Princess Nourah bint Abdulrahman University

Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA

Multiple signaling pathways have been implicated in the pathogenesis of ulcerative colitis (UC), including Sphingosine Kinase 1 (SPHK)/Sphingosine‐1‐Phosphate (S1P), AMP‐activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR)/NLR family pyrin domain‐containing 3 (NLRP3), zonula occludens‐1 (ZO‐1), and signal transducer and activator of transcription 3 (STAT3). We aimed to investigate the Colo protective and anti‐ulcerative effects of pentoxifylline (PTX) in a rat model of UC. Colitis was induced by intracolonic administration of 2 mL of 3% (v/v) acetic acid (AA). Thirty‐five rats were randomly assigned to five groups (n = 7 each): normal control, colitis, mesalamine, PTX, and a combination of PTX plus mesalamine. Disease activity was assessed using the disease activity index, colon weight and length measurements, histological examination, and immunohistochemical detection of caspase‐3. Colonic tissue homogenates were analyzed for interleukin‐6 (IL‐6), S1P, SPHK, mTOR, heme oxygenase‐1 (HO‐1), nuclear factor erythroid 2–related factor 2 (Nrf2), AMPK, and STAT3 levels. Gene expression of ZO‐1 and NLRP3 was also evaluated. Intracolonic AA induced marked functional, biochemical, and inflammatory damage to colonic tissue. Treatment with PTX, mesalamine, or their combination significantly attenuated these effects. Specifically, all treatments reduced levels of IL‐6, S1P, SPHK, mTOR, STAT3, NLRP3, and caspase‐3, while increasing levels of ZO‐1, HO‐1, Nrf2, and AMPK. The combination treatment group exhibited near‐complete restoration of normal colonic architecture, characterized by intact crypt morphology and minimal fibrosis in the lamina propria. PTX attenuated inflammation, apoptosis, and oxidative stress in colitis, supporting its potential as an adjuvant therapy in UC management.

The escalating impacts of climate change coupled with rapid population growth, and unsustainable consumption patterns, have created a global water crisis of unprecedented proportions. The availability of clean water is a fundamental human right, yet billions of people worldwide lack access to safe drinking water and basic sanitation. This necessitates the development of advanced wastewater treatment systems capable of producing high-quality effluent. In this study, we successfully synthesized highly efficient photocatalysts, specifically ZnS, bulk-g-C3N4, and bulk-g-C3N4/ZnS composites, using microwave-assisted technique. These materials were designed to serve as effective photocatalysts driven by visible light for environmental applications. The synthesized materials included ZnS, bulk-g-C3N4, and their composites at a 1:1, 1:2, and 1:3 weight ratios. Comprehensive characterization of the prepared composites using various techniques, including XRD, UV–Vis, FTIR, FESEM, EDS, HRTEM, and XPS was conducted. The cubic zinc blend structure and layered stacking arrangement for the ZnS, and bulk-g-C3N4 compounds were revealed by the composite material's XRD analysis; the sizes of ZnS, pure bulk-g-C3N4, and their composites with various ratios of ZnS/bulk-g-C3N4 (1:1, 1:2, 1:3) were 2.72 nm, 5.62 nm, 3.02 nm, 2.74 nm, and 2.69 nm, respectively. FTIR analysis revealed that the stretching vibrations of C = N and C≡N bonds were located inside certain spectrum regions. Peaks in the 1600–1800 cm⁻¹ range were seen for C = N bonds, while peaks in the 2350 cm⁻¹ range were observed for C≡N bonds. Moreover, the noticeable peaks observed between 1300 and 1570 cm⁻¹ are caused by the aromatic C-N stretching vibrations. The FESEM analysis showed that ZnS/bulk-g-C3N4 composites had a sheet-like nanohybrid morphology, whereas pure ZnS and bulk-g-C3N4 appeared as nanosheets and nanohybrids, respectively. The Zn, S, C, and N elements found in the produced materials were identified by EDS analysis, which also confirmed the lack of impurities. The HRTEM image of the ZnS/bulk-g-C3N4 (1:1) composite was used to quantify the interatomic distance between the ZnS atoms. The cubic zinc blend structure of ZnS was discovered to have a (111) plane that corresponds to a 0.31 nm lattice spacing. XPS revealed that Zn, S, C, and N were in the Zn 2p, S 2p, C 1 s, and N 1 s oxidation states. The photocatalytic performance of the different composites (e.g., 30 mg) was evaluated for the degradation of malachite green dye (e.g., 3 × 10⁵ M) in aqueous solution, utilizing a custom-built photocatalytic reactor equipped with a 250W halogen lamp under continuous magnetic stirring for 120 min. The findings indicated that the g-C3N4/ZnS composite photocatalysts exhibited superior degradation efficiency compared to the individual components, showing a degradation rates of 2% and 28% for pure bulk-g-C3N4 and ZnS, respectively. Remarkably, under visible light irradiation, the g-C3N4/ZnS composite with a 1:3 weight ratio demonstrated the highest photocatalytic efficiency, achieving 33.50%. The 1:1, and 1:2 weight ratios exhibited photocatalytic efficiencies of 16.79%, and 25.57%, respectively. Ultimately, these findings indicate that ZnS/bulk-g-C3N4 (1:3) can be regarded as an exceptionally effective photocatalyst for the removal and degradation of malachite green dye from wastewater.

