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This study provides an innovative and attractive analytical strategy to examine the numerical solution for the time-fractional Schrödinger equation (SE) in the sense of Caputo fractional operator. In this research, we present the Elzaki transform residual power series method (ET-RPSM), which combines the Elzaki transform (ET) with the residual power series method (RPSM). This strategy has the advantage of requiring only the premise of limiting at zero for determining the coefficients of the series, and it uses symbolic computation software to perform the least number of calculations. The results obtained through the considered method are in the form of a series solution and converge rapidly. These outcomes closely match the precise results and are discussed through graphical structures to express the physical representation of the considered equation. The results showed that the suggested strategy is a straightforward, suitable, and practical tool for solving and comprehending a wide range of nonlinear physical models. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a modification for the residual power series method that is used to solve fractional-order Helmholtz equations, which is called the Shehu-transform residual power series method (S T-RPSM). This scheme uses a combination of the Shehu transform (S T) and the residual power series method (RPSM). The fractional derivatives are taken with respect to Caputo order. The novelty of this approach is that it does not restrict the fractional order and reduces the need for heavy computational work. The results were obtained using an iterative series that led to an exact solution. The 3D graphical plots for different values of fractional orders are shown to compare S T-RPSM results with exact solutions. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance. [ABSTRACT FROM AUTHOR]

Simple Summary: Given the potential impact of climate change on the distributions of endemic species, it is critical to implement species recovery and habitat management measures to protect threatened species. This study used MaxEnt modeling to assess the species' habitat suitability in varying climate scenarios in the Western Himalayas and identified ten influential drivers. Our field-based observations of Tragopan melanocephalus show that the species typically lives at elevations between 1850 and 3800 m, which is consistent with the species' reported affinity for extremely high elevations between 1500 and 4000 m above sea level. Our findings reveal that areas with high and moderate suitability for the species are patchily distributed throughout the Western Himalayas, ranging from northeastern Pakistan to central Himachal Pradesh and Uttarakhand. Moreover, there are continuous strips of highly suitable habitats along the Pakistan–Afghanistan border, in the Kashmir region, and in the Annapurna region of Uttarakhand. The study observed that the Western Tragopan's habitat suitability may increase under future climate change scenarios, but additional research is needed to avert population collapses and identify other potential drivers of local extinction occurrences. To support increased biodiversity and lower risks under current and anticipated future climatic conditions, it is advised that the suitable areas identified be designated as nature reserves. According to the study's findings, a more effective wildlife management strategy might significantly help with the reintroduction of the Tragopan melanocephalus population into its suitable habitats in western Himalaya, thereby advancing the global objectives set for the UN Decade of Ecosystem Restoration targets (2021–2030). The impact of a changing climate, particularly global warming, often harms the distribution of pheasants, particularly those with limited endemic ranges. To effectively create plans of action aimed at conserving species facing threats such as the Western Tragopan, (Tragopan melanocephalus; Gray, 1829; Galliformes, found in the western Himalayas), it is crucial to understand how future distributions may be affected by anticipated climate change. This study utilized MaxEnt modeling to assess how suitable the habitat of the targeted species is likely to be under different climate scenarios. While similar studies have been conducted regionally, there has been no research on this particular endemic animal species found in the western Himalayas throughout the entire distribution range. The study utilized a total of 200 occurrence points; 19 bioclimatic, four anthropogenic, three topographic, and a vegetation variable were also used. To determine the most fitting model, species distribution modeling (SDM) was employed, and the MaxEnt calibration and optimization techniques were utilized. Data for projected climate scenarios of the 2050s and 2070s were obtained from SSPs 245 and SSPs 585. Among all the variables analyzed; aspect, precipitation of coldest quarter, mean diurnal range, enhanced vegetation index, precipitation of driest month, temperature seasonality, annual precipitation, human footprint, precipitation of driest quarter, and temperature annual range were recognized as the most influential drivers, in that order. The predicted scenarios had high accuracy values (AUC-ROC > 0.9). Based on the feedback provided by the inhabitants, it was observed that the livability of the selected species could potentially rise (between 3.7 to 13%) in all projected scenarios of climate change, because this species is relocating towards the northern regions of the elevation gradient, which is farther from the residential areas, and their habitats are shrinking. The suitable habitats of the Tragopan melanocephalus in the Himalayan region will move significantly by 725 m upwards, because of predicted climate change. However, the fact that the species is considered extinct in most areas and only found in small patches suggests that further research is required to avert a further population decline and delineate the reasons leading to the regional extinction of the species. The results of this study can serve as a foundation for devising conservation strategies for Tragopan melanocephalus under the changing climate and provide a framework for subsequent surveillance efforts aimed at protecting the species. [ABSTRACT FROM AUTHOR]

In the present work, a new fractional analytical scheme (NFAS) is developed to obtain the approximate results of fourth-order parabolic fractional partial differential equations (FPDEs). The fractional derivatives are considered in the Caputo sense. In this scheme, we show that a Taylor series destructs the recurrence relation and minimizes the heavy computational work. This approach presents the results in the sense of convergent series. In addition, we provide the convergence theorem that shows the authenticity of this scheme. The proposed strategy is very simple and straightforward for obtaining the series solution of the fractional models. We take some differential problems of fractional orders to present the robustness and effectiveness of this developed scheme. The significance of NFAS is also shown by graphical and tabular expressions. [ABSTRACT FROM AUTHOR]

