Abstract: Congenital cytomegalovirus (CMV) infection is the most common congenital viral infection worldwide and a leading cause of neurodevelopmental abnormalities, including sensorineural hearing loss, microcephaly, and developmental delay in neonates. Transmission occurs primarily through vertical maternal–fetal infection during early pregnancy. Although most infected neonates are asymptomatic at birth, a significant proportion develop long-term neurological sequelae. In low- and middle-income countries, including Nigeria, routine screening for congenital CMV is rarely performed, and molecular epidemiological data remain limited. This study aimed to determine the molecular prevalence and characterize congenital CMV infection among neonates attending tertiary health institutions in Jos, Nigeria. A cross-sectional molecular study was conducted among neonates aged ≤21 days recruited from three tertiary hospitals in Jos between January 2021 and December 2022. Buccal swab samples were collected and tested for CMV DNA using standardized in-house polymerase chain reaction (PCR). Positive samples were subjected to Sanger sequencing, and phylogenetic analysis was performed. Data were analyzed descriptively, and molecular prevalence was reported with exact 95% confidence intervals. Out of 180 neonates enrolled, one tested positive for CMV DNA, giving a molecular prevalence of 0.6% (95% CI: 0.02–3.1%). BLAST analysis revealed 98.9% nucleotide sequence similarity to Human herpesvirus 5 strain HAN22, and the sequence was assigned the GenBank accession number PV668598. Phylogenetic analysis showed clustering with previously reported African isolates. The CMV-positive neonate presented with microcephaly and small-for-gestational-age status. Although congenital CMV infection was rare in this cohort, molecular detection and genomic characterization provide valuable baseline data on CMV epidemiology in Nigeria and underscore the importance of continued surveillance and early diagnosis.

Abstract: Forecasting long-term time series (LTSF) requires a delicate balance between capturing global dependencies and preserving granular local dynamics. Although State Space Models (SSMs) and Transformers have been successful, a technical challenge remains, in which individual paradigms often fail to perform well over extended horizons due to the difficulty of simultaneously achieving linear-time efficiency and high-fidelity local refinement at the same time. This study introduces Mamba-LSTM-Attention (MLA), a novel hybrid architecture featuring a cascaded topology designed to bridge these dimensions. In this work, the core innovation is the hierarchical feature evolution mechanism, in which a Mamba module serves first as an efficient global encoder to capture long-range periodic trends at linear complexity. Following this, gated LSTM units are used to refine the algorithm at the micro-scale in order to filter noise and characterize non-linear local fluctuations. Lastly, a multi-head attention mechanism performs a dynamic feature re-weighting in order to focus on key historical signals. Systematic evaluations across four multivariate benchmark datasets demonstrate that MLA achieves exceptional cross-step forecasting stability. Most notably, on the ETTh1 dataset, MLA maintains a remarkably narrow Mean Squared Error (MSE) fluctuation range (0.127) as the forecasting horizon extends from T=96 to T=720. This empirical evidence confirms that the integrated Mamba module effectively mitigates the error accumulation typically encountered by vanilla LSTMs. While the current implementation faces an information bottleneck due to a single-point projection decoding strategy, the ablation studies (revealing a 19.76% MSE surge upon LSTM removal) validate the combination of the proposed architecture. This work establishes a robust framework for hybrid SSM-RNN modeling and a clear path for future performance enhancements through sequence-to-sequence mechanisms.

Abstract: Rates of Parkinson’s disease (PD) and dementia have risen globally to near-epidemic proportions. Conventional explanations including aging, toxins, genetics, and protein misfolding describe aspects of pathology but do not fully account for the synchrony of recent increases across regions. This paper explores whether chronic microbial persistence in modern food systems, particularly high-speed poultry-processing environments, may represent an underrecognized upstream contributor to PD and dementia risk. Publicly available data from the Global Burden of Disease study and FAOSTAT meat-consumption records indicate that PD incidence has increased alongside poultry consumption, especially in regions using high-throughput, chlorine-based processing such as the United States and China. In the United States, PD and dementia trends rise in near-parallel with poultry consumption, with a multi-year lag consistent with long prodromal intervals. In contrast, Israel shows stable or declining PD rates despite substantial poultry consumption, coinciding with kosher processing practices and long-standing post-BSE feed restrictions. These patterns support a microbial-ecological hypothesis in which biofilm-forming spirochetes persist through chemical sanitation, enter the food chain, and chronically stimulate gut-brain inflammation. This hypothesis paper is intended to stimulate empirical testing; all associations described are ecological and do not imply individual-level causation.

