Deep neural network (DNN) modelling for prediction of the mode of delivery.

One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work, we suggested a computerized decision-making process for deciding on the best birthing style. The data was collected from 101 pregnant subjects who were admitted to hospital in eastern India for delivery from January 2021 to September 2021.The data set had 101 instances & 11 variables. The response was a binary variable with "caesarean" & "vaginal" as the outputs. A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. The results support the use of these algorithms in the creation of a clinical decision system to help gynaecologists choose the most appropriate delivery method.

Hierarchical NiCo@NiOOH@CoMoO4 Core-Shell Heterostructure on Carbon Cloth for High-Performance Asymmetric Supercapacitors.

The fabrication of low-cost, effective, and highly integrated nanostructured materials through simple and reproducible methods for high-energy-density supercapacitors is highly desirable. Herein, an activated carbon cloth (ACC) is designed as the functional scaffold for supercapacitors and treated hydrothermally to deposit NiCo nanoneedles working as internal core, followed by a dip-dry coating of NiOOH nanoflakes core-shell and uniform hydrothermal deposition of CoMoO4 nanosheets serving as an external shell. The structured core-shell heterostructure ACC@NiCo@NiOOH@CoMoO4 electrode resulted in exceptional specific areal capacitance of 2920 mF cm-2 and exceptional cycling stability for 10 000 cycles. Moreover, the fabricated electrode is developed into an asymmetric supercapacitor which demonstrates excellent areal capacitance, energy density, and power density within the broad potential window of 1.7 V with a cycling life of 92.4% after 10 000 charge-discharge cycles, which reflects excellent cycle life. The distinctive core-shell structure, highly conductive substrate, and synergetic effect of coated material results in more electrochemical active sites and flanges for effective electrons and ion transportation. This unique technique provides a new perspective for cost-efficient supercapacitor applications.

Semi-Transparent, Pixel-Free Upconversion Goggles with Dual Audio-Visual Communication.

The intractable brittleness and opacity of the crystalline semiconductor restrict the prospect of developing low-cost imaging systems. Here, infrared visualization technologies are established with large-area, semi-transparent organic upconversion devices that bring high-resolution invisible images into sight without photolithography. To exploit all photoinduced charge carriers, a monolithic device structure is proposed built on the infrared-selective, single-component charge generation layer of chloroaluminum phthalocyanine (ClAlPc) coupled to two visible light-emitting layers manipulated with unipolar charges. Transient pump-probe spectroscopy reveals that the ClAlPc-based device exhibits an efficient charge dissociation process under forward bias. This process is indicated by the prompt and strong features of electroabsorption screening. Furthermore, by imposing the electric field, the ultrafast excited state dynamic suggests a prolonged charge carrier lifetime from the ClAlPc, which facilitates the charge utilization for upconversion luminance. For the first time, >30% of the infrared photons are utilized without photomultiplication strategies owing to the trivial spectrum overlap between ClAlPc and the emitter. In addition, the device can broadcast the acoustic signal by synchronizing the device frequency with the light source, which enables to operate it in dual audio-visual mode. The work demonstrates the potential of upconversion devices for affordable infrared imaging in wearable electronics.

A Gradient Lithiophilic Structure for Stable Lithium Metal Anodes with Ultrahigh Rate and Ultradeep Capacity.

Using three-dimensional current collectors (3DCC) as frameworks for lithium metal anodes (LMAs) is a promising approach to inhibit dendrite growth. However, the intrinsically accumulated current density on the top surface and limited Li-ion transfer in the interior of 3DCC still lead to the formation of lithium dendrites, which can pose safety risks. In this study, it reports that gradient lithiophilic structures can induce uniform lithium deposition within the interior of the 3DCC, greatly suppressing dendrite formation, as confirmed by COMSOL simulations and experimental results. With this concept, a gradient-structured zinc oxide-loaded copper foam (GSZO-CF) is synthesized via an easy solution-combustion method at low cost. The resulting Li@GSZO-CF symmetric cells demonstrate stable cycling performance for over 800 cycles, with an ultra-deep capacity of 10 mAh cm-2 even under an ultra-high current density of 50 mA cm-2 , the top results reported in the literature. Moreover, when combined with a LiFePO4 (LFP) cathode under a low negative/positive (N/P) capacity ratio of 2.9, the Li@GSZO-CF||LFP full cells exhibit stable performance for 200 cycles, with a discharge capacity of 130 mAh g-1 and retention of 85.5% at a charging/discharging rate of 1C. These findings suggest a promising strategy for the development of new-generation LMAs.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil).

