Temperature-dependent partitioning of 1,4-dioxane between atmospheric emissions and landfill leachate at waste incineration facilities.

In aerospace power systems, integrating temperature sensors and associated measurement circuits into local temperature control units usually requires additional space and complex design considerations. This not only increases the complexity of the system, but also imposes significant restrictions on equipment layout, especially in applications with strict volume and weight constraints. The temperature-sensitive, high-precision components in the power supply will drift due to the working environment temperature, resulting in a decrease in the accuracy of the power supply output, which places strict requirements on the performance of the local temperature control unit. These challenges highlight the urgent need for innovative solutions for aerospace power supplies. Therefore, this study proposes a novel temperature control method that uses diodes as temperature sensors and heat sources for temperature-sensitive, high-precision components in aerospace power supplies. By integrating the sensing and heating functions into a single diode, compensating for factors affecting the measurement accuracy and controlling the temperature of key high-precision components in the power supply, a stable voltage output can be achieved. This method solves the problem of installing multiple temperature sensors in the limited space of aerospace power supplies and reduces system complexity.

BackgroundThe Dynamic Appraisal of Situational Aggression - Inpatient Version (DASA-IV) is a brief violence risk assessment tool to evaluate imminent risk of aggression. There have been no previous studies to investigate its utility in an acute adult non-forensic psychiatric setting in Australia.ObjectiveTo examine the feasibility of DASA-IV implementation in an acute adult inpatient non-forensic psychiatric setting.MethodsThis study utilised a quasi-experimental design with pre- and post-testing. Subjective data from questionnaires administered to psychiatric staff on utility of DASA-IV and objective data on frequency of aggression incidents were collected.ResultsStaff reported higher confidence in deciding when medications, seclusion, and security and medical staff were required to manage aggression. DASA-IV was reported as valuable and easy to use. Lack of time and lack of DASA-IV training for non-regular nurses were barriers to implementation. Only the control unit had a statistically higher number of aggression incidents post-intervention.Conclusions DASA-IV is a feasible tool that improves staff confidence in managing aggression and reduces aggression incidents in an acute adult non-forensic inpatient psychiatric setting in a metropolitan tertiary public hospital. Its implementation may assist in aggression management in the acute adult non-forensic inpatient psychiatric setting.

With the growing demand for computation-intensive and latency-critical tasks in intelligent mining, the computing capabilities of terminal devices are becoming increasingly inadequate. Consequently, task offloading has emerged as a vital mechanism. However, existing approaches often depend on centralized resource allocation algorithms, which tend to produce suboptimal assignment decisions. As a result, tasks are frequently offloaded to inappropriate edge servers that become unstable under heavy upload traffic, leading to higher latency and increased energy consumption. To effectively assess system performance, we introduce the Overall Utility Value (OUV), which balances system delay and energy usage. In this paper, we present an edge computing task-offloading framework tailored for mining scenarios, which comprises a central control unit, distributed service nodes, and a large number of terminal devices. By leveraging the environmental awareness of the service nodes, we present a Cooperative Communication and Sensing Task Offloading Scheme (CCTS) designed to minimize both system latency (SL) and system energy consumption (SEC) through optimized task allocation and wireless bandwidth ratios. To tackle this optimization problem, we develop an Improved Gray Wolf Optimization algorithm integrated with a Feasibility Checking Algorithm (IGWO-FCA). Simulation results demonstrate that the IGWO-FCA achieves the lowest OUV, validating its effectiveness.

Recent advancements in photovoltaic water pumping systems (PVWPS) have garnered significant attention from researchers, driven by their reliance on clean solar energy and their potential for sustainable water management. A typical PVWPS configuration includes a three-phase asynchronous motor (ASM) coupled to a centrifugal pump and powered by a photovoltaic (PV) generator. To optimize power extraction from the PV source, a maximum power point tracking (MPPT) algorithm is employed in conjunction with a boost converter. The system integrates two main control strategies: an MPPT controller for the three-phase inverter and a Field-Oriented Control (FOC) scheme for the asynchronous motor. This study introduces an enhanced perturbation and observation (P&O) MPPT algorithm featuring adaptive step-size control. The proposed method effectively reduces steady-state oscillations and improves the efficiency of power extraction in PV systems. The second control unit, based on FOC, regulates the induction motor by generating the switching signals for the voltage source inverter (VSI). Together, these control units play a vital role in system operation, contributing to improved efficiency and overall performance. The key innovation of this work lies in the integration of the improved P&O MPPT method. Extensive simulation analyses conducted under various irradiation conditions demonstrate that the enhanced algorithm achieves superior tracking performance compared to conventional P&O methods. Furthermore, the effectiveness of the FOC strategy implemented with a two-level inverter has been experimentally validated. The control approach was executed using a dSPACE DS1104 digital signal processor board in an ASM drive system.

