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Significant advancements in the field of power electronics have created an ideal opportunity to introduce various topologies of multilevel inverters. These multilevel inverter topologies comprise different notable characteristics, such as staircase sinusoidal output voltage with high quality, a lowered number of power switches, no filter requirement, etc. In this literature, a new asymmetrical MLI topology is proposed to reduce the number of components of the inverter with admirable voltage-step creation. The proposed topology provides a 17-level, staircase-type, nearly sinusoidal output voltage waveform. The number of switches required for the proposed multilevel inverter topology is fewer compared to the existing topology for the same level. A carrier-based sinusoidal pulse-width modulation technique is used for the proposed topology at a switching frequency of 3 kHz. The functioning of the proposed inverter topology is thoroughly examined. A 17-level asymmetrical inverter is executed; both the MATLAB/SIMULINK as well as the experimental results using dSPACE-1103 controller. The simulation results are verified using the experimental results for the proposed 17-level multilevel inverter for modulation indexes of 1 and 0.6. [ABSTRACT FROM AUTHOR]

Deep learning techniques for gaze estimation usually determine gaze direction directly from images of the face. These algorithms achieve good performance because face images contain more feature information than eye images. However, these image classes contain a substantial amount of redundant information that may interfere with gaze prediction and may represent a bottleneck for performance improvement. To address these issues, we model long-distance dependencies between the eyes via Strip Pooling and Multi-Criss-Cross Attention Networks (SPMCCA-Net), which consist of two newly designed network modules. One module is represented by a feature enhancement bottleneck block based on fringe pooling. By incorporating strip pooling, this residual module not only enlarges its receptive fields to capture long-distance dependence between the eyes but also increases weights on important features and reduces the interference of redundant information unrelated to gaze. The other module is a multi-criss-cross attention network. This module exploits a cross-attention mechanism to further enhance long-range dependence between the eyes by incorporating the distribution of eye-gaze features and providing more gaze cues for improving estimation accuracy. Network training relies on the multi-loss function, combined with smooth L1 loss and cross entropy loss. This approach speeds up training convergence while increasing gaze estimation precision. Extensive experiments demonstrate that SPMCCA-Net outperforms several state-of-the-art methods, achieving mean angular error values of 10.13° on the Gaze360 dataset and 6.61° on the RT-gene dataset. [ABSTRACT FROM AUTHOR]

Background/Objectives: Early studies have suggested that the SARS-CoV-2 virus has a deleterious effect on bone mineral density and may increase the risk of pathological fractures. This study characterized vertebral compression fractures in patients with and without a prior diagnosis of COVID-19. Methods: Using a nationwide claims database, this retrospective study used ICD-10 billing codes to identify patients with a diagnosis of vertebral compression fracture from January 2020 to April 2022. Two cohorts were created based on whether the patients had a concurrent diagnosis of COVID-19. Patient demographics, comorbidities, and outcome measures were characterized by descriptive analysis. Results: In total, 413,425 patients met the inclusion criteria. Among them, a total of 23,148 patients (5.60%) had a diagnosis of COVID-19 at the time of their compression fracture. Among the COVID-19 patients, the incidences of vertebral compression fracture were 0.42% in 2020 and 0.33% in 2021, in comparison to the historical average yearly incidence of 0.17% across all patients. The patients with COVID-19 at the time of compression fracture diagnosis had a higher rate of vitamin D deficiency (OR: 1.25) and a lower rate of routine healing (OR: 0.61). The patients without COVID-19 were more likely to be osteoporotic (OR: 0.88), experience additional compression fractures (OR: 0.38), and have kyphoplasty or vertebroplasty (OR: 0.73). Conclusions: Despite lower rates of osteoporosis, patients with a concomitant COVID-19 diagnosis exhibited a higher incidence of compression fractures. Although more research is needed, these results support increasing bone health surveillance in patients with a history of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Background: Degenerative cervical myelopathy is a progressive neurological disorder that is commonly encountered in clinical practice and its incidence is expected to increase alongside the aging population. Given the importance of early and accurate diagnosis in this patient population, this narrative review aims to provide a repository of up-to-date information regarding pertinent patient history, physical exam findings, and potential alternate diagnoses. Methods: The PubMed database was queried for publications from 1 January 2019 to 19 March 2024. The search terms utilized are as follows: cervical myelopathy", "cervical spondylotic myelopathy", "degenerative cervical myelopathy", "epidemiology", "prevalence", "incidence", "etiology", "diagnosis", "differential", "symptoms", "clinical presentation", and "atypical symptoms". The resultant articles were reviewed for relevance and redundancy and are presented within the following categories: Natural History, Epidemiology, Clinical Presentation, Diagnosis, and Management. Results: Myelopathy patients often present with subtle and non-specific symptoms such as sleep disturbances, increased falls, and difficulty driving, which can lead to underdiagnosis and misdiagnosis. Failing to diagnose degenerative cervical myelopathy in a timely manner can result in progressive and irreparable neurological damage. Although many nonoperative treatment modalities are available, surgical decompression is ultimately recommended in most cases to limit further deterioration in neurological function and optimize long-term patient outcomes. Conclusions: A thorough clinical history and physical examination remain the most important diagnostic tools to avoid misdiagnosis and implement early treatment in this patient population. [ABSTRACT FROM AUTHOR]

