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Maintaining balance during quiet standing is a challenging task for the neural control mechanisms due to the inherent instabilities involved in the task. The feedback latencies and the lowpass characteristics of skeletal muscle add to the difficulty of regulating postural dynamics in real-time. Inverted-pendulum (IP) type robotic models have served as a popular paradigm to investigate control of postural balance. In this study, an in-depth neuromechanical postural control model is developed from physiological principles. The model comprises a single-segment IP robotic model, Hill-type muscle model, and proprioceptive feedback from the muscle spindle (MS) and golgi tendon organ (GTO). An optimal proportional-integral-derivative (PID) controller is proposed to realize effective postural control amid latencies in sensory feedback. The neural commands for postural stabilization are generated by a time-varying PID controller, tuned using linear quadratic regulator (LQR) principles. Computer simulations are used to assess the efficacy of the tuned PID-LQR controller. Sensitivity analysis of the controlled system shows a delay tolerance of 300ms. Preliminary empirical data in support of the mathematical model were obtained from perturbation experiments. The model response to perturbation torque, measured in terms of the center of mass (COM) excursion in the anterior-posterior (AP) direction, displays a high degree of correlation with the empirical data (ρ=0.91).

Traditional control of robotic systems relies on the availability of an exact model, which assumes complete knowledge of the robot’s parameters and all dynamic effects. However, this idealized scenario rarely holds in practice, as real-world interactions introduce unpredictable environmental influences, friction, and edge effects. This paper presents a novel data-driven approach to modeling and estimating robot dynamics by leveraging data collected during the robot’s movements. The proposed method operates without prior knowledge of the system parameters, thereby addressing the limitations of conventional model-based control strategies in complex and uncertain environments. Our unified data-driven framework integrates classical control theory with modern machine learning techniques, including system identification, physics-informed neural networks (PINNs), and deep learning. We demonstrate its efficacy in the case of a two-link robotic manipulator that achieves superior trajectory tracking and robustness to unmodeled dynamics. The technique is modular and can be extended to manipulators with more joints.

Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, also called Brain Computer Interface (BCI). However, achieving high accuracy in the electroencephalography signal-based decoding remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG–Audio–Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the two-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research.

Aims Cadmium (Cd) is one of the most toxic heavy metals. Cd tolerance ability differs among varieties in plants, but the underlying molecular mechanisms remain largely unknown. In this study, we identified genes that are involved in Cd stress responses and different Cd tolerances of two V. sativa varieties (Cd-tolerant variety (L3) and Cd-sensitive variety (ZM)). Methods Transcriptomic analysis using Illumina pair-end sequencing was carried out on root tissues of L3 and ZM grown with 5 (μM and 50 μM of Cd treatments. A de novo assembled V. sativa transcriptome was generated. Differentially expressed genes (DEGs) were assigned to Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and enrichment analysis was performed. The expression of selected DEGs were confirmed by quantitative reverse transcription–polymerase chain reaction (qRT-PCR). Results A total of 49,062 sequences were identified as unigenes. In the 5 μM Cd treatment, 69 and 28 differentially expressed unigenes were found as compared with the control in the L3 and ZM respectively, while in the 50 μM Cd treatment, 1036 and 335 differentially expressed unigenes were found in comparison with the control in L3 and ZM. Pathway enrichment analysis suggested that genes related to the cell wall, stress response, the glutathione pathway, metal transporters, and transcription factors are commonly up-regulated in response to Cd stress in both varieties. However, the expression of metal transporter genes and transcription factor genes showed significant differential responses to Cd stress. Conclusions In addition to the regulation of transcription by transcription factors, metal transporters play a vital role in controlling the different Cd tolerances of V. sativa varieties L3 and ZM.

The uptake and transport of iron (Fe) in plants are both important for plant growth and human health. However, little is known about the mechanism of Fe transport in plants, especially for crops. In the present study, the function of yellow stripe-like 13 (YSL13) in rice was analyzed. OsYSL13 was highly expressed in leaves, especially in leaf blades, whereas its expression was induced by Fe deficiency both in roots and shoots. Furthermore, the expression level of OsYSL13 was higher in older leaves than that in younger leaves. OsYSL13 was located in the plasma membrane. Metal measurement revealed that Fe concentrations were lower in the youngest leaf and higher in the older leaves of the osysl13 mutant under both Fe sufficiency and deficiency conditions, compared with the wild type and two complementation lines. Moreover, the Fe concentrations in the brown rice and seeds of the osysl13 mutant were also reduced. Opposite results were found in OsYSL13 overexpression lines. These results suggest that OsYSL13 is involved in Fe distribution in rice.

Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes that impairs gait and balance, increasing fall risk. This study investigated gait characteristics and postural control in individuals with DPN, compared to age- and gender-matched healthy controls. Fifteen DPN patients and fifteen controls underwent assessments of gait, static balance, and mobility. Gait parameters were measured during overground walking using motion capture and force platforms. Static balance was evaluated via tandem stance tests (eyes open/closed), while mobility was assessed with the Timed-Up-and-Go (TUG) test. Dynamic stability was assessed by computing the center-of-pressure Time-to-Contact (TTC) with the mediolateral (ML) stability boundary. We hypothesized that patients with DPN would exhibit an altered gait and reduced ML postural stability during walking. The study results show no significant differences in ML center-of-pressure (COP) excursion or its velocity during walking between groups. Patients with DPN walked relatively slowly, with shorter steps, and showed markedly poorer static balance (earlier failure during tandem stance test), as well as slower TUG performance. Clinically, these findings support routine fall risk screening in DPN using both static balance tests (e.g., tandem stance) and mobility measures (e.g., TUG or gait speed). These findings further suggest that while dynamic postural control during walking may be preserved, DPN patients exhibit gait adaptations and significant static balance deficits, highlighting the need for comprehensive balance assessment in this population.

Many basic Helix-Loop-Helix (bHLH) transcription factors precisely regulate the expression of Fe uptake and translocation genes to control iron (Fe) homeostasis, as both Fe deficiency and toxicity impair plant growth and development. In rice, three clade IVc bHLH transcription factors have been characterised as positively regulating Fe-deficiency response genes. However, the function of OsbHLH057, another clade IVc bHLH transcription factor, in regulating Fe homeostasis is unknown. Here, we report that OsbHLH057 is involved in regulating Fe homeostasis in rice. OsbHLH057 was highly expressed in the leaf blades and lowly expressed in the roots; it was mainly expressed in the stele and highly expressed in the lateral roots. In addition, OsbHLH057 was slightly induced by Fe deficiency in the shoots on the first day but was not affected by Fe availability in the roots. OsbHLH057 localised in the nucleus exhibited transcriptional activation activity. Under Fe-sufficient conditions, OsbHLH057 knockout or overexpression lines increased or decreased the shoot Fe concentration and the expression of several Fe homeostasis-related genes, respectively. Under Fe-deficient conditions, plants with an OsbHLH057 mutation showed susceptibility to Fe deficiency and accumulated lower Fe concentrations in the shoot compared with the wild type. Unexpectedly, the OsbHLH057-overexpressing lines had reduced tolerance to Fe deficiency. These results indicate that OsbHLH057 plays a positive role in regulating Fe homeostasis, at least under Fe-sufficient conditions.

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years.