Big Research Research Articles

Possible (Theo)Linguistic Research with St. Nicholai Velimirovich’s Slovesnik as a Material

This paper describes the manuscript of St. Nicholai Velimirovich named Slovesnik which was published as a book in 1997. It was written in the fourth or at the beginning of the fifth decade of the 20th century and is addressed to nuns of certain Serbian monasteries — spiritual daughters of Bishop Nicholai. There is poetry and prose, there are documents, memorials, historical and hagiography texts, miniatures, drawings, etc. in the manuscript. All of that was written and drawn with warm paternal love. As for poetry, there are hagiography texts: Canon to the Most Holy Theotokos Slovesnica, Troparion and Kontakion to St. Georgios, the poem Zidanje Vraćevšnice [Building of Vraćevšnica] — written as an epic folk song, and lyrical songs. Almost in every text, Church Slavonic and contemporary Serbian interfere. In several places in the manuscript, the text in Church Slavonic is translated into Serbian next to the original, so we can find two parallel texts. Linguistically, this interference between two languages is interesting, not only in the lexical and translational sense, but also graphically: St. Bishop Nicholai used not only old letters in the text in Serbian, but also diacritical signs, especially ~ on the letter(s). The basic directions of the (theo)linguistics research provided by the texts from this manuscript is the research in the fields of genre, translation, phonetic-phonology, lexical-semantic, derivation, stylistics, discourse, or text, and paremiology. The importance of the described edition for research in the field of pragmatics has to be especially emphasized. Pragmatics has a big research potential, as was said by Jean-Paul van Noppen — the founder of the term theolinguistics. He wrote that it is important “to delineate not only the semantics, i.e. what the words mean — what they describe, report, reflect and conjure up, but also the pragmatics, i.e. what the words do for people (which includes shaping their cognition)” (van Noppen 2011, 33). If we keep in mind who Saint Bishop Nicholai is addressing to in this manuscript (nuns, his spiritual daughters) — we have to state that this is an addressee who is very rarely in such a role. It is not easy to study communication in monasteries because the spiritual life of the monastic community is usually closed for observation, especially to linguists. Therefore, this edition has additional value as research material. Deeper and more detailed linguistic research of the texts briefly described in this paper will show and open some other directions and approaches of (theo)linguistic research. By all these approaches, mentioned and not mentioned in this paper, we hope, that both theolinguistics and linguistics, in general, can be enriched, as well as, in the first place, studies of contemporary Serbian language (Serbistics).

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

BackgroundMultiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare?MethodsThrough a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by.ResultsThe positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization.ConclusionsIn conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

Big Data Technology Oriented to Wetland Resource Ecosystem Value Evaluation.

In order to study a big data technology research for the evaluation of wetland resource ecosystem value. This paper proposes a wetland dimension oriented to the evaluation of wetland ecosystem services space attribute through big data coupling analysis framework. The framework used China's coastal wetlands as a case for empirical research and summarized the future direction of the research on the value evaluation of wetland ecosystem services in the era of big data. The result shows: Wetland Ecosystem Observation Network can obtain long-term series of dynamic data, remote sensing Earth observation can realize the integrated observation of space, space, and Earth, the combination of the two will help to build a wetland ecological big data observation system. The service value of China's coastal wetland ecosystem is 5010.32 × 108 yuan. The research results can effectively solve the problem of geographical heterogeneity and have reference value for the protection and management of the wetland ecosystem.

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

Alzheimer Caregiving Problems According to ADLs: Evidence from Facebook Support Groups.

