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This study examines the theoretical framework and practical application of machine learning models to predict high run chase outcomes in T-20 International (T-20I) cricket. Focusing on factors such as venue, toss outcomes, batting order, pitch conditions, and team rankings, the analysis investigates how these elements contribute to the probability of achieving high-scoring chases. A dataset comprising 458 T-20I matches from 2005 to 2024 was analyzed using seven machine learning models: Random Forest, Support Vector Machine, Naïve Bayes, Neural Networks, Decision Tree, XGBoost, and K-Nearest Neighbors. Model performance was systematically evaluated through metrics including accuracy, precision, recall, and Area under the Curve (AUC), with robustness confirmed via Monte Carlo simulations across variable match conditions. Results highlight Random Forest and Decision Tree models as the most accurate and reliable, consistent with theoretical expectations for ensemble and decision-based models in complex, multi-factor analyses. This research provides both theoretical and practical insights to enhance data-driven strategies, pre-match evaluations, and in-game decision-making for T-20I cricket performance analysis.Top of Form

This research analyzes the marine route plan from Shanghai to Vladivostok utilizing Dijkstra’s algorithm, Markov chain analysis, game theory, and congestion analysis. Dijkstra determines the route through Busan and Hungnam as the shortest, with a total distance of 2114 kilometers and minimal travel time. The Markov chain analysis supported the designated path by demonstrating greater transition probabilities compared to other routes, so establishing it as the most probable option. Experts in game theory, particularly on the Nash equilibrium, demonstrated that cooperation significantly reduced operating expenses. Further congestion research corroborated that the Shanghai-Busan-Hungnam-Vladivostok route offers a cost advantage, and even with the inclusion of congestion, the route remains less expensive. The study collectively advocates for the consideration of distance, likelihood, collaboration, and congestion while selecting the optimal maritime route, hence enhancing efficiency in maritime logistics.

Abstract Bacterial blight (BB) of rice caused by Xanthomonas oryzae pv. oryzae (Xoo), is an important disease in rice-growing countries, including Pakistan, where it was first reported in the mid-1970s. Transcription activator-like effectors (TALEs) play vital roles in many plant diseases caused by Xanthomonas spp.; however, Pakistani Xoo TALome diversity and their contribution to pathogenicity is largely unknown. In this study, 101 Xoo strains were screened using specific PCR primers. The genomic DNA from these strains underwent BamHI digestion and hybridized with the internal SphI fragment of PthXo1. Southern blot analysis revealed 16 to 20 putative tale fragments among the tested strains. These strains were further classified into 11 genotypes based on the number and size of the hybridizing bands. Genotypes 1, 2, 3, and 4 represented 24, 2, 51, and 17 strains, respectively. Pathogenicity assays on near-isogenic lines (NILs) containing different resistance (R) genes exhibited that CBB23 was incompatible with all tested Pakistani-Xoo genotypes, whereas IRBB5 and IRBB4 showed resistance against specific genotypes. In contrast, paddy trails on NILs containing single, double, and triple mutants of OsSWEET11a, OsSWEET13, and OsSWEET14 in the effector binding elements (EBEs) of cv. Kitaake revealed that KP-22 and LD-5 harbor novel virulent TAL effector/s. Interestingly, the expression analysis of six clade-III OsSWEET genes suggests that novel TALE/s targeting unidentified susceptibility gene/s. Altogether, this study highlights gene-for-gene relationships between tested rice lines and Pakistani-Xoo strains. This is the first report providing the diversity of TALEs and their relationship to R and S (susceptibility) genes. Further identification of novel virulent TALE/s and their cognate target/s is warranted to precisely elucidate their role in BB.

