Control of Active Suspension Systems Based on Mechanical Wave Concepts

Wave-based control (WBC) offers a relatively novel approach to the challenge of con-trolling flexible mechanisms by treating the interaction between actuator and system as the launch and absorption of mechanical waves. WBC is a robust approach but has been unexplored in active suspension systems to date. This study adapts WBC to a quarter-car suspension model. Having embedded an actuator as the active element of a car suspension, a novel but simple ’force impedance’ adaptation of WBC is introduced and implemented for effective vibration control. Testing with various input signals (pulse, sinusoidal, and random profile) highlights the active system’s significant ride comfort and rapid vibration suppression with zero steady-state error. Compared to two other models—one employing an ideal skyhook strategy and the other a passive sus-pension—the active system utilizing WBC outperforms across many criteria. The active controller achieves over 38% superior ride comfort compared to the skyhook model for a pulse road input. This is accomplished while adhering to WBC principles: relying solely on actuator-interface measurements, simplicity, cost-effectiveness, with no need for detailed system models, extensive sensors, or deep system knowledge

A Hospital-Based Exploration of Medication Adherence among Outpatients with COPD:Implications for Clinical Practice

Background: The management of COPD typically involves a multifaceted approach and a complex treatment regimen. Poor adherence to prescribedmedications can lead to worsened symptoms, increased exacerbations, and reduced quality of life among patients with COPD.Objective: This study aimed to assess medication adherence and associated factors in outpatients with COPD, which remain insufficiently investigated.Methods: A cross-sectional study was conducted at outpatient respiratory clinics in two major Jordanian hospitals. Data collection included sociodemographic and medical parameters. Medication adherence was assessed using a validated Arabic 4-item scale.Logistic regression was conducted to identify the variables associated with medication adherence.Results: Of the 702 participants, 68%reported poor medication adherence. Key determinants of medication adherence included age, inhaler technique,knowledge, comorbidities, concerns about side effects, dosing frequency, disease duration, and depression.Conclusion: Medication adherence in COPD patients, particularly in the elderly, and those with comorbidities, depression, and longer disease duration, isinadequate. Effective counselling and more convenient medication regimens are essential to improving adherence in this patient population

Food loss and waste reduction by using Industry 4.0 technologies:examples of promising strategies

Food loss and waste (FLW) represent a significant global issue, posing a threat to food sustainability on a worldwide scale. However, the growing awareness among consumers and the development of emerging technologies driven by the Fourth Industrial Revolution (Industry 4.0) present numerous opportunities to reduce FLW. This article provides a comprehensive examination of recently developed strategies for reducing FLW. The role of Industry 4.0 technologies, such as the Internet of Things, artificial intelligence, cloud computing, blockchain, and big data, is highlighted through examples of various promising initiatives. The results of this analysis show that the application of digital technologies to address the issue of FLW is on the rise globally, with Industry 4.0 technologies revolutionising many sectors, including the food sector. Further research is necessary, and closer collaboration between producers, distributors, consumers, and other actors involved in the food supply chain is still required to reduce FLW further.</p

Synthetic cannabinoids in e-cigarettes seized from English schools

Background and aims: People who use synthetic cannabinoids (SCs) report debilitating side effects and withdrawal symptoms, coupled with dependence. In the UK, SC use was believed to be largely restricted to prison, where they are the most common drug and associated with nearly half of non-natural deaths, or poly-drug users in the community who are also likely to be homeless. However, national media reporting has increasingly identified cases of children collapsing in schools, which are claimed to be associated with vaping and putatively involving a drug such as delta-9-tetrahydrocannabinol (THC) or SCs. We therefore conducted the first study to identify and quantify SCs in e-cigarettes routinely collected from schools in England. Design: E-cigarette and e-liquid samples seized by teachers in schools were identified through engagement with police forces and city councils in England. We sought agreements across broad geographical areas and based on acquiring the relevant approvals at a local level. Sample bias is considered in the analysis and reporting. Setting and cases: Samples were submitted from 27 secondary (age 11–18) schools from geographically distinct regions of England, representing a broad range of social metrics (free school meals, persistent absenteeism and special educational needs). All submitted samples were anonymised and no identifying information was collected. Analysis of samples was conducted both in a laboratory setting and in-field at local police stations. Measurements: Qualitative gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry were used to identify SCs and THC in e-cigarettes/liquid, with concentration measured by quantitative nuclear magnetic resonance spectroscopy. A subset of samples was screened for SCs and THC using a portable detector based on combined fluorescence and photochemical discrimination. Findings: E-cigarettes containing SC were identified in 77.8% of all participating schools and were detected in 17.4% of all samples seized. These were almost entirely in refillable devices and liquid bottles, with very few in single use products. The percentage of SC e-cigarettes in schools positively correlated with the fraction of pupils eligible for free school meals, a social deprivation metric (Pearson's correlation r = 0.65 and P = 0.003). Positive samples contained a median SC concentration of 0.42 (interquartile range = 0.77) mg mL−1 with a maximum of 3.6 mg mL−1. In contrast, few samples contained THC (1.2%). Conclusions: E-cigarettes containing synthetic cannabinoids were identified in three quarters of 27 secondary schools in England that were sampled.</p