Abstract: In this study the photocatalytic activity as a function of effective irradiance, photocatalytic quantum yield and reactant coverage was thoroughly assessed for the proper photoreactor (PhR) selection. PhR selection is a preponderant stage for photocatalytic processes, which has been an aspect not studied in detail in various scientific investigations. The emitted wavelength and effective irradiance of several PhRs, equipped with fluorescent and light emitting diodes (LEDs) lamps, were tested in the photodegradation of methylene blue (MB) in solid phase using AgTiC. Among all tested PhRs the one equipped with the low-pressure Hg lamp enhanced the photodegradation of MB. The above is due to the Hg lamp emitted UV-type radiation, which promotes the simultaneous photoactivation of the TiO2 and the surface plasmon resonance phenomenon of the Ag nanoparticles. Based on this study, it was determined that high values of effective irradiance promoted photocata-lytic activity because of the greater amount of photogenerated species [e-/h+]. Also, the ef-fective irradiance on the proper photocatalytic material slows down the recombination rate of the [e-/h+]. A kinetic photocatalytic model (KPM) was proposed to the description of photocatalytic reactions as a function of the effective irradiance, photocatalytic quantum yield and reactant coverage considering photocatalytic pseudo steady state according to the reactant equilibrium coverage (Langmuir isotherm) and the transfer processes of the photoinduced charge carrier species.

Abstract: Background: Mitral annular calcification (MAC) is associated with systemic atherosclerosis and cardiometabolic risk factors. Although hematologic inflammatory indices have been reported to be correlated with MAC, whether these associations persist after accounting for the cardiometabolic context in which MAC occurs remains unclear. Methods: In a prospective, cross-sectional study of consecutive adults, patients with mild MAC were compared to those without MAC. Individuals with major inflammatory conditions, advanced chronic kidney disease, cirrhosis, malignancy, autoimmune/acute inflammatory disorders, significant valvular disease, prosthetic valves/pacing devices, psychiatric disorders, or moderate-severe MAC were excluded. C-reactive protein (CRP) and hematological inflammatory indices including neutrophil-to-lymphocyte ratio (NLR), Systemic Inflammatory Response Index (SIRI), and lymphocyte-to-leukocyte ratio (LLR) were analyzed in relation to MAC status. Results: Among 205 patients, 134 had mild MAC and 71 had no MAC. Patients with MAC were older and displayed higher cardiometabolic burden, including more frequent dysglycemia, higher blood pressure and greater adiposity. In unadjusted comparisons inflammatory markers differed by MAC status: CRP (0.31 mg/dL vs. 0.18 mg/dL, p = 0.002), NLR (2.52 vs. 1.99, p = 0.032) and SIRI (1.27 vs. 1.04, p = 0.039), and LLR (0.26 vs. 0.29, p = 0.032). In multivariable logistic regression models, none of the inflammatory markers remained independently associated with MAC. In contrast, age (ORs 1.056 - 1.063 per year increase, p ≤ 0.001), prediabetes (ORs 2.43 - 3.63, p ≤ 0.001) and type 2 diabetes (OR 5.91 and 6.19, p ≤ 0.001) demonstrate consistent independent associations with MAC across all models. Conclusions: In this cardiometabolic population with mild MAC, inflammatory indices showed unadjusted differences but no independent associations with MAC after comprehensive cardiometabolic adjustment. These findings are most compatible with inflammatory markers primarily reflecting the cardiometabolic milieu in which MAC occurs rather than representing MAC-specific processes. Age and glucose metabolism abnormalities emerged as the dominant independent factors associated with mild MAC reinforcing the central role of metabolic disfunction in MAC pathogenesis.

