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Abstract Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.

Two groups of cotton growers participated in this study; the first, referred as “conventional cotton producers” (CCPs), continues to use traditional techniques. The second group, known as sustainable cotton producers (SCPs), adheres to practices that are socially acceptable, economically feasible, and environment friendly. This study was designed to undertake a thorough socio-economic analysis of CCP and SCP in terms of cost of production, yield, and adaptations for climate change. The data were collected from a total of 275 farming households (138 SCPs and 137 CCPs) from two districts, namely, Bahawalpur and Rajanpur, Punjab, Pakistan. The data were analyzed using the benefit–cost ratio (BCR), paired sampled t-test, and log–log regression model in SPSS. The results of the study revealed that the cost of production for SCP was significantly less than CCP and the value of BCR for SCP is higher than CCP. The results of the study also revealed that the land preparation costs, irrigation costs, and fertilizer and pesticide costs decrease the cotton yield of CCP, while land area owned and picking costs showed no significant influence on the yield of CCP. On the other hand, all cost items except fertilizer cost have a positive impact on CCP’s cotton yield. The results also indicated that SCP farmers adopted more of adaptation practices for climate change than CCP. This study concluded that SCP has significant advantages over CCP. Therefore, it is recommended that the government should prioritize incentives for SCP adoption to enhance yield and environmental sustainability in cotton farming.

Diesel engines are used extensively in heavy-duty transportation due to their high thermal efficiency and energy density, but they also contribute to environmental pollution. Water-in-diesel emulsions have emerged as an alternative method for decreasing NOx and emissions, but there are still obstacles to assuring engine performance and stability. Surfactants are used to stabilise the emulsion by decreasing the interfacial tension between the fuel and water. Studies on water-in-diesel emulsions published literature suggest that chemical surfactants have been used in the production of emulsified fuels. In addition, research have shown that biosurfactants are less harmful to the environment than chemical surfactants. However, only limited study has been conducted on the use of biosurfactants in emulsified fuel. Consequently, it is important to investigate the possible use of biosurfactants in applications using emulsified fuels. This research studies the categorization of surfactants and biosurfactants and emulsion methods for the development of emulsified fuel. This research also aids in the selection of the most suitable surfactant and biosurfactant for applications, particularly in the context of water-in-diesel emulsions and diesel-in-water emulsions, with the goal of developing an environmentally friendly, stable emulsified fuel that can reduce the emission effect and protect the environment.

Water-in-diesel emulsion (WIDE) fuel has been employed to reduce the emissions from diesel engines. Emulsified fuel in combustion chamber experiences secondary atomization, leading to micro-explosions that lower the combustion temperature and improve atomization. As a result, it reduces emissions and improves combustion efficiency. Emulsified fuels often face challenges with water separation over time; therefore, chemical surfactants have traditionally been used to prevent water separation from emulsified fuel. While biosurfactants offer a more environmentally friendly option compared to traditional chemical surfactants, there is a research gap regarding use of emulsified fuel with biosurfactant. This study aims to investigate the effect of biosurfactant concentration on emulsified fuel properties, stability, particles size, and microexplosions. In this study, six emulsified biodiesel fuels were developed with 10 % distilled water and biosurfactants (lecithin 63 % and cocamide DEA 37 %) ranging from 0.5 % to 3.0 %. The emulsion stability and properties of these fuels were examined. Further, Microscopy was employed to examine particle sizes distributions and suspended droplets hot plate technique with high-speed camera was used to record the micro-explosions. The results illustrate increase in biosurfactant concentration in emulsified fuel increases density and viscosity while reducing surface tension, droplet size, and micro-explosion peak temperature.

Abstract Type 1 diabetes mellitus (T1DM) is characterized by absolute insulin deficiency primarily due to autoimmune destruction of pancreatic β‐cells. The prevailing treatment for T1DM involves daily subcutaneous insulin injections, but a substantial proportion of patients face challenges such as severe hypoglycemic episodes and poorly controlled hyperglycemia. For T1DM patients, a more effective therapeutic option involves the replacement of β‐cells through allogeneic transplantation of either the entire pancreas or isolated pancreatic islets. Unfortunately, the scarcity of transplantable human organs has led to a growing list of patients waiting for an islet transplant. One potential alternative is xenotransplantation of porcine pancreatic islets. However, due to inter‐species molecular incompatibilities, porcine tissues trigger a robust immune response in humans, leading to xenograft rejection. Several promising strategies aim to overcome this challenge and enhance the long‐term survival and functionality of xenogeneic islet grafts. These strategies include the use of islets derived from genetically modified pigs, immunoisolation of islets by encapsulation in biocompatible materials, and the creation of an immunomodulatory microenvironment by co‐transplanting islets with accessory cells or utilizing immunomodulatory biomaterials. This review concentrates on delineating the primary obstacles in islet xenotransplantation and elucidates the fundamental principles and recent breakthroughs aimed at addressing these challenges.

In the quest to mitigate excessive CO2 emissions, the electrochemical reduction of CO2 (eCO2R) into multi-carbon fuels and vital chemical precursors emerges as a compelling strategy. Meticulous control of the C–C coupling on a catalyst surface is a grand challenge in the selective production of desired C2+ products. Ethane and propanol are among the most desirable C2+ products in the gas and liquid phase, respectively. Herein, we demonstrate facile femtosecond laser-enabled tuning of Cu selectivity towards ethane and propanol. The laser-enabled tailoring of the Cu surface induces a shift from C1 products to ethane and propanol. This shift in product composition is attributed to the concurrent creation of hierarchical porous structures, the stabilization of {111}, {200}, and {220} Cu2O facets, and the promotion of the Cu1+ oxidation state. These alterations collectively enhance the adsorption strength, leading to an increased propensity for C-C coupling and, consequently, an elevated selectivity toward C2+ products.

