Search Results (33)

Data has become a strategic production factor, but the institutional logic underlying data’s tradability, priceability, and governability remains insufficiently theorized. In response, this study develops a coevolutionary framework that connects conventional factor market theory with digital political economy, platform theory, and comparative institutional analysis. This study adopts a conceptual–analytical research design, integrating three research methods: theory synthesis, comparative institutional analysis, and policy-process interpretation. Through theoretical synthesis, institutional comparison, and policy-process interpretation, it analyzes the conditions under which data circulation becomes feasible, lawful, and economically sustainable. In addition, by combining transaction data, exchange listings, property rights registrations, network indicators, and regional policy variations, it formulates testable propositions and an empirical agenda. The study finds that data factor markets do not emerge automatically with digitalization; their formation requires three mutually reinforcing conditions: technologically reducing search, verification, privacy protection, and contract enforcement costs; institutionally realizing a modular definition of rights and establishing compliance boundaries; and market demand from firms, public agencies, and research organizations generating use-case-specific value. Meanwhile, this study revises the three-stage model of market evolution as a contingent and testable pathway—from administrative pilot allocation, through hybrid state–market professionalization, to ecosystem-based cross-domain circulation. It also clarifies a closed-loop dynamic mechanism consisting of external shocks, internal strategic feedback, and adaptive governance, which jointly shapes market boundaries, pricing rules, and competition patterns. Full article

Large-scale renewable energy bases increasingly employ automatic voltage control (AVC) to coordinate heterogeneous reactive-power resources. The resulting voltage regulation process inherently involves sampling, communication delay, and nonlinear device characteristics, which may induce nontraditional voltage oscillations and stability degradation that cannot be adequately captured by conventional continuous-time or small-signal analysis. This paper proposes a discrete-time nonlinear voltage stability analysis framework for renewable energy bases with multi-reactive-power-resource coupling under AVC-based coordinated control. The voltage regulation dynamics are formulated as a discrete-time nonlinear closed-loop system by incorporating sampled AVC actions, delayed voltage feedback, and nonlinear voltage–reactive-power coupling. An incremental system representation is constructed, and a strong-contraction-based stability criterion is derived using sector-bounded nonlinearity descriptions and linear matrix inequalities, providing a sufficient condition for global voltage convergence without local linearization. Extensive numerical studies are conducted on a representative renewable energy base with parallel and series coupling topologies. A total of 2916 randomized configurations are evaluated. The proposed criterion achieves consistency rates exceeding 96% for the parallel topology and 99% for the series topology when compared with time-domain simulations, while the probability of dangerous misjudgment remains below 1%. Scenario-based simulations further demonstrate that coupling topology plays a critical role in shaping voltage stability behaviors, and state-space analysis further supports the observed stability behaviors. These results indicate that nonlinear strong contraction offers an effective and practical stability notion for AVC-based voltage regulation in renewable energy bases. Full article

This paper addresses the critical challenge of detecting weak, small targets in sonar intensity images for linear-array active sonar, where target signatures are not only obscured by low signal-to-interference ratio (SIR) but also strongly resemble structural interference arising from beamforming processing. We propose an end-to-end detection method that integrates controllable simulation with spatiotemporal structure-aware modeling. First, a physics-informed simulation system is constructed, centered on the Bellhop ray-tracing model. It incorporates multiple environmental effects, including multi-highlight targets, spectrally shaped noise, range-dependent reverberation, discrete scatterers, multipath propagation, and platform perturbations. Through closed-loop SIR calibration and point spread function (PSF)-constrained automatic annotation, a high-fidelity dataset with traceable parameters is generated. Second, the YOLOv8-Mamba-P2 detection network is designed. It introduces gated long-range spatial mixing modules (inspired by Mamba) to model global context and enhance the ability to discriminate interference structures, and extends a P2 small-scale detection branch to improve the perception and localization capabilities for weak targets. This enables precise target detection within complex backgrounds. Experimental results demonstrate the algorithm’s superior performance in low-SIR and strong reverberation conditions, achieving significant improvements in recall and localization accuracy while maintaining real-time inference efficiency, offering a promising framework for sonar target detection under the simulated conditions considered, with potential applicability to complex marine environments pending further real-world validation. Full article

