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Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study. [ABSTRACT FROM AUTHOR]

The Kawahara equation exhibits signal dispersion across lines of transmission and the production of unstable waves from the water in the broad wavelength area. This article explores the computational analysis for the approximate series of time fractional Kawahara (TFK) and modified Kawahara (TFMK) problems. We utilize the Shehu homotopy transform method (SHTM), which combines the Shehu transform (ST) with the homotopy perturbation method (HPM). He's polynomials using HPM effectively handle the nonlinear terms. The derivatives of fractional order are examined in the Caputo sense. The suggested methodology remains unaffected by any assumptions, restrictions, or hypotheses on variables that could potentially pervert the fractional problem. We present numerical findings via visual representations to indicate the usability and performance of fractional order derivatives for depicting water waves in long-wavelength regions. The significance of our proposed scheme is demonstrated by the consistency of analytical results that align with the exact solutions. These derived results demonstrate that SHTM is an effective and powerful scheme for examining the results in the representation of series for time-fractional problems. [ABSTRACT FROM AUTHOR]

The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers are aware of. In our study, we focused on dynamic application security testing (DAST) to investigate cross-site scripting (XSS) attacks. We closely examined 23 popular WordPress plugins, especially those requiring user or admin interactions, as these are frequent targets for XSS attacks. Our testing uncovered previously unknown zero-day vulnerabilities in three of these plugins. Through controlled environment testing, we accurately identified and thoroughly analyzed these XSS vulnerabilities, revealing their mechanisms, potential impacts, and the conditions under which they could be exploited. One of the most concerning findings was the potential for admin-side attacks, which could lead to multi-site insider threats. Specifically, we found vulnerabilities that allow for the insertion of malicious scripts, creating backdoors that unauthorized users can exploit. We demonstrated the severity of these vulnerabilities by employing a keylogger-based attack vector capable of silently capturing and extracting user data from the compromised plugins. Additionally, we tested a zero-click download strategy, allowing malware to be delivered without any user interaction, further highlighting the risks posed by these vulnerabilities. The National Institute of Standards and Technology (NIST) recognized these vulnerabilities and assigned them CVE numbers: CVE-2023-5119 for the Forminator plugin, CVE-2023-5228 for user registration and contact form issues, and CVE-2023-5955 for another critical plugin flaw. Our study emphasizes the critical importance of proactive security measures, such as rigorous input validation, regular security testing, and timely updates, to mitigate the risks posed by zero-day vulnerabilities. It also highlights the need for developers and administrators to stay vigilant and adopt strong security practices to defend against evolving threats. [ABSTRACT FROM AUTHOR]

The present study presents a combination of two famous analytical techniques for the analytical solutions of linear and nonlinear time-fractional Emden–Fowler models. We combine the Elzaki transform (ET) and the homotopy perturbation method (HPM) for the development of the Elzaki transform homotopy perturbation method (ET-HPM). In this paper, we demonstrate that the Elzaki transform (ET) simplifies fractional differential problems by transforming them into algebraic formulas within the transform space. On the other hand, the HPM has the ability to discretize the nonlinear terms in fractional problems. The fractional orders are considered in the Caputo sense. The main purpose of this strategy is to use an alternative approach that has never been employed in the time-fractional Emden–Fowler model. This strategy does not require any variable or hypothesis constraints that ruin the physical nature of the actual problem. The derived series yields a convergent series using the Taylor series formula. The analytical data and visual illustrations for several kinds of fractional orders validate the effectiveness of the suggested scheme. The significant results demonstrate that our recommended strategy is quick and simple to use on fractional problems. [ABSTRACT FROM AUTHOR]

In this study, we derive a simple transportation scheme by post-optimizing the costs of a modified problem. The strategy attempts to make the original (mainly feasible) option more practicable by adjusting the building components' costs. Next, we employ the previously mentioned cell or area cost operators to gradually restore the modified costs to their initial levels, while simultaneously implementing the necessary adjustments to the "optimal" solution. This work presents a multi-goal, multi-item substantial transportation problem with interval-valued fuzzy variables, such as transportation costs, supplies, and demands, as parameters to maintain the transportation cost. This research addresses two circumstances where task ambiguity may occur: the interval solids transportation problem and the fuzzy substantial transportation issue. In the first scenario, we express data problems as intervals instead of exact values using an interval-valued fermatean neutrosophic number; in the second case, the information is not entirely obvious. We address both models when uncertainty solely affects the constraint set. For the interval scenario, we define an additional problem to solve. Our existing efficient systems have dependable transportation, so they are also capable of handling this new problem. In the fuzzy case, a parametric technique generates a fuzzy solution to the preceding problem. Since transportation costs have a direct impact on market prices, lowering them is the primary goal. Using parametric analysis, we provide optimal parameterization solutions for complementary situations. We provide a recommended algorithm for determining the stability set. In conclusion, we offer a sensitivity analysis and a numerical example of the transportation problem involving both balanced and imbalanced loads. [ABSTRACT FROM AUTHOR]

