Simple Modules Research Articles

Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes

In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple and parameter-free attention module (SimAM), and soft non-maximum suppression (Soft NMS) are introduced. In order to improve the accuracy of target recognition and fine localization based on a depth camera (eye-in-hand), the SENetV2 backbone network structure, dynamic head module, deformable attention mechanism, and chain-of-thought prompted adaptive enhancer network are introduced. After that, on the basis of constructing a dual camera platform for target hybrid recognition and localization, the hand–eye calibration, collection and production of image datasets required for model training are completed. Finally, for the docking of the oil filling port, the hybrid recognition and localization experimental tests are completed in sequence. The test results show that in target recognition and coarse localization based on the industrial camera, the recognition accuracy of the designed model reaches 99%, and the average localization errors in the horizontal and vertical directions are 2.22 mm and 3.66 mm, respectively. In target recognition and fine localization based on the depth camera, the recognition accuracy of the designed model reaches 98%, and the average errors in depth, horizontal, and vertical directions are 0.12 mm, 0.28 mm, and 0.16 mm, respectively. These not only verify the effectiveness of the target hybrid recognition and localization methods based on dual cameras, but also demonstrate that they meet the high-precision recognition and localization requirements in complex scenes.

The solenoidal Virasoro algebra and its simple weight modules

The aim of this paper is to construct a one dimensional universal central extension of the solenoidal Lie algebra W ( n ) μ introduced by Y. Billig and V. Futorny. The obtained Lie algebra is higher rank Virasoro algebra denoted by Vir ( n ) μ . Moreover, we study Vir ( n ) μ -Harich-Chandra modules. We obtain two kinds of Harich-Chandra modules, the intermediate series modules and generalized weight modules. Furthermore, we use the triangular decomposition of Vir ( n ) μ given by lexicographic order on Z n to construct new weight modules having infinite dimensional weight spaces.

Composition multiplicities of tensor products and branching multiplicities for GL(n)

Following work of Brundan and Kleshchev (2000), which considered tensor products with the natural module (and its dual) for GL ( n ) , we take the next fundamental module and explore the relationship between multiplicities of composition factors of tensor products of simple modules for GL ( n ) and branching multiplicities. In doing this we provide a method for determining the composition multiplicities for some composition factors of tensor products of simple modules with the wedge square of the dual natural module.

2D Fe/Co-MOF/SOX cascade reactors for fast noninvasive detection of sarcosine level in prostate cancer urine

Sarcosine plays a key role in screening for early prostate cancer. However, the several already reported peroxidase mimics immobilized with sarcosine oxidase (SOX) utilized to detect uriary sarcosine still have some limitations such as complex synthesis process, using of expensive heavy metals to mimic enzyme activity and long color development time. Herein, an inexpensive peroxidase-like 2D Fe/Co-MOF nanosheet was prepared by a simple solvent modulation method. The resultant 2D Fe/Co-MOF nanosheets have strong peroxidase activity, with its Vmax value for H2O2 of 15.3 × 10-8 M/s, being 1.76 times that of HRP. Then, using the 2D Fe/Co-MOF as a peroxidase model for anchoring natural SOX to construct 2D Fe/Co-MOF/SOX , which can act as a cascade reactor for detection of sarcosine. Considering the above properties, a platform for the detection of sarcosine was built based on a colorimetric method. Because of presence of the high ratio of Fe2+ caused by the electron transfer from Co2+ to Fe3+, large specific surface area and plentiful active sites, 2D Fe/Co-MOF/SOX with TMB (3,3′,5,5′-tetramethylbenzidine) colorimetric reagent could have fast color development and can be applied conveniently and fastly in an early screening tool for prostate cancer patients. The sarcosine could be quantified by peroxidase activity with a detection range of 1–400 μM and a limit of detection (LOD) of 0.324 μM. More importantly, the average sarcosine concentration of 21.367 μM and 1.871 μM was detected in patient’s and normal urine (n = 5), respectively, which showed an excellent screening effect and a great potential in early prostate cancer.

