Search Results (1,708)

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

While state-of-the-art massive acoustic arrays typically rely on costly, specialized FPGA architectures or rigid proprietary hardware, there is a growing need for modular, high-density sensing in complex aeroacoustics environments. This paper presents the electronic and acoustic design of a multiplexed, modular, scalable, and low-cost massive acoustic array (MxArray) founded on an embedded Linux system. The AM3358 SoC microprocessor collects audio data through its multichannel audio peripheral, where it simultaneously receives four Time-Division Multiplexing streams of 16 microphones each. This multiplexed scheme enables the handling of 64 microphones per module, whose acquisition synchronization is set with the Precision Time Protocol and a pulse injection hardware. The combination of both BeagleBone Black and microphones based on Micro-Electro-Mechanical Systems yields a cost-effective solution with built-in Ethernet connectivity and accessible software development through an embedded Linux environment with audio libraries for hardware control. Sensors are arranged in an Underbrink Spiral pattern on a four-layer printed-circuit board. The perforated thin layout minimizes any airborne disturbance, exploiting a distribution that simultaneously achieves a low sidelobe level and a narrow main lobe when used with a beamforming algorithm. Measurement results for the developed module are presented, as well as an evaluation of a full-scale system comprising 16 modules (1024 microphones) arranged in a honeycomb pattern. The resulting instrument offers a practical and scalable solution for applications that require a large number of simultaneous microphone measurements, such as beamforming technology for aeroacoustics applications. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

Experimentation in science education faces significant access limitations, both in face-to-face and distance learning settings; in light of this situation, remote laboratories are emerging as a strategic solution. The aim of this study is to present the design and development of an ultra-concurrent remote laboratory focused on the study of projectile motion. Using the Design-Based Research methodology, the resource has been structured around an iterative five-phase approach: design, data capture, development, test and improvement, and integration. The data acquisition system was developed using a hardware setup comprising a projectile launcher, photo gates, a digital interface and a time sensor, implemented and managed via the LabsLand platform. The laboratory integrates semi-parabolic and full-parabolic configurations via an interactive interface that guides the user from connecting components to the multimedia observation of real experimental data. The results of the experimental validation confirm the system’s viability, as the data obtained compare with ideal kinematic equations and reflect, as expected, the behaviour and physical limitations of the real-world environment. This laboratory offers a potential pedagogical advantage, reporting percentage errors around $3$–$11\%$, as it exposes students to experimental uncertainty whilst simultaneously ensuring simultaneous and free access for multiple users in science education. Full article

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

Image sensors produce high-dimensional visual data for classification algorithms. Deep Neural Networks (DNNs) achieve high accuracy but require large labeled datasets and computational and energy resources, limiting their use in embedded systems. Active Learning (ALrn) can reduce labeling effort by selecting samples based on informativeness scores, but it remains computationally expensive, especially for high-dimensional images. This work presents a hardware-accelerated approach for the instance selection stage based on a query strategy in uncertainty-based ALrn for image classification using a novel in-line top-k selection algorithm that avoids conventional sorting and reduces memory and computational requirements. The algorithm is implemented on an Xilinx ZYNQ-7000 System on Chip (SoC) using a Field Programmable Gate Array (FPGA)-based accelerator operating at 110 MHz, interfacing with an embedded Advanced RISC Machine (ARM) processor for data acquisition and communication via the Python Productivity for Zynq (PYNQ) framework. Experiments on diverse multiclass datasets demonstrate correctness within an ALrn setting, showing negligible performance deviation in the learning curves compared to software baselines. The accelerator achieves speedup of 231.7× and 22.9× over software baseline and optimized software implementation of the proposed algorithm, respectively, in query-strategy computation while consuming only 0.473 W, substantially lower than conventional Central Processing Unit (CPU)- and Graphics Processing Unit (GPU)-based platforms. These results demonstrate the efficiency and extensibility of the proposed accelerator across alternative ALrn designs and hardware platforms, where the computational cost of instance selection scales with the size of the unlabeled pool. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

