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Due to its low NO x emission index, the micromix burner technology is a promising alternative for using hydrogen in combustion. Various universities and research centers in Germany, England, and Spain have documented and studied this technology. However, the number of studies on micromix burners is limited, which hinders their implementation on an industrial scale. The present study aims to review developed works focused on micromix combustion technologies to identify the main gaps and research needs. A sample of 76 articles from 2008 was selected using the PRISMA methodology, which was categorized based on the study methodology, simulation software, and fuels used. An experimental gap has been identified in the combustion of hydrogen and methane in the selected article sample. This gap is a critical research need due to the opportunity to implement this technology in existing natural gas networks, facilitating the transition from fossil fuels to cleaner combustion processes. • Demonstrates the capability of micromix burners to achieve minimal NOx emissions through advanced air-fuel mixing techniques, making them a promising solution for hydrogen combustion. • Analyzes a wide range of studies using numerical simulations and experimental methods, emphasizing the need for their combined application to optimize burner performance. • Explores the feasibility of hydrogen-methane blends in micromix burners to leverage existing infrastructure while reducing greenhouse gas emissions. • Identifies key gaps, such as material selection and burner geometry design, proposing strategies to advance the scalability and industrial adoption of micromix technology. [ABSTRACT FROM AUTHOR]

Methane (CH4) is one of the most significant greenhouse gases responsible for about one-third of climate warming since preindustrial times, originating from various sources. Landfills are responsible for a large percentage of CH4 emissions, and population growth can boost these emissions. Therefore, it is vital to automate the process of CH4 monitoring over landfills. This study proposes a convolutional neural network (CNN) with an Atrous Spatial Pyramid Pooling (ASPP) mechanism, called PRISMethaNet, to automate the CH4 detection process using PRISMA satellite data in the 400–2500 nm spectral range. A total number of 41 PRISMA images from 17 landfill sites located in several countries, such as India, Nigeria, Mexico, Pakistan, Iran, and other regions, were used as our study areas. The PRISMethaNet model was trained using augmented data as the input, and plume masks were obtained from the matched filter (MF) algorithm. This novel proposed model successfully detected plumes with overall accuracy (OA), F1-score (F1), precision, and recall of 0.99, 0.96, 0.93, and 0.99, respectively, and quantification uncertainties ranging from 11 % to 58 %. An unboxing of the ASPP module using Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm demonstrated a strong relationship between larger dilation rates (DRs) and CH4 plume detectability. Importantly, the results highlighted that plume masks obtained by PRISMethaNet provided more accurate CH4 quantification rate compared to the statistical methods used in previous studies. In particular, the mean square error (MSE) for PRISMethaNet was approximately 1,102 kg/h, whereas the MSE for the commonly used statistical method was around 1,974 kg/h. [ABSTRACT FROM AUTHOR]

Evaporites like calcrete and gypsum are found largely in arid regions in association with playas, palaeochannels, and different source rocks. Identification and precise mapping of these minerals is important for the economy. The present study aims to identify and map the spatial distribution of gypsum in the Bikaner district (an arid region in western Rajasthan, Bharat) based on the analysis of distinct spectral responses. Recently high spectral resolution and atmospherically corrected PRISMA data were used in this study. During the initial processing of the data, bad bands were spectrally subset after a detailed visual inspection. Also, a minimum noise fraction was performed to remove noise as well as to reduce the dimensionality of the data as per the analysis of eigenvalues. Then, the distribution map of gypsum was generated using the application of Spectral Angle Mapper after matching the spectra obtained from the spectral library and image. The results were validated through a detailed field investigation as well as from lab-based analysis like X-ray Diffraction. Moreover, we have delineated a course of a palaeochannel based on the interpretation of ALOS PALSAR, Sentinel-2A, and their fused images using two different fusion algorithms such as Gram Schmidt and Intensity Hue and Saturation. Our analysis revealed that the gypsum deposits are mostly situated along the identified palaeochannel and with lithology such as fine aeolian sand and calcrete (Kankar). [ABSTRACT FROM AUTHOR]

Background/Objectives: The environmental problems associated with meat production, the pain and distress of animals, and health problems have contributed to the increased appreciation of meat alternatives. Methods: The review of patented inventions presenting plant-based meat analogues (PBMAs) issued in the years 2014–2023 was conducted according to PRISMA statements across the ISI Web of Science, as well as Google Patents and Espacenet Patent Search engines. Results: The analysis of 183 patents showed an increase in patent numbers in the years 2020–2022, with the greatest number of patents developed by teams consisting of two authors. The authors and patent applicants were mainly affiliated with the United States, while Société des Produits Nestlé S.A. emerged as the leader among applicant institutions. The International Patent Classification (IPC) codes were given to 177 patents, which were mainly classified as Human Necessities (Section A). In total, inventors mentioned 184 taxa, including 28 genera, 1 section, 144 species, 5 subspecies and 6 varieties of vascular plants. The majority of taxa showed a high edibility rating and belonged to perennials, mainly herbaceous plants representing the families Fabaceae, Poaceae and Brassicaceae. The analysis of patents showed that plants are a promising source of protein, lipids, fibre, polyphenols, starch and gum in meat analogues. At the same time, the noticed slight use of numerous taxa, despite the substantial content of valuable constituents as well as high edibility rates, presumably might be caused by their underutilization in numerous regions of the world. Conclusions: The direction of future studies should focus on searching for novel plant-based meat analogues based on underutilized, promising plant sources and investigations of their usefulness. [ABSTRACT FROM AUTHOR]

