Control Engineering Research Articles

Research progress on surface ligand engineering and size distribution control of PbS CQDs used in optoelectronic devices

Research progress on surface ligand engineering and size distribution control of PbS CQDs used in optoelectronic devices

Anti-Theft Vehicle Locking System

As an advancement in automobile industry, real time vehicle safety technology has arrived a long way. Vehicles become very essential part of human life hence vehicle’s safety is priority for its owner. Nowadays vehicle theft can happen anytime from anywhere such as common parking areas and some of the instable places. This paper is aimed for a novel Anti-Theft Vehicle Locking System. Here, with high-speed reliable area network, a sensor-based mechanism is interfaced with Engine Control Module (ECM). In order to prevent vehicle from theft fuel flow sensor observes ignition of engine and attached GSM sends an alert message to owner. In case of stolen, real-time vehicle tracking with global positioning system enables owner to control the engine with mobile phone so vehicle will not work any longer. This system functionality makes vehicle locking technique more efficient and provides a complete security from theft.

Evaluating Biological Risks in Biomedical Laboratories of Primary Health Care

Biomedical laboratories in primary health care centers play a critical role in disease detection, diagnosis, and management. However, the handling of diagnostic samples presents significant biological risks, particularly when biosafety measures are insufficient. This study focuses on analyzing the biological risks in 35 BSL-2 biomedical laboratories within health facilities in Athens, Greece, by examining compliance with biosafety regulations, personnel safety awareness, and biorisk management practices. A cross-sectional survey was conducted combining a customized checklist and a structured health and safety questionnaire, both developed based on the existing literature, including the international biosafety guidelines (BMBL 6th ed., WHO Biosafety Program Management, 2020). On-site evaluations were performed by a certified biorisk management advisor, and 158 laboratory professionals anonymously completed questionnaires on biosafety practices. The collected data were analyzed qualitatively, and where possible, quantitatively, by using SPSS software and p-values from the McNemar test. The results revealed widespread deficiencies in biosafety culture and risk management. Key gaps were identified in all layers of engineering controls, administrative controls, personal protective equipment (PPE), and emergency preparedness. Many laboratories failed to meet international biosafety standards set by organizations such as the WHO, CDC, and ECDC, as well as Greek legislation, highlighting the need for urgent improvements. To address these issues and mitigate the observed gaps, the implementation of comprehensive Biorisk Management Systems, enhanced biosafety training, and stricter enforcement of national and European biosafety regulations is strongly recommended. These measures are essential to protect laboratory personnel, the surrounding community, and the environment from lab-acquired infections and other biological threats.

Occupational exposure patterns and respiratory disease manifestations in industrial workers across multiple manufacturing sectors

Occupational respiratory diseases remain a significant concern in industrial settings, particularly within various manufacturing sectors. Workers are frequently exposed to a range of airborne contaminants, including dust, fumes, gases, and vapors, which can lead to various respiratory conditions. This comprehensive review examines the patterns of occupational exposures and their correlation with respiratory disease manifestations among industrial workers across multiple manufacturing sectors. Through detailed analysis of industry-specific exposure levels in textile, rubber, chemical, and steel manufacturing, the review identifies prevalent respiratory diseases such as byssinosis, occupational asthma, chronic obstructive pulmonary disease (COPD), and pneumoconiosis. Risk factors influencing disease severity, including duration and intensity of exposure, use of personal protective equipment, smoking habits, and pre-existing health conditions, are thoroughly evaluated. The review explores current mitigation strategies encompassing engineering controls, administrative measures, and robust training programs aimed at reducing occupational respiratory hazards. Additionally, it highlights emerging innovations in prevention and management, including artificial intelligence-driven monitoring systems and novel protective equipment. By synthesizing this comprehensive assessment, the paper aims to provide evidence-based recommendations for effective preventive measures to safeguard worker health and reduce the socioeconomic burden associated with occupational respiratory diseases.

Representation Formulas and Stability Analysis for Hilfer–Hadamard Proportional Fractional Differential Equations

This paper introduces a novel version of the Gronwall inequality specifically related to the Hilfer–Hadamard proportional fractional derivative. By utilizing Picard’s method of successive approximations along with the definition of Mittag–Leffler functions, we derive a representation formula for the solution of the Hilfer–Hadamard proportional fractional differential equation featuring constant coefficients, expressed in the form of the Mittag–Leffler kernel. We establish the uniqueness of the solution through the application of Banach’s fixed-point theorem, leveraging several properties of the Mittag–Leffler kernel. The current study outlines optimal stability, a new Ulam-type concept based on classical special functions. It aims to improve approximation accuracy by optimizing perturbation stability, offering flexible solutions to various fractional systems. While existing Ulam stability concepts have gained interest, extending and optimizing them for control and stability analysis in science and engineering remains a new challenge. The proposed approach not only encompasses previous ideas but also emphasizes the enhancement and optimization of model stability. The numerical results, presented in tables and charts, are provided in the application section to facilitate a better understanding.