In four-dimensional additive manufacturing (4DAM), specific external stimuli are applied in conjunction with additive manufacturing technologies. This combination allows the development of tailored stimuli-responsive properties in various materials, structures, or components. For shape-changing functionalities, the programming step plays a crucial role in recovery after exposure to a stimulus. Furthermore, precise tuning of the 4DAM process parameters is essential to achieve shape-change specifications. Within this context, this study investigated how the structural arrangement of infill patterns (criss-cross and concentric) affects the shape memory effect (SME) of compression cold-programmed PLA under a thermal stimulus. The stress–strain curves reveal a higher yield stress for the criss-cross infill pattern. Interestingly, the shape recovery ratio shows a similar trend across both patterns at different displacements with shallower slopes compared to a higher shape fixity ratio. This suggests that the infill pattern primarily affects the mechanical strength (yield stress) and not the recovery. Finally, the recovery force increases proportionally with displacement. These findings suggest a consistent SME under the explored interval (15–45% compression) despite the infill pattern; however, the variations in the mechanical properties shown by the stress–strain curves appear more pronounced, particularly the yield stress. [ABSTRACT FROM AUTHOR]

The dynamic behavior of flood waves on rivers is essential to flood prediction. Natural flood waves are complex due to tributary inputs, rainfall variations, and overbank flows, so this study examines hydropower dam releases, which are simpler to analyze because channel effects are isolated. Successive arrival times and heights of peaks along 9 rivers with multiple stream gauges downstream of hydroelectric dams show that flow peaks typically become exponentially lower and wider with distance. The propagation velocity of peaks increases with water depth and channel slope but decreases with downstream distance and greater channel tortuosity. A rich hierarchy of velocities was found. Hydropower pulses progress at or in slight excess of the theoretical celerity, which is faster than the propagation rate of average natural floods, which in turn exceeds the mean velocity of water in the channel, yet the water moves faster than the peaks of record floods. The progressive changes to the height, shape, and velocity of hydropower flow peaks are simulated by the first analytical solution to the convolution integral for a rectangular source pulse that is based on diffusion-advection theory. Available data support some widely held expectations while refuting others. An expanded definition of "water mining" is proposed. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to evaluate if imbalance influences complication rates, radiological outcomes, and patient-reported outcomes (PROMs) following adult spinal deformity (ASD) surgery. Methods: ASD patients with baseline and 2-year radiographic and PROMs were included. Patients were grouped according to whether they answered yes or no to a recent history of pre-operative loss of balance. The groups were propensity-matched by age, pelvic incidence–lumbar lordosis (PI-LL), and surgical invasiveness score. Results: In total, 212 patients were examined (106 in each group). Patients with gait imbalance had worse baseline PROM measures, including Oswestry disability index (45.2 vs. 36.6), SF-36 mental component score (44 vs. 51.8), and SF-36 physical component score (p < 0.001 for all). After 2 years, patients with gait imbalance had less pelvic tilt correction (−1.2 vs. −3.6°, p = 0.039) for a comparable PI-LL correction (−11.9 vs. −15.1°, p = 0.144). Gait imbalance patients had higher rates of radiographic proximal junctional kyphosis (PJK) (26.4% vs. 14.2%) and implant-related complications (47.2% vs. 34.0%). After controlling for age, baseline sagittal parameters, PI-LL correction, and comorbidities, patients with imbalance had 2.2-times-increased odds of PJK after 2 years. Conclusions: Patients with a self-reported loss of balance/unsteady gait have significantly worse PROMs and higher risk of PJK. [ABSTRACT FROM AUTHOR]

Neuroendocrine tumors (NETs) are a heterogeneous class of cancers, predominately occurring in the gastroenteropancreatic system, which pose a growing health concern with a significant rise in incidence over the past four decades. Emerging from neuroendocrine cells, these tumors often elicit paraneoplastic syndromes such as carcinoid syndrome, which can manifest as a constellation of symptoms significantly impacting patients' quality of life. The prognosis of NETs is influenced by their tendency for metastasis, especially in cases involving the liver, where the estimated 5-year survival is between 20 and 40%. Although surgical resection remains the preferred curative option, challenges emerge in cases of neuroendocrine tumors with liver metastasis (NELM) with multifocal lobar involvement, and many patients may not meet the criteria for surgery. Thus, minimally invasive and non-surgical treatments, such as locoregional therapies, have surfaced. Overall, these approaches aim to prioritize symptom relief and aid in overall tumor control. This review examines locoregional therapies, encompassing catheter-driven procedures, ablative techniques, and radioembolization therapies. These interventions play a pivotal role in enhancing progression-free survival and managing hormonal symptoms, contributing to the dynamic landscape of evolving NELM treatment. This review meticulously explores each modality, presenting the current state of the literature on their utilization and efficacy in addressing NELM. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present and study nonlinear bivariate C 1 quadratic spline quasi-interpolants on uniform criss-cross triangulations for the approximation of piecewise smooth functions. Indeed, by using classical spline quasi-interpolants, the Gibbs phenomenon appears when approximating near discontinuities. Here, we use weighted essentially non-oscillatory techniques to modify classical quasi-interpolants in order to avoid oscillations near discontinuities and maintain high-order accuracy in smooth regions. We study the convergence properties of the proposed quasi-interpolants and we provide some numerical and graphical tests confirming the theoretical results. [ABSTRACT FROM AUTHOR]