Hydrogel-based optical sensors have emerged as a versatile class of analytical materials that combine soft-matter processability, tunable network chemistry, and compatibility with luminescent, colorimetric, photonic, and hybrid transduction strategies. Progress in the field is driven not by a single sensing mechanism, but by the convergence of key advances in material functionalization, embedded selectivity, operation across diverse sample matrices, mechanical and analytical robustness, and usability beyond the laboratory. Current systems include framework-integrated, nanoparticle-doped, probe-functionalized, photonic-crystal, enzyme-immobilized, and device-coupled hydrogels, reflecting growing architectural diversity and application-oriented engineering. Selectivity has likewise advanced from basic interferent screening to recognition-specific, imprinted, and pattern-discriminative formats suited to complex environmental, food, biological, and wearable settings. Evidence of stability, reusability, and deformation tolerance further suggests that many platforms are moving beyond proof-of-concept demonstrations toward credible real-world operation. At the same time, translational priorities such as portability, smartphone readout, implantable and epidermal formats, and multifunctionality spanning antimicrobial action, adsorption, anti-counterfeiting, and device integration are becoming increasingly prominent. Together, these trends show that hydrogel-based optical sensing is maturing into a materially rich, application-responsive domain. The key challenge ahead is to unify materials design, selectivity control, durability, and deployability in standardized, reproducible, and clinically or environmentally credible sensing platforms. Full article

With the growing need for long-term secure communications in Internet-of-Things (IoT) and sensor-network environments, practical and robust post-quantum key-establishment mechanisms have become increasingly important. In this work, we revisit the ephemeral-only Ding key exchange (DKE) proposed at ACNS 2019, which is based on one-sample Ring Learning With Errors (Ring-LWE) with rounding, and the original analysis of which covers only passive security. Building on the DKE framework, we propose MoRo-KEM, a Module Learning With Errors (Module-LWE)-based key-encapsulation mechanism using rounding. First, we lift the construction from the Ring-LWE setting to the Module-LWE setting, retaining ring-level efficiency while enabling more flexible parameter choices and reducing reliance on rigid algebraic structure. Second, we replace discrete Gaussian sampling for secrets and errors with centered binomial sampling, thereby simplifying constant-time vectorized implementations while preserving the required noise behavior. Third, we extend the resulting key-exchange core to an IND-CPA-secure public-key encryption scheme and further obtain an IND-CCA-secure KEM via the Fujisaki–Okamoto transform. Finally, at security level $\mathrm{I}$, MoRo-KEM achieves a decryption failure rate of $2^{-166}$, lower than the $2^{-139}$ reported for CRYSTALS-Kyber, thus improving robustness against decryption-failure attacks. These properties make the proposed design attractive for secure key establishment among sensor nodes, edge devices, and gateways operating under constrained computation, memory, and communication budgets. Overall, our construction provides a concrete path from ephemeral key exchange to a practical IND-CCA-secure KEM instantiated over Module-LWE. Full article

This study proposes a novel anti-resonant fiber sensing structure based on a composite “egg-shaped” configuration with surface plasmon resonance (SPR) effect. By designing a novel anti-resonant structure consisting of a semicircle and a semi-ellipse and coating its inner surface with a gold film, the optimal structural parameters are determined through three sets of simulation experiments using temperature sensitivity as the criterion. The optimal sensing structure was applied to the simulated detection and analysis of cancer cells, aiming to provide value and reference for the application of high-sensitivity optical fiber sensor in the field of cancer cell detection. Simulation results show that the proposed sensing structure achieves a maximum temperature sensitivity (TS) of 3.86 nm/°C. For the detection of six different types of cancer cells, the maximum wavelength sensitivity (WS), optimal resolution (R), maximum figure of merit (FOM), maximum signal-to-noise ratio (SNR), and best limit of detection (LOD) reach 12,142.86 nm/RIU, 8.24 × 10⁻⁶, 3035.72 RIU⁻¹, 65.50, and 0.94 nm, respectively. Owing to its unique detection mechanism, the proposed sensing structure exhibits label-free characteristics and demonstrates balanced and excellent performance across all metrics for both temperature and cancer cell detection, showing broad application prospects and great potential in the fields of environmental monitoring and medical prevention and treatment. Full article

Bone densitometry in osteoporosis diagnosis via dual-energy X-ray absorptiometry (DXA) can benefit from advances in imaging detector technology. We devised a compact imaging scanner—DXA4A—using a photon-counting and energy-sensitive Timepix4 hybrid pixel detector (512 × 448 pixels, 55 µm pitch), for areal bone mineral density (aBMD) assessments in the distal radius and tibia in the clinic and for future in-flight astronauts’ bone health assessment. We present the design and Monte Carlo simulations of the scanner. A Timepix4 detector with a 1 mm thick CdTe sensor was tested in the laboratory with X-ray tube sources, acquiring first images of test samples. Monte Carlo simulations were implemented for scanner design and performance prediction, using 50 kVp unfiltered and 100 kVp Sm K-edge filtered spectra. With a digital twin of the scanner and patient wrist, we set up a virtual imaging study and determined the aBMD in the forearm of a patient (0.515 ± 0.048 g/cm²), in agreement with the clinical DXA value (0.571 g/cm² for the total forearm). This study highlights the feasibility of realizing a compact DXA scanner for the distal tibia and radius with spectral capabilities, exploiting Timepix4 hybrid detectors for its peculiar energy sensitivity and photon event timing properties for tissue identification. Full article