Background and study goal: Social media are a societal phenomenon today, including the oldest generation, yet they are seldom used in current health research to identify the needs of persons with Alzheimer’s disease (PADs) and their carers. There is an even bigger research gap in the analysis of caregivers’ communication in online support groups and its classification according to activities of daily living (ADLs). For this, the goal of this study is to identify real-life practices of informal caregivers who care for PADs based on the analysis of their communication in Facebook groups. Methods: A sample of 1603 contributions was obtained from support groups by keyword search, manual coding, and verification; thus, the contributions in the sample are relevant for the individual basic ADLs of PADs. Next, five main conversation topics were identified for each ADL. This was done using the topic extractor based on simple parallel threaded implementation of LDA with a sparse sampling scheme and data structure. Results: The qualitative dimension of research identified discussion topics as well as specific caregiver behavior patterns for each ADL. The quantitative dimension determining the level of engagement of group members in individual ADLs was also measured. The highest engagement was found in activities of feeding and drinking, followed by bathing. In contrast, the activities of dressing, continence, and toileting attract the lowest interest. Moreover, the causal links between the topics discussed within the areas of ADLs were identified. Conclusions: The acquired knowledge can help further research focus on the most problematic areas relevant for people with AD in order to increase their quality of life and at the same time reduce the caregiver burden. The study expands the information of the demands posed by the individual caregiver activities, specifically in the context of activity-based costing or time-based activity costing.

Least Significant Bit-Based Image Watermarking Mechanism

Now days, digital data security over network is a big issue. Digital data security and authentication is a big and challenge able research area in the field of computer science. A lot of security mechanisms are available for data authentication and privacy. For the purpose of data security and secure communication encryption mechanism are available, and also for data authentication several watermarking techniques are available. In this paper authors mainly focus research based on image watermarking approach by using least significant bit substitutions. Here authors represent comparative studies of different LSB based image watermarking approach.This paper mainly focuses on spatial domain based data hiding mechanism. The detail comparison is also given in the tabular form.

Research on Blockchain Technology and Media Industry Applications in the Context of Big Data

With the everyday use of big data technology, using big data resources effectively has become a key area of research. Media consumption creates enormous amounts of data in people’s digital lives. This data is not only very valuable to the company, but it also presents a number of concerns, including privacy breaches and data exploitation. Along with the continuous improvement of blockchain technology, the rational use of big data resources has become possible. Blockchain technology has expanded from the initial Bitcoin to the financial field and has become more widely used in the legal and media industries. This study catches the actual cases of blockchain technology in the media industry through Qingbo Big Data, analyzes the current global blockchain and media industry’s overall development status and the existing problems, and puts forward corresponding countermeasures to accelerate the integration and development of blockchain and media industry in the context of big data. The results show that the technical features of blockchain can solve the painful problems of copyright risk, algorithmic black box, and weak trust mechanism in the media industry, and the combination of the two can form a new burst of media productivity. The article discusses the broad application prospects of blockchain in the media industry, which is essential for big data and blockchain research. This paper composes the pioneering literature on the blockchain, catches the information on blockchain by keywords, analyzes the application prospect of blockchain in the media industry, and promotes the research and application of blockchain technology.

Monitoring of a Broad Set of Pharmaceuticals in Wastewaters by High-Resolution Mass Spectrometry and Evaluation of Heterogenous Catalytic Ozonation for Their Removal in a Pre-Industrial Level Unit

The removal of contaminants of emerging concern (CECs) occurring in wastewater effluents, such as pharmaceutically active substances (PhACs) and personal care products, pose a big research challenge since they can be a major source of pollution for water bodies and a danger to public health. The objective of this work was to perform a comprehensive monitoring of a broad set of PhACs (>130) in a wastewater treatment plant (WWTP) close to Thessaloniki (Greece), as well as to evaluate the potential of heterogeneous catalytic ozonation for the removal of CECs from wastewater through a continuous flow system. The high-resolution mass spectrometry analysis revealed the highest average concentrations for irbesartan (1817 ng/L). Antihypertensives along with antibiotics, psychiatrics, and β-blockers were found to aggravate the effluents. Removal efficiency after conventional treatment was >30%. The results from catalytic ozonation unit operation indicate that the introduction of a proper solid material that acts as catalyst can enhance the removal of CECs. A preliminary risk assessment using the risk quotient (RQ) revealed that irbesartan and telmisartan entail high acute risk. The overall results underline the urgent need to incessantly monitor PhACs and expand the toxicological studies to establish the sublethal and chronic effects on aquatic organisms.

