Selective Overview Research Articles

Future agricultures: The promise and pitfalls of a (re)turn to nature

This paper introduces the Outlook on Agriculture Special Issue on biomimicry and nature-based solutions. It provides a selective overview that will help frame and situate the collection, with a particular focus on agriculture and food production. The relationship between agriculture and nature is a central concern, and particularly how this relationship is framed by those promoting the idea that to overcome the multiple challenges it faces, agriculture must (re)turn to nature. The significance of different understandings of ‘nature-based solutions’, and the relative importance of biomimicry, are explored.

Endovascular Instead of Open Surgical Repair of Axillosubclavian Artery Injuries: An Evolving Paradigm Shift

Axillosubclavian injuries (ASI) comprise a small proportion of vascular injuries, yet their morbidity and mortality is high. This is often attributable to non-compressible bleeding in the apical thorax, hemodynamic instability, and the anatomically challenging location of these vessels making them difficult to access and control quickly. While the traditional management of ASI was with open surgical repair (OSR), recent years have seen an evolution towards less invasive endovascular repair (EVR). In patients with these injuries, EVR may be a safer alternative that achieves similar immediate results with significantly lower complication and mortality rates than the highly morbid open surgical option. In this article, we review and compare the two approaches, providing an overview of patient selection, anatomic considerations, techniques, postoperative management, and outcomes. With the advent of EVTM and more trauma team members capable of endovascular management of vascular trauma, a paradigm shift towards EVR for ASI is taking place.

Photocatalytic removal of gaseous ethanol, acetaldehyde and acetic acid: from a fundamental approach to real cases

ABSTRACT The photocatalytic oxidation of volatile organic compounds (VOCs) has been extensively investigated. With respect to water treatment, photocatalytic degradation of air pollutants is still less understood, but this has not prevented photocatalytic building materials and air purifiers to reach the market. Here, we provide a selective overview of the current understanding on VOC photocatalytic oxidation, focusing on ethanol, acetaldehyde, and acetic acid. Among the main indoor pollutants, these molecules are also oxidation intermediates of numerous VOCs. Their adsorption at the photocatalyst surface is first presented, based on theoretical and experimental evidence. Reaction intermediates are discussed, comparing proposed reaction mechanisms. The role of the photocatalyst features in directing adsorption and oxidation phenomena is highlighted, encompassing both TiO2 and emerging photocatalysts. We then critically discuss gaps in our knowledge, such as the effect of air humidity, multi-pollutant interactions and deactivation pathways. Finally, attempts to model VOC degradation in realistic conditions are reviewed.

Proposed EU AI Act - Presidency Compromise Text - Select Overview and Comment on the Changes to the Proposed Regulation

Proposed EU AI Act - Presidency Compromise Text - Select Overview and Comment on the Changes to the Proposed Regulation

Journal of Economic Literature Codes Classification System (JEL)

The Journal of Economic Literature codes classification system (JEL) published by the American Economic Association (AEA) is the de facto standard classification system for research literature in economics. The JEL classification system is used to classify articles, dissertations, books, book reviews, and working papers in EconLit, a database maintained by the AEA. Over time, it has evolved and extended to a system with over 850 subclasses. This paper reviews the history and development of the JEL classification system, describes the current version, and provides a selective overview of its uses and applications in research. The JEL codes classification system has been adopted by several publishers, and their instructions are reviewed. There are interesting avenues for future research as the JEL classification system has been surprisingly little used in existing bibliometric and scientometric research as well as in library classification systems.

Nutrition Concepts for the Treatment of Obesity in Adults.

Obesity caused by a positive energy balance is a serious health burden. Studies have shown that obesity is the major risk factor for many diseases like type 2 diabetes mellitus, coronary heart diseases, or various types of cancer. Therefore, the prevention and treatment of increased body weight are key. Different evidence-based treatment approaches considering weight history, body mass index (BMI) category, and co-morbidities are available: lifestyle intervention, formula diet, drugs, and bariatric surgery. For all treatment approaches, behaviour change techniques, reduction in energy intake, and increasing energy expenditure are required. Self-monitoring of diet and physical activity provides an effective behaviour change technique for weight management. Digital tools increase engagement rates for self-monitoring and have the potential to improve weight management. The objective of this narrative review is to summarize current available treatment approaches for obesity, to provide a selective overview of nutrition trends, and to give a scientific viewpoint for various nutrition concepts for weight loss.

