Miracle-dz/UniFAR-FTUs huggingface dataset:
Bryant Digital Repository Bryant Digital Repository

Purpose - The purpose of this study is to investigate the barriers and motivations for adopting RFID, the level of RFID implementation, the processes RFID is utilized in , and issues in the deployment of RFID. Design/methodology/approach - a survey instrument was developed based on a literature review. The survey was then distributed to the members of the Association for Operations Management (APICS) Rhode Island and Boston chapters. The results were then analyzed. Findings - It was found that the majority of the surveyed firms are not considering RFID implementation. Lack of a business case and lack of understanding were cited as their main concerns. For firms considering RFID implementation and firms that had implemented RFID, better inventory management, obtaining competitive advantage and cost reduction were the three most important motivations for adopting RFID. Financial concerns and the lack of a business case were the most prevalent issues. In addition, product tracking (pallets, cases and items) in shipping was the most cited RFID application. It was also found that considering firms are facing less pressure from customers to adopt RFID and reported a much higher degree of apprehension regarding potential issues than implementing firms reported for actual difficulties faced. Research limitations - One of the limitations was the small sample size (n=49) which may limit the generalizability of the results. Originality/value - By identifying barriers, motivations, and issues in the implementation of RFID, this study further educates practitioners on the challenges and opportunities of RFID, as well as provides direction to academicians for further research on this area.
Analysis of Temporal and Spatial Dynamics of Ecosystem Services and Trade-Offs/Synergies during Urbanization in the Loess Plateau, China

As a typical ecological fragile zone and an area with a high intensity of human activities, the Loess Plateau (LP) of China has significantly altered its ecosystem and the corresponding services under the influence of urbanization processes. However, most existing studies focus on the spatial and temporal changes of ecosystem services (ESs) and their interrelationships under the influence of ecological restoration works in the LP, leaving limited research on the impacts of urbanization on ESs. Therefore, this study constructed a research framework for exploring the spatio-temporal dynamics and interactions of ESs under the influence of urbanization based on time series data from 2000 to 2020. The results showed that: (1) based on the comprehensive urbanization level (CUL), developed and developing areas accounted for 5.63% of the total area; (2) for the whole LP, all ESs except Habitat Quality (HQ) showed an increased trend. HQ showed a trade-off with the other services, while there was a clear synergy between the other three types of services; (3) in terms of processes of urbanization, Carbon Sequestration, Water Yield and HQ gradually decreased with increased levels of urbanization, and Soil Conservation increased the least in developing areas. The trade-off between HQ and the other three services decreased with increasing urbanization, while the synergy between the other three services strengthened as urbanization deepened. These findings suggest that urbanization significantly impacts ESs. It is necessary to implement appropriate measures (e.g., sponge city construction, urban green space, etc.) to address the impacts of urbanization on ESs.
Fractal design of 3D-printing mechanical metamaterial undergoing tailorable zero Poisson’s ratio

With the development of mechanical metamaterials, structures with large and nonlinear deformations have attracted great research passions for their increasingly applications. Although it has been studied for decades, the mechanics of conflict between large deformation with non-buckling has not been well understood. In this study, a new type of mechanical metamaterial, of which the large deformation is achieved without buckling, has been proposed to achieve the zero Poisson’s ratio using fractal design. Effects of fractal iteration ( i ) and connection angle ( α ) on the Poisson’s ratio and mechanical stress-strain behaviors of 3D printing mechanical metamaterial were studied by finite element analysis and experimental tests. Finally, constitutive relationships among loading force, Poisson’s ratio, iteration time and connection angle have been explored and discussed to achieve a large and non-buckling deformation.
Revisiting Levees in Southern Texas using Love-Wave Multi-Channel Analysis of Surface Waves (MASW) with the High-Resolution Linear Radon Transform (HRLRT)

The primary objective of this work was to determine compressional and shear velocity distribution within the body of five levees and any relationship to existing core taken from the levee and airborne EM data. Several different types of seismic data were recorded at each of the five levee sites, each of which possessed unique core and/or EM characteristics. Several seismic data-analysis techniques were appraised during our main efforts in 2004, including, Pand S-wave refraction, Pand S-wave refraction tomography, Rayleigh and Love-wave surface-wave analysis using multi-channel analysis of surface waves (MASW), and Pand S-wave cross-levee tomography. While the P-wave methods provided reasonable results, the S-wave methods produced surprising shear-wave velocity (Vs) properties. The reason for the latter effect is not clear; possibly the result of mode conversion, which is likely at sites with Poisson’s ratio greater than 0.438. Furthermore, the Rayleigh-wave MASW method could not sample the levees due to lack of high-frequencies of the fundamental mode and complexities of higher modes and, as a result, there were no reliable Vs estimates for the levees. The most recent technological developments that included the use of the high-resolution linear radon transform (HRLRT) with the MASW method for imaging and Love wave inversion, encouraged us to revisit the analysis of horizontalcomponent data. The combined contribution of both techniques was essential to successfully obtaining Vs estimates that imaged to levees.
Physics and Geometry from a Lagrangian: Dirac Spinors on a Generalised Frame Bundle