Hyperspectral images have been extensively employed to map alterations related to various ore deposits, particularly those associated with porphyry copper deposits. The present study aims to evaluate the efficiency of the PRISMA (Precursore Iper Spettraledella Missione Applicativa) satellite imagery, that is an Italian satellite, in identifying and discriminating alteration zones (Phyllic, Argillic, Propylitic) related to a well-known porphyry copper mineralization system in Iran. The chosen algorithms for performance evaluations involve selective principle component analysis (SPCA), spectral band ratios, mixture-tuned matched filtering (MTMF), and linear spectral unmixing (LSU). In this study, new band ratios are also proposed to improve the efficiency of alteration mapping. The results are validated by field data and geological-alteration map demonstrating the overall accuracy of detecting different alteration zones including the Phyllic, Argillic, and Propylitic zones equal to 71.87%, 87.50%, 81.25% and 71.87%, and kappa coefficients of 58.85%, 80.54%, 70.55% and 57.52% for SPCA, band ratio, MTMF, and LSU algorithms, respectively. In current research, it is found that using PRISMA data, the band ratio algorithm is superior when compared to other implemented algorithms in detecting hydrothermal alteration associated with porphyry copper deposits. [ABSTRACT FROM AUTHOR]

In adoption, it is common to consider the adopted child, the adoptive parents, and the birth mother as part of the adoption galaxy. Yet, birth fathers are often missing elements in adoption‐focused research. This article aims to comprehensively understand the current knowledge regarding birth fathers' experiences in adoption. Adapting the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses approach, this review identified 100 peer‐reviewed articles published from 2000 to 2022 on birth fathers in adoption. This article used a mixed‐methods approach to analyze the landscape of the current research. Quantitative analysis confirmed that birth fathers in adoption are under‐researched worldwide. In the qualitative analysis, the following themes emerged regarding birth fathers: a lack of openness, negative stereotypes, gatekeeping, and emotional impacts. The findings demonstrate the importance of considering the wishes and support needs of birth fathers when their child is being placed for adoption. [ABSTRACT FROM AUTHOR]

Purpose: Financial anxiety has become a global concern and a growing research area with significant potential to contribute to the behavioral and personal finance literature. Despite this, the literature is fragmented and inconsistent. Prior studies vary greatly in the breadth of definitions and measures of financial anxiety. There has been no systematic evaluation of literature on financial anxiety antecedents, consequences, and coping strategies. This systematic review fills this gap. Design/methodology/approach: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Scopus and Web of Science and identified 55 eligible studies published between 2009 and 2024. Findings: Financial anxiety is defined and measured differently in different research domains. We identified several antecedents, including socio-demographic factors (e.g. gender, age, ethnicity, income, employment, racial background, and language proficiency), personality traits, compulsive and impulsive buying behavior, depression or other mental issues, family health issues, and the COVID-19 pandemic and consequences of financial anxiety, including psychological and psychic health, societal and personal relations, financial behavior and well-being, and job-related outcomes. In addition, the literature presents six financial anxiety coping strategies (self-imposed coping mechanisms, spiritual and theological resources, increased financial capability, social and family support, seeking professional help, and language proficiency training). Several future research directions are presented. Originality/value: This review represents the first systematic compilation and evaluation of the research findings on financial anxiety. [ABSTRACT FROM AUTHOR]

Humanlike robots are increasingly employed to provide frontline services. They are frequently designed with stereotypically feminine or masculine humanlike features which affect or bias consumer behavior in service encounters. This systematic review of 118 peer‐reviewed journal papers aims to comprehensively capture the current status of the field and identify important research gaps requiring further investigation. Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses protocol, a comprehensive framework is developed to conceptualize the process of customer interactions with humanlike service robots, depicting how humanlike service robots influence consumer behavior. Specifically, we identify interaction antecedents, consumer processing factors, outcomes of the interactions, and strengthening/attenuating factors. Based on the framework, the review concludes by identifying issues that future research should seek to solve to contribute to the field. This paper provides a deep understanding of service robot anthropomorphism in marketing and consumer research and proposes a future research agenda to advance knowledge of the field. [ABSTRACT FROM AUTHOR]

Communications require a large amount of network resources to support the heavy traffic generated by the Internet of Things (IoT). As a result, microelectronics and telecommunications have been exploring types of hardware capable of supporting these new network architectures and communication systems. New devices should be designed to offer high speed, ultra-wideband connectivity, and extensive coverage. In addition, they should use eco-friendly materials to respond to the needs of sustainable smart cities and meet the requirements of future communications. This literature review followed the PRISMA statement to identify research trends in the use of graphene in antennas for wireless communication applications. A total of 168 documents were analyzed, and one of the main findings is a considerable rise in research interest in this topic since 2012, with India emerging as the leading contributor of knowledge in this area. Furthermore, most applications have been developed for the terahertz band, and more recent studies have focused on utilizing MIMO technologies for 5G and 6G communication systems. In addition, most research has observed improvements in the performance and efficiency of antennas designed with graphene, observing in some results up to 9.35 dBi of gain and 97.6% in radiation efficiency for applications in the terahertz band. [ABSTRACT FROM AUTHOR]

