Yeungnam University

The high bioactivity and biocompatibility of hydroxyapatite (HAP) make it a useful bone graft material for bone tissue engineering. However, the development superior osteoconductive and osteoinductive materials for bone regeneration remains a challenge. To overcome these constraints, Cu‐doped hydroxyapatite (HAP(Cu)) from waste eggshells has been produced for bone tissue engineering. The materials produced were characterized using Fourier transform infrared spectroscopy, x‐ray diffraction, and photoelectron spectroscopy. The scanning microscopy images revealed that the developed HAP was a rod‐like crystalline structure with a typical 80–150 nm diameter. Energy‐dispersive x‐ray spectroscopy showed that the generated HAP was mostly composed of calcium, oxygen, and phosphorus. The Ca/P molar ratios in eggshell‐derived and copper‐doped HAP were 1.61 and 1.67, respectively, similar to the commercially available HAP ratio (1.67). The WST‐8 assay was used to assess the biocompatibility of HAPs with hBMSCs. HAP(Cu) in the media significantly altered the cytotoxicity of biocompatible HAP(Cu). The osteogenic potential of HAP(Cu) was demonstrated by greater mineralization than that of pure HAP or the control. HAP(Cu) showed higher osteogenic gene expression than pure HAP and the control, indicating its stronger osteogenic potential. Furthermore, we assessed the effects of sample‐treated macrophage‐derived conditioned medium (CM) on hBMSCs' osteogenesis. CM‐treated HAP(Cu) demonstrated a significantly higher osteogenic potential vis‐à‐vis pure HAP(Cu). These findings revealed that HAP(Cu) with CM significantly improved osteogenesis in hBMSCs and can be explored as a bone graft in bone tissue engineering.

The release of toxic contaminants from industries has adverse effects on environment and the living organisms. Therefore, in this research we studied a dual-purpose material, Zn-doped NiO, which could efficiently serve as a triethylamine (TEA) gas sensor, as well as photodegrade rhodamine B. Nanoparticles of nickel oxide (NiO) with zinc (Zn) incorporation were produced using the solvothermal technique. The XRD investigation revealed that the crystallite size ranged from 16 to 24 nm. Studies using FESEM and TEM have shown that the ZNO contains agglomerated, quasi-spherical particles. It was found that the optical band gap energy ranged from 3.46 to 3.67 eV. ZNO nanoparticles had the maximum gas sensing efficiency for TEA at an optimal operating temperature of 260 °C, with a 9% efficiency for an initial TEA gas concentration of 100 ppm. The ZNO nanoparticles with 5% Zn, exhibited 91% efficiency when subjected to 10 ppm of Rhodamine B degradation. Thus, ZNO exhibits potential as a versatile material that can operate as an effective gas sensor for TEA and as a photocatalyst for the degradation of Rhodamine B.

Trees are constantly exposed to environmental stresses due to climatic variations over time across geographical places. Trees have evolved various molecular processes to govern growth and development under challenging conditions, reducing the impact on their growth and development. Over time, stress measurement tools and software advanced dramatically, increasing our understanding of how woody plants respond physiologically to various stresses. However, it is only recently that advances in biochemical and next-generation sequencing approaches enabled us to investigate the complicated molecular networks that underpin the stress physiology in woody trees. This chapter looks at recent breakthroughs in understanding the molecular mechanisms underlying how trees adapt to different abiotic stresses. We further focus on functional, hormonal cascades, epigenetic, and small RNA-mediated responses in trees against abiotic stresses.

