This research aims to study and assess state-of-the-art physics-informed neural networks (PINNs) from different researchers’ perspectives. The PRISMA framework was used for a systematic literature review, and 120 research articles from the computational sciences and engineering domain were specifically classified through a well-defined keyword search in Scopus and Web of Science databases. Through bibliometric analyses, we have identified journal sources with the most publications, authors with high citations, and countries with many publications on PINNs. Some newly improved techniques developed to enhance PINN performance and reduce high training costs and slowness, among other limitations, have been highlighted. Different approaches have been introduced to overcome the limitations of PINNs. In this review, we categorized the newly proposed PINN methods into Extended PINNs, Hybrid PINNs, and Minimized Loss techniques. Various potential future research directions are outlined based on the limitations of the proposed solutions.

Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run.

Chalcones have been well examined in the extant literature and demonstrated antibacterial, antifungal, anti-inflammatory, and anticancer properties. A detailed evaluation of the purported health benefits of chalcone and its derivatives, including molecular mechanisms of pharmacological activities, can be further explored. Therefore, this review aimed to describe the main characteristics of chalcone and its derivatives, including their method synthesis and pharmacotherapeutics applications with molecular mechanisms. The presence of the reactive α,β-unsaturated system in the chalcone’s rings showed different potential pharmacological properties, including inhibitory activity on enzymes, anticancer, anti-inflammatory, antibacterial, antifungal, antimalarial, antiprotozoal, and anti-filarial activity. Changing the structure by adding substituent groups to the aromatic ring can increase potency, reduce toxicity, and broaden pharmacological action. This report also summarized the potential health benefits of chalcone derivatives, particularly antimicrobial activity. We found that several chalcone compounds can inhibit diverse targets of antibiotic-resistance development pathways; therefore, they overcome resistance, and bacteria become susceptible to antibacterial compounds. A few chalcone compounds were more active than conventional antibiotics, like vancomycin and tetracycline. On another note, a series of pyran-fused chalcones and trichalcones can block the NF-B signaling complement system implicated in inflammation, and several compounds demonstrated more potent lipoxygenase inhibition than NSAIDs, such as indomethacin. This report integrated discussion from the domains of medicinal chemistry, organic synthesis, and diverse pharmacological applications, particularly for the development of new anti-infective agents that could be a useful reference for pharmaceutical scientists.

It is no wonder that agriculture plays a vital role in the development of some countries when their economies rely on agricultural activities and the production of food for human survival. Owing to the ever-increasing world population, estimated at 7.9 billion in 2022, feeding this number of people has become a concern due to the current rate of agricultural food production subjected to various reasons. The advent of the Internet of Things (IoT) based technologies in the 21st century has led to the reshaping of every industry, including agriculture, and has paved the way for smart agriculture, with the technology used towards automating and controlling most aspects of traditional agriculture. Smart agriculture, interchangeably known as smart farming, utilizes IoT and related enabling technologies such as cloud computing, artificial intelligence, and big data in agriculture and offers the potential to enhance agricultural operations by automating and making intelligent decisions, resulting in increased efficiency and a better yield with minimum waste. Consequently, most governments are spending more money and offering incentives to switch from traditional to smart agriculture. Nonetheless, the COVID-19 global pandemic served as a catalyst for change in the agriculture industry, driving a shift toward greater reliance on technology over traditional labor for agricultural tasks. In this regard, this research aims to synthesize the current knowledge of smart agriculture, highlighting its current status, main components, latest application areas, advanced agricultural practices, hardware and software used, success stores, potential challenges, and countermeasures to them, and future trends, for the growth of the industry as well as a reference to future research.

Maintaining our standard of living and keeping the economy running smoothly is heavily reliant on a consistent supply of energy. Renewable energy systems create abundant energy by utilizing resources such as the sun, wind, earth, and plants. The demand for renewable energy is increasing, despite power scarcity, pollution, and climate change posing challenges to long-term development in the Association of Southeast Asian Nations (ASEAN), which has seen significant social and economic growth in recent years. To achieve its 23% renewable energy (RE) target, ASEAN can develop solar photovoltaic (PV) electricity. Members of the ASEAN have established regulations and incentives to encourage individuals and businesses to use renewable energy in the future. This paper explores Southeast Asian countries’ comprehensive fossil-free energy options, the region’s renewable energy potential, current capacity, goals, and energy needs. Through the ASEAN Plan of Action for Energy Cooperation (APAEC) 2016–2025 and the ASEAN Declaration on Renewable Energy, ASEAN is committed to reducing its greenhouse gas emissions and promoting sustainable development aligning with the Paris Agreement’s aim to limit global warming to well below 2 degrees Celsius above pre-industrial levels. Results show that decarbonizing the region’s energy system is possible, but current policies and actions must be altered to reach that target level. Further research is necessary to optimize the ASEAN region’s renewable resource technical potential and commercial viability with available technology.

Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21^st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.