Big Data Analysis Research on the Deep Integration of Intangible Cultural Heritage Inheritance and Art Design Education in Colleges and Universities

Intangible cultural heritage involves a large number of national traditions, or folk unique knowledge, language, and customs, derived from folk dance and music as well as traditional medicine and other forms of art. China’s intangible cultural heritage plays a key role in the whole national traditional culture. Strengthening the protection of intangible cultural heritage and promoting its continuous development is an important way and inevitable choice to strengthen cultural self-confidence and build a cultural power. As the builders and successors of the socialist cause, college students must strengthen their cultural self-confidence and shoulder important responsibilities in the construction of a cultural power. Big data is a new technology in the Internet era. Using big data technology to realize the collection, preservation, retrieval, reproduction, integration, dissemination, and utilization of intangible cultural heritage information can better meet the requirements of the times, so as to provide new opportunities, conditions, and means for the inheritance and development of intangible cultural heritage. This article will discuss the use of big data to analyze the research talent training of the deep integration of intangible cultural heritage inheritance and art design education in colleges and universities. This article introduces the background and significance of intangible cultural heritage inheritance and educational integration, introduces its related methods, and then designs a new mode of intangible cultural heritage learning in colleges and universities based on big data. Finally, this article applies the model to college education and then investigates the students’ experience of the new model curriculum, and comes to the conclusion that the proportion of people who choose to like and very much like the new model is 69.3%; 83.3% of the people who chose to agree and strongly agree with the new model were helpful to themselves. In short, the application and design of the new curriculum model is still relatively successful.

Sociology between big data and research frontiers, a challenge for educational policies and skills.

The paper focuses on the challenges posed within sociology and social research by the transformations created by the “data society”. To this end, the paper outlines some of the most significant elements for new frontier research which sociology is forced to confront also in relation to the challenges for educational policies and skills. While leading literature decries the need to promote alphabetising data, otherwise defined as data literacy, the idea proposed here is that it is necessary to work towards understanding data. An understanding that highlights the role sociology has firmly set itself since its foundation, that of studying and explaining the complexity of the relationships that characterize social life in every context and period.

Good Tech, Bad Tech: Policing Sex Trafficking with Big Data

Technology is often highlighted in popular discourse as a causal factor in significantly increasing sex trafficking. However, there is a paucity of robust empirical evidence on sex trafficking and the extent to which technology facilitates it. This has not prevented the proliferation of beliefs that technology is essential for disrupting or even ending sex trafficking. Big data analytics and anti-trafficking software are used in this context to produce knowledge and intelligence on sex trafficking. This paper explores the challenges and limitations of understanding exploitation through algorithms and online data. It also highlights the key dimensions of exploitation ignored in big data-oriented research on sex trafficking. By doing so, the paper seeks to advance our theoretical understanding of the trafficking–‍technology nexus, and it is argued that sex trafficking must be reframed along a continuum of exploitation that is sensitive to the social context of exploitation within the sex market.

Big Data in Forecasting Research: A Literature Review

With the boom in Internet techniques and computer science, a variety of big data have been introduced into forecasting research, bringing new knowledge and improving prediction models. This paper is the first attempt to conduct a literature review on full-scale big data in forecasting research. By source, big data in forecasting research fell into user-generated content data (from the users on social media in texts, photos, etc.), device-monitored data (by meteorological monitors, smart meters, GPS, etc.) and activity log data (for web searching/visiting, online/offline marketing, clinical treatments, laboratory experiments, etc.). Different data types, bearing distinctive information and characteristics, dominated different forecasting tasks, required different analysis technologies and improved different forecasting models. This survey provides an overall review of big data-based forecasting research, details what (regarding data types and sources), where (forecasting hotspots) and how (analysis and forecasting methods used) big data improved prediction, and offers insights into future prospects. • A review on full-scale big data in forecasting research is presented. • Big data in forecasting research fell into UGC data, device data and log data. • Three steps were taken: data collection, data process and prediction improvement. • For each data type, what, where and how big data improved prediction are detailed.

