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Human resource capability is an important factor in determining institutional capacities in digital records preservation. This study aims to assess human resource capabilities in supporting digital records preservation in Tanzania, with special reference to the Records and Archives Management Department (RAMD) and Registration, Insolvency and Trusteeship Agency (RITA).

This study employed a descriptive case study design with multiple cases to generate insights into the topic under inquiry. Simple random and purposive sampling methods were used to select study respondents. Data were collected using questionnaires, interviews and documentary review. Qualitative data collected were subjected to content analysis, whereas quantitative data were analysed using international business machine-corporation-statistical packages and service solution.

RAMD and RITA have minimal human resource capabilities, as they contend with a shortage of qualified staff and technical personnel for digital records preservation. The shortage of funds, lack of management commitment to preserving digital records, lack of staff awareness on digital records preservation and inadequate participation of records practitioners in digital records preservation emerged as pressing challenges the two agencies contended with.

This study’s recommendations include finding alternative sources of funding, employing digital records specialists and provision of training on digital records preservation. Furthermore, this study proposes a framework that institutions can use in assessing human resource capabilities for digital records preservation.

This study contributes new knowledge and insights on the role of human resource capabilities in supporting digital records preservation in a resource-poor country.

The purpose of this paper is to propose a method to compute RAMD indices to measure and improve the performance of skim milk powder production system of a dairy plant under real working conditions.

The present work is carried out by developing performance model based on Markov birth-death process. The skim milk powder production system consists of six units. The first order governing differential equations are derived using the mnemonic rule and further solved to calculate RAMD indices i.e. reliability, availability, maintainability, dependability, MTBF, MTTR and dependability ratio for each subsystem of the system.

The subsystem SS1 comprising of chiller and cream separator is the most critical from maintenance point of view, as the reliability, availability, maintainability, dependability, MTBF and dependability ratio indices are low as compared to those of other subsystems of skim milk powder production system of the dairy plant.

The RAMD indices of the present work is very useful for finding the critical subsystem and its effect on the performance of the system working under real working conditions. Further, based on findings the maintenance priorities for various subsystems can be decided.

This paper aims to present a high-level summary of the US archivist’s digital mandate for 2019, embodied in the publication “Managing Government Records”, issued on August 24, 2012, and a summary of US policy. The authors then consider the implications of the US e-recordkeeping initiative for lower-resource countries.

After setting out key elements of the US Archivist’s digital mandate, the paper proceeds to evaluate its policy implications for lower-resource countries based on the authors’ field experience and knowledge of case studies.

The USA is embarking on a state of the art approach for managing public sector archives in a digital form, with deadlines approaching for all federal agencies to manage e-mail and other e-records. Although a similar need exists in lesser-resourced countries, there are enormous barriers to successful implementation of a similar approach.

The archivist’s 2019 digital mandate assumes that the technology sector will embrace the needs of public sector agencies in working on applicable electronic archiving solutions.

The Archivist’s Directive has the potential to be an enormous driver of change in the records management profession with respect to future management of increasingly digital archive collections. Vast collections of public sector e-mail and other forms of e-records potentially will be preserved under the directive, raising the stakes that archivists and records managers work on solutions in the area of long-term preservation and future access.

The importance of capturing the activities of public-sector institutions in all countries for the purpose of openness, transparency and access cannot be overstated. In an increasingly digital age, new methods are needed to ensure that the historical record of governmental institutions is preserved and made accessible.

The US Archivist’s mandate represents a cutting-edge approach to long-term digital archiving with potential future applicability to the management of public sector records worldwide.

The United States is, without doubt, the most powerful nation in the world in terms of its economy. Some measure of this is exemplified by the fact that the Gross National Product (GNP) of California, treated as that of a country, would be seventh highest in the world. The City of Los Angeles alone, treated as a country, has a GNP twelfth largest on a world scale.

The purpose of this paper is to develop and describe a pixel‐based airborne hyperspectral remote sensing system approach for searching for missing and lost RMAF aircraft, especially the NURI helicopter that crashed in Genting‐Sempah, Malaysia on Friday 13 July 2007, ten minutes after leaving the RMAF 10th Squadron based in Sg. Besi Camp, Selangor, Malaysia. It was reported found crashed with all six air force crew onboard killed, four days later on 17 July 2007 at 13.25 hours 5 km North‐west of Genting‐Sempah‐Karak‐Highway.