Sacral insufficiency fractures commonly affect elderly women with osteoporosis and can cause debilitating lower back pain. First line management is often with conservative measures such as early mobilization, multimodal pain management, and osteoporosis management. If non-operative management fails, sacroplasty is a minimally invasive intervention that may be pursued. Candidates for sacroplasty are patients with persistent pain, inability to tolerate immobilization, or patients with low bone mineral density. Before undergoing sacroplasty, patients' bone health should be optimized with pharmacotherapy. Anabolic agents prior to or in conjunction with sacroplasty have been shown to improve patient outcomes. Sacroplasty can be safely performed through a number of techniques: short-axis, long-axis, coaxial, transiliac, interpedicular, and balloon-assisted. The procedure has been demonstrated to rapidly and durably reduce pain and improve mobility, with little risk of complications. This article aims to provide a narrative literature review of sacroplasty including, patient selection and optimization, the various technical approaches, and short and long-term outcomes. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study can be directly applied in the process of evaluating circular and resilient information systems in manufacturing, providing a practical tool for practitioners and researchers. Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely circular and resilient information systems (CRISs). These systems integrate with traditional ones to optimise key performance indicators (KPIs) related to circularity and resiliency. Despite extensive methodologies for evaluating traditional information systems, systems designed for circularity and resiliency need to be assessed in parallel and in depth. Existing evaluations focus on efficiency and user satisfaction but often neglect the unique demands of circularity and resiliency. This study introduces a novel evaluation methodology for CRISs. Through a case study of an innovative system and the established literature, we address real-life needs and challenges in manufacturing. In particular, the system serves the needs of three distinct case studies: Carbon Fibre-Reinforced Polymer (CFRP) waste utilisation in drone manufacturing, recovery of magnets from Waste Electrical and Electronic Equipment (WEEE), and the repurposing of citrus processing waste into juice by-products. Our methodology is built on the 5W1H method to make our approach context-specific and aligned with each case's unique requirements, making it also replicable for other industries. Our findings offer insights and a tool for practitioners and researchers to evaluate CRIS performance. The research highlights the importance of a two-fold evaluation approach for CRISs, evaluating both pilot-specific KPIs and the system's technical performance. Policy implications suggest the need for regulatory frameworks and incentives to support the adoption, as well as evaluation, of CRISs and promote sustainable and resilient industrial practices. [ABSTRACT FROM AUTHOR]

Available inventories of baryonic mass in the universe are based largely on galactic data and empirical calculations made >20 years ago. Values falling below cosmological estimates underlie proposals that certain rarified gassy regions could have extremely high T, which motivated absorption measurements and hydrodynamic models. Yet, the shortfall remains. We inventory the total baryonic mass, focusing on gravitational interactions and updated measurements. A recent analytical inverse method for analyzing galactic rotation curves quantified how baryon mass and associated volumetric density (ρ) depend on distance (r) from galactic centers. The model is based on the dynamical consequences of the observed oblate shape of galaxies and the Virial Theorem. The parameter-free solution provides ρ(r) ∝ 1/r2 which describes star-rich galactic interiors, gas-rich outer discoids, circumgalactic media, and gradation into intergalactic media. Independent observational determinations of baryonic ρ validate that our 1/r2 result describes baryons alone. This solution shows that total baryonic mass associated with any galaxy is 2.4 to 40 times detectable luminosity, depending on galaxy size and spacing. Luminosity data within 50 Mpc show that Andromeda equivalents separated by ~1 Mpc represent the local universe. Combining the above yields (6 ± 2) × 10−25 kg m−3 for the present-day universe. Three other approaches support this high density: (1) evaluating trends and luminosity data near 1000 Mpc; (2) using a recent estimate for the number of galaxies in the universe; (3) calculating an energy balance. We discuss uncertainties in the critical density. Implications of large baryonic ρ are briefly discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Primary liver cancer is the third most common cause of cancer-related deaths worldwide. Risk factors for primary liver cancer include chronic viral hepatitis B and C infections, alcohol abuse, non-alcoholic fatty liver disease, and obesity. Surgical resection and/or transplantation is the mainstay treatment for candidates with primary liver tumors. However, minimally invasive, image-guided locoregional therapies have become an integral part of liver cancer treatment and management, depending on staging. In this manuscript, the authors provide a comprehensive overview of the antineoplastic mechanisms underpinning locoregional therapies and the current state of the literature on the efficacy of these therapies for primary liver cancer. We also discuss emerging advances in treatment, such as the adjuvant use of immunotherapies and molecular targeting agents with locoregional therapy. This review highlights the emerging technological advancements and image-guided procedures used to treat primary liver cancer. Primary liver cancer is the leading cause of cancer-related deaths worldwide. with incidences predicted to rise over the next several decades. Locoregional therapies, such as radiofrequency or microwave ablation, are described as image-guided percutaneous procedures, which offer either a curative intent for early-stage hepatocellular carcinoma or bridging/downstaging for surgical resection or transplantation. Catheter-driven locoregional therapies, such as transarterial chemoembolization and radioembolization, induce tumor hypoxia, can be palliative, and improve survival for early-to-intermediate hepatocellular carcinoma and unresectable intrahepatic cholangiocarcinoma. Herein, we provide a comprehensive overview of the antineoplastic mechanisms underpinning locoregional therapies, different treatment approaches, and the current state of the literature for the efficacy of locoregional therapies for primary liver cancer. We also discuss emerging advancements, such as the adjuvant use of immunotherapies and molecular targeting agents with locoregional therapy, for the treatment of primary liver cancer. [ABSTRACT FROM AUTHOR]

