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The Need to Revitalise Drug Use Monitoring to Keep Pace With a More Dynamic, Digitally Enabled and Globally Connected Drug Market
No full textfals
Analytic-holistic tendencies differentially impact consumer response dependent on measurement method and context: A case study with chocolate
No full textAs sensory and consumer researchers work to better understand consumer decision-making, a focus on consumer product evaluation in environments that are closer to the real world than traditional central location tests has emerged. However, not all consumers respond in the same way across different environments. The notion that variations in cognitive styles, namely analytic-holistic tendencies, impact consumer response and susceptibility to context effects has been highlighted in existing literature. This typically sized consumer sensory study (n = 115) investigated whether grouping participants based on analytic-holistic tendencies provided additional insight into consumer response to chocolate in a traditional CLT and an immersive home virtual environment (VE). Whole-sample analysis indicated differences in sensory perception based on context, across both traditional sensory intensity questions and a speeded-response task. Furthermore, based on context, the holistic group (n = 56) exhibited changes in emotional and conceptual product association speeded-responses, whilst the analytic group showed changes in sensory product association speeded-responses. No between-group differences existed when considering liking or sensory perception. However, the analytic group (n = 59) exhibited more significant mean drops in liking than the holistic group when attributes were not Just-About-Right, which was particularly apparent in the VE environment. Findings indicate that the food-related cognitive thinking style tool used may not measure a single coherent construct. Nevertheless, using such a tool can provide insights into consumer decision-making. Results also suggest that context may have differential effects across certain groups of consumers; a consideration for sensory and consumer scientists when deciding on testing methods.fals
A framework of subject-specific expertise for out-of-field teachers: Translated for Science and English
No full textWhile teaching is a learning profession, learning to teach out-of-field (OOF) places subject-specific demands on teacher knowledge, practice and identity. Using Shulman's ‘signature pedagogies,’ we examine what OOF teachers need to know (thinking), do (performing), and be (identity). Through collaborative research, nine teacher educators from various disciplines identified pertinent themes leading to a framework of subject-specific expertise. The framework invites dialogue on relationships between subject-specific teacher identity and the four salient features of specialist teaching: inquiring, knowing, connecting, and pedagogical imperatives. The framework can support professional learning for OOF teachers and set a foundation for further research into this phenomenon.fals
Evaluation of the causes of infertility in dairy cattle on smallholder farms in Tanzania : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Science at Massey University, School of Veterinary Science, Manawatu
No full textSmallholder dairy cattle farming plays a crucial socio-economic role for many households across rural, peri-urban and urban areas in Tanzania. Despite its importance, the sector faces numerous challenges, with reproductive issues, particularly infertility, being among the major barriers to productivity and sustainability. This study explored farmer demographics and their knowledge, attitudes and practices (KAP) regarding infertility in dairy cattle, alongside an assessment of reproductive performance in cows and heifers. A cross-sectional survey was conducted on 301 smallholder farms across 13 districts in six key dairy-producing regions: Arusha, Kilimanjaro, Mbeya, Morogoro, Njombe and Tanga. Despite the shared similarities in demographics depicted by farmers, regional differences were notable. For instance, men dominated most regions except Njombe, where gender representation was more balanced. Education levels varied, with Morogoro farmers having higher education levels than others, while a higher proportion of farmers in Morogoro and Tanga had herds >4 animals. Acquisition of the first cattle beast also differed; cash purchases dominated, except in Mbeya, where cattle were often received as gifts (referred to as ‘kufufya’). The top-reported farming constraints included high input costs (93%), feed unavailability (71%), insufficient land (68%), and livestock diseases (62%). Overall, 95% of farmers reported having infertility in their herds, with the key reported causes being poor nutrition and housing (93%), livestock disease (89%), poor farm record keeping (85%) and poor heat detection (83%). Nearly all farmers (98%) considered infertility as having a major impact, with repeat breeding (95%) and failure to produce a calf in a year (69%) having the most impact. Reproductive performance was poor, with only 46% of animals pregnant and a median inter-calving interval of 468 days (despite excluding cows that were sold or culled before getting pregnant). Analysis of influencing factors showed that region was an important predictor of reproductive performance, followed by herd size and farmer experience. Larger herds were linked with better performance, while less experienced farmers surprisingly reported more pregnant animals, with better ability to recognise infertility signs. These findings highlight the need for regionally tailored strategies to improve reproductive outcomes and sustain smallholder dairy cattle farming in Tanzania
