Improving The Performance of Inventory Control – Taking W Company as an Example

The W company is facing a problem that their demand is intermittent. Because intermittent demand is difficult to predict, there are some models being created to deal with it. Using these models, such as Bootstrapping, Croston’s method, and Discreteauto-regressive-moving-average model, to predict and compare with the current one if any of them outperforms

Use electrochemistry to charge the next dynamic thermal metamaterials

Electrochemistry has enabled a wide range of important energy technologies such as fuel cells and batteries, emerging as a powerful tool to achieve active materials and devices with novel applications. In this Perspective, we highlight the great potential of electrochemistry in propelling the next generation of dynamic thermal metamaterials with a focus on thermal radiation applications. After a brief introduction of the mechanisms of electrochemistry to change material properties, we discuss the possibilities of achieving highly tunable thermal radiation features by electrochemically manipulating the carrier densities of plasmonic materials. Recent studies in the intersections between electrochemistry, metamaterials, and thermal radiation applications are reviewed, indicating an emerging research direction incorporating these three fields — electrochemically dynamic thermal metamaterials. Towards this direction, we anticipate a promising pathway of employing conducting polymers and point out its remarkable opportunities and potential challenges. We hope this perspective could encourage more researchers to contribute to the development of this interdisciplinary field targeting the next energy technologies and applications

Serum ferritin levels and polycystic ovary syndrome in obese and nonobese women

AbstractObjectiveThe aim of this study is to evaluate serum ferritin levels and polycystic ovary syndrome (PCOS)-related complications in obese and nonobese women.Materials and methodsThis retrospective study included 539 (286 with PCOS and 253 without PCOS).ResultsSerum ferritin correlated with menstrual cycle length, sex hormone-binding globulin, total testosterone, androstenedione, triglyceride, and total cholesterol in both obese and nonobese women. Obese women with high ferritin levels exhibited higher insulin resistance, impaired glucose tolerance, and liver enzymes (glutamic oxaloacetic transaminase, glutamic pyruvic transaminase) than obese women with low ferritin levels. However, among nonobese women, insulin resistance and risk of diabetes were not significantly different between the high and low ferritin groups. Independent of obesity, hypertriglyceridemia was the major metabolic disturbance observed in women with elevated serum ferritin levels.ConclusionElevated serum ferritin levels are associated with increased insulin resistance and risk of diabetes in obese women but not in nonobese women. However, higher serum ferritin levels were correlated with a greater risk of hyperglyceridemia in both obese and nonobese women. Therefore, hypertriglyceridemia in women with PCOS might be associated with iron metabolism

Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI

Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods

Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201