Novel Aczel–Alsina operations‐based interval‐valued intuitionistic fuzzy aggregation operators and their applications in multiple attribute decision‐making process

In the creation of better multiple attribute decision-making (MADM) patterns to address the ambiguity in the expanding sophisticated of expert systems, the hypothesis of interval-valued intuitionistic fuzzy sets has proven to be an effective and advantageous technique. We employ Aczel–Alsina operations to remedy the MADM issue, wherein all data supplied by decision-makers is conveyed as interval-valued intuitionistic fuzzy (IVIF) decision matrices with all components described by an IVIF number (IVIFN). This allows us to satisfy much more demands from fuzzy decision-making concerns (IVIFN). In the framework of IVIFNs, we primarily describe several novel Aczel–Alsina operations. On the basis of these operations, we construct several novel IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted averaging operator, the IVIF Aczel–Alsina order weighted averaging operator, and IVIF Aczel–Alsina hybrid averaging operator. We built up several features of such operators. We recommend an MADM technique dependent on the advanced IVIF aggregation operators. To demonstrate the effectiveness of the developed technique, we present an overview of research scientist selection. The experimental results show the viability and benefits of the created strategy by contrasting it with the different strategies. This paper reveals that some existing IVIF aggregation operators are particular instances of the operators induced in this paper.

VERTICO: The Virgo Environment Traced in CO Survey

Abstract We present the Virgo Environment Traced in CO (VERTICO) survey, a new effort to map 12CO (2–1), 13CO (2–1), and C18O (2–1) in 51 Virgo Cluster galaxies with the Atacama Compact Array, part of the Atacama Large Millimeter/submillimeter Array. The primary motivation of VERTICO is to understand the physical mechanisms that perturb molecular gas disks, and therefore star formation and galaxy evolution, in dense environments. This first paper contains an overview of VERTICO's design and sample selection, 12CO (2–1) observations, and data reduction procedures. We characterize global 12CO (2–1) fluxes and molecular gas masses for the 49 detected VERTICO galaxies, provide upper limits for the two nondetections, and produce resolved 12CO (2–1) data products (median resolution = 8″ ≈ 640 pc). Azimuthally averaged 12CO (2–1) radial intensity profiles are presented along with derived molecular gas radii. We demonstrate the scientific power of VERTICO by comparing the molecular gas size–mass scaling relation for our galaxies with a control sample of field galaxies, highlighting the strong effect that radius definition has on this correlation. We discuss the drivers of the form and scatter in the size–mass relation and highlight areas for future work. VERTICO is an ideal resource for studying the fate of molecular gas in cluster galaxies and the physics of environment-driven processes that perturb the star formation cycle. Upon public release, the survey will provide a homogeneous legacy data set for studying galaxy evolution in our closest cluster.

An overview of skew distributions in model-based clustering

The literature on non-normal model-based clustering has continued to grow in recent years. The non-normal models often take the form of a mixture of component densities that offer a high degree of flexibility in distributional shapes. They handle skewness in different ways, most typically by introducing latent ‘skewing’ variable(s), while some other consider marginal transformations of the original variable(s). We provide a selective overview of the main types of skew distributions used in the area, based on their characterization of skewness, and discuss different skew shapes they can produce. For brevity, we focus on the more commonly-used families of distributions.