Researchers are investigating eco-friendly binders like coconut shell ash (CSA), metakaolin (MK), and calcined clay (CC) as supplementary cementitious materials (SCM) in high-strength concrete (HSC). Abundantly available as industrial or agricultural waste, these materials, when combined with Portland cement (PC), offer synergistic benefits. This not only improves concrete performance but also addresses waste disposal issues, presenting a sustainable and environmentally friendly solution for long-term use in HSC production. However, this study performed on fresh and mechanical characteristics of HSC blended with CSA, MK, and CCA alone and together as SCM after 28 days of curing. A total of 504 samples of standard concrete were cast and the cubical samples were tested to achieve the targeted compressive strength about 80 MPa after 28 days. The experimental results indicated that the rise in tensile, flexural and compressive strengths of 9.62%, 8.27%, and 10.71% at 9% of CSA, MK, and CC as SCM after 28 days of curing. As SCM content increases, the density, porosity and water absorption of concrete decrease. Moreover, the workability of fresh concrete is getting reduced when the concentration of SCMs increases in HSC. In addition, the concrete's sustainability assessment revealed that employing 18% MK, CC, and CSA as SCM reduced carbon emissions by approximately 11.78%. It is suggested that using 9% CC, MK and CSA together in HSC yields the best results for practical applications in civil engineering.

Abstract Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs’ effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug’s features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.

Abstract In this manuscript, we implement the travelling wave solutions of the fractional (3+1) generalized computational nonlinear wave equation with gas bubbles via application of five mathematical methods. Liquids with gas bubbles primarily arise in various applications like science, engineering, and mathematical physics. The obtained solitary waves solutions have fruitful applications in engineering, science, life, nature and physics. Several novel soliton solutions of concerned model are established in the form of hyperbolic, trigonometric, exponential and rational functions. To handle all calculations and verification of obtained results, computational software Mathematica 12.1 is used. For the demonstration of the physical behaviour of concern model, some solutions are plotted graphical in 2-dimensional and 3-dimensional by imparting specific values to the parameters under constrain conditions. Finally, we intrigue both two and three dimensional to explain the physical behavior of the model.

Advances in deep reinforcement learning have made it possible to create artificial intelligence-based agents for games that use visual information to make decisions as accurately as humans. Novel procedures are often evaluated in two-dimensional games. However, they are relatively easy compared to three-dimensional games, which have a significantly larger state and action space and, more prominently, contain partially observable states. Thus, this paper trains agents with different reinforcement learning algorithms that work fine in contradiction of human players and in-built agents by evaluating them in the first-person shooter (FPS) game Doom using the VizDoom platform. The agents learned in three different scenarios (maps): ’ Defend the Center,’ ‘Deadly Corridor,’ and ‘Health gathering.’ C51-DDQN, DFP, and REINFORCE algorithms have been proven effective in this study. To assess how well the trained agents performed using various reinforcement learning algorithms, we compared the results of our research with other findings in the literature. Finally, this paper presents a comparative analysis and future research directions.

ABSTRACTAn effective low-cost nano NiO-In2O3 electrode material for oxygen evolution (OER) is presented. Electrochemical studies uncovered electrocatalytic recital in Ascorbic Acid, Hydrogen Peroxide (H2O2), and ethanol. The cubic crystal structure of NiO-In2O3 was revealed by XRD. FT-IR, FE-SEM and HR-TEM studies exploit the structure and morphology of NiO-In2O3. Electrochemistry of NiO-In2O3 uncovered high current density (900mA/cm2) at substantially low overpotential (230mV), realizing its OER recital. On top, high mass activity and turnover frequency by NiO-In2O3 comprehend improved electrical and semiconductive properties in H2O2. The NiO-In2O3 durability beyond 90 hours was estimated by chronopotentiometry (CP). The Impedance analysis (EIS) revealed low charge transfer resistance and high exchange current density. Given electrocatalytic studies, we found a direct relationship between NiO-In2O3 nanocomposite and the degradation of H2O2 compared to its counterparts. Hence, this strategy can be an alternative and potential source of hydrogen and oxygen production at commercial scale.Highlights A facile and effectual low-cost NiO-In2O3 electrocatalyst is developed for efficient OER in aqueous hydrogen peroxide.NiO-In2O3 nanocomposite showed high current density (900mA/cm2) at low overpotential grasping its oxygen evolution reaction (OER) concert.NiO-In2O3 Impedance analysis revealed its low charge transfer resistance, high exchange current density (Jexc.), high mass Activity, and high turnover frequency (TOF) in hydrogen peroxide, advocating enhanced electrical and semiconductive properties.NiO-In2O3 showed long-term durability (>90 h) at varying current densities, fostering its application as a potential electrocatalyst for OER/HER reactions.

Along-with the basic principles of Islamic jurisprudence the Quran and Sunnah, there are other legal supporting sources agreed upon by Muslims. Among these stands Ijma’ (Consensus legal Opinion) as the third primary source. This research studies the status of Ijma Fiqh Ja’fri and Fiqh Hanafi, by comparing the both schools, discussing the main concept of Ijma, its conditions, types and ways of implementations of its decisions. Both schools of thought have some commonalities and there are also some differences on its attainment. For example, in Jafari jurisprudence, it is not required that all jurists of the time must agree upon an opinion to be established. Instead, the important part of Ijma is the statement of an Imam. If the consensus includes a statement from one of the Imams of the Ahl al-Bayt or if it is generally known that the opinion of any Imam is included in the consensus, then it is considered an authority. On the other hand, in the Hanafi school of thought, the consensus of all jurists of the time is a prerequisite for Ijma. This article is an attempt to analyze perspectives of both schools of thought and present a comparative assessment of both theories comprehensively.

