Resource Use Efficiency of Cassava farmers in Akwa Ibom State, Nigeria

This study examined the resource use efficiency of cassava farmers in Akwa Ibom State, Nigeria. Data collated from 100 farmers selected using a multi stage random sampling technique were subjected to statistical and economic analyses to unveil the pattern of resource use efficiency in cassava enterprise. Result from the ordinary least square multiple regression informed that 92 percent of the variation in cassava output was explained by farm size, fertilizer use, cuttings and labour inputs, with only farm size and cuttings being significant at five percent level. Data on resource use efficiency reveal that the farmers were operating in the second stage of production as regards the use of land (farm size), cuttings and labour, thus, implying decreasing returns to scale, whereas for fertilizer use, they operated within the third stage of the production process as Marginal Physical Product (MPP) was below zero. Based on these findings, the study recommends policies to raise the level of resource use especially through the provision and maintenance of an efficient input delivery system. Keywords: Cassava Farmers, Resource Use Efficiency, Returns to Scale, Marginal Physical Produc

Lessons from the Congested Clique Applied to MapReduce

The main results of this paper are (I) a simulation algorithm which, under quite general constraints, transforms algorithms running on the Congested Clique into algorithms running in the MapReduce model, and (II) a distributed $O(\Delta)$-coloring algorithm running on the Congested Clique which has an expected running time of (i) $O(1)$ rounds, if $\Delta \geq \Theta(\log^4 n)$; and (ii) $O(\log \log n)$ rounds otherwise. Applying the simulation theorem to the Congested-Clique $O(\Delta)$-coloring algorithm yields an $O(1)$-round $O(\Delta)$-coloring algorithm in the MapReduce model. Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the $\mathcal{CONGEST}$ model), the major impediment to constructing fast algorithms is the $O(\log n)$ restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models.Comment: 15 page

Constructing Robust Channel Structures by Packing Metallacalixarenes: Reversible Single-Crystal-to-Single-Crystal Dehydration

The self-assembly process involving the dianion of trimesic acid (Htrim2−) and {Cu(tmen)}2+ templating cations (tmen = N,N,N′,N′-tetramethylethylenediamine) affords a new metallacalixarene, [Cu4(tmen)4(Htrim)4]·nH2O. The packing of the cyclic molecules in the crystal generates channels that are filled by water molecules. The dehydration−rehydration process of the crystals was found to be reversible

Lactate signalling regulates fungal β-glucan masking and immune evasion

AJPB: This work was supported by the European Research Council (STRIFE, ERC- 2009-AdG-249793), The UK Medical Research Council (MR/M026663/1), the UK Biotechnology and Biological Research Council (BB/K017365/1), the Wellcome Trust (080088; 097377). ERB: This work was supported by the UK Biotechnology and Biological Research Council (BB/M014525/1). GMA: Supported by the CNPq-Brazil (Science without Borders fellowship 202976/2014-9). GDB: Wellcome Trust (102705). CAM: This work was supported by the UK Medical Research Council (G0400284). DMM: This work was supported by UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/K000306/1). NARG/JW: Wellcome Trust (086827, 075470,101873) and Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377). ALL: This work was supported by the MRC Centre for Medical Mycology and the University of Aberdeen (MR/N006364/1).Peer reviewedPostprin

Application of Artificial Intelligence (AI)in Sustainable Building Lifecycle; ASystematic Literature Review

With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI’s potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI’s capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data‐driven analysis and prediction, while digital twins provide real‐time insights for informed decision‐making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices

The Rewiring of Ubiquitination Targets in a Pathogenic Yeast Promotes Metabolic Flexibility, Host Colonization and Virulence

Funding: This work was funded by the European Research Council [http://erc.europa.eu/], AJPB (STRIFE Advanced Grant; C-2009-AdG-249793). The work was also supported by: the Wellcome Trust [www.wellcome.ac.uk], AJPB (080088, 097377); the UK Biotechnology and Biological Research Council [www.bbsrc.ac.uk], AJPB (BB/F00513X/1, BB/K017365/1); the CNPq-Brazil [http://cnpq.br], GMA (Science without Borders fellowship 202976/2014-9); and the National Centre for the Replacement, Refinement and Reduction of Animals in Research [www.nc3rs.org.uk], DMM (NC/K000306/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments We thank Dr. Elizabeth Johnson (Mycology Reference Laboratory, Bristol) for providing strains, and the Aberdeen Proteomics facility for the biotyping of S. cerevisiae clinical isolates, and to Euroscarf for providing S. cerevisiae strains and plasmids. We are grateful to our Microscopy Facility in the Institute of Medical Sciences for their expert help with the electron microscopy, and to our friends in the Aberdeen Fungal Group for insightful discussions.Peer reviewedPublisher PD