Origami multifunctional metagrating for a mechanically controlled electromagnetic wavefront

Metasurface or artificial electromagnetic (EM) structures offer viable options for manipulating EM waves with compact periodic structures. Tunable metasurfaces are ideal for many engineering and scientific applications because they can manipulate EM wavefronts. However, it is challenging to implement tunable metasurfaces that can achieve multiple functions for integration. In addition, integrated multifunctional metasurfaces are often structurally complicated and bulky. This paper proposes a simple spatial modulation mechanism that uses Miura origami reconfigurable one-dimensional metagrating to achieve multiple control of EM wavefront. Theoretical predictions, numerical simulations, and experiments confirm and validate the basic concepts. In particular, the continuous geometric deformation of the Miura-ori lattice is a promising method for compensating the dispersion of characters in gradient metasurfaces. Considering origami structures’ outstanding mechanical properties and strong deformation abilities, this discovery opens up another avenue for lightweight and deployable meta-devices.

Local derivations of the modular Lie algebra $$\mathfrak {sl}(2)$$ to its all simple modules

Local derivations of the modular Lie algebra $$\mathfrak {sl}(2)$$ to its all simple modules

EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.

On representations of the super-Yangian of the queer Lie superalgebra

Let Q(n) be the queer Lie superalgebra. We determine conditions under which two 1-dimensional modules over the super-Yangian of Q(n) can be extended nontrivially. We describe the dual modules of the simple finite-dimensional modules over YQ(1). We use these results to describe blocks in the subcategory of finite-dimensional YQ(1)-modules admitting the zero generalized central character.

Versatile Molecular Structure Strategy Toward Highly Efficient Single‐Component White Organic Light–Emitting Diodes

AbstractLuminophores' dual emission (DE) properties hold great potential for realizing single‐component white organic light–emitting diodes (WOLEDs). This study illustrates that the unique and vibrant DE phenomena with different luminous mechanisms can be formed through simple modulation of molecular structures. Four target luminophores, namely 2‐TPE‐PPI, 2‐TPE‐PI, 2‐TPE‐An‐PPI, and 2‐TPE‐An‐PI, capable of DE under different conditions, are intentionally designed and successfully synthesized. Owing to the inherent flexibility of the minor molecular backbone and minor steric hindrance, 2‐TPE‐PPI and 2‐TPE‐PI exhibit DE spectra in dilute solutions with different solvent polarities. The intrinsic cause of the DE phenomenon in 2‐TPE‐An‐PPI and 2‐TPE‐An‐PI arises from the localized distribution of frontier molecular orbits resulting from the presence of an anthracene unit and the formation of an exciter group through intermolecular interactions involving anthracene. Remarkably, single‐emissive‐layer WOLEDs based on 2‐TPE‐An‐PPI and 2‐TPE‐An‐PI demonstrate stable white emission with CIE coordinates at (0.33, 0.39) and (0.30, 0.39), respectively, closely approaching the CIE coordinates of standard white light. Moreover, they maintain stable EL spectra from 4 to 10 V, an exceptional attribute rarely observed in many white light devices.

Cyclic wide subalgebras of semisimple Lie algebras

Let s ∈ + r be a Levi decomposable Lie algebra, with Levi factor s , and radical r . A module V of s ∈ + r is cyclic indecomposable if it is indecomposable and the quotient module V / r · V is a simple s -module. A Levi decomposable subalgebra of a semisimple Lie algebra is cyclic wide if the restriction of every simple module of the semisimple Lie algebra to the subalgebra is cyclic indecomposable. We establish a condition for a regular Levi decomposable subalgebra of a semisimple Lie algebra to be cyclic wide. Then, in the case of a regular Levi decomposable subalgebra whose radical is an ad-nilpotent subalgebra, we show that the condition is necessary and sufficient for the subalgebra to be cyclic wide. All Lie algebras, and modules in this article are finite-dimensional, and over the complex numbers.

Smooth representations of the extended Schrödinger–Virasoro algebra

The Schrödinger–Virasoro algebra was originally introduced during the process of investigating the free Schrödinger equations. In order to investigate vertex representations of the Schrödinger–Virasoro algebra, Unterberger introduced a new infinite-dimensional Lie algebra [Formula: see text] called the extended Schrödinger–Virasoro algebra in the context of two-dimensional conformal field theory and statistical physics. In this paper, we study simple smooth modules over [Formula: see text], which can be viewed as an extension of the Schrödinger–Virasoro algebra by a conformal current with conformal weight 1. More specifically, these modules are determined by simple modules over finite-dimensional solvable Lie algebras. Moreover, we present several equivalent descriptions for simple smooth modules over [Formula: see text].

