CHALLENGES AND OPPORTUNITIES FOR SOURCE WATER PROTECTION PLAN IMPLEMENTATION IN SASKATCHEWAN: LESSONS FOR CAPACITY BUILDING

Source water protection (SWP) is defined as a land use management and planning process aimed at the protection of surface and groundwater sources from contamination. Currently in Saskatchewan, the Water Security Agency is leading much of the planning and management with the goal of safe drinking water sources and reliable water supplies. The Water Security Agency has developed SWP planning initiatives across the southern portion of the province. Rates of SWP plan implementation in Saskatchewan are uneven and dependent on multiple factors. Using document review and key informant interviews, this study identifies factors facilitating and constraining source water protection plan implementation in selected areas and describes capacity building needs for SWP plans implementation in Saskatchewan. Results are discussed based on four capacity areas: financial, institutional, technical and social capacity. The results in this study show that capacity areas in need of improvement include stable financial resources, training opportunities for local watershed groups, public awareness, adequate stakeholder involvement, SWP plan re-evaluation, and information/data access. The result of this research contributes to the understanding of SWP plan implementation relating to capacity building needs at the watershed scale in the prairie region

DreamDrone

We introduce DreamDrone, an innovative method for generating unbounded flythrough scenes from textual prompts. Central to our method is a novel feature-correspondence-guidance diffusion process, which utilizes the strong correspondence of intermediate features in the diffusion model. Leveraging this guidance strategy, we further propose an advanced technique for editing the intermediate latent code, enabling the generation of subsequent novel views with geometric consistency. Extensive experiments reveal that DreamDrone significantly surpasses existing methods, delivering highly authentic scene generation with exceptional visual quality. This approach marks a significant step in zero-shot perpetual view generation from textual prompts, enabling the creation of diverse scenes, including natural landscapes like oases and caves, as well as complex urban settings such as Lego-style street views. Our code is publicly available.Comment: 16 pages, 12 figures, project page: https://hyokong.github.io/dreamdrone-page

Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation

Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language. However, it remains challenging for existing 3D generation methods to create subject-driven 3D content across diverse prompts. In this paper, we introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5 minutes. Our key insight is to harmonize the distributions of a multi-view diffusion model and an identity-specific 2D generative model, aligning them with the distribution of the desired 3D subject. Specifically, we design a co-evolution framework to reduce the variance of distributions, where each model undergoes a process of learning from the other through identity-aware optimization and subject-prior optimization, respectively. Extensive experiments demonstrate that our method can produce high-quality, consistent, and subject-specific 3D content with text-driven modifications that are unseen in subject image.Comment: Project page: https://liuff19.github.io/Make-Your-3

Improved design of the transmission mechanism of the of 4‑cylinder double‑acting Stirling engine

Four-cylinder U-shaped transmission mechanism design is closely related to operational stability, efficiency and life expectancy of Stirling engine system, as any deficiency of design of transmission mechanism may cause excessive reciprocating inertia force, centrifugal inertia force and counter-torque. Those intense forces and torques will transfer through the crankshaft bearings and the crankcase to supporting, resulting in the vibration of the Stirling engine and reducing the system operation stability and efficiency. According to features of four cylinder U-shaped drive mechanism, this paper built the counterweight theoretical model of transmission mechanism to obtain the values of counterweight and counter-balanced phase angle on crankshaft and output shaft. On this basis, dynamics simulation model of transmission mechanism can be established by multi-body dynamics simulation platform. Simulation results indicate that through certain improvement based on original design, the speed fluctuation coefficient of output shaft, left and right crankshafts is reduced by 19.2 %, 40.5 % and 37.4 % respectively; vibration displacement of the center of mass in output shaft is decreased by 19.5 %; average dynamic force and moment on engine body is diminished by 15.84 % and 20 % respectively; the weight of the flywheel can be declined by 50 % under steady working conditions. Above simulation results could verify the feasibility and effectiveness of improvement program aimed at dynamic balance. Meanwhile, this paper improves the power density of engine through the appropriate design of flywheel, striving to provide theoretical support for the design of transmission mechanism in Stirling engines

Priority-Centric Human Motion Generation in Discrete Latent Space

Text-to-motion generation is a formidable task, aiming to produce human motions that align with the input text while also adhering to human capabilities and physical laws. While there have been advancements in diffusion models, their application in discrete spaces remains underexplored. Current methods often overlook the varying significance of different motions, treating them uniformly. It is essential to recognize that not all motions hold the same relevance to a particular textual description. Some motions, being more salient and informative, should be given precedence during generation. In response, we introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM), which utilizes a Transformer-based VQ-VAE to derive a concise, discrete motion representation, incorporating a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token within the entire motion sequence. This approach retains the most salient motions during the reverse diffusion process, leading to more semantically rich and varied motions. Additionally, we formulate two strategies to gauge the importance of motion tokens, drawing from both textual and visual indicators. Comprehensive experiments on the HumanML3D and KIT-ML datasets confirm that our model surpasses existing techniques in fidelity and diversity, particularly for intricate textual descriptions.Comment: Accepted by ICCV202

Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth.Comment: IEEE the 44th Real-Time System Symposium (RTSS), 202

A brief analysis on the similarities and differences between the laws and regulations of animal aquatic products in Eurasian Economic Union and similar standards in China

The Eurasian Economic Union (EAEU) was established in 2015. At present, Russia, Kazakhstan, Belarus, Kyrgyzstan and Armenia are member countries. They are all important partners in the construction of the "Belt and Road" initiative. The mandatory technical regulations on animal aquatic products and their products from the EAEU were collected and summarized. The similarities and differences of key contents in relevant EAEU regulations and China’s national food safety standards such as definitions, scopes and categorizations of animal aquatic products and their products, as well as maximum levels of contaminates and pathogenic bacteria, parasitological safety requirements and veterinary drug residues were compared and analyzed. The possible causes of the above similarities and differences of animal aquatic products and their products were briefly discussed. The article could provide references for promoting trade in animal aquatic products and their products between China and EAEU member states, avoiding or resolving potential trade barriers and other issues, as well as for further exchanges and cooperation in technical regulations and standards between the two parties

A Compact and Low Profile Loop Antenna with Six Resonant Modes for LTE Smart phone

In this paper, a novel six-mode loop antenna covering 660-1100 MHz, 1710-3020 MHz, 3370-3900 MHz, and 5150-5850 MHz has been proposed for the application of Long Term Evolution (LTE) including the coming LTE in unlicensed spectrum (LTE-U) and LTE-Licensed Assisted Access (LTE-LAA). Loop antennas offer better user experience than conventional Planar Inverted-F Antennas (PIFA), Inverted-F Antennas (IFA), and monopole antennas because of their unique balanced modes (1?, 2?, …). However, the bandwidth of loop antennas is usually narrower than that of PIFA/IFA and monopole antennas due to these balanced modes. To overcome this problem, a novel monopole/dipole parasitic element, which operates at an unbalanced monopole-like 0.25? mode and a balanced dipole-like 0.5? mode, is first proposed for loop antennas to cover more frequency bands. Benefiting from the balanced mode, the proposed parasitic element is promising to provide better user experience than conventional parasitic elements. To the authors’ knowledge, the balanced mode for a parasitic element is reported for the first time. The proposed antenna is able to provide excellent user experience while solving the problem of limited bandwidth in loop antennas. To validate the concept, one prototype antenna with the size of 75×10×5 mm3 is designed, fabricated and measured. Both simulations and experimental results are presented and discussed. Good performance is achieved