We clarify the structure obtained in H\'elein and Vey's proposition for a variational principle for the Einstein-Cartan gravitation formulated on a frame bundle starting from a structure-less differentiable 10-manifold. The obtained structure is locally equivalent to a frame bundle which we term"generalised frame bundle". In the same time, we enrich the model with a Dirac spinor coupled to the Einstein-Cartan spacetime. The obtained variational equations generalise the usual Einstein-Cartan-Dirac field equations in that they are shown to imply the usualy field equations when the generalised frame bundle is a standard frame bundle.
The last straw for communities at risk? : climate change

Much has been said about the potential effects human induced climate change will have on Southern Africa, its biodiversity, its water resources, the economy of the region and the health of its people. However, this phenomenon is only one stressor in the lives of the area's most vulnerable communities, and should not be viewed in isolation, experts warn.
Evaluation of sediment properties using wind and turbidity observation and experimental investigation of the impact of macroalgal mats on sediment stability and flow dynamics in shallow tidal areas

Sediment resuspension and deposition are key processes governing intertidal morphodynamics and are crucially influenced both by physical and biological factors. The present thesis addresses two important aspects of sediment trans- port processes in intertidal areas. The first topic concerns the quantitative analysis of suspended sediment concentration and wind forcing to provide estimates of sediment properties, such as settling velocity and the critical shear stress for erosion, valid at a spatial scale which is relevant for the overall morphodynamics of the system. The first part of the thesis thus develops and describes a method to evaluate sediment properties based on the solution of a sediment mass conser- vation equation in the water column accounting for the effects of wind-induced and current-induced bottom shear stress. The method is applied by using data from a network of in situ sensors in the Venice lagoon and provides estimates which compare favorably with observations in the same shallow tidal areas, and are in line with what is expected for sandy-silt sediments. The proposed estimation method is direct and quantitative, it does not interfere with the local system physical and biological status, and lends itself to the monitoring of sed- iment parameters on a seasonal basis and for long periods of time with limited operational effort. The second, related, topic treated here concerns the labora- tory study of the impact of mats of Ulva intestinalis on sediment stability and flow dynamic in shallow tidal areas. Macroalgae are a controlling factor of flow and sediment stability, as they act to inhibit sediment erosion due to waves or currents and promote sedimentation. Algal mats are increasingly more frequent and extended in many coastal and estuarine intertidal habitats and it is thus important to quantitatively characterize their impact on the flow field and on sediment stability, in order to better understand ongoing degradation of coastal lagoons and to develop suitable mitigation and restoration measures. The second part of the thesis thus describes and analyzes a series of experiments performed in a large open-channel flume (the Total Environment Simulator - TES- facility at University of Hull, UK), set up with a bed of fine sand, partially covered by strands of U. intestinalis. The experiments allowed the accurate measurement, both in space and time, of flow velocity distributions, water levels, bed levels, and suspended sediment concentration. The presence of macroalgae was found to influence both the velocity distribution near the sediment bed, and the Reynolds shear stress acting on it. Direct inspection during the experiments and data analysis suggest that macroalgae exert a significant bio-stabilizing effect.
Securing Data Platforms: Strategic Masking Techniques for Privacy and Security for B2B Enterprise Data

In today's digital age, the imperative to protect data privacy and security is a paramount concern, especially for business-to-business (B2B) enterprises that handle sensitive information. These enterprises are increasingly constructing data platforms, which are integrated suites of technology solutions architected for the efficient management, processing, storage, and data analysis. It has become critical to design these data platforms with mechanisms that inherently support data privacy and security, particularly as they encounter the added complexity of safeguarding unstructured data types such as log files and text documents. Within this context, data masking stands out as a vital feature of data platform architecture. It proactively conceals sensitive elements, ensuring data privacy while preserving the information's value for business operations and analytics. This protective measure entails a strategic two-fold process: firstly, accurately pinpointing the sensitive data that necessitates concealment, and secondly, applying sophisticated methods to disguise that data effectively within the data platform infrastructure. This research delves into the nuances of embedding advanced data masking techniques within the very fabric of data platforms and an in-depth exploration of how enterprises can adopt a comprehensive approach toward effective data masking implementation by exploring different identification and anonymization techniques.
Generating Visually Realistic Adversarial Patch

