Comprehensive Review Research Articles

A Review of the Interactions between Tropical Cyclones and Environmental Vertical Wind Shear

Abstract Tropical cyclone (TC) structure and intensity are strongly modulated by interactions with deep-layer vertical wind shear (VWS)—the vector difference between horizontal winds at 200 and 850 hPa. This paper presents a comprehensive review of more than a century of research on TC–VWS interactions. The literature broadly agrees that a TC vortex becomes vertically tilted, precipitation organizes into a wavenumber-1 asymmetric pattern, and thermal and kinematic asymmetries emerge when a TC encounters an environmental sheared flow. However, these responses depend on other factors, including the magnitude and direction of horizontal winds at other vertical levels between 200 and 850 hPa, the amount and location of dry environmental air, and the underlying sea surface temperature. While early studies investigated how VWS weakens TCs, an emerging line of research has focused on understanding how TCs intensify under moderate and strong VWS (i.e., shear magnitudes greater than 5 m s−1). Modeling and observational studies have identified four pathways to intensification: vortex tilt reduction, vortex reformation, axisymmetrization of precipitation, and outflow blocking. These pathways may not be uniquely different because convection and vortex asymmetries are strongly coupled to each other. In addition to discussing these topics, this review presents open questions and recommendations for future research on TC–VWS interactions.

A Review of Robust Machine Scheduling

Robust optimization (RO) has been recognized as an effective means to deal with unanticipated events in highly uncertain and risky environments. This paper systematically reviews two types of emerging RO machine scheduling approaches—robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) methods—which usually offer tractable formulations and analytical results for machine scheduling problems under uncertainty. First, after highlighting the advantages of RO methods over the stochastic approach in terms of tractability and robustness, we use the bibliometric method to analyze the literature related to R-MS/DR-MS problems and classify them from the following aspects: (1) uncertain factors, (2) uncertainty descriptions, (3) robustness criteria, (4) machine environments and (5) solution methods. Second, we discuss the uncertainty descriptions, and the robust feasibility and robust optimality criteria. We further provide a state-of-the-art review of R-MS/DR-MS models in different machine environments and discuss the performance of the R-MS/DR-MS models. Third, we review and discuss the existing exact, approximation, online, and heuristic solution methods for solving R-MS/DR-MS models. Finally, we present future research opportunities in two promising areas: green machine scheduling problems and machine learning-enabled algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Machine scheduling plays an essential role in industrial and service systems, such as manufacturing, power generation, transportation and medical systems. However, in practice, scheduling systems usually operate in highly uncertain environments due to noisy measurements, prediction errors, and implementation deviations. To ensure robust feasibility and robust optimality, robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) approaches have been recently proposed to hedge against the uncertainties related to processing time, release time, due date, machine breakdown, etc. This paper provides a comprehensive review of the R-MS/DR-MS models and algorithms in different machine environments from the aspects of uncertainty descriptions, robustness criteria and solution methods. This paper further highlights the challenges of R-MS problems and provides promising and valuable research opportunities in terms of problem formulations and algorithm designs.

Classification Based on Ethnicity and Race: A Comprehensive Review

Classification Based on Ethnicity and Race: A Comprehensive Review

“Unlocking the Therapeutic Potential: A Comprehensive Review of the Pharmacological Activities of Shiitake Mushroom (Lentinula edodes)"

Abstract: Worldwide, edible mushrooms are prized for their medical and nutraceutical qualities. Because of their high protein content, low fat and cholesterol levels, low-calorie content, and abundance of vital vitamins, they are acknowledged as the new superfood of the future. This is considered that mushrooms are the only vegetarian source of vitamin D because they contain high amount of ergosterol and fat-soluble vitamins. Limited information exists regarding the composition and amount of fiber, although polysaccharides such as β-glucans increase the nutritional value of mushrooms. Mushrooms are a fantastic food because of their many pharmacological and nutraceutical qualities, as has been noted. Shiitake mushrooms are nutrient-dense, widely farmed fungal plants and possess various pharmacological and nutraceutical properties. The purpose of this review is to compile the most recent information on the health advantages and medicinal uses of shiitake mushrooms.