This study investigates the impact of industrial wastewater from leather, household, and marble sources on the growth, physiological traits, and biochemical responses of Momordica charantia (bitter melon). Industrial activities often lead to the release of contaminated effluents, which can significantly affect plant health and agricultural productivity. Water analysis revealed that leather effluent contained high concentrations of heavy metals, including cadmium (2.67 mg/L), lead (1.95 mg/L), and nickel (1.02 mg/L), all of which exceeded the recommended safety limits for irrigation. Seed germination was significantly reduced, with only 45% germination in seeds irrigated with leather effluent, compared to 90% in the control group. Similarly, in plants treated with leather wastewater, shoot length, and root length were reduced by 38% and 42%. Chlorophyll content showed a marked decline, with chlorophyll “a” reduced by 25% and chlorophyll “b” by 30% in wastewater-treated plants, indicating impaired photosynthetic activity. Antioxidant enzyme activity, including catalase and superoxide dismutase, increased by up to 40%, reflecting a stress response to heavy metal toxicity. These findings highlight that industrial wastewater severely disrupts plant metabolic processes, leading to stunted growth and physiological stress. To safeguard crop productivity and food security, stringent wastewater treatment protocols must be implemented to mitigate environmental contamination. Future research should focus on developing advanced remediation techniques and sustainable wastewater management practices to reduce heavy metal toxicity and enhance soil health.

Green nanomaterials are increasingly used to improve plant growth and phytochemical traits. This study employed Eucalyptus globulus leaf extract, a medicinal plant, as a bio-reductant and capping agent to synthesize copper oxide nanoparticles (CuO-NPs), which were applied as seed primers for Lactuca sativa (lettuce), an annual species prized for its short germination time and rich bioactive compounds. Characterization of CuO-NPs using FTIR, XRD, SEM, and EDX confirmed their purity, crystalline structure, and an average particle size of 74.66 nm. The CuO-NPs were applied at concentrations of 0.01 mg/ml, 0.02 mg/ml, 0.03 mg/ml, and 0.04 mg/ml. At the highest concentration (0.04 mg/ml), significant reductions in physical growth parameters were observed, with plant length, height, and width measuring 7.85 cm, 5.50 cm, and 3.48 cm, respectively, compared to 13.70 cm, 11.52 cm, and 11.18 cm in control plants. Phytochemical analysis identified tannins, alkaloids, phytosterols, saponins, flavonoids, and glycosides in all methanolic extracts, while carotenoids were absent at higher concentrations (0.03 mg/ml and 0.04 mg/ml) due to phytotoxicity. FTIR analysis revealed a prominent peak at 858 cm⁻¹ at 0.01 mg/ml, indicating the presence of antioxidant-rich aromatic phenyl compounds. In conclusion, the study demonstrates that CuO-NPs synthesized using Eucalyptus globulus extract enhance phytochemical constituents at optimal concentrations but inhibit growth and reduce key phytochemicals at higher doses. Future research should optimize nanoparticle concentrations to minimize phytotoxicity while maximizing beneficial effects on plant growth and bioactive compounds.

The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

Biochar is a versatile material increasingly recognized for its efficacy in wastewater treatment and environmental remediation. Herein, phosphoric acid-activated soybean stover biochar (ASSB) was prepared via torrefaction under inert atmospheric conditions and evaluated for its adsorption capabilities towards pharmaceutical Norfloxacin (NFX) alongside its CO2 capture potential. Comprehensive characterization using FT-IR, XRD, SEM, EDX, TGA-DTA, and BET analysis revealed a substantial surface area of 298.82 m² g⁻¹ for ASSB. Batch adsorption studies demonstrated remarkable maximum adsorption capacities of 203.05 mg g⁻¹ for NFX. Response surface methodology (RSM) facilitated the optimization of operational constraints. Isotherm and kinetic analyses indicated that the adsorption of NFX confirmed the Langmuir model and pseudo-second-order kinetics, respectively, with thermodynamic assessments confirming the spontaneity of the adsorption process. Column studies further validated the efficiency of ASSB in large-scale applications. Clark model was found to be the best fit out of non-linear Thomas, Bohart–Adams, and Wolborska column modelling. Regeneration capacity was checked using a gamma irradiation dose of 6 kGy with 84% removal up to the fifth cycle. This study underscores the multifunctional utility of ASSB in pharmaceutical wastewater treatment and CO2 capture (49.96 cc g⁻¹ at 273 K), highlighting its promising prospects for sustainable environmental applications.