The low solubility and enhanced fixation of zinc (Zn) in semi-arid and dry climates limits Zn uptake in plants. Zn deficiency in soil impairs crop production and human health, necessitating agricultural biofortification. A pot experiment was conducted to evaluate the effect of Zn and various types of manure on the Zn biofortification of lentils. The treatments, consisting of a control (Con), normal manure (NM), composted manure (CM), and acidified manure (AM), were applied under saline soil (EC 8.00 dS m−1) and non-saline soil (EC 1.48 dS m−1) conditions along with two levels of Zn, including Zn at 0 kg ha−1 (native soil Zn = 2.2 mg kg−1) and Zn at 25 kg ha−1 (62.2 mg Zn kg−1 soil was achieved). The AM was prepared by adding sulfur and sulfur-oxidizing bacteria to the composted manure. All the manures were applied at 1% (w/w), and ZnSO4 (33% Zn) was used as a Zn source. Lentil variety Masoor 2021 was cultivated as a test crop in five replications of each treatment arranged in a completely randomized design. Applying AM with Zn considerably increased the lentils' growth, yield, and Zn content under saline and non-saline conditions. Under non-saline soils, the treatment of AM + Zn significantly promoted the Zn content in the root (132.5%), shoot (91.7%), grain (49.1%), root length (79.7%), plant height (33.7%), and SPAD value (29.9%). Under saline conditions, application of AM + Zn promoted uptake of Zn in the root (218.5%), Zn content in the shoot (175.7%), Zn accumulation in the grain (107.7%), root length (109.7%), plant height (37.8%), and SPAD value (52.8%) compared to the control. According to the results, lentils should be grown with AM and Zn to increase their growth, yield, and Zn content significantly. This is a cost-effective and sustainable way to combat Zn deficiency in lentils. [ABSTRACT FROM AUTHOR]

This paper presents a novel Continuous Variable Transmission (CVT) design. CVT is highly beneficial for actuators with rotary output as it can improve the energy efficiency of the actuators by providing an optimum transmission ratio. This property of CVT is highly beneficial for fossil-fuel-based vehicles, electric vehicles, wind turbines, industrial robots, etc. With the exception of Spherical CVT and DH CVT, all known CVTs like push belt CVTs, toroidal CVTs, Milner CVTs, etc., require additional gear sets and clutches for direction reversal and neutral gear ratio. However, Spherical CVT and DH CVT have low torque capacity due to a single traction point constraint. Foregoing in view, a new CVT named CAD CVT has been developed. The paper presents the design conception, the operating principle, the transmission ratio, the torque capacity, frictional losses, and experimental verification of the basic functionality by manufacturing a Proof of Concept (PoC). The proposed CVT is the only CVT capable of independent direction reversal and high torque capacity as it can transmit torque through multiple traction points. The new CVT will significantly impact high-torque applications in different engineering applications, especially land transport consisting of heavy vehicles like trucks, buses, and trailers. [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]

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 developing countries like Pakistan, the preservation of the environment, as well as people's economies, agriculture, and way of life, are believed to be hampered by climate change. Understanding how people perceive climate change and its signs is essential for creating a variety of adaptation solutions. In this study, we aim to bridge the gap in current research within this area, which predominantly relies on satellite data, by integrating qualitative assessments of people's perceptions of climate change, thereby providing valuable ground-based observations of climate variability and its impacts on local communities. Field-based data were collected at different altitudes (upstream (US), midstream (MS), and downstream (DS)) of the Upper Indus Basin using both quantitative and qualitative assessments in 2017. The result shows that these altitudes are highly variable in many contexts: socioeconomic indicators of education, agriculture, income, women empowerment, health, access to basic resources, and livelihood diversifications are highly variable in the Indus Basin. The inhabitants of the Indus Basin perceive the climate changing around them and report impacts of this change as increase in overall temperatures (US 96.9%, MS 97%, DS 93.6%) and erratic rainfall patterns (US 44.1%, MS 73.3%, DS 51.0%) resulting in increased water availability for crops (US 38.6%, MS 39.7%, DS 54.8%) but also increasing number of dry days (US 56.7%, MS 85.5%, DS 67.1%). Communities at these altitudes said that agriculture was their primary source of income, making them particularly vulnerable to the effects of climate change and the dangers that go along with it. The insights are useful for determining what information and actions are required to support local climate-related hazard management in subtropical climate regions. Moreover, it is vital to launch a campaign to raise awareness of potential hazards, as well as to provide training and an early warning system. [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]