Design and Implementation of SRAM Using Verilog

Abstract: This comprehensive review delves into the intricate realm of the Static Random-Access Memory (SRAM) design and implementation, elucidating its pivotal role in shaping the performance, efficiency, and reliability of contemporary electronic systems, involving applications in ASIC devices. With a primary focus on the integration of Verilog, a hardware description language (HDL), the paper provides an in-depth background on SRAM, meticulously detailing its architecture, operation, and overall significance in electronic systems. The review meticulously addresses challenges inherent in SRAM design, encompassing considerations such as power consumption, speed, and area efficiency. Significantly, it underscores the crucial role of Verilog by delving into its syntax, modules, and procedural constructs, showcasing its application in modeling key SRAM components. The discussion extends to design trade-offs, including size, speed, and power consumption, illustrating how Verilog plays a pivotal role in facilitating optimal parameter optimization. The review seamlessly integrates technical concepts like RTL (Register Transfer Level), and procedural assignments, ensuring a comprehensive exploration of SRAM design intricacies. Additionally, it introduces terms such as testbench, bitcell and power reduction, further enhancing the reader's understanding of the complex interplay between Verilog and SRAM design.

A Survey: Fake News Detection Mobile App Using Machine Learning

Abstract: With the proliferation of digital information, the challenge of identifying and mitigating the spread of fake news has become increasingly crucial. This research paper provides a comprehensive review of existing technologies designed for the detection of fake news, with a particular focus on their adaptation to mobile applications. The study delves into various approaches, including natural language processing, machine learning algorithms, and deep learning models, highlighting their strengths and limitations. Additionally, it explores the integration of user-generated content analysis and social network features in enhancing the accuracy of fake news detection on mobile platforms. By synthesizing insights from recent advancements, this paper aims to inform the development of an effective mobile app for mitigating the impact of fake news in the digital landscape.

Exploring social remittances: A comprehensive review and recommendations for future research

This paper combines a variety of academic perspectives on social remittances, analyzing several key studies in the field. Each article is critically reviewed for its primary findings, methods, and limitations, focusing on identifying both the distinct contributions and shared elements among them. The comparative analysis brings to light the various ways social remittances influence and are present in both sending and receiving communities across cultural, economic, political, and social aspects. The paper concludes with strategic recommendations for future research, promoting a multidisciplinary approach, integrating technological advancements, and recognizing the changing nature of global migration. This review aims to provide a detailed guide for future investigations in the area of social remittances, seeking to deepen our comprehension of their complex role in the context of global migration.

Real-time data analytics in retail: A review of USA and global practices

Real-time data analytics has emerged as a pivotal tool in the retail sector, revolutionizing decision-making processes and operational strategies. This study delves into the extensive landscape of real-time data analytics in retail, offering a comprehensive review of practices both in the USA and on a global scale. In the United States, retail giants have harnessed real-time data analytics to gain unprecedented insights into consumer behavior, preferences, and market trends. From personalized marketing campaigns to dynamic pricing strategies, retailers leverage real-time analytics to optimize inventory management and enhance the overall customer experience. The integration of technologies like RFID, IoT devices, and advanced analytics platforms has facilitated the seamless flow of data, enabling retailers to respond swiftly to changing market dynamics. On a global scale, diverse retail markets have embraced real-time data analytics to stay competitive in an ever-evolving landscape. Cross-border e-commerce and the proliferation of online marketplaces have intensified the need for real-time insights into global consumer trends. Retailers worldwide are adopting predictive analytics to forecast demand, reduce stockouts, and streamline supply chain operations. Moreover, the integration of artificial intelligence and machine learning algorithms enhances the ability to analyze vast datasets, uncovering hidden patterns and predicting future market trends. Challenges such as data privacy concerns, integration complexities, and the need for skilled professionals are acknowledged in this review. Despite these challenges, the transformative impact of real-time data analytics on the retail sector is undeniable. As technology continues to advance, the synergy between real-time data analytics and retail practices is expected to deepen, fostering innovation and reshaping the industry's landscape. This study provides a glimpse into the dynamic and evolving realm of real-time data analytics in retail, offering insights into the strategies employed by both the USA and global players to thrive in an era of rapid technological change.

Environmental stewardship and corporate social responsibility: A review of case studies from the oil and gas sector

