD, XPS, TEM, and TEM-EDS to investigate their physio-chemical properties. HRTEM, XPS analysis shows that the photocatalyst has a large sheet-like morphology with well-dispersed small metallic Ag particles (<3 nm) on the WO3 nanoparticle's surface, with most particles near the edges. Ultraviolet–visible spectra analysis observed a large redshift in the absorbing band edge and decreased bandgap energy from 2.6 to 2.1 eV. Photocatalytic analysis at different concentrations of 1% Ag/WO3 under visible light indicated a high degradation efficiency. The largest degradation efficiency of Methylene Blue (MB) under visible light irradiation was (∼80%) in 120 min at 1 g/L catalyst dosage. The photodegradation of MB under visible light as a function of catalyst dose followed the pseudo-first-order kinetics. In addition, the catalyst shows high degradation efficiency and significant dose-dependent inhibition of Gram-negative E. Coli and the Gram-positive S. aureus. Furthermore, the catalyst showed excellent stability and recyclability.

There is a growing demand for new heterogeneous catalysts for cost-effective catalysis. Currently, the hysteresis phenomenon during low-temperature CO oxidation is an important topic in heterogeneous catalysis. Hysteresis provides important information about fluctuating reaction conditions that affect the regeneration of active sites and indicate the restoration of catalyst activity. Understanding its dynamic behavior, such as hysteresis and self-sustained kinetic oscillations, during CO oxidation, is crucial for the development of cost-effective, stable and long-lasting catalysts. Hysteresis during CO oxidation has a direct influence on many industrial processes and its understanding can be beneficial to a broad range of applications, including long-life CO2 lasers, gas masks, catalytic converters, sensors, indoor air quality, etc. This review considers the most recent reported advancements in the field of hysteresis behavior during CO oxidation which shed light on the origin of this phenomenon and the parameters that influence the type, shape, and width of the conversion of the hysteresis curves.

Ternary NiO/Ag/TiO2 heterojunction photocatalyst was prepared by deposition coprecipitation for visible light photocatalytic applications. Physicochemical properties of the synthesized NiO/Ag/TiO2 composite were characterized by X-ray diffraction, Brunauer–Emmett–Teller surface area measurement method, transmission electron microscopy, energy-dispersive X-ray spectroscopy techniques, X-ray photoelectron spectroscopy technique, and ultraviolet–visible absorption spectroscopy. The results suggest that the well-dispersed small metallic silver nanoparticles (<3 nm) facilitate electron transfer and bridge nickel oxide and titanium oxide. The photocatalytic degradation and the methylene blue (MB) dye kinetics were carried out on a ternary NiO/Ag/TiO2 composite and compared to bare TiO2 under visible light irradiation. The results indicate that NiO/Ag/TiO2 has superior MB photodegradation efficiency with a high reaction rate constant and low degradation time (93.15% within 60 min) compared to Ag/TiO2, NiO/TiO2, and bare TiO2. NiO/Ag/TiO2 nanocomposite was also investigated for the most common pharmaceutical waste degradation and exhibited excellent degradation efficiency. The enhancement of the composite’s performance could be attributed to the surface plasmonic resonance of the Ag nanoparticles, the formation of Schottky junctions at the Ag–TiO2 and Ag–NiO interface, and the p–n heterojunction between NiO and TiO2. Ag NPs act as a photosynthesizer and a photocatalyst, facilitate electron transfer, shift the absorption to the visible light region, reduce the band gap of TiO2, suppress the electron–hole recombination, and enhance the photocatalytic activity and stability as a result.

Abstract In this paper, we report oxidation time effect on highly porous silver oxide nanowires thin films fabricated using ultrasonic spray pyrolysis and oxygen plasma etching method. The NW’s morphological, electrical, and optical properties were investigated under different plasma etching periods and the number of deposition cycles. The increase of plasma etching and oxidation time increases the surface roughness of the Ag NWs until it fused to form a porous thin film of silver oxide. AgNWs based thin films were characterized using X-ray diffraction, scanning electron microscope, transmission electron microscope, X-ray photoemission spectroscopy, and UV–Vis spectroscopy techniques. The obtained results indicate the formation of mixed mesoporous Ag 2 O and AgO NW thin films. The Ag 2 O phase of silver oxide appears after 300 s of oxidation under the same conditions, while the optical transparency of the thin film decreases as plasma etching time increases. The sheet resistance of the final film is influenced by the oxidation time and the plasma application periodicity. Graphic abstract