Digital preservation encompasses policies, technologies, and strategies to ensure that digital objects and collections will sustain for future use. This study aimed at investigating the trends toward digital preservation in terms of in-house activities versus outsourcing by systematically reviewing the extant literature. The relevant studies were identified by accessing multiple databases and indexing and abstracting services. After retrieving the relevant literature, necessary filtering was done by following the PRISMA protocols, and thereafter the selected studies were finalized for further review. The meta-analysis of the final studies affirms a strong global preference of libraries, archives, and other cultural and memory organizations toward in-house activities for the preservation of their digital objects and collections compared to outsourcing digital preservation activities by third parties. The results of this study provide a holistic view of how digital assets are being preserved in memory institutions across the world and underpin the global management (in-house or outsourced) approaches for digital preservation. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are widely used in various applications. Collaborating two or more algorithms in a hybrid form has shown great improvements in terms of the algorithm's performance. This paper highlights the recently published work during the last decade from a quantitative perspective. The biometric measures include the number of publications, citations, average citations per publication, h-index, and field-weighted citation impact (FWCI) based on the data extracted from the Scopus database. Statistical measures, co-occurrence and co-authorship maps, and illustrative graphs have been implemented using software tools. According to the collected data, about 3469 articles have been published during the last decade with an increasing rate of 44.1 papers per year. Most of these articles have been published as journal articles with a percentage of 68.3%, followed by conference articles occupied 29.5%. China, India and Iran contributed the largest number of articles at 1076, 965, and 239, respectively. Parouha, Verma, and Kamel, are the top-ranked authors with 14, 10, and 9 publications, respectively. The most areas of interest are computer science, engineering and mathematics with publication percentages of 27.69%, 25.55% and 13.91%, respectively. The data presented in this paper gives the researchers a clear image of this hot topic to start new research. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management in developing nations, particularly Ethiopia, faces considerable issues, including high generation and low collection rates, unsafe disposal practices, and inadequate reuse and recycling efforts. This review looks into the current status of these challenges and possibilities for valorizing the potential of the organic fraction of MSW through composting and bioenergy. Aligned with global sustainability goals and emphasizing the promotion of resource efficiency and waste reduction, this review aims to identify the major challenges of MSW management in Ethiopia and valorize the MSW organic fraction to describe sustainable waste management solutions. The review process comprised a systematic approach through a comprehensive literature search of credible databases such as Google Scholar, Scopus, and PubMed from 2012 to 2023. To ensure relevance and reduce bias, 63 relevant studies were selected from an initial pool of 200 papers via a rigorous screening process utilizing the PRISMA protocol. The assessment of study quality with the AMSTAR‐2 improved the accuracy and reliability of the review's conclusions. The authors reviewed various challenges associated with MSW in Ethiopia, including MSW generation and collection rates, composition issues, and disposal technologies. A lack of community awareness and cooperation were identified as significant barriers to successful waste management practices in Ethiopia. This review also evaluated the viability of valorizing the organic fraction of MSW through bioenergy production and community‐level composting as sustainable alternatives. The findings of this review provide valuable insights and evidence‐based recommendations for addressing MSW concerns in Ethiopia. The findings can also serve to shape policy formulation, research goals, and practical interventions focused on enhancing solid waste management practices in developing nations. [ABSTRACT FROM AUTHOR]

This systematic review and meta-synthesis assesses the effectiveness of Conditional Cash Transfers (CCTs) and Unconditional Cash Transfers (UCTs) in Sub-Saharan Africa. Analyzing 27 studies (2014–2024) using PRISMA guidelines, the research examines impacts on financial stability, education, and health. CCTs generally outperform UCTs in achieving specific education and health targets by incentivizing behaviors, while UCTs offer greater flexibility, improving mental health and economic resilience in fragile settings. The findings stress the context-dependent nature of cash transfer programs and the need for tailored, integrated approaches. Policy recommendations emphasize designing programs based on local conditions and desired outcomes. [ABSTRACT FROM AUTHOR]

With the global population projected to exceed 10.9 billion by 2100, agriculture faces significant strain. One approach to alleviate this pressure is through the use of blockchain technology, which could improve the traceability and transparency of agricultural products. This study investigates blockchain's characteristics, advantages, and challenges to determine its suitability for agricultural supply chain management (ASCM). The methodology involved selecting keywords and conducting searches for papers published between 2017-2021 across multiple scholarly databases including Google Scholar, Scopus, Cross Ref, Science Direct, and Emerald Insight. The PRISMA method was employed for the literature review, resulting in the analysis of 91 papers. The analysis identified the top ten most commonly discussed blockchain characteristics. Findings suggest that blockchain technology offers advantages such as increased operational efficiency, enhanced management data transparency, intelligent contract management, and mitigation of fraud, errors, and financial losses in ASCM. However, blockchain adoption faces challenges including regulatory hurdles, stakeholder relationships, data ownership, scalability issues, and knowledge gaps. This study contributes to the understanding of blockchain's potential in ASCM and underscores the importance of addressing these challenges for its effective implementation. [ABSTRACT FROM AUTHOR]

