Search Results (206)

Standard maturity methods for concrete monitoring rely primarily on temperature history, often neglecting the influence of internal relative humidity (RH) on hydration kinetics and self-desiccation risks. Continuous in situ monitoring of internal RH remains a challenge due to the high cost, proprietary nature, and lack of reproducibility of existing solutions. This study evaluates a low-cost, open-source embedded sensor array designed to characterize early-age curing behavior through trend-based monitoring—defined here as the evaluation of ensemble consistency and repeatability rather than absolute metrological traceability. The prototype system, based on SHT31 sensors controlled by an ESP32 microcontroller, was embedded in high-performance concrete cylinders (f′c = 45 MPa) to capture the exothermic hydration peak and the equilibration of internal humidity. Results demonstrate that while the sensor encapsulation introduced a geometric disturbance that reduced compressive strength by approximately 25%—a limitation requiring mitigation in structural applications—the system successfully captured reproducible curing transitions. The proposed framework provides an accessible tool for experimental research into internal curing conditions, offering a digital complement to traditional surface-based quality control. Full article

Background/Objectives: Stewartia pseudocamellia Maxim. (S. pseudocamellia) has been reported to possess antioxidant and anti-inflammatory properties and contains various bioactive flavonoids and phenolic compounds. These components may contribute to neuroprotective effects relevant to depression and cognitive dysfunction. This study was conducted to evaluate the effects of 20% ethanolic extract from S. pseudocamellia leaves (ESP) on chronic unpredictable mild stress (CUMS)-induced depressive-like behaviors and cognitive dysfunction in C57BL/6 mice. Methods: C57BL/6 mice were divided into six groups: normal control (NC), normal sample (NS; ESP 100 mg/kg), CUMS, L-theanine (Thea; 4 mg/kg), ESP 50 mg/kg, and ESP 100 mg/kg groups. Phytochemical profiling of ESP was performed using ultra-performance liquid chromatography–quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS). Depressive-like behaviors and cognitive function were assessed, along with stress-related hormonal regulation and associated cellular signaling pathways. Results: Phytochemical profiling of ESP identified procyanidin B2, epicatechin, rutin, catechin gallate, kaempferol 3-O-glucoside, and quercitrin as major constituents. ESP significantly alleviated CUMS-induced depressive-like behaviors and improved spatial learning and memory. These effects were associated with modulation of stress-related hormones in serum and hypothalamic–pituitary–adrenal (HPA) axis–related proteins in the brain. ESP also enhanced antioxidant defense by activating the Nrf2 signaling pathway and improving mitochondrial function. Furthermore, ESP attenuated neuroinflammation and apoptosis by regulating the TLR4/NF-κB and JNK pathways, and promoted neuroplasticity by modulating cholinergic activity, with enhanced BDNF/TrkB signaling in the cerebral cortex and hippocampus. Conclusions: Collectively, these findings suggest that ESP exerts protective effects against CUMS-induced depressive-like behaviors and cognitive deficits in a preclinical model. Full article

Breast cancer is the most frequently diagnosed malignancy among women worldwide, with the luminal A subtype being the most prevalent. Several studies have reported a complex interplay between breast cancer cells and the local microbiome; however, the mechanisms by which tumor cell-secreted factors influence bacterial biological properties remain insufficiently explored. In this study, we established an in vitro model that partially recapitulates the luminal A breast cancer microenvironment by exposing three breast-associated bacterial species, Pseudomonas aeruginosa, Enterococcus faecalis, and Escherichia coli, to conditioned media (CM) derived from MCF-7 (tumor) or MCF-10A (non-tumor control) cell lines. A combination of complementary approaches, including ultrastructural morphological assessment, biofilm formation assays, antimicrobial susceptibility testing, and virulence gene abundance profiling by genomic qPCR, was employed to reveal distinct tumor-microbiota interactions. Exposure to MCF-7 CM induced dose-dependent structural alterations in P. aeruginosa and E. faecalis, with pronounced membrane blebbing and structural disruption in E. faecalis. Biofilm formation was differentially modulated in a species- and concentration-dependent manner, with a persistent increase observed in E. coli. Antibiotic susceptibility profiles were selectively altered in E. faecalis, which displayed increased sensitivity to vancomycin, penicillin, and imipenem, along with decreased sensitivity to chloramphenicol. P. aeruginosa exhibited increased sensitivity to imipenem along with reduced sensitivity to meropenem and gentamicin, whereas no significant changes were observed in E. coli. qPCR analyses demonstrated that MCF-7 CM was associated with enrichment of multiple virulence-associated genes (e.g., lasB, exoS, pilB, plcH, fsrC, esp, fimH, and papG), reflecting enhanced pathogenic and adhesive potential. Collectively, these findings suggest that luminal A breast cancer-derived factors can reprogram microbial phenotypes in a species-specific manner, providing mechanistic insight into breast tumor-microbiome crosstalk and a platform to explore microbiome-targeted interventions. Full article

