Practical Scenarios Research Articles

A Review: Recent Advances of Piezoelectric Photocatalysis in the Environmental Fields.

Piezoelectric photocatalysis can effectively suppress the recombination of electron holes during the course of photocatalysis, which has been widely applied in environmental and energy catalysis. Its advantage is that when the piezoelectric effect happens, a built-in electric field is formed inside the catalyst, which improves the separation efficiency of photogenerated charge carriers and obtains more excellent photocatalytic performance. The efficient conversion of mechanical energy to chemical energy can be realized through the synergistic effect of the piezoelectric effect, and photocatalysis is greatly significant in solving the energy crisis and providing environmental protection. Therefore, we organized a more complete review to better understand the mechanism and system of piezoelectric photocatalysis. We briefly introduce the principle of the piezoelectric effect, the existing types of piezoelectric photocatalysts, the practical application scenarios, and the future challenges and feasible methods to improve catalytic efficiency. The purpose of this review is to help us broaden the idea of designing piezoelectric photocatalysts, clarify the future research direction, and put it into more fields of environmental protection and energy reuse.

Underwater Optical Imaging: Methods, Applications and Perspectives

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

Diffusion‐based Human Motion Style Transfer with Semantic Guidance

Abstract3D Human motion style transfer is a fundamental problem in computer graphic and animation processing. Existing AdaIN‐based methods necessitate datasets with balanced style distribution and content/style labels to train the clustered latent space. However, we may encounter a single unseen style example in practical scenarios, but not in sufficient quantity to constitute a style cluster for AdaIN‐based methods. Therefore, in this paper, we propose a novel two‐stage framework for few‐shot style transfer learning based on the diffusion model. Specifically, in the first stage, we pre‐train a diffusion‐based text‐to‐motion model as a generative prior so that it can cope with various content motion inputs. In the second stage, based on the single style example, we fine‐tune the pre‐trained diffusion model in a few‐shot manner to make it capable of style transfer. The key idea is regarding the reverse process of diffusion as a motion‐style translation process since the motion styles can be viewed as special motion variations. During the fine‐tuning for style transfer, a simple yet effective semantic‐guided style transfer loss coordinated with style example reconstruction loss is introduced to supervise the style transfer in CLIP semantic space. The qualitative and quantitative evaluations demonstrate that our method can achieve state‐of‐the‐art performance and has practical applications. The source code is available at https://github.com/hlcdyy/diffusion-based-motion-style-transfer.

Shared Accountability Shaping the Destinies of Individual and Groups of Nonhuman Primates

At the California National Primate Research Center (CNPRC), the preferred housing for rhesus macaques involves maintaining them in complex social groups outdoors, primarily for breeding purposes. This functionally appropriate environment promotes effective coping through the expression of species-typical behaviors and important aspects of species-typical social structure, thus enabling normal animal development, higher reproductive success, and the production of high-quality biological models. Despite the benefits, social housing introduces challenges like trauma from aggressive interactions. These challenges necessitate a delicate balance between tolerating some aggression and preventing repeated targeting of individuals. Therefore, the CNPRC has established a multidisciplinary working group of behavioral management experts, veterinarians, animal care, and researchers that meets regularly to review cases of animals that may need to be removed from their social group. We discuss the criteria and decision-making processes employed to manage and mitigate aggression. We describe the systematic approach to identifying at-risk individuals and the comprehensive evaluation process that guides whether to relocate an animal from their groups or not. Considerations include the welfare of the individual and the group’s social stability. This paper provides case studies demonstrating how the working group applies these criteria and processes in practical scenarios, highlighting the complexities and challenges of such decisions.