This research paper proposes the concept of a voice-controlled smart wheelchair that enables mobility, safety and independence for physically disabled persons. This smart wheelchair incorporates various hardware and software modules, including the Raspberry Pi Zero 2 Was the brain unit, into its framework to facilitate intelligent operation. A microphone unit is designed to recognize voice commands and process them through machine learning-based speech recognition technology to enable hands-free operation of the wheelchair. To ensure the safety of the user, a physical mushroom-type emergency stop button is integrated to instantly stop the wheelchair when pressed. The wheelchair movement components include 350 watts DC brushed motors and a strong structure to ensure smooth movement. The motor speeds can be regulated through the intelligent control unit to enable smooth and efficient movement within the indoor and outdoor environments. Location tracking in real time is provided through a GPS module, while in emergency situations, location updates are sent to caregivers using a GSM module. In addition, the text-to-speech feedback provides audio confirmation for system status and user commands, further improving usability for those users challenged by vision or physical capabilities. The power input shall be provided through a rechargeable lithium-ion battery pack with BMS and voltage regulation, allowing for continuous operation in a safe manner. Overall, the workflow that can be realized consists of system wake-up through a wake word, followed by voice command processing, safety validation, execution of motor control, GPS based location monitoring, and data logging for analysis. This assistive mobility in a smart wheelchair, as proposed, is cost effective, user-friendly, and safety-focused, showing in practice how to effectively incorporate voice control, embedded systems, and real-time communication technologies.

This paper presents the design and development of a portable, low-cost mechanical ventilator intended for emergency respiratory support in resource-constrained healthcare environments. The proposed system automates a conventional Bag Valve Mask (BVM) using a stepper motor– driven lead screw mechanism to deliver controlled ventilation. Key parameters such as tidal volume, respiratory rate, airway pressure, and inspiration–expiration ratio are regulated using a microcontroller-based control unit. Real-time feedback from an airway pressure sensor and a pulse oximeter ensures patient safety by preventing over-pressurization and maintaining adequate oxygenation. Experimental evaluation using an artificial lung model demonstrates stable and repeatable ventilation performance within clinically safe limits. Due to its low power consumption, portability, and affordability, the proposed ventilator is well suited for use in rural healthcare centers, ambulances, and emergency disaster-response situations. Keywords -Ventilator, Bag Valve Mask (BVM), Low-Cost, Low-Power, Portable, Automated Ventilation

The most significant infections via needle-stick injuries (NSIs) involve hepatitis B, hepatitis C, and HIV, and the most common device is a hollow-bore needle or suture needle. This study aimed to determine the epidemiology and associated factors that contribute to occupational exposures to blood and bodily fluids at a hospital in South Trinidad and Tobago. This study was conducted retrospectively from 2011 to 2016 in one tertiary public health institution in Trinidad and Tobago. Data were extracted from incident reports, occupational health records, and infection prevention and control (IPC) unit documentation. Primary outcomes included demographic characteristics, occupation, and departmental distribution of NSIs. Secondary outcomes assessed the circumstances of injury, anatomical site, glove use, laboratory investigations, and administration of post‑exposure prophylaxis (PEP). A total of 221 healthcare workers reported NSIs, with a mean age of 34.97 years. Most affected individuals were women and clinical staff. "Other" (e.g., psychiatry, or in transit outside wards/units) accounted for the highest proportions of injuries, followed by internal medicine, while the IPC unit received the majority of first reports. Procedures were the most common activity associated with injury, followed by discarding needles and recapping. Fingers were the most frequently affected sites, particularly the right index and left ring fingers. Documentation gaps were notable, especially regarding glove use and time to reporting. PEP was administered in most cases, and official reporting to the IPC unit was associated with a higher likelihood of receiving PEP. Laboratory testing commonly included renal and liver function tests, complete blood counts, and screening for blood‑borne pathogens. NSIs reflect ongoing challenges in occupational safety. Strengthening IPC training, improving reporting practices, promoting safer sharps handling, and implementing safety‑engineered devices are essential to reducing NSI incidence and improving post‑exposure management.

