Search Results (255)

The increasingly widespread concept of Industry 4.0 and Digital Twins, as well as the competitive global markets, make it essential to produce goods in a timely and cost-effective manner. Therefore, one of the most important requirements for current production scheduling systems is to automate the control of production progress and to quickly correct schedules in response to feedback from production machines while minimizing the number of changes in the order of tasks to be performed. In this paper, we propose a new extension to the dispatching rules, which takes into account the uncertainty of job processing times and allows for the production of robust schedules. The important feature of the proposed extension is that it does not increase the computational time of the underlying techniques, i.e., it has no negative impact on their most important advantage over other production scheduling algorithms. The results obtained from the real data of a large international offset printing company with various machine and job heterogeneities confirm the effectiveness of the proposed EPO extension in producing schedules that are indeed robust to changes in production time. Full article

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article

Background/Objectives: Puberty is a critical stage in sheep development when reproductive capability is established, but the hormonal mechanisms underlying this transition remain incompletely understood. This study aimed to investigate the dynamic changes in estradiol (E2) levels and the expression patterns of estrogen receptors (ERα and ERβ) during puberty in Duolang sheep, a breed characterized by early sexual maturity and high reproductive efficiency. Methods: A total of 18 female Duolang sheep were assigned to three developmental stages (n = 6 per group): prepuberty (145 days), puberty (within 0 h of first estrus), and postpuberty (+3 days). Serum E2 concentrations and the mRNA and protein levels of ERα and ERβ were assessed in the hypothalamus, pituitary, and ovary. Additionally, primary ovarian granulosa cells (GCs) were isolated and stimulated in vitro with increasing concentrations of E2 (0–1000 ng/mL) to evaluate the dose-dependent expression of ERα, ERβ, and gonadotropin-releasing hormone (GnRH). Results: E2 levels peaked at the onset of puberty and declined thereafter. ERα expression in the hypothalamus and pituitary decreased during puberty but rebounded postpuberty, indicating a role in negative feedback regulation. In contrast, ovarian ERα expression reached its highest level during puberty, while ERβ expression in the ovary gradually increased from prepuberty to postpuberty. In GCs, ERα exhibited a biphasic expression pattern, peaking at 250 ng/mL E2 and decreasing at higher concentrations. ERβ and GnRH expression levels increased in a dose-dependent manner. Conclusions: These findings suggest that ERα primarily mediates E2 feedback within the hypothalamus–pituitary axis, whereas ERβ is associated with ovarian development and may regulate GnRH expression during the pubertal transition. The study provides new insights into the hormonal regulation of puberty in Duolang sheep and offers potential biomarkers for improving reproductive efficiency through targeted breeding strategies. Full article

Calcium (Ca²⁺) signaling is a fundamental regulatory mechanism controlling essential processes in the endothelium, vascular smooth muscle cells (VSMCs), and the extracellular matrix (ECM), including maintaining the endothelial barrier, modulation of vascular tone, and vascular remodeling. Cytosolic free Ca²⁺ concentration is tightly regulated by a balance between Ca²⁺ mobilization mechanisms, including Ca²⁺ release from the intracellular stores in the sarcoplasmic/endoplasmic reticulum and Ca²⁺ entry via voltage-dependent, transient-receptor potential, and store-operated Ca²⁺ channels, and Ca²⁺ elimination pathways including Ca²⁺ extrusion by the plasma membrane Ca²⁺-ATPase and Na⁺/Ca²⁺ exchanger and Ca²⁺ re-uptake by the sarco(endo)plasmic reticulum Ca²⁺-ATPase and the mitochondria. Some cell membranes/organelles are multifunctional and have both Ca²⁺ mobilization and Ca²⁺ removal pathways. Also, the individual Ca²⁺ handling pathways could be integrated to function in a regenerative, capacitative, cooperative, bidirectional, or reciprocal feed-forward or feed-back manner. Disruption of these pathways causes dysregulation of the Ca²⁺ signaling dynamics and leads to pathological cardiovascular conditions such as hypertension, coronary artery disease, atherosclerosis, and vascular calcification. In the endothelium, dysregulated Ca²⁺ signaling impairs nitric oxide production, reduces vasodilatory capacity, and increases vascular permeability. In VSMCs, Ca²⁺-dependent phosphorylation of the myosin light chain and Ca²⁺ sensitization by protein kinase-C (PKC) and Rho-kinase (ROCK) increase vascular tone and could lead to increased blood pressure and hypertension. Ca²⁺ activation of matrix metalloproteinases causes collagen/elastin imbalance and promotes vascular remodeling. Ca²⁺-dependent immune cell activation, leukocyte infiltration, and cholesterol accumulation by macrophages promote foam cell formation and atherosclerotic plaque progression. Chronic increases in VSMCs Ca²⁺ promote phenotypic switching to mesenchymal cells and osteogenic transformation and thereby accelerate vascular calcification and plaque instability. Emerging therapeutic strategies targeting these Ca²⁺-dependent mechanisms, including Ca²⁺ channel blockers and PKC and ROCK inhibitors, hold promise for restoring Ca²⁺ homeostasis and mitigating vascular disease progression. Full article

Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NSCT) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NSCT has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NSCT coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components. Full article

This paper will examine a methodology to enable the usage of Human Digital Twins (HDTs) for Quality Assurance in the aerospace manufacturing domain. Common-place hardware and infrastructure, including cloud-based facility security cameras, cloud-based commercial virtual environments, a virtual reality (VR) headset, and artificial intelligence (AI) detection algorithms, have been connected via application programming interfaces (API) to enable a 24-h surveillance and feedback capability for a representative aerospace manufacturing cell. Human operators who perform defined manufacturing assembly operations in real life in the cell can utilize this methodology to digitize their performance and provide objective evidence of conformity and safety messaging for their human-centric manufacturing operation in real time. The digitization of real human-centric performance using this methodology creates the foundation for a HDT. This paper will present the application of HDTs in a manner that can easily be scaled across manufacturing operations while utilizing technologies that are already commonly inserted into existing manufacturing operations, which facilitates the exploration of HDT concepts without the need for expensive capital purchases and emerging technologies. Full article

The ongoing exploration of brain–computer interfaces (BCIs) provides deeper insights into the workings of the human brain. Motor imagery (MI) tasks, such as imagining movements of the tongue, left and right hands, or feet, can be identified through the analysis of electroencephalography (EEG) signals. The development of BCI systems opens up opportunities for their application in assistive devices, neurorehabilitation, and brain stimulation and brain feedback technologies, potentially helping patients to regain the ability to eat and drink without external help, move, or even speak. In this context, the accurate recognition and deciphering of a patient’s imagined intentions is critical for the development of effective BCI systems. Therefore, to distinguish motor tasks in a manner differing from the commonly used methods in this context, we propose a fractal dimension (FD)-based approach, which effectively captures the self-similarity and complexity of EEG signals. For this purpose, all four classes provided in the BCI Competition IV 2a dataset are utilized with nine different combinations of seven FD methods: Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. The resulting features are then used to train five machine learning models: linear, Gaussian, polynomial support vector machine, regression tree, and stochastic gradient descent. As a result, the proposed method obtained top-tier results, achieving 79.2% accuracy when using the Katz vs. box-counting vs. correlation dimension FD combination (KFD vs. BCFD vs. CDFD) classified by LinearSVM, thus outperforming the state-of-the-art TWSB method (achieving 79.1% accuracy). These results demonstrate that fractal dimension features can be applied to achieve higher classification accuracy for online/offline MI-BCIs, when compared to traditional methods. The application of these findings is expected to facilitate the enhancement of motor imagery brain–computer interface systems, which is a key issue faced by neuroscientists. Full article

Although the roles of proteoglycans (PGs) have been well documented in the development and homeostasis of the temporomandibular joint (TMJ), how the glycosaminoglycan (GAG) chains of PGs contribute to TMJ chondrogenesis and osteogenesis still requires explication. In this study, we found that FAM20B, a hexokinase essential for attaching GAG chains to the core proteins of PGs, was robustly activated in the condylar mesenchyme during TMJ development. The inactivation of Fam20b in craniofacial neural crest cells (CNCCs) dramatically reduced the synthesis and accumulation of GAG chains rather than core proteins in the condylar cartilage, which resulted in a hypoplastic condylar cartilage by severely promoting chondrocyte hypertrophy and perichondral ossification. In the condyles of Wnt1-Cre;Fam20b^f/f mouse embryos, enlarged Ihh- and COL10-expressing domains indicated premature hypertrophy resulting from an attenuated IHH-PTHRP negative feedback in condylar chondrocytes, while increased osteogenic markers, canonical Wnt activity, and type-H angiogenesis verified the enhanced osteogenesis in the perichondrium. Further ex vivo investigations revealed that the loss of Fam20b decreased the domain area but increased the activity of HH signaling in the embryonic condylar mesenchyme. Moreover, the abrogation of GAG chains in heparan sulfate and chondroitin sulfate proteoglycans led to a rapid up- and then downregulation of HH signaling in condylar chondrocytes, implicating a “slow-release” manner of growth factors controlled by GAG chains. Overall, this study revealed a comprehensive role of the FAM20B-catalyzed GAG chain synthesis in the chondrogenic and osteogenic differentiation of the embryonic TMJ condyle. Full article