Context-aware feature reconstruction for class-incremental anomaly detection and localization

With the development of deep learning, the unsupervised visual anomaly detection and localization task has gained significant attention in both academia and industry, where only normal data are used for training. Existing methods for this task typically train using all training data simultaneously. However, in practical industrial scenarios, new product classes are usually introduced incrementally, leading to the sequential availability of training data. Such scenarios demand methods for class-incremental anomaly detection and localization (CADL). The main challenge of class-incremental learning is to retain knowledge of old classes when learning new classes. In this article, we aim to effectively leverage limited exemplars of old classes to retain knowledge for the CADL task. Achieving this goal requires a model that can efficiently capture anomaly-identification-related knowledge from limited exemplars. Considering that pixel-level anomaly identification requires an understanding of the surrounding context, we treat context within inputs as valuable anomaly-identification-related knowledge and design a context-aware feature reconstruction (CFR) model to capture such knowledge. Moreover, to avoid inter-class context conflict that may arise with class increments, we design an intermediate feature organization strategy. This strategy and output-level knowledge distillation jointly form dual constraints to regularize the model at both mid-feature and output levels. Utilizing the CFR model with Dual Constraints, the proposed CFRDC can effectively retain old-class knowledge while learning new classes, thus addressing the CADL task. Experimental results on the commonly-used MVTec-AD dataset demonstrate the effectiveness and outstanding performance of the proposed method in CADL.

Sensitivity by Parametricity

The work of Fuzz has pioneered the use of functional programming languages where types allow reasoning about the sensitivity of programs. Fuzz and subsequent work (e.g., DFuzz and Duet) use advanced technical devices like linear types, modal types, and partial evaluation. These features usually require the design of a new programming language from scratch—a significant task on its own! While these features are part of the classical toolbox of programming languages, they are often unfamiliar to non-experts in this field. Fortunately, recent studies (e.g., Solo) have shown that linear and complex types in general, are not strictly needed for the task of determining programs’ sensitivity since this can be achieved by annotating base types with static sensitivity information. In this work, we take a different approach. We propose to enrich base types with information about the metric relation between values, and we present the novel idea of applying parametricity to derive direct proofs for the sensitivity of functions. A direct consequence of our result is that calculating and proving the sensitivity of functions is reduced to simply type-checking in a programming language with support for polymorphism and type-level naturals. We formalize our main result in a calculus, prove its soundness, and implement a software library in the programming language Haskell–where we reason about the sensitivity of canonical examples. We show that the simplicity of our approach allows us to exploit the type inference of the host language to support a limited form of sensitivity inference. Furthermore, we extend the language with a privacy monad to showcase how our library can be used in practical scenarios such as the implementation of differentially private programs, where the privacy guarantees depend on the sensitivity of user-defined functions. Our library, called Spar, is implemented in less than 500 lines of code.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Optimization Study on Nozzle Selection Based on the Influence of Nozzle Parameters on Jet Flow Field Structure

Currently, the primary method for controlling red tides in the ocean involves spraying water solutions with special chemicals as solutes. High-pressure spraying results in the formation of typical jet structures. In this study, numerical simulation methods are employed to investigate the velocity variations, turbulent characteristics, and gas content distribution of jet flow fields under different initial jet flow pressures, cone angles, and nozzle diameters. Based on practical application scenarios, cluster analysis is used to explore the similarities and differences in jet equivalent diameters under different parameter conditions. The research findings indicate the following. (1) The difference of jet velocity distribution at the far field exit will be enlarged with the increase in the nozzle cone angle. When the nozzle cone angle is 4 mm, the velocity uniformity at the outlet is the best. (2) The TKE of the flow field has no consistent change law along the central axis. At the jet exit, the TKE shows an obvious multi-peak structure. (3) The gas content demonstrates a typical “double-valley” feature at the jet outlet cross-section. Increasing the initial pressure leads to a decrease in the gas content within the jet due to reduced entrainment, while the entrainment range remains largely constant. (4) Cluster analysis reveals that the similarity of jet flow width when it reaches the water surface is minimal compared to other operating conditions when the initial pressure is 0.36 MPa, the cone angle is 115°, and the nozzle diameter is 2 mm. All conditions can be categorized into two or three groups to ensure jet effectiveness. The study results provide scientific guidance for selecting spray devices for controlling red tides in the ocean.