Extremely slow recession of the Moon from the Earth has been recently proposed and attributed to conversion of Earth's axial spin to lunar orbital momentum. This hypothesis is inconsistent with long-standing recognition that the Moon's orbit involves three-body interactions. This and other short-comings, such as Earth's spin loss being internal, are summarized here. Considering point-masses is justified by theory and observational data on other moons. We deduce that torque in the Earth-Moon-Sun system increases eccentricity of the lunar orbit but decreases its inclination over time. Consequently, the average lunar orbital radius is decreasing. We also show that lunar drift is too small to be constrained through lunar laser ranging measurements, mainly because atmospheric refraction corrections are comparatively large and variations in lunar cycles are under-sampled. Our findings support co-accretion and explain how orbits evolve in many-body point-mass systems. [ABSTRACT FROM AUTHOR]

Orthopaedic and related care has the potential to present unique obstacles for patients with a range of autism manifestations. In this review, we aim to describe and analyze the literature on autistic patients' experience within orthopaedics and closely related fields. This literature search utilized the PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature databases. Three major concepts were built into the search terms: (1) patients on the autism spectrum; (2) patient experience; and (3) movement sciences, including orthopaedics, physical medicine and rehabilitation (PM&R), occupational therapy (OT), and physical therapy (PT). Our search yielded 35 topical publications, with the major topic areas addressed as follows: (1) clinical and perioperative management, (2) therapy interventions, (3) participation in exercise and social play, (4) sensory management and accommodations, (5) caregiver/parent training and involvement in care, (6) healthcare needs and barriers to care, and (7) utilization of technology. In the current literature, there are no studies that attempt to directly assess autistic patient experience with care practices and clinical environments in orthopaedics. Rigorous, direct examination of the experience of autistic patients within clinical orthopaedic settings is urgently needed to address this gap. [ABSTRACT FROM AUTHOR]

Kepler's discoveries were permitted by his remarkable insight to place the Sun at the focus of an elliptical planetary orbit. This coordinate system reduces a 2-dimensional orbit to a single spatial dimension. We consider an alternative coordinate system centered on the "image focus," which is the symmetrical (mirror) counterpart of the "real focus" occupied by the Sun. Our analytical approach provides new purely geometric formulae and an exact relationship for the dynamic property of orbital time. In addition, considering the mirror symmetry of the ellipse leads to a simple approximation: the radial hand of an orbital clock rotates counterclockwise at a nearly steady angular velocity 2π/T about the "image focus," where T is the orbital period. This approximation is a useful pedagogic tool and has good accuracy for orbits with low to moderate eccentricities, since the deviation from the exact result goes as eccentricity squared. Planetary comparisons are made. In particular, the angular speeds of Mercury and Jupiter are highly variable in the geocentric and heliocentric reference frames, but are nearly constant in the image focus reference frame. Our findings resolve whether the image focus is the location for observing uniform motion of an elliptical orbit, and pertain to their stability. [ABSTRACT FROM AUTHOR]

Shadows degrade image quality and complicate interpretation, underscoring the importance of accurate shadow detection for many image analysis tasks. However, due to the complex backgrounds and variable shadow characteristics of remote sensing images (RSIs), existing methods often struggle with accurately detecting shadows of various scales and misclassifying dark, non-shaded areas as shadows. To address these issues, we proposed a comprehensive shadow detection network called MAMNet. Firstly, we proposed a multi-scale spatial channel attention fusion module, which extracted multi-scale features incorporating both spatial and channel information, allowing the model to flexibly adapt to shadows of different scales. Secondly, to address the issue of false detection in non-shadow areas, we introduced a criss-cross attention module, enabling non-shadow pixels to be compared with other shadow and non-shadow pixels in the same row and column, learning similar features of pixels in the same category, which improved the classification accuracy of non-shadow pixels. Finally, to address the issue of important information from the other two modules being lost due to continuous upsampling during the decoding phase, we proposed an auxiliary branch module to assist the main branch in decision-making, ensuring that the final output retained the key information from all stages. The experimental results demonstrated that the model outperformed the current state-of-the-art RSI shadow detection method on the aerial imagery dataset for shadow detection (AISD). The model achieved an overall accuracy (OA) of 97.50%, an F 1 score of 94.07%, an intersection over union (IOU) of 88.87%, a precision of 95.06%, and a BER of 4.05%, respectively. Additionally, visualization results indicated that our model could effectively detect shadows of various scales while avoiding false detection in non-shadow areas. Therefore, this model offers an efficient solution for shadow detection in aerial imagery. [ABSTRACT FROM AUTHOR]

RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance. To address this issue, we propose a method for RGB-T salient object detection that enhances performance through wavelet transform and channel-wise attention fusion. Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. First, input features are passed through the channel-wise criss-cross module (CCM) for cross-modal information fusion, adaptively adjusting the importance of features to generate rich fusion information. Subsequently, the multi-scale fusion information is input into the feature selection wavelet transforme module (FSW), which selects beneficial low-frequency and high-frequency features to improve feature aggregation performance and achieves higher segmentation accuracy through long-distance connections. Extensive experiments demonstrate that our method outperforms 22 state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study aims to assess the implementation effectiveness of the online platform for digital competence (DC) certification in schools. The testing platform was a prototype of a DC certification system developed and piloted during 2019 in primary and secondary schools in six European countries involving more than 800 teachers and 6000 students. The study resulted in positive proof that the effective integration and evaluation of the DC acquisition, evaluation, and certification within formal curricula in primary and secondary schools is possible. In addition, it was confirmed that information quality is a significant predictor of the impact on the platform end-users. In contrast, the quality of service is not a significant predictor of a successful implementation of the cloud-based platform with an intuitive user interface and proper online help, i.e., massive open online courses (MOOCs). Furthermore, the developed instrument can help schools implement and assess platforms for DC certification and help policymakers pursue and monitor the implementation of such platforms in schools. [ABSTRACT FROM AUTHOR]

