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The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

Greenhouses have long been important in the advancement of agricultural operations because they provide regulated settings for optimal plant growth. With the introduction of real-time monitoring and automation capabilities, the Internet of Things (IoT) integration into greenhouse systems represents a revolutionary change. This abstract delves into the wider field of greenhouse technology, highlighting the role that IoT plays in improving agricultural in controlled environments. Conventional greenhouses provide plants with a protected environment, but they might not be as accurate or flexible. Intelligent control of environmental conditions is made possible by the introduction of IoT-enabled greenhouses, which utilize data exchange protocols, actuators, and sensors that are networked. The project aims to elevate traditional greenhouse models by integrating Node-RED and MQTT technologies. Transitioning from a Blynk-based prototype showcases the system's versatility. Other key components, including NodeMCU, sensors for real-time data, and LED lighting, collaborate to redefine controlled environment agriculture. The Raspberry Pi serves as a central hub, facilitating seamless communication through Node-RED and MQTT. This advanced greenhouse system harmonizes cutting-edge technologies, showcasing a commitment to sophistication and adaptability in agricultural practices.

Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.

By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.

This research project explores the factors influencing consumers' purchase intention via live streaming in Malaysia, including trustworthiness, social influence, perceived ease of use, and attractiveness. The study utilized both primary and secondary data, with questionnaires administered through Google Forms to 207 respondents in the urban areas of Malaysia. This study used Smart PLS software (version 3.0), and the findings suggest that trustworthiness, peer or social influence, and perceived ease of use have a significant relationship with purchase intention via live streaming among consumers in Malaysia, whereas attractiveness does not support this hypothesis. The findings of this study can assist new startups and entrepreneurs in gaining a better understanding of the livestreaming selling concept using the Source Credibility Theory. [ABSTRACT FROM AUTHOR]

Electrical energy is often wasted through human negligence when people do not switch off electrical appliances such as lighting after leaving a place. Such a scenario often happens in a classroom when the last person leaves the class and forgets to switch off the electrical appliances. Such wastage may not be able to be afforded by schools that are limited financially. Therefore, this research proposed a simple and cost-effective system that can analyze whether there is or is not a human presence in the classroom by applying a counter to count the total number of people entering and leaving the classroom based on the sensing signals of a set of dual PIR sensors only and then correlating this to automatically turn on or off the electrical appliances mentioned. The total number of people identified in the classroom is also displayed on an LCD screen. A TRIZ approach is used to support the ideation of the system. The system can switch on several electrical output loads simultaneously when the presence of people is detected and switch them off when there are no people in the classroom. The proposed system can be expanded to be used in homes, offices, and buildings to prevent the high cost of electricity consumption caused by the negligence of people. This enables smarter control of electricity consumption. [ABSTRACT FROM AUTHOR]

This study compares and correlates young and elderly people's pinch force and endurance, and proposes inventive solutions contextualised from these statistical evaluations using TRIZ techniques. An experiment was performed with 64 young and old adults who were required to pinch an apparatus while wearing a glove with pressure sensors. Participants held their pinch until they could no longer sustain it. Pinch force and endurance time were measured and analysed using Pearson's correlation and T-Tests. The analyses confirmed that pinch force decreases with lower endurance and vice versa. Apart from the significant difference in elderly and young people's pinch force and endurance, researchers found that young people sustained pinches longer and at a higher force than elderly people. TRIZ strategies suggested that it is better for objects to 'grasp' the elderly's fingers in pinch activities rather than the usual way. Elderly people could attempt pinching periodically to conserve endurance and reduce injury risks from highly sustained exertions. This study provides an explicit pathway in developing innovative solutions that help prevent finger-related musculoskeletal risks among elderly people. These solutions have the potential in contributing to advances in ergonomics, mechanical design and biomechanics in the form of hand prosthetics and hand exoskeletons. [ABSTRACT FROM AUTHOR]

BACKGROUND: While studies have investigated relationships among learning motivation, social presence, and cognitive presence, there appear to be no studies on the inclusion of industry talks and the theory of inventive problem-solving (TRIZ) in strengthening engineering students' learning motivation, social presence, and cognitive presence within a blended learning setting. OBJECTIVE: This study investigated the influence of industry talks and TRIZ on learning motivation, social presence, and cognitive presence in a blended learning environment. METHODS: Data samples were obtained from 98 engineering students in a blended learning course and analysed using Spearman's correlation test, regression, ANOVA, and t-test. RESULTS: Findings suggested that TRIZ and industry talks strongly, positively, and significantly correlated with learning motivation, social presence, and cognitive presence. A well-rounded learning experience compounded of TRIZ and industry talks significantly affected learning motivation, social presence, and cognitive presence, thereby enhancing students' programme outcome (PO) achievement. CONCLUSIONS: These findings can be attributed to the students' independent learning capabilities with TRIZ and industry talks. Analogically, embracing TRIZ and industry talks helps turn blended learning into a "sweet instead of bitter pill to swallow" for engineering students in the face of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

BACKGROUND: The selection of students at academic institutions has been a challenging affair given multiple criteria that need to be considered by the institution. Additionally, multiple evaluators and decision makers are involved in the student selection process, rendering it inconsistent. The complexity and subjectiveness in such decisions making requires new and innovative approach in order to be more systematic and transparent. OBJECTIVE: This paper presents an innovative methodology for student selection for admission into an Institute of Higher Learning (IHL) using Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Drawing on the success of using these methods in other fields, this study applies the technique and principles on student selection process. METHOD: Fuzzy Analytical Hierarchy Process (FAHP) is used in determining the weights of the criteria by the decision makers which avoids the vagueness and inconsistencies in decision making process and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method ranks finds out the best alternative solution for student selection by calculating the relative closeness from the positive ideal solution. RESULTS AND CONCLUSION: This research finds using the hybrid method is effective in student selection for IHL and makes the process efficient and bias-free. This method can be applied to various fields and uses where multi-criteria decision making is involved. [ABSTRACT FROM AUTHOR]