In nature, symmetry is all around us. The symmetry framework represents integer partial differential equations and their fractional order in the sense of Caputo derivatives. This article suggests a semi-analytical approach based on Aboodh transform (A T) and the homotopy perturbation scheme (HPS) for achieving the approximate solution of time-fractional porous media and heat transfer equations. The A T converts the fractional problems into simple ones and obtains the recurrence relation without any discretization or assumption. This nonlinear recurrence relation can be decomposed via the use of the HPS to obtain the iterations in terms of series solutions. The initial conditions play an important role in determining the successive iterations and yields towards the exact solution. We provide some numerical applications to analyze the accuracy of this proposed scheme and show that the performance of our scheme has strong agreement with the exact results. [ABSTRACT FROM AUTHOR]

Shannon entropy, also known as information entropy or entropy, measures the uncertainty or randomness of probability distribution. Entropy is measured in bits, quantifying the average amount of information required to identify an event from the distribution. Shannon's entropy theory initiates graph entropies and develops information-theoretic magnitudes for structural computational evidence of organic graphs and complex networks. Graph entropy measurements are valuable in several scientific fields, such as computing, chemistry, biology, and discrete mathematics. In this study, we investigate the entropy of fractal-type networks by considering cycle, complete, and star networks as base graphs using degree-based topological indices. [ABSTRACT FROM AUTHOR]

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study's findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach. [ABSTRACT FROM AUTHOR]

This research investigated the best economically viable power source with the least environmental impact and socially acceptable for the maize crop. Maize is one of the key economic crops in Pakistan. Solar-, electric-, and diesel-powered drip irrigation systems (DIS) were considered for comparative study. We selected 45 sites of maize crop to collect the data, with an area of 1–3 ha, from three divisions. For economic viability, the benefit:cost ratio, life cycle cost, and payback period were calculated, and CO2 emissions were calculated to assess the environmental impact. The SPSS model was used for one-way ANOVA followed by post hoc and chi-squared tests to check the significance level between all power sources. It was found that the B-C of electric power, solar, and diesel drip irrigation systems was 1.65, 1.52, and 1.44, respectively. Solar, diesel, and electricity power DIS have CO2 emissions of 0.02, 0.730, and 1.106 tons/ha, respectively. The research concludes that solar power and electric power are the best sources for the environment and economically, respectively. It is recommended that solar power DIS be subsidized, which will help to lower CO2 emissions and reduce the electricity shortfall in Pakistan. [ABSTRACT FROM AUTHOR]

News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) volatilization from paddy fields is a major issue which leads to poor fertilizer use efficiency and is considered a severe threat to the atmosphere. The previous research studies gave importance to the use of nitrogen fertilizers to mitigate NH3 volatilization, while very little emphasis was given to the role of other fertilizers, such as phosphorus (P), for the alleviation of NH3 volatilization in rice fields. Considering P importance herein, we conducted two consecutive field experiments using an innovative, controlled-release, phosphorus-blended fertilizer (CRPBF, with levels CRP0, CRP1, and CRP2). We compared CP0 (in which no fertilizer was applied), CP1 (112.5 kg P ha−1 P of locally recommended fertilizers), and CP2: (P and K blended fertilizers) to determine the best possible way to reduce NH3 volatilization without affecting the yield and quality of rice. The results of the study suggested that the yield of rice increased significantly with the application of CRP1 (11.11 t ha−1) and CRP2 (11.99 t ha−1). The addition of CRP1 and CRP2 to the rice field also enhanced yield-related components, i.e., panicle weight, total spikelets per unit area, spikelets per panicle, and above-ground biomass. CRP0 showed a lower yield and related components when compared to CP2. The addition of CRP1 and CRP2 demonstrated lower protein contents when compared to other treatments. The CRPBF application improved starch content and taste scores, and reduced the chalkiness of the rice grain during both years. The results showed a decreasing trend in NH3 volatilization from CRPBF amendments by improving the nitrogen use efficiency traits when compared to other treatments: CRP2, CRP1, and CRP0 reduced NH3 volatilization by 45%, 35%, and 15%, respectively. The results of this study indicate that, due to the episodic nature of NH3 volatilization, CRPBFs with 50% P and 100% P can markedly reduce NH3 volatilization from paddy fields without compromising the yield and quality of the crop, and could be a promising alternative to the ordinary commercial fertilizers used in rice fields. [ABSTRACT FROM AUTHOR]

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan). [ABSTRACT FROM AUTHOR]

Anaerobic liquid dairy digestate is a by-product of dairy waste anaerobic digestion from dairy operations and is associated with environmental risks if not handled properly, particularly nutrient leaching losses, water contaminations, and greenhouse gas emissions. We tested the applications of anerobic digestate (AD) as a biofertilizer and water source in greenhouse vegetable production to integrate food production and industry waste management for sustainable environments. We used a deep water culture system to assess the effects of AD effluent alone, inorganic nutrient solution (NS), and a combination of AD and NS on the growth, yield, and phytonutrient profile and heavy metal contamination assessment of hydroponically produced lettuce. Lettuce produced in AD had a lower leaf area, total chlorophyll content, and fresh biomass; however, it displayed significantly higher chicoric acid (200%), chlorogenic acid (67%), luteolin (800%), quercetin-3-O-β-d-glucuronide (378%), quercetin-3-glucoside (200%), quercetin-3-O-(6″-O-malonyl)-β-D-glucoside (1077%), folate (248%), pantothenic acid (200%), total phenolics (111%), total antioxidants (44%), and soluble sugars (253%) compared to control (inorganic feed). The AD-produced lettuce also showed significantly lower heavy metal bioaccumulation risks associated with the human consumption. Based on various results, we may conclude that AD utilization in hydroponics can offer a sustainable solution to harvest a better lettuce yield, higher phytonutrients, and environmental benefits. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to introduce a new scheme, known as the Aboodh homotopy integral transform method ( A HITM), for the approximate solution of wave problems in multi-dimensional orders. The Aboodh integral transform ( A IT) removes the restriction of variables in the recurrence relation, whereas the homotopy perturbation method (HPM) derives the successive iterations using the initial conditions. The convergence analysis is provided to study a wave equation with multiple dimensions. Some computational applications are considered to show the efficiency of this scheme. Graphical representation between the approximate and the exact solution predicts the high rate of convergence of this approach. [ABSTRACT FROM AUTHOR]