The intricate architecture of the intestinal epithelium, crucial for nutrient absorption, is constantly threatened by environmental factors. The epithelium undergoes rapid turnover, which is essential for maintaining homeostasis, under the control of intestinal stem cells (ISCs). The central regulator, Wnt/β-catenin signaling plays a key role in intestinal integrity and turnover. Despite its significance, the impact of environmental factors on this pathway has been largely overlooked. This study, for the first time, investigates the influence of Cd on the intestinal Wnt signaling pathway using a mouse model. In this study, male BALB/c mice were administered an environmentally relevant Cd dose (0.98 mg/kg) through oral gavage to investigate the intestinal disruption and Wnt signaling pathway. Various studies, including histopathology, immunohistochemistry, RT-PCR, western blotting, ELISA, intestinal permeability assay, and flow cytometry, were conducted to study Cd-induced changes in the intestine. The canonical Wnt signaling pathway experienced significant downregulation as a result of sub-chronic Cd exposure, which caused extensive damage throughout the small intestine. Increased intestinal permeability and a skewed immune response were also observed. To confirm that Wnt signaling downregulation is the key driver of Cd-induced gastrointestinal toxicity, mice were co-exposed to LiCl (a recognized Wnt activator) and Cd. The results clearly showed that the harmful effects of Cd could be reversed, which is strong evidence that Cd mostly damages the intestine through the Wnt/β-catenin signalling axis. In conclusion, this research advances the current understanding of the role of Wnt/β catenin signaling in gastrointestinal toxicity caused by diverse environmental pollutants.

Time synchronization among smart city nodes is critical for proper functioning and coordinating various smart city systems and applications. It ensures that different devices and systems in the smart city network are synchronized and all the data generated by these devices is consistent and accurate. Synchronization methods in smart cities use multiple timestamp exchanges for time skew correction. The Skew Integrated Timestamp (SIT) proposed here uses a timestamp, which has time skew calculated from the physical layer and uses just one timestamp to synchronize. The result from the experiment suggests that SIT can be used in place of multiple timestamp exchanges, which saves computational resources and energy.

ABSTRACT: Essential oils (EO), ascorbic acid, sugars, carotenoids, flavonoids, dietary fiber, polyphenols, and trace minerals are found in citrus residue. It gives animals energy and promotes health. On a dry matter basis, the citrus pulp is composed of 7% crude protein, 14% crude fiber, 21.6% nitrogen detergent fiber, 2.5% fat, 24.4% total sugars, and 12.1% ME MJ/kg. It has been reported that the natural antioxidant content of citrus pulp has a beneficial effect on growth and microbial and immunological parameters. The literature indicates that the ultimate weight and weight gain of poultry are significantly (P > 0.05) greater with 7.5% inclusion. Growing knowledge of the health benefits of lactic acid bacteria (LAB) in producing beneficial metabolites has led to interest in developing LAB-containing products for use in biofeed businesses. The consumption of fermented citrus residue significantly decreased blood cholesterol levels. Fermentation results in the production of many compounds (including organic acids, exopolysaccharides, bioactive peptides, phenolic compounds, and gamma-aminobutyric acid), which have many multidimensional functions for maintaining the health and well-being of poultry. During fermentation, the pH may quickly decrease, and harmful bacterial and fungal organisms may be substantially retarded at the early stage of ensiling. The published literature has shown that the fermentation of citrus waste with different probiotic strains, such as Lactobacillus acidophilus, Limosilactobacillus fermentum, Lactiplantibacillus plantarum, Pediococcus pentocaseus, and Lacticaseiobacillus paracasei, in the diet has fantastic effects on the conversion of citrus waste into fermented high-quality feed with extended shelf life and sensory value. Citrus waste lactic acid fermentation may be a viable option for producing nutritional biofeed for poultry, but there is a lack of related research on poultry, so more research on food-grade bacterial fermentation is needed.

Abstract When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews’s correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

The Fractional Nonlinear Schrödinger Model (FNLSM) with Kerr law nonlinearity, a popular model for simulating a variety of physical events, is the subject of this study. Our research has two main goals. First, we want to identify novel soliton solutions for the FNLSM with Kerr law nonlinearity, including bright, single, exponential, periodic, hyperbolic, dark and combinations thereof. We use a modified Sardar sub-equation technique to achieve these solutions, which have not yet been published. Secondly, demonstrate that the model is stable and sensitive, examine its modulation instability and sensitivity analysis. To validate the physical relevance of our results, we present 2D, 3D and contour plots with appropriate parameter values. Our results indicate that this research's methodology is effective computationally faster and provides comprehensive and standard solutions. In engineering, computational physics and fiber optics, it can be helpful in resolving more complicated occurrences. By offering novel approaches to currently studied features of FNLSM, this paper advances computational physics. The results that have been presented illustrate the potential for our method to fundamentally alter how FNLSM is understood and modeled. In this dynamic field, we believe that our research offers novel opportunities for investigation and advancement. To the best of our knowledge, this study represents a unique approach to investigating the FNLSM with Kerr law nonlinearity.

A non-linear dynamic model describing the spread of any infectious disease, based on different categories of individuals: susceptible (S), exposed (E), infected (I), quarantined (Q), recovered (R), dead (D), and vaccinated (V) is presented. The aim of the analysis is to develop a sensitivity technique for fuzzy systems, leveraging the application of fuzzy set theory and its differential calculus. This designed method can significantly generate the sensitivity profile of each measured model output with respect to varying values of input parameters. Additionally, the collective influence of all parameters sensitivities on model is simulated in terms of system sensitivities. Numerical simulations by MATLAB, demonstrate the solution of the fuzzy model and its sensitivities concerning the impreciseness, thereby developing a more generalized and accurate disease model.