Rice is an important diet source for the majority of the world's population, and meeting the growing need for rice requires significant improvements at the production level. Hybrid rice production has been a significant breakthrough in this regard, and the floral traits play a major role in the development of hybrid rice. In grass species, rice has structural units called florets and spikelets and contains different floret organs such as lemma, palea, style length, anther, and stigma exsertion. These floral organs are crucial in enhancing rice production and uplifting rice cultivation at a broader level. Recent advances in breeding techniques also provide knowledge about different floral organs and how they can be improved by using biotechnological techniques for better production of rice. The rice flower holds immense significance and is the primary focal point for researchers working on rice molecular biology. Furthermore, the unique genetics of rice play a significant role in maintaining its floral structure. However, to improve rice varieties further, we need to identify the genomic regions through mapping of QTLs (quantitative trait loci) or by using GWAS (genome-wide association studies) and their validation should be performed by developing user-friendly molecular markers, such as Kompetitive allele-specific PCR (KASP). This review outlines the role of different floral traits and the benefits of using modern biotechnological approaches to improve hybrid rice production. It focuses on how floral traits are interrelated and their possible contribution to hybrid rice production to satisfy future rice demand. We discuss the significance of different floral traits, techniques, and breeding approaches in hybrid rice production. We provide a historical perspective of hybrid rice production and its current status and outline the challenges and opportunities in this field. [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]

This study aimed to assess the concentrations of copper (Cu), zinc (Zn), iron (Fe), and manganese (Mn) in the edible parts of grapefruit and kinnow fruit irrigated with sewage water (SW), tube-well water (TW), and canal water (CW). Preparation of the samples used in the study for metal analysis was carried out via the wet acid digestion method. Atomic absorption spectrometry (AAS) was used for metal determination. According to the results, Cu concentration ranged from 0.152 to 0.754 mg/L in water, 5.254 to 41.659 mg/kg in soil, and 0.128 to 0.864 mg/kg in fruit samples. Zn concentration varied from 0.574 to 2.723 mg/L in water, 17.812 to 112.954 mg/kg in soil, and 2.658 to 42.642 mg/kg in fruit samples. Fe concentration ranged from 0.254 to 1.245 mg/L in water, 10.635 to 48.638 mg/kg in soil, and 1.062 to 7.584 mg/kg in fruit samples. Mn concentration ranged from 0.154 to 0.638 mg/L in water, 51.283 to 183.865 mg/kg in soil, and 0.136 to 1.464 mg/kg in fruit samples. The Pollution Load Index (PLI) indicated that Cu and Mn exceeded a PLI value of one, and Zn had a PLI > 1 only in sewage water-irrigated sites. Bioconcentration Factor (BCF), Enrichment Factor (EF), Daily Intake of Metal (DIM), and Health Risk Index (HRI) values for all metals were within permissible limits, indicating no immediate health risks associated with consuming these fruits. [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 lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia's hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground evaluation of three reanalysis datasets (ERA5, MEERA2, and APHRO) and five satellite datasets (PERSIANN-PDIR, CHIRPS, GPM-SM2Rain, SM2Rain-ASCAT, and SM2Rain-CCI). Several temporal scales (daily, monthly, seasonal (winter, spring, summer, autumn), and annual) of all the GPDS were analyzed across the complete spatial domain and point-to-pixel scale from January 2000 to December 2013. The validation of GPDS was evaluated using evaluation indices (Root Mean Square Error, correlation coefficient, bias, and relative bias) and categorical indices (False Alarm Ratio, Probability of Detection, success ratio, and Critical Success Index). The performance of all GPDS was also analyzed based on different elevation zones (≤1500, ≤2500, >2500 m). According to the results, the daily estimations of the spatiotemporal tracking abilities of CHIRPS, APHRO, and GPM-SM2Rain are superior to those of the other datasets. All GPDS performed better on a monthly scale than they performed on a daily scale when the ranges were adequate (CC > 0.7 and r-BIAS (10)). Apart from the winter season, the CHIRPS beat all the other GPDS in standings of POD on a daily and seasonal scale. In the summer, all GPDS showed underestimations, but GPM showed the biggest underestimation (−70). Additionally, the CHIRPS indicated the best overall performance across all seasons. As shown by the probability density function (PDF %), all GPDS demonstrated more adequate performance in catching the light precipitation (>2 mm/day) events. APHRO and SM2Rain-CCI typically function moderately at low elevations, whereas all GPDS showed underestimation across the highest elevation >2500 m. As an outcome, we strongly suggest employing the CHIRPS precipitation product's daily, and monthly estimates for hydroclimatic applications over the hilly region of Tajikistan. [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]