(1) Background: The development of generative artificial intelligence (GAI) is transforming higher education. This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices. (2) Methods: Following PRISMA guidelines, a comprehensive search strategy was employed to locate scientific articles on GAI in higher education published by Scopus and Web of Science between January 2023 and January 2024. (3) Results: The search identified 102 articles, with 37 meeting the inclusion criteria. These studies were grouped into three themes: the application of GAI technologies, stakeholder acceptance and perceptions, and specific use situations. (4) Discussion: Key findings include GAI's versatility and potential use, student acceptance, and educational enhancement. However, challenges such as assessment practices, institutional strategies, and risks to academic integrity were also noted. (5) Conclusions: The findings help identify potential directions for future research, including assessment integrity and pedagogical strategies, ethical considerations and policy development, the impact on teaching and learning processes, the perceptions of students and instructors, technological advancements, and the preparation of future skills and workforce readiness. The study has certain limitations, particularly due to the short time frame and the search criteria, which might have varied if conducted by different researchers. [ABSTRACT FROM AUTHOR]

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model's universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge. [ABSTRACT FROM AUTHOR]

Summary: Plant‐Based Milk (PBM) alternatives have been a hype recently, drawing the high focus of investors, developers, and researchers. However, despite this surge in interest, the market size and potential of these products remain unclear. While several studies explored consumer perspectives on these alternatives, the variability in findings remains poorly understood. This systematic review aims to investigate consumer perceptions of PBM and to identify the factors associated with higher acceptance. Adhering to the PRISMA‐P methodology, searches on two major databases – Scopus and Web of Science – were conducted. Research papers published between 2019 and 2024 were screened to capture the most recent insights, resulting in thirty‐one articles. Among these studies, 71% were surveys, 50% were conducted in Europe and 46% in America. Additionally, 78% of the studies had a sample size of 500 participants or fewer, 71% assessed the perception of PBM alternative in general, while equally, 12.9% focused on soy‐based milk and 12.9% on coconut‐based milk. Overall, the studies evaluated preference for PBM over dairy milk, motives for consumption and other relevant factors. The research describes the socio‐demographic criteria associated with the high adoption rates of these products, the reasons for choosing dairy substitutes (such as curiosity, health benefits, or social influence), and the perceived advantages and barriers to their use. The expected outcomes of this study can provide valuable insights for designing effective marketing strategies for PBM and fostering the development of sustainable and healthy food systems. [ABSTRACT FROM AUTHOR]

Due to its inherent complexities in the process and the dynamic interactions with external environmental factors, the construction industry is widely considered one of the most hazardous industries worldwide. With advancements in artificial intelligence (AI), construction safety management practices have increasingly used knowledge-driven approaches. Such incorporation of knowledge-based methods has led to significant improvements in various elements of construction safety management systems, including hazard identification and risk assessment, selection of risk mitigation strategies, analysis of accident information, sharing of health and safety knowledge, access to regulations, and identification of applicable safety requirements. Against this background, this paper presents a systematic literature review to provide an overview of the current state of the art in the use of knowledge-driven approaches in construction safety management. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure, this study reviews how the knowledge-driven approach is utilized in the construction safety management field to automate different activities that come under it. Journal papers published from 2000 were considered for this review, and the analysis focused on the contributions of research, the evolution of knowledge-driven approaches, sources of incorporated knowledge, methods of system development, yearly publications, and publication by journals. The results provide a comparison of related studies over two decades and offer insights into trends and gaps in this research field. Notably, the trend analysis shows a dramatic increase in the number, as well as the depth, of research efforts utilizing AI techniques for analyzing unstructured data, such as construction images and texts from construction documents, and drawing data-based decisions for accident prevention. [ABSTRACT FROM AUTHOR]

The increasing integration of brain–computer interfaces (BCIs) with augmented reality (AR) presents new possibilities for immersive and interactive environments, particularly through the use of head-mounted displays (HMDs). Despite the growing interest, a comprehensive understanding of BCI-AR systems is still emerging. This systematic review aims to synthesize existing research on the use of BCIs for controlling AR environments via HMDs, highlighting the technological advancements and challenges in this domain. An extensive search across electronic databases, including IEEEXplore, PubMed, and Scopus, was conducted following the PRISMA guidelines, resulting in 41 studies eligible for analysis. This review identifies key areas for future research, potential limitations, and offers insights into the evolving trends in BCI-AR systems, contributing to the development of more robust and user-friendly applications. [ABSTRACT FROM AUTHOR]

Background: The incidence of Chikungunya in tropical Africa is still of major epidemiological significance. This study aims to determine the prevalence of chikungunya in East Africa through a systematic review and meta‐analysis of published studies. Methods: We conducted a comprehensive search across six electronic databases—Web of Science, PubMed, ScienceDirect, Scopus, and Google Scholar—using specific keywords to address the worldwide impact of chikungunya following the Preferred Reporting Items for Systematic Reviews and Meta‐analysis (PRISMA) guidelines. A meta‐analysis was performed on our eligible studies using the random effect model. Results: Our search returned 40 eligible articles involving 4122 Chikungunya cases in 13 East African nations. These studies, conducted between 2014 and 2024 across 13 East African nations, provided diverse data on chikungunya prevalence. The overall pooled prevalence of chikungunya in East Africa was 20.6% (95% CI: 18.8%–22.5% and I2 = 99.62%). Subgroup analyses revealed variations in prevalence across different countries, study designs, detection methods, and publication years. Notably, Rwanda and Djibouti exhibited high prevalence rates of 63.0% and 50.4%, respectively, while Kenya and Somalia reported a moderate prevalence of 12.2%. The detection methods also influenced prevalence rates, with RT‐PCR studies indicating a higher prevalence (28.3%) compared to ELISA (19.3%). Conclusion: The study highlights the significant burden of chikungunya in East Africa, and the findings underscore the need for targeted public health interventions and improved surveillance to manage and control chikungunya outbreaks in the region. [ABSTRACT FROM AUTHOR]

Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily "all over the world." This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies' quality assessment result of "moderate quality." This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area. [ABSTRACT FROM AUTHOR]

AbstractThis systematic literature review examines the barriers, strategies, and accessibility issues affecting the engagement and retention of learners with disabilities in Massive Open Online Courses (MOOCs). Employing the PRISMA procedures, this study systematically identifies and analyzes relevant literature from five prestigious databases: (a) Scopus, (b) ScienceDirect, (c) Emerald, (d) SpringerLink, and (e) EBSCOhost in order to identify (1) the common barriers experienced by learners with disabilities in MOOCs, (2) the strategies that can be employed to improve the engagement and participation of special needs students in MOOCs and (3) influence of accessibility on engagement and retention rates of learners with disabilities in MOOCs. After conducting the screening and eligibility analysis according to the pre-defined criteria, 19 studies of out 624 sources were finally selected between 2014–2024. The review underscores the necessity for comprehensive policy frameworks and institutional commitment to inclusivity. It emphasizes the adoption of universal design principles are essential in bridging the accessibility gap. These measures are pivotal in fostering an inclusive learning environment in MOOCs, significantly enhancing engagement and retention rates among learners with disabilities. The insights from this review provide a solid foundation for educators, policymakers, and platform developers to create more inclusive and supportive online learning experiences for students with disabilities. [ABSTRACT FROM AUTHOR]

In 2008, the UK government introduced Employment and Support Allowance (ESA) as a welfare policy for individuals who are unable to work due to sickness or disability. Our research project examined the cultural landscape that has been made ‘available’ for those living with chronic health conditions whilst claiming welfare. To increase the transparency of a Foucauldian-inspired discursive analysis, PRISMA reporting guidelines were incorporated into a Media Framing Analysis. News articles and policy documents were collected as data corpora. This paper outlines how this innovative approach was developed; sharing samples of the analytical findings to illustrate what was achieved through a systematic compiling of a data set. A genealogical approach to health-based welfare shows how temporal and dualistic discourses of illness synonymise ‘health’ with a capability for employment. Methodological considerations are explored; illuminating how a systematic approach to data compilation can improve the transparency, rigour and potential transferability of discursive analyses. [ABSTRACT FROM AUTHOR]

Conversational Agents (CAs, chatbots) are systems with the ability to interact with users using natural human dialogue. While much of the research on CAs for sexual health has focused on adult populations, the insights from such research may not apply to CAs for youth. The study aimed to comprehensively evaluate the state-of-the-art research on sexual health CAs for youth. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we synthesized peer-reviewed studies specific to sexual health CAs designed for youth over the past 14 years. We found that most sexual health CAs were designed to adopt the persona of health professionals to provide general sexual and reproductive health information for youth. Text was the primary communication mode in all sexual health CAs, with half supporting multimedia output. Many sexual health CAs employed rule-based techniques to deliver pre-written expert knowledge on sexual health; yet most sexual health CAs did not have the safety features in place. While youth appreciated accessibility to non-judgmental and confidential conversations about sexual health topics, they perceived current sexual health CAs provided limited sexual health information that is not inclusive of sexual and/or gender minorities. Our review brings to light sexual health CAs needing further development and evaluation and we identify multiple important areas for future work. While the new trend of large language models (LLMs) based CAs can make such technologies more feasible, the privacy and safety of the systems should be prioritized. Finally, best practices for risk mitigation and ethical development of sexual health CAs with and for youth are needed. [ABSTRACT FROM AUTHOR]

The Mechanical properties of concrete significantly deteriorate when exposed to fire, with explosive spalling posing a major threat to structural integrity. This highlights the need for effective insulation systems to protect concrete structures in fire conditions. However, current insulation solutions are typically installed externally, requiring additional labour and materials. This review explores the potential of incorporating Phase Change Materials (PCMs) directly into concrete as an embedded insulation system. As a latent heat storage material, PCM offers promising fire-resistant properties, reducing the need for external insulation while improving the fire performance of concrete. This paper presents a bibliometric analysis and a systematic review of recent publications on the use of PCMs in concrete and their fire performance applications. One thousand two hundred and four publications retrieved from Scopus© and Web of Science databases were passed through the PRISMA flowchart to obtain a database of recent literature on the study area. The current trend of research shows a shift towards inorganic PCMs in concrete. There is a significant lack of studies on using inorganic and eutectic PCM in concrete for fire applications and existing studies show inorganic PCM can be a viable solution. Commercial availability and lack of synthesised encapsulated PCM were found to be barriers to research. [ABSTRACT FROM AUTHOR]