Data driven intelligent model for quality management in healthcare

It is the need of today’s world, to deliver with quality health care services to meet the health needs of target populations. The healthcare system includes procedures of prevention and screening of all types of diseases, their treatment and diagnostics, recent research and development. These procedures must be maintained at a desired level of excellence, which comes under quality management. Quality management in healthcare incorporates with making of various quality policies, quality planning and assurance, quality control and quality improvement. Quality improvement (QI) is the scheme used for betterment of the services delivered to the patients, such as diagnosis and treatment. If these schemes are recent and advanced technology based, services provided would be cost effective, accurate, less time consuming and hassle-free for both healthcare provider as well as patients. In this study we are applying artificial intelligent and machine learning techniques to enhance the diagnosis accuracy of the liver fibrosis which is caused by hepatitis C virus (HCV). Generally, the SLBs (serial liver biopsies) are utilized to diagnose the liver fibrosis levels (LFLs), which is the gold standard method in this domain. However, SLB has various impediment and not appropriate to the patients which leads to higher prognosis cost with invasive way. So, there is a big research gap in the medical field to find out the alternative non-invasive approach/method for SLB. The proposed data-driven intelligent model for identification of liver fibrosis using hybrid approach is designed and implemented to overcome the SLBs problems with higher diagnostic accuracy. The empirical mode decomposition (EMD) approach is used to extract the IMFs (intrinsic mode functions), which are used as input features to the ANN-J48 algorithm based intelligent classifiers. The proposed approach shows the evidence for utilization in a non-invasive way to diagnose the LFLs without high level clinical expert skills.

Review on the use of Molecular Docking as the First Line Tool in Drug Discovery and Development

Molecular docking has now become a novel approach for drug discovery in recent years. Computer-aided drug design is an area that is rapidly growing and has seen many successes. Many big pharmaceutical industries, academic resources and research personnel are using the tools for new drug development. In this review, we have discussed briefly different molecular docking methods, software used in the molecular docking process and their application in drug discovery. Molecular docking is a configuration-based virtual tryout in which computer-generated three dimensional structures of small molecules are given the freedom to interact with the target structure in assorted positions, orientation, and conformations.

A machine learning-based QSAR model for predicting phenols cytotoxicity

Cytotoxicity is a very important aspect that gains big research interest in toxicology and pharmacology as it is related to whether a compound may cause cell damage, necrosis or apoptosis and this contributes effectively to determine the toxicity potential of a compound and to cancer treatment studies as well. The complexity and the sensitivity of cytotoxicity assays and the involvement of animal tests in many instances increase the need for rapid and reliable alternative methods. Quantitative structure-activity relationships (QSAR) are relevant techniques that provide mathematical models and help in chemicals screening and in predicting biological activities and eventually cytotoxicity. Because of their capacity to handle complex problems, machine learning contributed substantially to the QSAR field's evolvement. In this study, we established a predictive QSAR model based on machine learning, namely deep neural network to predict the cytotoxicity of phenols. The model exhibited high performances in predicting new compounds. It was proved that hydrophobic, steric and electronic effects are relevant in determining the cytotoxicity variability of phenols against Tetrahymena pyriformis.

EVALUATION OF URINARY BIOMARKERS FOR THE EARLY DETECTION OF BLADDER CANCER

Background: Bladder cancer is one of the most common cancers characterized by high rates of tumor recurrence throughout the patient’s life. Urine biomarkers detect the disease in its early stages to enhance patient prognosis, lower mortality and lessen expensive procedures such as cystoscopy. Objectives: The purpose of this paper is to assess the performance of the biomarkers within urine for the early diagnosis of bladder cancer and compare the results with conventional approaches. Study design: A Cross Sectional study Place and duration of study. Department of General Surgery Ziauddin University Hospital from jan 2021 to dec 2021 Methods: A total of 150 patients, 90 cases with bladder cancer and 60 controls were assessed. NMP22, UroVysion, and BLCA-4 were utilized in the urinary samples with biomarkers. Diagnostic accuracy, sensitivity, and specificity of each of the markers were calculated using the test results. Quantitative descriptive data was used in statistical comparison where standard deviation (SD) and p-value were used to test the significance of the findings. Results: The urinary biomarkers demonstrated fairly inconsistent mean sensitivities and specificities for early detection. Detectivity of NMP22 was 82% (SD ± 4.5), of UroVysion 78% (SD ± 3.2), of BLCA-4 was 85% (SD ± 5.1). The sensitivity of the tests were at 75% (SD ± 3.8) for NMP22, 82% (SD ± 4.0) for UroVysion and 80% (SD ± 4.3) for BLCA-4. The p-values for all biomarkers regarding difference in performance compared with conventional approaches were <0.05, which revealed statistical difference. Conclusions: Several urinary markers, such as BLCA-4, have been also identified to provide the potential for early diagnosis of bladder cancer in humans. It is therefore clear that these non invasive procedures are effective, highly sensitive and specific investigations in place of cystoscopy. Additional big research trials using these deadlines still have to be conducted in order to determine their efficacy in clinical practice.