WHAT IS KNOWN AND OBJECTIVE: Constipation is a common disorder among long-term care (LTC) patients due to several factors. However, there are no systematic reviews investigating the use of laxatives for chronic constipation in LTC settings. This study aims to explore the safety and efficacy of laxatives in LTC patients. METHODS: A systematic review of randomized controlled trials (RCTs) describing the efficacy and safety of laxatives for chronic constipation in LTC patients was conducted using the following databases and search engines: MEDLINE, Cochrane Database of Systematic Reviews, ScienceDirect, ProQuest and Google Scholar. Two of the investigators independently performed the searches, and the data were extracted using a standardized data abstraction tool. RESULTS AND DISCUSSION: Seven RCTs involving 444 patients were included in the review. These studies included senna (with or without fibre, ie Plantago ovata), lactulose, sodium picosulphate, docusate sodium, docusate calcium, isotonic and hypotonic polyethylene glycol and Chinese herbal medicine. Senna and lactulose were the most studied laxatives in LTC patients, and senna was found to be superior to or as effective as other laxatives. Generally, the frequency and severity of adverse drug reactions (ADRs) were similar between the arms of the studies, and no serious ADRs were reported. WHAT IS NEW AND CONCLUSION: Considering the short duration of the trials, the lack of trials including newer laxatives and the low quality of some of the included trials, the long-term efficacy and safety of these laxatives are not conclusive. There is a need to conduct more robust RCTs that include newer agents to evaluate long-term outcomes.

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

Several metal oxides, such as tungsten oxide (WO3), are considered superior sensing materials for hydrogen sulfide (H2S) detection in the ambient environment. In this study, silver loaded tungsten oxide nanoparticles were prepared by microwave-assisted chemical route. Nanoparticle thin films were deposited on substrates with electrical electrodes to explore their gas sensing and electrical properties. The morphology, crystal structure, and chemical state were examined using x-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and x-ray photoelectron spectroscopy (XPS). The sensing properties of the sample were tested at different temperatures varying from room temperature to 200 °C. The Ag2O/WO3 nanoparticles showed enhanced H2S sensing abilities at very low temperatures (close to room temperature) and concentrations as low as 10 ppm compared to bare WO3. Moreover, the effect of annealing and comparison with physical mixing was studied and showed that non-annealed Ag-loaded WO3 produced the best response. The results can be attributed to the decrease in the energy bandgap due to increasing the Ag content and the strong interaction between the Ag and WO3 and silver to silver oxide transformation at low annealing temperatures. This study demonstrates that Ag2O/WO3 sensors offer enhanced low-temperature sensing with reasonable response time, low operating power, and simple fabrication that can be promising sensors for H2S, which may be utilized as wearable devices and in the industrial setting.

The objective of this study is to overview the literature relative to color, as an environmental constituent, and its impacts in healthcare environments. Borrowing from the environmental behavioral paradigm, this study attempts to decipher myths and misconceptions as well as highlight well-evidenced research findings. Broad Literature review journal articles and reports of empirical studies in multiple disciplines were studied to identify theories, which could have design implications for color in healthcare design. Despite the incongruity and fragmentation of previous studies, it emerges from this overview that color impacts healthcare outcomes by reducing medical errors, promoting the sense of well-being, reducing stress, improving patients sleep, reducing length of stay, reducing spatial disorientation, increasing patient satisfaction, and increasing staff morale and productivity. While the review of literature indicates major findings relative to the impact of color on healthcare environments, yet ambiguities remained to be addressed. Previous studies on the use of color in healthcare environment have illustrated that there are some obvious, replicable, behavioral and perceptual effects from color that addressed their use in certain ways for design. However, color must be observed and analyzed in the contextual application to avoid generalizations about color perception and mood affects.