Abstract: Improving the efficiency of energy consumption, conversion, and transmission on merchant ships has become a critical challenge due to rising fuel costs, increasingly stringent environmental regulations, and the introduction of operational efficiency requirements such as the IMO CII. Existing energy-efficiency metrics are predominantly based on abso-lute or design-oriented indicators and do not adequately capture the latent reserves of energy savings embedded in ship energy systems. This study addresses this gap by proposing a methodological framework for quantifying energy efficiency through the concept of relative energy-saving potential. The proposed approach integrates ship energy balance analysis with a hierarchical assessment of relative theoretical, technical, and economical-ly feasible energy-saving potentials. The methodology is demonstrated through an illustrative case study of a medium-size product tanker, focusing on the main engine, auxiliary generators, pumping systems, and HVAC loads. The results indicate that a significant share of energy losses can be systematically identified and progressively constrained by technical and economic feasibility considerations, providing a transparent basis for prior-itizing energy efficiency measures. The study concludes that relative energy-saving potentials offer an effective and scalable foundation for ship energy management, supporting SEEMP implementation, CII compliance strategies, and integration into digital twin and AI-based energy management systems.

Abstract: Background/Objectives: Anxiety and suicidal ideation are major mental health concerns during the perinatal period, impacting both mothers and newborns. While studies from South America address this issue, no prior systematic reviews have synthesized the findings. This review and meta-analysis aimed to explore the relationship between anxiety and suicidal ideation in perinatal women in South American countries. Methods: A systematic review and meta-analysis were registered in PROSPERO (CRD42025631849). Database searches were conducted in PubMed, Scopus, Web of Science, SciELO, and LILACS, including studies published up to December 2024, with no restrictions on year of publication. Study quality was evaluated using Joanna Briggs Institute tools. A random-effects model was used for the meta-analysis, following PRISMA guidelines. Results: Suicidal ideation was linked to 23 variables, such as age, race, depression, mother-infant bonding, violence, marital status, drug use, planned pregnancy, anxiety, low birth weight, and preterm birth. Anxiety was associated with 10 variables, including age, race, marital status, hyperglycemia, disabilities, eating habits, and mother-child relationship. The meta-analysis revealed a strong, significant association between suicidal ideation and violence, with affected women being 2.84 times more likely to report ideation. Conclusions: Violence and marital status emerged as key factors, reinforcing the need for screening and maternal mental health policies.

Abstract: This study is intended to examine how Human-AI collaboration-based identity threat appraisals in the form of loss of autonomy and loss of skill, triggers professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models Perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity strengthens these associations for loss of autonomy suggesting employees high in AI-inclusive identity exhibit stronger professional identity threat and higher cyberloafing under autonomy loss. This study used self-reported data from a single cultural context, which may limit generalizability. The counterintuitive effect of AI-inclusive identity highlights the need for future research to examine when it serves as a protective versus a risk-enhancing factor. When integrating AI, organizations should mitigate autonomy and skill-erosion appraisals through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI–human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as Human-AI collaboration–driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human–AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions.

Abstract: We address the control problem of a hybrid photovoltaic-grid water pumping system. This system includes a photovoltaic panel, an AC/DC PWM rectifier connected to an electric grid, a DC/AC PWM inverter paired with a Multiphase Induction Motor MPIM, and a centrifugal water pump. The control objectives are to ensure that the water pump flow rate follows a reference signal, regulate the norm of the rotor flux of the motor to its nominal value, maintain the DC link voltage at a reference value for optimal power point tracking (MPPT), and achieve satisfactory power factor correction (PFC). A nonlinear model of the controlled system is developed, followed by the synthesis of a multiloop nonlinear controller using the backstepping technique. This controller ensures global asymptotic stability in closed-loop operation and achieves all control objective.