In the advanced landscape of materials science, particularly in the development of ceramic materials, artificial intelligence (AI) emerged as a transformative tool for accelerating innovation. This study proposed a comprehensive analysis of the Transformer-based Inverse Learning model to optimize component and process recommendations. K-Nearest Neighbors (KNN) imputation was first applied, improving data accuracy and completeness to address data gaps. Subsequently, Variational Autoencoders (VAE) were used for data augmentation, enriching the dataset’s diversity. The Transformer model, leveraging this enhanced data, demonstrated strong predictive performance, achieving an R2 score of 0.966 for component analysis and an outstanding R2 score of 0.982 for process analysis in Barium Titanate (BaTiO3) material data. These results show the effectiveness of combining imputation, augmentation, and advanced AI modeling in capturing complex material properties. The study highlights the potential of AI-driven methodologies to significantly improve prediction accuracy in material discovery, offering valuable insights for developing future ceramic materials.

This work encompasses a novel highly compact Split Ring Resonator (SRR) based quad-band Dielectric Resonator Antenna (DRA) for 4G and 5G applications. The proposed antenna comprises of an extended rectangular feedline and a rectangular DR in combination with two SRRs. The DRA uses the extended rectangular microstrip feedline for providing better impedance matching and enhanced performance. Remarkably, the introduction of SRRs on either side of the feedline enables the DRA to resonate at four distinct frequency bands, i.e., 1.62 GHz, 3.03 GHz and 4.7 GHz and 6.56 GHz respectively. The two lower operating bands with narrow band response are due to SRRs while two upper bands with wide band characteristics are due to rectangular DR. The simulated performance of the rectangular DRA is validated through measurements, performed on a fabricated prototype. At the corresponding resonance frequencies, the suggested design exhibits −10 dB impedance bandwidths of 9.2%, 5%, 8.3%, and 5.6%. With a notable peak gain of 2.3 dBi and an efficiency of 93%, the suggested design has excellent radiation pattern stability. To the best of authors knowledge, this is the first design where SRRs are incorporated with rectangular DR to achieve multiband operations. The simulated and measured findings show a strong agreement, highlighting the SRR-based DRA appropriateness for multiband wireless applications (such as satellite television, radio astronomy, Bluetooth, WiFi, and 6G), while demonstrating its significant contribution to the field.

The growing demand for wireless communication in biomedical engineering has opened new research frontiers. This paper introduces an innovative miniaturized ( $4\times 9$ mm2) multiple-input multiple-output (MIMO) circularly polarized (CP) implantable antenna for a wireless capsule endoscopy (WCE) system. The presented device features two Hilbert curve-shaped meandered resonators. The space-filling characteristic inherent in the Hilbert curve contributes to miniaturization and bandwidth enhancement of up to 250 MHz to cover possible detuning between diverse organs. Simultaneously, employing the curve’s inherent natural symmetry and adopting an orthogonal port arrangement led to a noteworthy improvement in isolation up to 27 dB with an enhanced gain of −27.9 dBi. Comprehensive simulation analysis of antenna-integrated WCE systems in homogeneous and hetrogenous environments is conducted in the stomach, small intestine, and large intestine. Without the need for an external decoupling structure, the system demonstrated good MIMO characteristics with isolation of 27 dB. A fabricated prototype has been tested in a saline solution and minced pork. A good match between the measured and simulated results has been observed. At both ports, the axial ratio (

Abstract Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts. Traditional deep learning models rely on single-point predictions, limiting their ability to provide uncertainty measures essential for robust clinical decision-making. To solve this issue, Bayesian approximation approaches have evolved and are gaining market traction. In this work, we implemented a transfer learning approach, building upon the DenseNet-121 convolutional neural network to detect diabetic retinopathy, followed by Bayesian extensions to the trained model. Bayesian approximation techniques, including Monte Carlo Dropout, Mean Field Variational Inference, and Deterministic Inference, were applied to represent the posterior predictive distribution, allowing us to evaluate uncertainty in model predictions. Our experiments on a combined dataset (APTOS 2019 + DDR) with pre-processed images showed that the Bayesian-augmented DenseNet-121 outperforms state-of-the-art models in test accuracy, achieving 97.68% for the Monte Carlo Dropout model, 94.23% for Mean Field Variational Inference, and 91.44% for the Deterministic model. We also measure how certain the predictions are, using an entropy and a standard deviation metric for each approach. We also evaluated the model using both AUC and accuracy scores at multiple data retention levels. In addition to overall performance boosts, these results highlight that Bayesian deep learning does not only improve classification accuracy in the detection of diabetic retinopathy but also reveals beneficial insights about how uncertainty estimation can help build more trustworthy clinical decision-making solutions.

