Search Results (261)

This paper presents a comprehensive design, implementation, and feasibility analysis of a sustainable styrene production plant that integrates conventional ethylbenzene dehydrogenation with bio-based feedstock processing. Located on the Gujarat coastline, India, the proposed facility is designed to produce 220,000 tonnes per year of styrene while addressing environmental, social, and economic sustainability objectives. Agricultural corn stover sourced from local farms is valorised via pretreatment, enzymatic hydrolysis, and two-phase simultaneous saccharification and fermentation (TPSSF), achieving ethanol yields of up to 477 kg per tonne of biomass. Bioethanol is subsequently alkylated with benzene over a beta-zeolite catalyst, delivering a 95% conversion with respect to bioethanol and 85.8% selectivity to ethylbenzene. This intermediate is then dehydrogenated at 550 °C using a potassium-promoted iron oxide catalyst to produce styrene with a 96.2% selectivity. Extensive heat integration, benzene recycling, and a steam-to-ethylbenzene ratio of 7:1 enhance energy efficiency and process robustness. Techno-economic analysis demonstrates that the plant remains commercially viable and profitable despite the incorporation of renewable feedstocks. The utilisation of corn stover reduces biomass waste and greenhouse gas emissions, lowers dependence on fossil resources, and supports local employment and supply chains. By embedding sustainability, safety, process integration, and economic assessment within a unified design framework, this work provides an industry-relevant and transferable model for low-carbon styrene manufacture aligned with UN Sustainable Development Goals 7, 12 and 13. Full article

Surface acoustic wave (SAW) resonators are widely employed to design radio-frequency (RF) filters in wireless communication. To adapt to various application scenarios, the article proposes an equivalent circuit model based on the Butterworth–Van Dyke (BVD) model for SAW devices operating with spurious modes and at extreme ambient temperatures. In cases where the resonance frequencies of the spurious modes are close to those of the main mode, isolation capacitances (IC) are proposed in the modeling process. With the IC, the different resonance frequencies produced by the proposed equivalent circuit model can be flexibly adjusted. The extreme temperature influence on the SAW resonators is investigated using the proposed model. In the temperature-dependent test environment, the performance of the SAW devices changes, and these changes are captured by the proposed model. Especially for the resonators’ spurious-mode frequencies, which are less influenced by temperature near room temperature unless extreme temperatures are applied. The parameters motional resistance R_m and motional inductance L_m are considered temperature-dependent and are used to describe the influence of the ambient temperature. The RF characteristics of the SAW devices are modeled with the proposed model and verified with measurement data. The consistent results between the measured data and simulated data indicate that the proposed model is accurate and the modeling work is effective. Full article

Background/Objectives: Cardiac auscultation is an important method for identifying cardiovascular abnormalities, but conventional methods are limited by examiner-dependent variability and sensitivity. The automatic classification of phonocardiogram (PCG) has the potential to be applied for standardized cardiac screening, but still has to deal with signal non-stationarity and the extraction of discriminative features. This investigation develops a computational framework integrating wavelet analysis with entropy-based features for distinguishing normal and abnormal heart sounds. Methods: The study employs the PhysioNet Computing in Cardiology Challenge 2016 database. Signal processing includes resampling, zero-phase Butterworth band-pass filtering (20–800 Hz), and median absolute deviation normalization. Time–frequency representations are generated through continuous wavelet transform (CWT) and synchrosqueezed CWT using analytic Morlet wavelets. Multiple entropy measures including Shannon, Rényi, Tsallis, spectral, permutation, and sample entropies are computed globally and across four physiologically motivated frequency bands (20–80, 80–200, 200–400, 400–800 Hz). A regularized multi-layer perceptron with dropout performs classification. Evaluation employs stratified 5-fold cross-validation with recording-level partitioning to prevent data leakage. Results: The best configuration using standard CWT with 800 Hz bandwidth achieved test-set AUROC of 0.972, balanced accuracy of 0.915, and 96% sensitivity maintained at 90% specificity. Contrary to expectations, standard CWT outperformed synchrosqueezed CWT with AUROC advantage of +0.038. Also, the band-specific entropy analysis provided the largest performance contribution with +4.1% to AUROC, confirming frequency-localized pathological signatures. Conclusions: This methodology demonstrates how the conventional wavelet analysis integrated with entropy engineering achieves state-of-the-art performance. Also, it maintains computational efficiency (1.2 s extraction and classification) and interpretability, offering practical potential for point-of-care cardiac screening in resource-limited settings. Full article

