Search Results (950)

Tropospheric ozone (O₃) pollution is a major concern in urban environments because of its toxicity for both people and vegetation. This paper review provides an overview of atmospheric mechanisms, as well as the potential and best management practices of urban greening for reducing O₃ pollution in cities. Urban greening has often been proposed as a cost-effective solution to reduce O₃ pollution, but its effectiveness depends on careful species selection and integration with broader air quality management strategies. Ozone is a secondary pollutant and the volatile organic compounds emitted by vegetation (BVOCs) can play a prominent role in O₃ formation. A list of recommended and to-avoid species is given here to drive future planting at city scale. Planting low BVOC-emitting species and combining greening with reductions in anthropogenic emissions are key to maximizing benefits and minimizing unintended increases in O₃. Public and non-public institutions should carefully select plant species in consultation with expert scientists from the early stages, e.g., by considering local conditions and pollutant dynamics to design effective greening interventions. Collaborative planning among urban ecologists, atmospheric scientists, and municipalities is thus crucial to ensure that greening interventions contribute to overall air quality improvements rather than inadvertently enhancing O₃ formation. Such improvements will also translate into plant protection from O₃ stress. Therefore, future directions of research and policy integration to achieve healthier, O₃-resilient urban ecosystems are also provided. Full article

This research adapts and tests the Smart Village Index (SVI) as a multidimensional technological model designed to assess the digital readiness, institutional maturity, and infrastructural connectivity of rural areas in Serbia. The research was undertaken in 10 rural municipalities that are representative of various phases of digital transformation and development typologies. The dimensions included in the analysis were six, which are information and communication technologies, digital governance, leadership and local competences, community participation, a sustainable economy, and infrastructure. The results indicated significant regional differences: About 30% of the municipalities, including Aranđelovac, Kanjiža, and Arilje, fall into the group of smart villages with developed infrastructure and high institutional readiness. About 40% of the municipalities, such as Titel, Knjazevac, and Despotovac, are in the phase of transiting to digital, while the remaining 30% (Knić, Rekovac, Žabari, and Crna Trava) still present a low level of digital connectivity, with limited capacities in their institutions. This research supports the fact that the successful digital transformation of rural communities requires a balance between technological development, institutional support, and social inclusion. The Smart Village Index (SVI) proposed is a robust way to evaluate the digital readiness of villages and to inform targeted policies on achieving sustainable rural development in Serbia. In addition to its analytical and evaluative role, the Smart Village Index (SVI) is a digital–technological innovation and a computational tool that unites data modeling, algorithmic standardization, and digital analytics in order to measure the level of digital readiness of a rural community. It therefore crosses over the thresholds of the conventional social scientist construct and gives a technological implementation that is within the threshold of technology being a reproducible and data-driven instrument for the real-life planning of digital governance and rural development. Full article

Earthquake predictability remains a central challenge in seismology. Are earthquakes inherently unpredictable phenomena, or can they be forecasted through advances in technology? Contemporary seismological research continues to pursue this scientific milestone, often referred to as the ‘Holy Grail’ of earthquake prediction. In the direction of earthquake prediction based on historical data, the Grammatical Evolution technique of GenClass demonstrated high predictive accuracy for earthquake magnitude. Similarly, our research team follows this line of reasoning, operating under the belief that nature provides a pattern that, with the appropriate tools, can be decoded. What is certain is that, over the past 30 years, scientists and researchers have made significant strides in the field of seismology, largely aided by the development and application of artificial intelligence techniques. Artificial Neural Networks (ANNs) were first applied in the domain of seismology in 1994. The introduction of deep neural networks (DNNs), characterized by architectures incorporating two hidden layers, followed in 2002. Subsequently, recurrent neural networks (RNNs) were implemented within seismological studies as early as 2007. Most recently, grammatical evolution (GE) has been introduced in seismological studies (2025). Despite continuous progress in the field, achieving the so-called “triple prediction”—the precise estimation of the time, location, and magnitude of an earthquake—remains elusive. Nevertheless, machine learning and soft computing approaches have long played a significant role in seismological research. Concerning these approaches, significant advancements have been achieved, both in mapping seismic patterns and in predicting seismic characteristics on a smaller geographical scale. In this way, our research analyzes historical seismic events from 2004 to 2011 within the latitude range of 21°–79° longitude range of 33°–176°. The data is categorized and classified, with the aim of employing grammatical evolution techniques to achieve more accurate and timely predictions of earthquake magnitudes. This paper presents a systematic effort to enhance magnitude prediction accuracy using GE, contributing to the broader goal of reliable earthquake forecasting. Subsequently, this paper presents the superiority of GenClass, a key element of the grammatical evolution techniques, with an average error of 19%, indicating an overall accuracy of 81%. Full article

