Search Results (34)

Microcontroller-based embedded systems integrating multiple sensors are increasingly required to support continuous data acquisition, on-board processing, and long-term storage within tightly coupled hardware–software architectures. In such platforms, overall performance is often constrained not by computational capability but by storage I/O behavior, particularly under real-time constraints and concurrent workloads. This study presents a comprehensive empirical evaluation of eMMC storage performance on an STM32U5 microcontroller running the ThreadX RTOS. The proposed methodology combines multi-dimensional stress testing, controlled task concurrency (0–4 tasks), and long-duration aging analysis (90 h), together with timing variability assessment under electrical stress and interrupt-driven preemption. Both synthetic workloads and realistic sensor-node scenarios with heterogeneous and asynchronous access patterns are considered. The results highlight significant performance limitations, including up to 98% throughput degradation under four concurrent tasks and a nonlinear increase in metadata latency as free space decreases below 40% (from 10 ms to over 200 ms for file creation). Additionally, timing jitter increases by 2–5× under voltage variation and interrupt load. Based on these findings, practical firmware-level design guidelines are derived, including sector-aligned buffering, dedicated I/O task architectures, and proactive capacity management, enabling substantial improvements in throughput and latency. This study provides quantitative insights and reproducible methodologies for optimizing storage subsystems in multi-sensor embedded applications. Full article

Down syndrome (DS) is associated with persistent language impairments that extend beyond early childhood, yet evidence from agglutinative languages remains limited. While morphosyntactic weaknesses have been well-documented in Indo-European languages, less is known about how such difficulties are manifested in Turkish, a language in which grammatical relations are primarily marked through morphology. In addition, short-term memory (STM) limitations, particularly in verbal domains, are characteristic of DS and may contribute to language outcomes. This study examined the interaction between morphosyntax and STM in Turkish-speaking children and adolescents with DS. A cross-sectional observational design was employed, including 12 monolingual Turkish-speaking participants with DS (aged 6;7–15;11) and 10 TD peers matched on nonverbal mental age. Participants completed standardized assessments of syntax and morphology, spontaneous language sampling, and STM tasks assessing verbal and visual memory. Children with DS performed significantly below controls on syntactic comprehension and production as well as morphological measures, with larger effects observed for syntax. Noun morphology was less accurate than verb morphology, likely reflecting increased morphophonological complexity. Regression analyses indicated that auditory digit span predicted sentence comprehension, whereas nonword repetition predicted morphological production indexed by mean length of utterance in morphemes. Substantial inter-individual variability was observed within the DS group. These findings suggest that morphosyntactic outcomes in Turkish-speaking children with DS are closely linked to verbal STM capacities and vary considerably across individuals, underscoring the importance of integrated assessment and individualized intervention planning. Future research with larger samples is warranted to confirm and extend these preliminary findings. Findings should be interpreted cautiously due to the limited sample size and are presented as preliminary descriptive evidence. This study provides initial data on Turkish-speaking individuals with Down syndrome. Full article

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg. Full article

Advancements in technology and the emergence of vehicle-to-everything communication encourage new research approaches. Continuously sharing data through the onboard unit, connected vehicles (CVs) have proven to be a valuable source of real-time microscopic traffic data. Utilizing CVs as mobile sensors is a key driver for traffic safety improvement and increasing the effective operative road capacity. Data obtained from CVs can be effectively processed using speed transition matrices (STMs) while preserving spatial and temporal characteristics. This research proposes a new approach to adaptive traffic signal control utilizing STMs and a cooperative multi-agent learning system for the environment of CVs. To confirm its effectiveness, the concept is tested in a simulated environment of an intersection network, comparing different CVs’ penetration rates and cooperation coefficients between agents. Full article