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

The aim of this work was to study the characteristics of composites fabricated from cellulose (CL) and chitosan (CS) blends that were reinforced with different tannic acid (TA) and gallic acid (GA) concentrations. The structure and antibacterial activity of CL/CS composite films were investigated with the addition of TA and GA. The composite films were subjected to mechanical, water contact angle (WCA), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Scanning electron microscopy (SEM), and antibacterial activity tests. The FTIR results and the uniform dense SEM images confirmed the interaction of TA and GA with the CL/CS blends. The water vapor transmission rate (WVTR) of films with 5 wt% TA and GA improved by 23.02 %. The tensile strength of the CCTA-3 film was 27.67 MPa, demonstrating higher tensile properties compared to films made from CL and CS blend film (13.20 MPa). The prepared films also showed increased resistance to moisture and water, as indicated by their higher water contact angle (WCA) values (59.43°). The antibacterial activity of the films was effective against food-borne bacteria such as S. aureus and E. coli due to the addition of TA and GA. The shelf-life of cherry tomatoes increased by approximately 15 days when covered in CCTA-3 instead of polyethylene film. Based on the results, CL/CS blend films containing TA could be beneficial for use in active food packaging.

Tissue engineering and regenerative medicine need biocompatible and functional scaffolds that promote cell survival and tissue regeneration. Blending polyurethane (PU) and polycaprolactone (PCL) nanofibers with bioactive ingredients such as almond oil may provide beneficial qualities. This work seeks to manufacture and describe PU-PCL nanofibers infused with almond oil at different concentrations (1%, 3%, and 5%) to evaluate their suitability as scaffolding materials. Electrospinning methods were used to fabricate nanofibers with a uniform 1:1 ratio of PU and PCL, integrating almond oil at designated percentages. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy verified the presence of polyurethane (PU), polycaprolactone (PCL), and almond oil in the nanofibers. The ATR-FTIR spectra clearly revealed absorption bands corresponding to the molecular structures of polyurethane (PU), polycaprolactone (PCL), and almond oil. The survivability of human dermal fibroblast (HDF-1) cells was evaluated over 72 h using MTT assays, demonstrating increased cell viability in nanofiber groups with 1%, 3%, and 5% almond oil relative to control groups. The 3% almond oil group exhibited a significant increase in cell survival, indicating enhanced biocompatibility. Statistical analysis, conducted using unpaired t-tests and one-way ANOVA, indicated that PU-PCL-almond oil nanofibers have notable biocompatibility and may function as useful scaffolding materials for tissue engineering.

Two modular systems were synthesized composed of triphenylamine (ZnTPAP) and pyrene (ZnPyP) covalently linked at meso position of the Zn(II) porphyrins. Both compounds behaved as energy transfer antenna and orthogonal units to enhance the electron donating ability of Zn(II) porphyrins. Detailed photophysical and aggregation studies reveal that an appreciable electronic interaction exists between peripheral units to the porphyrin π-system so that they behave like strong donor materials. The electrochemical and computational studies demonstrate delocalization of the frontier highest occupied molecular orbital (−5.08 eV) over the triphenylamine entities (ZnTPAP) in addition to the porphyrin macrocycle. Fluorescence experiments with ZnTPAP and ZnPyP in the presence of different nitro analytes at various concentrations show turn-off fluorescence behaviour and exhibit superior selectivity towards 2,4-dinitrophenol (DNP) with limit of detection (LOD) of ~ 2.3 and 9.2 ppm for ZnTPAP and ZnPyP. Photoinduced electron transfer process is involved in the static and dynamic fluorescence quenching process. A Stern–Volmer quenching association constant (Ksv) determination revealed that ZnTPAP is more sensitive than the ZnPyP. This is attributed to the strong donating behaviour of TPA units caused by intermolecular interaction through metal center and strong π–π interactions with nitro analytes. The present study provides new insights into the ability to tune the affinity and selectivity of porphyrin-based sensors utilising electronic factors associated with the central Zn(II) ion. Furthermore, a smartphone-interfaced portable fluorimetric method by recognising colour variations in RGB and the luminance (L) values facilitate sensitive and real-time sensing at low concentration levels will have a significant impact on development of a new class of chemosensors. Graphical Abstract