The study of dynamic systems appears in various aspects of dynamical structures such as decomposition, decoupling, observability, and controllability. In the present research, we study the controllability of fractional stochastic systems (FSF) and examine the Poisson jumps in finite dimensional space where the fractional impulsive neutral stochastic system is controllable. Sufficient conditions are demonstrated with the aid of fixed point theory. The Mittag-Leffler (ML) matrix function defines the controllability of the Grammian matrix (GM). The relation to symmetry is clear since the controllability Grammian is a hermitian matrix (since the integrand in its definition is hermitian) and this is the complex version of a symmetric matrix. In fact, such a Grammian becomes a symmetric matrix in the specific scenario where the controllability Grammian is a real matrix. Some examples are provided to demonstrate the feasibility of the present theory. [ABSTRACT FROM AUTHOR]

This article presents an idea of a new approach for the solitary wave solution of the modified Degasperis–Procesi (mDP) and modified Camassa–Holm (mCH) models with a time-fractional derivative. We combine Laplace transform (L T) and homotopy perturbation method (HPM) to formulate the idea of the Laplace transform homotopy perturbation method (L HPTM). This study is considered under the Caputo sense. This proposed strategy does not depend on any assumption and restriction of variables, such as in the classical perturbation method. Some numerical examples are demonstrated and their results are compared graphically in 2D and 3D distribution. This approach presents the iterations in the form of a series solutions. We also compute the absolute error to show the effective performance of this proposed scheme. [ABSTRACT FROM AUTHOR]

Emotional stress is believed to be associated with increased tumor progression. Stress-induced epigenetic modifications can contribute to the severity of disease and poor prognosis in cancer patients. The current study aimed to investigate the expression profiles along with the prognostic significance of psychological stress-related genes in metastatic breast cancer patients, to rationalize the molecular link between emotional stress and cancer progression. We profiled the expression of selected stress-associated genes (5-HTT, NR3C1, OXTR, and FKBP5) in breast cancer including the stress evaluation of all participants using the Questionnaire on Distress in Cancer Patients–short form (QSC-R10). A survival database, the Kaplan–Meier Plotter, was used to explore the prognostic significance of these genes in breast cancer. Our results showed relatively low expressions of 5-HTT (p = 0.02) and OXTR (p = 0.0387) in metastatic breast cancer patients as compared to the non-metastatic group of patients. The expression of NR3C1 was low in tumor grade III as compared to grade II (p = 0.04). Additionally, the expression of NR3C1 was significantly higher in patients with positive estrogen receptor status. However, no significant difference was found regarding FKBP5 expression in breast cancer. The results suggest a potential implication of these genes in breast cancer pathology and prognosis. [ABSTRACT FROM AUTHOR]

The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From 2010 to 2018, all precipitation products were evaluated on daily, monthly, seasonal, and annual bases at a point-to-pixel scale and over the entire spatial domain. The accuracy of the products was evaluated using commonly used evaluation and categorical indices, including Root Mean Square Error (RMSE), Correlation Coefficient (CC), Bias, Relative Bias (rBias), Critical Success Index (CSI), Success Ratio (SR) Probability of Detection (POD), and False Alarm Ratio (FAR). The results show that: (1) Over the entire country, the spatio-temporal distribution of observed precipitation could be represented by IMERG and TRMM products. (2) All products (reanalysis and SPPs) demonstrated good agreement with the reference data at the monthly scale compared to the daily data (CC > 0.7 at monthly scale). (3) All other products were outperformed by IMERG and TRMM in terms of their capacity to detect precipitation events throughout the year, regardless of the season (i.e., winter, spring, summer, and autumn). Furthermore, both products (IMERG and TRMM) consistently depicted the incidence of precipitation events across Pakistan's various topography and climatic regimes. (4) Generally, CHIRPS and ERA5 products showed moderate performances in the plan areas. PERSIANN, PERSIANN-CCS, PDIR, PERSIANN-CDR, and MEERA2 products were uncertain to detect the occurrence and precipitation over the higher intensities and altitudes. Considering the finding of this assessment, we recommend the use of daily and monthly estimates of the IMERG product for hydro climatic studies in Pakistan. [ABSTRACT FROM AUTHOR]

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR. [ABSTRACT FROM AUTHOR]