Detecting intrusions on in-vehicle networks from voltage characteristics has become a popular technique. However, an effective mechanism for voltage identification of Electronic Control Units requires both a sound clustering algorithm to determine the correct number of devices on the network and an efficient classifier that allows updates in order to handle changes due to environmental conditions. Firstly, we explore the use of HDBSCAN in order to cluster ECUs based on voltage characteristics. While HDBSCAN is a highly effective algorithm, which has the merit of having only a few parameters that need to be tuned, our results show that finding the optimal parametrization is not that straight-forward. We test two well-known methods and an empirical selection in order to determine optimal choices for the largest existing dataset that contains voltage samples from ten vehicles. Secondly, we use the Nearest Centroid classifier to identify ECUs based on their fingerprints, which offers the advantage of an extremely small memory footprint and an efficient updating mechanism for the centroids. Thus, the method is both efficient and capable of adapting to environmental changes, which is a known demand for voltage-based identification. The proposed methodology demonstrates a very high detection rate that is specific to voltage-based techniques, i.e., true acceptance rate greater than 99.93% and false acceptance rate lower than 0.03%, even when faced with changing environmental conditions when updates are used. It also features an easy to update mechanism and a minimal memory footprint that is 4 to 20 times smaller than baseline classifiers such as SVM and RF.

Entropy-augmented deep reinforcement learning with adaptive exploration for integrated energy and motor thermal management in hybrid electric vehicles

Impact of partial load operation on the exergoenvironmental performance of coal power plant

The conventional shale gas production methods usually have a complex network of control systems at the wellheads which can be controlled by several autonomous parts. This de-centralized configuration causes system instability and higher maintenance expenses. To solve these problems, one of the solutions proposed in this work is the intelligent control valve which combines sensors, regulating valves and control units into a unified system, simplifies the structure of the wellhead control system, increases the efficiency of regulation and enhances the stability of the system. Multiple control modes, including production control mode, pressure control mode, and piston gas lift mode are available in the intelligent control valve, which allows it to adapt to changing production conditions flexibly and optimize gas production rates and economic benefits at the wellhead. Results indicate that the intelligent control valve exhibits excellent stability and meets the remote control requirements of gas wells. After installing the intelligent control valve, the average daily gas production of Well A increased significantly, from 2.14×10⁴ m³/d to 2.34×10⁴ m³/d, representing a 6.86% increase compared to pre-installation levels. The average gas production efficiency improved by 5.44%, with good drainage and gas production effects. The single-well intelligent control valve system can generate an economic benefit of 355,600 yuan within six months, demonstrating high potential for promotion and application.

RISC-V has significant demand due to its efficiency, scalability, and open-source instruction set architecture. Its simple and modular design makes it highly suitable for both academic and industrial applications. This paper presents the design and implementation of an RV32M processor using a single-cycle architecture, where each instruction is executed within one clock cycle. The proposed design includes key modules such as instruction memory, control unit, register file, and arithmetic logic unit (ALU). This pro cessor is efficient to perform arithmetic, logical, and branch operations, following a load/store architecture. This design is implemented using Verilog HDL and verified through simulation, which demonstrates the functionality and efficient execution of instructions. This provides an understanding of RISC-V processor design and foundation for pipelined implementation.

In today’s world of embedded systems, microprocessors and microcontrollers are widely used in nearly every electronic application. While these devices deliver high performance and compact designs, their internal structure often remains unclear to students and beginners. This means that learners frequently do not understand how a processor executes instructions at the hardware level. To tackle this issue, this paper outlines the design and implementation of an 8-bit Central Processing Unit (CPU) created using discrete Bipolar Junction Transistors (BJTs) and standard Logic Gate ICs. The proposed CPU design includes key processing units such as the Arithmetic Logic Unit (ALU), Program Counter (PC), Accumulator Register, Control Unit, and Clock Generator, all built from basic digital components like logic gates, flip-flops, and transistor-based circuits. A 555 timer IC is used as the system clock, enabling the processor to run at a low frequency. This allows for clear observation of individual instruction cycles. The CPU operates on a standard fetch, decode, execute cycle, processing simple instructions such as LOAD, ADD, SUBTRACT, JUMP, and HALT. This project offers a complete view of data paths, control signals, and timing relationships, helping learners connect theoretical ideas with real-world implementation. The system is affordable, works at safe voltage levels, and acts as a useful educational tool for understanding computer architecture and the basics of digital electronics.

Agriculture is the foundation of our economy, and modern technologies are changing how farming is done. One of the biggest problems in farming is the manual application of pesticides and fertilizers, which is time-consuming, labor intensive, and harmful due to chemical exposure. To fix this, this project introduces a drone-based agricultural spraying system that automates the spraying process, cutting down on human work and making things more efficient. The system uses an Arduino as its main control unit to manage all the parts. The drone has several features: an ultrasonic sensor to maintain the spraying height, a flow sensor to monitor the amount of pesticide sprayed, and a GPS module to track its position. With these tools, the drone can cover big areas efficiently and ensure even spraying, helping to make farming smarter and more sustainable.