The trematode Clinostomum marginatum, secretes excretory-secretory products (ESPs) which have the potential to increase the viability and antioxidant activity of probiotic strains. The aim of this study was to identify the ESP profile of C. marginatum and to evaluate its anti-inflammatory activity in RAW 264.7 macrophages, as well as its effect on the viability and antioxidant activity of a consortium of bacteria comprising Lactobacillus and/or Bifidobacterium. C. marginatum was maintained in RPMI-1640 medium for ESP collection. Anti-inflammatory activity was assessed in LPS-stimulated RAW264.7 cells treated with 800 µg/mL of ESPs, measuring cell viability, nitric oxide production, and the relative expression of pro-inflammatory cytokines (IL-6, TNF-α, INF-γ) and the COX-2 gene by qPCR. The influence of ESPs (800–1600 µg/mL) on probiotic viability and antioxidant activity was determined using MTT, DPPH, hydroxyl, and superoxide radical scavenging assays. C. marginatum showed 74% survival in vitro, and SDS-PAGE analysis revealed three major protein bands in the ESPs (47, 54, and 58 kDa). ESP treatment significantly reduced nitric oxide and the mRNA expression of pro-inflammatory markers in LPS-activated macrophages. ESPs supplemented at 1200 µg/mL optimized the growth kinetics of Lactobacillus (specific growth rate μ_L = 1.12 h⁻¹, doubling time t_d = 0.62 h) and Bifidobacterium (μ_B = 1.09 h⁻¹, t_d = 0.63 h) compared to control conditions. In conclusion, ESPs from C. marginatum exhibited significant anti-inflammatory and antioxidant effects while enhancing bacterial viability, which positions them as promising candidates for biotherapeutics agents in the management of inflammatory control and gut microbiota modulation. Full article

Background/Purpose: Primary urethral cancer is a rare malignancy, accounting for less than 1% of all urogenital cancers. Current epidemiological data from Europe are scarce and outdated. Therefore, the analyzes and comparison of the incidence and mortality of PUC in selected European countries, with particular focus on Poland, based on the most recent available registry data, were performed. Methods: Our study is based on country-level data and is descriptive in nature. Incidence data for PUC were obtained from the national cancer registries of Poland, Latvia, Slovenia, and Hungary. Mortality data were sourced from the WHO Mortality Database. Age-standardized incidence rates were calculated for two time intervals (2000–2009 and 2010–2019). Age-standardized mortality rates for individuals aged ≥45 years were calculated using the European Standard Population (ESP2013). Trends in incidence and mortality in Poland were analyzed using a five-year moving average. Results: The highest incidence of PUC was observed in Hungary, while Poland showed the lowest incidence. Latvia had the highest ASMRs for both sexes, whereas Poland and Greece reported the lowest mortality rates. Despite slight annual fluctuations, the overall PUC mortality rate in Poland has remained stable. Our study is limited by the relatively short analyzed period (2000–2021), restricted availability of C68.0 incidence data from national cancer registries, and incomplete mortality data in the WHO mortality database. Conclusions: This first contemporary comparative analysis of PUC epidemiology in Europe highlights the rarity of this malignancy and the limited data availability. Based on the knowledge drawn from the literature presented in the article on the impact of centralization on the increase in overall survival and the decrease in mortality in rare cancers, the authors believe that centralization of care can improve PUC patient outcomes. Full article