The Implementation of Evidence-Based Obesity Education Curricula to Prevent Cancer in a Predominantly Mexican–American Community on the U.S.-Mexico Border

Although cancer is the leading cause of death among Mexican-Americans, few community-based programs target obesity reduction as a way to reduce the prevalence of obesity-related cancer in underserved populations. Evidence suggests that obesity correlates with 13 types of cancer. The objective is to provide an overview of evaluation and selection of evidence-based content; details of the implementation process; modifications needed to tailor education programs to specific needs of different target audiences; and demonstrate challenges of implementing a community-based prevention program intended to reduce cancer incidence and mortality in Mexican-Americans. We used the Social Cognitive Theory (SCT) to develop a 10-topic menu of educational classes using elements of multiple evidence-based curricula. Outcome measures for physical activity and nutrition were determined using the International Physical Activity Questionnaire (IPAQ) and the Dietary Screener Questionnaire (DSQ). Weight status was determined using weight, body fat, and body mass index (BMI). To date, 2845 adults received wellness education from our program. Multiple delivery models were used to reach a larger audience; they included a 4-week model, 5-week model, employer model, low-income housing, 1- and 2-h sessions, and clinic encounters. Individuals were given education at multiple community locations including senior centers (14%), churches (0.6%), employers (17.6%), low-income housing (8.2%), community centers (16.6%), clinics (11.5%), and schools (32.5%). Our study indicates that our delivery model is feasible and can disseminate evidence-based obesity education. Further investigation is necessary to assess long-term behavioral change and to assess the most effective model for delivery.

An overview of microneedle applications, materials, and fabrication methods.

Microneedle-based microdevices promise to expand the scope for delivery of vaccines and therapeutic agents through the skin and withdrawing biofluids for point-of-care diagnostics – so-called theranostics. Unskilled and painless applications of microneedle patches for blood collection or drug delivery are two of the advantages of microneedle arrays over hypodermic needles. Developing the necessary microneedle fabrication processes has the potential to dramatically impact the health care delivery system by changing the landscape of fluid sampling and subcutaneous drug delivery. Microneedle designs which range from sub-micron to millimetre feature sizes are fabricated using the tools of the microelectronics industry from metals, silicon, and polymers. Various types of subtractive and additive manufacturing processes have been used to manufacture microneedles, but the development of microneedle-based systems using conventional subtractive methods has been constrained by the limitations and high cost of microfabrication technology. Additive manufacturing processes such as 3D printing and two-photon polymerization fabrication are promising transformative technologies developed in recent years. The present article provides an overview of microneedle systems applications, designs, material selection, and manufacturing methods.

An overview of tests on high-dimensional means

Testing high-dimensional means has many applications in scientific research. For instance, it is of great interest to test whether there is a difference of gene expressions between control and treatment groups in genetic studies. This can be formulated as a two-sample mean testing problem. However, the Hotelling T2 test statistic for the two-sample mean problem is no longer well defined due to singularity of the sample covariance matrix when the sample size is less than the dimension of data. Over the last two decades, the high-dimensional mean testing problem has received considerable attentions in the literature. This paper provides a selective overview of existing testing procedures in the literature. We focus on the motivation of the testing procedures, the insights into how to construct the test statistics and the connections, and comparisons of different methods.

Ethics and digital collections: a selective overview of evolving complexities

PurposeDigital collections are becoming more commonplace at libraries, archives and museums around the world, creating potential for improved accessibility to information that may otherwise remain hidden and further support for intellectual exploration. As a result of the growing potential for digital collections to inform and influence, the conversation surrounding ethics and digital collections needs to be continually examined and adapted as technologies evolve, user expectations change and digital information plays an increasing role in our everyday lives. In this context, this paper presents an overview of multifaceted ethical realities that impact the how, why and what digital information is created, accessed and preserved.Design/methodology/approachWritten from the perspective of a digital collections librarian, this paper relies on existing research in presenting ethical considerations and complements that research with professional observations in providing subsequent reflections on addressing challenges in the age of digital information.FindingsThere are and should be considerations given to not only what information is contained in a given collection, but also how that information is selected, accessed and consumed by the public. The conclusions offered are designed to provoke reflection on the evolving and interconnected nature of information and ethics in the context of digital collections.Originality/valueInformation ethics is multifaceted, with one of those facets relating directly to digital collections. This paper demonstrates that digital collections are more complex than simply a collection of digitized documents and photographs. As the field of information management continually evolves and adapts, so, too, do the ethical realizations identified in this paper, all of which go beyond the (virtual) walls of a library, archive or museum, and carry the potential to have a long-term impact concerning information and its integrity, equity and access.

