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Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment. The adaptation of hyperparameters has a great impact on the overall learning process and the learning processing times. Hyperparameters should be accurately estimated while training DRL algorithms, which is one of the key challenges that we attempt to address. This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the optimum control strategy in an autonomous driving control problem. DDPG is capable of handling complex environments, which contain continuous spaces for actions. To evaluate the proposed algorithm, the Open Racing Car Simulator (TORCS), a realistic autonomous driving simulation environment, was chosen to its ease of design and implementation. Using TORCS, the DDPG agent with optimized hyperparameters was compared with a DDPG agent with reference hyperparameters. The experimental results showed that the DDPG’s hyperparameters optimization leads to maximizing the total rewards, along with testing episodes and maintaining a stable driving policy.

High-power laser delivery with near-diffraction-limited beam quality is typically limited to tens of metres distances by nonlinearity-induced spectral broadening inside the glass core of delivery fibres. Anti-resonant hollow-core fibres offer not only orders-of-magnitude lower nonlinearity but also loss and modal purity comparable to conventional beam-delivery fibres. Using a single-mode hollow-core nested anti-resonant nodeless fibre with 0.74 dB km−1 loss, we demonstrate the delivery of 1 kW of near-diffraction-limited continuous-wave laser light over a 1 km distance, with a total throughput efficiency of ~80%. From simulations, a further improvement in transmitted power or length of more than one order of magnitude should be possible in such air-filled fibres, and considerably more if the core is evacuated. This paves the way to multi-kilometre, kilowatt-scale power delivery that is potentially useful not only for future manufacturing and subsurface drilling but also for new scientific possibilities in sensing, particle acceleration and gravitational wave detection.Microstructured optical fibre is shown to be able transmit high-power laser light over long distances with high throughput efficiency.

This study aims to evaluate the suitability of subsurface layers for infrastructure development using geophysical and geotechnical studies. Six seismic refraction and six 2D geoelectic profiles were conducted in the study area to analyze the geotechnical characteristics of the subsoil in order to assess its suitability for construction projects. According to the geophysical investigation, there are two main geoelectric strata, each with a different lithology and thickness. The first layer has high resistivity values of more than 200 (Ωm) and its thickness is between 3.5 and 6.3 m. The second layer's resistivity ranges from 0.3 to 200 (Ωm). The findings indicate that the near-surface area of the study site is composed of two layers in addition to the surface layer, which consists of semi-consolidated wadi fill deposits of gravel, sand, and silt with rock fragments. The first layer comprises fractured limestone with clay intercalation, followed by a second layer of marly limestone, which transitions into marl and clay. Both compressional (P) and shear (S) waves were detected and analyzed. The shallow seismic refraction technique indicates that the velocity waves (Vp) in the first and second layers range from 133 to 770 and 790 to 3100 m/s respectively. Various engineering parameters for the second layer were determined, including elastic moduli (bulk modulus, Poisson’s ratio, rigidity modulus, and Young’s modulus), competence scales (stress ratio, concentration index, material index and density gradient), and bearing capacity (ultimate and allowable). The average Poisson ratio for the first and second layers is 0.235. The ultimate bearing capacity (Qu) of the first layer ranges from 379.1 to 1031.2 gm/cm 2 and between 1040.8 and 1548.5 gm/cm 2 of the second layer. Allowable bearing capacity (Qall) for the first layer is between 126.4 and 343.7 gm/cm 2 , and for the second layer, it is between 346.9 and 516.2 gm/cm 2 . According to the geotechnical investigation's findings, the range of liquid limit (LL) values is between 41.20 and 86.30%, the range of plastic limit (PL) values is between 18 and 35.25%, the range of plasticity index (PI) values is between 14.17 and 51.45%, and the range of free swelling values is between 60 and 170%. However, the study identified high-risk areas in the study area. These zones are located in the first and second layers represented by strongly swelling clay layers and strongly fractured limestone, which will have a major impact on the road and buildings. Therefore, this study recommends addressing these problems before any development to protect these areas from subsidence and collapse.

Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher than the existing techniques.

This paper presents several nonparametric estimators for the Sharma–Taneja–Mittal entropy measure of a continuous random variable with known support, utilizing spacing, a local linear model, and a kernel function. The properties of these estimators are discussed. Their performance was also examined through real data analysis and Monte Carlo simulations. In the Monte Carlo experiments, the proposed Sharma–Taneja–Mittal entropy estimators were employed to create a test of goodness-of-fit under the standard uniform distribution. The suggested test statistics demonstrate strong performance, as evidenced by a comparison of their power with that of other tests for uniformity. Finally, we examine a classification issue in the recognition of patterns to underscore the significance of these measures.

This study explores a sustainable approach for synthesizing silver nanocomposites (AgNCs) with enhanced antimicrobial and bioactivity using safe Lactobacillus strains and a whey-based medium (WBM). WBM effectively supported the growth of Lactobacillus delbrueckii and Lactobacillus acidophilus , triggering a stress response that led to AgNCs formation. The synthesized AgNCs were characterized using advanced spectroscopic and imaging techniques such as UV‒visible, Fourier transform infrared (FT-IR) spectroscopy, transmission electron (TEM), and scanning electron microscopy with energy dispersive X-ray analysis (SEM–Edx). Lb acidophilus -synthesized AgNCs in WBM (had DLS size average 817.2–974.3 ± PDI = 0.441 nm with an average of metal core size 13.32 ± 3.55 nm) exhibited significant antimicrobial activity against a broad spectrum of pathogens, including bacteria such as Escherichia coli (16.47 ± 2.19 nm), Bacillus cereus (15.31 ± 0.43 nm), Clostridium perfringens (25.95 ± 0.03 mm), Enterococcus faecalis (32.34 ± 0.07 mm), Listeria monocytogenes (23.33 ± 0.05 mm), methicillin-resistant Staphylococcus aureus (MRSA) (13.20 ± 1.76 mm), and filamentous fungi such as Aspergillus brasiliensis (33.46 ± 0.01 mm). In addition, Lb acidophilus -synthesized AgNCs in WBM exhibit remarkable free radical scavenging abilities, suggesting their potential as bioavailable antioxidants. These findings highlight the dual functionality of these biogenic AgNCs, making them promising candidates for applications in both medicine and nutrition. Graphical Abstract

Background Colorectal cancer (CRC) is frequently associated with thrombosis with thrombotic events, such as deep vein thrombosis or pulmonary embolism, often correlate with poor clinical outcomes. Coagulation markers have been suggested as potential prognostic indicators for CRC severity. However, the relationship with clinicopathological characteristics in CRC remains unclear. Purpose This study aims to examine the relationship between routine coagulation markers and clinicopathological characteristics in CRC patients. Patients and methods A retrospective analysis was conducted on 100 patients with confirmed diagnosis of CRC, classified according to the 2018 edition of the American Joint Committee on Cancer Tumor/Node/Metastasis staging system for malignant tumors. Clinicopathological characteristics and routine coagulation tests including prothrombin time, and international normalized ratio, activated partial thromboplastin time, prothrombin activity, thrombin time, fibrinogen, d-dimer, platelet count, were evaluated. Spearman correlation was used to assess correlations with clinicopathological characteristics. Additionally, univariate and multivariate ordinal regression analysis were conducted to detect the independent predictors for CRC aggressiveness. Results Our data documents several associations between coagulation markers and cancer progression markers. Specifically, positive correlations were identified between fibrinogen and d-dimer levels and each of the following: carcinoembryonic antigen, carbohydrate antigen, tumor stage, node involvement, and metastasis. Regression analysis showed, d-dimer (OR = 1.102, p  < 0.001) and fibrinogen (OR = 1.002, p  < 0.001) are independent predictors of high-risk CRC cases. Conclusion Fibrinogen and d-dimer may serve as independent predictive biomarkers for CRC aggression. Their clinical utility could support personalized treatment plans for CRC patients.

