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Bread wheat ( Triticum aestivum L. ) production faces increasing instability under rainfed and semi-arid conditions, necessitating the development of cultivars adapted to these environments. However, the genetic basis of physiological growth dynamics and their connection to yield under water-limited conditions remains poorly understood, limiting breeding progress. To address this, six genetically diverse parental lines were crossed in a full diallel mating design (Method I, Model I) and evaluated with their F₁ hybrids using a randomized complete block design with three replications across seven phenological stages (Zadok scale Z-20 to Z-92). Analysis of variance showed highly significant ( P  < 0.01) genetic variation among genotypes for all growth, yield, and phenological traits. Among parental lines, Pakistan-2013 displayed superior performance, with early heading (110.5 days), longer grain-filling duration (46.1 days), the highest thousand-grain weight (47.3 g), and the maximum grain yield (27.6 g plant⁻¹). In contrast, NR-514 was the weakest parent, with the lowest thousand-grain weight (32.0 g) and grain yield (12.2 g plant⁻¹). Among direct crosses, Pakistan-2013 × NR-499 showed superiority, with the maximum number of tillers (16.1 plant⁻¹), the highest grains per spike (65.1), the highest thousand-grain weight (51.2 g), and the highest grain yield (31.5 g plant⁻¹). Reciprocal effects were most notable in NR-499 × Pakistan-2013, with significant positive RCA effects for thousand-grain weight (4.933**) and grain yield (4.929**), indicating substantial maternal contributions. General combining ability (GCA) effects identified Pakistan-2013 as the best general combiner for yield-related traits (GCA = 3.785** for TGW; 3.556** for GYPP), suggesting dominant additive gene action. Specific combining ability (SCA) effects were highest in Borlaug-2016 × NR-516 for thousand-grain weight (5.626**) and grain yield (7.471**). Additionally, Pakistan-2013 × NR-499 showed significant positive SCA for grain yield (2.835*), indicating strong non-additive gene action (dominance/epistasis) and heterotic potential in these elite hybrids. Stage-dependent gene action was observed for growth traits, with additive effects prevailing during early stages and non-additive effects influencing reproductive stages. Baker’s ratio (> 0.70) and GCA: SCA ratios (> 1) confirmed the predominance of additive gene action for most traits, supporting early-generation selection. These findings establish a genetic framework that links growth dynamics, phenology, and yield components, offering practical strategies for developing climate-adapted wheat ideotypes for rainfed ecosystems. Future research should focus on multi-environment trials to confirm the stability of combining ability and utilize molecular marker-assisted selection, thereby speeding up the introgression of favorable alleles into breeding programs.

In this paper, a generalized nontriangular normal form is presented to facilitate designing a recursive integral backstepping control for the class of underactuated nonholonomic systems, i.e., wheeled mobile robots (WMRs) that perform posture stabilization and trajectory tracking in environments without obstacles. Based on the differential geometry theory, we develop a multiple input multiple output (MINO) generalization of normal form using the input-output feedback linearization technique. Then, the change of variables (diffeomorphism) transform the state-space model of WMR, incorporating both kinematic and dynamic models into nontriangular normal form. As a result, the system dynamics can be represented as internal and external dynamics. The nonlinear internal dynamics of WMR pose serious challenges to design a suitable controller due to its internal dynamics being not minimum phase and non-strict feedback form structure. The proposed backstepping controller is designed in two steps. First, a standard integral backstepping controller is designed to stabilize the robot’s orientation angle. Then, a recursive integral backstepping control technique is applied to achieve asymptotic convergence of position error to zero. Hence, both asymptotic posture stabilization and trajectory tracking are achieved in semi-global regions, except the nonzero initial condition of the orientation angle. The asymptotic stability of the entire closed-loop system is shown using the Lyapunov criteria.

This paper presents the mathematical model and control of a voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI considered in this paper is six switches three-phase Pulse Width Modulated (PWM) inverter, whose output active and reactive power is controlled in the dq reference frame. The control strategy presented here is state feedback control with disturbance cancellation. This disturbance signal is either provided by a voltage sensor or estimated using a presented extended high gain observer (EHGO). The control strategy without EHGO requires a current sensor and a voltage sensor, and the control strategy with EHGO requires only a current sensor. The EHGO is saving the requirement of a voltage sensor. The stability analysis of the presented control strategy is showing that the error is ultimately bounded in the presence of disturbance, formed due to Pulse Width Modulated (PWM) inverters. The microgrid is simulated using the SimPowerSystems Toolbox of MATLAB/Simulink. The simulation results are also showing the effectiveness of the proposed control strategy, that the output active and reactive power control is achieved with ultimately bounded errors. The comparison of the proposed control with the PI-based control scheme is also presented, and it is shown that better reference tracking with the desired settling time of “0.04 s” is achieved with the proposed control.