Simple Summary: The Himalayan pheasants have many cultural, economic, and environmental values. Still, unfortunately, the species is under anthropogenic pressure and biological hazards, leading to a rapid decline in its population. The current study aims to identify the human pressures imposed by major infrastructure development projects, illegal hunting, deforestation, pollution, non-timber forest collections, and other natural activities that severely damage the pheasant habitat and population in the study area. Higher altitude valleys with less disturbed habitats have significantly higher encounter rates and densities than those at lower altitudes and higher human pressure. This research suggests that conservation biologists and the wildlife department of Khyber Pakhtunkhwa should formulate approaches for effective conservation and better habitat management. It may include public awareness regarding pheasant's beneficial aspects, pre-implementations of the wildlife management plan for a major infrastructure development project, and strict rules and policies around deforestation and habitat degradation. The study also recommends that the habitat of the pheasants, specifically the threatened area of Western Tragopan, be considered a legally reserved area of conservation to improve its conservation status. Pheasants play a distinctive and significant role in high altitudinal ecosystems. These are good indicators of environmental changes, and their presence determines the health and balance of the bio-network. Recent human pressure continues to degrade their populations, and some pheasant species are already extinct. Therefore, the current study focuses on pheasant abundance and emerging conservation issues. The pheasant population was assessed using "Call count methods" and analyzed by DISTANCE software. The results revealed that the valleys where human interference is minimum had significantly higher encounter rates and densities of pheasants. At the same time, the pheasant population was severely affected, whether found at lower or higher altitudes, showing seasonal migration toward human settlements. The habitat suitability modeling was performed using the MaxEnt model and showed that human activities overlap with the suitable natural habitats of pheasants. The threats were identified using a systematic questionnaire survey from the nearest villages of the potential habitat, and particular attention was paid to valleys where human pressures were found to be high. Major infrastructure development projects, illegal hunting, and deforestation were identified as the major threats to the pheasant population. The study concluded that proper conservation measures are required to protect pheasants in their potential habitats. [ABSTRACT FROM AUTHOR]

The symmetry design of the system contains integer partial differential equations and fractional-order partial differential equations with fractional derivative. In this paper, we develop a scheme to examine fractional-order shock wave equations and wave equations occurring in the motion of gases in the Caputo sense. This scheme is formulated using the Mohand transform (MT) and the homotopy perturbation method (HPM), altogether called Mohand homotopy perturbation transform (MHPT). Our main finding in this paper is the handling of the recurrence relation that produces the series solutions after only a few iterations. This approach presents the approximate and precise solutions in the form of convergent results with certain countable elements, without any discretization or slight perturbation theory. The numerical findings and solution graphs attained using the MHPT confirm that this approach is significant and reliable. [ABSTRACT FROM AUTHOR]

The algebras of the symmetry operators for the Klein–Gordon equation are important for a charged test particle, moving in an external electromagnetic field in a space time manifold on the isotropic hydrosulphate. In this paper, we develop an analytical and numerical approach for providing the solution to a class of linear and nonlinear fractional Klein–Gordon equations arising in classical relativistic and quantum mechanics. We study the Yang homotopy perturbation transform method (Y HPTM), which is associated with the Yang transform (Y T) and the homotopy perturbation method (HPM), where the fractional derivative is taken in a Caputo–Fabrizio (CF) sense. This technique provides the solution very accurately and efficiently in the form of a series with easily computable coefficients. The behavior of the approximate series solution for different fractional-order ℘ values has been shown graphically. Our numerical investigations indicate that Y HPTM is a simple and powerful mathematical tool to deal with the complexity of such problems. [ABSTRACT FROM AUTHOR]

This document provides an overview of a special issue of the journal "Algorithms" focused on "Deep Neural Networks and Optimization Algorithms." The issue includes seven papers that address theoretical and practical issues in this area. The papers cover a range of topics, including preprocessing pregnancy examination data, sampling in neural network training, neural network boosting for logistic regression, generating artificial EEG data, enhancing QR code source printer identification, gas tracking in chemical industry parks, and developing a graph neural network-based surrogate model. The document concludes by noting the increasing use of AI in cybersecurity and the three main directions of research in this field. The authors of the document express their gratitude to the authors, referees, and the editorial office of Algorithms. [Extracted from the article]

Nitrogen (N) plays a vital role in plant growth; however, the yield response to N fertilizer is regularly declining in the major cropping systems due to overuse and the upper limit of yields. Heavy losses of N are also documented due to its excessive use in the rice-wheat rotation system, resulting in low nitrogen-use efficiencies (NUE) and environmental problems. Therefore, a three-year field experiment was performed with different N managements to investigate the impact of reduced N input rates in this exhaustive cropping system. Reducing the N application rates did not affect the wheat and rice yields significantly, only during the second wheat season was the yield slightly reduced as compared to higher N input treatments. Decreasing the N input rates in the prior crop and present crop, and the interactions of both rice and wheat seasons (R × W) did not influence the yields of either crop. A reduction in N fertilizer had a considerable impact on dry matter production during the wheat seasons but demonstrated no effect during the rice season. The accumulation of N was significantly reduced during both crop seasons by lowering the rate of N application. In addition, the NUE indices were significantly influenced by N fertilizer application rates. In conclusion, N fertilizer input rates for both rice and wheat crops can be lowered as compared to present fertilization rates without any risk of yield decline. Lowering the N input increases the NUE and effectively reduces N losses, and soil N status can also be maintained. [ABSTRACT FROM AUTHOR]