Floods are among the most destructive natural disasters, causing significant loss of life, property damage, and disruption to communities. The necessity for creative and practical flood monitoring and prevention technologies has been highlighted in recent years by the frequency and intensity of floods that are growing. Flood management uses cutting-edge technology and approaches, as well as, their potential to increase disaster resilience. This study reviews flood monitoring and prevention strategies from a data analytic perspective, particularly those involving the Internet of Things (IoT), and machine learning. The utilization of IoT, data analytics, and machine learning tools within cutting-edge solutions facilitates real-time data collection, predictive modeling, and informed decision-making. With the help of community involvement and potential catastrophe, resilience improved and safeguarded the people, and property damages in flood-prone areas. Techniques for flood monitoring are explored including remote sensing, IoT, ground-based solutions, machine learning, and early flood alert systems concerning their processes involving data acquisition, data integration, scalability, real-time monitoring, infrastructure, and accuracy. In addition, current challenges are these approaches are discussed and future research directions are outlined. Key findings indicate the integration of these technologies to enhance disaster resilience by providing real-time monitoring and early warning systems, hence drastically reducing the impact caused by floods. This paper presents flood monitoring techniques through remote sensing, IoT, ground-based solutions, machine learning, and early flood alert systems in such aspects as data acquisition, integration, scalability, real-time monitoring, infrastructure, and accuracy. These techniques, however, are all still bedeviled by challenges such as high implementation costs, maintenance difficulties, and the feature of a robust communication infrastructure. In this regard, research in the future will be directed to cost-effectiveness in solution designing, improving the accuracy of predictive models, and wider engagement involving the community in flood risk management.

In this paper, the adaptive intelligent control problem for a class of nonlinear switched stochastic cyber-physical systems (CPSs) with output constraint under deception attacks is studied. The output-dependent function is introduced to convert the system into an unconstrained system. In order to eliminate the effects caused by the attacks, the attack gains are introduced into the coordinate transformations and then the Nussbaum function technology is used to deal with the unknown attack gains. Based on the new coordinate transformations, the corresponding controllers are designed for each subsystem using the compromised states. Furthermore, a switching event-triggered mechanism (ETM) is proposed, which can not only resolve asynchronous switching problem without any strict restrictions, but also effectively save communication resources. Meanwhile, a segment constant variable is introduced into the ETM, which is helpful to find a strict positive lower bound for two consecutive triggering intervals to obtain the absence of Zeno behavior. It is shown from the Lyapunov stability theory that all signals in the closed-loop system are bounded in probability under arbitrary switching. Finally, the simulation results validate the effectiveness of the proposed method.

The observer-based stabilization of network-based interval type-2 (IT2) semi-Markov jump models is investigated, where a new control strategy based on redundant channels is proposed to overcome the adverse effects of packet loss. The IT2 fuzzy method is adopted to describe the nonlinear objects, which overcomes the uncertainty problem of the traditional T-S fuzzy model. An observer-based control strategy is adopted due to the difficulty of obtaining complete state information under a complicated network environment. The main novelty is that redundant channels are adopted to construct a suitable observer-based feedback control scheme, and the auxiliary variable is introduced to solve the matrix dimension problem to achieve better dynamic performance of IT2 fuzzy networked semi-Markov jump models, improve the stability and anti-interference ability of the system, and overcome the difficulty caused by potential packet loss. Based on the semi-Markov kernel, IT2 fuzzy, and the Lyapunov functions dependent on the elapsed time, sufficient criteria are constructed for the considered system to ensure the $\sigma$ -error mean-square stability. Furthermore, the expected solvability is constructed for an observer-based feedback controller under a linear matrix inequality framework. Ultimately, the tunnel circuit model verifies the proposed control method.