The current study investigated proximal femur bone shape asymmetry and its associations with cartilage composition and functional performance in individuals with hip osteoarthritis (OA). Forty-seven participants with hip OA (mean age: 53.77 ± 12.47 years; 22 females; BMI: 24.49 ± 4.0 kg/m²) were included in this study. Bilateral hip MRI was performed using a 3.0 T MR scanner with 3D proton density fat-saturated CUBE and MAPSS sequences. Automatic segmentation of the proximal femur was achieved using a U-Net framework refined through a human-in-the-loop annotation strategy, followed by three-dimensional bone shape analysis to quantify asymmetry. Cartilage relaxation times were assessed using atlas-based segmentation and quantification, while functional activity was evaluated according to OARSI-recommended criteria. The proposed proximal femur bone segmentation showed a DSC of 96.48% (95%-CI: 96.33–96.64) and Hausdorff Distance of 4.66 mm (95%-CI: 3.80–5.51). Increased bone shape asymmetry in the posterior–lateral–superior region of the proximal femur was associated with functional activity in the chair stand test (rho = −0.41; p = 0.006), and the anterior–lateral–inferior region demonstrated a comparatively higher significant positive correlation ($\mathrm{rho}=0.37$; p = 0.006) with the ${\mathrm{T}}_{1\mathrm{rho}}$values of the acetabular cartilage region. Overall, the findings indicate that region-specific proximal femoral bone shape asymmetry in hip OA is associated with cartilage characteristics and functional impairment, highlighting the potential value of bone shape features as imaging biomarkers relevant to clinical function. Full article

The article discusses the control of nonlinear processes with first-order dominant dynamics, focusing on implementation using modern hardware available in various programmable devices and embedded systems. The first two approaches rely on linearization with an ultra-local process model, considering small changes of the process input and output around a fixed operating point, which can be adjusted through gain scheduling with the setpoint variable. This model is used to configure either the historically established automatic reset controller (ARC) or a stabilizing proportional (P) controller enhanced by an inversion-based disturbance observer (DOB). This solution can be interpreted as an application of modern control theory (MCT), as DOB-based control (DOBC) or as advanced disturbance rejection control (ADRC). Alternatively, they can be viewed as a special case of automatic offset control (AOC) based on two types of linear process models. In the third design method, setpoint tracking by exact linearization (EL) is extended with a nonlinear DOB designed using the inverse of the nonlinear process dynamics (EEL). The fourth approach augments EL-based tracking with a DOB derived from the transfer functions of nonlinear processes (NTF). An illustrative example involving the control of a liquid reservoir with a variable cross-section clarifies motivation for the definition of (linear) local and ultra-local process models as well as their advantages in designing robust control that accounts for process uncertainties. Thus, the speed, homogeneity, and shape of transient responses, the ability to reconstruct disturbances, control signal saturation, and measurement noise attenuation are evaluated according to the assumptions specified in the controller design. The novelty of the paper lies in presenting a unifying perspective on several seemingly different control options under the impact of measurement noise. By explaining their essence, advantages, and disadvantages, it provides a foundation for controlling more complex time-delayed systems. The paper emphasizes that certain aspects of controller design, often overlooked in traditional linearization procedures, can significantly improve closed-loop properties. Full article