This study presents the design of a model-free control scheme for the load-following operation of a Pressurized Heavy Water Reactor (PHWR), which is a 70th-order Multi-Input Multi-Output, strongly coupled and open-loop unstable energy-generating system. To design a model-free controller for a PHWR, we propose a novel cross-coupled conditional error surface and a modified funnel algorithm-based funnel control scheme. The proposed control scheme reduces the effects of inter-zonal coupling and improves the steady-state accuracy without degrading the transient performance. The proposed control scheme is of low complexity and does not require system dynamics for controller design. A closed-loop stability analysis is carried out to ensure the boundedness of the closed-loop system trajectories. Furthermore, the proposed control scheme is evaluated by simulating different scenarios, which demonstrate its effectiveness.

This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous (dq) reference frame via a sliding mode control strategy. The control strategy required only output voltages; other states of the system were estimated by using a high-gain observer. The power-sharing among multiple inverters was achieved by solving power flow equations of the electrical network. The stability analysis showed that the error was ultimately bound in the case of the real PWM inverter and/or with a nonlinear load in the electrical network. The microgrid was simulated using the SimPowerSystems Toolbox from MATLAB/Simulink. The simulation results show the effectiveness of the proposed control scheme. The output voltage regulation of the inverter and power-sharing was achieved with the ultimately bounded error for the linear load. Later, the nonlinear load was also included in the electrical network and the error was shown to remain ultimately bounded. The output voltage regulation and power-sharing were achieved with the nonlinear load in the system.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input–output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

Exploiting Radio Frequency Identification (RFID) technology in healthcare systems has become a common practice, as it ensures better patient care and safety. However, these systems are prone to security vulnerabilities that can jeopardize patient privacy and the secure management of patient credentials. This paper aims to advance state-of-the-art approaches by developing more secure and private RFID-based healthcare systems. More specifically, we propose a lightweight RFID protocol that safeguards patients’ privacy in the Internet of Healthcare Things (IoHT) domain by utilizing pseudonyms instead of real IDs, thereby ensuring secure communication between tags and readers. The proposed protocol has undergone rigorous testing and has been proven to be secure against various security attacks. This article provides a comprehensive overview of how RFID technology is used in healthcare systems and benchmarks the challenges faced by these systems. Then, it reviews the existing RFID authentication protocols proposed for IoT-based healthcare systems in terms of their strengths, challenges, and limitations. To overcome the limitations of existing approaches, we proposed a protocol that addresses the anonymity and traceability issues in existing schemes. Furthermore, we demonstrated that our proposed protocol had a lower computational cost than existing protocols and ensured better security. Finally, our proposed lightweight RFID protocol ensured strong security against known attacks and protected patient privacy using pseudonyms instead of real IDs.

The toxic dye pigments, even in small quantities, can damage ecosystems. Removing organic, inorganic, and microbiological contaminants from wastewater via heterogeneous photocatalysis is a promising method. Herein, we report the band structure tuning of ZnO/CuO nanocomposites to enhance photocatalytic activity. The nanocomposites were synthesized by a chemical approach using step-wise implantation of p-type semiconductor CuO to n-type semiconductor ZnO. Various characterization techniques such as X-ray diffraction analysis (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray analysis (EDX) and UV spectroscopy were used to investigate the crystal structure, surface morphology, elemental composition and optical properties of the synthesized samples. As the CuO content increased from 10% to 50% in ZnO/CuO nanocomposites, the optical bandgap decreased from 3.36 to 2.14 eV. The photocatalytic activity of the samples was evaluated against the degradation of methylene blue (MB) under visible irradiation. Our study demonstrates a novel p–n junction oxide photocatalyst based on wt. 10% CuO/ZnO with superior photocatalytic activity. Effectively 66.6% increase in degradation rate was achieved for wt. 10% CuO/ZnO nanocomposite compared to pure ZnO nanoparticles.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).

The study endeavored to analyze the risk perception, sense of place, and disaster preparedness in response to landslide disaster–prone mountain areas of Gilgit-Baltistan, Pakistan. To this end, we surveyed 315 rural residents of two vulnerable landslide districts (Hunza and Nagar) of Gilgit-Baltistan. To explore the relationships between the dimensions of risk perception, sense of place, and disaster preparedness, we used partial least squares (PLS) structural equation modeling (SEM) to test the hypotheses. The results derived from PLS-SEM have implied that there is a significant negative relationship between risk perception (apprehension and unidentified) with a sense of place (bond with society and place dependence). It was observed that the residents usually overestimate the risks of disasters due to their limited scientific knowledge regarding disaster occurrence, which reduces their dependencies on the place. We revealed that disaster preparedness enhances the place attachment and reduces the apprehension of landslides in the study area. This study devotes to government and relevant agencies to devise policies that can help relocate the vulnerable rural settlements, develop, and educate the masses on disaster mitigation and prevention strategies, and help prepare a suitable landslide management plan.