Dynamic carbon budgets and carbon debts for Aotearoa New Zealand and its building sector
No full textThe remaining carbon budget (RCB) is a crucial parameter when setting climate budgets for nations and economic sectors that want to measure their progress in climate change mitigation. The Paris Agreement is the most widely used and accepted climate change mitigation target, and the global RCB specified by the Intergovernmental Panel for Climate Change (IPCC) provides the carbon budget remaining from the beginning of 2020 that can be emitted as CO2 before the Paris Agreement’s target is exceeded. This research investigates the global RCB allocation to the national and building sector level in Aotearoa New Zealand, including consideration of different sharing approaches and modelling of potential future dynamic parameters for the RCB allocation, that are required to stay below 1.5 °C warming between the years 2024 and 2050. The average national RCB ranges from 159 to 339 MtCO₂ from year 2024; based on an average annual emissions rate of 38 MtCO₂, it will be depleted in 4–8 years. Therefore, this study proposed a dynamic carbon debt framework that provides a more realistic representation of dynamic RCBs and the carbon debt over future years. Key findings include the urgency of timely interventions, the need for additional mitigation strategies beyond the current policy approach which is largely focused on increased plantation forestry, and the usefulness of time-disaggregated carbon budgeting to address exhaustion of the RCB. Overall, this study demonstrates the relevance of dynamic budgeting to guide effective climate policy at both the national and building sector levels.fals
The global geopolitical-energy uncertainty index and total factor productivity: New evidence from firm-level analysis
No full textThis paper examines the impact of the global geopolitical-energy uncertainty (GEU) on firm-level total factor productivity, considering variation across countries, industries, and firm sizes. Employing the novel GEU index proposed by Dang et al. (2024a) and firm-level annual data from 2001 to 2023, we find strong evidence that the GEU index negatively affects firm productivity. There is heterogeneity in the GEU index's impact. Firms in developed countries such as the US, UK, France, and Germany are more negatively affected, whereas Canadian firms show a positive response. Energy-intensive firms and smaller firms experience stronger negative impacts. Mechanism analysis further demonstrates that both firm level characteristics and macroeconomic energy conditions shape productivity responses to GEU. Higher profitability reduces the negative impact of GEU shocks, while higher cost intensity and higher global energy prices amplify the adverse effects, increasing productivity losses. Our baseline results remain robust under different robustness checks. The paper's findings offer guidance for firms to develop effective strategies to manage risks during periods of heightened geopolitical-energy uncertainty.fals
Subclinical mastitis in New Zealand grazing dairy ewes 2: Relationships among somatic cell count, California Mastitis Test, and milk culture, and risk factors for elevated aerobic plate count
No full textOur objectives were, in grazing dairy ewes, (1) to describe SCC, California Mastitis Test (CMT) score, and ewe-level milk aerobic plate count (APC), (2) to explore the relationship between CMT and SCC, (3) to identify risk factors for elevated APC, and (4) to find the optimal SCC threshold for diagnosis of IMI. Gland-level milk samples were collected from ∼15 randomly selected ewes on each of 20 New Zealand dairy sheep farms at early, mid, and late lactation in a repeated cross-sectional study. Aerobic bacterial culture and CMT (measured on a scale of 0, trace, 1, 2, or 3) were performed at the gland level, and SCC and APC at the ewe level using composite milk samples. Milk samples were collected from 893 ewes, 870 of which had complete SCC and culture data. Geometric mean SCC was 169,039 (95% CI: 153,921–185,641) cells/mL, varying between farms and decreasing across visits. A CMT score ≥1 in one or both glands occurred in 21.2% of ewes. Mean log10 SCC increased linearly with CMT score, but the correlation between the ewe's highest gland-level CMT score and SCC was moderate (Kendall's tau = 0.47, 95% CI 0.43–0.52). Bacteria were isolated from 86 (9.9%) ewes, with the most common bacteria being NAS (7.0% of glands) and Staphylococcus aureus (0.6% of glands). A SCC threshold of ∼400,000 cells/mL had the greatest Youden's index for diagnosing IMI using a single SCC measurement. The APC was below the limit of quantification (1 × 103 cfu/mL) in 78.0% of ewes, and <100 × 103 cfu/mL in 96.9% of ewes, and varied between visits and farms. Using a mixed Bayesian ordinal regression model, elevated CMT score and SCC, positive milk culture, and subclinical mastitis, but not udder asymmetry, were confirmed as risk factors for elevated APC. These findings provide baseline milk quality data for New Zealand grazing dairy ewes, confirm that udder health should be considered when investigating elevated bulk milk APC, and can be used to help producers manage SCC, subclinical mastitis, and APC, as well as informing further research. Findings specific to New Zealand's emerging sheep dairy industry offer a benchmark for pastoral systems internationally and highlight the importance of udder health to bulk milk quality.fals