Conjugated linoleic acid (CLA) is perceived to protect the body from metabolic diseases. This study was conducted to determine the effect of Lactiplantibacillus plantarum (Lp. plantarum) on CLA production and sensory characteristics of cheddar cheese. Lp. plantarum can convert linoleic acid (LA) to CLA. To increase CLA in cheddar cheese and monitor the conversion of LA to CLA by Lp. plantarum, the LA content of cheese milk (3.4% fat) was increased by partially replacing fat with safflower oil (85% LA of oil) at 0, 3, 6, and 9% concentrations (T1, T2, T3, and T4). Furthermore, Lp. plantarum 108 colony-forming units (CFU)/mL (8 log CFU mL−1) was added in all treatments along with traditional cheddar cheese culture (Lactococcus lactis ssp. lactis and L. lactis ssp. cremoris). After 30 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.75, 6.72, 6.65, and 6.55 log CFU g−1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.35, 6.27, 6.19, and 6.32 log CFU g−1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.41, 6.25, 6.69, and 6.65 log CFU g−1. GC-MS analysis showed that concentrations of CLA in the 90 days' control, T1, T2, T3, and T4 were 1.18, 2.73, 4.44, 6.24, and 9.57 mg/100 g, respectively. HPLC analysis revealed that treatments containing Lp. plantarum and LA presented higher concentrations of organic acids than the control sample. The addition of safflower oil at all concentrations did not affect cheese composition, free fatty acids (FFA), and the peroxide value (POV) of cheddar cheese. Color flavor and texture scores of experimental cheeses were not different from the control cheese. It was concluded that Lp. plantarum and safflower oil can be used to increase CLA production in cheddar cheese. [ABSTRACT FROM AUTHOR]

A variety of foods fermented with lactic acid bacteria (LAB) serve as dietary staples in many countries. The incorporation of health-promoting probiotics into fermented milk products can have profound effects on human health. Considering the health benefits of Yakult, the current study was undertaken to develop an enriched Yakult-like fermented skimmed milk drink by the addition of two probiotic strains, namely Lacticaseibacillus casei (Lc) and Lacticaseibacillus rhamnosus (Lr). The prepared drinks were compared in terms of various parameters, including their physicochemical properties, proximate chemical composition, mineral estimation, microbial viable count, antioxidant activity, and sensory evaluation. Each strain was employed at five different concentrations, including 1% (T1), 1.5% (T2), 2% (T3), 2.5% (T4), and 3% (T5). The prepared Yakult samples were stored at 4 °C and analyzed on days 0, 7, 14, 21, and 28 to evaluate biochemical changes. The findings revealed that the concentration of the starter culture had a significant (p ≤ 0.05) impact on the pH value and moisture and protein contents, but had no marked impact on the fat or ash content of the developed product. With the Lc strain, Yakult's moisture content ranged from 84.25 ± 0.09 to 85.65 ± 0.13%, whereas with the Lr strain, it was from 84.24 ± 0.08 to 88.75 ± 0.13%. Protein levels reached their highest values with T5 (3% concentration). The acidity of all treatments increased significantly due to fermentation and, subsequently, pH showed a downward trend (p ≤ 0.05). The total soluble solids (TSS) content decreased during storage with Lc as compared to Lr, but the presence of carbohydrates had no appreciable impact. The drink with Lc exhibited a more uniform texture and smaller pore size than Yakult with Lr. Except for the iron values, which showed an increasing trend, the contents of other minerals decreased in increasing order of the added probiotic concentration used: 1% (T1), 1.5% (T2), 2% (T3), 2.5% (T4), and 3% (T5). The highest lactobacilli viable count of 8.69 ± 0.43 colony-forming units (CFU)/mL was observed with the T1 Lr-containing drink at the end of the storage period. Regarding the storage stability of the drink, the highest value for DPPH (88.75 ± 0.13%) was found with the T1 Lc drink on day 15, while the highest values for FRAP (4.86 ± 2.80 mmol Fe2+/L), TPC (5.97 ± 0.29 mg GAE/mL), and TFC (3.59 ± 0.17 mg GAE/mL) were found with the T5 Lr drink on day 28 of storage. However, the maximum value for ABTS (3.59 ± 0.17%) was noted with the T5 Lr drink on the first day of storage. The results of this study prove that Lc and Lr can be used in dairy-based fermented products and stored at refrigerated temperatures. [ABSTRACT FROM AUTHOR]