This study formulates a multi-objective, multi-item solid transportation issue with parameters that are neutrosophic Z-number fuzzy variables such as transportation costs, supplies, and demands. This work covers two scenarios where uncertainty in the problem can arise: the fuzzy solid transportation problem and the interval solid transportation problem. The first scenario arises when we represent data problems as intervals instead of exact values, while the second scenario arises when the information is not entirely clear. We address both models when the uncertainty alone impacts the constraint set. In order to find a solution for the interval case, we generate an additional problem. Since this auxiliary problem is typical of solid transportation, we can resolve it using the effective techniques currently in use. In the fuzzy scenario, a parametric method is used to discover a fuzzy solution to the earlier issue. Parametric analysis identifies that the best parameterized approaches to complementary problems are characterized by the application of parametric analysis. We present a suggested algorithm for determining the stability set. Finally, we provide a numerical example and sensitivity analysis for the transportation problem, which is both symmetrical and asymmetrical. [ABSTRACT FROM AUTHOR]

The astonishing increase in temperature presents an alarming threat to crop production worldwide. As evident by huge yield decline in various crops, the escalating drastic impacts of heat stress (HS) are putting global food production as well as nutritional security at high risk. HS is a major abiotic stress that influences plant morphology, physiology, reproduction, and productivity worldwide. The physiological and molecular responses to HS are dynamic research areas, and molecular techniques are being adopted for producing heat tolerant crop plants. In this article, we reviewed recent findings, impacts, adoption, and tolerance at the cellular, organellar, and whole plant level and reported several approaches that are used to improve HS tolerance in crop plants. Omics approaches unravel various mechanisms underlying thermotolerance, which is imperative to understand the processes of molecular responses toward HS. Our review about physiological and molecular mechanisms may enlighten ways to develop thermo-tolerant cultivars and to produce crop plants that are agriculturally important in adverse climatic conditions. [ABSTRACT FROM AUTHOR]

Symmetries are crucial to the investigation of nonlinear physical processes, particularly the evaluation of a differential problem in the real world. This study focuses on the investigation of the Kadomtsev–Petviashvili (KP) model within a (3+1)-dimensional domain, governing the behavior of wave propagation in a medium characterized by both nonlinearity and dispersion. The inquiry employs two distinct analytical techniques to derive multiple soliton solutions and multiple solitary wave solutions. These methods include the modified Sardar sub-equation technique and the Darboux transformation (DT) . The modified Sardar sub-equation technique is used to obtain multiple soliton solutions, while the DT is introduced to develop two bright and two dark soliton solutions. These solutions are presented alongside their corresponding constraint conditions and illustrated through 3-D, 2-D, and contour plots to physically portray the derived solutions. The results demonstrate that the employed analytical techniques are useful and have not yet been explored in the context of the analyzed models. The proposed methodologies are valuable and can be applied to additional nonlinear evolutionary models employed to describe nonlinear physical models within the domain of nonlinear science. [ABSTRACT FROM AUTHOR]

Aging is a complex process indicated by low energy levels, declined physiological activity, stress induced loss of homeostasis leading to the risk of diseases and mortality. Recent developments in medical sciences and an increased availability of nutritional requirements has significantly increased the average human lifespan worldwide. Several environmental and physiological factors contribute to the aging process. However, about 40% human life expectancy is inherited among generations, many lifespan associated genes, genetic mechanisms and pathways have been demonstrated during last decades. In the present review, we have evaluated many human genes and their non-human orthologs established for their role in the regulation of lifespan. The study has included more than fifty genes reported in the literature for their contributions to the longevity of life. Intact genomic DNA is essential for the life activities at the level of cell, tissue, and organ. Nucleic acids are vulnerable to oxidative stress, chemotherapies, and exposure to radiations. Efficient DNA repair mechanisms are essential for the maintenance of genomic integrity, damaged DNA is not replicated and transferred to next generations rather the presence of deleterious DNA initiates signaling cascades leading to the cell cycle arrest or apoptosis. DNA modifications, DNA methylation, histone methylation, histone acetylation and DNA damage can eventually lead towards apoptosis. The importance of calorie restriction therapy in the extension of lifespan has also been discussed. The role of pathways involved in the regulation of lifespan such as DAF-16/FOXO (forkhead box protein O1), TOR and JNK pathways has also been particularized. The study provides an updated account of genetic factors associated with the extended lifespan and their interactive contributory role with cellular pathways. [ABSTRACT FROM AUTHOR]

Photoreforming biomass-derived waste such as glycerol into hydrogen fuel is a renewable hydrogen generation technology that has the potential to become important due to unavoidable CO2 production during methane steam reforming. Despite tremendous efforts, the challenge of developing highly active photocatalysts at a low cost still remains elusive. Here, we developed a novel photocatalyst with a hybrid support comprising reduced graphene oxide (rGO) and TiO2 nanorods (TNR). rGO in the hybrid support not only performed as an excellent scavenger of electrons from the semiconductor conduction band due to its suitable electrochemical potential, but also acted as an electron transport highway to the metal co-catalyst, which otherwise is not possible by simply increasing metal loading due to the shadowing effect. A series of hybrid supports with different TNR and rGO ratios were prepared by the deposition method. Pd nanoparticles were deposited over hybrid support through the chemical reduction method. Pd/rGO-TNRs photocatalyst containing 4 wt.% rGO contents in the support and 1 wt.% nominal Pd loading demonstrated hydrogen production activity ~41 mmols h−1g−1, which is 4 and 40 times greater than benchmark Au/TiO2 and pristine P25. The findings of this works provide a new strategy in optimizing charge extraction from TiO2, which otherwise has remained impossible due to a fixed tradeoff between metal loading and the detrimental shadowing effect. [ABSTRACT FROM AUTHOR]