This study proposes a non-invasive dual respiratory monitoring system for intensive care units (ICU), based on a triaxial MEMS accelerometer ADXL345 attached to the thoracic cage and a nasal NTC thermistor, interfaced with an Arduino Mega 2560 microcontroller. The accelerometer signal is filtered through a second-order Butterworth low-pass filter (fc = 0.6 Hz) and double-integrated to obtain thoracic displacement ΔD, while the thermistor detects respiratory cycles through the thermal variation in nasal airflow. The system detects respiratory rate in the range of 4–60 breaths/minute, with amplitudes ΔD of 2–5 mm, providing superior sensor redundancy over individual methods. Full article

Determining the Remaining Useful Life (RUL) in roller bearings is of utmost importance in rotary machinery. Knowing the present state and acting before a failure occurs is the most important aspect in industrial setups. This research presents an effective methodology to determine the RUL state of roller bearings by successfully using different combinations of Daubechies order and decomposition levels of Wavelet Transforms and applying machine learning methods. A dataset comprising temperature and vibration signals collected from a roller bearing test rig was developed for this study. These signals were then filtered using Butterworth bandpass filter for vibration signal filtering and moving average filter for temperature signal filtering followed by splitting the signal into overlapping windows. Then the signals are subjected to Wavelet Packet Transform followed by statistical feature extraction. In the classification phase, machine learning models such as the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) were used to classify the RUL state in roller bearing. While analyzing different wavelet types (db1 to db10) through seven decomposition levels, this research determined that a db4 wavelet at the third level was identified as optimal for detecting RUL state in roller bearing. The results show that Support Vector Machine (SVM) classifier achieved maximum classification accuracy of 97.68 ± 0.64%, which is higher than the other classification models used in this study. These results show that the careful calibration of wavelet parameters, combined with an efficient machine learning model can provide a reliable solution for real-time machine health monitoring and predictive maintenance of rotating equipment. Full article

South Africa has been receiving a high number of undocumented immigrants for years. As Africa’s second largest economy and the biggest in the South African Development Community region, it has witnessed a surge in illegal migration. Some undocumented immigrants marry local women, establishing spousal and familial relations. The government has taken a legal stand against undocumented immigrants. It deports thousands annually. A grassroots movement, Operation Dudula, has initiated efforts to locate undocumented immigrants. Its modus operandi has been condemned for lacking ubuntu. A void is left when undocumented immigrants are deported, leaving their families in South Africa. Some South African women have turned to TikTok to express their views on migration and familyhood. This study attempts to answer these questions: What are the TikTok discursive practices of South African women in spousal relations with African immigrants? In what ways do the women legitimise the relationships? How does TikTok function as a subaltern counter-public formation? Data were gathered through digital archival research and subjected to a multimodal critical discourse analysis. The findings show that the women celebrate the relations as an achievement. They construct them as pathways to prosperity. The women also invoked racial discourses to legitimise the relations. Through TikTok, they recontextualised discourses on migration by deconstructing dominant narratives that project African immigrants through lenses of criminality. Full article

Insulators are essential for the secure and uninterrupted functioning of high-voltage transmission lines. However, since insulators are exposed to the outdoor environment, they are inevitably affected by environmental conditions such as icing. Accumulation of ice on insulator surfaces adversely impacts insulation efficacy and elevates surface leakage currents, resulting in power outages. This research presents a spectrogram-based convolutional neural network (CNN) model for identifying icing conditions on the surfaces of glass, porcelain, and silicone insulators. Insulators are labeled in three classes under laboratory conditions: ice-free, slightly iced (t < 12 mm), and iced (t > 20 mm). High voltage was applied at three distinct levels ranging from 10 to 50 kV, considering the icing conditions of each insulator, and leakage current signals were recorded. The Butterworth and smoothing filters were first applied to the leakage current signals, which were then transformed into spectrogram images using the Fourier transform and used as input for the created CNN architecture. Additionally, spectrogram images were also applied to AlexNet, GoogLeNet, and ResNet-50 architectures. The suggested CNN architecture attained an accuracy of 97.78% to 100% across all operating situations for glass and silicone insulators while demonstrating a classification success rate of 82.22% to 100% for porcelain insulators. Experiments indicate that the accuracy rates of established models in the literature (AlexNet, GoogLeNet, and ResNet-50) diminished to as low as 73%, particularly in porcelain insulator data, thereby validating the developed model’s proficiency in differentiating during icing detection processes and its adaptability to varying conditions. Full article