A Structured Method to Generate Self-Test Libraries for Tensor Cores

Modern computing systems increasingly rely on specialized hardware accelerators, such as Graphics Processing Units (GPUs), to meet growing computational demands. GPUs are essential for accelerating a wide range of applications, from machine learning and scientific computing to safety-critical domains like autonomous systems and aerospace. To enhance performance, modern GPUs integrate dedicated in-chip units, such as Tensor Cores(TCs), which are designed for efficient mixed-precision matrix operations. However, as semiconductor technologies scale down, reliability challenges emerge. Permanent hardware faults caused by aging, process variations, or environmental stress can lead to Silent Data Corruptions, which silently compromise computation results. In order to detect such faults, self-test libraries (STLs) are widely used, corresponding to suitably crafted pieces of code, able to activate faults and propagate their effects to visible points (e.g., the memory) and possibly signal their occurrence. This work introduces a structured method for generating STLs to detect permanent hardware faults that may arise in TCs. By leveraging the parallelism and regular structure of TCs, the method facilitates the creation of effective STLs for in-field fault detection without hardware modifications and with minimal requirements in terms of test time and memory. The proposed approach was validated on an NVIDIA GeForce RTX 3060 Ti GPU, installed in a Hewlett-Packard Z2 G5 workstation with an Intel Core i9-10800 CPU and 32 GB RAM, available at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy.This setup was used to address stuck-at faults in the arithmetic units of TCs. The results demonstrate that the methodology offers a practical, scalable, and non-intrusive solution for enhancing GPU reliability, applicable in both high-performance and safety-critical environments.

Insight into Lysine Selection via Controlled Degradation of 2,4-Diaminobutyric Acid and Ornithine Polypeptides.

The selection of twenty canonical amino acids in protein synthesis is well-established, yet the reasons behind their natural selection remain unclear. Specifically, why structurally similar analogs, such as L-2,4-diaminobutyric acid (Dab) and L-ornithine (Orn), are not chosen over L-lysine (Lys) is unknown. These analogs differ only in alkyl chain length, raising the question of whether such variations influence polypeptide stability and natural selection. To investigate how unnatural amino acids affect the stability of synthesized polypeptides, we present the efficient synthesis of o-nitrobenzyl carbamate-caged photo-responsive polypeptide amphiphiles based on Dab and Orn via polymerization of N-phenoxycarbonyl-functionalized amino acid precursors. Depending on the self-assembly methods, these poly(ethylene glycol)-b-polypeptide hybrid block copolymers (HBCPs) formed various nanostructures, including two-dimensional discs and oblate vesicles. Upon photoirradiation, the newly liberated primary amines in the polypeptide side chains underwent spontaneous cyclization with backbone amide linkages to generate thermodynamically stable 5-membered or 6-membered lactams. Such a backbone degradation process eventually led to the disassembly and disintegration of the assemblies. These findings provide new insights into the natural selection of Lys over its analogs, Dab and Orn, in proteins, and open new possibilities for creating functional polypeptide-based materials in controlled drug delivery, tissue engineering, and other biomedical applications.

Insight into Lysine Selection via Controlled Degradation of 2,4‐Diaminobutyric Acid and Ornithine Polypeptides

The selection of twenty canonical amino acids in protein synthesis is well‐established, yet the reasons behind their natural selection remain unclear. Specifically, why structurally similar analogs, such as L‐2,4‐diaminobutyric acid (Dab) and L‐ornithine (Orn), are not chosen over L‐lysine (Lys) is unknown. These analogs differ only in alkyl chain length, raising the question of whether such variations influence polypeptide stability and natural selection. To investigate how unnatural amino acids affect the stability of synthesized polypeptides, we present the efficient synthesis of o‐nitrobenzyl carbamate‐caged photo‐responsive polypeptide amphiphiles based on Dab and Orn via polymerization of N‐phenoxycarbonyl‐functionalized amino acid precursors. Depending on the self‐assembly methods, these poly(ethylene glycol)‐b‐polypeptide hybrid block copolymers (HBCPs) formed various nanostructures, including two‐dimensional discs and oblate vesicles. Upon photoirradiation, the newly liberated primary amines in the polypeptide side chains underwent spontaneous cyclization with backbone amide linkages to generate thermodynamically stable 5‐membered or 6‐membered lactams. Such a backbone degradation process eventually led to the disassembly and disintegration of the assemblies. These findings provide new insights into the natural selection of Lys over its analogs, Dab and Orn, in proteins, and open new possibilities for creating functional polypeptide‐based materials in controlled drug delivery, tissue engineering, and other biomedical applications.

