Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexicon is developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentiment scores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. In the second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis of Roman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrieval metrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The results showcase the potential for improving software engineering tasks related to user feedback analysis and product development.

In this paper, we discuss the distributed optimal Sliding Mode Control (SMC) issue for the isomorphic complex network in view of learning theory. Firstly, a dynamic distributed SMC strategy is introduced innovatively by virtue of Reinforcement Learning (R-L) theory, and on this basis, the overall tracking error closed-loop system is formulated between the complex network and the target system. For the overall error system, we provide the detail schemes for stability analysis and SMC control synthesis. The corresponding criteria and SMC algorithm are proposed via the solvable inequality constraints. Subsequently, the reachability issue is also analyzed for the pre-designed sliding mode surface. To conclude the paper, we check some Unmanned Aerial Vehicles (UAVs) as the example under the complex network framework, and then, we also verify the effectiveness of the proposed stability criterion and control algorithm in this paper.

After-sale service plays an essential role in the electronics retail industry, where providers must supply the required repair parts to consumers during the product warranty period. The rapid evolution of electronic products prevents part suppliers from maintaining continuous production, making it impossible to supply spare parts consistently during the warranty periods and requiring the providers to purchase all necessary spare parts on Last Time Buy (LTB). The uncertainty of customer demand in spare parts brings out difficulties to maintain optimal spare parts inventory. In this paper, we address the challenge of forecasting spare parts demand and optimizing the purchase volumes of spare parts during the regular monthly replenishment period and LTB. First, the problem is well defined and formulated based on the dynamic economic lotsize model. Second, a transfer function model is constructed between historical demand values and product sales, aiming to identify the length of warranty period and forecast the spare part demands. In addition, the linear Model Predictive Control (MPC) scheme is adopted to optimize the purchase volumes of spare part considering the inaccuracy in the demand forecasts. A real-world case considering different categories of spare parts consumption is studied. The results demonstrate that our proposed algorithm outperforms other algorithms in terms of forecasting accuracy and the inventory cost.

In autonomous driving, an unprotected left turn is a highly challenging scenario. It refers to the situation where there is no dedicated traffic signal controlling the left turns; instead, left-turning vehicles rely on the same traffic signal as the through traffic. This presents a significant challenge, as left-turning vehicles may encounter oncoming traffic with high speeds and pedestrians crossing against red lights. To address this issue, we propose a Model Predictive Control (MPC) framework to predict high-quality future trajectories. In particular, we have adopted the infinity norm to describe the obstacle avoidance for rectangular vehicles. The high degree of non-convexity due to coupling terms in our model makes its optimization challenging. Our way to solve it is to employ Sequential Convex Optimization (SCP) to approximate the original non-convex problem near certain initial solutions. Our method performs well in the comparison with the widely used sampling-based planning methods.

This paper investigates the distributed fixed-time attitude coordinated control problem for multiple spacecraft subject to actuator saturation under the directed topology. First, a distributed fixed-time observer is presented for each follower spacecraft to estimate the leader spacecraft’s states. Compared with the commonly used fixed-time observer, the settling time of the proposed fixed-time observer can be easily adjusted by some free design parameters. Next, a distributed fixed-time control scheme is derived by using the estimates of the leader spacecraft's states and the adding a power integrator technique. When considering actuator saturation, an auxiliary system is utilized to compensate the saturation. Further, a rigorous theoretical proof is provided to show that the practical fixed-time stability of the closed-loop system is ensured. Finally, simulation results illustrate the benefits and effectiveness of the developed control scheme.