Abstract: The objective of this study was to synthesize silver nanoparticles (AgNPs) utilizing an eco-friendly approach with Ocimum lamiifolium leaf extract as a biological reducing agent. The research focused on investigating how various experimental conditions influenced the stability and particle size of the AgNPs. The characterization of the synthesized nanoparticles involved multiple techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), UV-visible spectroscopy, particle size analysis, Polydispersity Index (PDI), and zeta potential measurements. During the reduction process, a noticeable color change from colorless to grey indicated successful conversion of Ag+ to Ag°. The UV-vis spectra revealed a maximum absorption at 467 nm, confirming nanoparticle formation. The average particle size was found to be 65.37 nm with a PDI of 0.241, suggesting a relatively uniform size distribution. The zeta potential was measured at -29.85 mV, indicating good colloidal stability over time. FTIR analysis identified various functional groups associated with phytochemicals, supporting the role of plant compounds in reduction and stabilization. XRD patterns confirmed the face-centered cubic (FCC) crystalline structure of the AgNPs. Furthermore, antibacterial testing showed increased inhibitory zones with higher AgNP concentrations, with the minimum inhibitory zone of 4 mm and maximum of 15.45 mm against E. coli. The green synthesis mechanism involved reduction, stabilization, membrane disruption, reactive oxygen species (ROS) production, and bacterial cell death. Overall, AgNPs produced via Ocimum lamiifolium extract demonstrated enhanced stability and effective antibacterial activity, highlighting the potential of plant-based green synthesis methods for biomedical applications.

Abstract: Grassland state assessment is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB), aboveground net primary production (ANPP) and net primary production (NPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017-2024. We synthesized grassland extent data, collected extensive field measurements, and used remote sensing-based biophysical proxies to build an ensemble of eight linear models for spatial extrapolation at 10 m resolution. The proxies explained 11-41% of observed biomass (BM) variability. The ensemble mean ANPP was 325.7±56.6 gBM m2 year1 (median: 316 gBM m2 year1), with modest overall interannual variability. Upscaled country-wide ANPP averaged 35.1±9.2 Mt BM year-1 annually (range: 31.9-38.3; median: 35.9 Mt BM year-1). Uncertainty from grassland area estimation was roughly twice that from model choice. Using literature and local data, NPP was estimated at 421±110 gC m2 year-1 (median value), showing low interannual variability. Results highlight grassland area uncertainty as the dominant source of error in biomass estimation, rather than the remote sensing models themselves.

Abstract: An innovative pin-fin integrated thermoelectric device (iTED) is numerically modeled to quantify the effect of thermal flow conditions, namely inlet temperature (\( T_{\textrm{in}} \)) and Reynolds number (Re), as well as electrical load resistance (\( R_{\textrm{load}} \)), on the thermal-electric-fluid coupled performance. Quantification of the simultaneous thermal-fluid-electric behavior of the iTED was pursued through the implementation of an explicitly-coupled solution algorithm developed in ANSYS Fluent's User Defined Scalar (UDS) environment. The effects of \( T_{\textrm{in}} \), Re, and \( R_{\textrm{load}} \), which were varied between 350 and 650 K, 3,000 and 15,000, and 0.01 and \( 10^{6} \% \) of the internal device resistance (\( R_{\textrm{int}} \)), respectively, on the open-circuit voltage, current, power output (\( \dot{W} \)), heat input, pumping power, device thermal conversion efficiency (\( \eta \)), and dimensionless performance index (\( \zeta \)), were evaluated for a fixed cold-side temperature of 300 K. The performance of the iTED was then compared to that of a conventional TEG. At a \( T_{\textrm{in}} \) of 650 K, the iTED achieved a maximum \( \dot{W} \) of 23.9 W when \( R_{\textrm{load}}=R_{\textrm{int}} \) at a Re of 15,000, a maximum \( \eta \) of \( 8.1\% \) when \( R_{\textrm{load}} \) < \( R_{\textrm{int}} \) at a Re of 10,000, and a maximum \( \zeta \) of 7.8 at a Re of 3,000. In comparison, the conventional device achieved a \( \dot{W} \) of 5.2 W, an \( \eta \) of 2.9%, and a \( \zeta \) of 1.6 at the same \( T_{\textrm{in}} \) and Re of 15,000, 6,000, and 3,000, respectively. The proposed multiphysics numerical modeling illustrates marked improvements via device restructuring.