Traditional medicines are becoming more popular as people become more aware of the dangers of synthetic pharmaceuticals. Tribulus terrestris L., (Gokharu) an annual herbaceous plant, has been extensively utilized by herbalists for numerous medicinal purposes. T. terrestris has been studied for its multiple therapeutic effects, including immunomodulatory, aphrodisiac, anti-urolithic, absorption enhancer, cardioprotective, antidiabetic, anti-inflammatory, hypolipidemic, neuro-protective, anticancer, and analgesic properties. Saponins and flavonoids are two examples of beneficial substances that have recently been found in T. terrestris. These chemicals are very important for a variety of therapeutic effects. Numerous studies have shown that T. terrestris products and various parts may have antioxidant, anti-inflammatory, anti-cancer, anti-diabetic, testosterone-boosting, and liver protective effects. According to the published evidence, T. terrestris boosts testosterone secretion, regulates blood pressure, and protects the human body against injuries. The cardiovascular, reproductive, and urinary systems are all severely impacted. Due to its potent bioactive compounds, the literature evaluated from a wide range of sources including books, reports, PubMed, ScienceDirect, Wiley, Springer, and other databases demonstrated the extraordinary potential to treat numerous human and animal ailments. Our review is different from other published articles because we explored its importance for humans and especially in veterinary like poultry health. It could also be used as an aphrodisiac to treat different fertility-related disorders in human and animal science. More research into the pharmacodynamics of herbs like T. terrestris is needed so that it can be used in a wider variety of nutraceutical products for humans and poultry.

The stochastic nonlinear Schrödinger model (SNLSM) in (1+1)-dimension with random potential is examined in this paper. The analysis of the evolution of nonlinear dispersive waves in a totally disordered medium depends heavily on the model under investigation. This study has three main objectives. Firstly, for the SNLSM, derive stochastic precise solutions by using the modified Sardar sub-equation technique. This technique is efficient and intuitive for solving such models, as shown by the generated solutions, which can be described as trigonometric, hyperbolic, bright, single and dark. Secondly, for obtaining numerical solutions to the SNLSM, the algorithms described here offer an accurate and efficient technique. Lastly, investigate the phase plane analysis of the perturbed and unperturbed dynamical system and the time series analysis of the governing model. The results show that the numerical and analytical techniques can be extended to solve other nonlinear partial differential equations in physics and engineering. The results of this study have a significant impact on how well we comprehend how solitons behave in physical systems. Additionally, they may serve as a foundation for the development of improved numerical techniques for handling challenging nonlinear partial differential equations.

In this paper, we examine the solitary wave solutions of the generalised (2 + 1) extended Kadomtsev–Petviashvili (eKP) equation, which is used as model for the surface waves and internal waves in straits or channels and describes the dynamics of nonlinear waves in plasma physics and fluid dynamics. We secure distinct soliton solutions by employing two amelioration methodologies, namely the improved F-expansion method and the new Kudryashov method. These methods explore several unique forms of solitary wave solutions, including dark, rational, singular, periodic, and exponential ones. In order to illustrate propagation of some reported wave solutions, the graphs associated with these solutions are presented by choosing appropriate parametric values in 2D, 3D, contour, and density plots with the use of the symbolic software Mathematica 13.0. The presented solutions realize very significant fact for investigation the qualitative interpretations for various phenomena in our nature. The computed solutions revealed that the applied methods are influential, efficious and skilful and can be a best way to handle other higher order nonlinear evolution equations.

In this research, a dynamical (3 + 1)-dimensional nonlinear Schrödinger model (NLSM) is under consideration which is used to model the propagation of ultra-short optical pulses in highly-nonlinear media. This aforementioned model has been widely applied in Bose–Einstein condensates, nonlinear optical fiber communication plasma physics, hydrodynamics, elastic media and other diversified areas of mathematical physics and engineering. This study has two main objectives. Firstly, to obtain some novel solutions in the shape of singular, dark, periodic and plane wave solutions that are innovative and not previously found in the literature. Secondly, an amended extended tanh-function method with modulation instability (MI) is used for this model. As far as we know, this has never been investigated in this manner. The 2-D, 3-D and contour plots have been defined using the required parametric values to support the results of physical compatibility. According to the examined results, the method used in this study to retrieve consisting and conventional solutions is efficient and more immediate in computing and can be thought of as a useful tool in resolving more difficult problems that arise in the field of engineering, physics, mathematics and fiber optics.

In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.

The current study explores the prospective of a nitrogen-doped graphene (NG) incorporated into ZnSe-TiO2 composites via hydrothermal method for supercapacitor electrodes. Structural, morphological, and electronic characterizations are conducted using XRD, SEM, Raman, and UV analyses. The electrochemical study is performed and galvanostatic charge-discharge (GCD) and cyclic voltammetry (CV) are evaluated for the supercapacitor electrode material. Results demonstrate improved performance in the ZnSe-NG-TiO2 composite, indicating its potential for advanced supercapacitors with enhanced efficiency, stability, and power density. Specific capacity calculations and galvanic charge-discharge experiments confirmed the promising electrochemical activity of ZnSe-NG-TiO2, which has a specific capacity of 222 C/g. The negative link among specific capacity and current density demonstrated the composite’s potential for high energy density and high-power density electrochemical devices. Overall, the study shows that composite materials derived from multiple families can synergistically improve electrode characteristics for advanced energy storage applications.