Currently, academia is grappling with a significant problem—a lack of engagement. Humankind has gone too far into exploring entertainment options, while the education system has not really kept up. Millennials love playing games, and this addiction can be used to engage and motivate them in the learning process. This study examines the effect of digital game-based learning on student engagement and motivation levels and the gender differences in online learning settings. This study was conducted in two distinct phases. A game-based and traditional online quizzing tools were used to compare levels of engagement and motivation, as well as to assess the additional parameter of gender difference. During the first phase of the study, 276 male and female undergraduate students were recruited from Sophomore Seminar classes, and 101 participated in the survey, of which 83 were male and 18 were female. In the second phase, 126 participants were recruited, of which 107 (63 females and 44 males) participated in the anonymous feedback surveys. The results revealed that digital game-based learning has a more positive impact on student engagement and motivation compared to traditional online activities. The incorporation of a leaderboard as a gaming element in the study was found to positively impact the academic performance of certain students, but it could also demotivate some students. Furthermore, female students generally showed a slightly higher level of enjoyment toward the games compared to male students, but they did not prefer a comparison with other students as much as male students did. The favorable response from students toward digital game-based activities indicates that enhancing instruction with such activities will not only make learning an enjoyable experience for learners but also enhance their engagement. [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]

Oilseed crops require several micronutrients to support their physiological functions and reproductive phases. A deficiency of these nutrients can significantly reduce the yield and oil quality of oilseed crops. Soil application of micronutrients can reduce their deficiency and improve plant growth, yield, and oil quality. Oilseed rape (Brassica napus L.) is an important oilseed crop that produces oil with low levels of saturated fat and high levels of beneficial omega-3 fatty acids, which renders it a widely used cooking oil. However, the yield and oil quality of oilseed rape are significantly affected by the deficiency of boron (B) and zinc (Zn). This two-year field study determined the influence of sole and combined soil application of B and Zn on the physiological attributes of plants, seed and oil yields, and oil quality under semiarid climatic conditions. Nine different B and Z combinations, i.e., B0 + Zn0 (control), B0 + Zn8, B0 + Zn10, B1 + Zn0, B1 + Zn8, B1 + Zn10, B2 + Zn0, B2 + Zn8, and B2 + Zn10 (kg ha−1), were included in the study. Sole and combined application of B and Zn significantly altered physiological attributes, seed and oil yields, and oil quality. The highest values for plant height, number of siliques per plant, number of seeds per silique, 1000-seed weight, seed and oil yields, oil quality (higher stearic acid, palmitic acid, oleic acid, linoleic acid, linolenic acid, and lower erucic acid), and physiological traits (protein concentration, soluble sugar concentration, chlorophyll concentration, photosynthesis and transpiration rates, and stomatal conductance) were recorded with the combined application of 2 + 8 kg ha−1 B and Zn, respectively, during both years of this study. The lowest values of yield- and oil-quality-related traits and physiological attributes were recorded for the control treatment. A dose-dependent improvement was recorded in B and Zn contents in leaves, and the highest values were recorded with the combined soil application of 2 + 10 kg ha−1 B + Zn, respectively. It can be concluded that 2 + 8 kg ha−1 B + Zn should be applied to oilseed rape for higher seed and oil yields and better oil quality under semiarid climatic conditions. [ABSTRACT FROM AUTHOR]

Willingness to communicate (WTC) in a second language (L2) is one of the key concepts in L2 communication and learning. Previously, research practice in this area has been more concerned with investigating the relationship between individual negative emotions and WTC. However, insufficient attention has been given to a wide range of positive and negative emotions contributing to WTC. This study aims to address the impact of emotions (positive and negative) and self-perceived communication competence (SPCC) on WTC in an L2 in the context of Pakistan. The broaden-and-build theory of positive emotions has driven theoretical reasoning. Higher education students (N = 401) were considered the participants of this study and were approached conveniently. Data were processed and analyzed in SPSS. The findings revealed that positive emotions and SPCC positively influence WTC in an L2. On the contrary, negative emotions have a negative impact on WTC in an L2. Additionally, SPCC appears to be a significant moderator in the relationship between emotions (positive and negative) and WTC in an L2. The study's findings have important practical implications for Pakistan's Higher Education Commission (HEC) in drafting and implementing policies to enhance students' WTC within higher education classrooms. [ABSTRACT FROM AUTHOR]

Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin's biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins. [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]

Spring durum wheat occupies over 0.5 M ha in Kazakhstan and represents an important domestic and export commodity. This study aimed to characterize 151 durum wheat cultivars and advanced lines originating from eight breeding programs of the Kazakhstan–Siberia Spring Wheat Improvement Network (KASIB) between 2003 and 2018. The phenotypic characterization was performed in two contracting evaluation sites more than 1000 km apart (Almaty in the Southeast and Shortandy in the North) for two years and a total of 11 agronomic traits were recorded. Field trials at both locations followed regional agronomy practices, including sowing, harvesting, and genotype evaluation using a randomized complete block design (RCBD). The growing season was longer in Almaty, resulting in a higher number of grains per spike. Though grains are smaller in size with an overall higher yield, 243 g/m2 versus 170 g/m2, there was no correlation between germplasm performance at the two sites. Molecular characterization was performed with 10 iPBS-retrotransposons primers that resulted in a total of 345 bands and showed a mean polymorphism of 91.9%. Mean values of gene diversity (0.251), Shannon's information index (0.388), and expected heterozygosity (0.233) revealed a relatively high level of genetic diversity in the KASIB set. AMOVA revealed higher genetic variations due to differences within the populations. Marker-based cluster analysis, including STRUCTURE and neighbor-joining algorithms, divided the material into two populations with clear differences in geographic origin. Superiors and diverse germplasm identified in the study are recommended for marker assisted selection and breeding. [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]

Background and Objectives: The increase in antimicrobial resistance (AMR) across countries has seriously impacted the effective management of infectious diseases, with subsequent impact on morbidity, mortality and costs. This includes Pakistan. Antimicrobial surveillance activities should be mandatory to continually assess the extent of multidrug-resistant bacteria and the implications for future empiric prescribing. The objective of this retrospective observational study was to monitor the susceptibility pattern of microbes in Pakistan. Materials and Methods: Clinical samples from seven laboratories in Punjab, Pakistan were collected between January 2018 and April 2019, with Punjab being the most populous province in Pakistan. The isolates were identified and their antimicrobial susceptibility was tested using the Kirby-Bauer disc diffusion assay and micro broth dilution methods. The antibiotics assessed were those typically prescribed in Pakistan. Results: In total, 2523 bacterial cultural reports were studied. The most frequently isolated pathogens were Staphylococcus aureus (866, 34.3%), followed by Escherichia coli (814, 32.2%), Pseudomonas aeruginosa (454, 18.0%) and Klebsiella pneumoniae (269, 10.7%). Most pathogens were isolated from pus (1464, 58.0%), followed by urine (718, 28.5%), blood (164, 6.5%) and sputum (81, 3.2%). Conclusions: The findings suggest that current antimicrobial options are severally restricted in Pakistan due to the emergence of multidrug-resistant pathogens. This calls for urgent actions including initiating antimicrobial stewardship programs to enhance prudent prescribing of antibiotics. This includes agreeing on appropriate empiric therapy as part of agreed guidelines, in line with the WHO EML and AWaRe book, whilst awaiting culture reports. This is alongside other measures to reduce inappropriate antimicrobial prescribing and reverse the threat of rising AMR. [ABSTRACT FROM AUTHOR]

In order to assess the effects of climate change and land use change on Rawal Dam, a major supply of water for Rawalpindi and Islamabad, this study uses hydrological modeling at the watershed scale. The HEC-HMS model was used to simulate the hydrological response in the Rawal Dam catchment to historical precipitation. The calibrated model was then used to determine how changes in land use and climate had an impact on reservoir inflows. The model divided the Rawal Dam watershed into six sub-basins, each with unique features, and covered the entire reservoir's catchment area using data from three climatic stations (Murree, Islamabad Zero Point and Rawal Dam). For the time spans of 2003–2005 and 2006–2007, the model was calibrated and verified, respectively. An excellent fit between the observed and predicted flows was provided by the model. The GCM (MPI-ESM1-2-HR) produced estimates of temperature and precipitation under two Shared Socioeconomic Pathways (SSP2 and SSP5) after statistical downscaling with the CMhyd model. To evaluate potential effects of climate change and land use change on Rawal Dam, these projections, along with future circumstances for land use and land cover, were fed to the calibrated model. The analysis was carried out on a seasonal basis over the baseline period (1990–2015) and over future time horizon (2016–2100), which covers the present century. The findings point to a rise in precipitation for both SSPs, which is anticipated to result in an increase in inflows throughout the year. SSP2 projected a 15% increase in precipitation across the Rawal Dam catchment region until the end of the twenty-first century, while SSP5 forecasted a 17% increase. It was determined that higher flows are to be anticipated in the future. The calibrated model can also be utilized successfully for future hydrological impact assessments on the reservoir, it was discovered. [ABSTRACT FROM AUTHOR]