Available data on insulating, semiconducting, and metallic solids verify our new model that incorporates steady-state heat flow into a macroscopic, thermodynamic description of solids, with agreement being best for isotropic examples. Our model is based on: (1) mass and energy conservation; (2) Fourier's law; (3) Stefan–Boltzmann's law; and (4) rigidity, which is a large, yet heretofore neglected, energy reservoir with no counterpart in gases. To account for rigidity while neglecting dissipation, we consider the ideal, limiting case of a perfectly frictionless elastic solid (PFES) which does not generate heat from stress. Its equation-of-state is independent of the energetics, as in the historic model. We show that pressure-volume work (PdV) in a PFES arises from internal interatomic forces, which are linked to Young's modulus (Ξ) and a constant (n) accounting for cation coordination. Steady-state conditions are adiabatic since heat content (Q) is constant. Because average temperature is also constant and the thermal gradient is fixed in space, conditions are simultaneously isothermal: Under these dual restrictions, thermal transport properties do not enter into our analysis. We find that adiabatic and isothermal bulk moduli (B) are equal. Moreover, Q/V depends on temperature only. Distinguishing deformation from volume changes elucidates how solids thermally expand. These findings lead to simple descriptions of the two specific heats in solids: ∂ln(cP)/∂P = −1/B; cP = nΞ times thermal expansivity divided by density; cP = cVnΞ/B. Implications of our validated formulae are briefly covered. [ABSTRACT FROM AUTHOR]

Parents and peers play critical roles in the socialization of children and adolescents, yet investigations on the role played by parents vs. peers have been largely separate for many years. To address this problem, we invited leading scholars in the field to collectively tell a complex story of the part that parents and peers together play in the development of children and adolescents. The resulting Special Issue is a collection of papers highlighting current conceptualizations and empirical work in this area, with a focus on additive, multiplicative, and transactional mechanisms that link parent and peer relational contexts to each other and to child/adolescent social and emotional development. Two papers present new conceptual models, six illustrate empirical work in the field, and one paper that provides a comprehensive review of the literature. The stories that are conveyed in the issue are both innovative and complex. [ABSTRACT FROM AUTHOR]

Vehicle detection with optical remote sensing images has become widely applied in recent years. However, the following challenges have remained unsolved during remote sensing vehicle target detection. These challenges include the dense and arbitrary angles at which vehicles are distributed and which make it difficult to detect them; the extensive model parameter (Param) that blocks real-time detection; the large differences between larger vehicles in terms of their features, which lead to a reduced detection precision; and the way in which the distribution in vehicle datasets is unbalanced and thus not conducive to training. First, this paper constructs a small dataset of vehicles, MiVehicle. This dataset includes 3000 corresponding infrared and visible image pairs, offering a more balanced distribution. In the infrared part of the dataset, the proportions of different vehicle types are as follows: cars, 48%; buses, 19%; trucks, 15%; freight, cars 10%; and vans, 8%. Second, we choose the rotated box mechanism for detection with the model and we build a new vehicle detector, ML-Det, with a novel multi-scale feature fusion triple cross-criss FPN (TCFPN), which can effectively capture the vehicle features in three different positions with an mAP improvement of 1.97%. Moreover, we propose LKC–INVO, which allows involution to couple the structure of multiple large kernel convolutions, resulting in an mAP increase of 2.86%. We also introduce a novel C2F_ContextGuided module with global context perception, which enhances the perception ability of the model in the global scope and minimizes model Params. Eventually, we propose an assemble–disperse attention module to aggregate local features so as to improve the performance. Overall, ML-Det achieved a 3.22% improvement in accuracy while keeping Params almost unchanged. In the self-built small MiVehicle dataset, we achieved 70.44% on visible images and 79.12% on infrared images with 20.1 GFLOPS, 78.8 FPS, and 7.91 M. Additionally, we trained and tested our model on the following public datasets: UAS-AOD and DOTA. ML-Det was found to be ahead of many other advanced target detection algorithms. [ABSTRACT FROM AUTHOR]

A lightweight bearing fault detection approach based on an improved residual network is presented to solve the shortcomings of previous fault diagnostic methods, such as inadequate feature extraction and an excessive computational cost due to high model complexity. First, the raw data are turned into a time–frequency map using the continuous wavelet transform, which captures all of the signal's time- and frequency-domain properties. Second, an improved residual network model was built, which incorporates the criss-cross attention mechanism and depth-separable convolution into the residual network structure to realize the important distinction of the extracted features and reduce computational resources while ensuring diagnostic accuracy; simultaneously, the Meta-Acon activation function was introduced to improve the network's self-adaptive characterization ability. The study findings indicate that the suggested approach had a 99.95% accuracy rate and a floating point computational complexity of 0.53 GF. Compared with other networks, it had greater fault detection accuracy and stronger generalization ability, and it could perform high-precision fault diagnostic jobs due to its lower complexity. [ABSTRACT FROM AUTHOR]

