Search Results (10,508)

Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article

Background: Osteodysplastic syndromes comprise a very diverse group of clinically and genetically heterogeneous disorders characterized by defects in bone and connective tissue development, as well as in bone density. Here, we report the case of a 48-year-old female with a complex medical history characterized by bone dysplasia, hyperostosis, and partial tooth agenesis. Methods: Genetic testing was performed using WES analysis and Sanger sequencing. Molecular modeling analysis and dynamics simulation explored the impact of detected pathogenic variants. Results: The genetic analysis detected multiple pathogenic variants in genes CREB3L1, SLCO2A1, SFRP4, LRP5, and LRP6, each of which has been associated with rare osteodysplastic syndromes. The patient was homozygous for the same rare alleles associated with three of the identified autosomal recessive disorders osteogenesis imperfecta type XVI, primary hypertrophic osteoarthropathy, and metaphyseal dysplasia Pyle type. She also had a variant linked to autosomal dominant endosteal hyperostosis and a variant previously associated with increased risk of osteoporosis and bone fractures. Two of the detected variants are predicted to cause abnormal splicing, while molecular modeling and dynamics simulations analysis suggest that the other three variants probably confer altered local secondary structure and flexibility that may have functionally devastating consequences. Conclusions: Our case highlights the rare coexistence of multiple osteodysplastic syndromes in a single patient that may complicate differential diagnosis. Furthermore, this case emphasizes the necessity for early genetic investigation of such complex cases with overlying phenotypic traits, followed by genetic counseling, facilitating orchestration of clinical interventions and allowing prevention and/or prompt management of manifestations. Full article

This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: $\left(i\right)$ grid-connected and islanded operation; $\left(ii\right)$ peak shaving; $\left(iii\right)$ voltage support; $\left(iv\right)$ load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS. Full article

Glycolysis and oxidative phosphorylation are the main pathways of cellular energy production. Glucose is metabolized via glycolysis to generate pyruvate, which, under anaerobic conditions, is converted into lactate, while, under aerobic conditions, pyruvate enters mitochondria for oxidative phosphorylation to produce more energy. Accordingly, mitochondrial dysfunction disrupts the energy balance. Lactate, historically perceived as a harmful metabolic byproduct. However, emerging research indicates that lactate has diverse biological functions, encompassing energy regulation, epigenetic remodeling, and signaling activities. Notably, the 2019 study revealed the role of lactate in regulating gene expression through histone and non-histone lactylation, thereby influencing critical biological processes. Metabolic reprogramming is a key adaptive mechanism of cells responding to stresses. The Warburg effect in tumor cells exemplifies this, with glucose preferentially converted to lactate for rapid energy, accompanied by metabolic imbalances, characterized by exacerbated aerobic glycolysis, lactate accumulation, suppressed mitochondrial oxidative phosphorylation, and compromised mitochondrial function, ultimately resulting in a vicious cycle of metabolic dysregulation. As molecular bridges connecting metabolism and epigenetics, lactate and lactylation offer novel therapeutic targets for diseases like cancer and neurodegenerative diseases. This review summarizes the interplay between metabolic reprogramming and mitochondrial dysfunction, while discussing lactate and lactylation’s mechanistic in the pathogenesis of related diseases. Full article

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article

Background: A critical review of the literature demonstrates that masticatory apparatus with an artificial oral environment is of interest in the fields including (i) dental science; (ii) food science; (iii) the pharmaceutical industries for drug release. However, apparatus that closely mimics human chewing and oral conditions has yet to be realised. This study investigates the vital role of dental morphology and form–function connections using two-bite test parameters for effective drug release from medicated chewing gum (MCG) and compares them to human chewing efficiency with the aid of a humanoid chewing robot and a bionics product lifecycle management (PLM) framework with built-in reverse biomimetics—both developed by the first author. Methods: A novel, bio-engineered two-bite testbed is created for two testing machines with compression and torsion capabilities to conduct two-bite tests for evaluating the mechanical properties of MCGs. Results: Experimental studies are conducted to investigate the relationship between biting force and crushing/shearing and understand chewing efficiency and effective mastication. This is with respect to mechanochemistry and power stroke for disrupting mechanical bonds releasing the active pharmaceutical ingredients (APIs) of MCGs. The manuscript discusses the effect and the critical role that jaw physiology, dental morphology, the Bennett angle of mandible (BA) and the Frankfort-mandibular plane angle (FMA) on two-bite test parameters when FMA = 0, 25 or 29.1 and BA = 0 or 8. Conclusions: The impact on other scientific fields is also explored. Full article

