British University in Egypt

Abstract We investigate the realization of a nonsingular cosmological bounce in metric f ( R ) gravity using a controlled exponential deformation of the Starobinsky $$R^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> model. Adopting a smooth Gaussian-type bouncing scale factor, we first demonstrate a no-go result showing that a positive-curvature vacuum bounce cannot be supported by the model $$f(R)=R+\alpha R^{2}(1-e^{-R/R_b})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>R</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mi>R</mml:mi> <mml:mo>+</mml:mo> <mml:mi>α</mml:mi> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>-</mml:mo> <mml:msup> <mml:mi>e</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>R</mml:mi> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>b</mml:mi> </mml:msub> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> alone. We then show that a minimal extension obtained by introducing a constant term restores the bounce exactly, with the constant fixed algebraically by the bounce condition. A systematic parameter-space scan is performed to identify regions free of ghost and tachyonic instabilities. Working in the Einstein frame, we study the evolution of scalar and tensor perturbations across the bounce and show that both remain finite and well behaved. Our results establish a minimal, perturbatively stable realization of a vacuum bounce in f ( R ) gravity that goes beyond background-level constructions.

Abstract Although optimization algorithms have made significant progress in a wide range of power system applications, many research areas and case studies remain unexplored and require additional investigation. Therefore, this paper tackles the coordination process of overcurrent relays (OCRs) in large-scale and complicated networks comprising hundreds of decision variables and constraints. In this context, savannah bengal tiger optimizer (SBTO) is employed to solve the OCR’s coordination of the 42-bus and 30-bus power grids by optimizing pickup current, time dial, and operating curve. It is worth noting that the 42-bus network comprises 291 decision variables and 713 constraints while the 30-bus incorporates 333 decision variables and 1119 constraints: indicating challenging problem formulation to be optimized. Thus, high-performance computing is necessary to provide the necessary computational efficiency and real-time results since the suggested method requires parallel processing capabilities for large-scale simulations and high-resolution models. The objective function (OF) is adapted to have a minimal total operating time (TOT) along with fulfilling problem constraints. Exceptional results of the utilized SBTO are validated against other benchmark algorithms such as genetic algorithm, particle swarm optimizer, water cycle algorithm, and gray wolf optimizer. Obviously, SBTO effectively meets the coordination criteria by achieving a minimal OF and incurring only a limited number of constraint violations. Furthermore, SBTO attains the minimum possible TOT of 19.66 s among other competitors for the 42-bus study case. This approach highlights SBTO’s capability in effectively addressing highly complex optimization problems with numerous constraints.

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R2 = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R2 = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors.

ABSTRACT Polylactide (PLA) and polycaprolactone (PCL) biocomposites were prepared with modified rice husk (RH). In this context, RH was treated with maleic anhydride (MA) and maleated PLA (i.e., coupling agent [CA]) forming modified RH (i.e., RH‐MA and RH‐CA, respectively). Several formulations were tested from biocomposites of PLA and PCL with different percentages of modified RH where the best results were obtained when using 5% with respect to the used mixing method. Pure PLA and PCL‐ modified RH biocomposites were first obtained, then each of them was further involved in cross blends' formation with each other. Different characteristics were studied for the prepared modified RH polymeric biocomposites for future expected applications in biomedical fields and packaging. Accordingly, several PLA and PCL‐ modified RH biocomposites were toxic on proliferation of cancer cells with inhibitory effect against biofilm formation of bacterial cells in case of MSSA or Pseudomonas aeruginosa . Also, the prepared biocomposites recorded low water vapor and oxygen transmission rates (WVTR, OTR) which indicated their possible application as packaging materials for food at low moisture level or for dry food.

This multicentre randomized controlled clinical trial aims to investigate the regenerative effects of various thicknesses and types of barrier materials with and without bone grafting in a rabbit calvaria model. One hundred male rabbits were partitioned into two groups: one without bone graft (NB) and one with bone grafting (BG). The groups were further divided into five subgroups, n = 10 each: C (control); SC (0.3 mm single-layered collagen); DC (0.6 mm double-layered collagen); L1 (0.5 mm cortical collagenated bone barrier); and L2 (1.0 mm cortical collagenated bone barrier). In all experimental groups, each distinct type of barrier was applied following the creation of a 10 mm circular defect in the calvaria of each rabbit. After 24 weeks, the calvariae were examined by histologic and histomorphometric analyses. The utilization of cortical bone barriers increased bone formation in all experimental groups. For Group NB, the histological score significantly differed among subgroups (p < 0.001). L1 and L2 subgroups had more favorable histological scores than the control groups (p < 0.001). Furthermore, the L2 subgroup had a higher histological score than the SC subgroup (p < 0.001). In Group BG, histological score significantly differed among subgroups (p < 0.001). DC, L1, and L2 subgroups had higher histological scores than the controls (p < 0.02), (p < 0.001), and (p < 0.001), respectively. The L2 subgroup had a higher histological score than the SC subgroup (p < 0.01). The BG group had significantly higher histological scores overall compared to the NB group based on barriers (p < 0.05). Within the limits of this model, the 1.0 mm cortical lamina barrier demonstrated the most favorable regenerative performance, consistently achieving higher histologic scores and more advanced tissue maturation than thinner cortical lamina or collagen membranes. These findings indicate that barrier architecture, particularly thickness and mechanical stability, plays an important role in promoting predictable bone regeneration.