Genomic sequences that form three-stranded triplexes (TPXs) under physiological conditions (called T-flipons) play an important role in defining DNA nucleosome-free regions (NFRs). Within these NFRs, other flipon types can cycle conformations to actuate gene expression. The transcripts read from the NFR form condensates that engage proteins and small RNAs. The helicases bound then trigger RNA polymerase release by dissociating the 7SK ribonucleoprotein. The TPXs formed usually incorporate RNA as the third strand. TPXs made only from DNA arise mostly during DNA replication. Many small RNA types (sRNAs) and long noncoding (lncRNA) can direct TPX formation. TPXs made with circular RNAs have greater stability and specificity than those formed with linear RNAs. LncRNAs can affect local gene expression through TPX formation and transcriptional interference. The condensates seeded by lncRNAs are updated by feedback loops involving proteins and noncoding RNAs from the genes they regulate. Some lncRNAs also target distant loci in a sequence-specific manner. Overall, lncRNAs can rapidly evolve by adding or subtracting sequence motifs that modify the condensates they nucleate. LncRNAs show less sequence conservation than protein-coding sequences. TPXs formed by lncRNAs and sRNAs help place nucleosomes to restrict endogenous retroelement (ERE) expression. The silencing of EREs starts early in embryogenesis and is essential for bootstrapping development. Once the system is set, EREs play a different role, with a notable enrichment of Short Interspersed Nuclear Repeats (SINEs) in Enhancer–Promoter condensates. The highly programmable TPX-dependent processes create a chromaverse capable of many complexities. Full article

This paper aims to solve the problems of poor mobility, passability, and stability in heavy-duty vehicles, and proposes an active suspension system control strategy based on position–force feedback optimal control to coordinately enhance vehicle passability and wheel grounding performance. Firstly, a two-degrees-of-freedom one-sixth vehicle active suspension model and a valve-controlled hydraulic actuator system model are constructed, and the advantages of impedance control in robot compliance control are integrated to analyze their applicability in hydraulic active suspension. Next, a position feedback controller and force feedback LQG optimal controller for fuzzy PID control are designed, the fuzzy PID-LQG (FPL) integrated method is applied to the hydraulic active suspension system, and the dynamic load of the wheel is tracked by impedance control to obtain the spring mass displacement correction. Then, a suspension system model under the excitation of a C-class road surface and a 0.11 m raised road surface is constructed, and the dynamic simulation and comparison of active/passive suspension systems are carried out. The results show that, compared with PS and LQR control, the body vertical acceleration, suspension dynamic deflection, and wheel dynamic load root-mean-square value of the proposed FPL integrated control active suspension are reduced, which can effectively reduce the body vibration and wheel dynamic load and meet the design objectives proposed in this paper, effectively improving vehicle ride comfort, handling stability, passability, and wheel grounding performance. Full article

In video human action-recognition tasks, motion tempo describes the dynamic patterns and temporal scales of human motion. Different categories of actions are typically composed of sub-actions with varying motion tempos. Effectively capturing sub-actions with different motion tempos and distinguishing category-specific sub-actions are crucial for improving action-recognition performance. Convolutional Neural Network (CNN)-based methods attempted to address this challenge, by embedding feedforward attention modules to enhance the action’s dynamic representation learning. However, feedforward attention modules rely only on local information from low-level features, lacking contextual information to generate attention weights. Therefore, we propose a Sub-action Motion information Enhancement Network (SMEN) based on motion-tempo learning and feedback attention, which consists of the Multi-Granularity Adaptive Fusion Module (MgAFM) and Feedback Attention-Guided Module (FAGM). MgAFM enhances the model’s ability to capture crucial sub-action intrinsic information by extracting and adaptively fusing motion dynamic features at different granularities. FAGM leverages high-level features that contain contextual information in a feedback manner to guide low-level features in generating attention weights, enhancing the model’s ability to extract more discriminative spatio-temporal and channel-wise features. Experiments are conducted on three datasets, and the proposed SMEN achieves top-1 accuracies of 52.4%, 63.3% on the Something-Something V1 and V2 datasets, and 76.9% on the Kinetics-400 dataset. Ablation studies, evaluations, and visualizations demonstrate that the proposed SMEN is effective for sub-action motion tempo and representation learning, and outperforms compared methods for video action recognition. Full article