In this article, we discuss various solitary wave solutions that are extremely important in applied mathematics. In order to construct accurate solution to nonlinear fractional PDEs, we have employed the Khater technique to nonlinear equation for 3-dimensional fractional Wazwaz-Benjamin-Bona-Mahony (WBBM) using conformable fractional dervatives. We obtain novel sets of solutions by employing this method including both periodic kink and periodic singular kink solution, singular periodic waves solution, dark solitons, bell shaped soliton solution, bell shaped singular solution, bright solution and smooth periodic wave solutions. In order to comprehend the physical principles and significance of the technique, solutions have been graphically represented. The results acquired to demonstrate the efficiency of the computational technique for the WBBM equation, our findings unambiguously demonstrate that the suggested approach is a useful, potent and simple way for experimental result for various kinds of non-integer order differential equations in the engineering and applied sciences.

The fractional-order nonlinear Gardner and Cahn–Hilliard equations are often used to model ultra-short burst beams of light, complex fields of optics, photonic transmission systems, ions, and other fields of mathematical physics and engineering. This study has two main objectives. First, the main objective of this investigation is to solve the fractional-order nonlinear Gardner and Cahn–Hilliard equations by using the modified auxiliary equation method, which is not found in the literature. Second, the exact and approximate solutions of these equations are obtained by utilizing the fractional conformable residual power series algorithm and the modified auxiliary equation method. For the analytical and numerical solutions to two equations, we employ two separate techniques and establish consistency between the precise answers that are derived and the compatible numerical solution. To the best of our knowledge, this method of solving equations has never been investigated in this manner. The 2D and 3D contours have been defined using appropriate parametric values to support the physical compatibility of the results. The assessed findings suggested that the approach used in this study to recover inclusive and standard solutions is approachable, efficient, and faster in computing and can be considered a useful tool in resolving more complex phenomena that arise in the field of engineering, mathematical physics, and optical fiber.

In this current research, we investigate a new generalized two-dimensional nonlinear wave equation of engineering physics using two versatile approaches. Nonlinear wave models are very important in that they play a key role in modeling diverse occurrences in cosmology, solid mechanics and fluid dynamics, astrophysics, mathematical physics and engineering. The research work is based on three steps. Firstly, soliton solutions are secured via the modified Sardar sub-equation method (MSSEM) and the generalized exponential rational function method (GERFM). Secondly, for bifurcation analysis phase portraits of an equation are erected. Also, using the Galilean transformation, the given equation plane dynamical system is erected and all possible phase portraits are provided. Lastly, the modulation instability of the equation is examined. In order to better comprehend how the nonlinear wave equation depicts physical processes, the wave dynamics of the solutions are displayed to give the results a more physical perspective. The use of these findings in physics, engineering and complex media is essential.

Abstract In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.

The notion of a fuzzy subset is used to introduce certain subclasses of analytic functions. Mainly, this article presents several inclusion results and integral preserving properties. Also, certain applications of the analytic functions in terms of fuzzy structure will be discussed.

A vast range of pesticides have been routinely employed for plant protection throughout the last few decades. Pesticides can enter non-target organisms in various ways, posing health hazards. Exposure to different environmental pollutants, including pesticides, can affect the human gut flora. Metabolites generated from the gut microbiota play an essential role in the host’s health by regulating metabolic homeostasis. A disruption in this equilibrium can lead to the emergence of numerous illnesses and their etiology. Pesticides have been shown in a few recent studies to harm the host’s gut microbiome. As a result, there is an urgent need to investigate the impact of pesticides on gut microbiota-mediated immunity. Metabolic alterations in the host may give a better understanding of pesticide-induced harm. This review highlights the potential consequences of pesticide exposure on gut microbiota composition and function, mainly focusing on how it might alter the production of secondary metabolites with potential downstream implications for host health.

Helicobacter pylori (H. pylori) is a Gram-negative bacterium that colonizes the gastric mucosa and is associated with various gastrointestinal disorders. H. pylori is a pervasive pathogen, infecting nearly 50% of the world’s population, and presents a substantial concern due to its link with gastric cancer, ranking as the third most common cause of global cancer-related mortality. This review article provides an updated and comprehensive overview of the current understanding of H. pylori infection, focusing on its pathogenesis, diagnosis, and treatment strategies. The intricate mechanisms underlying its pathogenesis, including the virulence factors and host interactions, are discussed in detail. The diagnostic methods, ranging from the traditional techniques to the advanced molecular approaches, are explored, highlighting their strengths and limitations. The evolving landscape of treatment strategies, including antibiotic regimens and emerging therapeutic approaches, is thoroughly examined. Through a critical synthesis of the recent research findings, this article offers valuable insights into the contemporary knowledge of Helicobacter pylori infection, guiding both clinicians and researchers toward effective management and future directions in combating this global health challenge.

Aflatoxins, a group of mycotoxins, represent a heterogeneous class of secondary metabolites that pose a significant risk to food safety and public health due to their potent toxicity. Aflatoxins are widely distributed in the environment, with high levels frequently observed in hot and humid conditions. There is an ongoing development of various methods for detecting aflatoxins in food and feed samples. Herein, a review of these methods is presented with special emphasis on molecularly imprinted polymers (MIPs) as selective materials for aflatoxins’ detection. The key findings of various methods for real-time analysis of food and feed samples are presented and analyzed, providing a comparative assessment of their performance. Furthermore, the challenges and limitations of these methods are discussed, considering their commercialization prospects and real-world requirements.