Integral Linear Quadratic Regulator Sliding Mode Control for Inverted Pendulum Actuated by Stepper Motor

Stabilization and tracking problems for cart inverted pendulums under disturbances and uncertainties have posed significant challenges for control engineers. While various controllers have been designed for an inverted pendulum, they often overlook the calibration error of the pendulum angle in practical implementations, which degrades the control performance. Incorrect calibration of the pendulum angle in upright equilibrium position generates an offset of cart position errors. To solve this problem, an augmented model comprising integral cart position errors was first constructed. Afterwards, a sliding mode control was designed for this system based on a linear quadratic controller, to facilitate implementation. Additionally, a stepper motor was employed in the inverted pendulum to enhance the control performance and widen applicability in industrial settings. The effectiveness and performance of the proposed controller were validated by means of experimental studies, focusing on stabilization control and tracking control of a cart inverted pendulum actuated by a stepper motor.

Wide-Range Variable Cycle Engine Control Based on Deep Reinforcement Learning

In this paper, a controller design method based on deep reinforcement learning is proposed for a wide-range variable cycle engine with a turbine interstage mixed architecture. The PID controller is subject to limitations, including single-input single-output limitations, low regulation efficiency, and poor adaptability when confronted with contemporary variable cycle engines that exhibit complex and multi-variable operating conditions. To solve this problem, this paper adopts a deep reinforcement learning method based on a deep deterministic policy gradient algorithm, and it applies an action space pruning technique to optimize the controller, which significantly improves the convergence speed of network training. In order to verify the control performance, two typical flight conditions are selected for simulation experiments as follows: in the first scenario, H = 0 km and Ma = 0, while in the second scenario, H = 10 km and Ma = 0.9. A comparison of the simulation results shows that the proposed deep reinforcement learning controller effectively addresses the engine’s multi-variable coupling control problem. In addition, it reduces response time by an average of 44.5%, while maintaining a similar overshoot level to that of the PID controller.

Hydrogen and point defect introduction into 4H-SiC by plasma treatment

Abstract This study investigates the effect of hydrogen plasma treatment (HPT) on defect introduction and carrier lifetime in SiC. Secondary ion mass spectrometry shows increased hydrogen diffusion at higher HPT temperatures and complete hydrogen release after annealing. Microwave photoconductivity decay measurements show reduced carrier lifetime after HPT, which partially recovers after annealing but remains lower than that of non-HPT-treated samples. Deep level transient spectroscopy observes point defects introduced by HPT, and they remain even after annealing at 1600°C. Therefore, HPT is a scalable, cost-effective approach to induce point defects for mitigating bipolar degradation and improve SiC device reliability through defect engineering and carrier lifetime control.

Mechanical Response Mechanism and Yield Characteristics of Coal Under Quasi-Static and Dynamic Loading

During deep mining engineering, coal bodies are subjected to complex geological stresses such as periodic roof pressure and blasting impacts, which may induce mechanical property deterioration and trigger severe rock burst accidents. This study systematically investigated the mechanical characteristics and failure mechanisms of coal under strain rates on two orders of magnitude through quasi-static cyclic loading–unloading experiments and split Hopkinson pressure bar (SHPB) tests, combined with acoustic emission (AE) localization and crack characteristic stress analysis. The research focused on the differential mechanical responses of coal-rock masses under distinct stress environments in deep mining. The results demonstrated that under quasi-static loading, the stress–strain curve exhibited four characteristic stages: compaction (I), linear elasticity (II), nonlinear crack propagation (III), and post-peak softening (IV). The peak strain displayed linear growth with increasing cycle, accompanied by a failure mode characterized by oblique shear failure that induced a transition from gradual to abrupt increases in the AE counts. In contrast, under the dynamic loading conditions, there was a bifurcated post-peak phase consisting of two unloading stages due to elastic rebound effects, with nonlinear growth of the peak strain and an interlaced failure pattern combining lateral tensile cracks and axial compressive fractures. The two loading conditions exhibited similar evolutionary trends in crack damage stress, though a slight reduction in stress occurred during the final dynamic loading phase due to accumulated damage. Notably, the crack closure stress under quasi-static loading followed a decrease–increase pattern with cycle progression, whereas the dynamic loading conditions presented the inverse increase–decrease tendency. These findings provide theoretical foundations for stability control in underground engineering and prevention of dynamic hazards.