The interaction between IrO2 and TiO2 (anatase) in non-isothermal reduction conditions has been studied by the temperature programmed reduction technique. IrO2 clusters are of sizes between 0.5 and 0.9 nm as determined from High Resolution Transmission Electron Microscopy (HRTEM). Largely, two main regions for reduction were found and modeled at ca. 100 and 230 °C. The first region is attributed to the partial reduction of IrO2 clusters, while the second one is due to reduction of the formed crystalline (rutile IrO2), during TPR, to Ir metal. Two methods for calculating kinetic parameters were tested. First, by applying different ramping rates on a 3.5 wt.% IrO2/TiO2 using Kissinger's method. The apparent activation energy values for the first and second reduction regions were found to be ca. 35 and 100 kJ/mol, respectively. The second method was based on fitting different kinetic models for the experimental results in order to extract qualitative information on the nature of interaction during the reduction process. It was found that the first reduction is largely due to the amount of IrO2 (reactant concentration) while the second one involved phase boundary effect as well as nucleation. [ABSTRACT FROM AUTHOR]

Nanotechnology is a booming avenue in science and has a multitude of applications in health, agriculture, and industry. It exploits materials' size at nanoscale (1–100 nm) known as nanoparticles (NPs). These nanoscale constituents are made via chemical, physical, and biological methods; however, the biological approach offers multiple benefits over the other counterparts. This method utilizes various biological resources for synthesis (microbes, plants, and others), which act as a reducing and capping agent. Among these sources, microbes provide an excellent platform for synthesis and have been recently exploited in the synthesis of various metallic NPs, in particular iron. Owing to their biocompatible nature, superparamagnetic properties, small size efficient, permeability, and absorption, they have become an integral part of biomedical research. This review focuses on microbial synthesis of iron oxide nanoparticles using various species of bacteria, fungi, and yeast. Possible applications and challenges that need to be addressed have also been discussed in the review; in particular, their antimicrobial and anticancer potentials are discussed in detail along with possible mechanisms. Moreover, some other possible biomedical applications are also highlighted. Although iron oxide nanoparticles have revolutionized biomedical research, issues such as cytotoxicity and biodegradability are still a major bottleneck in the commercialization of these nanoparticle-based products. Addressing these issues should be the topmost priority so that the biomedical industry can reap maximum benefit from iron oxide nanoparticle-based products. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) and Alzheimer's disease (AD) are neurodevelopmental and neurodegenerative disorders affecting two opposite ends of life span, i.e., childhood and old age. Both disorders pose a cumulative threat to human health, with the rate of incidences increasing considerably worldwide. In the context of recent developments, we aimed to review correlated symptoms and genetics, and overlapping aspects in the mechanisms of the pathogenesis of ASD and AD. Dementia, insomnia, and weak neuromuscular interaction, as well as communicative and cognitive impairments, are shared symptoms. A number of genes and proteins linked with both disorders have been tabulated, including MECP2, ADNP, SCN2A, NLGN, SHANK, PTEN, RELN, and FMR1. Theories about the role of neuron development, processing, connectivity, and levels of neurotransmitters in both disorders have been discussed. Based on the recent literature, the roles of FMRP (Fragile X mental retardation protein), hnRNPC (heterogeneous ribonucleoprotein-C), IRP (Iron regulatory proteins), miRNAs (MicroRNAs), and α-, β0, and γ-secretases in the posttranscriptional regulation of cellular synthesis and processing of APP (amyloid-β precursor protein) have been elaborated to describe the parallel and overlapping routes and mechanisms of ASD and AD pathogenesis. However, the interactive role of genetic and environmental factors, oxidative and metal ion stress, mutations in the associated genes, and alterations in the related cellular pathways in the development of ASD and AD needs further investigation. [ABSTRACT FROM AUTHOR]

Current research into the role of engineered nanoparticles in drug delivery systems (DDSs) for medical purposes has developed numerous fascinating nanocarriers. This paper reviews the various conventionally used and current used carriage system to deliver drugs. Due to numerous drawbacks of conventional DDSs, nanocarriers have gained immense interest. Nanocarriers like polymeric nanoparticles, mesoporous nanoparticles, nanomaterials, carbon nanotubes, dendrimers, liposomes, metallic nanoparticles, nanomedicine, and engineered nanomaterials are used as carriage systems for targeted delivery at specific sites of affected areas in the body. Nanomedicine has rapidly grown to treat certain diseases like brain cancer, lung cancer, breast cancer, cardiovascular diseases, and many others. These nanomedicines can improve drug bioavailability and drug absorption time, reduce release time, eliminate drug aggregation, and enhance drug solubility in the blood. Nanomedicine has introduced a new era for drug carriage by refining the therapeutic directories of the energetic pharmaceutical elements engineered within nanoparticles. In this context, the vital information on engineered nanoparticles was reviewed and conferred towards the role in drug carriage systems to treat many ailments. All these nanocarriers were tested in vitro and in vivo. In the coming years, nanomedicines can improve human health more effectively by adding more advanced techniques into the drug delivery system. [ABSTRACT FROM AUTHOR]