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen of public health concern, requiring a One Health approach to clarify its transmission and distribution. However, its prevalence and genomic characteristics in livestock and companion animals remain underexplored in low-income countries. We investigated prevalence and genomic features of STEC in animals in western Ghana, representing the first genomic report of STEC in Ghana. Fecal samples (97) were collected from goats (n = 33), sheep (n = 33), dogs (n = 30), and a cat (n = 1), with STEC detected in 12.1% of goats and sheep samples. Whole-genome sequencing identified serotypes O38:H26, O43:H2, and O157:H7. stx1c and stx2b genes were detected in O38:H26 and O43:H2, whereas stx2c and key virulence genes (chuA, eae, esp, nle, tir, and toxB) were exclusively found in O157:H7. Phylogenetic analysis revealed that O38:H26 isolates form a cluster closely related to clinical strains from the UK. O43:H2 isolates exhibited diverse stx profiles, linking animal, environmental, and clinical strains from North America and the UK. O157:H7 isolates were genetically similar to European clinical and food-derived strains, suggesting that goats and sheep are important STEC reservoirs in Ghana, offering data for public health risk assessment and effective One Health-based control strategies. Full article

Industrial Internet of Things (IIoT) deployments increasingly rely on low-cost microcontrollers and single-board computers to stream operational telemetry for monitoring, control, and predictive maintenance, yet the canonical “TLS-to-broker” model does not protect message content from a compromised or curious MQTT broker. This study therefore designs and implements a practical, application-layer end-to-end (E2E) encryption pipeline spanning an ESP32 data client (C++/mbedTLS), an untrusted MQTT broker, and a Raspberry Pi gateway (Python/PyCryptodome) using AES-256-GCM with Additional Authenticated Data (AAD). Sensor measurements are serialized as compact JSON, encrypted and authenticated on the ESP32, framed into a binary record, Base64-encoded for MQTT payload carriage, and verified/decrypted only at the gateway. Experiments on ESP32-WROOM-32 and Raspberry Pi 4 show an average ESP32 packet-preparation latency of 41.754 ms (JSON 1.0 ms; AES-GCM 29.5 ms; Base64 11.2 ms), robust rejection of ciphertext tampering and unauthorized devices via MAC verification and whitelist checks, and 99.72% decrypt-and-store success over a one-hour run (718/720 messages). These results indicate that commodity IIoT hardware can support practical and replicable E2E confidentiality and integrity without sacrificing operational throughput, while eliminating the MQTT broker as a de facto man-in-the-middle. Full article

Thermal limitations restrict the performance of low-cost, vertically stacked embedded systems. This paper presents a self-referencing digital twin framework for thermal and task management in a multi-device ESP32-S3 stack. The system combines a Mixture-of-Experts (MoE) model for task allocation with a neural network for short-term temperature prediction. Acting as a lightweight digital replica of the physical stack, the digital twin continuously monitors device states, forecasts thermal behavior 30 s into the future, and adapts workload distribution accordingly. The MoE model evaluates each device individually and asynchronously, estimating the portion of workload it should receive based on current state features including SoC temperature, CPU frequency, stack position, and recent task history. A separate neural network predicts future temperatures using real-time data from local and neighboring devices, enabling proactive thermal-aware scheduling. Training data for both models is collected through controlled experiments involving fixed-frequency operation and structured frequency switching with idle phases. All predictions and control actions are driven by in-built sensor feedback from the ESP32-S3 microcontrollers. The resulting digital twin supports distributed task scheduling based on temperature and works well in simple, low-cost edge systems with heat constraints. In one-hour experiments on a 6 ESP32-S3 stack, the proposed scheduling method completes up to 572 computation rounds at a 50 °C temperature limit, compared with 493 and 542 rounds under logistic regression based control and 534 rounds at fixed 240 MHz operation, while keeping peak temperature at 51 °C. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