Featured Application: The study provides a conceptual and methodological approach for the evaluation of Digital Competence in the school context. Most of all, it helps highlight four factors that, according to teaching staff, describe the quality of the proposed model when applied to daily teaching practices. The progressive shift from a transmissive to a competence-based approach emphasises the need to design new models and tools for competence assessment. In order to address this need, this study, firstly, introduces the assessment model for digital competence in primary and secondary schools developed within the H2020 CRISS project. The Competence Assessment Model (CAM) (1) embraces the integration pedagogy approach in which competences are embedded within the curriculum and (2) provides competence assessment scenarios (CAS). Secondly, the study presents the results of the evaluation of its quality from the teachers' perspective through an instrument designed on the basis of nine competence assessment quality criteria. The CAM was tested in a large-scale pilot study conducted in 535 primary and secondary schools across Europe. The outcomes show that the CAM mainly fulfills two out of the nine quality criteria: meaningfulness and authenticity. However, the exploratory factor analysis also reveals that all these quality criteria can be summarized into four key quality factors that more accurately describe how CAM is experienced in school teaching. Out of these, the most relevant is the (1) efficiency over time, student monitoring, support and evaluation. The three secondary factors identified are (2) fairness and cognitive complexity, (3) meaningfulness and authenticity and (4) reproducibility and transparency. The study provides a conceptual and methodological approach for the evaluation of Digital Competence in the school context. Most of all, it helps highlight four factors that, according to teaching staff, describe the quality of the proposed model when applied to daily teaching practices. [ABSTRACT FROM AUTHOR]

In the accelerated phase of urbanization, intelligent surveillance systems play an increasingly pivotal role in enhancing urban management efficiency, particularly in the realm of parking lot administration. The precise identification of small and overlapping targets within parking areas is of paramount importance for augmenting parking efficiency and ensuring the safety of vehicles and pedestrians. To address this challenge, this paper delves into and amalgamates cross-attention and multi-spectral channel attention mechanisms, innovatively designing the Criss-cross and Multi-spectral Channel Attention (CMCA) module and subsequently refining the CMCA-YOLO model, specifically optimized for parking lot surveillance scenarios. Through meticulous analysis of pixel-level contextual information and frequency characteristics, the CMCA-YOLO model achieves significant advancements in accuracy and speed for detecting small and overlapping targets, exhibiting exceptional performance in complex environments. Furthermore, the study validates the research on a proprietary dataset of parking lot scenes comprising 4502 images, where the CMCA-YOLO model achieves an mAP@0.5 score of 0.895, with a pedestrian detection accuracy that surpasses the baseline model by 5%. Comparative experiments and ablation studies with existing technologies thoroughly demonstrate the CMCA-YOLO model's superiority and advantages in handling complex surveillance scenarios. [ABSTRACT FROM AUTHOR]

Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 × 10 − 3 , 1.387641 × 10 − 3 , 1.719946 × 10 − 3 , and 1.686104 × 10 − 3 , respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution. [ABSTRACT FROM AUTHOR]

Galactic mass consistent with luminous mass is obtained by fitting rotation curves (RC = tangential velocities vs. equatorial radius r) using Newtonian force models, or can be unambiguously calculated from RC data using a model based on spin. In contrast, mass exceeding luminous mass is obtained from multi-parameter fits using potentials associated with test particles orbiting in a disk around a central mass. To understand this disparity, we explore the premises of these mainstream disk potential models utilizing the theorem of Gauss, thermodynamic concepts of Gibbs, the findings of Newton and Maclaurin, and well-established techniques and results from analytical mathematics. Mainstream models assume that galactic density in the axial (z) and r directions varies independently: we show that this is untrue for self-gravitating objects. Mathematics and thermodynamic principles each show that modifying Poisson’s equation by summing densities is in error. Neither do mainstream models differentiate between interior and exterior potentials, which is required by potential theory and has been recognized in seminal astronomical literature. The theorem of Gauss shows that: (1) density in Poisson’s equation must be averaged over the interior volume; (2) logarithmic gravitational potentials implicitly assume that mass forms a long, line source along the z axis, unlike any astronomical object; and (3) gravitational stability for three-dimensional shapes is limited to oblate spheroids or extremely tall cylinders, whereas other shapes are prone to collapse. Our findings suggest a mechanism for the formation of the flattened Solar System and of spiral galaxies from gas clouds. The theorem of Gauss offers many advantages over Poisson’s equation in analyzing astronomical problems because mass, not density, is the key parameter. [ABSTRACT FROM AUTHOR]

The current deep learning-based image fusion methods can not sufficiently learn the features of images in a wide frequency range. Therefore, we proposed IFormerFusion, which is based on the Inception Transformer and cross-domain frequency fusion. To learn features from high- and low-frequency information, we designed the IFormer mixer, which splits the input features through the channel dimension and feeds them into parallel paths for high- and low-frequency mixers to achieve linear computational complexity. The high-frequency mixer adopts a convolution and a max-pooling path, while the low-frequency mixer adopts a criss-cross attention path. Considering that the high-frequency information relates to the texture detail, we designed a cross-domain frequency fusion strategy, which trades high-frequency information between the source images. This structure can sufficiently integrate complementary features and strengthen the capability of texture retaining. Experiments on the TNO, OSU, and Road Scene datasets demonstrate that IFormerFusion outperforms other methods in object and subject evaluations. [ABSTRACT FROM AUTHOR]