Teaching foreign language grammar: New solutions, old problems

AbstractThe place of grammar instruction in second or foreign language (L2) pedagogy has always been highly controversial and although there is currently consensus that such pedagogic intervention is beneficial, numerous questions remain as to how it should most propitiously be conducted. Different theoretical perspectives have resulted in a wide array of instructional options intended to enhance the effectiveness of grammar teaching, both with respect to ensuring fuller understanding of grammar rules and helping learners use these rules in communicative interaction. The paper provides a selective overview of such “new solutions” and argues that they have not really contributed much to solving the “old problems” of grammar instruction. Reasons for this state of affairs are considered and some suggestions for how the situation can be improved are offered.

A Harmony of Judeo-Christian Eschatology and Messianic Prophecy

This essay presents a selective overview of the main themes of Judeo-Christian eschatological prophecy. Particular attention is paid to the significance of successive biblical covenants, prophecies of the “day of the Lord,” differences between personal and collective resurrection, and expectations of the Messianic era. Although the prophets of the Hebrew Bible and Christian New Testament lived and wrote in diverse historical and social contexts, their foresights were remarkably consistent and collectively offered a coherent picture of the earth’s last days, the culmination of human history, and the prospects of the afterlife. This coherence reflects the interrelated character of Judaic and Christian theology and the unity of the Judeo-Christian faith.

A Review of Feature Selection and Classification Approaches for Heart Disease Prediction

Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research.

Spin–Orbit-Entangled Electronic Phases in 4d and 5d Transition-Metal Compounds

Complex oxides with 4d and 5d transition-metal ions recently emerged as a new paradigm in correlated electron physics, due to the interplay between spin–orbit coupling and electron interactions. For 4d and 5d ions, the spin–orbit coupling, ζ, can be as large as 0.2–0.4 eV, which is comparable with and often exceeds other relevant parameters such as Hund’s coupling JH, noncubic crystal field splitting Δ, and the electron hopping amplitude t. This gives rise to a variety of spin–orbit-entangled degrees of freedom and, crucially, non-trivial interactions between them that depend on the d-electron configuration, the chemical bonding, and the lattice geometry. Exotic electronic phases often emerge, including spin–orbit assisted Mott insulators, quantum spin liquids, excitonic magnetism, multipolar orderings and correlated topological semimetals. This paper provides a selective overview of some of the most interesting spin–orbit-entangled phases that arise in 4d and 5d transition-metal compounds.

Memories from the future: constative and performed identities in ideologized spaces

Although communism was a Western creation its last consequences were implemented in southeastern Europe. In addition to the imposed aspects, there were local enthusiasms and excesses of zeal (euphemistically speaking), which attest to the existence of an identity matrix and a common mentality. Countries with an authoritarian tradition have absorbed this ideology of simultaneous denationalization and supra-nationalization to the deepest. And after the fall of the Iron Curtain in 1989, the Southeast European space preserved mass nostalgia: Stalin, Tito and Ceausescu are still guardianship figures for many of various social categories. Imperialist stability and/or glory are two of the most important reasons for forgetting communist terror. The research tries to identify and analyze the sources of historical instability that has an impact on the post-communist present - the communist heritage still looming large-, as well as to demystify certain stigmas unconditionally applied to Southeast European civilizations: corruption, laziness, negative Balkanization, frivolity and lack of consistency. This is a selective overview which aims to decant common mentalities of synchrony in relation to diachrony.

Robust high dimensional factor models with applications to statistical machine learning.

Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.

A selective overview of deep learning.

Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in computer vision, natural language processing, etc. From the statistical and scientific perspective, it is natural to ask: What is deep learning? What are the new characteristics of deep learning, compared with classical methods? What are the theoretical foundations of deep learning? To answer these questions, we introduce common neural network models (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets) and training techniques (e.g., stochastic gradient descent, dropout, batch normalization) from a statistical point of view. Along the way, we highlight new characteristics of deep learning (including depth and over-parametrization) and explain their practical and theoretical benefits. We also sample recent results on theories of deep learning, many of which are only suggestive. While a complete understanding of deep learning remains elusive, we hope that our perspectives and discussions serve as a stimulus for new statistical research.