A-181 The Effectiveness Module to Reduce Laboratory Error Reporting Rates

Abstract Background In a hospital in central Taiwan, the number of tests conducted in 2022 was 1.88 million, there were 26 cases of laboratory error reports, with an error rate of 14 cases per million. Error reports may lead to diagnostic errors, posing a significant risk to patient safety. Therefore, our team aims to establish a simple and universal module that can be applicable to various laboratory operations to minimize errors. Methods Initially, we conducted an analysis of error causes, identifying 25 cases of human error and 1 case of computer system error. Human errors were categorized according to the laboratory process, including 4 specimen identification errors, 3 procedural errors, 9 typing errors, 7 validation of analytical data errors, and 2 instances of failure to communicate issues during handover. The proximate cause of human error was non-compliance with SOPs, and the root cause was inadequate SOPs leading to inconsistent practices. We reviewed the workflow, implemented corrective actions, revised SOPs, and provided training for personnel. Additionally, three important strategies were introduced: 1.High-Frequency Audits: Corrective actions were included in the internal audit checklist, initially increasing the frequency to at least once a month for the first 3 months, followed by at least once every 6 months within the next 6 months. If no further occurrences were noted, audits could be conducted annually or not. Each audit should be completed within 20 minutes to avoid excessive burden, with direct observation of operations and random questioning being the preferred audit methods. 2. Audit deficiencies should be documented: Personnel were requested to conduct cause analysis and improvements. This not only expressed the supervisor's commitment to improvement but also provided an opportunity to discover reasons why countermeasures cannot not be implemented, allowing for strategic adjustments until successful implementation. 3.Case-Based Training: Benchmark cases were developed into teaching materials, and compiled errors regularly for looking for patterns or trends to identity key steps. Make these key steps the focus of your educational training. Results In 2023, 1.94 million tests were conducted, there were 4 error reports, with an error rate of 3 cases per million, all were attributed to human error. In another hospital in southern Taiwan, laboratory error reports were primarily due to human error. From June 2022 to July 2023, a total of 3.46 million tests were conducted, there were 14 error reports and an error rate of 5 cases per million. After implementing the same improvement module from August to December 2023, a total of 1.56 million tests were conducted, there were 2 error reports and an error rate of 2 cases per million, all were attributed to human error. Conclusions We applied this improvement module to two hospitals in central and southern Taiwan, resulting in a significant reduction in error rate. The key factors of effectiveness were utilizing high-frequency audits to ensure sustained results, and demonstrating the positive attitude of supervisors during the process. This motivated personnel to demand higher standards from themselves, ultimately showcasing remarkable improvements.

Narrow and wide regular subalgebras of semisimple Lie algebras

A subalgebra of a semisimple Lie algebra is wide if every simple module of the semisimple Lie algebra remains indecomposable when restricted to the subalgebra. A subalgebra is narrow if the restrictions of all non-trivial simple modules to the subalgebra have proper decompositions. A semisimple Lie algebra is regular extreme if any regular subalgebra of the semisimple Lie algebra is either narrow or wide. We determine necessary and sufficient conditions for a simple module of a semisimple Lie algebra to remain indecomposable when restricted to a regular subalgebra. As a natural consequence, we establish necessary and sufficient conditions for regular subalgebras to be wide, a result which has already been established by Panyushev for essentially all regular solvable subalgebras [10]. Next, we show that establishing whether or not a regular subalgebra of a simple Lie algebra is wide does not require consideration of all simple modules. It is necessary and sufficient to only consider the adjoint representation. Then, we show that all simple Lie algebras are regular extreme. Finally, we show that no non-simple, semisimple Lie algebra is regular extreme.

Rigidity dimensions of self-injective Nakayama algebras

Rigidity dimension is a new homological dimension which is intended to measure the quality of the best resolution of an algebra. In this paper, we determine the rigidity dimensions of self-injective Nakayama algebras An,m with n simple modules and the Loewy length m⩾n.