IntroductionDiabetes and thyroid dysfunction often co-exist. One autoimmune disorder always invites another and it has been reported that such co-morbid ailments always become detrimental to the health of the patients.Materials and methodsIn our previous work, we elucidated the interactions of diabetes and hypothyroidism on testicular development and spermatogenesis. However, the present study illuminates the interface between diabetes and hyperthyroidism, where 16 ICR pregnant primiparous mice were used and subsequently 48 male pups were randomly selected (n=12/group) and separated into 4 groups: control (C), diabetic (D), diabetic + hyperthyroidism (DH) and hyperthyroidism (H).ResultsComputerized sperm analyses showed significant reductions in count by 20% and increases of 15% in D and H animals, respectively, vs. controls. However, rapid progressive sperm motility was significantly lower only in D (30%) compared with C mice. Our histomorphometric investigation depicted damaging effects on testicular and epididymal tissues; the stroma adjacent to the seminiferous tubules of the D mice revealed edematous fluid and unstructured material. However, in the epididymis, germ cell diminution contraction of tubules, compacted principal and clear cells, lipid vacuolization, atypical cellular connections, exfoliated epithelial cells, and round spermatids were conspicuous in DH mice.DiscussionCollectively, our experiment was undertaken to ultimately better recognize male reproductive disorders in diabetic-hyperthyroid patients.

Proteases are considered one of the most imperative groups of enzymes and are used in bioremediation processes, leather, detergents, pharmaceutical and the food industry. The foremost aim of this study was to extract, purify, and characterize the protease enzyme from cotton seeds. Purification of the neutral protease was achieved with ammonium sulphate, which gave the best results at 60% concentration with a specific activity of 51 units/mg protein and 1.52 purification fold with a percentage yield of 10.5%. The protease was active and stable at a wide range of pH from 4.0–10.0 with an optimum pH of 7.0. The highest activity of the purified enzyme was found at 20 °C. The enzyme was thermally stable and retained 25% of its activity at 50o C. The activity of cottonseeds protease Fraction-IV was enhanced by 20% with ZnCl2 and 15% with CoCl2 as enzyme samples were heated for 10 minutes. The Casein and peptone assays were also performed to check its catalytic activity. Furthermore, the maximum hydrolysis rate (Vmax) and apparent Michaelis–Menten constant (Km) values of the purified protease were 19 μmol/min and 0.08 mol/L, respectively, while activation energy was found to be 12.47 KJ/mol.

Abstract Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

PURPOSEAcute lymphoblastic leukemia (ALL) represents around 70% of pediatric leukemia. In high-income countries, the 5-year survival is above 90%, but survival in low- and middle-income countries is inferior. This study documents the treatment outcome and prognostic factors of pediatric ALL in Pakistan.MATERIALS AND METHODSIn this prospective cohort study, all newly diagnosed patients with ALL/lymphoblastic lymphoma from age 1 to 16 years enrolled between January 1, 2012, and December 31, 2021, were included. The treatment was based on the standard arm of the UKALL2011 protocol.RESULTSData from 945 patients with ALL, including 597 males (63.2%), were analyzed. The mean age at diagnosis was 5.73 ± 3.51 years. Pallor was the commonest presentation in 95.2% followed by fever in 84.2% of patients. The mean WBC count was 56.6 ± 103.4 × 109/L. Neutropenic fever followed by myopathy was the most common complication during induction. In univariate analysis, the high WBC count (P ≤ 0.001), intensive chemotherapy (P ≤ 0.001), malnutrition (P = .007), poor response to induction chemotherapy (P = .001), delayed presentation (P = .004), and use of steroids before chemotherapy (P = .023) significantly adversely affected overall survival (OS). The delayed presentation was the most significant prognostic factor in the multivariate analysis (P ≤ .002). After a median follow-up of 54.64 ± 33.80 months, the 5-year OS and disease-free survival (DFS) were 69.9% and 67.8%, respectively.CONCLUSIONIn this largest cohort of childhood ALL from Pakistan, a high WBC count, malnutrition, delayed presentation, previous steroids use, intensive chemotherapy, and poor response to the induction chemotherapy were associated with decreased OS and DFS rates.

Rural people, particularly in developing nations, rely on livestock as a key source of income. In Pakistan, rural people depend profoundly on buffalo, cows, sheep, and goats to earn their livelihood. The systems of agricultural production are at risk because of the negative effects of climate change. It badly affects production and quality of milk and meat, animal health, productivity, breeding, feed, and rangelands of livestock production. Climate change risks assessment and adaptation are required to minimize losses from these effects, which are not just technical but also socioeconomically significant. Hence, based on data collected from 1080 livestock herders using a multistage sampling technique in Punjab, Pakistan this study aims to assess perceived impact of climate change on livestock production and to assess coping strategies. In addition, determinants of adaptation strategies and their effects on livestock production was also estimated. Binary Logistic Regression was used to identify the drivers of adaptation strategies. In addition, Multi Group Analysis (MGA) in Partial Least Squares Path Modelling (PLS-PM) was applied to compare adapter and non-adapter of climate change adaptation strategies. Findings indicated that there are spread of various diseases to livestock due to adverse effects of climatic variability. There was reduction in the availability of the livestock's feed. Moreover, competition of water and land resources of livestock was also increasing. Low production efficiency resulted in decline of milk yield and meat production. Likewise, mortality of livestock, increased in still births, reduction in reproductive performance, decline in animal fertility, longevity, and general fitness, decreased birthing rates, rises in age at foremost calving in beef cattle was also prevailing. There were different adaptation policies used by farmers to handle with climate change and these were influenced by several demographic, socioeconomic, and agronomic aspects. Findings indicated that nexus of risk perception, adaptation plans and their determinants are beneficial to reduce the consequences of climatic variability and it improve the wellbeing of the herders. Risk management system may be created to protect livestock against losses caused by extreme weather events by providing awareness regarding influence of climate change on livestock. Easy and cheaper credit should be provided to the farmers to manage with the vulnerabilities of climate change.

