With the development of globalization and diversification, more and more people attach importance to English, and a great number of primary schools in China begin to attach importance to English teaching. As an international mainstream English teaching method, phonics has gradually been used in primary school education in China. Phonics guides students to match letters or letter combinations in the words with sounds, and read or spell words through these pronunciation rules, so that students can learn the vocabulary in a relaxed and pleasant way. It will also reduce obstacles to reading and writing words, and improve students’ learning efficiency. However, there are still some problems in primary school English teaching in China, such as lack of systematic teaching, neglect of phonetic symbol learning and neglect of word meaning, which need to be further improved so that phonics can better assist primary school English teaching.

The objective of this study was to develop a model based on fuzzy rules to evaluate the effects caused by varying the dosages of two soil fertilizers (mineral and organic) on root diameter. Fuzzy logic is a method that presents a language, more appropriate to day-to-day life, as the sky is a bit cloudy. For the input variables of this system the mineral and an organic fertilizer were used, for the output the root diameter, in cm. After optimization of the input rules, it can be seen that for the application of the fertilizers (mineral and organic) the best dosages were from 15 to 60 and 20 to 60 g·m^-2, respectively. With this application of fuzzy rules in real data, it is possible to take these benefits to those involved in the production chain of radish, resulting in a reduction in the dosages of products and improving its final profitability.

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

The chemical reinforcement of sandy soils is usually carried out to improve their properties and meet specific engineering requirements. Nevertheless, conventional reinforcement agents are often expensive; the process is energy-intensive and causes serious environmental issues. Therefore, developing a cost-effective, room-temperature-based method that uses recyclable chemicals is necessary. In the current study, poly (styrene-co-methyl methacrylate) (PS-PMMA) is used as a stabilizer to reinforce sandy soil. The copolymer-reinforced sand samples were prepared using the one-step bulk polymerization method at room temperature. The mechanical strength of the copolymer-reinforced sand samples depends on the ratio of the PS-PMMA copolymer to the sand. The higher the copolymer-to-sand ratio, the higher the sample’s compressive strength. The sand (70 wt.%)-PS-PMMA (30 wt.%) sample exhibited the highest compressive strength of 1900 psi. The copolymer matrix enwraps the sand particles to form a stable structure with high compressive strengths.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.