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Diet and Foraging in the Waibira Chimpanzee Community, Budongo Central Forest Reserve, Uganda
Get PDFForaging is a fundamental aspect of the behavioural ecology of any species. Chimpanzees (Pan troglodytes) are generalist omnivores that inhabit a continuous range of forest environments. Accordingly, substantial differences in feeding ecology exist across chimpanzee sub‐species and populations. Despite a persistent importance for the role of ripe fruit, chimpanzee diets typically include a large variety of food types. While considerable data exist on the foraging behaviour and diets of chimpanzees, these are typically limited to studies of single communities in distinct populations. Previous studies in the Budongo forest, Uganda, have focused on the Sonso community; less is known of the foraging behaviour of the neighbouring Waibira community. Here, we present detailed descriptive data on diet, activity, and food availability from this community. These were collected between October 2016 and June 2017 from focal observations of ten adult males and nine adult females, phenological monitoring of 168 chimpanzee food trees, and 4 ha of botanical plots. These chimpanzees generally conformed to the view of this species as a ripe fruit specialist, but were notably less frugivorous than other study communities and showed a considerable reliance on young leaves, in particular the leaves of Celtis mildbraedii, and on the seeds of Cynometra alexandrii during the dry season. Dietary diversity was similar to that of the neighbouring Sonso community, and our results support the idea that significant folivory is a general foraging strategy for Budongo Forest chimpanzees
The appraisal model of conspiracy theories (AMCT): Applying appraisal theories to understand emotional and behavioral reactions to conspiracy theories
Get PDFBeliefs in conspiracy theories are related to a diverse set of emotional and behavioral consequences. However, a theoretical model detailing when a conspiracy theory is more likely to elicit confrontation compared to withdrawal, indirect aggression or community-building is missing. We argue that appraisals provide the missing link between conspiracy beliefs and their consequences, proposing the Appraisal Model of Conspiracy Theories (AMCT). Based on appraisal theories of emotions, we outline how the focus on different features that vary between conspiracy theories and the situations in which they are embedded (i.e., focus on secrecy vs. gained knowledge; powerlessness vs. option of confrontation; harm to oneself vs. others) facilitate specific appraisals, resulting in different behavioral outcomes. We also outline how the AMCT helps to reconcile inconsistent research on conspiracy beliefs by providing better predictions about their emotional and behavioral consequences
A socio-legal analysis of abortion in Romania: From inspiring ‘The Handmaid’s Tale’ to post-Dobbs developments
No full textThis chapter provides a socio-legal analysis of the status of abortion in Romania, offering insights into one of the most severe limitations of reproductive autonomy in European history and its aftermath. The chapter begins with a discussion of Romania’s abortion ban during the communist era, a restriction so severe that it served as inspiration for Margaret Atwood’s ‘The Handmaid’s Tale’. It then follows the socio-legal trajectory of abortion after the fall of communism, when abortion was legalized, until the recent global developments following the US Supreme Court’s decision in Dobbs v. Jackson Women’s Health Organization, which effectively put an end to abortion rights under the US Constitution. Overall, by looking at Romania as a case study, the chapter shows that the status and accessibility of abortion are deeply contingent on factors extending beyond the legal realm, such as historical legacies, religion, international developments and the involvement of transnational actors
An effective mitigation strategy to hedge against absenteeism of occasional drivers
Get PDFCompanies can use occasional drivers to increase efficiency on last-mile deliveries. However, as occasional drivers are freelancers without contracts, they can decide at short notice whether they perform delivery requests. If they do not perform their tasks, this is known as driver absenteeism, which obviously disrupts the operations of companies. This paper tackles this problem by developing an auction-based system, including a mitigation strategy to hedge against the absenteeism of occasional drivers. According to this strategy, a driver can bid not only for serving bundles but also to act as a reserved driver. Reserved drivers receive a fee to ensure their presence but are not guaranteed to be assigned to a specific bundle. The problem is modeled as a two-stage stochastic problem with recourse activation. To solve this problem, this paper develops a self-learning matheuristic (SLM) and an iterated local search (ILS) that exploits SLM as a local search operator. Through an extensive computational study, this paper shows the clear dominance of the newly proposed approach in terms of solution quality, run times, and customers’ perceived quality of service compared against three different deterministic approaches. The Value of the Stochastic Solution, a well-known stochastic parameter, is also analyzed. Finally, the identikit of the perfect reserved driver, based on data observed in optimal solutions, is discussed
Benefits Conditionality in the United Kingdom: Is It Common, and Is It Perceived to Be Reasonable?