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery.

Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson's algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

Transparent organic upconversion devices displaying high-resolution, single-pixel, low-power infrared images perceived by human vision.

Crystalline photodiodes remain the most viable infrared sensing technology of choice, yet the opacity and the limitation in pixel size reduction per se restrict their development for supporting high-resolution in situ infrared images. In this work, we propose an all-organic non-fullerene-based upconversion device that brings invisible infrared signal into human vision via exciplex cohost light-emissive system. The device reaches an infrared-to-visible upconversion efficiency of 12.56% by resolving the 940-nm infrared signal (power density of 103.8 µW cm-2). We tailor a semitransparent (AVT, ~60%), large-area (10.35 cm2), lightweight (22.91 g), single-pixel upconversion panel to visualize the infrared power density down to 0.75 µW cm2, inferring a bias-switching linear dynamic range approaching 80 dB. We also demonstrate the possibility of visualizing low-intensity infrared signals from the Face ID and LiDAR, which should fill the gap in the existing technology based on pixelated complementary metal-oxide semiconductors with optical lenses.

Rural mothers' beliefs and practices about diagnosis, treatment, and management of children health problems: A qualitative study in marginalized Southern Pakistan.

Introduction: Appropriate health-seeking beliefs and practices are indispensable for the survival and development of children. In this study, we explore childcare beliefs and practices of rural mothers and analyze the different ways childhood illness is diagnosed and managed in a marginalized rural community in Southern Pakistan. Methods: Using purposive sampling, in-depth interviews are conducted to obtain qualitative data from 20 illiterate and rural mothers in addition to 15 healthcare providers in the district Rajanpur of South Punjab. Results and discussion: The findings reveal that rural mothers' access to healthcare and therapeutic programs is impeded due to geographical isolation, structural inequalities, poverty, and illiteracy. Consequently, evil eyes, witchcraft, and spirits are recognized as potential threats to children's health and nutrition. Therefore, the treatment of childhood morbidity and malnutrition is mostly performed with folk, domestic, herbal, magico-religious remedies, and spiritual healing methods. The current study also highlights that many low-income and rural mothers tend to normalize childhood illness when they become unable to advocate for their children's health and nutrition. Besides improving low-income mothers' access to healthcare facilities, health education and risk communication at the field level through field health staff could be most effective for health promotion.

Impacts and Strategies Behind COVID-19-Induced Economic Crisis: Evidence from Informal Economy.

The COVID-19 pandemic has put pressure on the informal sector, especially in developing countries. Regarding the case study found in Yogyakarta Special Region (Indonesia), this research focuses on workers in the informal sector with the following objectives: (1) to assess the impacts of COVID-19 pandemic on informal workers' conditions, (2) to identify their strategies for surviving the crisis, and (3) to analyze the existing social safety net to support their livelihood. This study surveyed 218 respondents who worked in the informal, non-agricultural sector. The data were analyzed using descriptive statistical techniques. The results confirmed that most respondents underwent a decrease in working hours and incomes. In general, they have particular coping mechanisms to survive. The results also found that most respondents had high hopes for social assistance to stabilize their livelihood. Several government programs had been issued, either by improving policies before the pandemic or by creating new ones. However, there were many barriers and challenges to implementing them so that some recommendations had been suggested in this study to help the informal workers to become more resilient.

Lung contusion.

Lung contusion usually follows blunt trauma to the chest. If not properly diagnosed and adequately treated, it could at times be fatal as well. This case is being presented to highlight the radiological findings of this condition.

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent 'curcumin.' Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.