Based on comprehensive experimental datasets—proximate/ultimate analyses, XPS, solid-state ¹³C NMR, and Raman spectroscopy—we constructed and optimized a compositionally faithful macromolecular model of SG coking coal. Using density-functional theory (DFT) calculations, we simulated electrostatic-potential (ESP) fields and frontier molecular orbitals (FMO) to probe elementary oxidation steps relevant to combustion, and focused on how heteroatom speciation and carbon ordering govern site-selective reactivity. Employing multi-peak deconvolution and parameter synthesis, we obtained an aromatic fraction f_a = 76.56%, a bridgehead-to-periphery ratio X_BP = 0.215, and Raman indices I_D1/I_G ≈ 1.45 (area) with FWHM(G) ≈ 86.7 cm⁻¹; the model composition C₁₉₀H₁₄₄N₂O₂₁S and its predicted ¹³C NMR envelope validated the structural assignment against experiment. ESP–FMO synergy revealed electron-rich hotspots at phenolic/ether/carboxyl and thiophenic domains and electron-poor belts at H-terminated edges/aliphatic bridges, rationalizing carbon-end oxidation of CO, weak electrostatic steering by O₂/CO₂, and a benzylic H-abstraction → edge addition → O-insertion/charge-transfer sequence toward CO₂/H₂O, with thiophenic sulfur comparatively robust. We quantified surface functionalities (C–O 65.46%, O–C=O 24.51%, C=O 10.03%; pyrrolic/pyridinic N dominant; thiophenic-S with minor oxidized S) and determined a naphthalene-dominant, stacked-polyaromatic architecture with sparse alkyl side chains after Materials Studio optimization. The findings are significant for mechanistic understanding and control of coking-coal oxidation, providing actionable hotspots and a reproducible workflow (multi-probe constraints → model building/optimization → DFT reactivity mapping → spectral back-validation) for blend design and targeted oxidation-inhibition strategies. Full article

Controlling the microclimate is vital in fruit fly breeding. This project develops an automated IoT system for monitoring and controlling temperature and relative humidity in chambers, optimizing processes through accessible and flexible technology. It uses a multi-layer system starting with the application layer with the ESP32 for data acquisition and actuator control. The second layer is the network layer, and the perception layer uses VisionFive2 with MQTT and HTTP protocols for communication, as well as NodeRed for flow orchestration and MySQL for data management and storage. In the validation, there are absolute errors of ±0.434 °C and ±0.5 RH, which are values within the acceptable ranges for these applications. Full article

This study presents the design and validation of an AI-enhanced embedded IoT system for real-time industrial sensor calibration. The proposed platform integrates a PT100 temperature sensor and a 4–20 mA pressure transmitter with an ESP32 microcontroller, enabling on-device data acquisition, processing, and wireless transmission. A lightweight multilayer perceptron (MLP) neural network, trained in Python with a hybrid dataset (synthetic and experimental) and deployed on the ESP32 via JSON weight files, performs local inference to estimate ideal sensor outputs and compute key performance metrics. Experimental tests under controlled laboratory conditions confirmed high accuracy, with efficiency above 98.6%, RMSE below 0.005 V, and absolute uncertainty margins of ±0.5 °C and ±0.07 bar. Additionally, 95% confidence intervals for RMSE and standard deviation demonstrated statistical reliability across all operating points. The prototype also addresses practical constraints, including ESP32 ADC nonlinearity, energy consumption, and multi-sensor scalability, while remaining portable and low-cost. The integration of edge AI capabilities demonstrates the feasibility of executing accurate neural network models directly on embedded microcontrollers, eliminating reliance on cloud-based processing. The proposed solution provides a robust proof-of-concept that is scalable, cost effective, and suitable for industrial IoT applications, predictive maintenance, and Industry 4.0 environments, with future work focusing on long-term drift evaluation and validation under real industrial conditions. Full article