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results. Full article

Loop shaping the controller for high-order systems, especially in the presence of flexible transmission components such as elastic shafts, gearboxes, and belts commonly found in servo systems, poses significant challenges. Therefore, developing a non-parametric, versatile tuning algorithm that adapts to multi-order systems is essential for general control applications. This article first obtains the frequency characteristics of plants through a frequency sweep. Then, based on preset frequency domain specifications, the boundaries representing disturbance rejection and stability constraints are defined in the complex plane with explicit mathematical and graphical expressions. Subsequently, a system of equations is developed based on the tangency between the open-loop curve of the system and the boundaries in the complex plane. On this basis, a versatile tuning algorithm is designed to calculate parameters of a PI controller cascaded with a low-pass filter that ensures the system meets the preset constraints. The proposed approach does not rely on parametric modeling, and the zeros and poles of the controller can be flexibly placed. Experimental validation is carried out on mechanical platforms. Full article

Traditional approaches to changing health behaviours have primarily focused on education and raising awareness, assuming that increased knowledge leads to better decisions. However, evidence suggests these methods often fail to result in sustained behavioural change. The dual-process theory of decision-making highlights that much of our behaviour is driven by automatic, intuitive processes, which educational interventions typically overlook. Compounding this challenge, behavioural research is often conducted on small groups, making it difficult to scale insights into broader societal issues, where behaviour is influenced by complex, interconnected factors. This review advocates for integrating behavioural science with systems approaches (including systems thinking and approaches to complex adaptive systems) as a more effective approach to resolving complex societal issues, such as public health, sustainability, and social equity. Behavioural science provides insights into individual decision-making, while systems approaches offer ways of understanding, and working with, the dynamic interactions and feedback loops within complex systems. The review explores the commonalities and differences between these two approaches, highlighting areas where they complement one another. Design thinking is identified as a useful structure for bridging behavioural science and systems thinking, enabling a more holistic approach to problem-solving. Though some ideological challenges remain, the potential for creating more effective, scalable solutions is significant. By leveraging the strengths of both behavioural science and systems thinking, one can create more comprehensive strategies to address the “wicked problems” that shape societal health and well-being. Full article

This study presents an innovative control strategy for enabling ships to perform automatic U-turns in restricted waters, with a focus on minimizing energy consumption and reducing wear on the steering gear. The strategy integrates a closed-loop gain-shaping algorithm with nonlinear feedback control, applied to a nonlinear motion mathematical model specifically designed for low-speed operations in shallow waters. The simulations, conducted under a Beaufort wind scale conditions up to No. 5 and water depths of 15 m, demonstrate that ships can successfully execute automatic U-turns within a distance three times their length. The incorporation of nonlinear feedback technology significantly reduces energy consumption and steering gear wear, with specific improvements including a reduction in the average rudder angle by up to 18.26%, a reduction in the mean absolute error (MAE) by up to 3.6%, a reduction in the mean integrated absolute (MIA) by up to 13.55%, and a reduction in the mean total variation (MTV) by up to 36.36%. These enhancements not only optimize the control effect but also extend the service life of the steering gear, thereby contributing to more sustainable maritime operations. Theoretical proofs and Matlab-based simulations validate the effectiveness of the controller, highlighting its potential for energy savings and improved navigational efficiency in challenging maritime environments. Full article