Two-stage optimal PMU placement for distribution systems

The rapid growth of distributed energy resources has highlighted the importance of monitoring the system state of distribution networks. One key technology that enhances observability in distribution systems is the Phasor Measurement Unit (PMU). However, there are still challenges in effectively deploying PMUs in practice. One challenge is the solution multiplicity in optimal PMU placement (OPP). Another challenge arises from the limited deployment space available at distribution nodes, leading to incomplete observability and the need for optimization to ensure node observability. To address the challenge of solution multiplicity, this work proposes a two-stage OPP approach that considers critical node observability. The first stage optimization model determines the minimum number of PMUs required to meet observability requirements. The second stage model identifies the solution with the greatest observability redundancy while satisfying practical constraints. To validate the effectiveness of our approach, this work conducts simulations using both IEEE testing systems and a real-world distribution feeder. These simulations provide empirical evidence of the benefits of the proposed OPP model and demonstrate its applicability in practical scenarios.

Board Games and Entrepreneurial Skills Acquisition: A Mixed-Methods Study on the Efficacy of the EntreComp Framework in Higher Education

The landscape of higher education has witnessed a paradigmatic shift towards methodologies that are more interactive and engaging. The integration of gamification and board games as active learning methodologies in higher education, as well as the instruction in entrepreneurial competencies, has garnered significant interest for its potential to engage and motivate students, thereby enhancing learning outcomes. Gamification, characterized by the incorporation of game design elements into non-game contexts, offers a captivating and novel learning experience that fosters intrinsic motivation and active participation among students. One of the main ways of applying gamification is through board games, providing hands-on opportunities for students to apply theoretical knowledge in practical scenarios. A critical aspect of this educational innovation is its contribution to the instruction of entrepreneurial competencies, a domain gaining traction within higher educational settings. The European Entrepreneurship Competence Framework (EntreComp) stands at the forefront of this educational endeavor, offering a comprehensive blueprint for cultivating entrepreneurial skills and mindsets. This study is anchored in the context described above, with a focused aim to delineate which entrepreneurial competencies, as outlined by the EntreComp framework, are most effectively developed through these novel educational interventions. To facilitate this investigation, the "Game It Away!" project was initiated, encompassing three pilot studies. These pilots innovatively employed board games as the medium through which participants could acquire and hone their entrepreneurial skills. A key component of the learning process within these pilots was the maintenance of a learning journal by the students. This journal, filled with various reflective questions, provided a structured mechanism for participants to document and reflect upon their learning journey throughout the project. In this way, by using qualitative and quantitative methodologies, it is possible to verify the evolution of certain competences. The integration of gamification and board games within educational settings, particularly through the lens of the "Game It Away!" project, represents a pioneering approach to fostering entrepreneurial competencies among students. This study, leveraging both qualitative and quantitative methodologies, has illuminated the substantial potential of these innovative pedagogical strategies to revolutionize the teaching and acquisition of entrepreneurial skills as outlined by the EntreComp framework.

Bernstein operational matrix of differentiation to analyze the conductive, convective, and radiative non-linear sublimation model with temperature-dependent internal heat generation

Theoretical modeling of solid-vapor phase change process, known as sublimation, is of much interest in the pharmaceutical industry and freeze-drying technology. While past theoretical studies on sublimation assume that the phase change material (PCM) remains in direct contact with the heat source or sink with no heat loss due to the surrounding temperature, in most practical scenarios, PCM experiences less heat on its surface due to the loss of some amount of heat in the surrounding environment. Considering the impact of heat radiation on the porous surface is essential for ensuring the precision of the phase change process. The present study reports a theoretical model of a sublimation problem where heat generation is in proportion to the local temperature and loss of heat due to thermal radiation occurs in the porous medium. In connection with thermal radiation, a convective boundary condition is assumed at the surface of the porous body. The solution of the non-linear mathematical model is determined numerically via the Bernstein operational matrix of differentiation method. It is found that temperature-dependent heat generation enhances the temperature within the porous medium and thus, the rate of sublimation increases, while it is delayed due to heat loss through the radiation.