Low-temperature superconductivity has been known since 1957 to be described by BCS theory for effective single-band metals controlled by the density of states at the Fermi level, very far from band edges, the electron–phonon coupling constant l, and the energy of the boson in the pairing interaction w₀, but BCS has failed to predict high-temperature superconductivity in different materials above about 23 K. High-temperature superconductivity above 35 K, since 1986, has been a matter of materials science, where manipulating the lattice complexity of high-temperature superconducting ceramic oxides (HTSCs) has driven materials scientists to grow new HTSC quantum materials up to 138 K in HgBa₂Ca₂Cu₃O₈ (Hg1223) at ambient pressure and near room temperature in pressurized hydrides. This perspective covers the major results of materials scientists over the last 39 years in terms of investigating the role of lattice inhomogeneity detected in these new quantum complex materials. We highlight the nanoscale heterogeneity in these complex materials and elucidate their special role played in the physics of HTSCs. Especially, it is highlighted that the geometry of lattice and charge complex heterogeneity at the nanoscale is essential and intrinsic in the mechanism of rising quantum coherence at high temperatures. Full article

Background: The National Institutes of Health (NIH) established the Research Centers in Minority Institutions (RCMI) Program in response to the Congressional language in House Report 98-911 to establish research centers in predominantly minority institutions that offered doctoral degrees in the health professions and/or health-related sciences. The National Institute on Minority Health and Health Disparities (NIMHD) recognizes the critical role of the RCMI in conducting biomedical research and providing healthcare to communities impacted by health disparities. The RCMI Coordinating Center (RCMI-CC) supports the Consortium of 23 competitively funded RCMI Centers, with a collaborative infrastructure, to stimulate research partnerships and harness the research talents of the many gifted scientists and health professionals to collectively support investigator development, and advance health disparities research. Objectives: This manuscript presents the national and public health impact of the RCMI-CC as it works to help RCMI achieve their primary goals. Methods: We describe the organization of the RCMI Consortium and evaluate the impact of the overall RCMI Program, as measured by highly competitive NIH awards, high-impact publications, and other metrics. Results/Impact: In addition to the competitive research R01 and equivalent awards, publications, and patents, RCMI-CC implementation of the National Research Mentoring Network (NRMN), and health services research in RCMI–clinical research networks, collectively highlight the national and public health impact, as measured by collaborative scientific excellence, investigator development, and community engagement. Conclusions: The RCMI-CC and RCMI Consortium collectively demonstrate national and public health impact, with externally validated quantifiable metrics and return on investment. Full article

Bladder carcinoma (BC) is evaluated as the ninth most common cancer worldwide and the sixth most common cancer among men. The determination of the occurrence and stage of the disease is a significant diagnostic task. An alternative to an invasive biopsy may be the determination of biomarkers in patient samples such as bladder tissue, blood serum, plasma, or urine samples. The aim of this paper is to review reports on the role of proteases in bladder cancer and their determination in cancerous samples. Proteases can be classified in several groups depending on their catalytic residue, most commonly aspartic, cysteine, serine, metalloproteinases, and others. A review was made of papers reporting cysteine cathepsins: B, L, H, V, S, aspartyl cathepsin D, and metalloproteinases MMP 1, 2, 3, 7, 9, 10, 14, and 15, as well as ubiquitin-specific proteases USP 1, 2a and 5. The majority of the reviewed papers show an increase in marker concentration in bladder cancer samples versus a control. Only a few of them provide quantitative information about MMP biomarkers in bladder tissue (cancerous and control tissue), and none give such information about cathepsins. Levels of the order of µg/g protein are characteristic of MMP biomarkers in urinary bladder tissue. Most reported concentrations of proteases in blood serum/plasma and urine are at levels of ng/mL, both cancerous and control samples. It is concluded that the reviewed papers do not provide a clear picture concerning the use of proteases as bladder cancer biomarkers or concerning the levels of particular proteases in control samples. Potential new analytical tools for protease determination are discussed. More work in this area is necessary, especially by scientists equipped with new analytical tools. Full article