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that causes a substantial decline in cognitive functions and affects memory, thinking abilities, and daily behavior. The most prominent hallmark of AD pathogenesis is the formation of amyloid-β plaques, among other associated pathways such as neurofibrillary tangles, mitochondrial dysfunction, neuroinflammation, and oxidative stress. Butyrylcholinesterase (BuChE), an acetylcholine-degrading enzyme, plays a critical role in the progression of Alzheimer’s disease, particularly through its involvement in amyloid-β plaque formation. Thus, the inhibition of BuChE is considered a valuable therapeutic strategy for the management of AD. The present study aimed to identify potential bioactive chemicals from naturally occurring dietary compounds that could improve neurocognitive function and appear as a viable treatment for AD by inhibiting the function of BuChE. A small library of 44 natural dietary chemicals from a variety of dietary plants was subjected to comprehensive in silico studies, including molecular docking, molecular mechanics generalized born surface area (MM-GBSA) calculations, pharmacokinetics assessments, toxicity profiles, molecular dynamics (MD) simulation, and density functional theory (DFT) analysis. These studies revealed that CID 129886986 and CID 115269 showed stronger binding affinities with drug-likeness and no toxicity than the FDA-approved standard drug, Donepezil. Additionally, they exhibited strong structural stability with fewer fluctuations throughout the simulation, making them promising candidates for Alzheimer’s disease treatment. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-98092-y.

This study presents a novel approach to enhancing the performance of CsPbIBr2 perovskite solar cells by incorporating 10% CsPbI3 via spray coating. X-ray diffraction (XRD) confirms the α-cubic crystal structure, with improved crystallinity and increased crystallite size from 33.4 nm (pure) to 39.9 nm (modified), reducing dislocation line density (8.97 × 10¹⁴ to 6.28 × 10¹⁴ m⁻²). Optical analysis shows a bandgap reduction from 2.13 eV to 2.04 eV, enhancing light absorption and charge transport. Dielectric properties also improve, with the real dielectric constant increasing from 11.74 to 11.93. The modified perovskite film exhibits stronger PL intensity than the pure film, indicating reduced non-radiative recombination. Electrochemical impedance spectroscopy (EIS) indicates a significant reduction in charge transfer resistance (Rct) from 103.27 Ω to 29.61 Ω, with increased recombination resistance (Rrec) from 5597.13 Ω to 5877.44 Ω, leading to superior charge transport. The modified perovskite solar cell exhibits superior performance, achieving an increased short-circuit current density (10.73 to 12.92 mA/cm²) and power conversion efficiency (10.48% to 12.91%) as confirmed by current density voltage (JV) measurement. Electrochemical impedance spectroscopy reveals reduced charge transfer resistance, improving electron mobility and suppressing recombination. These advancements highlight the potential of modified perovskites in high-efficiency photovoltaics. Graphical Abstract With the incorporation of CsPbI3, the modified perovskite-based device showed the PCE upto 12.91%.