Exploring Automated Assertion Generation via Large Language Models

Unit testing aims to validate the correctness of software system units and has become an essential practice in software development and maintenance. However, it is incredibly time-consuming and labor-intensive for testing experts to write unit test cases manually, including test inputs ( i.e., prefixes) and test oracles ( i.e., assertions). Very recently, some techniques have been proposed to apply Large Language Models (LLMs) to generate unit assertions and have proven the potential in reducing manual testing efforts. However, there has been no systematic comparison of the effectiveness of these LLMs, and their pros and cons remain unexplored. To bridge this gap, we perform the first extensive study on applying various LLMs to automated assertion generation. The experimental results on two independent datasets show that studied LLMs outperform six state-of-the-art techniques with a prediction accuracy of 51.82% \(\sim\) 58.71% and 38.72% \(\sim\) 48.19%. The improvements achieve 29.60% and 12.47% on average. Besides, as a representative LLM, CodeT5 consistently outperforms all studied LLMs and all baselines on both datasets, with an average improvement of 13.85% and 26.64%, respectively. We also explore the performance of generated assertions in detecting real-world bugs, and find LLMs are able to detect 32 bugs from Defects4J on average, with an improvement of 52.38% against the most recent approach EditAS . Inspired by the findings, we construct a simplistic retrieval-and-repair-enhanced LLM-based approach by transforming the assertion generation problem into a program repair task for retrieved similar assertions. Surprisingly, such a simplistic approach can further improve the prediction accuracy of LLMs by 9.40% on average, leading to new records on both datasets. Besides, we provide additional discussions from different aspects ( e.g., the impact of assertion types and test lengths) to illustrate the capacity and limitations of LLM-based approaches. Finally, we further pinpoint various practical guidelines ( e.g., the improvement of multiple candidate assertions) for advanced LLM-based assertion generation in the near future. Overall, our work underscores the promising future of adopting off-the-shelf LLMs to generate accurate and meaningful assertions in real-world test cases and reduce the manual efforts of unit testing experts in practical scenarios.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

8481 A 32 Year Old Male With Recurrent Acute Pancreatits and Severe Hypertriglyceridemia: Importance of Considering Genetic Variants

Abstract Disclosure: M.F. Salazar Zelaya: None. J.D. Sibaja Jiménez: None. Background : Hypertriglyceridemia is a common medical scenario in daily practice, causing a variety of symptoms including recurrent episodes of acute pancreatits, which can be severe. Multiple genetic variants can lead to different spectrums of the disease. CLINICAL CASE: A 32 year old male police officer presented to our Endocrinology Outpatient Clinic with a history of long standing hypertriglyceridemia and six previous episodes of acute pancreatits, two in the last twelve months. Familiy history was negative for lipid disorders. The patient had adecuate adherence to diet and exercise as well to prescribed medications, which included Fenofibrate 250 mgs/day and Rosuvastatin 40 mgs/day. The patient denied consumption of alcoholic beverages. Recent abdominal ultrasound was reported without abnormalities. His lipid panel reported elevated triglycerides at 5831 mg/dl (n<150 mg/dl), Total Cholesterol normal at 147 (n<200 mg/dl), low HDL levels at 12 mg/dl (range 12-92 mg/dl) and direct LDL Cholesterol at 120 mg/dl. His TSH value was normal at 1.02 uUI/mL (range 0.55-4.78 uUI/mL). Creatinine level was normal at 0.81 mg/dl (range 0.7-1.3 mg/dl). Liver function tests were normal with AST level at 25 IU/L (range 13-39 IU/L) ALT levels at 22 IU/L (range 7-52 IU/L) and alcaline phosphatase at 62 IU/L (range 34-104 IU/L).His fasting glucose level were normal at 82 mg/dl (n<100 mg/dl). Due to a high Familial Chylomicronemia Syndrome clinical score (13 points) we proceeded with genetic analysis of this patient. Next Generation Sequencing for genes related with chylomicronemia (APOA5, APOC2, GPIHBP1,LMFI, LPL) was performed to determine gene sequence plus copy number variation.Two heterocigote variants were reported, both in the LPL gene. The first one: LPL (NM 000237.3) c.644>A;p.Gly215Glu heterozygous is a missense variant already reported in the literature, registered in ClinVar (ID 1522) and the Leiden Open Variation Database. This variant is classified as pathogenic according to criteria PS3, PM1, PP3, PP5 of the American College of Medical Genetics and Genomics. The second one: LPL (nm 000237.3): c.383C>A;p.Thr128Asn;heterozygous is also a missense variant which to the extent of our knowledge has previously been reported only once in the literature: ClinVar (ID 1520537). This variant is classified currently as of unknown clinical significance according to PM2 criteria. There is however and entry reported (PMID: 8778602) of another variant affecting the same codon (p.Thr128AIa) that is associated to chylomicronemia syndrome. Conclusion: In cases of severe hypertriglyceridemia, suspicion and determination of gene variants are of great importance nowadays as they could eventually guide treatment, given the availability in some countries (and currently pending FDA approval) of new pharmacological therapies such as apo C-III inhibitors. Presentation: 6/2/2024