Rosacea is a chronic dermatosis which affects negatively patients' quality of life (QoL). There is shortage of high-quality evidence comparing the efficacy of ivermectin cream (IVM) 1% with other available topical choices. Besides, the well-documented impaired of self-esteem and stigmatization of rosacea patients make essential to address which treatment provides the greatest psychological and social benefit. Our objective is to critically review and appraise the efficacy of IVM 1% in PPR and the impact in patients' QoL against other options. We carried out a literature search from PubMed, MEDLINE, EMBASE, Cochrane, and clinicaltrials.gov using the following descriptors: "rosacea" AND "ivermectin." Efficacy was assessed with the Investigator Global Assessment (IGA), and the impact on QoL was based on the DLQI score. Six studies from four published articles were included. The meta-analysis estimated that more participants achieved "success" (IGA ≤ 1) and "complete clearance" (IGA = 0) with IVM1%. The overall effect estimate for IGA ≤ 1 was: 1.56 [1.23-1.97], whereas for IGA = 0, it was: 1.72 [1.40-2.11]. The rate of participants achieving lower DLQI score, and thus, better QoL was with IVM 1%. The overall effect estimate was: 1.71 [1.34-2.18] at week 16# and 1.64 [1.38-1.94] at week 52#. This meta-analysis confirms IVM 1% cream as the most effective topical treatment and it satisfies the impairment of social life with sustained better QoL. Further studies extending this period of remission are warranted, as well as researches about the potential application of this agent combined with other agents. KEY POINTS: Question: What is the current efficacy of ivermectin versus other choices in papulopustular rosacea and its impact on patients' quality of life? Findings: In this meta-analysis, ivermectin showed higher efficacy than metronidazol, azelaic acid, and placebo measured by Investigator Global Assessment. Parallely, the DLQI score highlighted that this agent was more beneficious in both short and long-term. Meaning: This meta-analysis gives strong evidence that ivermectin is the most effective topical treatment. Besides, this agent provides the greatest psychological benefit as it satisfies the stigmatization of rosacea patients as well as the impairment of social and working life with a sustained better QoL above other alternatives.

Low-temperature carbon monoxide (CO) oxidation on silver/silica aerogel (Ag/SiO2 AG) catalyst prepared by one-pot sol–gel synthesis followed by supercritical ethanol drying method is reported. Highly stable and sinter-proof catalyst led to easy reactant diffusion to the active sites. The Ag/SiO2 AG catalyst showed enhanced catalytic activity toward low-temperature CO oxidation by preventing agglomeration of silver nanoparticles inside pores and facilitating well-dispersed active sites to enhance the mass heat transfer in the mesopores. Catalyst pre-treatment conditions were found to play a crucial role in achieving high CO conversion efficiency at low light-off temperatures. Inverse counter-clockwise CO oxidation hysteresis was found to occur after the first run. The active sites contributing to this enhanced catalytic behavior were confirmed to be Ag0 from XPS, XRD, and TEM analysis. The catalyst exhibited good thermal stability up to 450 °C over repeated number of cycles.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing older population-especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in older people, both at home and in LTCFs. VR thus holds potential in allowing older individuals to gradually adapt to their new environments-thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. In this review article, we concisely discuss the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.

Native and cross-linked aerogel monoliths were fabricated in a few hours using a technique that does not require solvent exchange prior to supercritical drying.

We present the synthesis of polymer cross-linked silica alcogels in a matter of seconds by illuminating the solution of TEOS, hexanedioldiacrylate, Eosin Y and amine with a laser beam (<italic>λ</italic> = 532 nm).

Carbon monoxide (CO) oxidation is considered an important reaction in heterogeneous industrial catalysis and has been extensively studied. Pd supported on SiO2 aerogel catalysts exhibit good catalytic activity toward this reaction owing to their CO bond activation capability and thermal stability. Pd/SiO2 catalysts were investigated using carbon monoxide (CO) oxidation as a model reaction. The catalyst becomes active, and the conversion increases after the temperature reaches the ignition temperature (Tig). A normal hysteresis in carbon monoxide (CO) oxidation has been observed, where the catalysts continue to exhibit high catalytic activity (CO conversion remains at 100%) during the extinction even at temperatures lower than Tig. The catalyst was characterized using BET, TEM, XPS, TGA-DSC, and FTIR. In this work, the influence of pretreatment conditions and stability of the active sites on the catalytic activity and hysteresis is presented. The CO oxidation on the Pd/SiO2 catalyst has been attributed to the dissociative adsorption of molecular oxygen and the activation of the C-O bond, followed by diffusion of adsorbates at Tig to form CO2. Whereas, the hysteresis has been explained by the enhanced stability of the active site caused by thermal effects, pretreatment conditions, Pd-SiO2 support interaction, and PdO formation and decomposition.

Many students who enroll in introductory statistics courses do not have positive attitudes about the subject. A 2012 wide-ranging study by Schau and Emmioglu showed that student attitudes do not tend to improve after completing an introductory statistics course. However, there is a need for more studies about attitudes in introductory statistics courses that utilize reform teaching methods. In this article, we present findings about student attitudes toward statistics in both a teacher-centered lecture-based class and a student-centered active learning class, taught by the same instructor. The overall results of this study were consistent with those reported in the study by Schau and Emmioğlu. Although on an overall level, it seemed that attitudes did not change for both classes, when each attitude component was analyzed on a deeper level, from both a quantitative and a qualitative perspective, differences were found between the two classes for the components of Effort, Affect and Cognitive Competence, Interest, and Difficulty.