Background: Anhedonia, defined as the diminished capacity to experience pleasure, represents a core negative symptom in first-episode psychosis (FEP) with profound implications for functional outcomes and long-term prognosis. Despite its clinical significance, comprehensive understanding of anhedonia prevalence, underlying mechanisms, and optimal intervention strategies in early psychosis remains limited. Objectives: To systematically examine the prevalence and characteristics of anhedonia in FEP patients, explore neurobiological mechanisms, identify clinical correlates and predictive factors, and evaluate intervention efficacy. Methods: Following PRISMA 2020 guidelines, we conducted comprehensive searches across PubMed, Embase, PsycINFO, and Web of Science databases from January 1990 to June 2025. Studies examining anhedonia and negative symptoms in FEP patients (≤24 months from onset) using validated assessment instruments were included. Quality assessment was performed using appropriate tools for study design. Results: Twenty-one studies comprising 3847 FEP patients met inclusion criteria. Anhedonia prevalence ranged from 30% at 10-year follow-up to 53% during acute phases, demonstrating persistent motivational deficits across illness trajectory. Factor analytic studies consistently supported five-factor negative symptom models with anhedonia as a discrete dimension. Neuroimaging investigations revealed consistent alterations in reward processing circuits, including ventral striatum hypofunction and altered network connectivity patterns. Social anhedonia demonstrated stronger associations with functional outcomes compared to other domains. Epigenetic mechanisms involving oxytocin receptor methylation showed gender-specific associations with anhedonia severity. Conventional antipsychotic treatments showed limited efficacy for anhedonia improvement, while targeted psychosocial interventions demonstrated preliminary promise. Conclusions: Anhedonia showed high prevalence (30–53%) across FEP populations with substantial clinical burden (13-fold increased odds vs. general population). Meta-analysis revealed large effect sizes for anhedonia severity in FEP vs. controls (d = 0.83) and strong negative correlations with functional outcomes (r =·−0.82). Neuroimaging demonstrated consistent ventral striatum dysfunction and altered network connectivity. Social anhedonia emerged as the strongest predictor of functional outcomes, with independent suicide risk associations. Conventional antipsychotics showed limited efficacy, while behavioral activation approaches demonstrated preliminary promise. These findings support anhedonia as a distinct treatment target requiring specialized assessment and intervention protocols in early psychosis care. Full article

The various forms of Airy’s differential equation are discussed in this work, together with the special functions that arise in the processes of their solutions. Further properties of the arising integral functions are discussed, and their connections to existing special functions are derived. A generalized form of the Scorer function is obtained and expressed in terms of the generalized Airy and Nield–Kuznetsov functions. Higher derivatives of all generalized functions arising in this work are obtained together with their associated generalized Airy polynomials. A computational procedure for the generalized Scorer function is introduced and applied to computing and graphing it for different values of its index. The solution of an initial value problem involving the generalized Scorer function is obtained. Full article

This study aimed to assess the vulnerability of transportation networks and identify critical nodes and regions within a multimodal transportation system. While previous research has predominantly focused on centrality measures to evaluate node importance from an accessibility perspective, this study emphasizes the need to evaluate network vulnerability comprehensively in response to rapid socioeconomic changes. We propose a vulnerability function that integrates network topology and connectivity. First, we defined the vulnerability of individual nodes and regional clusters. Second, we developed a methodology to evaluate the defined vulnerabilities systematically. Finally, we applied the framework to Korea’s multimodal transportation network, conducting a case study to validate the effectiveness and applicability of the proposed function. Conclusively, this study presents a comprehensive vulnerability assessment framework for multimodal transportation networks, offering valuable insights to support robust decision making for enhancing sustainable and efficient transportation systems. Full article

