Review of fan-use rates in field studies and their effects on thermal comfort, energy conservation, and human productivity

This paper is a literature review of field studies on fan-use rates and their effects on thermal comfort, energy conservation, and human productivity. In the assessed literature, fans are more popular in Asia, and more used in mixed-mode (MM) and naturally ventilated (NV) buildings than in air-conditioned (AC) buildings. On the basis of collected fan-use models, probit regression models of fan-use rates and ambient environments were obtained and indicate that the essential trigger of fan-use is a warm environment rather than building types. This result helps us to understand the control behaviors and comfort requirements of occupants. Also, fans could provide benefits in three aspects: widening neutral temperatures, saving energy, and improving occupants’ productivity. First, using fans in buildings elevates the neutral temperature and the upper limit of neutral zone (0.5 thermal sensation scale) averages by about 3 K in ranges from 25.7℃ to 28.7℃ and 27.5℃ to 30.7℃, respectively. Second, fan-use reduces AC-use rates in MM buildings in summer. The regression models based on the collected AC-use rate models illustrate that, on average, AC-use is expected to be reduced by about 15% in summer when fans are used. Third, providing occupants access to fans could improve occupants’ productivity. Based on the limited data available, a 3-K temperature extension is achieved by fans ensuring productivity not decreasing. This review could shed some light on the extension of the neutral temperature range, predictions of MM buildings’ energy consumptions, and methods to enhance productivity. Additionally, this review suggests some valuable directions for future research on fans

Gasdermins in sepsis

Sepsis is a hyper-heterogeneous syndrome in which the systemic inflammatory response persists throughout the course of the disease and the inflammatory and immune responses are dynamically altered at different pathogenic stages. Gasdermins (GSDMs) proteins are pore-forming executors in the membrane, subsequently mediating the release of pro-inflammatory mediators and inflammatory cell death. With the increasing research on GSDMs proteins and sepsis, it is believed that GSDMs protein are one of the most promising therapeutic targets in sepsis in the future. A more comprehensive and in-depth understanding of the functions of GSDMs proteins in sepsis is important to alleviate the multi-organ dysfunction and reduce sepsis-induced mortality. In this review, we focus on the function of GSDMs proteins, the molecular mechanism of GSDMs involved in sepsis, and the regulatory mechanism of GSDMs-mediated signaling pathways, aiming to provide novel ideas and therapeutic strategies for the diagnosis and treatment of sepsis

Performance Analysis of Free-space Quantum Key Distribution Using Multiple Spatial Modes

In the diffraction-limited near-field propagation regime, free-space optical quantum key distribution (QKD) systems can employ multiple spatial modes to improve their key rate. Here, we analyze QKD using the non-orthogonal flat-top focused beams. Although they suffer from a rate penalty, their ease of implementation makes them an attractive alternative to the well-studied orthonormal Laguerre-Gauss (LG) modes. Indeed, in the presence of turbulence, the non-orthogonal modes may achieve higher QKD rate than the LG modes.Comment: 13 pages, 5 figures (if I count all the subfigures the total number is 9) submitted to Optics expres

Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Large Language Models (LLMs) have proven their exceptional capabilities in performing language-related tasks. However, their deployment poses significant challenges due to their considerable memory and storage requirements. In response to this issue, weight-only quantization, particularly 3 and 4-bit weight-only quantization, has emerged as one of the most viable solutions. As the number of bits decreases, the quantization grid broadens, thus emphasizing the importance of up and down rounding. While previous studies have demonstrated that fine-tuning up and down rounding with the addition of perturbations can enhance accuracy in some scenarios, our study is driven by the precise and limited boundary of these perturbations, where only the threshold for altering the rounding value is of significance. Consequently, we propose a concise and highly effective approach for optimizing the weight rounding task. Our method, named SignRound, involves lightweight block-wise tuning using signed gradient descent, enabling us to achieve outstanding results within 400 steps. SignRound competes impressively against recent methods without introducing additional inference overhead. The source code will be publicly available at \url{https://github.com/intel/neural-compressor} soon

MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

MDAM-DRNet: Dual Channel Residual Network with Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases\u27 spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model\u27s ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases

On Two-Level State-Dependent Routing Polling Systems with Mixed Service

Based on priority differentiation and efficiency of the system, we consider an N+1 queues’ single-server two-level polling system which consists of one key queue and N normal queues. The novel contribution of the present paper is that we consider that the server just polls active queues with customers waiting in the queue. Furthermore, key queue is served with exhaustive service and normal queues are served with 1-limited service in a parallel scheduling. For this model, we derive an expression for the probability generating function of the joint queue length distribution at polling epochs. Based on these results, we derive the explicit closed-form expressions for the mean waiting time. Numerical examples demonstrate that theoretical and simulation results are identical and the new system is efficient both at key queue and normal queues