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article

This paper presents TwinP2G, a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling, including electricity, green hydrogen, natural gas, and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles, including data scientists and energy experts, using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally, TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid. Full article

In recent years, animal welfare scientists working in professionally managed settings have increasingly focused on promoting resilience to enhance the quality of life of individual animals. Resilience—defined as an animal’s capacity to be minimally affected by a disturbance or to rapidly return to the physiological, behavioral, cognitive, health, affective, and production states that pertained before exposure to a disturbance—involves various systems and dynamic processes. There is evidence that resilience can be measured using a suite of species-specific indicators, including both behavioral measures and physiological biomarkers. These indicators should be tracked for individuals of the same species over time and across various conditions, events, and experiences. Large-scale, multi-institutional studies allow welfare scientists to collect cross-sectional data to identify “resilient phenotypes” for the species of interest. Ultimately, the focus should be on improving outcomes for individual animals as they face particular stressors, challenges, and environmental disturbances over their lifetime. Animal care specialists play a crucial role in helping animals build resilience by providing opportunities to engage in cognitive challenges, stimulating environments, and species-appropriate social interactions. This review defines resilience for animal welfare scientists, as well as discusses how to measure and promote resilience in animals residing in zoos and aquariums. Full article

Hans Paulsen was one of the first scientists who believed that chemistry should be applied to biology and medicine. His interest in natural products and their roles solidified in the 1970s. He passed on his knowledge to hundreds of students and coworkers and advanced science with many national and international collaborators. No matter where he was, at home or travelling, he was always curious and keen to learn, from chemistry to enzymes, their roles in diseases, and the possible applications of synthetic compounds. His creative chemistry and synthesis of novel compounds made essential contributions to elucidating the mechanisms and pathways of glycoprotein biosynthesis. This review describes the biosynthetic pathways of the O- and N-glycans of glycoproteins and studies of novel substrates and inhibitors developed by Hans Paulsen’s group. Full article

Over 150 years, pathology has transformed remarkably, from the humble beginnings of microscopic tissue examination to today’s revolutionary advancements in digital pathology and artificial intelligence (AI) applications. This review briefly retraces the evolution of microscopes and highlights breakthroughs in complementary tools and techniques that laid the foundation for modern surgical pathology, recently expanded into a new dimension with digital pathology. Digital pathology marked a pivotal turning point by addressing the longstanding limitations of conventional microscopy, paving the way for AI integration. AI now revolutionizes pathology workflows, offering unprecedented opportunities for automated diagnostics, enhanced precision, accelerated research, and advanced medical education. Despite widespread consensus on AI as complementary to pathologists, rare studies critically explore the feasibility of a fully autonomous, pathologist-independent diagnostic workflow. Given the rapid advancement of AI, it is timely to examine whether mature AI systems might realistically achieve diagnostic autonomy. Thus, this review uniquely addresses this gap by evaluating the feasibility, limitations, and implications of a disruptive, pathologist-free diagnostic model. This exploration raises critical questions about the evolving role of pathologists in an era increasingly defined by automation. Can pathologists adapt to emerging trends, maintain their central role in patient care, and leverage AI effectively, or will their traditional roles inevitably diminish? Could the continued advancement of AI eventually prompt a return of pathologists to their initial mid-19th century role as scientist scholars, removed from frontline diagnostics? Ultimately, we assess whether AI can independently sustain diagnostic accuracy and decision making without pathologist oversight. Full article

Fossils are portals to the past, providing researchers with vital information about the evolution of life on Earth throughout the geological eras. The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years. The bibliographic survey revealed that destructive methods continue to play a significant role in scientific production related to this topic, particularly in studies on 3D morphologies, diagenesis, nutritional ecology, dating, elucidating dietary or habitat preferences, or understanding the physiology of extinct species. However, noninvasive tools, such as Raman spectroscopy, are rapidly rising, particularly when integrated with imaging techniques. As such, fossil research continues to advance even beyond the borders of our planet, exploring extraterrestrial samples in a quest to unlock the universal mystery of life. At the same time, the advent of advanced AI methods—particularly model chatbots that rival the capabilities of experienced scientists—has facilitated and enhanced data interpretation and classification. As fossil research evolves, upcoming technological advancements in spatial resolution, penetration depth, and detection sensitivity will integrate state-of-the-art spectroscopic tools. This will undoubtedly take fossil research to new heights, generating breakthroughs that optimize analysis while preserving invaluable specimens. Overall, the present study offers a holistic overview of analytical techniques through meta-analysis and bibliometric mapping, including a critical assessment of commonly used methods and offering a glimpse into the integration of machine learning and AI tools in fossil research. Full article

Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. Our quantum-like model, representing one of the few, if only, mathematical models of interdependence, captures the tradeoffs in energy expenditures a team chooses as it consumes its available energy on its structure versus its performance, measured by the uncertainty (entropy) relationship generated. Here, we outline the support for our quantum-like model of uncertainty relations, our goals in this study, and our future plans: (i) Redundancy reduces interdependence. This first finding confirms the existence of interdependence in systems, both large and small. (ii) Teams with orthogonal roles perform best. This second finding is the root cause of humans, including scientists, being unable to appreciate the role of interdependence in “squeezing” states of teams. (iii) Cognitive reports may not equal behavior. The last finding allows us to tie our research together and to account for the absence of social scientists from leading the mathematical science of teams. In this article, we review the need for a mathematics for the future of team operations, the literature, the mathematics in our model of agents with full agency (viz., intelligent and interdependent), our hypothesis that freely organized teams enjoy significant advantages over command decision-making (CDM) systems, and results from the field. We close with future plans and a generalization about squeezing states to control interdependent systems. Full article

Biodiversity is crucial for humankind. It encompasses three main levels: ecosystem, species, and intraspecific genetic diversity. Species consist of populations that exhibit deoxyribonucleic acid (DNA) variability, which is a key component of intraspecific genetic diversity. In turn, intraspecific genetic diversity is directly linked with the term population genetic structure (PGS). There is a great deal of uncertainty and confusion surrounding the concept of the PGS of species in the scientific literature, yet the term PGS is central to population genetics, and future research is expected to focus on the evolutionary continuum from populations to species. Therefore, it is necessary for current biologists and the next generation of scientists to acquire a better understanding of a PGS, both as a term and a concept, as well as the various roles PGSs play within a biodiversity context. This knowledge can then be applied to the expansion of both practical and theoretical science. Finding answers and reaching a consensus among the scientific community on certain questions regarding PGSs could expand the horizons of population genetics and related research disciplines. The major areas of interest and research are PGSs’ roles in the processes of microevolution and speciation, the sustainable use of natural resources, and the conservation of genetic diversity. Other important aspects of this perspective review include proposals for scientific definitions of some terms and concepts, as well as new perspectives and explanations that could be used as a basis for future theoretical models and applied research on PGSs. In conclusion, a PGS should be viewed as a fragile genetic mosaic encompassing at least three spatial dimensions and one temporal dimension. Full article

This review examines the emerging role of manganese-based nanoparticles (Mn-NPs) in detecting heavy metal pollutants in environmental matrices. Heavy metals such as cadmium, lead, zinc, and copper pose serious environmental and health concerns due to their tendency to persist in ecosystems and accumulate in living organisms. As a result, there is a growing need for reliable methods to detect and remove these pollutants. Manganese nanoparticles offer unique advantages that scientists could consider as replacing other metal nanoparticles, which may be more expensive or more toxic. The physicochemical properties of Mn-NPs—including their multiple oxidation states, magnetic susceptibility, catalytic capabilities, and semiconductor conductivity—enable the development of multi-modal sensing platforms with exceptional sensitivity and selectivity. While Mn-NPs exhibit inherently low electrical conductivity, strategies such as transition metal doping and the formation of composites with conductive materials have successfully addressed this limitation. Compared to noble metal nanoparticles (Au, Ag, Pd) and other base metal nanoparticles (Bi, Fe₃O₄), Mn-NPs demonstrate competitive performance without the drawbacks of high cost, complex synthesis, poor distribution control, or significant aggregation. Preliminary studies retrieved from the Scopus database highlight promising applications of manganese-based nanomaterials in electrochemical sensing of heavy metals, with recent developments showing detection limits in the sub-ppb range. Future research directions should focus on addressing challenges related to scalability, cost-effectiveness, and integration with existing water treatment infrastructure to accelerate the transition from laboratory findings to practical environmental applications. Full article