Agriculture contributes to environmental degradation by using unsustainable farming practices, such as the intensive use of chemical fertilizers and fossil fuels. With China's growing environmental concerns, new environmental laws have begun to disseminate information about environmental preservation among residents. This legislation plays a major role in developing individual behavior. Thus, the purpose of this study was to investigate the moderating role of environmental laws in relation to environment-protecting intentions and behavior based on their legal cognition. Based on 1350 randomly selected farmers from Jiangxi Province, we confirm the theory of planned behavior through a structural equation model. The results demonstrate that the perceived behavior control, personal factors, and attitude of farmers directly contribute to both types of farmer's intentions, i.e., citizen and activist; and indirectly to both types of farmer's environmental-oriented behavior (citizen and activist). The inward attitude effect was stronger than the outward attitude in both types of intentions of farmers. The farmers' legal cognition also moderated their environment-protecting intentions and behaviors. This implies that as the farmer's legal familiarity grows, the direct effect of both types of farmer's intentions on the farmer's environmentally oriented behavior continues to increase. Awareness programs, financial support, and extension services should be enhanced in rural areas, which may develop the farmers' proenvironmental behavior and lead to them adopting environmentally friendly farm practices. [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 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 theory of capabilities describes the need for a country to adopt different capabilities to enhance its productivity through the production of diversified and complex goods. These capabilities are not independent of the human, physical, institutional, legal systems, and gross value chain (GVC) of a country. Therefore, the current study analyzed the relationship between GVC, institutional quality, human capital development, and the economic fitness of different countries. This study used panel data from 131 countries for the period of 2007–2019. The generalized method of moments (GMM), fully modified ordinary least square (FMOLS), and quantile regression showed that GVC, institutional quality, and human capital development have a big positive effect on a country's economic health. The quantile regression results also showed that GVC, institutional quality, and skilled human capital have a positive and significant effect on the economic fitness of all three quantiles (25th, 50th, and 75th). Overall, the study concludes that greater GVC participation, political stability, effective government, good rules of law, high regulatory quality, and good human capital all enhance the country's capabilities to produce diversified and complex goods that increase its market share in the global competitive market. Thus, the government should formulate policies in such a way that they promote participation in GVC to overcome the limitations of resource availability and poor technology. In addition, it should be ensured that effective policy implementation is in place to facilitate business without unjust means, violence, etc. [ABSTRACT FROM AUTHOR]

Based on the perceptions of the local farmers, this study aims to assess the effects of socioeconomic factors and climatic change on the groundwater livelihood system, with a particular focus on in situ Persian wheels/dug wells. Farmers' perceptions of climate change and how it is affecting their way of life in the Soan River Basin have also been evaluated to determine the most appropriate adaptive interventions. Information and literature about dug wells was unavailable, which stressed the need to carry out this survey. A structured close-ended questionnaire was designed and administered with as much quantitative data as possible. Random sampling opted for a 5 km buffer zone across the Soan River and its tributaries. Union councils having more than 50% of their area lying in the buffer zone were surveyed, and data was collected. Fifty UCs fell within this criterion, and six dug wells from each Union Council were surveyed. The results of our survey collecting local farmer's perceptions determined that about 70% of respondents agreed about climate change in the Soan Basin of Pakistan, and 62% of farmers reported that climate change severely impacted their livelihood by affecting agricultural productivity and water availability. Ninety-two percent reported summer becoming hot, 72% highlighted that winters are becoming less cold, and 96% reported that average annual rainfall has decreased compared to 10 years before. About 72% of respondents indicated that available water in their dug wells had decreased, and 80% of respondents explained that their crop yield had decreased compared to 10 years before. Sixty percent preferred drip and 35% sprinkler irrigation as efficient water management practices to cope with water shortages. Ninety-five percent of farmers were ready to use solar pumps for irrigation to tame high pumping costs. The study recommends integrating solar pumping with drip and sprinkler irrigation systems to enhance farmers' cropped area and productivity. These vulnerable farmers can enhance their resilience and profitability by adopting high-value agriculture (tunnel farming, off-season vegetables, etc.) instead of conventional crops. [ABSTRACT FROM AUTHOR]