This work presents the synthesis of a chitosan-based nanocomposite of crosslinked chitosan-citrate/SnO2 nanoparticles (CTN-CT/SnO2) for methyl blue (MB) dye removal from aqueous solutions. Box-Behnken design (BBD) was implemented to examine the impact of three variables on the adsorption of MB dye: A: CTN-CT/SnO2 dose (0.02–0.08 g), B: pH (4–10), and C: time (10–30) min. The BET surface area of the CTN-CT/SnO2 nanocomposite was determined to be 9.90 m²/g. Furthermore, the mean pore diameter was 7.05 nm, and the total pore volume was measured to be 0.0174 cm³/g. The CTN-CT/SnO2 nanocomposite demonstrates predominantly polycrystalline properties, as evidenced by its average crystallite size of 23.76 nm. Kinetic modeling of MB dye adsorption was conducted using pseudo-first-order, pseudo-second-order, and intra-particle diffusion models. The results demonstrate that the pseudo-first-order kinetic model best describes the MB adsorption by CTN-CT/SnO2. Adsorption isotherm models, including Langmuir, Dubinin–Radushkevich, Freundlich, and Temkin, were applied to understand the MB adsorption behavior. The Freundlich model exhibited the best fit (R² = 0.98), suggesting a multilayer adsorption process. Thermodynamic analysis revealed negative Gibbs free energy values (ΔG° = − 8.137 to − 12.587 kJ/mol), indicating the spontaneity of the adsorption. Additionally, positive values for enthalpy (ΔH° = 36.086 kJ/mol) and entropy (ΔS° = 0.1483 kJ/molK) suggest that the process is endothermic and accompanied by an increase in disorder at the interface. The optimal conditions for maximal MB elimination (96.53%) were determined by the BBD model findings, which identified a pH of 9, a CTN-CT/SnO2 dose of 0.045 g, and a contact duration of 27.8 min. The maximal absorption capacity of the CTN-CT/SnO2 nanocomposite at 25 °C for the MB dye was 511.92 mg/g. The hydrogen bonding, electrostatic interaction, n–π interaction, and Yoshida H-bonding were postulated as the mechanisms behind the adsorption of MB dye onto the CTN-CT/SnO2 nanocomposite. The work presents a highly efficient CTN-CT/SnO2 nanocomposite as a potential adsorbent for effectively eliminating organic dye from water-based solutions.

Copper-based anti-perovskites Cu3HX (X = S, Se, Te) are investigated using density functional theory (DFT) within the full-potential linearized augmented plane wave (FP-LAPW) method. Structural properties are computed using the PBE-GGA exchange potential, while the Tran–Blaha modified Becke–Johnson (TB-mBJ) potential is employed for accurate band structure and density of states calculations. These materials crystallize in a cubic phase (Pm-3m) and exhibit indirect bandgaps of 1.36 eV (Cu3HS), 1.71 eV (Cu3HSe), and 1.75 eV (Cu3HTe) along X→R. Small charge carrier effective masses enhance optical conductivity, with Cu3HS having the lowest exciton binding energy (0.037 eV). Mechanical analysis reveals that Cu3HSe has the highest Young’s, shear, and bulk moduli, while Cu3HX (X = Se, Te) exhibit ductile behavior. Optical properties, including dielectric constants, polarization, and absorption spectra, are analyzed in detail. These findings provide fundamental insights into the physical properties of Cu3HX anti-perovskites.