This article presents an environmental assessment of emissions from a solid waste landfill in the Karaganda Region of the Republic of Kazakhstan in order to study the dynamics of methane release and determine its energy potential. The study is based on an analysis of a 13-hectare facility that has been operating since 2015 in a reclaimed quarry with an average annual accumulation volume of up to 4000 tons. The methodology includes a detailed analysis of the morphological composition of waste (57% of the organic fraction) and consideration of regional climatic parameters for modeling the phase-specific formation of biogas, according to the approved national methodology. It has been established that, by 2030, the volume of methane will be 81.7–92.6 tons/year. Based on the data obtained, a set of environmental protection measures is proposed, including the installation of special pipes for degassing and the introduction of automated monitoring based on stationary sensors. The results confirm the technical feasibility of using landfill gas as an alternative energy resource and can serve as a scientific and methodological basis for designing environmentally safe landfills in a sharply continental climate and intensive industrial infrastructure. Full article

Wireless Sensor Networks (WSNs) are increasingly evolving toward intelligent distributed systems in which local sensing, on-device inference, and collaborative model training are becoming central to scalable Internet of Things (IoT) deployments. However, the practical adoption of Federated Learning (FL) in WSN-oriented environments remains constrained by three major challenges: limited and unevenly depleted node energy, heterogeneous non-IID local data distributions, and variable client reliability during collaborative training. This paper proposes a Trust- and Energy-Aware Federated Learning (TEA-FL) framework specifically designed for resource-constrained WSN settings, in which client participation and server-side aggregation are jointly guided by residual energy estimates and dynamically updated trust scores. The proposed method prioritizes reliable, energy-efficient sensor nodes while reducing the impact of weakly aligned or low-quality local updates during global aggregation. The framework is evaluated on two representative WSN/IoT-oriented proxy benchmarks, Human Activity Recognition (HAR) and UNSW-NB15 intrusion detection, under both IID and Dirichlet-based non-IID federated partitions. Under non-IID HAR partitioning, TEA-FL improved final accuracy from 0.6752 with FedAvg to 0.7636 and final Macro-F1 from 0.5623 to 0.7185. On the more challenging non-IID UNSW-NB15 benchmark, TEA-FL achieved the highest final Macro-F1, 0.3711, compared with 0.3230 for FedAvg and 0.3323 for the trust-only baseline, although with a lower final accuracy. These results indicate that TEA-FL is particularly useful when final-round robustness, class-balanced behavior, and client sustainability are more relevant than maximizing a single peak intermediate accuracy value. Additional ablation and unreliable-client experiments further show that the trust–energy-aware aggregation component is particularly influential and that TEA-FL can improve behavior under selected low-quality participation scenarios, although it should not be interpreted as a complete Byzantine-robust defense. Overall, the findings suggest that jointly modeling update consistency and residual energy offers a practical, lightweight pathway toward more dependable and sustainable federated intelligence in next-generation WSN and IoT deployments. Full article

Metalloporphyrins play an important role in biomedicine, catalysis, and energy, among other fields, due to their structural complexity and functional diversity. In this study, GO was used as the precursor support and chitosan was employed to reduce and functionalize GO into chitosan-functionalized rGO. Furthermore, metalloporphyrins were covalently linked to the amino side chains of chitosan via an amide crosslinking method, and a series of metalloporphyrin–chitosan-functionalized rGO nanocomposites were designed and synthesized. A set of poly(metalloporphyrin–chitosan)-functionalized rGO working electrodes was constructed by drop-coating onto glassy carbon electrodes, and their electrocatalytic performance toward dopamine was investigated in PBS solution. Finally, zinc(II) porphyrin, with the best performance, was selected as the core catalytic unit to fabricate an enzyme-free dopamine sensor. Under optimal working conditions, the sensor exhibited a sensitivity of 0.30 mA mM⁻¹cm⁻², a linear detection range of 0.001~1.0 mM, and a low detection limit of 0.05 μM (S/N = 3). The sensor showed anti-interference ability against various interfering ions and electroactive substances, as well as good stability and repeatability. Full article