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features, thus limiting the feature expression of the models. To address this problem, we propose a novel SAA network, that is, Scale-and-Attention-Aware Networks, to apply different attention to different temporal-length streams, while further exploring both spatial and channel attention on separate streams with a newly proposed Criss-Cross Channel Attention Module ( C 3 A M ). Experiments on public VSR datasets demonstrate the superiority of our method over other state-of-the-art methods in terms of both quantitative and qualitative metrics. [ABSTRACT FROM AUTHOR]

Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting. [ABSTRACT FROM AUTHOR]

Based on the synergistic effect of ripening and hydrogen ion etching in a hydrothermal solution, a simple, facile, and low-cost new strategy was demonstrated to prepare multi-channel surface-modified amorphous Fe2O3 nanospheres as anodes for Li-ion batteries in this study. Compared with polycrystalline Fe2O3, the conversion reaction between amorphous Fe2O3 and lithium ions has a lower Gibbs free energy change and a stronger reversibility, which can contribute to an elevation in the cycle capability of the electrode. Meanwhile, there are abundant active sites and more effective dangling bonds/defects in amorphous materials, which is beneficial to promote charge transfer and lithium-ion migration kinetics. The Galvanostatic intermittent titration analysis results confirmed that the amorphous Fe2O3 electrode had a higher Li+ diffusion coefficient. In addition, the surfaces of the amorphous nanospheres are corroded to produce multiple criss-cross channels. The multi-channel surface structure can not only increase the contact area between Fe2O3 nanospheres and electrolyte, but also reserve space for volume expansion, thereby effectively alleviating the volume change during the intercalation-deintercalation of lithium ions. The electrochemical performance showed that the multi-channel surface-modified amorphous Fe2O3 electrode exhibited a higher specific capacity, a more stable cycle performance, and a narrower voltage hysteresis. It is believed that amorphous metal oxides have great potential as high-performance anodes of next-generation lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Existing elevator systems lack the ability to display the number of people waiting on each floor and inside the elevator. This causes an inconvenience as users cannot tell if they should wait or seek alternatives, leading to unnecessary time wastage. In this work, we adopted edge computing by running the MobileNet–Single-Stage Object Detection (SSD) algorithm on edge devices to recognize the number of people inside an elevator and waiting on each floor. To ensure the accuracy of people counting, we fine-tuned the SSD parameters, such as the recognition frequency and confidence thresholds, and utilized the line of interest (LOI) counting strategy for people counting. In our experiment, we deployed four NVIDIA Jetson Nano boards in a four-floor building as edge devices to count people when they entered specific areas. The counting results, such as the number of people waiting on each floor and inside the elevator, were provided to users through a web app. Our experimental results demonstrate that the proposed method achieved an average accuracy of 85% for people counting. Furthermore, when comparing it to sending all images back to a remote server for people counting, the execution time required for edge computing was shorter, without compromising the accuracy significantly. [ABSTRACT FROM AUTHOR]

Empirical laws proposed for the decline in star spin with time have heretofore been tested using ambiguous fitting models. We develop an analytical inverse model that uses histogram data to unequivocally determine the physical law governing how dwarf star spin depends on time (t) and mass (M). We analyze shapes of paired histograms of axial rotation period (П) and angular velocity (ω = 2π/П) to utilize the fact that a variable and its reciprocal are governed by the same physics. Copious data on open clusters are used to test the formula ∂ω/∂t ∝ − ωn where n is unrestricted, and thus covers diverse possibilities. Histogram conjugates for each of 15 clusters with 120 to 812 measurements provide n = 1.13 ± 0.19. Results are independent of initial spin rate, bin size, cluster parameters, and star mass. Notably, 11 large clusters with mostly M-types yield fits with n = 1.07 ± 0.12. Associations behave similarly. Only exponential decay (n = 1) explains the similar shapes of the conjugate histograms for the spin period and angular velocity, despite the asymmetric (inverse) relationship of these variables. This rate law is consistent with viscous dissipation. Forward modeling confirms that n is near unity and further shows that coeval formation of all stars in a cluster does not occur. We therefore explore a constant rate of star production, which is reasonable for tiny stars. Inverse models show that episodic production increases with mass, but is unimportant below ~0.55 MSun. We infer star and cluster ages, and find that star production becomes less regular with time, as interstellar gas and dust are progressively depleted. Our new analytical approach of extracting a physical law from conjugate histograms is general and widely applicable. [ABSTRACT FROM AUTHOR]

Interplanetary interactions are the largest forces in our Solar System that disturb the planets from their elliptical orbits around the Sun, yet are weak (<10−3 Solar). Currently, these perturbations are computed in pairs using Hill's model for steady-state, central forces between one circular and one elliptical ring of mass. However, forces between rings are not central. To represent interplanetary interactions, which are transient, time-dependent, and cyclical, we build upon Newton's model of interacting point-mass pairs, focusing on circular orbits of the eight largest bodies. To probe general and evolutionary behavior, we present analytical and numerical models of the interplanetary forces and torques generated during the planetary interaction cycles. From symmetry, over a planetary interaction cycle, radial forces dominate while tangential forces average to zero. Our calculations show that orbital perturbations require millennia to quantify, but observations are only over ~165 years. Furthermore, these observations are compromised because they are predominantly made from Earth, whose geocenter occupies a complex, non-Keplerian orbit. Eccentricity and inclination data are reliable and suggest that interplanetary interactions have drawn orbital planes together while elongating the orbits of the two smallest planets. This finding is consistent with conservation principles governing the eight planets, which formed as a system and evolve as a system. [ABSTRACT FROM AUTHOR]