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Abstract Diabetes is a prevalent and serious metabolic disorder affecting millions globally, and it poses extensive health risks due to elevated blood glucose levels. One promising approach for managing diabetes is the inhibition of α-glucosidase, an enzyme that plays a crucial role in carbohydrate metabolism. Targeting α-glucosidase can help delay glucose absorption, thus controlling postprandial blood sugar spikes. Dihydropyrimidones, a core structural class present in various biologically active natural compounds, have been recognized for their diverse therapeutic potential, including anti-diabetic properties. In this study, we evaluated a library of previously synthesized 37 Dihydropyrimidone derivatives to assess their potential as α-glucosidase inhibitors. We identified 34 derivatives with significant inhibitory activity, exhibiting IC50 values in the range of 5.30–56.72 µM. Among these, compounds 2, 4–7, 9–11, 13–16, 31, 32, and 33 demonstrated high potency, with IC50 values below 20 µM; the most active compound, 5, achieved an IC50 of 5.30 µM. A detailed kinetic study on compound 5 revealed a competitive inhibition mode with a Ki value of 16.10 ± 0.0075 µM. Additionally, cytotoxicity assays confirmed that compound 5 is non-toxic to BJ cell lines, underscoring its safety for therapeutic use. The computational studies further supported the inhibitory potential by illustrating key interactions and binding affinities between the Dihydropyrimidone derivatives and the α-glucosidase, highlighting these compounds as promising candidates for diabetes management.

Introduction: In the 1st week of life, hyperbilirubinemia is the most common clinical condition in newborns that has to be evaluated and treated. It is also a prominent cause of readmission to the hospital. Some newborns experience noticeable, potentially dangerous bilirubin levels, which can directly increase the risk of severe brain damage, despite it being a benign, postnatal, transitory phenomenon. The current investigation looked at the relationship between cord blood bilirubin levels and the predictability of pathological jaundice. Materials and Methods: Cord blood bilirubin was estimated at birth. Bilirubin estimation was done at 48 h, 72 h, and 5–7 days of life using a trans-bilirubinometer. The study was approved by the Institutional Ethical Committee. Results: Significant hyperbilirubinemia was found in 3.7% of the neonates. A statistically significant correlation was found between cord blood bilirubin and the development of significant hyperbilirubinemia at 48 h of life. Gender, gestational age, birth weight, and use of Oxytocin have no correlation with cord bilirubin or the subsequent development of jaundice. A cord blood value of >2 mg/dL has a high negative predictive value (98%), sensitivity (83%), specificity (41%), and positive predictive value (5.3%) in predicting the future development of future pathological jaundice. Conclusions: A high negative predictive value in our study suggests that healthy term babies with cord bilirubin ≤2 mg/dL can be discharged early with assurance to parents. Babies with cord blood bilirubin >2 mg/dL should be followed more frequently.

Carissa carandas L. is a wild food plant, however, is cultivated for its fruits to be utilized to make pickles, jams, and other similar products. Fruit of this plant has been intensively investigated for its bioactive potential; however, a scanty study is reported on the leaf, which is usually wasted. In the present study, ethanolic extract of the leaf of C. carandas was analyzed for its total bioactive contents and found rich in phenolics and flavonoids (26.67 ± 0.33 mg GAE/g extract and 18.42 ± 0.05 mg RE/g extract, respectively). These findings were substantiated due to UHPLCMS/MS analysis, which disclosed the presence of about 110 secondary metabolites, mostly lignans, flavonoids, and terpenoids. In DPPH and ABTS free radical inhibition assays, the extract showed inhibitory values as 47.51 ± 0.09 and 93.09 ± 2.8 mg TE/g extract, respectively. Its further antioxidant potential was observed in CUPRAC and FRAP assays, wherein the metal reducing power was recorded as 102.05 ± 1.03 and 67.24 ± 0.9 mg TE/g extract, respectively, while in phosphomolybdenum and metal chelating assays, the extract also disclosed potential activity (0.92 ± 0.02 mmol TE/g extract and 21.42 ± 0.34 mg EDTAE/g extract). The extract was also significantly active against tyrosinase (45.69 ± 0.7 mg KAE/g extract) and showed mild activities against AChE (3.61 ± 0.16 mg GALAE/g extract), BChE (1.43 ± 0.2 mg GALAE/g extract), α-amylase (0.46 ± 0.01 mmol ACAE/g extract) and α-glucosidase (1.05 ± 0.02 mmol ACAE/g extract). In computational studies, binding affinities for AChE and BChE enzymes were from −4.21 to −12.70 and −7.58 to −12.53 kcal/mol, respectively, confirming the results of the invitro tests and ADME studies. The current finding suggested that C. carandas may be considered as a source for several nutraceutical formulations.