This study presents the design, implementation, and field validation of a low-cost Internet of Things (IoT)-enabled embedded platform for real-time energy efficiency assessment of heat pump systems. The platform integrates an ESP32-based microcontroller with calibrated temperature, flow rate, and electrical current sensors to automate the determination of the coefficient of performance (COP). Unlike conventional studies that rely on high-cost instrumentation or laboratory-only trials, this work emphasizes a portable and scalable solution deployable in real environments. A three-month continuous deployment demonstrated high measurement accuracy against certified reference instruments, achieving mean absolute error (MAE) values of 0.29 °C (temperature), 0.0027 L/min (flow rate), and 0.110 A (current). Under steady-state conditions, the daily COP ranged from 1.43 to 4.43, diverging from the nominal manufacturer value of 3.83 and confirming the influence of operational and environmental factors reported by field studies. These results indicate that the platform serves not only as a diagnostic and monitoring tool but also as a replicable framework for predictive maintenance, operational optimization, and sustainable energy management in both grid-connected and off-grid applications, bridging the gap between engineering implementation and scientific assessment of heat pump performance. Full article

Phosphate tailings (PTs) are typical industrial byproducts that can rapidly neutralize soil acidity. However, their acid-neutralizing efficacy, long-term application optimization mechanisms, and high-yield regulation pathways for crops remain unclear. This study conducted a corn-potato crop rotation field trial on acidic soils, investigating the effect of different PT application rates (T: CK, 0 t·ha⁻¹; PTs-1, 6 t·ha⁻¹; PTs-2, 9 t·ha⁻¹; PTs-3, 15 t·ha⁻¹) in a multiple cropping system (C: late autumn potatoes (LAP)-early spring potatoes (ESP)-summer maize (SM)). The results showed that two consecutive applications of 9 t·ha⁻¹ of PTs produced optimal results, increasing the LAP yield by 12.82% and the soil quality by 76.51%, while improving the ESP soil quality by 46.21%. The higher yield was mainly attributed to a significant increase in the soil pH (0.72–1.58 units) and enhanced chemical and biological properties (higher exchangeable calcium (ExCa), exchangeable magnesium (ExMg), the total exchangeable salt base ion (TEB), and catalase (CAT) and urease (UE) content and lower soil exchangeable acidity (EA), exchangeable hydrogen ion (ExH), and exchangeable aluminum (ExAl) levels). Notably, a synchronized increase in the total phosphorus (TP) and total potassium (TK) during LAP cultivation, combined with simultaneous growth of TP, available nitrogen (AN), and available phosphorus (AP) during ESP cultivation, and a significant increase in TP and AP during SM cultivation, effectively promoted crop yield. Furthermore, continuous PT application significantly enriched phosphorus (P)-soluble functional bacteria, such as Actinomycetes and Chloroflexota, and enhanced the stability of bacterial-fungal cross-boundary networks. In summary, optimal acidity levels and favorable soil texture improved soil quality, consequently increasing corn and potato yields. This study reveals for the first time that PTs can substantially increase crop production via a synergistic mechanism involving acid-base balance, structural improvement, and microbial activation. Not only does this provide a novel strategy for rapidly improving acidic soils, but it also establishes a solid theoretical and technical foundation for utilizing PT resources. Full article

With the increasing prevalence of Internet of Things (IoT) devices in areas like authentication, data protection, and access control, general purpose microcontrollers (MCUs) have become the primary platform for security-critical apps. However, the expense of these attacks has decreased significantly in recent years, making them a viable threat to MCU-based devices. We present a framework-driven perspective with a comparative survey of MCU fault injection resilience for IoT. The survey supports—and is organized around—the procedural evaluation framework we introduce. We discuss the basic requirements for security first, and then categorize the common types of hardware intrusion, including side-channel attacks, fault injection attacks, and invasive methods. We synthesize reported security technologies employed by MCU vendors, such as TrustZone/TEE, Physical Unclonable Functions (PUF), secure boot, flash encryption, secure debugging, and tamper detection, in the context of FIA scenarios. A comparison of representative MCUs—STM32U585, NXP LPC55S69, Nordic nRF54L15, Espressif ESP32-C6, and Renesas RA8M1—highlights cost–security trade-offs relevant to token-class deployments. We position this work as a framework/perspective: an evidence-first FI evaluation protocol for token-class MCUs, a portable checklist unifying PSA/SESIP/CC expectations, and a set of concrete case studies (e.g., ESP32-C6 secure boot hardening). We do not claim a formal systematic review. Full article