Mixed-scale cross-modal fusion network for referring image segmentation

Referring image segmentation aims to segment the target by a given language expression. Recently, the bottom-up fusion network utilizes language features to highlight the most relevant regions during the visual encoder stage. However, it is not comprehensive that establish only the relationship between pixels and words. To alleviate this problem, we propose a mixed-scale cross-modal fusion method that widens the interaction between vision and language. Specially, at each stage, pyramid pooling is used to augment visual perception and improve the interaction between visual and linguistic features, thereby highlighting relevant regions in the visual data. Additionally, we employ a simple multi-scale feature fusion module to effectively combine multi-scale aligned features. Experiments conducted on Standard RIS benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the- art approaches. Moreover, we conducted experiments on different visual backbones respectively, and the proposed method yielded better and significantly improved performance results.

Achieving ultra-high energy storage performance in simple systems through minimal element substitution

Dielectric capacitors are essential components of modern advanced electronic devices and power systems based on their ultra-fast charging and discharging speeds and supreme power densities. Nevertheless, how to comprehensively boost their energy storage density and storage efficiency is still an insurmountable challenge. Here, we report a simple micro-chemical polarizability modulation strategy that enables SrTiO3-based dielectric materials to achieve excellent energy storage properties. The energy density and energy efficiency are increased to as high as 76 J∙cm−3 and 72 % from 0.4 J∙cm−3 and 46.7 % of pure STO, respectively, which is the largest value in micro-substitution (less than 3 %). Our results show that the introduction of trace amounts of elements with high ionic polarizabilities (Mn, V) facilitates the increase of chemical disorder and the formation of stable and highly dynamic polar nanoregions (PNRs), which reduces the hysteresis of the polarization switching and improves the polarization at the breakdown field strength, synergistically fostering the energy storage performance. The results of X-ray photoelectron spectroscopy and scanning electron microscopy reveal that the micro-substitution of Mn and V promote the reduction of oxygen vacancies and grain size, improving the breakdown resistance and stability of the capacitor. This present approach is expected to be widely used in the development of high-performance dielectrics.

Contramodules for algebraic groups: Induction and projective covers

In this paper we investigate contramodules for algebraic groups. Namely, we give contramodule analogs to two 20th century results about comodules. Firstly, we show that induction of contramodules over coordinate rings of algebraic groups is exact if and only if the associated quotient variety is affine. Secondly, we give an inverse limit theorem for constructing projective covers of simple G-modules using G-structures of projective covers of simple modules of the first Frobenius kernel, G1. We conclude by showing that the inverse limit theorem is a special case of a more general phenomenon between injective covers in k[G]-Comod and projective covers in k[G]-Contra.

A note on the Loewy lengths of baby Verma modules for modular Lie algebras

Let G be a connected reductive algebraic group over an algebraically closed field k of prime characteristic p, and g=Lie(G). We study the representations of the reductive Lie algebra g with p-character χ of standard Levi-form in this note. We obtain the similar results about the translation functor and the wall-crossing functor of simple modules parallelling to the representations of algebraic groups (cf. [16, II.Lem.7.20]). Moreover, we get the Loewy lengths of baby Verma modules provided the Vogan Conjecture holds.

A Tomato Recognition and Rapid Sorting System Based on Improved YOLOv10

In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations.

SSFPN: Selective Spatial Feature Pyramid Network for Remote Sensing Object Detection

Abstract Object detection, particularly for oriented small objects, benefits greatly from the generation of multi-scale feature maps by the feature fusion module. Despite the success of series of Feature Pyramid Network models in general object detection, their application in remote sensing object detection has received limited attention. Small oriented objects require contextual information for detection, while various types of objects need different long-range context. In this paper, we propose an intuitive and simple fusion module to be added in Feature Pyramid Network called Selective Spatial Feature Pyramid Network (SSFPN) to address these challenges. SSFPN dynamically adjusts the spatial receptive field across multi-scale feature maps, enhancing the modeling of contextual variations among different objects in remote sensing scenarios. In extensive experiments, SSFPN has achieved competitive results, i.e., an improvement of 0.34% after adding SSFPN to the Oriented RCNN model. The codes are available at https://github.com/Atlantisming/SSFPN.