Research has shown that language ability can vary enormously depending on variables such as musical ability, musical training, and second and/or foreign language experience. In this study, we simulated initial foreign language learning conditions in which learners must recognize and match unfamiliar language input. We recruited 500 participants with different levels of foreign language experience, different levels of musical training and different socio-economic backgrounds. Their auditory phonological pattern recognition ability, short-term memory (STM) capacity, musical ability, musical self-estimation, educational status, and socio-economic status (SES) were assessed. Both overall and group-specific analyses were conducted to investigate the impact of these variables. For the group-specific analysis, participants were assigned to four groups based on the presence or absence of musical training and extensive foreign language experience. For the overall analysis, regression models were applied to the entire sample to examine the combined effects of all variables. Group-specific analyses revealed that both musical training and extensive foreign language experience contributed to individual differences in the ability to recognize phonological patterns in unintelligible auditory stimuli. A key finding was that musical training appeared to have a stronger influence on auditory phonological pattern recognition than extensive foreign language experience, particularly in the early stages of language learning. This suggests that musical training may exert a greater impact on initial phonetic acquisition processes than extensive foreign language proficiency, especially when the language stimuli are relatively poor in linguistic content. The overall analysis revealed that musical variables, short-term memory capacity, socioeconomic status, and educational status all contributed to individual differences in auditory phonological pattern recognition. Notably, the most significant finding of the overall analysis was the association between SES and auditory phonological pattern recognition in unfamiliar speech—a result that challenges the notion of aptitude measures as stable and environment-independent and highlights the potential influence of environmental factors on this capacity. Full article

This study presents a strut-and-tie modeling (STM) framework for reinforced concrete (RC) deep beams strengthened with intraply hybrid composites (IRCs), integrating comprehensive experimental data from beams with three different span lengths (1.0 m, 1.5 m, and 2.0 m). Although the use of fiber-reinforced polymers (FRPs) for shear strengthening of RC members is well established, limited attention has been given to the development of STM formulations specifically adapted for hybrid composite systems. In this research, three distinct IRC configurations—Aramid–Carbon (AC), Glass–Aramid (GA), and Carbon–Glass (CG)—were applied as U-shaped jackets to RC beams without internal transverse reinforcement and tested under four-point bending. All experimental data were derived from the authors’ previous studies, ensuring methodological consistency and providing a robust empirical basis for model calibration. The proposed modified STM incorporates both the axial stiffness and effective strain capacity of IRCs into the tension tie formulation, while also accounting for the enhanced diagonal strut performance arising from composite confinement effects. Parametric evaluations were conducted to investigate the influence of the span-to-depth ratio (a/d), composite configuration, and failure mode on the internal force distribution and STM topology. Comparisons between the STM-predicted shear capacities and experimental results revealed excellent correlation, particularly for deep beams (a/d = 1.0), where IRCs substantially contributed to the shear transfer mechanism through active tensile engagement and confinement. To the best of the authors’ knowledge, this is the first study to formulate and validate a comprehensive STM specifically designed for RC deep beams strengthened with IRCs. The proposed approach provides a unified analytical framework for predicting shear strength and optimizing the design of composite-strengthened RC structures. Full article

In the last decade, German forests have been decimated because of extreme events such as drought and windthrow, and bark beetle infestations that occur in the aftermath, primarily in monoculture Norway spruce stands. It is essential for decision makers in forest management to have an educated estimation of potential future loss. We have developed a model to predict future canopy cover loss in German spruce forests. Since, past canopy cover loss is a key predictor, we adapt the spatio-temporal matrix (STM) method used for predicting urban growth, to work with a canopy-cover-loss time-series product based on earth observation data. We configure a hybrid neural network model using the STM, its percentiles along with climatic and topographic data to produce the probability information of canopy cover loss in German spruce forests in the next year. The prediction results from the model show a good capacity of prediction, as validation results present an AUC of the ROC space as high as 82.3%. Our results show that future canopy cover loss can be predicted with reasonable accuracy using open-access earth-observation time-series data supplemented by environmental data without the need for site specific in situ data collection. Full article