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data's spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments. [ABSTRACT FROM AUTHOR]

Government agencies, energy consumers, and other societal groups have all shown concern and attention for the energy management of buildings. Relevant statistical data, however, indicate that most public buildings continue to consume large amounts of energy overall and that the issues of low energy usage and energy waste have not materially improved. As a result, this study reviewed the state of progress and potential directions for future research in the field of building energy management in public buildings using a data-driven approach. Relevant studies were obtained from three databases—Web of Science, Scopus, and China National Knowledge Infrastructure—based on certain search phrases. The text mining program VOS viewer was then used to examine the material. We provide a thorough examination of the study techniques and material, as well as a visual representation of the keywords and current state of the field. According to this study, the range of data processing outcomes; the flexibility of research system standards; and the availability of a comprehensive, unified assessment system are the main factors contributing to the practical issues facing building energy management today. Based on the geographic distribution and state of energy development, this study is the first to examine possible research avenues for building energy management in public buildings through cross-fusion research on passive energy-saving design and subjective behavioral energy-saving. It offers a foundation for developing the building energy management system best practice model in the future. [ABSTRACT FROM AUTHOR]

Farmers' adoption of commodity futures and options contracts (CFCs) in agriculture shows substantial variation across different regions. Sociodemographic and farm characteristics fail to fully account for these observed differences. Hence, research focussing on understanding the behavioural factors that influence the decision to adopt CFCs is gaining popularity. Although past research has addressed these behavioural factors, a comprehensive synthesis is needed. This study provides a systematic literature review to systematically categorise, taxonomically standardise and summarise behavioural factors influencing farmers' decisions to adopt CFCs. Results are reported via the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) scheme. In the systematic literature review, 41 usable studies were identified. The identified behavioural factors were classified into eleven core behavioural factors and grouped into three categories: dispositional, social and cognitive. In addition, a systematic literature map was created, which shows behavioural factors' inclusion frequency and share of being statistically significant in the identified studies. These approaches offer a framework for researchers and practical insights for trainers, policymakers and farmers. The results could be used, for instance, to develop strategies to promote more informed decision among farmers regarding the use of CFCs. [ABSTRACT FROM AUTHOR]

The landscape of phishing email threats is continually evolving nowadays, making it challenging to combat effectively with traditional methods even with carrier-grade spam filters. Traditional detection mechanisms such as blacklisting, whitelisting, signature-based, and rule-based techniques could not effectively prevent phishing, spear-phishing, and zero-day attacks, as cybercriminals are using sophisticated techniques and trusted email service providers. Consequently, many researchers have recently concentrated on leveraging machine learning (ML) and deep learning (DL) approaches to enhance phishing email detection capabilities with better accuracy. To gain insights into the development of deep learning algorithms in the current research on phishing prevention, this study conducts a systematic literature review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. By synthesizing the 33 selected papers using the SLR approach, this study presents a taxonomy of DL-based phishing detection methods, analyzing their effectiveness, limitations, and future research directions to address current challenges. The study reveals that the adaptability of detection models to new behaviors of phishing emails is the major improvement area. This study aims to add details about deep learning used for security to the body of knowledge, and it discusses future research in phishing detection systems. [ABSTRACT FROM AUTHOR]

This systematic literature review aims to evaluate and synthesize the effectiveness of various embedding techniques—word embeddings, contextual word embeddings, and context-aware embeddings—in addressing Meaning Conflation Deficiency (MCD). Using the PRISMA framework, this study assesses the current state of research and provides insights into the impact of these techniques on resolving meaning conflation issues. After a thorough literature search, 403 articles on the subject were found. A thorough screening and selection process resulted in the inclusion of 25 studies in the meta-analysis. The evaluation adhered to the PRISMA principles, guaranteeing a methodical and lucid process. To estimate effect sizes and evaluate heterogeneity and publication bias among the chosen papers, meta-analytic approaches were utilized such as the tau-squared (τ2) which represents a statistical parameter used in random-effects, H-squared (H2) is a statistic used to measure heterogeneity, and I-squared (I2) quantify the degree of heterogeneity. The meta-analysis demonstrated a high degree of variation in effect sizes among the studies, with a τ2 value of 8.8724. The significant degree of heterogeneity was further emphasized by the H2 score of 8.10 and the I2 value of 87.65%. A trim and fill analysis with a beta value of 5.95, a standard error of 4.767, a Z-value (or Z-score) of 1.25 which is a statistical term used to express the number of standard deviations a data point deviates from the established mean, and a p-value (probability value) of 0.2 was performed to account for publication bias which is one statistical tool that can be used to assess the importance of hypothesis test results. The results point to a sizable impact size, but the estimates are highly unclear, as evidenced by the huge standard error and non-significant p-value. The review concludes that although contextually aware embeddings have promise in treating Meaning Conflation Deficiency, there is a great deal of variability and uncertainty in the available data. The varied findings among studies are highlighted by the large τ2, I2, and H2 values, and the trim and fill analysis show that changes in publication bias do not alter the impact size's non-significance. To generate more trustworthy insights, future research should concentrate on enhancing methodological consistency, investigating other embedding strategies, and extending analysis across various languages and contexts. Even though the results demonstrate a significant impact size in addressing MCD through sophisticated word embedding techniques, like context-aware embeddings, there is still a great deal of variability and uncertainty because of various factors, including the different languages studied, the sizes of the corpuses, and the embedding techniques used. These differences show how future research methods must be standardized to guarantee that study results can be compared to one another. The results emphasize how crucial it is to extend the linguistic scope to more morphologically rich and low-resource languages, where MCD is especially difficult. The creation of language-specific models for low-resource languages is one way to increase performance and consistency across Natural Language Processing (NLP) applications in a practical sense. By taking these actions, we can advance our understanding of MCD more thoroughly, which will ultimately improve the performance of NLP systems in a variety of language circumstances. [ABSTRACT FROM AUTHOR]