This review presents a comprehensive review of case studies focusing on environmental stewardship and corporate social responsibility (CSR) within the oil and gas sector. The oil and gas industry plays a pivotal role in global energy production, yet its operations often pose significant environmental and social challenges. In response, companies in this sector are increasingly embracing environmental stewardship and CSR principles to mitigate negative impacts and foster sustainable practices. The review examines a diverse range of case studies from various regions and contexts, highlighting both successful initiatives and notable challenges faced by oil and gas companies in integrating environmental stewardship and CSR into their operations. Key themes explored include environmental conservation efforts, community engagement, stakeholder collaboration, regulatory compliance, and technological innovation. Several case studies demonstrate innovative approaches adopted by oil and gas companies to minimize environmental degradation, such as implementing advanced technologies for emission reduction, investing in renewable energy projects, and restoring ecosystems affected by exploration and extraction activities. Moreover, successful CSR programs are showcased, emphasizing community development initiatives, education, healthcare, and infrastructure improvement projects aimed at enhancing the quality of life for local populations impacted by oil and gas operations. However, challenges persist, including balancing economic interests with environmental and social responsibilities, navigating complex regulatory landscapes, and addressing stakeholder concerns effectively. Despite these challenges, the case studies underscore the importance of proactive engagement, transparent communication, and continuous improvement in fostering environmental stewardship and CSR within the oil and gas sector. The review offers insights into best practices, lessons learned, and future directions for promoting sustainability and responsible business practices in the oil and gas industry, thereby contributing to the broader discourse on corporate environmental responsibility and societal impact mitigation.

Automating financial reporting with natural language processing: A review and case analysis

In the evolving landscape of financial reporting, the integration of Natural Language Processing (NLP) emerges as a beacon of innovation, promising to redefine the paradigms of accuracy, efficiency, and compliance. This paper embarks on a scholarly expedition to explore the transformative potential of NLP within the realm of financial disclosures, navigating through the intricate interplay of technological advancements and regulatory frameworks. The study meticulously analyzes the application of NLP techniques in automating financial reporting, unraveling the complexities of implementation and the multifaceted challenges therein through a qualitative research design. Through a comprehensive review of the literature and empirical data, the paper illuminates the efficacy of NLP in enhancing the precision and reliability of financial reports while also delving into stakeholders' perceptions regarding its adoption. The findings reveal a significant improvement in reporting efficiency and accuracy, underscored by the strategic importance of addressing implementation hurdles and regulatory considerations. The study culminates in a set of cogent recommendations, advocating for the development of a robust framework for NLP applications in financial reporting, alongside a clarion call for ongoing research into sophisticated NLP models and scalable solutions. In essence, this paper not only charts a course for the future integration of NLP in financial reporting but also stands as a testament to the indelible impact of technological innovation on the financial industry. It beckons the academic and professional communities to forge a collaborative path towards realizing the full potential of NLP, thereby ushering in a new era of transparency and insight in financial disclosures.

Strategic human resource management for sustainability in creative industries: A review and framework proposal

Strategic Human Resource Management (SHRM) has emerged as a critical approach for fostering sustainability within the dynamic landscape of creative industries. This review presents a comprehensive review of existing literature on SHRM in the context of creative industries, culminating in a proposed framework aimed at enhancing sustainability in these sectors. The review begins by examining the unique challenges faced by creative industries, characterized by rapid technological advancements, evolving consumer preferences, and intense competition. In this context, traditional HRM approaches are often insufficient, necessitating a strategic alignment between HR practices and organizational objectives to ensure long-term viability. Key themes identified in the literature review include talent management, organizational culture, employee engagement, and leadership development. Effective talent management strategies are crucial for attracting, retaining, and developing skilled individuals who drive innovation and creativity within creative firms. Furthermore, fostering a supportive organizational culture that values diversity, collaboration, and experimentation is vital for promoting sustainability in these industries. Employee engagement emerges as a critical factor influencing organizational performance and resilience, particularly in creative environments where intrinsic motivation plays a significant role. Moreover, leadership development programs tailored to the unique needs of creative professionals can empower individuals to navigate uncertainty and drive strategic initiatives that contribute to organizational sustainability. Building upon these insights, this review proposes a framework for SHRM in creative industries, integrating strategic planning, talent management, culture development, and leadership practices. The framework emphasizes the alignment between HR strategies and business goals, emphasizing the importance of agility, innovation, and adaptability in response to dynamic market conditions. Strategic Human Resource Management plays a pivotal role in enhancing sustainability within creative industries by fostering a culture of innovation, empowering employees, and cultivating leadership capabilities. The proposed framework provides a roadmap for organizations seeking to navigate the complexities of the creative landscape while maximizing long-term success and resilience.