This study investigates the issues of non-unique model configurations and insufficient guidance for reinforcement design encountered when applying the strut-and-tie model (STM) method to reinforced concrete deep beams with openings. Using concrete deep beam specimens with three openings as a case study, the topology optimization method was employed to establish the initial STM, which was subsequently refined through crack propagation simulation technology to develop the final optimized STM for guiding reinforcement design. Experimental investigations and comparative analyses with existing literature demonstrate that the proposed approach offers significant advantages in controlling initial concrete cracking, improving structural load-bearing capacity, and reducing steel reinforcement consumption for such perforated deep beams designed with this optimized STM methodology. Full article

The purpose of this study is to investigate the effects of verbal working memory (VWM) interventions on reading speed, accuracy, and comprehension in elementary school students diagnosed with specific learning disabilities (SLD). Given the limited research on the role of VWM in reading performance, this study fills a critical gap in the literature. A pre-test and post-test design was employed, with an experimental group (n = 14) receiving VWM interventions over 4 weeks, while the control group (n = 12) received no intervention. The intervention focused on enhancing VWM and verbal short-term memory (V-STM) through structured cognitive tasks, including rehearsal techniques and phonological loop strengthening activities, delivered over 24 sessions. Results showed that although VWM interventions significantly enhanced VWM capacity (t(24) = 3.39, p < 0.05, d = 1.48), they did not lead to significant improvements in reading speed or accuracy. However, a statistically significant improvement in reading comprehension was observed (p = 0.04, d = 0.92). These findings suggest that while enhancing VWM may not directly improve reading fluency, it can positively affect comprehension. The study highlights the importance of considering VWM in educational interventions targeting reading comprehension and recommends further research into other cognitive and linguistic factors influencing reading speed and accuracy. Additionally, future studies should explore the long-term effects of diverse intervention strategies on reading outcomes. Full article

Solid-state DC circuit breakers provide crucial support for the safe and reliable operation of low-voltage DC distribution networks. A hardware topology based on a cascaded structure with dual-stage, current-limiting, small-capacity, solid-state DC circuit breakers has been proposed. The hardware topology uses a series–parallel configuration of cascaded SCR (thyristors) and MOSFETs (metal oxide semiconductor field-effect transistors) in the transfer branch, which enhances the breaking capacity of the transfer branch. Additionally, a secondary current-limiting circuit composed of an inductor and resistor in parallel is integrated at the front end of the transfer branch to effectively improve the current-limiting performance of the circuit breaker. Meanwhile, a dissipation branch is introduced on the fault side to reduce the energy consumption burden on surge arresters. For the power supply system of the hardware part, a capacitor-powered method is adopted for safety and efficiency, with a capacitor switch serially connected to the capacitor power supply for high-precision control of the power supply. Current detection branches are introduced into each branch to provide conditions for the on–off control of semiconductor switching devices and experimental data analysis. The high-frequency control of semiconductor devices is achieved using optocoupler signal isolation chips and high-speed drive chips through a microcontroller STM32. Simulation verification based on MATLAB/SIMULINK software and experimental prototype testing have been conducted, and the results show that the hardware topology is correct and effective. Full article

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, and health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on a global scale. However, traditional physical and statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence methods are increasingly applied to aerosol parameter retrieval, yet most current approaches focus on end-to-end single-parameter retrieval without considering the inherent relationships among multiple aerosol properties. In this study, we propose a sequence-to-sequence aerosol parameter joint retrieval algorithm based on the transformer model S2STM. Unlike conventional end-to-end single-parameter retrieval methods, this algorithm leverages the encoding–decoding capabilities of the transformer model, coupling multi-source data such as polarized satellite, meteorological, model, and surface characteristics, and incorporates a physically coherent consistency loss function. This approach transforms traditional single-parameter numerical regression into a sequence-to-sequence relationship mapping. We applied this algorithm to global observations from the Chinese polarimetric satellite (the Particulate Observing Scanning Polarimeter, POSP) and simultaneously retrieved multiple key aerosol optical and microphysical parameters. Event analyses, including dust and pollution episodes, demonstrate the method’s responsiveness in hotspot regions and events. The retrieval results show good agreement with ground-based observation products. This method is also adaptable to satellite instruments with various configurations (e.g., multi-wavelength, multi-angle, and multi-dimensional polarization) and can further improve its spatiotemporal generalization performance by enhancing the spatial balance of ground station training datasets. Full article