Towards Long-Term Operational and Geo-Mechanical Stability of Underground Hydrogen Storage in Salt Caverns

Hydrogen is rapidly gaining momentum as a cornerstone of the global transition to cleaner energy systems, providing a versatile and low-carbon fuel alternative across a range of industries, including power generation and transportation. However, large-scale hydrogen storage poses significant challenges, as effective storage solutions are critical for balancing supply and demand, especially for renewable energy sources. Underground salt caverns present a promising option for hydrogen storage due to their unique mechanical properties, such as high impermeability and self-sealing behaviour under stress. These characteristics make salt caverns particularly suited for long-term, safe, and high-pressure hydrogen storage.To ensure the reliable performance of hydrogen storage systems, it is essential to thoroughly understand the behaviour of salt caverns under operational conditions. This study adopts a comprehensive geo-mechanical modelling approach to assess the performance of salt caverns repurposed for hydrogen storage. By employing finite difference modelling, we simulate the stress, deformation, and displacement responses of the cavern structure. The geological model is developed based on real-world data from the Zechstein Group in East Yorkshire, United Kingdom, a region with favourable geological conditions for underground storage. The study incorporates detailed sensitivity analyses to evaluate the influence of varying operational parameters, such as injection pressures and cycle frequencies, on the stability of the caverns. These analyses provide a clear understanding of how operational stresses affect the long-term behaviour of the storage system, including potential risks related to creep, subsidence, and deformation.The results offer crucial insights into the optimization of design and operational parameters for hydrogen storage caverns. This work contributes to the development of safe, efficient, and scalable hydrogen storage infrastructure, which is a critical enabler for the widespread adoption of hydrogen as a clean energy vector

Enhancing Forensic Audio Transcription with Neural Network-Based Speaker Diarization and Gender Classification

Forensic audio transcription is often compromised by low-quality recordings, where indistinct speech can hinder the accuracy of conventional Automatic Speech Recognition (ASR) systems. This study addresses this limitation by developing a machine learning-based approach to improve speaker diarization, a process critical for distinguishing between speakers in sensitive audio data. Previous research highlights the inadequacy of traditional ASR in forensic settings, particularly where audio quality is poor and speaker overlap is common. This paper presents a neural network specifically designed for gender classification, using 20 key acoustic features extracted from real forensic audio data. The model architecture includes input, hidden, and output layers tailored to differentiate male and female voices, with dropout regularization to prevent overfitting and hyperparameter optimization ensuring robust generalization across test data. The neural network achieved an average recall of 86.81%, F1 score of 85.67%, precision of 87.95%, and accuracy of 86.83% across varied audio conditions. This model significantly improves transcription accuracy, reducing errors in legal contexts and supporting judicial processes with more reliable, interpretable evidence from sensitive audio data

Enhancing Forensic Audio Transcription with Neural Network-Based Speaker Diarization and Gender Classification

Forensic audio transcription is often compromised by low-quality recordings, where indistinct speech can hinder the accuracy of conventional Automatic Speech Recognition (ASR) systems. This study addresses this limitation by developing a machine learning-based approach to improve speaker diarization, a process critical for distinguishing between speakers in sensitive audio data. Previous research highlights the inadequacy of traditional ASR in forensic settings, particularly where audio quality is poor and speaker overlap is common. This paper presents a neural network specifically designed for gender classification, using 20 key acoustic features extracted from real forensic audio data. The model architecture includes input, hidden, and output layers tailored to differentiate male and female voices, with dropout regularization to prevent overfitting and hyperparameter optimization ensuring robust generalization across test data. The neural network achieved an average recall of 86.81%, F1 score of 85.67%, precision of 87.95%, and accuracy of 86.83% across varied audio conditions. This model significantly improves transcription accuracy, reducing errors in legal contexts and supporting judicial processes with more reliable, interpretable evidence from sensitive audio data

Reimagining Higher Education Learning Spaces:Assembling Theory, Methods, and Practice

The Higher Education (HE) sector faces an increasingly challenging environment with key themes identified as: the economic and social fallout from the Covid-19 pandemic,shifting politics, changing expectations around education, and technological advances(Marshall et al., 2024). There are particular concerns about aspects including: a mismatch between tuition fee caps and inflation, reduced government grants (Atherton et al., 2024),decreasing numbers of international students (Bolton et al., 2024), concerns over pensions, pay, and working conditions (University and College Union, 2022), new thresholds for student outcomes (Office for Students, 2022), and gaps around research funding(Butland, 2022)