A review of sustainable project management practices in modern LNG industry initiatives

This paper presents a comprehensive review of sustainable project management practices within the modern LNG industry, aiming to analyze the strategies, challenges, and opportunities associated with sustainable development in LNG initiatives. The literature review encompasses an examination of sustainable project management principles, emphasizing the importance of environmental stewardship, social responsibility, and economic viability in LNG projects. Case studies from various LNG initiatives worldwide are analyzed to identify best practices and frameworks for integrating sustainability into project management methodologies. These studies highlight innovative approaches such as carbon footprint reduction, community engagement programs, and economic diversification strategies, demonstrating the multifaceted nature of sustainable project management in the LNG sector. Methodologically, this review adopts a systematic approach, utilizing established criteria for selecting and analyzing relevant literature and case studies. Data collection methods include extensive literature searches and case study analysis to gather insights into the implementation and effectiveness of sustainable project management practices in the LNG industry. The findings reveal a growing trend towards the adoption of sustainable practices in LNG projects, driven by regulatory requirements, investor preferences, and stakeholder expectations. However, several challenges persist, including technological limitations, regulatory complexities, and cost implications. Nonetheless, these challenges present opportunities for innovation and collaboration, fostering the development of new technologies and strategies to enhance sustainability in LNG project management. In conclusion, this review underscores the importance of sustainable project management in the modern LNG industry, emphasizing the need for proactive measures to address environmental, social, and economic concerns while meeting energy demands. The insights provided offer valuable guidance for industry stakeholders, policymakers, and researchers seeking to advance sustainability objectives in LNG initiatives.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Reviewing predictive analytics in supply chain management: Applications and benefits

Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definitions. Drawing upon existing research, the paper analyzes real-world applications, case studies, and successful implementations in areas such as demand forecasting, inventory management, and supply chain visibility. Methodologically, the paper outlines the criteria for selecting relevant studies, details the search strategies employed, and highlights the sources contributing to the comprehensive understanding of predictive analytics in SCM. The exploration of applications focuses on how predictive analytics is revolutionizing demand forecasting, optimizing inventory levels, and enhancing supply chain visibility. Through case studies and examples, the paper illustrates the practical implications of implementing predictive analytics in these key areas. Real-world examples and data-driven insights underscore the transformative impact of predictive analytics on SCM processes. Despite its numerous advantages, challenges and limitations exist in the implementation of predictive analytics. This paper examines common hurdles and proposes strategies to overcome these challenges, offering a balanced perspective on the practical implications of integrating predictive analytics into supply chain management. Looking towards the future, the paper discusses emerging trends and technologies in predictive analytics, anticipating advancements that will further shape the landscape of SCM analytics. It summarizes key findings, outlines implications for practitioners and researchers, and suggests avenues for future research in the dynamic field of predictive analytics in supply chain management.

A comprehensive review of strategic management practices in satellite telecommunications, highlighting the role of data analytics in driving operational efficiency and competitive advantage

Satellite telecommunications play a crucial role in modern connectivity, enabling global communication, broadcasting, and internet access. This paper presents a comprehensive review of strategic management practices in the satellite telecommunications industry, with a particular focus on the role of data analytics in enhancing operational efficiency and achieving competitive advantage. The satellite telecommunications sector is characterized by rapid technological advancements, evolving market dynamics, and increasing demand for bandwidth-intensive services. To thrive in this competitive landscape, companies in the industry must adopt strategic management practices that enable them to effectively leverage their resources, capabilities, and market opportunities. One of the key strategic management practices in satellite telecommunications is the integration of data analytics into business operations. Data analytics encompasses the collection, analysis, and interpretation of large volumes of data to extract valuable insights and inform decision-making. By harnessing the power of data analytics, satellite telecommunications companies can optimize their network performance, enhance customer experience, and drive innovation. Data analytics can also help companies in the satellite telecommunications industry to identify new market trends, customer preferences, and competitive threats. By analyzing data from various sources, including satellite sensors, customer feedback, and market research, companies can gain a deeper understanding of their operating environment and make informed strategic decisions. Furthermore, data analytics can enable satellite telecommunications companies to enhance their operational efficiency by optimizing resource allocation, minimizing downtime, and improving network reliability. By using predictive analytics, companies can anticipate equipment failures and proactively address potential issues, thereby reducing maintenance costs and improving service quality. In conclusion, this paper highlights the importance of strategic management practices in the satellite telecommunications industry, with a focus on the role of data analytics in driving operational efficiency and competitive advantage. By adopting data-driven decision-making processes, satellite telecommunications companies can position themselves for long-term success in a rapidly evolving industry landscape.