Deep neural networks (DNNs) are vulnerable to various types of adversarial examples, bringing huge threats to security-critical applications. Among these, adversarial patches have drawn increasing attention due to their good applicability to fool DNNs in the physical world. However, existing works often generate patches with meaningless noise or patterns, making it conspicuous to humans. To address this issue, we explore how to generate visually realistic adversarial patches to fool DNNs. Firstly, we analyze that a high-quality adversarial patch should be realistic, position irrelevant, and printable to be deployed in the physical world. Based on this analysis, we propose an effective attack called VRAP, to generate visually realistic adversarial patches. Specifically, VRAP constrains the patch in the neighborhood of a real image to ensure the visual reality, optimizes the patch at the poorest position for position irrelevance, and adopts Total Variance loss as well as gamma transformation to make the generated patch printable without losing information. Empirical evaluations on the ImageNet dataset demonstrate that the proposed VRAP exhibits outstanding attack performance in the digital world. Moreover, the generated adversarial patches can be disguised as the scrawl or logo in the physical world to fool the deep models without being detected, bringing significant threats to DNNs-enabled applications.
Investigating Technology Usage Span by Analyzing Users' Q&A Traces in Stack Overflow

Choosing an appropriate software development technology (e.g., programming language) is challenging due to the proliferation of diverse options. The selection of inappropriate technologies for development may have a far-reaching effect on software developers' career growth. Switching to a different technology after working with one may lead to a complex learning curve and, thus, be more challenging. Therefore, it is crucial for software developers to find technologies that have a high usage span. Intuitively, the usage span of a technology can be determined by the time span developers have used that technology. Existing literature focuses on the technology landscape to explore the complex and implicit dependencies among technologies but lacks formal studies to draw insights about their usage span. This paper investigates the technology usage span by analyzing the question and answering (Q&A) traces of Stack Overflow (SO), the largest technical Q&A website available to date. In particular, we analyze 6.7 million Q&A traces posted by about 97K active SO users and see what technologies have appeared in their questions or answers over 15 years. According to our analysis, C# and Java programming languages have a high usage span, followed by JavaScript. Besides, developers used the . NET framework, iOS&Windows Operating Systems (OS), and SQL query language for a long time (on average). Our study also exposes the emerging (i.e., newly growing) technologies. For example, usages of technologies such as SwiftUI, . NET-6.0, Visual Studio 2022, and Blazor WebAssembly framework are increasing. The findings from our study can assist novice developers, startup software industries, and software users in determining appropriate technologies. This also establishes an initial benchmark for future investigation on the use span of software technologies.
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion

Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery. In this work, we propose a pretraining method for molecule joint auto-encoding (MoleculeJAE). MoleculeJAE can learn both the 2D bond (topology) and 3D conformation (geometry) information, and a diffusion process model is applied to mimic the augmented trajectories of such two modalities, based on which, MoleculeJAE will learn the inherent chemical structure in a self-supervised manner. Thus, the pretrained geometrical representation in MoleculeJAE is expected to benefit downstream geometry-related tasks. Empirically, MoleculeJAE proves its effectiveness by reaching state-of-the-art performance on 15 out of 20 tasks by comparing it with 12 competitive baselines.
Application of AI intelligent vision detection technology using deep learning algorithm

This study aims to design efficient and reliable artificial intelligence vision detection models to improve detection efficiency and accuracy. The study filters defect-free images by image preprocessing and region of interest detection techniques. AlexNet network is enhanced by introducing attention mechanism modules, deep separable convolutions, and more to effectively boost the network's feature extraction capacity. An area convolutional neural network is developed to rapidly identify and locate defects on steel plate surfaces, utilizing an enhanced AlexNet network for feature extraction. Results demonstrated that the algorithm attained an average detection rate of 98 % and can identify defects in a minimal time of only 0.0011 seconds. For the detection of six types of steel plate defects, the average accuracy of the optimized fast regional convolutional neural network reached more than 0.9, especially for the detection of small-size defects with excellent performance. This improved AlexNet network has a great advantage in F1 value. The conclusion of the study shows that the designed artificial intelligence vision detection model has high detection accuracy, speed, and performance stability in steel plate surface defect detection and has a wide range of application prospects.
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection

Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the assumption prevails that large training datasets allow the training of higher-performing UAD models. However, in this work, we show that using only very few training samples can already match - and in some cases even improve - anomaly detection compared to training with the whole training dataset. We propose three methods to identify prototypical samples from a large dataset of in-distribution samples. We demonstrate that by training with a subset of just ten such samples, we achieve an area under the receiver operating characteristics curve (AUROC) of $96.37 \%$ on CIFAR10, $92.59 \%$ on CIFAR100, $95.37 \%$ on MNIST, $95.38 \%$ on Fashion-MNIST, $96.37 \%$ on MVTec-AD, $98.81 \%$ on BraTS, and $81.95 \%$ on RSNA pneumonia detection, even exceeding the performance of full training in $25/67$ classes we tested. Additionally, we show that the prototypical in-distribution samples identified by our proposed methods translate well to different models and other datasets and that using their characteristics as guidance allows for successful manual selection of small subsets of high-performing samples. Our code is available at https://anonymous.4open. science/r/uad_prototypical_samples/
Enhancing Molecular Property Prediction via Mixture of Collaborative Experts

Molecular Property Prediction (MPP) task involves predicting biochemical properties based on molecular features, such as molecular graph structures, contributing to the discovery of lead compounds in drug development. To address data scarcity and imbalance in MPP, some studies have adopted Graph Neural Networks (GNN) as an encoder to extract commonalities from molecular graphs. However, these approaches often use a separate predictor for each task, neglecting the shared characteristics among predictors corresponding to different tasks. In response to this limitation, we introduce the GNN-MoCE architecture. It employs the Mixture of Collaborative Experts (MoCE) as predictors, exploiting task commonalities while confronting the homogeneity issue in the expert pool and the decision dominance dilemma within the expert group. To enhance expert diversity for collaboration among all experts, the Expert-Specific Projection method is proposed to assign a unique projection perspective to each expert. To balance decision-making influence for collaboration within the expert group, the Expert-Specific Loss is presented to integrate individual expert loss into the weighted decision loss of the group for more equitable training. Benefiting from the enhancements of MoCE in expert creation, dynamic expert group formation, and experts' collaboration, our model demonstrates superior performance over traditional methods on 24 MPP datasets, especially in tasks with limited data or high imbalance.
An Efficient S and Se Co-Annealing Strategy for Performance Enhancement of Solution-Processed Submicron CuIn(S,Se)2 Solar Cells

Solution-processed CuIn(S,Se)2 (CISSe) solar cells with a submicron absorber (500 nm < thickness < 1 μm) can effectively reduce the deposition time and manufacturing cost. However, both, sensitive shunt paths and serious rear interface recombination limit the performance of submicron CISSe solar cells. In this work, Cu-In-S precursor films are comparatively annealed under Se or Se and S conditions. When the precursor films are annealed in pure Se, the efficiency of the CISSe solar cells can significantly improve from 7.51% to 8.57% with increasing Se content. The Se and S annealing condition enables further efficiency improvement, CISSe solar cell with an efficiency of 10.44% is achieved with the absorber annealed in high Se and S content. The origins of the efficiency enhancement may be found in a widened bandgap and effectively passivated shunt paths, resulting in a significant improvement of Voc (601.2 mV) and FF (68%).
Morcegos Urbanos: Status do Conhecimento e Plano de Ação para a Conservação no Brasil

Urban bats: Knowledge Status and Action Plan for Conservation in Brazil This study analysis information on the bat species recorded in urban and suburban areas in the aspects of distribution, shelters, colonies size, diet, health aspects and conservation. Data were obtained at the Center for Zoonosis Control (CCZ), Center for Health Surveillance and Environmental, Research Institutes and experience and studies carried by the authors. A total of 47 species was recorded as follows: 17 Phyllostomidae, 16 Molossidae, 11 Vespertilionidae, two Emballonuridae and one Noctilionidae. It was verified that insectivorous bats were dominant, representing 64% of the recorded species. Three species were present only in the urban area and nine in the suburban area, the other species could be found in both sites. The most common species in, at least three cities, are Artibeus lituratus and Tadarida brasiliensis. Molossus molossus was a species not so commom in south states and commom in Sao Paulo and Distrito Federal. Eptesicus brasiliensis, Lasiurus blossevillii, L. ega and Promops nasutus are rare in all states. It is also discussed some aspects of the impact that bats generate and suffer in large cities, and proposes an action plan for the conservation of this group in urban environments.