Methodology for assessing the spherical plain bearings durability

The article presents an improved method for assessing the durability of spherical plain bearings, operating under the conditions of oscillatory motion, based on the processing of an array of experimental data obtained in laboratory conditions as well as during operation of the product. One of the options of mathematical modeling was applied, which uses regression methods to approximate the function of one variable. As an object of research, spherical plain bearings installed in aircraft (engine) control systems and operating in cyclic rocking motion are considered. Spherical bearings are plain bearings, and the amplitude of their rocking movements can vary over a wide range from ±20° to ±60°. The problems of calculating the service life of plain bearings are well studied and documented in the relevant standards GOST 1144-75 “Rolling bearings. Methods for calculating durability”, GOST 15084-78 “Plain bearings. Methods for calculating durability”. Spherical bearings are plain bearings, but at the same time, there are many technical applications in which bearings operate under conditions of oscillatory motion, for example, in flap drive units, in aircraft control systems, etc. Features of operating conditions determine the specific types of defects identified in experimental studies. It is shown that the known methods for calculating the durability of plain bearings do not allow obtaining acceptable practical results in assessing the service life of bearings operating in cyclic rocking motion.

Benzotriazole (BTA): A Review of the Current Research into Toxicity

ABSTRACT This review aims to appraise the current state of research on safe practice for benzotriazole (BTA) use by conservators. Employed as a corrosion inhibitor when treating copper alloys, concerns about BTA safety were raised in the 1970s and have never been fully contextualized. The authors summarize published research and focus on three assessments released by international governments. The article also reviews the chemical action of BTA and typical application methods. Health and safety concerns are addressed, and engineering controls and personal protective equipment (PPE) are recommended. Research into environmental toxicity is also presented, illustrating another avenue of concern. Finally, the article offers additional avenues for future research and mentions ongoing experimentation with organic, non-toxic corrosion inhibitor alternatives.

Control Engineer Roles in the Next Power Market Transition

This survey of power operations and power markets is a collaboration between members of academia and industry. It describes the thinking behind rules in organized electricity markets, which are rooted in the theory of efficient markets, and why this theory is ill suited to address the range of challenges in real-world power systems operations. The mismatch between market theory and reality includes a lack of consideration of fixed costs and a lack of understanding of value to the consumers. Moreover, the efficient equilibrium is the solution to a risk-neutral, single-objective optimal control problem. Every control engineer knows that such solutions are rarely the final answer to a practical control problem. It is hoped that this work will inspire the control community to collaborate with industry in the design of the next generation of power markets.

Urban Air Mobility Research Challenges and Opportunities

This article reviews the literature on urban air mobility (UAM), examining both the research challenges it presents and the transformative opportunities that make these challenges worth addressing. While UAM has historical precedents, the current iteration is born of novel aircraft technology, primarily electric vertical takeoff and landing (eVTOL) and electric short takeoff and landing (eSTOL) aircraft. These advances raise new questions in aerodynamics, control, and integration with urban infrastructure. We explore several key research areas, including aircraft design, vertiport development, network planning, and air traffic management. We also address the scalability challenges in air traffic management for high-density UAM operations and the potential of autonomous and remotely piloted systems. If new aircraft are to birth a new urbanism, they will do so by integrating aircraft engineering and computational intelligence in control, systems, robotics, and human factors.