A numerical investigation of unsteady boundary layer flow with heat and mass transfer of non-Newtonian fluid model, namely, Jeffrey fluid subject, to the significance of Soret and Dufour effects is carried out by using the local nonsimilarity method and homotopy analysis method. An excellent agreement in the numerical results obtained by both methods is observed and we establish a new mathematical approach to obtain the solutions of unsteady-state flow with heat and mass transfer phenomenons. Similarity transformation is applied to governing boundary layer partial differential equations to obtain the set of self-similar, nondimensional partial differential equations. Graphical results for different emerging parameters are discussed. The dimensionless quantities of interest skin friction coefficient, Sherwood number, and Nusselt number are discussed through tabulated results. The main novelty of the current work is that the average residual error of the m t h -order approximation of the OHAM scheme for steady-state solution is decreased for higher-order approximation. Further, a rapid development of the boundary layer thickness with the increasing values of dimensionless time τ is observed. It is noted that for large values of τ , the steady state in the flow pattern is gained. It is worth mentioning that the magnitude of Sherwood number is increased with the increasing values of Schmidt number S c and Dufour number D f . The magnitude of local Nisselt number is increased for the increasing values of Soret number, S r . [ABSTRACT FROM AUTHOR]

The critical component of HCI is face recognition technology. Emotional computing heavily relies on the identification of facial emotions. Applications for emotion-driven face animation and dynamic assessment are numerous (FER). Universities have started to support real-world face expression recognition research as a result. Short video clips are continually uploaded and shared online, building up a library of videos on various topics. The enormous amount of movie data appeals to system engineers and researchers of autonomous emotion mining and sentiment analysis. The main idea is that categorizing things may be done by looking at how individuals feel about specific issues. People might choose to have a basic or complex facial appearance. People worldwide continually express their feelings through their faces, whether they are happy, sad, or uncertain. An online user can visually express themselves through a video's editing, music, and subtitles. Additionally, before the video data can be used, noise in the data must frequently be eliminated. Automatically figuring out how someone feels in a video is a challenging task that will only get harder over time. Therefore, this paper aims to show how facial recognition video analysis can be used to show how sentiment analysis can help with business growth and essential decision-making. To determine how people are affected by reviewers' writing, we use a technique for deciding emotions in this analysis. The feelings in movies are assessed using machine learning algorithms to categorize them. A lightweight machine learning algorithm is proposed to help in Aspect-oriented emotion classification for movie reviews. Moreover, to analyze real and published datasets, experimental results are compared with different Machine Learning algorithms, i.e., Naive Bayes, Support Vector Machine, Random Forest, and CNN. The proposed approach obtained 84.72 accuracy and 79.24 sensitivity. Furthermore, the method has a specificity of 90.64 and a precision of 90.2. Thus, the proposed method significantly increases the accuracy and sensitivity of the emotion detection system from facial feature recognition. Our proposed algorithm has shown contribution to detect datasets of different emotions with symmetric characteristics and symmetrically-designed facial image recognition tasks. [ABSTRACT FROM AUTHOR]

Within the context of electron transfer during the catalytic water oxidation reaction, the Ir-based system is among the most active. The reaction, mimicking photosynthesis II, requires the use of an electron acceptor such the Ce4+ cation. This complex reaction, involving adsorbed water at the interface of the metal cation and Ce4+, has mostly been studied in homogenous systems. To address the ambiguity regarding the gradual transformation of a homogenous system into a heterogeneous one, we prepared and studied a heterogeneous catalyst system composed of IrO2, with a mean particle size ranging from about 5 Å to 10 Å, dispersed on a TiO2 anatase support, with the objective of probing into the different parameters of the reaction, as well as the compositional changes and rates. The system was stable for many of the runs that were conducted (five consecutive runs with 0.18 M of Ce4+ showed the same reaction rate with TON > 56,000) and, equally importantly, was stable without induction periods. Extraction of the reaction rates from the set of catalysts, with an attempt to normalize them with respect to Ir loading and, therefore, to obtain turnover frequencies (TOF), was conducted. While, within reasonable deviations, the TOF numbers extracted from TPR and XPS Ir4f were close, those extracted from the particle shape (HR-STEM) were considerably larger. The difference indicates that bulk Ir atoms contribute to the electron transfer reaction, which may indicate that the reaction rate is dominated by the reorganization energy between the redox couples involved. Therefore, the normalization of reaction rates with surface atoms may lead to an overestimation of the site activity. [ABSTRACT FROM AUTHOR]

The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells' parameters. [ABSTRACT FROM AUTHOR]

Cymbopogon citratus is a medicinal and well-known aromatic plant which is usually used as a substitute for green-tea with extraordinary phytomedicinal potential. It is of great importance because it offers several promising health effects. The objective of the study was to investigate the antioxidant activity and total phenolic content of lemongrass leaves extracted by maceration and ultrasound-assisted extraction techniques. Different concentrations of both acetone and ethanol (50% and 70%) as solvents were tested for their radical scavenging activity against the stable free radical DPPH in quantization using a spectrophotometric assay. The ultrasound-assisted extraction technique had advantageous results in the polyphenols extraction yield (26.68%), TPC (61 mg GAE/g of extract) and DPPH (73.85%) compared to the maceration technique. Overall, the results were significant from 50% ethanol and the DPPH results were more significant from 70% ethanol than other concentrations. It is concluded that the ultrasound-assisted extraction is a better extraction technique for polyphenols than other conventional techniques. [ABSTRACT FROM AUTHOR]