Background: Periapical pathosis in periapical radiographs must be properly diagnosed for the success of endodontic treatment but is often muddled by 2D imaging limitations and subjective interpretation. Artificial intelligence (AI) offers a solution, but whether the diagnostic granularity of AI versus human clinicians in everyday clinical practice has been adequately explored remains to be addressed. The purpose of this study was to evaluate the diagnostic accuracy of ChatGPT-5 in detecting periapical radiographic abnormalities compared with the three-expert consensus reference standard. Methods: In this diagnostic accuracy retrospective study, 270 periapical radiographs were independently read by a large language model (ChatGPT-5) and a three-board-certified oral radiologist consensus. The AI was given a standardized prompt to label radiographic features, like the presence of periapical radiolucency, border, shape, and integrity of lamina dura. Diagnostic accuracy, agreement (Cohen's κ), and predictors of correct AI classification were compared with the expert consensus reference standard. Results: ChatGPT-5 demonstrated high sensitivity (87.5%) but low specificity (12.5%), resulting in an overall diagnostic accuracy of 50.0%. This performance profile reflects a tendency toward over-identification of pathology, with the model classifying 87.5% of radiographs as abnormal compared with 50.0% by expert consensus. Agreement was almost perfect for anatomical localization (arch, κ = 0.857) but poor for binary abnormality detection (κ = 0.000). For morphological descriptors, statistically significant disagreement was observed for lesion border characterization (κ = 0.127; p < 0.001), whereas lesion shape demonstrated only descriptive divergence without reaching statistical significance (κ = 0.359). Root resorption assessment also differed significantly between evaluators (p = 0.046). Regression analysis showed that well-defined corticated borders (OR = 60.25, p < 0.001) and first molar-associated lesions (OR = 32.55, p < 0.001) were significant predictors of correct AI classification. Conclusions: This study demonstrates that while ChatGPT-5 Vision can visually interpret periapical radiographs with high sensitivity, limited specificity and inconsistent morphological feature characterization restrict its reliability for independent clinical diagnosis. The AI system tends to over-diagnose systematically and categorizes lesions more structurally and defined compared to dental experts. AI has the potential for being optimized as a sensitive first-screening test, but its findings must be validated by dental professionals to avoid false positives and ensure proper characterization.

Environmental Impact Assessment (EIA) plays a critical role in ensuring sustainable development by identifying and mitigating the adverse effects of major construction projects. However, current EIA practices, especially in developing countries, often lack a systematic approach to identifying risks and evaluating and selecting mitigation strategies, leading to suboptimal environmental protection. This is coupled with a lack of data, inconsistencies in the quality of reports, and a low compliance rate with environmental management plans, which characterize mega infrastructure projects. In this regard, the research aims to develop a structured digital framework for identifying, assessing, and prioritizing mitigation strategies in EIAs for large-scale construction projects. The research method combines a review of the existing literature and case studies of past EIAs to gather insights into common mitigation measures and their effectiveness. Based on this, key environmental impacts are analyzed and potential mitigation strategies are categorized. A multi-criteria decision analysis (MCDA) framework is proposed to evaluate mitigation strategies using the Analytical Hierarchy Process (AHP) to rank and select optimal mitigation approaches. Eventually, the research develops an EIA Risk and Mitigation Management framework (EIA-RMMS), which is a digital system developed to facilitate EIA implementation, indicating standardized risk types and potential mitigation measures. The EIA-RMMS links to project management and enables integration with Artificial Intelligence and Machine Learning. The proposed framework is applied to three case study metro line projects in Egypt to prove its effectiveness in data analysis, decision-support and report structuring. The findings are valuable for EIA practitioners and project developers seeking to align infrastructure development with ecological and social sustainability goals.