Eco-sustainable practices through strategic HRM: A review and framework for SMEs in the creative industries

In recent years, there has been a growing recognition of the importance of eco-sustainable practices in the business world, particularly within the context of small and medium-sized enterprises (SMEs) operating in the creative industries. This review presents a comprehensive review and framework for integrating eco-sustainability into the strategic human resource management (HRM) practices of SMEs in the creative industries. The creative industries, encompassing sectors such as design, advertising, fashion, and media, play a significant role in shaping cultural trends and consumer behavior. However, these industries also have a substantial environmental footprint, stemming from resource-intensive production processes, waste generation, and high energy consumption. Recognizing these challenges, there is a growing imperative for SMEs in the creative industries to adopt eco-sustainable practices. Strategic HRM offers a promising approach for integrating eco-sustainability into the core operations of SMEs. By aligning HRM practices with the organization's sustainability goals, SMEs can foster a culture of environmental responsibility and innovation among employees. This review presents a framework that outlines the key dimensions of eco-sustainable HRM, including recruitment and selection, training and development, performance management, and employee engagement. Effective recruitment and selection processes are crucial for attracting individuals who align with the organization's sustainability values. Training and development initiatives can equip employees with the knowledge and skills needed to implement eco-friendly practices in their roles. Performance management systems should include sustainability metrics to evaluate employee contributions to eco-sustainable goals. Additionally, fostering employee engagement through communication, participation, and recognition can enhance commitment to eco-sustainability initiatives. Through the integration of eco-sustainable practices into strategic HRM, SMEs in the creative industries can not only reduce their environmental impact but also gain a competitive edge by appealing to environmentally conscious consumers and investors. This review provides a roadmap for SMEs seeking to navigate the intersection of sustainability and human resource management, ultimately contributing to a more environmentally sustainable future for the creative industries.

A Survey on Evaluation of Large Language Models

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate , where to evaluate , and how to evaluate . Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

Emotion malleability beliefs matter in emotion regulation: a comprehensive review and meta-analysis

ABSTRACT Individuals’ beliefs about the malleability of emotions have been theorised to play a role in their psychological distress by influencing emotion regulation processes, such as the use of emotion regulation strategies. We conducted a meta-analysis to test this idea across studies with a focus on the relationships between emotion malleability beliefs and five distinct emotion regulation strategies: cognitive reappraisal, suppression, avoidance, rumination, and acceptance. Further, using two-stage meta-analytic structural equation modelling (TSSEM), we examined whether the emotion regulation strategies mediate the cross-sectional relationship between emotion malleability beliefs and psychological distress across studies. Thirty-seven studies were included in the meta-analyses and 55 cross-sectional studies were included in the TSSEM. Results demonstrated that, across studies, emotion malleability beliefs were significantly associated with greater use of putatively helpful strategies (particularly with cognitive reappraisal) and less use of putatively unhelpful strategies (particularly with avoidance). The use of cognitive reappraisal and avoidance partially mediated the relationship between emotion malleability beliefs and psychological distress. These results highlight the importance of considering beliefs about the malleability of emotions in the context of emotion regulation. These findings suggest the potential role of emotion malleability beliefs in interventions for individuals with emotion regulation-related difficulties and psychological distress.

Criminal responsibility and profile of Turkish firesetters with schizophrenia

This study aimed to examine a subgroup of Turkish firesetters diagnosed with schizophrenia spectrum disorders (SSD) for their motives, individual and offense-related characteristics and courses of action, and their association with expert assessment of criminal responsibility. A cohort of 50 arsonists with SSD, who underwent assessment for criminal responsibility by the Council of Forensic Medicine in Turkiye between 2009 and 2022, was analyzed using a comprehensive retrospective file review. Multivariate analyses indicated that being female, older, experiencing hallucinations at the time of the offense, having a primary diagnosis of SSD other than psychotic disorder not otherwise specified, and no prior convictions were associated with a higher likelihood of being found not criminally responsible. Singling out a definitive profile of SSD arsonists without criminal responsibility is challenging. Our findings, based on forensic expert assessments, suggest that positive psychotic symptoms and criminal history play crucial roles in determining criminal responsibility in this group.

Recent Advances in 4D Printing of Advanced Materials and Structures for Functional Applications.

4D printing has attracted tremendous worldwide attention during the past decade. This technology enables the shape, property, or functionality of printed structures to change with time in response to diverse external stimuli, making the original static structures alive. The revolutionary 4D-printing technology offers remarkable benefits in controlling geometric and functional reconfiguration, thereby showcasing immense potential across diverse fields, including biomedical engineering, electronics, robotics, and photonics. Here, a comprehensive review of the latest achievements in 4D printing using various types of materials and different additive manufacturing techniques is presented. The state-of-the-art strategies implemented in harnessing various 4D-printed structures are highlighted, which involve materials design, stimuli, functionalities, and applications. The machine learning approach explored for 4D printing is also discussed. Finally, the perspectives on the current challenges and future trends toward further development in 4D printing are summarized.