Mode of Biochar Application to Vertisols Influences Water Balance Components and Water Use Efficiency of Maize (Zea mays L.)

Vertisols belong to a group of soils with high fertility but poor physical properties of swelling when wet and shrinking and cracking when dry. The swelling inhibits infiltration, resulting in flooding, limiting the production of upland crops. Biochar (<BC) application has been shown to reduce the shrink-swell behaviour of Vertisols. However, the mode of biochar application to these soils may affect the effectiveness of the amendment. This study investigated the water relations and maize (Zea mays L.) growth under two BC application modes: (i) biochar applied into cracks that develop with drying, C, and (ii) biochar that was surface broadcast and incorporated into the topsoil, FM. A control treatment did not receive any BC amendment. Maize was grown on the BC-amended Vertisols using the two modes of application in a greenhouse under two seasonal water regimes of 610 and 450 mm. The results showed that the proportion of total water application lost to runoff was 37%, 49% and 53% for C, FM and control treatments, respectively. Both maize yield and Water Use Efficiency (WUE), for the C treatments were significantly (p < 0.05) higher than those for FM treatments. The maize yield under the C treatments was 19% over the control. Similarly, the WUE for the C treatments was 28% above the control treatment. It is concluded that the application of biochar into cracks is a more effective way of improving the water relations and upland crop productivity and WUE in Vertisols than the traditional surface incorporation

Analyzing Domino Effects Occurring on Gasoline Storage Tanks at the Bulk Oil Storage and Transportation (BOST) Depot

Since processed crude oil products are very vulnerable (susceptible) and highly flammable to cause massive catastrophes, such as fire and explosion, which are frequent and can create a chain reaction (Domino effects). This research was carried out at the Bulk Oil Storage and Transportation LTD depot on the Accra plain in Ghana where gasoline and Gasoil are stored. The research was conducted on a flammable gasoline area subjected to a vapor cloud explosion and the hazardous zone. Analyzing domino effects, propagation of a gasoline flammable vapor cloud caused by the explosion, ALOHA (Areal Location of Hazardous Atmospheres) software was used to find out how to apply effective safety measures to prevent future risks at any BOST facilities across the country. After the analysis, it was realized that 5.0 miles to the west-south-west the fuel concentration in the air was 2100 ppm lower than the explosive limit (LEL), and could not be as severe as that at 2.3 miles distance from the source point (12600 ppm LEL) with a greater fuel concentration in the atmosphere. The results made available would be useful in maximizing (improving) safety at the facility, residential area, and as well as minimizing future incidents

Productivity of Soybean under Projected Climate Change in a Semi-Arid Region of West Africa: Sensitivity of Current Production System

The production of soybean is gaining more attention in West Africa. In light of projected changes in climate, there is a need to assess the potential impacts on yield productivity and variability among farmers. An evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate soybean productivity under both historical (1980–2009) and projected climate scenarios from multiple general circulation models (GCMs) under two representative concentration pathways (RCPs): 4.5 and 8.5. Agronomic data from 90 farms, as well as multiple soil profile data, were also used for the impact assessment. Climate change leads to a reduction (3% to 13.5% across GCMs and RCPs) in the productivity of soybean in Northern Ghana. However, elevated atmospheric carbon dioxide has the potential to offset the negative impact, resulting in increased (14.8% to 31.3% across GCMs and RCPs) productivity. The impact of climate change on yield varied widely amongst farms (with relative standard deviation (RSD) ranging between 17% and 35%) and across years (RSD of between 10% and 15%). Diversity in management practices, as well as differences in soils, explained the heterogeneity in impact among farms. Variability among farms was higher than that among years. The strategic management of cultural practices provides an option to enhance the resilience of soybean productivity among smallholder

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. In: Proceedings of the 1986 IEEE international conference on robotics and automation. 1986. Vol. 3, pp.1645–1650.Berglund, M., & Karltun, J. (2007). Human, technological and organizational aspects influencing the production scheduling process. International Journal of Production Economics, 110(1-2), 160-174. doi:10.1016/j.ijpe.2007.02.024Besbes, W., Teghem, J., & Loukil, T. (2010). Scheduling hybrid flow shop problem with non-fixed availability constraints. European J. of Industrial Engineering, 4(4), 413. doi:10.1504/ejie.2010.035652Bhattacharyya, S., & Koehler, G. J. (1998). Learning by Objectives for Adaptive Shop-Floor Scheduling. Decision Sciences, 29(2), 347-375. doi:10.1111/j.1540-5915.1998.tb01580.xBitran, G. R., & Tirupati, D. (1988). OR Practice—Development and Implementation of a Scheduling System for a Wafer Fabrication Facility. Operations Research, 36(3), 377-395. doi:10.1287/opre.36.3.377Buxey, G. (1989). Production scheduling: Practice and theory. European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. 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Climate Change Impact and Variability on Cereal Productivity among Smallholder Farmers under Future Production Systems in West Africa