Fabry disease (FD) results from a lack of activity of the lysosomal enzyme α-Galactosidase A (α-Gal A), leading to the accumulation of glycosphingolipids in several different cell types. Protein supplementation by pDNA or mRNA delivery presents a promising strategy to tackle the underlying genetic defect in FD. Protein-coding nucleic acids in FD can be either delivered to the most affected sites by the disease, including heart, kidney and brain, or to specialized organs that can act as a production factory of the enzyme, such as the liver. Lipid-based systems are currently at the top of the ranking of non-viral nucleic acid delivery systems, and their versatility allows the linking to the surface of a wide range of molecules to control their biodistribution after intravenous administration. This systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines and provides an overview and discussion of the targeting ligands that have been employed so far to actively vectorize intravenously administered non-viral vectors based on lipid carriers to clinically relevant organs in the treatment of FD, for protein-coding nucleic acid (pDNA and mRNA) supplementation. Among the thirty-two studies included, the majority focus on targeting the liver and brain. The targeting of the heart has been reported to a lesser degree, whereas no articles addressing kidney-targeting have been recorded. Although a great effort has been made to develop organ-specific nucleic acid delivery systems, the design of active-targeted carriers with high quality, good clinical translation, and large-scale manufacturing capacity is still challenging. [ABSTRACT FROM AUTHOR]

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy. [ABSTRACT FROM AUTHOR]

Achieving Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lifestyles and promote well-being for all, is a critical focus within the context of construction health and safety, particularly for construction workers. A significant factor influencing the health and safety of construction workers is their consumption of alcohol and tobacco (A&T). This article presents a systematic review of the research on A&T use among construction workers, an analysis of key findings, research methodologies, and recommendations. A knowledge-mapping approach is applied to 108 articles sourced from the Web of Science (WOS), generating clusters of relevant keywords to illustrate the interrelationships between A&T usage and construction workers. The knowledge map reveals 40 distinct keyword distributions, organised into five clusters, highlighting the key connections between alcohol, tobacco, and construction workers. From an initial pool of 336 articles from WOS and Scopus, 26 were selected based on the PRISMA protocol for further analysis. A review of these 26 studies demonstrates a predominant use of mixed-methods and quantitative research approaches. The findings show the heightened vulnerability of construction workers to hazardous A&T consumption, its adverse health effects, and the urgent need for comprehensive interventions aimed at reducing or eliminating this risk.

Abstract As the pharmaceutical industry continues to evolve, the integration of Industry 4.0 technologies has emerged as a pivotal force in reshaping conventional practices, and as such it has a transformative impact on sustainable development in the pharmaceutical sector. This interdisciplinary literature review addresses the effects of Industry 4.0 on pharmaceutical industry’s workforce, safety concerns, and sustainable development efforts, together with challenges and benefits associated with the integration of advanced technologies in different countries. The employed research methodologies are qualitative, which incorporate PRISMA or Systematic Reviews and Meta-Analyses. This explanatory research consists of published data (articles, conference papers, books) explaining the adoption of the 4th Industry revolution by the pharmaceutical sector employing a qualitative methodology which incorporates PRISMA or Systematic Reviews and Meta-Analyses. By filtering out the published activity, an inflection point in the development of Industry 4.0 has been determined: the COVID-19 outbreak and the disruptions it brought to the supply chain of the pharmaceutical industry. A univariate bibliographic analysis focused in the meta-COVID-19 era, were the initial effects of Industry 5.0. Are taken into consideration, has been performed, revealing the relationship between the global health crisis and published activity by country. One of the most important outcomes is that local Governments’ policies and initiatives aiming to promote improved productivity, efficiency, and innovation are associated with published activity in the field.

As population growth continues, so does consumption and the need for basic human necessities, necessitating ongoing development. This development process often relies on non-renewable resources, generates waste, and emits harmful gases, usually neglecting sustainability. The Circular Economy (CE) concept can be applied to support sustainability in the construction industry. This regenerative system replaces the linear economy, focusing on optimizing material utilization and value throughout the product lifecycle and reducing waste generation. Through a Systematic Literature Review based on the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, this research provides an overview of CE practice trends that the construction industry in Indonesia can adopt based on lessons learned from other countries. The results indicate that CE implementation in Indonesia is highly relevant and important for supporting sustainability from economic, environmental, and social perspectives. To achieve these benefits, Indonesia must overcome various barriers, including less supportive policies, low consumer demand, limitations in the supply chain, and technological and infrastructure constraints. The CE principles commonly applied by the Indonesian construction industry in waste are reduction, reuse, and recovery. To ensure the successful implementation of CE in the construction sector, the Indonesian construction industry can learn from the best CE practices implemented in other countries and adopt effective and efficient strategies to achieve sustainability. However, successful implementation requires commitment and collaboration from various stakeholders, including the government, industry, and academia.