In the public health sector and the field of medicine, the popularity of data mining and its usage in knowledge discovery and databases (KDD) are rising. The growing popularity of data mining has discovered innovative healthcare links to support decision making. For this reason, there is a great possibility to better diagnose patient's diseases and maintain the quality of healthcare services in hospitals. So, there is an urgent need to make disease diagnosis possible by discovering the hidden patterns from the patients' history information in developing countries. This work is a step towards how to use the extracted knowledge to enhance the quality of healthcare facilities. In this paper, we have proposed a web-centered hospital information management system (HIMS) that identifies frequent patterns from the data with eye disorder patients using the association rule-based Apriori data mining technique. The proposed framework has the capability to overcome all the key issues and problems in the current hospital information management system regarding data analysis and reporting services. For this purpose, data were collected from more than 1000 university students (China citizens) both online and manually (printed questionnaire). After applying the Apriori algorithm on the collected data, we revealed that almost 140 individuals out of 1035 had myopia (near-sighted disorder), at current age of 22 years, and that there were no male patients found with myopia. We concluded that their clinical relevance and utility can generate favorable results from prospective clinical studies by mapping out the habits or lifestyles that potentially lead to fatal diseases. In the future, we plan to extend this work to fully automate HIMS to help practitioners to diagnose the reasons of various diseases by extracting patient lifestyle patterns. [ABSTRACT FROM AUTHOR]

This study examines the link between a firm's energy efficiency and their participation in global value chains (GVCs). Both countries' GVCs participation and positioning indices are designed to define the features of countries' participation in global value chains. We begin with a theoretical approach of how GVC participation influences energy efficiency. The sample size of 54 nations from 2000 to 2019 is then investigated for empirical analysis using FE and 2SLS methods. The results show that the impact of countries' participation in GVCs is unknown and the development of global value chains positioning increases the energy efficiency in selected countries. Further, it is found that the expansion of GVCs positioning index increases energy efficiency and the effect of forward GVCs positioning on energy efficiency is larger than the effect of backward GVCs positioning. Furthermore, increasing GVC participation in wealthy nations reduces the energy efficiency of the manufacturing industries, but increasing GVC participation in developing countries raises the energy efficiency of manufacturing industries, somewhat opposing the pollution haven approach. A key policy recommendation is that countries actively participate in GVCs to encourage energy efficiency at the macro-level. [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]

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]

Graphs are useful for analysing the structure models in computer science, operations research, and sociology. The word metric dimension is the basis of the distance function, which has a symmetric property. Moreover, finding the resolving set of a graph is NP-complete, and the possibilities of finding the resolving set are reduced due to the symmetric behaviour of the graph. In this paper, we introduce the idea of the edge-multiset dimension of graphs. A representation of an edge is defined as the multiset of distances between it and the vertices of a set, B ⊆ V (Γ) . If the representation of two different edges is unequal, then B is an edge-multiset resolving a set of Γ. The least possible cardinality of the edge-multiset resolving a set is referred to as the edge-multiset dimension of Γ. This article presents preliminary results, special conditions, and bounds on the edge-multiset dimension of certain graphs. This research provides new insights into structure models in computer science, operations research, and sociology. They could have implications for developing computer algorithms, aircraft scheduling, and species movement between regions. [ABSTRACT FROM AUTHOR]

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in the mountainous regions of Central Asia. Performance analysis of Satellite-Based Precipitation Datasets (SBPD) is very critical before their direct hydro-climatic applications. This study assessed the ground validation of four SBPDs (IMERG, TRMM, PERSIANN-CDR, and PERSIANN-CSS). From January 2000 to December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), and annual scales at the entire spatial domain and point-to-pixel scale. The performance of SBPD was analyzed by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (r-Bias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI). Results revealed that: (1) IMERG's spatiotemporal tracking ability is better as compared to other datasets with appropriate ranges (CC > 0.8 and r-BIAS (±10)). The performance of all SBPDs is more capable on a monthly scale as compared to a daily scale. (2) In terms of POD, the IMERG outperformed all other SBPD on daily and seasonal scales. All SBPD showed underestimations in the summer season, and PERSIANN-CCS showed the most significant underestimation (−70). Moreover, the IMERG signposted the most satisfactory performance in all seasons. (3) All SBPD showed better performance in capturing the light precipitation events as indicated by the Probability Density Function (PDF%). Moreover, the performance of PERSIANN-CDR and TRMM is acceptable at low topography; the performance of PERSIANN-CCS is very poor in diverse topographical and climatic conditions over Tajikistan. Therefore, we advocate the use of daily, monthly, and seasonal estimations of IMERG precipitation product for hydro-climatic applications over the mountainous domain of Central Asia. [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]