Agriculture inWest Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate, thus, pose a threat since crop production is mainly rain-fed. The impact of climate change and its variation on the productivity of cereals in smallholder settings under future production systems in Navrongo, Ghana and Nioro du Rip, Senegal was assessed in this study. Data on management practices obtained from household surveys and projected agricultural development pathways (through stakeholder engagements), soil data, weather data (historical: 1980–2009 and five General Circulation Models; mid-century time slice 2040–2069 for two Representative Concentration Pathways; 4.5 and 8.5) were used for the impact assessment, employing a crop simulation model. Ensemble maize yield changes under the sustainable agricultural development pathway (SDP) wer

UK Greenhouse Gas Inventory 1990 to 2021: annual report for submission under the Framework Convention on Climate Change

This is the United Kingdom’s National Inventory Report (NIR) submitted in 2023 to the United Nations Framework Convention on Climate Change (UNFCCC). It contains national greenhouse gas emission estimates for the period 1990-2021, and descriptions of the methods used to produce the estimates. The greenhouse gas inventory (GHGI) is based on the same datasets used by the UK in the National Atmospheric Emissions Inventory (NAEI) for reporting atmospheric emissions under other international agreements. The GHGI is therefore consistent with these other air emissions inventories where they overlap. The greenhouse gas inventory is compiled on behalf of the UK Department for Energy Security and Net Zero (DESNZ) for the Science and Innovation for Climate and Energy (SICE) Directorate, by Ricardo Energy & Environment. We acknowledge the positive support and advice from DESNZ throughout the work, and we are grateful for the help of all those who have contributed to this NIR. A list of the contributors can be found in Chapter 18. The GHGI is compiled according to the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines (IPCC, 2006). Each year the inventory is updated to include the latest data available. Improvements to the methodology are backdated as necessary to ensure a consistent time series. Methodological changes are made to take account of new data sources, or new guidance from IPCC, and new research, sponsored by DESNZ or otherwise

HLA haplotypes associated with hemochromatosis mutations in the Spanish population

BACKGROUND: The present study is an analysis of the frequencies of HLA-A and -B antigens and HLA haplotypes in two groups of individuals homozygous for the two main HFE mutations (C282Y and H63D) and a group heterozygous for the S65C mutation. METHODS: The study population includes: 1123 healthy individuals, 100 homozygous for the C282Y mutation, 138 homozygous for the H63D mutation and 17 heterozygous for the S65C mutation. HFE and HLA alleles were detected using DNA-based and microlymphocytotoxicity techniques respectively. RESULTS: An expected significant association between C282Y and the HLA-A3/B7 haplotype was found, but other HLA haplotypes carrying the -A3 antigen were found: HLA-A3/B62 and HLA-A3/B44. Also, a significant association between H63D mutation and HLA-A29/B44 haplotype was found, and again other HLA haplotypes carrying the HLA-A29 antigen were also found: HLA-A29/B14 and HLA-A29/B62. In addition, the S65C mutation seems to be associated with a HLA haplotype carrying the HLA-A26 antigen. CONCLUSION: These findings clearly suggest that HLA-A3/B7 and HLA-A29/B44 are the ancestral haplotypes from which the C282Y and H63D mutations originated, respectively. The frequencies of these mutations in different populations, their geographical distribution, and the degree of the statistical association to the ancestral haplotypes, suggest that the H63D mutation must have occurred earlier than the C282Y mutation

Promoting the achievement in schools of children and young people in care

As of March 2017, there were 72,670 children and young people in care in England. The number of looked after children has continued to increase steadily over the last eight years. Sixty per cent of these children are in care because of abuse or neglect and three-quarters are placed in foster care arrangements. Children and young people who are in or have experienced care remain one of the lowest performing groups in terms of educational outcomes. The average Attainment 8 score for children in care is 19.3 compared to 44.5 for non-looked after children and 19.3 for children in need. In 2017 there was an increase in the percentage of children in care achieving a pass in English and Mathematics from 17.4% to 17.5% and also in entering EBacc. Care leavers can experience poorer employment and health outcomes after leaving school compared to their peers. They are over-represented amongst the offender population and those who experience homelessness. However, research is emerging to show that children and young people in care can have very positive experiences of school if they are supported effectively to reach their full potential academically and socially. The purpose of this report is to share practice in local authorities (LA) from across England and Wales that is contributing to improved outcomes and school experiences for children and young people in care. The case studies were all undertaken as part of the Promoting the Achievement of Looked after Children (PALAC) programme between 2014 and 2017. This report presents an account of the programme, including the activities undertaken by the participants and the outcomes of the programme to date for pupils in care and staff in the participating virtual schools (VS) and local authorities