This study explored the nutritional qualities of black mash bean flour impacted by fermentation, gelatinization and their combination. Protease activity, in vitro protein digestibility (IVPD), and protein solubility were evaluated for both fermented and unfermented black mash bean flour samples. Interestingly, when compared to fermented samples and gelatinized mash bean flour (GMBF), raw mash bean flour (RMBF) showed the highest protein solubility. Due to enzymatic activity and protein hydrolysis, fermentation with Lactobacillus E14 and Saccharomyces cerevisiae MK-157 enhanced protein solubility. Prolonged fermentation timeframes were positively correlated with protease activity and IVPD, suggesting the degradation of complex proteins. The highest levels of IVPD (90.12%) and protease activity (5546.78 U/g) were detected in GMBF fermented by S. cerevisiae MK-157, indicating the impact of fermentation on protein breakdown. The Lactobacillus E14-fermented RMBF exhibited 5521.45 U/g of protease activity and 87.54% IVPD. The mineral content of the fermented samples was substantially greater than that of the unfermented GMBF. The increased calcium, magnesium, potassium, sodium, iron, zinc, and copper levels for the GMBF fermented by S. cerevisiae MK-157 indicated that fermentation facilitated the release of minerals from chelated compounds. Microscopic examination revealed higher protein quantities in the fermented flour as well as hydrolyzed and broken starch granules embedded in the protein matrix. Large mash bean proteins were shown to be proteolyzed during fermentation into smaller peptides by SDS PAGE. Storage proteins were extensively broken down during fermentation with S. cerevisiae MK-157, whereas Lactobacillus E14 displayed a more focused pattern of hydrolysis. The nutritional profile of black mash bean flour was improved by fermentation with Lactobacillus E14 and S. cerevisiae MK-157.

Gossypiboma is a unique postoperative complication characterized by the presence of a surgical sponge after surgery. This condition is seldom encountered. Owing to its serious medicolegal implications it remains underreported leading to a blurred clinical spectrum. This fact is also depicted in our case which presents an array of nonspecific symptoms painting a picture of possible malignancy. The case highlights the need for strict adherence to surgical protocols to avoid such instances of clinical negligence. Furthermore, our report provides the medical community with apt data to promptly diagnose such cases. A 40-year-old Asian hypertensive and diabetic female with a history of total hysterectomy presented with a 7-month history of progressive abdominal swelling, vomiting, and weight loss. The initial clinical presentation pointed to possible malignancies but subsequently revealed a 16 × 4 × 6 cm gossypiboma near the ileocecal junction. Following the removal and resection of 2.5 feet of the ileum, the patient underwent a right hemicolectomy, ileostomy, and colostomy. The postoperative recovery was uneventful, and the patient was discharged in stable condition. This case reiterates the importance of adhering to established surgical protocols and how minor negligence can have devastating and horrid effects. This case highlights the need for the fusion of medicine with technology< which prompts the use of innovations such as RFID-tagged sponges and electronic counting to minimize human error. Increased awareness and reporting of gossypibomas will aid in their timely diagnosis and management, ultimately reducing patient morbidity and associated legal challenges.