Abstract Since the outbreak of the COVID-19 pandemic in 2020, tens of thousands of scientific articles have been published. Researchers are enthusiastic to uncover the effects of COVID-19. This study aims to critically assess the methods researchers used to analyze the impact of COVID-19 on food security. The PRISMA and Bibliometric Methods were utilized in this study. The results of the PRISMA indicate that most researchers utilize quantitative methods, 61%, compared to 32% for qualitative and 7% for mixed methods. These findings provide important data. Firstly, researchers continue relying on quantitative methods (multiple linear regression and Chi-square). Secondly, 32% of the articles utilized qualitative and only two qualitative methods, phenomenological and case studies, have been identified. No researchers use grounded theory research (GTR) and ethnographic studies (SE). Thirdly, using mixed method among COVID-19 researchers is uncommon; only 3% of researchers employed it. Next, the results of the bibliometric reveal that the USA and China contribute the most to the publication on the impact of COVID-19 on food security. A review of research publications on the impact of COVID-19 on food security shows an alarming increase in food loss and waste due to excessive household food purchases. This trend has the potential to endanger food availability in the future. Recommendations to the government include offering incentives to reduce food loss and waste, improving supply chain coordination, training, and technical breakthroughs and innovations, especially for small-scale farmers. The government should support food banks and farmers' markets to shorten the value chain and connect farmers with consumers.

The level of surveillance and preparedness against epidemics varies across countries, resulting in different responses to outbreaks. When conducting an in-depth analysis of microinfection dynamics, one must account for the substantial heterogeneity across countries. However, many commonly used statistical model specifications lack the flexibility needed for sound and accurate analysis and prediction in such contexts. Nonlinear mixed effects models (NLMMs) constitute a specific statistical tool that can overcome these significant challenges. While compartmental models are well-established in infectious disease modeling and have seen significant advancements, Nonlinear Mixed Models (NLMMs) offer a flexible approach for handling heterogeneous and unbalanced repeated measures data, often with less computational effort than some individual-level compartmental modeling techniques. This study provides an overview of their current use and offers a solid foundation for developing guidelines that may help improve their implementation in real-world situations. Relevant scientific databases in the Research4life Access initiative programs were used to search for papers dealing with key aspects of NLMMs in infectious disease modeling (IDM). From an initial list of 3641 papers, 124 were finally included and used for this systematic and critical review spanning the last two decades, following the PRISMA guidelines. NLMMs have evolved rapidly in the last decade, especially in IDM, with most publications dating from 2017 to 2021 (83.33%). The routine use of normality assumption appeared inappropriate for IDM, leading to a wealth of literature on NLMMs with non-normal errors and random effects under various estimation methods. We noticed that NLMMs have attracted much attention for the latest known epidemics worldwide (COVID-19, Ebola, Dengue and Lassa) with the robustness and reliability of relaxed propositions of the normality assumption. A case study of the application of COVID-19 data helped to highlight NLMMs’ performance in modeling infectious diseases. Out of this study, estimation methods, assumptions, and random terms specification in NLMMs are key aspects requiring particular attention for their application in IDM.

The identification of medicinal plant species is a crucial task for assessing the status of our bioresources. Conventional methods primarily rely on taxonomy and laboratory-based instruments, which are time-consuming and require the requisite expertise. Thus, there is an escalating demand for efficient techniques that can quickly identify these precious species. The advent of Hyperspectral Remote Sensing (HRS) with artificial intelligence has significantly increased the scope of HRS techniques by offering rapid and precise plant identification. This study utilised non-imaging HRS handheld sensors to build a spectral repository for 10 important medicinal plant species from diverse locations across Indian Himalayan states, representing varying altitudinal and ecological conditions. The spectral repository encompasses 1237 distinct spectral signatures obtained from the leaves and canopies of the targeted plant species. Subsequently, an identification model has been developed using Random Forest (RF) with several feature selection methods, and it has been revealed that the RF model, coupled with wrapper-based feature selection, is an effective combination for classifying the targeted plant species. The calibration and test datasets accounted for accuracies of 87.87% and 91.39%, respectively, with corresponding kappa coefficients of 0.85 and 0.89. Furthermore, the developed RF model was applied to 'PRISMA' satellite data to identify Saussurea costus crops in farmers' croplands, achieving a classification accuracy of 81.31% and a kappa coefficient of 0.76. Therefore, the study highlights the potential of integrating RF, in-situ HRS, and satellite HRS for the non-destructive, precise, and accurate identification of medicinal plants that can significantly contribute to biodiversity conservation and sustainable resource management. [Display omitted] • Spectral repository with 1237 signatures of 10 Himalayan medicinal plant species. • Medicinal plant identification/classification model developed using Random Forest. • Compared filter, wrapper, and embedded approaches for optimal wavelength selection. • Among above approaches, wrapper-based method showed greater potential. • PRISMA data exhibited potential to identify plants in the field from space. [ABSTRACT FROM AUTHOR]