This study develops a numerical strategy for finding the approximate solution of the nonlinear foam drainage (NFD) equation with a time-fractional derivative. In this paper, we formulate the idea of the Laplace homotopy perturbation transform method (LHPTM) using Laplace transform and the homotopy perturbation method. This approach is free from the heavy calculation of integration and the convolution theorem for the recurrence relation and obtains the solution in the form of a series. Two-dimensional and three-dimensional graphical models are described at various fractional orders. This paper puts forward a practical application to indicate the performance of the proposed method and reveals that all the outputs are in excellent agreement with the exact solutions. [ABSTRACT FROM AUTHOR]

This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine's cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research. [ABSTRACT FROM AUTHOR]

The aim of this study was to improve the solubility and prevent the ulcerogenic effect of flurbiprofen. Initially, binary and ternary solid dispersions (BSDs and TSDs) of flurbiprofen were prepared by using non-ordered mesoporous silica and gelucire. After preformulation testing (solubility, flow properties, % yield, and entrapment efficiency), four formulations were selected for further detailed studies. Solid-state characterization of optimized formulations (S1, S6, S7, and S12) showed successful drug incorporation in the solid dispersion at the molecular state without any noticeable interactions. The in vitro solubility and release study showed an increase in solubility and 98–100% of drug release in 30–45 min. The in vivo gastro-protective effect of the optimized formulations containing flurbiprofen and silica (1:1) with 25% w/w gelucire (S6 and S12) showed a reduction in the gastric lesion index (GLI) after four days of treatment. Moreover, histological images of the stomach lining (S6 and S12) illustrated normal epithelial cells and a partially protected mucosal membrane. Thus, TSD exhibited a significant increase in solubility and the dissolution rate and reduced the gastric ulceration. Therefore, TSDs are dubbed as efficacious carriers to enhance the bioavailability of flurbiprofen while simultaneously reducing its side effects. [ABSTRACT FROM AUTHOR]

The capture and utilization of rainwater by crops under various mulching conditions have great importance in agriculture production systems, especially in dry-prone regions. Understanding the effect of mulching on rainwater use efficiency growth and yield of a crop is very important. For this purpose, field experiments were conducted in 2017 and 2018 to evaluate the potential of ridge-furrow mulching on maize growth and development under rain-fed conditions. The field study compared four treatments, i.e., ridge-furrow without mulch (WM), black plastic mulch (BM), transparent plastic mulch (TM) and grass mulch (GM). The BM treatment consistently increased the soil moisture and temperature, resulting in earlier emergence, as well as increased plant height and plant biomass, compared to the WM treatment. Compared to WM, the two-years mean yield of maize with BM, TM and GM were recorded to be increased by 33.6%, 28.1% and 10.8%, respectively. The BM produced a maximal crop growth rate at 90 days after sowing (DAS) as specified by a greater leaf area index. Transpiration rate and leaf stomatal conductance were significantly higher with BM and TM than with WM, however, the BM treatment showed the highest net photosynthetic rate in both years. Net income for the BM treatment was the highest (USD 1226 ha−1) of all the treatments and USD 335 ha−1 greater than WM. As growth, yield and net income of maize were improved with BM, therefore this treatment was found to be the most effective for maize production in rain-fed conditions. This system is evaluated at a small scale, hence to maximize its effectiveness on a large scale, a simulation design needs to be developed. [ABSTRACT FROM AUTHOR]

The conversion of solar to chemical energy is one of the central processes considered in the emerging renewable energy economy. Hydrogen production from water splitting over particulate semiconductor catalysts has often been proposed as a simple and a cost-effective method for large-scale production. In this review, we summarize the basic concepts of the overall water splitting (in the absence of sacrificial agents) using particulate photocatalysts, with a focus on their synthetic methods and the role of the so-called "co-catalysts". Then, a focus is then given on improving light absorption in which the Z-scheme concept and the overall system efficiency are discussed. A section on reactor design and cost of the overall technology is given, where the possibility of the different technologies to be deployed at a commercial scale and the considerable challenges ahead are discussed. To date, the highest reported efficiency of any of these systems is at least one order of magnitude lower than that deserving consideration for practical applications. [ABSTRACT FROM AUTHOR]

Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well. [ABSTRACT FROM AUTHOR]

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient. [ABSTRACT FROM AUTHOR]

Ergot alkaloids are novel pharmaceutical and therapeutic agents synthesized in this study using fungal species Penicillium citrinum. To get the maximum yield of ergot alkaloids a statistical process of response surface methodology was employed using surface culture fermentation technique. Initially, the strain of Penicillium was improved using physical (ultraviolet (UV) and chemical (ethyl methane sulfonate (EMS) treatments to get the maximum yield of ergot alkaloids through surface culture fermentation technique. After improving the strain, survival rate of colonies of Penicillium citrinum treated with UV and EMS was observed. Only 2.04% living colonies were observed after 150 min of exposure of Penicillium citrinum in UV light and 3.2% living colonies were observed after 20 min of the exposure in EMS. The mutated strains of Penicillium citrinum were screened for their production of ergot alkaloids and after fermentation experiments, maximum yield was obtained from PCUV-4 and PCEMS-1 strains. After strain improvement, Plackett–Burman design (PBD) and Box–Behnken design (BBD) of RSM were employed and 10-fold yield enhancement (35.60 mg/100 mL) of ergot alkaloids was achieved. This enhancement in yield of ergot alkaloids proved the positive impacts of RSM and UV on the yield of ergot alkaloids. The study provides a cost effective, economical and sustainable process to produce medically important ergot alkaloids which can be used in various pharmaceutical formulations to treat human diseases. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area. [ABSTRACT FROM AUTHOR]

Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques. [ABSTRACT FROM AUTHOR]