Anthropogenic and climatic pressures can transform contiguous forests into smaller, less connected fragments. Forest biodiversity and ecosystem functioning can furthermore be compromised or enhanced. We present a descriptive analysis of forest fragmentation in Bavaria, the largest federal state in Germany. We calculated 22 metrics of fragmentation using forest polygons, aggregated within administrative units and with respect to both elevation and aspect orientation. Using a forest mask from September 2024, we found 2.384 million hectares of forest across Bavaria, distributed amongst 83,253 forest polygons 0.1 hectare and larger. The smallest patch category (XS, <25 ha) outnumbered all other size classes by nearly 13 to 1. Edge zones accounted for more than 1.68 million hectares, leaving less than 703,000 hectares as core forest. Although south-facing slopes dominated the state, the highest forest cover (~36%) was found on the least abundant east-oriented slopes. Most of the area is located at 400–600 m.a.s.l., with around 30% of this area covered by forests; however, XL forest patches (>3594 ha) dominated higher elevations, covering 30–60% of land surface area between 600 and 1400 m.a.s.l. The distribution of the largest patches follows the higher terrain and corresponds well to protected areas. K-means clustering delineated 3 clusters, which corresponded well with the predominance of patchiness, aggregation, and edginess within districts. Full article

High-precision positioning is the foundation for the functionality of various intelligent agents. In complex environments, such as urban canyons, relative pose estimation using cameras is a crucial step in high-precision positioning. To take advantage of the ability of an inertial measurement unit (IMU) to provide relatively accurate gravity prior information over a short period, we propose a minimal solution method for the relative pose estimation of a stereo camera system assisted by the IMU. We rigidly connect the IMU to the camera system and use it to obtain the rotation matrices in the roll and pitch directions for the entire system, thereby reducing the minimum number of corresponding points required for relative pose estimation. In contrast to classic pose-estimation algorithms, our method can also calculate the camera focal length, which greatly expands its applicability. We constructed a simulated dataset and used it to compare and analyze the numerical stability of the proposed method and the impact of different levels of noise on algorithm performance. We also collected real-scene data using a drone and validated the proposed algorithm. The results on real data reveal that our method exhibits smaller errors in calculating the relative pose of the camera system compared with two classic reference algorithms. It achieves higher precision and stability and can provide a comparatively accurate camera focal length. Full article

Sensory dysregulation represents a core challenge in autism spectrum disorder (ASD), affecting perception, behavior, and adaptive functioning. The brain’s ability to reorganize, known as neuroplasticity, serves as the basic principle for therapeutic interventions targeting these deficits. Neuroanatomical mechanisms include altered connectivity in the sensory and visual cortices, as well as in the limbic system and amygdala, while imbalances of neurotransmitters, in particular glutamate and gamma-aminobutyric acid (GABA), contribute to atypical sensory processing. Traditional therapies used in sensory integration are based on the principles of neuroplasticity. Increasingly, new treatments use this knowledge, and modern therapies such as neurofeedback, transcranial stimulation, and immersive virtual environments are promising in modulating neuronal circuits. However, further research is needed to optimize interventions and confirm long-term effectiveness. This review discusses the role of neuroplasticity in the etiopathogenesis of sensory integration deficits in autism spectrum disorder. The neuroanatomical and neurotransmitter basis of impaired perception of sensory stimuli is considered, and traditional and recent therapies for sensory integration are discussed. Full article