Waste management has become a critical environmental and social challenge due to rapid urbanization and population growth. Improper segregation of waste reduces recycling efficiency and increases health risks for workers involved in manual sorting. This paper presents an Automated Waste Segregation System using a Robotic Arm, designed to segregate waste into metal and non-metal categories. The proposed system uses an inductive proximity sensor for metal detection, a microcontrollerbased control unit, and a servo motor driven robotic arm for physical segregation. The system minimizes human intervention, improves safety, and provides a compact and cost-effective solution suitable for small-scale applications. Experimental results show reliable detection and accurate placement of waste materials into respective bins.

Switched Reluctance Motors (SRMs) have a basic design, robustness, and fault-tolerant nature, offering scope for their extensive use in industrial and automotive applications. Still, their performance is hampered by significant disadvantages, including acoustic noise and high torque ripple, whose solutions often involve expensive driver circuits. The goal of this project is to design and create a controller for a Switched Reluctance Motor with the intention of minimising cost while maintaining acceptable torque ripple and efficiency levels. The suggested controller utilises an Arduino Nano, implementing extreme seeking optimisation for ideal turn-on and turn-off angles, as well as appropriate phase excitation control. Compared to traditional control techniques, the developed controller offers acceptable output voltage pulses and other characteristics within the acceptable range for the operation. Effective controller design improves SRM cost-effectiveness considerably while also keeping the design simple for easy scalability.

Abstract - The aviation sector continues to face environmental challenges with aircraft emission. This project aims to provide viable and economical solutions that include various sensors and a microcontroller to monitor the aviation ecosystem continuously. The system utilizes a gas sensor to monitor the quantity and concentration of noxious gases in the air and continually monitor carbon emissions. An ultrasonic distance sensor is included in the design to assist in his measurement and estimate of the fuel level. Also, the system accounts for the fuel level and the rate of fuel that is being consumed of combustion and the carbon emissions that are being produced. The DS18B20 sensor accounts for the combustion temperature because the combustion temperature affects the efficiency of fuel consumption. The central control unit for the sensor system is an ESP32 microcontroller. The sensors are interfaced and provide inputs that the ESP32 will process to provide outputs of gas concentration, distance, and temperature. The ESP32 microcontroller engineer is designed to provide wireless connectivity for remote mobile and cloud monitoring of the system. In the design, a range for emissions is designed that, when the emissions monitored are greater than this, the system will provide an alarm to notify personnel. Key Words: HC-SR04(ultrasonic distance sensor), ESP32(microcontroller), DS 18B20(temperature sensor)

Enhancing the robustness and fault tolerance of finite-state machines (FSMs) is crucial for safety-critical systems, such as transportation control systems and medical equipment. This issue becomes particularly important when developing control units for unmanned aerial vehicles (UAVs), which are exposed to external disturbances from electronic warfare (EW) systems. Under such conditions, traditional methods for creating fault-tolerant finite-state machines (FTFSMs), initially designed to address the effects of ionizing radiation that cause rare single-event upsets (SEUs), are often ineffective. This paper proposes a novel method for developing FTFSMs that can withstand multi-bit upsets (MBUs) affecting the FSM’s wires and memory cells due to external disturbances. The FTFSM architecture additionally includes an output register and a concurrent error detection (CED) circuit. When a fault is detected, the FTFSM switches to standby mode. Once the external disturbance ceases, the FTFSM resumes normal operation from the point of interruption without altering the control algorithm. In cases of critical errors, the FSM circuit can be reconfigured via the system processor. Experimental studies have shown that the proposed approach incurs exceptionally low overhead costs. Additionally, the paper presents a technique for calculating the probability of fault detection for FTFSMs implemented in field-programmable gate arrays (FPGAs).

Abstract - Smart Helmet with Safety and Monitoring Features, this project seeks to promote the safety of cyclists riding two-wheelers using accident-preventing real-time monitoring and controls. In particular, the project utilizes a bike control unit featuring an ESP32, LCD screen, DC motor controlling bike movement, MPU6050 tilt sensor detecting any accidents, GPS tracking device, and an RF433 receiver. A smart helmet control unit comprises the following components: RF433 transmitter, limit switch that detects whether or not the cyclist is wearing a helmet, and an alcohol detector to measure the sobriety level of the cyclist. Information concerning whether the helmet and alcohol detector indicate that the cyclist is sober or not is transmitted from the smart helmet unit to the bike unit. Apart from providing accident-preventing control, the project also features accident monitoring capabilities. The bike unit transmits real-time data concerning accident indication, helmet presence, alcohol indication, and GPS coordinates to the Mobile Application. Key Words: Smart Helmet, Accident Detection, Location, ESP32, Alcohol Sensor, Monitoring