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Urbanization is causing soil sealing and ecosystem fragmentation, affecting soil health, biodiversity, and carbon storage potential. While green infrastructure is being promoted to address these challenges, small-scale habitats such as urban crevice soils (UCSs), referred to as soils in the gaps between concrete and asphalt surfaces in heavily urbanized areas, remain overlooked. The aim of this study was to determine whether UCSs are advantageous ecological units that sustain microbiological life and perform ecosystem services. This study quantified soil heterotrophic respiration, microbial biomass carbon (MBC) and nitrogen (MBN), soil organic carbon (SOC) and inorganic carbon (SIC), and total nitrogen (TN) in UCSs (with and without plants), nearby roadside soils, and soils from a switchgrass cropland in an urban farm within the Nashville metropolitan area in Tennessee, USA. On average, UCSs exhibited up to 436.2 %, 59.4 %, 217.6 %, and 266.9 % higher SOC, MBC, MBN, and C/N ratio compared to roadside and switchgrass soils, respectively. UCSs with plants have the highest microbial biomass, highlighting the synergistic role of plant presence in enhancing microbial function. These findings challenge the belief that urban soils are universally degraded and biologically inert, and regard UCSs as dispersed, small-scale contributors to urban ecosystem services. UCSs could serve as scalable, low-cost nature-based solutions that support resilient and sustainable cities amid rapid urbanization and environmental stress. Future studies should evaluate the ecological potential of UCSs as microhabitats for microbial biodiversity conservation, carbon storage, and ecosystem service delivery across various cities of different scales. 

Rapid urbanization leads to soil degradation, threatens the ecological benefits of urban soils and vegetation, and deteriorates the urban microenvironment to address climate resilience, pollution, soil degradation, and biodiversity loss. We reviewed the one-century-long history, origins, classification, and characteristics of urban anthropogenic soils (UAS) while emphasizing rarely studied and untapped potential of the crevice soil, plant, and abiotic conditions in the designated cracks of concrete materials of urban roadside. The long-overlooked crevice soils share features with UAS, such as being shallow, heterogeneous, and existing under harsh environmental conditions. Our urban crevice studies were conducted in the Nashville metropolitan area in Tennessee, USA. We collected soil samples from crevices with and without plants, nearby roadsides, and one switchgrass cropland in an urban farm. A total of 34 different plant species growing in crevices were identified, and only 12 species are native to the area, suggesting the dominance of non-native species in crevice soils. Regardless of the presence of plants, the crevice soils showed significantly higher temperature, pH, electrical conductivity, and a lower moisture content than roadside soils and switchgrass soils. The crevice micro-environment thus preserved precious soil resources, promoted urban biodiversity, and inspired innovative strategies for future sustainable urban design. Our ongoing efforts further examine heavy metals, organic and inorganic pollutants, and microbial composition, activity, and function in crevice soils and their counterparts in the urban environment. 

Many important security properties can be formulated in terms of flows of tainted data, and improved taint analysis tools to prevent such flows are of critical need. Most existing taint analyses use whole-program static analysis, leading to scalability challenges. Type-based checking is a promising alternative, as it enables modular and incremental checking for fast performance. However, type-based approaches have not been widely adopted in practice, due to challenges with false positives and annotating existing codebases. In this paper, we present a new approach to type-based checking of taint properties that addresses these challenges, based on two key techniques. First, we present a new type-based tainting checker with significantly reduced false positives, via more practical handling of third-party libraries and other language constructs. Second, we present a novel technique to automatically infer tainting type qualifiers for existing code. Our technique supports inference of generic type argument annotations, crucial for tainting properties. We implemented our techniques in a tool TaintTyper and evaluated it on real-world benchmarks. TaintTyper exceeds the recall of a state-of-the-art whole-program taint analyzer, with comparable precision, and 2.93X-22.9X faster checking time. Further, TaintTyper infers annotations comparable to those written by hand, suitable for insertion into source code. TaintTyper is a promising new approach to efficient and practical taint checking. 

Null-pointer exceptions are serious problem for Java, and researchers have developed type-based nullness checking tools to prevent them. These tools, however, have a downside: they require developers to write nullability annotations, which is time-consuming and hinders adoption. Researchers have therefore proposed nullability annotation inference tools, whose goal is to (partially) automate the task of annotating a program for nullability. However, prior works rely on differing theories of what makes a set of nullability annotations good, making comparing their effectiveness challenging. In this work, we identify a systematic bias in some prior experimental evaluation of these tools: the use of “type reconstruction” experiments to see if a tool can recover erased developer-written annotations. We show that developers make semantic code changes while adding annotations to facilitate typechecking, leading such experiments to overestimate the effectiveness of inference tools on never-annotated code. We propose a new definition of the “best” inferred annotations for a program that avoids this bias, based on a systematic exploration of the design space. With this new definition, we perform the first head-to-head comparison of three extant nullability inference tools. Our evaluation showed the complementary strengths of the tools and remaining weaknesses that could be addressed in future work. 

New AI-designed RF pulses increase bandwidth and sensitivity for¹H-¹⁵N HSQC spectra of metabolites.