Microgreens, having short growth cycles and efficient nutrient uptake, are ideal candidates for biofortification. This study investigated the effects of selenium (Se) and zinc (Zn) on photosynthetic performance in four hydroponically grown Brassica microgreens (broccoli, pak choi, kohlrabi, and kale), using direct and modulated chlorophyll a fluorescence and chlorophyll-to-carotenoid ratios (Chl/Car). The plants were treated with Na₂SeO₄ at 0 (control), 2, 5, and 10 mg/L or ZnSO₄ × 7H₂O at 0 (control), 5, 10, and 20 mg/L. The results showed species-specific responses with Se or Zn uptake. Selenium enhanced photosynthetic efficiency in a dose-dependent manner for most species (8–26% on average compared to controls). It increased the plant performance index (PI_tot), particularly in pak choi (+62%), by improving both primary photochemistry and inter-photosystem energy transfer. Kale and kohlrabi exhibited high PSII-PSI connectivity for efficient energy distribution, with increased cyclic electron flow around PSI and reduced Chl/Car up to 8.5%, while broccoli was the least responsive. Zinc induced variable responses, reducing PI_tot at lower doses (19–23% average decline), with partial recovery at 20 mg/L (9% average reduction). Broccoli exhibited higher susceptibility, with inhibited Q_A re-oxidation, low electron turnover due to donor-side restrictions, and increased pigment ratio (+3.6%). Kohlrabi and pak choi tolerated moderate Zn levels by redirecting electron flow, but higher Zn levels impaired PSII and PSI function. Kale showed the highest tolerance, maintaining stable photochemical parameters and total electron flow, with increased pigment ratio (+4.5%) indicating better acclimation. These results highlight the beneficial stimulant role of Se and the dual essential/toxic nature of Zn, thus emphasizing genotype and dose-specific optimizations for effective biofortification. Full article

To characterize a phenomenological model of a mechanical oscillator, it is important to know the properties of the envelope of the three main physical motion variables: deviation from equilibrium, velocity, and acceleration. Experimental data show that friction forces restrict the shape of these functions. A linear, exponential, or more abrupt decay can be observed depending on the different physical systems and conditions. This paper aimed to contribute to clarifying the role that some types of friction forces play in these shapes. Three types of friction—constant sliding friction, pressure drag proportional to the square of velocity, and friction drag proportional to velocity—were considered to characterize the line connecting the maxima and minima of displacement for a generic mechanical harmonic oscillator. The ordinary differential equation (ODE), describing the harmonic oscillator simultaneously containing the three types of dissipative forces (constant, viscous, and quadratic), was numerically solved to obtain energy dissipation, and the extrema of both displacement and velocity. The differential equation ruling the behavior of the amplitude, as a function of the friction force coefficients, was obtained from energy considerations. Solving this equation, we obtained analytical functions, parametrized by the force coefficients that describe the oscillator tail. A comparison between these functions and the predicted oscillator ODE extrema was made, and the results were in agreement for all the situations tested. Information from the velocity extrema and nulls was enough to obtain a second function that rules completely the ODE solution. The correlations obtained allow for the reverse operation: from the identified extremum data, it was possible to identify univocally the three friction coefficients fitting used in the model. Motion equations were solved, and some physical properties, namely energy conservation and work of friction forces, were revisited. Full article

Throughout the generations, traditional academic writing skills development has taught students in socio-human programs to express their knowledge and thoughts with an evidence-based foundation, helping them make a special connection with their professional fields. However, a lack of digital learning and writing resources in this process has been identified. This study of the social work field connects digital academic writing, social protection functionality, and research innovations, identifying and exploring open-access (OA) educational and social resources for social work higher education (SWHE). Applying content analyses to online documents and websites, we identified key terms characteristic of social work, following a standard approach on formulating research questions, identifying categories, creating a code book, sampling, and measuring information. The research questions were as follows: How is digital academic writing being developed in social work education programs in Romanian universities? Where do researchers, students, teachers, and professionals gather OA digital information and data for academic innovation? What kind of OA information and data are contained in websites for academic writing? We also used OA socio-educational resource analysis to derive digital, evidence-based, and academic writing codes. The frequencies of these elements in documents and websites were examined. Professional samples of four OA documents and five academic and non-academic Romanian websites with extensions were processed. Furthermore, information from a non-academic official website concerning social protection functionality was observed, identified, and measured. We concluded that academic writing is not included as an independent course in the curricula of Romanian social work programs at universities; this topic is rarely researched. Digital and evidence-based education is also a marginalized topic in socio-human scientific resources. OA information, laws, reports, and statistics were identified. Information on scientific research, academic–non-academic partnerships, descriptions of good practices, and human resources information was lacking. In conclusion, this study contributes to increasing productivity and developing digital academic skills in social work education and research. Full article