This paper investigates the problem of stability analysis for aperiodic sampled-data load frequency control system with multiple time-varying delays. A discrete-time model of the load frequency control system including both generation units and energy storage systems is constructed firstly. Then, the form of feedback interconnection composing of nominal linear system and uncertainty is bulit, where uncertain operators are found to describe the aperiodic sampled-data interval and time-varying delays. Next, a uniformly exponential stability criterion is developed utilizing integral quadratic constraint analysis. Based on the stability criterion, algorithms are presented to obtain more accurate delay margins and maximum allowable sampled-data intervals. Finally, case studies are conducted to demonstrate the validity and benefits of proposed stability criterion.

A novel redundant channels-based sliding mode control (SMC) strategy is proposed to overcome the adverse effects of packet dropouts during signal transmission of tidal stream turbine system. To deal with the random characteristics of water velocity, the tidal stream turbine model is modeled as semi-Markov jump system. Due to the change of environmental factors, the actual state information of tidal turbine system is usually difficult or impossible to obtain, and the output feedback method is adopted to estimate the state information. The novelty is that the output feedback method is adopted to design the sliding surface, combining with the redundant channels method to obtain the redundant channels-based SMC mechanism, which overcomes the influence of uncertain parameters and packet dropouts during the signal transmission. Based on the elapsed-time-dependent Lyapunov function and SMK framework, the σ-error mean-square stability is realized. Furthermore, a suitable SMC method is constructed to realize the quasi-sliding mode. A simulation example is given to verify the effectiveness of the design method.

A viable technology for the future wireless communication system to obtain extremely high information rates with improved coverage is the collaborative incorporation of an intelligent reflecting surface (IRS) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. An IRS provides a virtual line-of-sight (LoS) path to enhance the wireless system’s capacity. However, accurate channel state information is essential for the complete utilization of IRS and mmWave MIMO systems. Existing channel estimators based on orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL) entail large pilot overhead and matrix inversion. Therefore, these techniques offer low spectral efficiency and high computational complexity. To overcome the limitations of existing estimators, we propose an online variable step-size zero-attracting least mean square (VSS-ZALMS) based algorithm for IRS-assisted mmWave hybrid MIMO system channel estimation. Further, we derive analytical expressions for the range of step-size and regularization parameters to improve estimation accuracy and convergence rates. Moreover, we conduct an analysis of IRS location, spectral efficiency, complexity analysis, and pilot overhead requirements. Simulation results are then compared with OMP, SBL, and oracle least square for benchmarking. The results corroborate superiority of the proposed approach concerning accuracy, complexity, and robustness compared to the existing estimators.

This brief addresses the optimal control issue of a three-phase grid-connected voltage source inverter under electromagnetic interference. Aiming to reduce the computational burden brought by the phase-locked loop, grid voltage modulation is used to transform the nonlinear inverter system into a linear system. A novel auxiliary variable is proposed to design a linear quadratic regulator, and the controller can exponentially stabilize the system globally. The nonlinear observer is used to estimate the grid voltage, and the unscented Kalman filter is utilized to filter the output of the nonlinear observer and the current sampling data. The control scheme proposed in this brief can achieve satisfactory transient and steady-state performance under severe electromagnetic interference. The simulation validates the theoretical analysis and the effectiveness of the proposed control scheme.

In this paper, an adaptive sequential convex programming is presented for rapid ascent trajectory optimization of Mars ascent vehicle (MAV) with multiple flight phases. A modified Chebyshev-Picard iteration algorithm with error feedback integral quasi-linearization in the form of a second-order system is used to deal with the dynamic hard constraints in the optimal control problem, so as to improve the convergence performance of the sequential convex programming (SCP). Then, the adaptive trust-region strategy and the adaptive node number strategy are combined to further improve the computational speed in the case of undesirable initial guess. Numerical simulations of the Mars ascent problem are given to show that this method has superior performance in terms of convergence and computational time compared to existing optimization methods.