This study applied the solid-state reaction technique to synthesize g-C3N4, Fe3O4, and g-C3N4/Fe3O4 composites in various ratios. XRD confirmed the formation of g-C3N4, Fe3O4, and a g-C3N4/Fe3O4 heterostructure. SEM confirmed the rod-shaped structure of Fe2O3 and the layered-like fabrication of g-C3N4. The Eg of g-C3N4/Fe3O4 was approximately 1.9 eV, making it a beneficial composite material for visible response in photocatalysis activity, which was confirmed by UV-Vis spectroscopy. Dielectrics were used to study ferrite nanoparticles and provide information on the mechanism of conductivity in the parts of the dielectric that responded to an applied alternating electric field. In polycrystalline ceramics, the resistive and capacitive grains, contributions, electrode specimen interfaces, and grain boundaries may all be distinguished using impedance analysis, a crucial tool for the study of complicated electrical performance. The g-C3N4/Fe3O4 composite material showed high photocatalytic activity against methylene blue (MB) dye.

In this study, silica-encapsulated gadolinium was doped in lanthanum strontium manganite nanoparticles (NPs) with different concentrations using the citrate–gel auto-combustion method. We focused on tuning the Curie temperature and enhancing the specific absorption rate (SAR) of silica-coated gadolinium-doped lanthanum strontium manganite NPs to make them suitable for self-controlled magnetic hyperthermia. The samples were characterized by using transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), and magnetic measurements to examine the structural, optical, and magnetic properties of the manganite NPs. While our results exhibit a successful doping of gadolinium in lanthanum strontium manganite NPs, we further prepared magnetic core NPs with sizes between 20 and 50 nm. The Curie temperature of the NPs declined with increasing gadolinium doping, making them promising materials for hyperthermia applications. The Curie temperature was measured using the magnetization (M-T) curve. Magnetic heating was carried out in an external applied AC magnetic field. Our present work proved the availability of regulating the Curie temperature of gadolinium-doped lanthanum strontium manganite NPs, which makes them promising candidates for self-controlled magnetic hyperthermia applications.

The health care delivery system in Pakistan is in a process of rapid growth, and it consists of public and private sectors. The provincial government is preliminarily responsible for providing health care facilities within its province except in federally administered areas. In Pakistan, there are three tiers of governmental healthcare delivery systems, which comprise government, semi-government, and parastatal organizations. The purpose of this study is to understand the rapid increase in influx of patients in major city hospitals of the Punjab province. The reasons for patient flow towards the major city areas can vary, but our point of focus is mainly on those patients who are prescribed to get a computed tomography (CT) scan done for better diagnosis and early treatment, including but not limited to roadside accident cases. The study targets the define, measure, analyze, improve and control (DMAIC) problem solving approach to assess the potential cause of CT patient flow and the challenges that the health department is facing to cater to such a patient niche and process while minimizing the congestion in city government hospitals. The approach in this study summarizes with numerous quality tools like Voice of Customer (VOC) which is customer feedback, Customer Output Process Input and Supplier (COPIS) processes use for high-level process map to know what your customer value, Approver Resource Member Interested (ARMI) chart which is use to analyze the stakeholders on management of the project, evaluation with the help of fishbone diagram and house of quality, and for process improvement methodology we use the team brainstorming technique and the kaizen 5-why technique. The technique came up with an idea of a public-private partnership (PPP) project—partnership between public agency and private firm, as the health care industry in Punjab province is going through budgetary issues. In a PPP project, the Government will allocate the space to a private firm to build the facility on their own and provide quality service for CT scan diagnosis to the public of Punjab. The study identified the top 10 critical factors that the patients have expected from the government to be provided on priority. The Kaizen process improvement methodology has been adopted to provide the possible solution of government budgetary issues. The set of tools in this study can be adopted by other PPP projects to enhance the project performance.

IntroductionWhile the prevalence of hunger decreased globally, it has risen in sub-Saharan Africa in recent years mainly due to rapid population growth, low agricultural productivity, and economic downturns. This study was conducted to examine food security determinants at the household levels among the beneficiaries and non-beneficiaries rice farmers of the Kano river irrigation project in Nigeria.MethodsCross-sectional data were used for the analysis while multi-stage sampling technique was used to obtain data from 382 respondents, out of which 217 were project beneficiaries and 165 non-beneficiaries; using a semi-structured questionnaire. Data were analyzed using Household food security index and Logit regression model.Results and discussionThe results showed that 72.6% of the beneficiaries' households were food secure, against the non-beneficiaries, who had 65.4% food secure households. The findings also revealed that the beneficiaries have 11 and 4% food insecurity depth and severity respectively. Non-beneficiaries, on the other hand, had 17 and 8% food insecurity depth and severity, respectively. Extension contact, farm size, rice output, and educational attainment were the positive determinants of food security. Similarly, determinants that could increase food insecurity identified were; credit constraints and household size. It is recommended that the design of a food security strategy should be multi-dimensional and should encompass social, institutional and economic transformation of small scale farmers. Addressing the identified determinants is also crucial for enhancing the food security status in the study area.

The fractional coupled Konno-Onno model, which is frequently used in numerous fields of scientific and engineering disciplines, is being investigated in the current study in order to gain an understanding of complex phenomena and systems. The two main goals of this study are to be accomplished. Firstly, the research aims to identify novel solitons for the fractional coupled Konno-Onno model using the unified technique, which is currently absent from the literature. Secondly, a novel strategy that hasn't been previously investigated is phase portrait analysis for both perturbed and non-perturbed dynamical systems. The current study uses appropriate parametric values in phase plane analysis, 2D, 3D, and density plots to ensure the results are physically compatible. The results validate the claim that the technique used in this research to produce complete and uniform responses is not only simple to use and effective, but also substantially faster in computing. The technique is useful for resolving more complex phenomena that arise in engineering and mathematical physics.