The growing threat of cyberattacks is a severe concern to governments, military organizations, and industries, especially with the increasing use of Internet of Things (IoT) devices. To tackle this issue, researchers are working on ways to predict and prevent these attacks by studying how malware spreads. In this study, we use a discrete-time approach to better model how cyberattacks spread across IoT networks. We also focus on the role of firewalls, developing a strategy to optimize their effectiveness in slowing down the spread of malware. Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. These insights are supported by numerical simulations based on real-world data.

The theoretical model developed for electromagnetic wave propagation at uniform plasma-lithium fluoride-uniform plasma waveguide structure has yielded insightful results, demonstrating the significant influence of collision frequency, plasma frequency, and waveguide thickness on the propagation characteristics. The normalized phase constant, attenuation, propagation length, and penetration depth exhibit clear dependencies on these parameters, which are crucial for optimizing waveguide design. These results offer promising applications in sensing systems, integrated circuits, and subwavelength optics in THz frequency regimes.

Convolutional neural networks (CNNs), renowned for their efficiency in image analysis, have revolutionized pattern and structure recognition in visual data. Despite their success in image-based applications, CNNs face challenges when applied to tabular data due to the lack of inherent spatial relationships among features. This weakness can be overcome if the original tabular data is expanded to create an enhanced image that exhibits pseudo-spatial relationships. This paper introduces an original approach that transforms tabular data into a format suitable for CNN processing. The Novel Algorithm for Convolving Tabular Data (NCTD) applies mathematical transformations including rotation translation and reflection, to simulate spatial relationships within the data, thereby constructing a data structure analogous to a 2D synthetic image. This transformation enables CNNs to process tabular data efficiently by leveraging automated feature extraction and enhanced pattern recognition. The NCTD algorithm was extensively evaluated and compared with traditional machine learning algorithms and existing methods on ten benchmark datasets. The results showed that NCTD consistently surpassed the majority of competing algorithms in nine out of ten datasets, indicating its potential as a robust tool for extending CNN applicability beyond conventional image-based domains, particularly in complex classification and prediction. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-01568-0.

This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress and heart rate fluctuations. In particular, it is especially helpful for managing cardiovascular disease, tracking stress, improving athletic performance, and providing health care. The device was tested with several machine learning models, such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and Stochastic Gradient Descent (SGD) to identify irregular heart rhythms. Using the SVM model, the system detects problems with an average accuracy of 98%. In the future, MyWear—designed as a wearable T-shirt—will seamlessly integrate with mobile applications for real-time data visualization, enhancing patient outcomes and fostering greater user engagement.

In the digital age, images permeate every facet of our lives, often carrying critical information for organizations, institutions, and even nation-states. Ensuring their security against unauthorized access is paramount. This research introduces a novel image encryption algorithm designed to safeguard the integrity and confidentiality of sensitive gray-scale digital images. The algorithm leverages the theory of axis-aligned bounding boxes, translating the input image into a 3D representation. Within this 3D space, two identically sized boxes are generated and assessed for overlap. If the boxes do not overlap, the pixels within them are swapped. This pixel-swapping process, guided by random numbers generated from a 5D multi-wing hyper-chaotic map, is repeated numerous times to infuse confusion into the image. To further enhance security, diffusion effects are introduced by performing an XoR operation between the confused image and random numbers provided by the piece-wise linear chaotic map. This study employs private key cryptography and utilizes four gray-scale images to validate the feasibility and effectiveness of the proposed method. Simulation has been carried out in the Pythonic ecosystem. So, the algorithms presented in this study are designed to closely resemble Python code. Comprehensive validation metrics attest to the robustness of the cipher, achieving an information entropy of 7.99985 and a computational speed of 0.3987 seconds. These results underscore the potential of this encryption approach for practical, real-world applications. These applications span military, diplomacy & government, commerce, showbiz, industry, and social life etc.