In this era of perpetual advancement and innovation, the term “smart” is frequently misused. Linking smartness to a city should reflect and solve multiple problems with a single solution. A city, district, or area can only be smart when it contemplates different development axes rather than having just a single strength. This work is an effort to make an area of Varanasi in Uttar Pradesh, India, smart by concentrating the actions on five principal axes—Environment, Energy, Mobility, Community, and Economy. Practical indicators have been selected and well formalised to obtain an output value that can support the methodology to rank each action in its executable manner. Software like ENVI-met (to simulate greening and pollution) and PVSyst (to simulate rooftop solar PV) have been used to simulate the actions proposed, and a detailed discussion for each result has been presented. The methodology involves the creation of a model based on morphological, structural, and environmental data, as well as using SWOT analysis and community feedback to identify key areas for intervention. The results demonstrate the effectiveness of the proposed interventions, with notable reductions in CO₂ emissions, improved air quality, and significant energy savings through the implementation of Nature-Based Solutions, solar PV systems, and electric mobility. Full article

This study aimed to shorten firefighter search times during indoor fires, allowing more people to be rescued, by enhancing disaster-prevention capabilities using building technologies. In indoor fires, fatalities are often caused by the failure of firefighters to rescue individuals in a timely manner. The question of how to effectively increase the probability of survival while waiting for rescue behind closed doors warrants in-depth research and analysis. Therefore, to ensure that people live in safe environments, there is an urgent need to develop a building door panel material with an emergency call function to prevent such incidents from occurring. Utilizing the PRISMA method, we conducted a comprehensive review of the existing literature to identify the key issues and limitations associated with the current search-and-rescue techniques. Subsequently, the identified primary factors were analyzed using the TRIZ method to determine the key factors that influence the success of rescuing trapped individuals, and a notification system was designed to address this issue. Based on the premise that it is advisable to wait for rescue during a fire, we utilized a smartphone to scan a QR code and transmit the exact location information to the fire department. Through extensive participation and feedback from firefighters, we developed a rescue notification door panel and obtained a patent for it. This system can significantly reduce the time required for search-and-rescue operations in fire incidents. The experimental results show a reduction of one-third in search times. Full article

The rapid urbanization of recent decades has intensified climate change challenges, demanding sophisticated solutions to build resilient and sustainable cities. A key aspect of sustainable urban planning is decentralizing and democratizing its processes, which requires citizen involvement from the early design stages. While current solutions such as digital tools, participatory workshops, gamification, and social media can enhance participation, they often exclude non-experts or those lacking digital skills. To address these limitations, this manuscript proposes a VR/AR gamified solution using open-source software and open GIS data. Specifically, it investigates the euPOLIS game as an innovative participatory tool offering an alternative to traditional approaches. This game decentralizes urban planning by shifting technical tasks to experts while citizens engage interactively, focusing solely on proposing solutions. To explore the potential of the proposed methodology, the euPOLIS game was demonstrated as a workshop activity in TNOC 2024 Festival, where 30 individuals from different academic background (i.e., citizens, architects, planners, etc.) voluntarily engaged and provided their impressions and feedback. The findings suggest that gamified solutions such as serious/simulation AR/VR games can effectively promote co-design, co-participation, and co-creation in urban planning in an inclusive and engaging manner. Full article

As an important policy tool for guiding the development and protection of territorial space, the specific impact of spatial planning on the evolution of territorial space and the effects of its implementation have not yet been fully recognized. At the planning formulation stage, the feedback cycle of the implementation effect of planning is too long, and the adjustment is too slow to take effect. This directly affects the effectiveness and relevance of planning implementation. In this study, we propose a framework for pre-assessment of regional spatial planning based on the evolution of territorial space. We construct an indicator system from four dimensions to pre-assess the effectiveness of territorial spatial planning. The results show that the land use change simulation model, based on historical data coupled with planning elements, achieves an accuracy of 0.8457, which can accurately reflect the impact of planning on the evolution of land space. The results of the evaluation show that: (1) Planning elements such as concentrated urban construction areas, schools, and other public service facilities are the main driving forces behind the future evolution of territorial space. (2) Regional spatial planning has a significant effect on adjusting the spatial layout and optimizing the spatial form, and it has a certain effect on restricting the total scale of the construction area, improving the efficiency of land use, and lowering carbon emissions. (3) Existing planning strategies are still too rigid, so further enhancement of “flexibility” and “blank space” in planning is necessary. Additionally, guidance for improving the efficiency of land development needs to be further strengthened. The main contribution of this study is to provide a reliable pre-evaluation framework for policymakers and scholars. This will help identify problems and shortcomings at the planning formulation stage, allowing them to be optimized and adjusted in a timely manner. Under the guidance of scientific and reasonable planning, it will further promote the green and high-quality development and protection of territorial space. Full article