Spectral reconstruction from RGB image to hyperspectral image: Take the detection of glutamic acid index in beef as an example

The use of spectral reconstruction (SR) to recovery RGB images to full-scene hyperspectral image (HSI) is an important measure to achieve real-time and low-cost HSI applications. Taking the detection of glutamic acid index for 360 beef samples as an example, the feasibility of using 11 state-of-the-art reconstruction algorithms to achieve RGB to HSI in complex food systems was investigated. The multivariate correlation analysis was used to prove that RGB is a projection of three-channel comprehensive coverage wide-band information. The comprehensive quality attributes (PSNR-Params-FLOPS) was proposed to determine the optimal reconstruction model (MST++, MST, MIRNet, and MPRNet). Moreover, SSIM values and t-SNE were introduced to evaluate the consistency of the reconstruction results. Finally, Lightweight Transformer was used to establish the detection models of Raw-HSI, RGB and SR-HSI for the prediction of glutamic acid index for beef. The results showed that the MST++ model exhibited the best performance in SR, with RMSE, PSNR, and SSIM values of 0.015, 36.70, and 0.9253, respectively. Meanwhile, the prediction effect of MST++ (R2P = 0.8422 and RPD = 2.46) reconstructed was close to the Raw-HSI (R2P = 0.8526 and RPD = 2.69). The results provide practical application scenarios and detailed analysis ideas for RGB-to-HSI.

Diffraction tomography for incident Herglotz waves

Abstract Diffraction tomography is an inverse scattering technique used to reconstruct the spatial distribution of the material properties of a weakly scattering object. The object is exposed to radiation, typically light or ultrasound, and the scattered waves induced from different incident field angles are recorded. In conventional diffraction tomography, the incident wave is assumed to be a monochromatic plane wave, an unrealistic simplification in practical imaging scenarios. In this article, we extend conventional diffraction tomography by introducing the concept of customized illumination scenarios, with a pronounced emphasis on imaging with focused beams. More specifically, we consider incident Herglotz waves and extend the classical Fourier diffraction theorem to this setting. This yields a new two-step reconstruction process which we comprehensively evaluate through numerical experiments.

An exact branch-and-price-and-cut algorithm for a practical and large-scale dial-a-ride problem

Vehicle routing problems are encountered in various practical transportation scenarios. One such problem is the dial-a-ride problem (DARP), which involves transporting efficiently passengers from their origin to their destination and whose objective is to design vehicle routes minimizing total costs and accommodating all pickup-and-delivery requests. The solution must also adhere to capacity limitations, time windows, and a maximum ride time for each passenger. This paper proposes an exact branch-price-and-cut algorithm to solve a practical variant of the DARP, where both the passengers and vehicle fleet are heterogeneous, and other practical constraints such as break requirements and maximum route duration are imposed. In this algorithm, column generation which alternates between solving a master problem and subproblems, is used to compute lower bounds in the search tree. We develop a labeling algorithm to efficiently handle these subproblems. The effectiveness of the algorithm is evaluated on a real-world case study involving 849 heterogeneous passengers and more than 70 available vehicles, as well as on 10 smaller instances (with 300 to 820 trips) extracted from another very large real-world dataset. To the best of our knowledge, this 849-trip instance is the largest DARP instance reported to be solved to optimality in the literature.

