India

The demand-supply gap in infrastructure became evident with the initiation of economic reforms in 1990. There are complex political arrangements around infrastructure in India's 29 states and seven union territories. Growth in the provision of infrastructure has grown markedly in the past 30 or so years. Two cases are considered. The first case concerns solid municipal waste management. In the mid-1990s, "Diamond City? (pseudonym) faced a health crisis because of poor management of municipal solid waste. Since then, municipal solid waste management became a focus area of improvement for the urban local body of Diamond City (ULB-CD). ULB-CD started using an "unbundling? approach for MSW management. By contrast, "BEATLE City? (pseudonym) is the capital of a north-eastern state in India, which has witnessed a rapid increase in population since 2005 and has become the largest metropolis in the northeast of the country. Urbanization has been the increased generation of municipal solid waste. The municipal corporation of BEATLE (ULB-BEATLE) is an urban local body responsible for municipal solid waste management. Deficiencies across all components of the municipal solid waste supply chain were evident: primary collection and transportation, secondary transportation, processing, and disposal. There were limited door-to-door collections of municipal solid waste, lack of secondary collection points and processing facilities, and unscientific dumping of municipal solid waste in low-lying areas. ULB-BEATLE used a public-private partnership that did little to improve the situation

Molecular mechanism of androgen receptor mutation in multigenerational mild androgen insensitivity syndrome.

OBJECTIVE: Androgen insensitivity syndrome (AIS) due to androgen receptor (AR) mutations creates a spectrum of clinical presentations based on residual AR function with the mildest impairment creating mild AIS (MAIS) whose undefined molecular mechanism and subtle clinical features leave it less understood and underdiagnosed. DESIGN: In silico modeling and in vitro androgen bioassay of the mutated AR are used to identify its structural and physiological mechanism. Clinical features and responses to high-dose testosterone treatment of three cases of MAIS across a six-generation family pedigree are described. METHODS: Structural and dynamic in silico molecular modeling and in vitro yeast-based androgen bioassays of the mutant AR are employed. Three cases of MAIS with consistent (gynecomastia and micropenis) and variable (infertility) clinical features across generations are reported, and the effects of high-dose testosterone treatment are studied. RESULTS: The missense AR exon 8 mutation (nucleotide aga → gga, p.R872G arginine to glycine), known to cause an increased ligand dissociation rate in mutant AR in binding assays, was analyzed. Modeling shows that the mutation weakens the closure energy of the 'lid' of the ligand-binding pocket, allowing easier ligand dissociation from the binding site but with unimpaired in vitro androgen bioactivity. High-dose testosterone treatment for 3 years in one young man caused increased virilization and height growth but was ineffective for treating micropenis. Genetic counseling allowed effective prediction of MAIS risks in progeny for carrier and noncarrier sisters. CONCLUSIONS: The differential diagnosis and clinical management of MAIS is reviewed. The novel molecular mechanism of an AR ligand-binding domain mutation in MAIS may be present in other cases of MAIS

Grand Challenges in SportsHCI

The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being

Minimum Torque Ripple Control for Brushless Doubly-Fed Induction Generator-DC System under Power Winding Open-Phase Fault

In order to cope with the severe torque ripple caused by the open-phase fault of the power winding (PW) in the brushless doubly-fed induction generator-dc (BDFIG-DC) system, a novel minimum torque ripple control method is proposed in this article. The proposed method does not rely on additional external hardware devices and can minimize the torque ripple in the faulty system, thereby reducing damage to the machine bearings. Firstly, this article analyzes the harmonic components of the torque caused by PW open-phase fault in details, and derives the simplified torque expression. Secondly, by regulating the 3rd and 5th harmonic currents of PW, a minimum torque ripple control method is proposed. To obtain the real-time reference values of PW 3rd and 5th harmonic currents, the Newton's gradient descent method is adopted. Finally, the effectiveness of the proposed method is proven by comprehensive experimental results on a 5-kVA BDFIG-DC system platform

Expectations or rational expectations? A theory of systematic goal deviation

A planner uses goals to manage a preference disagreement over effort provision with a doer. Goals set output expectations for the doer which affect her behavior due to reference-dependent, loss-averse preferences over output. We characterize the planner's optimal goal and explore when it is aspirational versus achievable. Specifically, we show that the optimal goal is achieved by the doer only if the extent of preference disagreement is relatively small. Instead, when the extent of preference disagreement is large, the doer falls short of the optimal goal. The stochasticity of output plays an important role in generating this prediction within our model

The bronchiectasis microbiome: current understanding and treatment implications.

PURPOSE OF REVIEW: Advances in DNA sequencing and analysis of the respiratory microbiome highlight its close association with bronchiectasis phenotypes, revealing fresh opportunities for diagnosis, stratification, and personalized clinical intervention. An under-recognized condition, bronchiectasis is increasingly the subject of recent large-scale, multicentre, and longitudinal clinical studies including detailed analysis of the microbiome. In this review, we summarize recent progress in our understanding of the bronchiectasis microbiome within the context of its potential use in treatment decisions. RECENT FINDINGS: Diverse microbiome profiles exist in bronchiectasis, in line with the established disease heterogeneity including treatment response. Classical microbiology has established Pseudomonas aeruginosa and Haemophilus influenza as two microbial markers of disease, while holistic microbiome analysis has uncovered important associations with less common bacterial taxa including commensal an/or pathobiont species, including the emerging role of the fungal mycobiome, virome, and interactome. Integration of airway microbiomes with other high-dimensional biological and clinical datasets holds significant promise to determining treatable traits and mechanisms of disease related to the microbiome. SUMMARY: The bronchiectasis microbiome is an emerging and key area of study with significant implications for understanding bronchiectasis, influencing treatment decisions and ultimately improving patient outcomes

Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study

The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.</jats:p

Polymers in contemporary art objects at the Art Gallery of New South Wales: a study using infrared spectroscopy

The common occurrence of polymer-based objects in museum and gallery collections means that conservators and curators require a knowledge of the polymer composition used in order to best address the care of an object. Polymer-based artworks can be examined and characterised using infrared spectroscopy. In this study, a variety of polymers from contemporary artworks in the collection of the Art Gallery of New South Wales were examined using this technique. In addition to providing guidance on polymer components, the findings of this study have demonstrated the importance of the identification of additives in the formulation of the polymer systems employed by artists. Additives including fillers, plasticisers and processing agents present in appreciable concentrations in commercial polymers, complicate the interpretation of the infrared spectra of these materials. The findings of this collaborative study contribute to a growing resource of information on polymers in heritage collections