A method for smart grid intrusion detection through explainable deep learning

Over the years, cyber-attacks have increased drastically, and their execution changed with time. One of the targets of cyber criminals is trying to obtain sensitive information from mobile, cloud, or generally IoT devices. To avoid those risks, different countermeasures have been developed and implemented. For instance, the IEC 60870-5-104 protocol was developed to define the systems used for remote control in electrical engineering and power system automation applications. Starting from these considerations, in this paper, we propose an intrusion detector based on explainable Deep Learning (DL) that is able to detect possible attacks. In a nutshell, we consider several DL models, i.e., AlexNet, DenseNet, EfficientNet, Inception, LeNet, MobileNet, ResNet50, Standard CNN, VGG16, and VGG19 to understand whether a network trace (stored in a PCAP file) is related to an attack. Moreover, to explain of the model attack prediction, we resort to two different Class Activation Mapping algorithms available in the literature: Grad-CAM++ and Score-CAM. As the last step, we also calculated the IF/IM-SSIM index to strengthen the robustness of the top-performing model and evaluate the similarity between the two CAM algorithms. Experimental results show the effectiveness of the proposed method, and we obtained an accuracy equal to 0.900 with the DenseNet. In conclusion, we applied the exact steps to a new dataset to confirm that the proposed methodology is scalable and applicable to other datasets and achieved promising results.

A novel risk-based access control engine in zero trust architecture for IoT network

The Internet of Things (IoT) and Wireless Sensor Network (WSN) market has grown recently. Therefore, security issues on IoT Networks are considerably important. This study proposes the zero trust-enabled trust evaluation mechanism that continuously evaluates the trust of neighbor nodes to prevent attackers from endangering the availability and reliability of the overall IoT networks. In zero trust, each node's behavior should be considered to avoid damage to the overall network. This study uses various indicators to evaluate the trust of each interaction behavior, including capability, feature, activity, and data contents. This study evaluates the behaviors through the ability of the interaction node and the expected behavior. The experiment result shows that the proposed method can detect and mitigate the four types of attacks in Routing Protocol for Low-Power and Lossy Network (RPL), and the proposed method has less misjudgment.

Meticulous Design of High-Polarity Interface Material for FACsPbI3 Perovskite Solar Cells with Efficiency of 26.47.

Designing new interface materials with the multifunctions of upper film crystallization control, interfacial defects passivation, and interfacial energy level regulation is crucial for developing efficient and stable perovskite solar cells (PSCs). Herein, a high polarity interfacial material, 2-cyano-N,N,N-trimethylammonium bromide (CNCB), was synthesized to engineer the buried interface between SnO2 and perovskite of the PSCs. Comprehensive theoretical and experimental investigations demonstrate that CNCB interacts with perovskite precursors (PbI2 and FAI) to regulate crystallization kinetics, yielding perovskite films with preferred orientation and reduced defects. Simultaneously, CNCB chemically interacts with both SnO2 and perovskite surfaces, effectively passivating oxygen vacancies in SnO2 and undercoordinated Pb2⁺ defects at the perovskite buried surface. Furthermore, the high dipole moment of CNCB induces beneficial interfacial polarization, optimizing energy level alignment and suppressing non-radiative recombination. The CNCB-modified FACsPbI3 PSCs achieve a champion power conversion efficiency (PCE) of 26.47% with exceptional operational stability, retaining 87.14% of their initial efficiency after 1000 h of continuous 1-sun illumination. This work establishes a molecular design paradigm for multifunctional interfacial materials in perovskite optoelectronics, highlighting the synergistic roles of crystallization control, defect passivation, and dipole engineering in high-performance devices.

Meticulous Design of High‐Polarity Interface Material for FACsPbI3 Perovskite Solar Cells with Efficiency of 26.47%

AbstractDesigning new interface materials with the multifunctions of upper film crystallization control, interfacial defects passivation, and interfacial energy level regulation is crucial for developing efficient and stable perovskite solar cells (PSCs). Herein, a high polarity interfacial material, 2‐cyano‐N,N,N‐trimethylammonium bromide (CNCB), was synthesized to engineer the buried interface between SnO2 and perovskite of the PSCs. Comprehensive theoretical and experimental investigations demonstrate that CNCB interacts with perovskite precursors (PbI2 and FAI) to regulate crystallization kinetics, yielding perovskite films with preferred orientation and reduced defects. Simultaneously, CNCB chemically interacts with both SnO2 and perovskite surfaces, effectively passivating oxygen vacancies in SnO2 and undercoordinated Pb2⁺ defects at the perovskite buried surface. Furthermore, the high dipole moment of CNCB induces beneficial interfacial polarization, optimizing energy level alignment and suppressing non‐radiative recombination. The CNCB‐modified FACsPbI3 PSCs achieve a champion power conversion efficiency (PCE) of 26.47% with exceptional operational stability, retaining 87.14% of their initial efficiency after 1000 h of continuous 1‐sun illumination. This work establishes a molecular design paradigm for multifunctional interfacial materials in perovskite optoelectronics, highlighting the synergistic roles of crystallization control, defect passivation, and dipole engineering in high‐performance devices.