Abstract: Fish are essential for global nutrition and food security, particularly for coastal communities. The European sardine Sardina pilchardus, a key marine resource in Portugal and Spain, experienced severe population declines during the 2000s. Although management measures have helped its recovery, ensuring compliance and verifying product origin, especially given its high reliance on imports, requires robust traceability tools. This study examines the spatial and temporal variability of elemental fingerprints (EF) of S. pilchardus scales collected from fishing harbors in Galicia (Spain) and mainland Portugal to assess their effectiveness in confirming the place and time of capture. Moreover, to improve cost-efficiency, it also evaluates how temporal variability influences the accuracy of predictive models to confirm capture location when samples from different years are used for model development and testing. Random Forest models developed with samples from 2018 and 2019 correctly classified over 95% of the specimens by location within each year. Capture time classification achieved 95.3% accuracy. However, applying the 2018 model to samples from 2019 reduced accuracy to 24.4%. Despite this constraint, the EF of fish scales provides a practical and reliable method for verifying capture time and origin, thereby reducing mislabeling and promoting the sustainable management of S. pilchardus stocks.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multi-dimensional framework for evaluating vulnerability in historic buildings where fragility cannot be explained by structural indicators alone. Existing models prioritise load-bearing behaviour but overlook typological discontinuity, spatial fragmentation and erosion of cultural or architectural value. STVSM addresses this through three weighted sub-indices—structural vulnerability (SV), typological degradation (TV) and heritage value (HV)—each calibrated using expert-derived micro–macro coefficients. Field-based deterioration scores (0–1) are multiplied by these final weights to produce SV, TV and HV values, then merged into a Conservation Priority Index (CPI).The model is applied to twenty-five buildings in three heritage contexts: Cumalıkızık traditional houses, vernacular dwellings in Balıkesir–Karesi and nineteenth-century Greek Orthodox churches in Bursa. The churches yield the highest CPI values due to roof loss, wall deformation and spatial discontinuity, reinforced by cultural significance. Vernacular houses show moderate structural deterioration but marked typological distortion linked to later additions and façade alterations. Cumalıkızık houses present heterogeneous conditions, combining preserved structures with material decay.By quantifying structural behaviour, typological integrity and heritage value within a single analytical system, STVSM offers a transparent and repeatable basis for conservation prioritisation across diverse historic building stocks.

Abstract: The Migdal effect has traditionally been viewed as a quantum phenomenon, with its explanation relying on assumptions such as non-adiabatic transitions and quantum coupling. This paper, based on the Great Tao Model, constructs a complete explanatory framework for this effect within classical physics: following an impact, the nucleus undergoes classical accelerated motion, inducing a dynamic distortion in its charge existence field, and transfers energy continuously to the extranuclear electrons via classical electrostatic interaction, leading to their excitation or ionization. Through quantitative derivation of the existence field distortion intensity, electron energy gain, and the geometric relationship of the double-track feature, all predictions of this theory are in complete agreement with the quantitative observational results of the direct measurement experiment, including the "co-vertex double-track" characteristic and the electron energy range.. Further analysis indicates that neutrinos, due to their extremely small mass, impart recoil energies far below the effect's threshold, while Subtrons, lacking charge interaction, cannot trigger the effect at all. This paper systematically analyzes the fundamental differences between the classical and quantum mechanical explanations regarding physical reality, energy transfer mechanisms, and theoretical self-consistency, and clarifies the underlying reason why the Migdal effect cannot be used to detect Subtrons (dark matter). This study not only confirms the universality of classical physical laws at the microscopic scale, providing a novel, physically real, and logically self-consistent paradigm for understanding the Migdal effect, but also offers clear guidance for the strategic direction of frontier experiments such as dark matter detection.

Abstract: The increasing adoption of artificial intelligence (AI) in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities; however, applying AI within cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. This study proposes an Anti-Sherif AI-driven cybersecurity audit model that integrates AI-based analytics with human expert judgment to support a more adaptive, risk-informed auditing process. Grounded in design science research, the model combines conventional binary compliance checks with AI-derived intelligence and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including ISO/IEC 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional audits. The results indicate that combining AI-driven insights with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed model provides a foundation for evolving cybersecurity auditing from periodic compliance assessments toward continuous, intelligence-supported assurance.