A vibroacoustic model for low-frequency sound transmission loss in circular membranes with added strip mass

Noise control strategies involve physical barriers in the transmission path. Conventional barriers conform to the mass law which states an inverse relation between sound transmission, barrier mass, and incident frequency. Practical scenarios for low-frequency noise attenuation require prohibitively thick barriers making them infeasible. Mass-loaded membranes exhibit marked differences in their dynamic nature from that of bare membranes as they do not necessarily conform to the mass law. Being thin, compact, and lightweight makes them geometrically ideal for most applications. The paper presents an analytical model based on the Newtonian approach for the vibroacoustic behavior of a pre-stretched elastic circular membrane with rigidly attached strip mass under normal incidence. The point collocation approach distributes the effect of the strip mass on the membrane as a collection of discretized concentrated loads along the interfacial boundary resulting in a summation that is easier to solve. The peak and dip frequencies of sound transmission are determined for circular membranes with eccentric and central strip mass. The present study evaluates strip mass-loaded membranes' capability to attenuate noise at low frequencies as an unconventional physical barrier.

Anchor Space Optimal Transport as a Fast Solution to Multiple Optimal Transport Problems.

In machine learning, optimal transport (OT) theory is extensively utilized to compare probability distributions across various applications, such as graph data represented by node distributions and image data represented by pixel distributions. In practical scenarios, it is often necessary to solve multiple OT problems. Traditionally, these problems are treated independently, with each OT problem being solved sequentially. However, the computational complexity required to solve a single OT problem is already substantial, making the resolution of multiple OT problems even more challenging. Although many applications of fast solutions to OT are based on the premise of a single OT problem with arbitrary distributions, few efforts handle such multiple OT problems with multiple distributions. Therefore, we propose the anchor space OT (ASOT) problem: an approximate OT problem designed for multiple OT problems. This proposal stems from our finding that in many tasks the mass transport tends to be concentrated in a reduced space from the original feature space. By restricting the mass transport to a learned anchor point space, ASOT avoids pairwise instantiations of cost matrices for multiple OT problems and simplifies the problems by canceling insignificant transports. This simplification greatly reduces its computational costs. We then prove the upper bounds of its 1 -Wasserstein distance error between the proposed ASOT and the original OT problem under different conditions. Building upon this accomplishment, we propose three methods to learn anchor spaces for reducing the approximation error. Furthermore, our proposed methods present great advantages for handling distributions of different sizes with GPU parallelization.

Comprehensive Insights on MXene-Based TENGs: from Structures, Functions to Applications.

The rapid advancement of triboelectric nanogenerators (TENGs) has introduced a transformative approach to energy harvesting and self-powered sensing in recent years. Nonetheless, the untapped potential of TENGs in practical scenarios necessitates multiple strategies like material selections and structure designs to enhance their output performance. Given the various superior properties, MXenes, a kind of novel 2D materials, have demonstrated great promise in enhancing TENG functionality. Here, this review comprehensively delineates the advantages of incorporating MXenes into TENGs, majoring in six pivotal aspects. First, an overview of TENGs is provided, stating their theoretical foundations, working modes, material considerations, and prevailing challenges. Additionally, the structural characteristics, fabrication methodologies, and family of MXenes, charting their developmental trajectory are highlighted. The selection of MXenes as various functional layers (negative and positive triboelectric layer, electrode layer) while designing TENGs is briefed. Furthermore, the distinctive advantages of MXene-based TENGs and their applications are emphasized. Last, the existing challenges are highlighted, and the future developing directions of MXene-based TENGs are forecasted.