Abstract-It is widely recognized that besides the quality of service (QoS), the energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy efficiency model is first proposed for multiple-input multiple-output orthogonal-frequency-divisionmultiplexing (

Background Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.Methods We did a multicentre, prospective study to develop models using slit-lamp or retinal fundus images from participants in three hepatobiliary departments and two medical examination centres. Included participants were older than 18 years and had complete clinical information; participants diagnosed with acute hepatobiliary diseases were excluded. We trained seven slit-lamp models and seven fundus models (with or without hepatobiliary disease [screening model] or one specific disease type within six categories [identifying model]) using a development dataset, and we tested the models with an external test dataset. Additionally, we did a visual explanation and occlusion test. Model performances were evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1* score.

In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a tradeoff makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this paper, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multitiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose PORA, an efficient and distributed predictive offloading and resource allocation scheme for multi-tiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild-value of predictive information to achieve a notable latency reduction, even with prediction errors.

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The dramatic improvements in the growth rate and breast muscle size and yield in broilers through the intensive genetic selection, and the improvement in nutrition and management over the past 50 years have introduced serious abnormalities that influenced the quality of breast meat. The abnormalities include pale-soft-exudative (PSE) conditions, deep pectoral muscle (DPM) myopathy, spaghetti meat (SM), white striping (WS), and woody breast (WB) that have serious negative implications to the broiler meat industry. The incidences of PSE and DPM have been known for several decades, and their prevalence, etiology and economic impact have been well discussed. However, other abnormalities such as SM, WS and WB conditions have been reported just for few years although these conditions have been known for some time. The newly emerging quality issues in broilers are mainly associated with the Pectoralis major muscles, and the incidences have been increased dramatically in some regions of the world in recent years. As high as 90% of the broilers are affected by the abnormalities, which are expected to cause from $200 million to $1 billion economic losses to the U.S. poultry industry per year. So, this review mainly discusses the histopathological characteristics and biochemical changes in the breast muscles with the emphasis on the newly emerging abnormalities (SM, WS, and WB) although other abnormalities are also discussed. The impacts of the anomalies on the nutritional, functional, mechanical and sensory quality of the meat and their implications to the poultry industry are discussed.