The initial processing of Citrus Reticulata ‘Chachi’ involves peeling as a crucial step. Currently, there is some semi-automatic peeling equipment available. However, due to the requirement of adjusting the orientation of Citrus Reticulata ‘Chachi’ to ensure the stem (or navel) is facing upwards before peeling and because the peeling process must retain the stem as a marker for fresh fruit picking, the loading of Citrus Reticulata ‘Chachi’ for peeling still solely relies on manual operation, resulting in low efficiency and poor standardization. With the rapid growth of the pericarp of the Citrus Reticulata ‘Chachi’ industry, semi-automatic processing equipment is no longer able to meet production demands. The loading issue before peeling Citrus Reticulata ‘Chachi’ is a complex hand–eye coordination problem. In response to this issue, this paper proposes a novel Citrus Reticulata ‘Chachi’ orientation adjustment mechanism (COAM). This mechanism utilizes frictional force to adjust the orientation of Citrus Reticulata ‘Chachi’. First, the conceptual design and kinematic modelling analysis of the mechanism were conducted. Next, the omnidirectional friction-driven wheels were optimized in design. Subsequently, a prototype was manufactured and assembled to conduct validation tests on its open-loop motion performance. Finally, a visual feedback-guided algorithm was introduced to complement the kinematic model, enabling the automatic and rapid adjustment of Citrus Reticulata ‘Chachi’ orientation. The experimental results indicate that the COAM designed in this study can effectively and rapidly adjust the orientation of Citrus Reticulata ‘Chachi’ fruits of different sizes and shapes. It demonstrates strong adaptability, and under visual feedback guidance, the orientation adjustment error is less than 10% of the fruit’s diameter. This meets the requirements for automated production in the initial processing of Citrus Reticulata ‘Chachi’. The research presented in this paper also provides new insights for the orientation adjustment and loading of similar spherical fruits. Full article

Deep-brain stimulation (DBS) is a highly effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric diseases. It has been established as a first-line tool in the treatment of these conditions for the past two decades. Closed-loop deep-brain stimulation (CLDBS) advances this tool further by automatically adjusting the stimulation parameters in real time based on the brain’s response. In this context, this paper presents a low-noise amplifier (LNA) and a neurostimulator circuit fabricated using the low-power/low-voltage 65 nm CMOS process from TSMC. The circuits are specifically designed for implantable applications. To achieve the best tradeoff between input-referred noise and power consumption, metaheuristic algorithms were employed to determine and optimize the dimensions of the LNA devices during the design phase. Measurement results showed that the LNA had a gain of 41.2 dB; a 3 dB bandwidth spanning over three decades, from 1.5 Hz to 11.5 kHz; a power consumption of 5.9 µW; and an input-referred noise of 3.45 µV_RMS, from 200 Hz to 11.5 kHz. The neurostimulator circuit is a programmable Howland current pump. Measurements have shown its capability to generate currents with arbitrary shapes and ranging from −325 µA to +318 µA. Simulations indicated a quiescent power consumption of 0.13 µW, with zero neurostimulation current. Both the LNA and the neurostimulator circuits are supplied with a 1.2 V voltage and occupy a microdevice area of 145 µm × 311 µm and 88 µm × 89 µm, respectively, making them suitable for implantation in applications involving closed-loop deep-brain stimulation. Full article

Accurate sample heating is vital for nucleic acid extraction and amplification, requiring a sophisticated thermal cycling process in nucleic acid detection. Traditional molecular detection systems with heating capability are bulky, expensive, and primarily designed for lab settings. Consequently, their use is limited where lab systems are unavailable. This study introduces a technique for performing the heating process required in molecular diagnostics applicable for point-of-care testing (POCT), by presenting a method for crafting customized heaters using freely patterned nichrome (NiCr) wire. This technique, fabricating heaters by arranging protrusions on a carbon black-polydimethylsiloxane (PDMS) cast and patterning NiCr wire, utilizes cost-effective materials and is not constrained by shape, thereby enabling customized fabrication in both two-dimensional (2D) and three-dimensional (3D). To illustrate its versatility and practicality, a 2D heater with three temperature zones was developed for a portable device capable of automatic thermocycling for polymerase chain reaction (PCR) to detect Escherichia coli (E. coli) O157:H7 pathogen DNA. Furthermore, the detection of the same pathogen was demonstrated using a customized 3D heater surrounding a microtube for loop-mediated isothermal amplification (LAMP). Successful DNA amplification using the proposed heater suggests that the heating technique introduced in this study can be effectively applied to POCT. Full article