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel. [ABSTRACT FROM AUTHOR]

Proposed explanations of galactic rotation curves (RC = tangential velocity vs. equatorial radius, determined from Doppler measurements) involve dramatically different assumptions. A dominant, original camp invoked huge amounts of unknown, non-baryonic dark matter (NBDM) in surrounding haloes to reconcile RC simulated using their Newtonian orbital models (NOMs) for billions of stars in spiral galaxies with the familiar Keplerian orbital patterns of the few, tiny planets in our Solar System. A competing minority proposed that hypothetical, non-relativistic, non-Newtonian forces govern the internal motions of galaxies. More than 40 years of controversy has followed. Other smaller groups, unsatisfied by explanations rooted in unknown matter or undocumented forces, have variously employed force summations, spin models, or relativistic adaptations to explain galactic rotation curves. Some small groups have pursued inverse models and found no need for NBDM. The successes, failures, and underlying assumptions of the above models are reviewed in this paper, focusing on their mathematical underpinnings. We also show that extractions of RC from Doppler measurements need revising to account for the effect of galaxy shape on flux-velocity profiles and for the possible presence of a secondary spin axis. The latter is indicated by complex Doppler shift patterns. Our findings, combined with independent evidence such as hadron collider experiments failing to produce non-baryonic matter, suggest that a paradigm shift is unfolding. [ABSTRACT FROM AUTHOR]

Spiral galaxies and their rotation curves have key characteristics of differentially spinning objects. Oblate spheroid shapes are a consequence of spin and reasonably describe galaxies, indicating that their matter is distributed in gravitationally interacting homeoidal shells. Here, previously published equations describing differentially spinning oblate spheroids with radially varying density are applied to 51 galaxies, mostly spirals. A constant volumetric density (ρ, kg m−3) is assumed for each thin homeoid in these formulae, after Newton, which is consistent with RCs being reported simply as a function of equatorial radius r. We construct parameter-free inverse models that uniquely specify mass inside any given r, and thus directly constrain ρ vs. r solely from velocity v (r) and galactic aspect ratios (assumed as 1:10 for spirals when data are unavailable). Except for their innermost zones, ρ is proven to be closely proportional to rn, where the statistical average of n for all 36 spirals studied is −1.80 ± 0.40. Our values for interior densities compare closely with independently measured baryon density in appropriate astronomical environments: for example, calculated ρ at galactic edges agrees with independently estimated ρ of intergalactic media (IGM). Our finding that central densities increase with galaxy size is consistent with behavior exhibited by diverse self-gravitating entities. Our calculated mass distributions are consistent with visible luminosity and require no non-baryonic component. [ABSTRACT FROM AUTHOR]

Spiral galaxies are spinning, internally densified objects. The Virial Theorem explains galactic rotation curves via its linkage of the rotation rate to the gravitational self-potential (Ug) and the moment of inertia of oblate spheroids. We devise a new analytical solution that allows galactic mass and volumetric density (kg m−3) profiles to be extracted from velocity and its derivative as functions of equatorial radius. This inverse model of rotation curves is direct, unambiguous, and parameter-free. To probe galactic evolution, we combine energy minimization, angular momentum conservation, and the Virial Theorem. The characteristic flat shape of spiral galaxies results from an initial vertical collapse of a spinning, colossal molecular cloud, which reduces Ug while conserving angular momentum. Subsequent inward densification further lowers Ug, producing bulges, but conserving angular momentum requires mass loss, achieved by the outward movement of the distal parts of the spiral arms. Many of the evolutionary patterns of spiral galaxies are exhibited by the changing shapes of hurricanes during formation and dissipation. In contrast, elliptical galaxies evolve from a cloud with roughly random orbits into progressively rounder, internally denser objects, with angular momentum conserved by the development of vertically oriented jets. Galactic evolution is governed by the initial inventory of mass and angular momentum, resulting in separate paths for elliptical and spiral galaxies, as is codified in Hubble's tuning fork diagram. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has been gaining importance in the operational weather forecast, being essential for economic and social development. Conventional methods of precipitation nowcasting are mainly focused on the task of radar echo extrapolation. In recent years, deep learning methods have been used in this task. Nevertheless, raising the accuracy and extending the lead time of prediction remains as a challenging problem. To address the problem, we proposed a Multi-Scale Criss-Cross Attention Context Sensing Long Short-Term Memory (MCCS-LSTM). In this model, Context Sensing framework (CS framework) focuses on contextual correlations, and Multi-Scale Spatiotemporal block (MS block) with criss-cross attention is designed to extract multi-scale spatiotemporal feature and full-image dependency. To validate the effectiveness of our model, we conduct experiments on CIKM AnalytiCup 2017 data sets and Guangdong Province of China radar data sets. By comparing with existing deep learning models, the results demonstrate that the MCCS-LSTM has the best prediction performance, especially for predicting accuracy with longer lead times. [ABSTRACT FROM AUTHOR]