With the rapid expansion of 5G millimeter-wave communications, there is a strong demand for compact, low-loss, and high-selectivity filtering components. This paper presents the design and analysis of a compact dual-band substrate integrated waveguide (SIW) bandpass filter operating at 28 GHz and 38 GHz for 5G applications. The proposed structure employs shunt iris-loaded resonators integrated within the SIW cavity to achieve dual-band operation with improved frequency selectivity. The designed filter provides a narrow passband of 1.2 GHz at 28 GHz and a wider passband of 2.6 GHz at 38 GHz, while maintaining a compact footprint of 8.5 mm × 6.2 mm. It is implemented on a Rogers RT/Duroid 5880 substrate (${\epsilon }_r$ = 2.2, h = 0.508 mm), ensuring low dielectric loss and stable high-frequency performance. The simulated results demonstrate excellent return loss of 44.2 dB at 28.1 GHz and 52.7 dB at 38.3 GHz, along with a low insertion loss of approximately 0.68 dB, confirming efficient signal transmission. Furthermore, the design is validated using a simulation with ADS of second-order Butterworth equivalent circuit, providing design simplicity and demonstrating the feasibility of practical fabrication. On the other hand, it is well suited for integration into compact 5G front-end modules requiring high performance, miniaturization, and dual-band operation. Full article

Human activity recognition (HAR) using Wi-Fi channel state information (CSI) offers a privacy-preserving and contactless sensing modality suitable for smart homes, healthcare monitoring, and pervasive mobile Internet of Things (IoT) environments. However, existing CSI-based HAR approaches often suffer from computational inefficiency, high latency, and limited feasibility on resource-constrained embedded platforms. This work presents STAR (Sensing Technology for Activity Recognition), an edge AI-optimized framework that integrates lightweight temporal modeling, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR employs a streamlined three-layer Gated Recurrent Unit (GRU) architecture that reduces model parameters by 33% compared to conventional Long Short-Term Memory (LSTM) designs while maintaining strong temporal modeling capability. To enhance signal quality, STAR incorporates a multi-stage pre-processing pipeline consisting of median filtering, an eighth-order Butterworth low-pass filtering, and empirical mode decomposition (EMD) to denoise CSI amplitude measurements and extract stable spatial-temporal features. For on-device deployment, the system is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU) and interfaced with an ESP32-S3 CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human-presence detection using a compact 97.6k-parameter model. INT8-quantized inference achieves a processing throughput of 33 MHz with only 8% CPU utilization, achieving a six-fold improvement in inference speed over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments. Full article

Chronic stress and depression-adjacent emotional states affect over 970 million people worldwide, yet their continuous, objective detection from physiological signals remains unsolved, particularly when cues are subtle and conventional approaches fail. Single modality classifiers capture only part of the picture, and binary valence/arousal formulations collapse emotionally distinct states into the same category, leaving conditions like sadness and depression characterised by Low-Valence Low-Arousal (LVLA) responses without reliable detection. A hybrid deep learning model combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders was developed to jointly classify four emotional quadrants from multimodal physiological data in the Database for Emotion Analysis using Physiological signals (DEAP) dataset. Electroencephalography (EEG), Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), and respiration signals from 32 participants were preprocessed using fourth-order Butterworth filtering, trained with Adam optimisation, and evaluated through an 80/20 stratified split with five-fold cross-validation. The system achieved 91.2% four-quadrant valence–arousal accuracy (95% CI: 89.1–93.3%), with LVLA recall reaching 91.3%, outperforming all partial-hybrid variants. These findings demonstrate that hierarchical, attention-based fusion of physiological modalities can reliably distinguish stress from depression-adjacent states, offering a practical pathway toward continuous, non-invasive mental health monitoring on wearable platforms. Full article

Studentpreneurship has gained prominence in South Africa as universities are increasingly expected to foster innovation, job creation, and youth participation in the economy. However, despite the establishment of incubators, entrepreneurship centres, mentorship programmes, and EDHE-aligned initiatives, support for studentpreneurs remains unevenly implemented, poorly integrated, and inconsistently accessible, particularly within a historically disadvantaged university. This study examines how university support mechanisms shape the experiences, challenges, and business development trajectories of studentpreneurs in a South African university. Guided by Entrepreneurial Ecosystem Theory, the study adopts a qualitative research design involving in-depth interviews with 15 studentpreneurs. Thematic analysis reveals significant gaps in awareness, accessibility, and continuity of institutional support. While students valued motivational workshops, pitching opportunities, and limited mentorship, these interventions lacked sustained follow-up, sector-specific guidance, and financial or infrastructural resources necessary for business growth. The study contributes to South African entrepreneurship scholarship by highlighting the lived realities of studentpreneurs at a historically disadvantaged university and by proposing institutional reforms to build more coherent, equitable, and sustainable studentpreneurship ecosystems. Full article

Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring. Full article

Rainfed agriculture is the most common type of agriculture in South Africa among smallholder farmers, accounting for the majority of the arable land. In a country with so much potential, only about 8% of the arable land is under irrigation. In response, the South African post-apartheid government has invested in the establishment of irrigation schemes in rural provinces such as the Eastern Cape to promote the sustainability of smallholder farming systems. Despite these efforts, the participation of farmers in these schemes remains low. This study investigated socioeconomic and environmental factors that affect farming households’ level of participation in irrigation schemes and intensity. Cross sectional data was collected from 209 households using a multi-stage sampling procedure. Descriptive statistics was used to analyse the socio-economic and environmental factors. A double hurdle model was used to analyse both participation in irrigation and the intensity of participation. The study results reveal that agriculture is largely practised by elderly farmers with an average age of 54 years and largely female-dominated (58%). On average, farmers have 7.5 years of schooling and 12 years of farming experience. Econometric findings demonstrate that participation is significantly influenced by market access, whereas participation intensity is driven by market access, market information and the level of education. The study recommends strengthening gender-targeted agricultural support systems, improved water access through expanded and well-maintained irrigation infrastructure and improving market access. In addition, enhanced extension training support and youth-focused agricultural programmes are required to build productive capacity and ensure the long-term sustainability of irrigation schemes. Full article

The increasing emphasis on sustainable construction has positioned regulatory frameworks as critical drivers of sustainable building construction project delivery (SBCPD), particularly in developing countries such as South Africa. However, the effectiveness of different regulatory instruments remains insufficiently understood. This study investigates the influence of regulatory factors on SBCPD by examining two key constructs: Compulsory Enforcement and Incentivisation (CEI) and the Sustainable Building National Framework (SBNF). A quantitative research design was adopted, and data were analysed using Principal Component Analysis (PCA), Confirmatory Factor Analysis (CFA), and Structural Equation Modelling (SEM) to assess the relationships between regulatory mechanisms and project delivery outcomes. The findings reveal that CEI does not exhibit a statistically significant influence on SBCPD when modelled as a combined construct, despite showing significance when tested independently. This suggests that aggregating compulsory and voluntary regulatory instruments may weaken their explanatory power due to underlying interaction effects. In contrast, SBNF demonstrates a strong and statistically significant positive influence on SBCPD, highlighting the critical role of government-led policies, institutional frameworks, and certification systems in shaping sustainable construction practices. The study contributes to theory by advancing our understanding of regulatory hybridity and the role of institutional drivers in sustainable construction. In practice, the findings underscore the need for coherent, well-articulated policy frameworks, strengthened enforcement capacity, and strategic alignment between voluntary and mandatory instruments. The study concludes that government-led frameworks remain the primary catalyst for sustainable construction delivery in developing economies. These insights provide valuable guidance for policymakers and industry stakeholders seeking to enhance sustainability performance in the built environment. Full article

The prevalence of chronic conditions such as hypertension, diabetes, and Human Immunodeficiency Virus (HIV) is rising globally, yet access to continuous care remains limited, particularly in rural low- and middle-income countries. This study evaluated the acceptability and psychosocial predictors of retention in a linkage-to-care (LTC) intervention for patients with chronic conditions in rural South Africa. We conducted a cross-sectional analytical study with a retrospective cohort component among 1673 patients diagnosed with hypertension, diabetes, and/or HIV in Limpopo Province, South Africa. Acceptability and psychosocial factors were assessed cross-sectionally using a theory-informed, interviewer-administered questionnaire between January and June 2024. Retention in care over the preceding six months (July–December 2023) was extracted from routine clinic records and classified as consistent (no gaps > 6 months between visits) or inconsistent (≥1 gap > 6 months. Logistic regression examined associations between psychosocial factors and retention outcomes, adjusting for age, gender, marital status, and diagnostic category. Overall, 25.1% of participants maintained consistent retention over six months, while 74.9% were retained inconsistently. Acceptability of the LTC intervention varied significantly by diagnosis (p < 0.001): 79.5% of participants with multimorbidity rated the intervention as acceptable compared to 54.9% with hypertension, 64.5% with diabetes, and 46.8% with HIV. However, only 12.8% of multimorbid participants agreed that intervention activities fit well with their daily lives. In adjusted analyses, participants who were not happy to participate had 85% lower odds of consistent retention (adjusted odds ratio [AOR] = 0.15, 95% CI: 0.09–0.22) and 7.2 times higher odds of inconsistent retention (AOR = 7.2, 95% CI: 4.8–10.9). Most participants supported de-identified data sharing, though privacy concerns were elevated among those with multimorbidity. Acceptability of LTC interventions differs by diagnosis, with multimorbid patients reporting poorer alignment with daily routines. Retention is strongly associated with emotional engagement and self-efficacy, suggesting that LTC interventions should integrate psychosocial support and be contextually adapted for multimorbid patients in rural settings. Full article