Development of Literature Database of Korean Ritual Studies and Intelligent Platform

The Korean Ritual Studies Series is a compilation of the works about Korean ritual studies, which makes a great contribution to scholarship in this area. Relying on a number of major national programs in China and South Korea, digital collation and research the Korean Ritual Studies Series was made to create into a “literature database of Korean ritual studies” by cooperation between the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU) which is a key research institute of humanities and social science approved by the Ministry of Education of the People’s Republic of China, and the research institutions of Kyungsung University including the Center for the Study of Chinese Characters in Korea, the Research Society of Korean Ritual Studies and the Academy of Korean Studies. The basic framework of the database contains six sub-databases, which are fully correlated with each other according to relevant attributes. The database provides search capability, with three search functions, including catalog index, full-text retrieval and a word index. It is a major achievement of an international cooperation program supported by a multilevel international platform. It provides a large sustainable development space for the “literature database of Korean ritual studies”, which is an indispensable and important part of the “public database of Chinese characters” created by the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU). The three key breakthroughs are needed for its further development: (I) Achieving the data association, comparative research and web publishing of Chinese and Korean ritual literature, establishing an Asian big data ritual research and application platform, supporting the research of Chinese and Korean ritual cultures, the communication history of ritual culture, the development history of Chinese characters and the research of many other disciplines, and meeting the multi-functional needs of ritual research, teaching and communication while completing a comprehensive digital connection of the internal resources of the database. (II) Deeply tapping into the massive Chinese character writing and memory data to create a 21st century database for written texts in the Sinosphere on the basis of digital processing and research of Korean ritual literature. (III) Realizing the multidimensional intelligent automatic recognition and interpretation of original literature written and memorized in Chinese characters such as ancient Chinese and Korean ritual literature, family mottoes and letters of noble families and great clans as well as folk handcopied books, and opening a new era of intelligent interpretation and research of the written texts in the Sinosphere through image recognition technology.

The Ethical Implications of Big Data Research in Public Health: "Big Data Ethics by Design" in the UK-REACH Study.

In this article, we analyze legal and ethical issues raised in Big Data health research projects in the Covid-19 era and consider how these issues might be addressed in ways that advance positive values (e.g., furtherance of respect for persons and accordance with relevant legal frameworks) while mitigating or eliminating any negative aspects (e.g., exacerbation of social inequality and injustice). We apply this analysis specifically to UK-REACH (The United Kingdom Research Study into Ethnicity and Covid-19 Outcomes in Healthcare Workers), a project with which we are involved. We argue that Big Data projects like UK-REACH can be conducted in an ethically robust manner and that funders and sponsors ought to encourage similar projects to drive better evidence-based public policy in public health. As part of this, we advocate that a Big Data ethics-by-design approach be undertaken when such projects are constructed. This principle extends the work of those who advocate ethics by design by addressing prominent issues in Big Data health research projects; it holds that ethical values and principles in Big Data health research projects are best adhered to when they are already integrated into the project aims and methods at the design stage. In advocating this principle, we present a unique perspective regarding pressing ethical problems around large-scale, data-driven Covid-19 research, as well as legal issues associated with processing ostensibly anonymized health data.