Abstract: We show that the GBFA method of Elgindy (2025), originally developed for 0 < α < 1, extends to all α > 0 without altering its algorithmic structure. Elgindy's transformation τ = t(1 − y1/α) remains valid for all α > 0 and preserves the numerical framework, ensuring that interpolation, quadrature, and error analysis carry over unchanged. For α > 1, the mapping induces only Hölder regularity at y = 0, which a ects quadrature accuracy. We quantify this e ect and show that the interpolation error retains its original convergence properties. To restore higher-order endpoint smoothness, we introduce a ϕ(α)-generalized transformation that enforces Cr regularity for any prescribed r ≥ 0, accelerating quadrature convergence while preserving the GBFA structure. Numerical experiments con rm high accuracy and robustness across all α > 0, demonstrating that the uni ed GBFA formulation provides an e cient, non-adaptive, xed-node approach for arbitrary-order RLFIs.

Abstract: Traveling wave ultrasonic motors (TWUMs) are critical components in precision systems, their performance is susceptible to degradation under dynamic disturbances in harsh operating environments. This paper presents a monolithic U-shaped rotor designed to intrinsically achieve quasi-zero stiffness (QZS). Unlike conventional QZS systems that rely on assembling discrete positive and negative stiffness elements, the proposed design generates the target mechanical characteristic through the tailored nonlinear response of a unified U-shaped structure, thereby improving preload stability. Through exploring the critical parameters of the rotor cross-section, the finite element method (FEM) is employed to optimize the geometry configuration and characterize the mechanical performances. Simulation results show that the QZS behavior, demonstrating a stable force plateau of 320 ± 10 N across a 0.7 mm displacement range. A maximum von Mises stress of 788 MPa is obtained, well within the material's safety margin, thereby ensuring the structural integrity. Experimental tests validate the effectiveness of the proposed design. This compact, monolithic U-shaped rotor provides a robust and reliable QZS solution, demonstrating significant potential for enhancing the stability of TWUMs in applications prone to harsh environments such as extreme high and low temperatures, thermal cycling conditions, shock environments.

Abstract: Based on the latest cosmological observational and theoretical advancements, this paper proposes a unified systems theory framework, conceptualizing the universe as an adaptive ecosystem with a fundamental architecture of "spacetime-dark matter-dark energy". Through the metaphor of a "computer operating system", it elaborates on the roles of spacetime as a structural entity, dark matter as a gravitational framework, and dark energy as a functional core. It also explores the implications of this framework for understanding physical laws, cosmic evolution, and the nature of information, attempting to establish a unified theory for comprehending the universe. It is important to emphasize that all physical theories are essentially sets of "models" or "metaphorical systems" used to explain and predict observational phenomena. The "operating system" paradigm presented herein is also an approximate description of the complex cosmic reality, not the ultimate answer.

Abstract: The advent of autonomous systems has propelled the integration of artificial intelligence (AI) and machine learning (ML) techniques, particularly deep reinforcement learning (DRL), to enhance decision-making capabilities. This research paper conducts an exhaustive survey of state-of-the-art DRL algorithms, focusing on their applicability and performance within the realm of autonomous systems. To find out how flexible and useful DRL algorithms are in real life, our research covers a lot of different areas, such as robots, self-driving cars, and unmanned flying vehicles. The study takes a close look at the most important parts of these algorithms, like neural network designs, exploration-exploitation strategies, and payment processes, to see how they affect how well independent systems work. Additionally, the study goes into detail about the problems and restrictions that come with using DRL in self-driving systems, covering everything from sample waste to safety concerns. Wealso look at newdevelopments andimprovements in DRLthatmightbeable to get around current problems and make way for future innovations in driverless technology. As a valuable resource for researchers, engineers, and practitioners working on the development and deployment of autonomous systems, this brief survey shows the pros, cons, and opportunities that come with different DRL algorithms in this ever-changing field.