Get PDFProgramme‐level data suggest that increasing numbers of claimants are subject to work‐related behavioural requirements in countries like the United Kingdom. Likewise, academic qualitative research has suggested that conditionality is pervasive within the benefits system, and is often felt to be unreasonable. However, there is little quantitative evidence on the extent or experience of conditionality from claimants' perspectives. We fill this gap by drawing on a purpose‐collected survey of UK benefit claimants (n = 3801). We find that the stated application of conditionality was evident for a surprisingly small proportion of survey participants—even lower than programme‐level data suggest. Unreasonable conditionality was perceived by many of those subject to conditionality, but not a majority, with, for example, 26.2% believing that work coaches do not fully take health/care‐related barriers into account. Yet, alongside this, a substantial minority of claimants not currently subject to conditionality (22.4%) report that conditionality has negatively affected their mental health. We argue that reconciling this complex set of evidence requires a more nuanced understanding of conditionality, which is sensitive to methodological assumptions, the role of time and implementation and the need to go beyond explicit requirements to consider implicit forms of conditionality. In conclusion, we recommend a deeper mixed‐methods agenda for conditionality research
On Cross-Validated Estimation of Skew Normal Model
Get PDFSkew normal model suffers from inferential drawbacks, namely singular Fisher information when it is close to symmetry and diverging of maximum likelihood estimation. This causes a large variation of the conventional maximum likelihood estimate. To address the above drawbacks, Azzalini and Arellano-Valle (2013) introduced maximum penalised likelihood estimation (MPLE) by subtracting a penalty function from the log-likelihood function with a pre-specified penalty coefficient. Here, we propose a cross-validated MPLE to improve its performance when the underlying model is close to symmetry. We develop a theory for MPLE, where an asymptotic rate for the cross-validated penalty coefficient is derived. We further show that the proposed cross-validated MPLE is asymptotically efficient under certain conditions. In simulation studies and a real data application, we demonstrate that the proposed estimator can outperform the conventional MPLE when the model is close to symmetry
Density Functional Theory in Forensic Science: Applications and Challenges
Get PDFForensic science is evolving to tackle increasingly complex criminal investigations, where traditional methods may fall short. This review examines how computational chemistry enhances forensic techniques by providing detailed insights into molecular interactions, reaction mechanisms, and spectroscopic properties. A particular focus is placed on density functional theory (DFT), which has emerged as a powerful, cost‐effective tool that balances computational efficiency and accuracy, making it ideal for studying a wide range of forensic compounds without the need for physical samples. Applications discussed include the analysis of new psychoactive substances (NPS), detection of food adulterants, development of chemical sensors, and forensic examination of luminol for blood detection, fingerprint visualization, and the analysis of explosives. Key advances in various DFT functionals are highlighted, along with prospects for integrating DFT with machine learning and sustainable practices to further expand its impact in forensic science
Simple recombinant monoclonal antibody production from Escherichia coli
Get PDFAntibodies are valuable biological reagents used in a wide range of discovery research, biotechnology, diagnostic and therapeutic applications. Currently both commercial and lab scale antibody production is reliant on expression from mammalian cells, which can be time consuming and requires use of specialist facilities and costly growth reagents. Here we describe a simple, rapid and cheap method for producing and isolating functional monoclonal antibodies and antibody fragments from bacterial cells that can be used in a range of laboratory applications. This simple method only requires access to basic microbial cell culture and molecular biology equipment, making scalable in-house antibody production accessible to the global diagnostics, therapeutics and molecular bioscience research communities
Examination of risks in circular supply chains using transition management lens: Towards a circular economy in emerging markets
No full textWe perform a multidimensional and integrated investigation of risks associated with circular supply chains (CSC), drawing on Transition Management Theory (TMT). This research focuses on e-waste from the Indian electronics industry, a waste stream with significant recovery potential and one of the fastest-growing in emerging economies. Drawing on TMT, the study (i) institutionalises risk management activities in circular systems to operationalise the transition towards CE; (ii) quantifies CSC risks at operational, tactical, and strategic levels and measure the total risk exposure of CSCs; (iii) comprehensively cogitates the operational, socio-environmental, and financial implications of CSCs risks and (iv) considers uncertainty in operations research (OR) models by applying a fuzzy set theory, evidential reasoning algorithm, and expected utility theory based model to evaluate and profile the CSCs risks. The proposed model contributes to the application of decision analysis and risk analysis approaches in the sustainability domain and can efficiently model uncertain, subjective, and incomplete data. Our findings reveal that customers’ reluctance to purchase reprocessed products represents the most critical challenge to the effectiveness of CSCs. Furthermore, contrary to conventional perspectives, organizations are strategically shifting toward adopting circular practices. However, they often lack the practical means and resources to implement these strategies effectively
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