What to watch for in 2022

MOLECULAR EDITING What to watch for in 2022Experts predict next year’s big chemistry research trends ShareShare onFacebookTwitterWechatLinked InRedditEmail C&EN, 2021, 99 (45), pp 46–47December 20, 2021Cite this:C&EN 99, 45, 46-47(Credit: C&EN/Shutterstock)Figure1of7Javier García Martínez (Credit: Rive Technology)Figure1of7Vernon Morris (Credit: Jamie Cepernich, Barry Evans Studio, Pinole, CA)Figure2of7Laura-Isobel McCall (Credit: University of Oklahoma)Figure3of7Eugene Y.-X. Chen (Credit: Colorado State University)Figure4of7Corinna Schindler (Credit: Courtesy of Corinna Schindler)Figure5of7Diego Solis-Ibarra (Credit: Courtesy of Diego Solis-Ibarra)Figure6of7Javier García Martínez, president-elect, International Union of Pure and Applied Chemistry“Among all the exciting new trends in catalysis, I would like to mention the photocatalytic conversion of CO2 into useful chemicals. In the last few years, we have seen not only significant improvements in overall CO2 conversion yields, but also in selectivity towards increasingly complex and useful molecules with multiple carbon atoms and a variety of functional groups. Based on the impressive advances in this field over the past few years, I am convinced that during 2022, we will see some significant breakthroughs in terms of what we can do with CO2 using only sunlight to drive its conversion into useful chemicals.”Note: García Martínez is a member of C&EN’s advisory board.Vernon Morris, atmospheric chemist, Arizona State University“The highly publicized social unrest in 2021 inspired calls to the STEM [science, technology, engineering, and mathematics] community to redress the institutionalized systems, mechanisms, and attitudes of exclusion that assure racially biased outcomes in science and technology. Equity-minded scientists will continue to challenge our community to move beyond the status-quo perceptions of who gets to become a scientist and who gets recognition as a producer of knowledge. But if past is precedent, look for heightened backlash against these efforts. Meanwhile, inclusive, interdisciplinary scientific teams will catalyze the development of robust, equity-centered solutions to complex challenges associated with air pollution (particularly in megacities) and environmental justice.”Laura-Isobel McCall, analytical chemist, University of Oklahoma“Now that several new methods have expanded our ability to annotate metabolite features, I am hopeful that scientists beyond the groups that developed these methods will begin to implement them. I will be watching for new biological insights generated from analytical chemistry and computational structure prediction tools. A key step will be increasing confidence in the output of in silico tools, especially with regards to rankings of predicted structures. I also think that we’re all hungry to reclaim the sense of community that was lost during the pandemic. This may lead to a growth in collaborative research projects, with new perspectives on the ‘tough questions.’ ”Eugene Y.-X. Chen, polymer chemist, Colorado State University“The plastics problem is not just about the plastic pollution crisis that everyone witnesses but also about energy and climate change, as plastics manufacturing is predicted to account for 20% of global petroleum consumption and 15% of the carbon budget by 2050. Furthermore, while this is a global problem, R&D activities are not yet globally connected. In 2022, with researchers hoping to travel more freely, we will see a burst of cross-disciplinary, collaborative activities not only in publications but also in international summits and conferences towards our common goal of creating innovative and sustainable solutions to meet this urgent challenge of our time.”Corinna Schindler, synthetic organic chemist, University of Michigan“In organic chemistry, I expect that we will see the trend of skeletal or molecular editing continue to grow. Mark Levin’s young research group at the University of Chicago created a lot of interest earlier this year with their work on skeletal editing through nitrogen deletion. They developed a creative and unexpected approach that got many chemists in the field fascinated. The question is very captivating—can you take a complex molecule and turn it into a very different one (that would take many steps to make) by one simple synthetic transformation? I anticipate that chemists will build on these results to quickly get their hands on new, complex molecules that will have important biological functions.”Diego Solis-Ibarra, materials chemist, National Autonomous University of Mexico “After a little over a decade of excitement and development, 2022 is likely to be the year in which perovskite photovoltaics will finally see the light at a commercial stage. This milestone will surely bring a lot of knowledge, questions, and challenges to the field. Simultaneously, perovskites and perovskite-inspired materials will further venture into new territories beyond solar cells and light-emitting diodes. Frontier applications, such as spintronics, detectors, transistors, and catalysts, are some of the exciting avenues that the field will undoubtedly be exploring.”Learn more in C&EN’s 2022 predictions webinar at cenm.ag/2022predictions.