This study presents the design and implementation of a piezoelectric power harvesting device to capture vibrational energy from pipelines to self-powered IoT devices. The device utilizes key components along with the PPA-1001 piezoelectric sensor, the STM32F103C8T6 microcontroller, and LTC-3588 energy harvesting power supply. Experimental results verified the system’s performance in harvesting power within a specific frequency range of 10 Hz to 50 Hz, with the foremost overall performance at 30 Hz. The device generated the highest voltage of 3.3 V, delivering a power output of 2.18 mW, which is sufficient to power low-power electronic devices. The device maintained solid performance across a temperature range of 40 °C to 50 °C, underscoring its robustness in various environmental situations. The findings highlight the capacity of this form of generation to offer a sustainable power source for remote pipeline tracking, contributing to stronger protection and operational efficiency. Full article

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. Full article

A deficit in short-term memory (STM) functions characterizes many neurodevelopmental disorders, in particular, specific learning disorders. Hence, there is a need to develop a web-based platform capable of testing specific variables and administration conditions in a controlled manner. The platform herein presented allows for the assessment of short-term memory (STM) items and order components in a series of different conditions. Stimulus types, presentation, and response modalities were appropriately selected to assess the impact of those variables on memory performances. The usefulness of such a systematic, fine-grained analysis of STM functions was tested by applying the complete assessment in a group of 100 school-age children (47 Typically Developing children and 53 children with learning disorders) and evaluating the capacity of the software to highlight different specific memory processes activated during reading, writing, and calculation. A cluster analysis was applied to the learning performances of the whole group, and a four-cluster solution representing the best division into subgroups of learning disorders (affecting reading, writing, and mathematical skills, variously combined) also showed clear-cut differences in the children’s STM profiles. This confirms the potential and the usefulness of the tool for the characterization of STM in school-age children. Full article

Bacterial surface display platforms have been developed for applications such as vaccine delivery and peptide library screening. The type V secretion system is an attractive anchoring motif for the surface expression of foreign proteins in gram-negative bacteria. SadA belongs to subtype C of the type V secretion system derived from Salmonella spp. and promotes biofilm formation and host cell adherence. The inner membrane lipoprotein SadB is important for SadA translocation. In this study, SadA was used as an anchoring motif to expose heterologous proteins in Salmonella typhimurium using SadB. The ability of SadA to display heterologous proteins on the S. typhimurium surface in the presence of SadB was approximately three-fold higher than that in its absence of SadB. Compared to full-length SadA, truncated SadAs (SadA⁸⁷⁷ and SadA²⁶⁹) showed similar display capacities when exposing the B-cell epitopes of urease B from Helicobacter pylori (UreB158–172aa and UreB349–363aa). We grafted different protein domains, including mScarlet (red fluorescent protein), the urease B fragment (UreBm) from H. pylori SS1, and/or protective antigen domain 4 from Bacillus anthracis A16R (PAD4), onto SadA⁸⁷⁷ or SadA¹²⁹². Whole-cell dot blotting, immunofluorescence, and flow cytometric analyses confirmed the localization of Flag×3-mScarlet (~30 kDa) and Flag×3-UreBm-mScarlet (~58 kDa) to the S. typhimurium surface using truncated SadA⁸⁷⁷ or SadA¹²⁹² as an anchoring motif. However, Flag×3-UreBm-PAD4-mScarlet (~75 kDa) was displayed on S. typhimurium using SadA¹²⁹². The oral administrated pSadBA¹²⁹²-FUM/StmΔygeAΔmurI and pSadBA⁸⁷⁷-FUM/StmΔygeAΔmurI could elicit a significant mucosal and humoral immunity response. SadA could thus be used as an anchoring motif for the surface expression of large heterologous proteins as a potential strategy for attenuated bacterial vaccine development. Full article