Introduction: Hydrogen is considered as a clean alternative green energy future fuel. Since the Honda-Fujishima effect for photoelectrochemical water splitting is known, there has been a substantial boost in this field. Numerous photocatalysts based on metals, semiconductors, and organic-inorganic hybrid-systems have been proposed. Several factors limit their efficiency, e.g., a stable PEC-WS setup, absorbing visible light, well-aligned band energy for charge transfer, electrons and holes, and their separation to avoid recombination and limited water redox reactions. Metallic doping and impregnation of stable and efficient co-catalysts such as Pt, Ag, and Au showed enhanced PEC-WS. We used Cobalt-based co-catalyst with molybdenum oxysulfide photocatalyst for effectual solar-driven water splitting. Objectives: To develop photocatalysts for efficient PEC processes capable of absorbing sufficient visible light, good band energy for effective charge transfer, inexpensive, significant solar-to-chemical energy conversion efficiencies. Above all, it is developing such PEC-WS systems that will be commercially viable for renewable energy resources. Methods: We prepared Molybdenum oxysulphide-cobalt phosphate photocatalyst for PEC-WS through a facile hydrothermal route using ammonium heptamolybdate, thiourea, and metallic Cobalt precursors. Results: An effectual photocatalyst is produced for solar-driven water splitting. The conformal morphology of MoOxSy-CoPi nanoflowers is a significant feature, as observed under FE-SEM and HR-TEM. XRD confirmed the degree of purity and orthorhombic crystal structure of MoOxSy-CoPi. EDX and XPS identify the elemental compositions and corresponding oxidation states of each atom. A 2.44 eV band-gap energy is calculated for MoOxSy-CoPi from the diffused reflectance spectrum. Photo- Electrochemical Studies (PEC) under 1-SUN solar irradiation revealed 7-8 folds enhanced photocurrent (∼ 3.5 mA/cm2) generated from MoOxSy-CoPi/FTO in comparison to Co-PI/FTO (∼ 0.5 mA/cm2) and MoOxSy-/FTO respectively, within 0.5 M Na2SO4 electrolyte (@pH=7) and standard three electrodes electrochemical cell. Conclusion: Our results showed MoOxSy-CoPi as promising photocatalyst material for improved solar-driven photoelectrochemical water splitting system.

Nuclear magnetic resonance spectroscopy (NMR) is a non-destructive and rapid meat analytical technique. Since meat has high proton content, the quality of fresh meat can be assessed by exciting proton spin using NMR laboratory apparatus. NMR allows the assessment of intrinsic properties including animal development stages and muscle type, as well as extrinsic factors including processing and storage conditions that affect the technological traits of meat. It can be used to determine the water-holding capacity of meat, water distribution in muscles, meat authentication, fat content, post mortem aging, rigor mortis, and changes in metabolomes, among others. Changes in NMR relaxation times are associated with the changes in muscle components and their distribution as affected by various processing conditions. Since, industrial assessment is now demanding rapidity and real-time assessment, it is therefore essential to evaluate the potential of NMR in contrast to other techniques. Therefore, this review article presents the principle of working of NMR, various methods available, quality traits of meat that can be evaluated using this technique, and a brief history. This review article will help the industry to adopt novel NMR-based meat quality assessment tools for rapid quality determinations.

Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from https://data.mendeley.com/datasets/k5fz57zd9z/1.

This study assessed the equity in irrigation water uses and its impacts on paddy yield and water productivity among rice farmers in the Kano River Irrigation Project. Two hundred and twenty-five (225) respondents were used for the data collection from January to June 2021. The data were analyzed using the Kruskal Walli’s test, Tukey kramer post-Hoc test, physical water productivity, and the Logit model. The results indicated that the downstream farmers had the lowest mean yield (1625 kg/acre), lowest access to irrigation water, and lowest irrigation water use (2430 m3/acre). However, they had the highest water productivity (0.66 kg/m3) against 0.44 kg/m3 and 0.58 kg/m3 for middle and upstream farmers, respectively. The logit regression results disclosed that the farm locations, quantity of irrigation water, access to irrigation water, and training on water use were statistically significant determinant of paddy output. Efforts to teach farmers about the actual crop water requirements are needed to promote irrigation water efficiency and conserve scarce resources for other competing users.

Biofilms are complex communities of microorganisms that grow on surfaces and are embedded in a matrix of extracellular polymeric substances. These are prevalent in various natural and man-made environments, ranging from industrial settings to medical devices, where they can have both positive and negative impacts. This review explores the diverse applications of microbial biofilms, their clinical consequences, and alternative therapies targeting these resilient structures. We have discussed beneficial applications of microbial biofilms, including their role in wastewater treatment, bioremediation, food industries, agriculture, and biotechnology. Additionally, we have highlighted the mechanisms of biofilm formation and clinical consequences of biofilms in the context of human health. We have also focused on the association of biofilms with antibiotic resistance, chronic infections, and medical device-related infections. To overcome these challenges, alternative therapeutic strategies are explored. The review examines the potential of various antimicrobial agents, such as antimicrobial peptides, quorum-sensing inhibitors, phytoextracts, and nanoparticles, in targeting biofilms. Furthermore, we highlight the future directions for research in this area and the potential of phytotherapy for the prevention and treatment of biofilm-related infections in clinical settings.

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.