Protecting river banks from turbulent flow is crucial for sustainable development. This study aims to evaluate the performance of submerged hockey groynes on river flow characteristics. This study employs numerical simulations were conducted using ANSYS Fluent, a computational fluid dynamics (CFD) software, to examine flow patterns, mean streamlines, mean velocity profiles, bed shear stresses, and vortex kinetic energy around the groynes. The simulations utilized a laboratory flume with a hockey groyne model at three orientation angles (60°, 90°, and 120°) and three submergence ratios (75, 100, and 125%) at varying discharges (0.0057, 0.0087, and 0.0119 m³/sec). This research contributes to the understanding of submerged hockey groynes' effectiveness in riverbank protection, highlighting specific configurations that align with field requirements and offering a basis for future studies on sustainable river management strategies. The findings suggest that a submergence ratio between 75 and 100% optimizes flow characteristics, and an orientation angle of 90° provides the most effective configuration for reducing shear stress and enhancing flow stability. The validation results demonstrated that the simulations effectively modeled the flow dynamics around the groynes. A single hockey groyne exhibited a minimum scour depth, with maximum shear stresses significantly lower than those observed for a series of elliptic groynes. A direct correlation was found between bed shear stress and maximum scour across all submergence ratios and orientation angles. While the study provides valuable insights into groyne performance, the laboratory conditions may not fully replicate real-world scenarios, which could affect the generalizability of the results.

Drought is a critical abiotic stress significantly reducing global wheat production, especially under climate fluctuations. Investigating wheat genetic variability using physiological and agronomic characteristics is essential for advancing breeding to enhance drought resilience and ensure sustainable production in light of global population growth. The genetic diversity and associations among traits of fourteen diverse genotypes of bread wheat in drought-stressed and well-watered conditions were studied, focusing on physiological and agronomic responses. Significant variations were detected among irrigation regimes, genotypes, and their interactions for all assessed characteristics. Drought stress substantially declined chlorophyll a (Chl a ) and b (Chl b ), net photosynthetic rate (NPR), transpiration rate (Tr), stomatal conductance (gs), membrane stability index (MSI), relative water content (RWC), plant height (PH), yield-related attributes, and grain yield. Conversely, it significantly increased malondialdehyde content, proline content (ProC), and activities of antioxidant enzymes, including catalase (CAT), ascorbate peroxidase (APX) and superoxide dismutase (SOD). The genotypes, G3 (L-1117), G8 (L-120), and G12 (L-1142) exhibited superior drought tolerance, maintaining high photosynthetic efficiency, RWC, antioxidant enzyme activity, and grain yield. Under drought conditions, these genotypes achieved grain yields of 6.32 t/ha (G8), 5.97 t/ha (G12), and 5.84 t/ha (G3), significantly surpassing the other genotypes. Genotypic classification and drought tolerance indices confirmed the superiority of G3, G8, and G12 as drought-resilient candidates, while G2, G5, G7, and G14 exhibited lower adaptability. Genotypic stability analysis (additive main effects and multiplicative interaction (AMMI) and ranking biplot) indicated that G3, G8, G6, and G12 were highly stable across diverse environments, making them promising candidates for wheat breeding programs. Agronomic traits such as PH, number of grains per spike (NGPS), and thousand kernel weight (TKW) were positively associated with drought tolerance. Furthermore, the multivariate analyses, including principal component analysis (PCA), correlation, and path analysis, highlighted the significance of RWC, MSI, chlorophyll content, and antioxidant enzymes in sustaining yield under drought stress. Broad-sense heritability estimates were high for key drought-related traits, particularly APX, SOD, and NGPS, indicating strong genetic potential for selection. These findings indicated the importance of integrating physiological and biochemical markers into breeding programs to develop high-yielding drought-tolerant wheat varieties, contributing to sustainable wheat production under water-limited conditions.