Background Several methods were introduced for enamel biomimetic remineralization that utilize a biomimetic analogue to interact and absorb bioavailable calcium and phosphate ions and induce crystal nucleation on demineralized enamel. Amelogenin is the most predominant enamel matrix protein that is involved in enamel biomineralization. It plays a major role in developing the enamel’s hierarchical microstructure. Therefore, this study was conducted to evaluate the ability of an amelogenin-inspired peptide to promote the remineralization potential of fluoride and a supersaturated calcium phosphate solution in treating artificially induced enamel carious lesions under pH-cycling regimen. Methods Fifty enamel slices were prepared with a window (4*4 mm 2 ) on the surface. Five samples were set as control healthy enamel and 45 samples were subjected to demineralization for 3 days. Another 5 samples were set as control demineralized enamel and 40 enamel samples were assigned into 8 experimental groups (n=5) (P/I, P/II, P/III, P/AS, NP/I, NP/II, NP/III and NP/AS) according to peptide treatment (peptide P or non-peptide NP) and remineralizing solution used (I; calcium phosphate solution, II; calcium phosphate fluoride solution, III; fluoride solution and AS; artificial saliva). Samples were then subjected to demineralization/remineralization cycles for 9 days. Samples in all experimental groups were evaluated using Raman spectroscopy for mineral content recovery percentage, microhardness and nanoindentation as healthy, demineralized enamel and after pH-cycling. Data were statistically analysed using two-way repeated measures Anova followed by Bonferroni-corrected post hoc test for pairwise multiple comparisons between groups. Statistical significance was set at p= 0.05. Additionally, XRD, FESEM and EDXS were used for crystal orientation, surface morphology and elemental analysis after pH-cycling. Results Nanocrystals clumped in a directional manner were detected in peptide-treated groups. P/II showed the highest significant mean values in mineral content recovery (63.31%), microhardness (268.81±6.52 VHN), elastic modulus (88.74±2.71 GPa), nanohardness (3.08±0.59 GPa) and the best crystal orientation with I 002 / I300 (1.87±0.08). Conclusion Despite pH changes, the tested peptide was capable of remineralizing enamel with ordered crystals. Moreover, the supplementary use of calcium phosphate fluoride solution with peptide granted an enhancement in enamel mechanical properties after remineralization.

Wasps, members of the order Hymenoptera, are distributed in different parts of the world, including Brazil, Thailand, Japan, Korea, and Argentina. The lifestyles of the wasps are solitary and social. Social wasps use venom as a defensive measure to protect their colonies, whereas solitary wasps use their venom to capture prey. Chemically, wasp venom possesses a wide variety of enzymes, proteins, peptides, volatile compounds, and bioactive constituents, which include phospholipase A2, antigen 5, mastoparan, and decoralin. The bioactive constituents have anticancer, antimicrobial, and anti-inflammatory effects. However, the limited quantities of wasp venom and the scarcity of advanced strategies for the synthesis of wasp venom’s bioactive compounds remain a challenge facing the effective usage of wasp venom. Solid-phase peptide synthesis is currently used to prepare wasp venom peptides and their analogs such as mastoparan, anoplin, decoralin, polybia-CP, and polydim-I. The goal of the current review is to highlight the medicinal value of the wasp venom compounds, as well as limitations and possibilities. Wasp venom could be a potential and novel natural source to develop innovative pharmaceuticals and new agents for drug discovery.

The complementary dual of entropy has received significant attention in the literature. Due to the emergence of many generalizations and extensions of entropy, the need to generalize the complementary dual of uncertainty arose. This article develops the residual cumulative generalized fractional extropy as a generalization of the residual cumulative complementary dual of entropy. Many properties, including convergence, transformation, bounds, recurrence relations, and connections with other measures, are discussed. Moreover, the proposed measure’s order statistics and stochastic order are examined. Furthermore, the dynamic design of the measure, its properties, and its characterization are considered. Finally, nonparametric estimation via empirical residual cumulative generalized fractional extropy with an application to blood transfusion is performed.