The purpose of this study was to automatically classify different motor subtypes of Parkinson’s disease (PD) on arterial spin labelling magnetic resonance imaging (ASL-MRI) data using support vector machine (SVM). This study included 38 subjects: 21 PD patients and 17 normal controls (NCs). Based on the Unified Parkinson’s Disease Rating Scale (UPDRS) subscores, patients were divided into the tremor-dominant (TD) subtype and the postural instability gait difficulty (PIGD) subtype. The subjects were in a resting state during the acquisition of ASL-MRI data. The automated anatomical atlas 3 (AAL3) template was registered to obtain an ASL image of the same size and shape. We obtained the voxel values of 170 brain regions by considering the location coordinates of these regions and then normalized the data. The length of the feature vector depended on the number of voxel values in each brain region. Three binary classification models were utilized for classifying subjects’ data, and we applied SVM to classify voxels in the brain regions. The left subgenual anterior cingulate cortex (ACC_sub_L) was clearly distinguished in both NCs and PD patients using SVM, and we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21%, and AUCmax = 0.9585). For the right supramarginal gyrus (SupraMarginal_R), SVM distinguished the TD group from the other groups with satisfactory diagnostic rates (accuracy = 84.21%, sensitivity = 63.64%, specificity = 92.59%, and AUCmax = 0.9192). For the right intralaminar of thalamus (Thal_IL_R), SVM distinguished the PIGD group from the other groups with satisfactory diagnostic rates (accuracy = 89.47%, sensitivity = 70.00%, specificity = 6.43%, and AUCmax = 0.9464). These results are consistent with the changes in blood perfusion related to PD subtypes. In addition, the sensitive brain regions of the TD group and PIGD group involve the brain regions where the cerebellothalamocortical (CTC) and the striatal thalamocortical (STC) loops are located. Therefore, it is suggested that the blood perfusion patterns of the two loops may be different. These characteristic brain regions could become potential imaging markers of cerebral blood flow to distinguish TD from PIGD. Meanwhile, our findings provide an imaging basis for personalised treatment, thereby optimising clinical diagnostic and treatment approaches. Full article

This paper proposes a novel, fast, and automatic modeling method to build a virtual model with minimum degrees of freedom (DOFs) without the need for FE models or human judgment. The proposed program uses the iterative closest point (ICP) algorithm to analyze the mode shape vector of structural dynamic characteristics to define the position and DOFs of the joints between structural components. After the multi-body dynamics model was developed in software, it was converted into an SSM to connect the servo loop model. Then, the mechatronic integration analysis was performed to verify the dynamic characteristics of the tool center point (TCP) and the workbench in the experiment and simulation. The model created by the proposed identification process has a small DOF and can accurately simulate the dynamic characteristics of a machine. This model can be used for dynamic testing and control strategy development in mechatronic integration. Full article

The fingerprint is an important biological feature of the human body, which contains abundant biometric information. At present, the academic exploration of fingerprint gender characteristics is generally at the level of understanding, and the standardization research is quite limited. A robust approach is presented in this article, Dense Dilated Convolution ResNet Autoencoder, to extract valid gender information from fingerprints. By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details, and the global reception field can be extended. The results were explored from three aspects: (1) Efficiency of DDC-ResNet. We conducted experiments using a combination of 6 typical automatic feature extractors with 9 classifiers for a total of 54 combinations are evaluated in our dataset; the experimental results show that the combination of methods we used achieved an average accuracy of 96.5%, with a classification accuracy of 97.52% for males and 95.48% for females, which outperformed the other experimental combinations. (2) The effect of the finger. The results showed that the right ring finger was the most effective for finger classification by gender. (3) The effect of specific features. We used the Class Activating Mapping method to plot fingerprint concentration thermograms, which allowed us to infer that fingerprint epidermal texture features are related to gender. The results demonstrated that autoencoder networks are a powerful method for extracting gender-specific features to help hide the privacy information of the user’s gender contained in the fingerprint. Our experiments also identified three levels of features in fingerprints that are important for gender differentiation, including loops and whorls shape, bifurcations shape, and line shapes. Full article