Interferenceless perfect absorption holds significant promise for applications in photodetection, photovoltaics, and medical diagnostics. In this study, an effective composite metamaterial with nanoscale thickness, composed of hexagonal Boron nitride (hBN) and lithium fluoride (LiF) layers is presented. Using effective medium approximation theory, the proposed design achieves broadband interferenceless perfect absorption, independent of polarization, either transverse magnetic or transverse electric. The structure employs a noninterferometric approach, enabling near‐zero reflectance over a wide incident angle (θi=0°–80°\left(\theta\right)_{i}=0 \circ - 80 \circ). Notably, by adjusting the relative layer thicknesses, the hBN–LiF composite structure offers substantial control over its absorption characteristics. The absorption frequency can be blue‐shifted by increasing the hBN thickness or red‐shifted by increasing the LiF thickness. This flexible tunability makes the structure an ideal candidate for photodetection, infrared sensing, and other light‐manipulation technologies.

Carbon dioxide can be efficiently captured from post-combustion flue gases via membrane technology to mitigate global warming issues. Efficient carbon capture composite membranes were concocted by impregnating titania nanoparticles into polyethersulfone matrix by opting phase inversion and solution casting methods. Morphological, mechanical, structural and thermal characteristics of prepared membranes were thoroughly analyzed via various characterization techniques. Carbon capture performance of synthesized membranes reported in terms of CO2 permeance and CO2/N2 permselectivity was enhanced by titania impregnation. In contrast to pristine polyethersulfone membrane, doping of titania nanoparticles in varying loadings significantly enhanced carbon separation efficacy of composite membranes. Optimum titania loading of 5% in pristine polymer resulted in a significant enhancement of 62% in CO2 permeability and 77% in CO2/N2 selectivity, indicating substantial improvement in gas separation performance of synthesized membranes. Experimentally obtained permeation results of different gases through prepared mixed-matrix membranes containing varied amounts of titania nanofiller were found satisfactory when compared to ideal scenario by applying different theoretical models based on two- and three-phase morphological systems.

Electrochemical water splitting is an effective strategy that can be utilized to obtain energy from sustainable sources. Still, the substantial overpotential necessary for sluggish OER (oxygen evolution reaction) hinders extensive application. Herein, we synthesized CeO2@PANI hybrid as an electrocatalyst for OER via the hydrothermal procedure. The synthesized electrocatalyst exhibited superior OER efficacy than pure CeO2. The CeO2@PANI hybrid was thoroughly studied using several analytical techniques including SEM (scanning electron microscopy), TGA (thermogravimetric analysis), BET (Brunauer–Emmett–Teller) and XRD (X-ray diffraction). These studies show that hybrid material has good crystallinity, particle like morphology and cubic framework, with a significant surface area. The electrocatalytic efficacy of the CeO₂@PANI hybrid was assessed in a basic solution (1.00 M KOH), demonstrating a reduced overpotential (η) of 226 mV, a Tafel value (36 mV/dec) at a current density (j) of 10 mA/cm² and a minimal charge transfer resistance (Rct) of 0.8 Ω. The composite also displayed 30 hours of durability as evaluated by CA (chronoamperometry). The produced hybrid’s high catalytic efficiency can be associated with CeO2 dispersed on the surface of PANI, which enhances electronic conduction. The hybrid of CeO2 and PANI outcomes in higher surface area, more active regions, less resistivity along with exceptional durability that contribute to the increased efficiency for OER procedure. Graphical Abstract

This paper presents a theoretical investigation of a functionally graded hydro-poroelastic semiconductor material subjected to photo-thermoelasticity theory. The material properties, including thermal conductivity, elasticity, and porosity, are assumed to vary spatially following a functionally graded distribution. A one-dimensional problem is formulated to analyze the coupled interactions between the hydro-semiconductor medium’s thermal, mechanical, and electronic transport phenomena. The governing equations incorporate hydrodynamic effects, poroelasticity, and semiconductor carrier transport under the influence of thermal and photonic excitation. The Laplace transform technique is employed to obtain analytical solutions in main physical fields. Numerical results are derived using inverse Laplace transformation, and the effects of functionally graded parameters on wave propagation and heat transport are examined. Graphical analysis illustrates the impact of grading index and porosity on the material’s response. The results highlight the significance of functional grading in tailoring the behavior of hydro-poroelastic semiconductors for advanced technological applications, including optoelectronic devices, photodetectors, and thermal management systems.