Digital Gazetteers: Review and Prospects for Place Name Knowledge Bases
No full textGazetteers typically store data on place names, place types, and the associated coordinates. They play an essential role in disambiguating place names in online geographical information retrieval systems for navigation and mapping, detecting and disambiguating place names in text, and providing coordinates. Currently, there are many gazetteers in use derived from many sources, with no commonly accepted standard for encoding the data. Most gazetteers are also very limited in the extent to which they represent the multiple facets of the named places yet they have potential to assist user search for locations with specific physical, commercial, social, or cultural characteristics. With a focus on understanding digital gazetteer technologies and advancing their future effectiveness for information retrieval, we provide a review of data sources, components, software and data management technologies, data quality and volunteered data, and methods for matching sources that refer to the same real-world places. We highlight the need for future work on richer representation of named places, the temporal evolution of place identity and location, and the development of more effective methods for data integration.fals
Enforcing Good Deeds: Investment Efficiency of Indian Firms Going Through CSR Law
No full textWith the enactment of the 2013 government mandate, Indian corporations meeting specific criteria no longer have the discretion to forgo CSR expenditures. Previous studies have reported negative capital market reactions to this regulatory intervention. In contrast, our study offers a long-term perspective on the impact of the CSR law on firms’ investment efficiency. Using a difference-in-differences framework, this study examines publicly listed Indian firms from 2011 to 2018, capturing a clean pre- and post-mandate window that isolates the structural impact of the CSR law while excluding confounding and shocks such as the COVID-19 crisis. Thus, the paper focuses on identifying the long-term institutional and structural effects of CSR rather than short-term cyclical fluctuations. We find that the CSR law leads to an increase in the investment efficiency of affected firms, driven primarily by reductions in agency conflicts and information asymmetry. This effect is more pronounced among firms with a strong presence of active monitoring groups, such as Hindu-owned promoters and institutional investors. Improved efficiency is also profound among firms located in areas with a lower Human Development Index (HDI) and Gender Diversity Index (GDI). Our findings demonstrate the positive impact of mandatory CSR law on capitalism and present insights for policymakers for regulators as ESG and CSR mandates are increasingly debated and adopted.fals
Transforming evidence synthesis: A systematic review of the evolution of automated meta-analysis in the age of AI
No full textExponential growth in scientific literature has heightened the demand for efficient evidence-based synthesis, driving the rise of the field of automated meta-analysis (AMA) powered by natural language processing and machine learning. This PRISMA systematic review introduces a structured framework for assessing the current state of AMA, based on screening 13,216 papers (2006–2024) and analyzing 61 studies across diverse domains. Findings reveal a predominant focus on automating data processing (52.5%), such as extraction and statistical modeling, while only 16.4% address advanced synthesis stages. Just one study (approximately 2%) explored preliminary full-process automation, highlighting a critical gap that limits AMA’s capacity for comprehensive synthesis. Despite recent breakthroughs in large language models and advanced AI, their integration into statistical modeling and higher-order synthesis, such as heterogeneity assessment and bias evaluation, remains underdeveloped. This has constrained AMA’s potential for fully autonomous meta-analysis (MA). From our dataset spanning medical (67.2%) and non-medical (32.8%) applications, we found that AMA has exhibited distinct implementation patterns and varying degrees of effectiveness in actually improving efficiency, scalability, and reproducibility. While automation has enhanced specific meta-analytic tasks, achieving seamless, end-to-end automation remains an open challenge. As AI systems advance in reasoning and contextual understanding, addressing these gaps is now imperative. Future efforts must focus on bridging automation across all MA stages, refining interpretability, and ensuring methodological robustness to fully realize AMA’s potential for scalable, domain-agnostic synthesis.fals