The objective is achieved by exploring the use of a very high 1 m² spatial resolution (pixel‐based) airborne hyperspectral imaging system near real‐time data processing within two hours after landing. The UPM‐Aeroscan's AISA is a state‐of‐the‐art aircraft mounted commercial hyperspectral sensor operated for SAR development application by Forest Geospatial Information and Survey Lab (FGISL)/Aeroscan Precision (M) Sdn Bhd. in Block C3, UPM‐MTDC Tech Centre, Lebuh Silikon, Universiti Putra Malaysia. It is designed to provide a near real time, frequent, repetitive, accurate and reliable pushbroom instrument that acquires images in hundreds of registered, contiguous narrow spectral band passes such that for each element it is possible to derive a complete reflectance spectrum between the tree crowns, a damaged or slashed tree canopy and an anomaly especially metal like foreign object that penetrates into the tree crowns of a dense forest. Using an advanced Spectral Angle Mapper (SAM) digital analysis and two archives spectral signatures of flying and “parked” NURI helicopters, in addition to ground supporting data from the civilian eye‐witnesses, spectral matching of images is applied to identify and map the missing helicopter.

Despite the bad weather hampering the search and air surveillance‐to‐ground verifications, the NURI helicopter was reported found crashed with all six air force crew onboard killed, four days later based on the GPS suspected locations analysed and mapped from the UPM‐APSB's AISA airborne hyperspectral sensor data. The GPS locations given by the image were, however, not accurate for ground verifications since an old military topographical map were used. The GPS locations from the airborne image was transferred on to a Google Earth image in the Operations Room but, for ground verifications, a different set of reference topographical maps were used. However, the suspected search target sites were not that far away from the actual sighted RMAF NURI helicopter wreckage.

The value and contribution of this research are the successful application of operating a pixel‐based airborne hyperspectral sensor to locate missing military helicopters in SAR. It is expected that the UPM‐APSB's AISA airborne hyperspectral sensor can be of further use in future SAR for missing civilian helicopters or commercial aircraft.

The purpose of this study was to identify and assess the regulatory and institutional constraints in managing procurement records in Tanzania’s procuring entities.

This study used a mixed study design. There were explorative case study and questionnaire survey study methods used sequentially. In total, 15 procuring entities were used for exploratory case study, while 200 respondents were administered with questionnaires. A 75 per cent response rate was realised.

Results indicated that management and care of procurement records is constrained by regulatory and institutional constraints. The identified and assessed constraints were inter alia: incapacity of institutional actors, inadequate regulatory and institutional arrangements, inadequacy of storage space, equipment and facilities and insufficiency of security and safety measures.

This research focussed on the procuring entities found in Dar es Salaam, which accounts for 40.72 per cent of the total procuring entities in Tanzania. Based on this, the generalisation of research findings can be sought in that particular context.

Findings imply that procurement records management and care is highly influenced by the constraining factors that hinder efficient records keeping in most procuring entities in Tanzania.

Majority of procurement management units and user departments’ staff were found to possess inadequate knowledge, skills and competences in management and care of procurement records. The procuring entities should ensure that procurement staffs are trained in records and archives management practices.

This study contributes towards adding knowledge to the existing body of knowledge on the procurement records and archives management systems.

The demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.

This paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).

Nature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.

This paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.

Availability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.

In this paper, the objective is to perform mathematical modeling to optimize the steady-state availability of a multi-state repairable crushing system of a sugar plant using the evolutionary algorithm of Particle Swarm Optimization (PSO). The system availability is optimized by evaluating the optimal values of failure and repair rate parameters concerned with the subsystem of the system.

Mathematical modeling of the multi-state repairable system is performed to develop the first-order differential equations based on the exponential distribution of the failure and repair rates. These differential equations are recursively solved to obtain the availability under normalizing conditions. The availability of the system is optimized by using the PSO algorithm. The results obtained by PSO are validated by using the Genetic Algorithm (GA).

The availability analysis of the system concludes that the cane preparation (F1) is critical of the crushing system and the optimized availability of the system using PSO is achieved as high as 87.12%.

A crushing system of the sugar plant is evaluated as it is the main system of the sugar plant. The maintenance data associated with failure and repair rate parameters were analyzed with the help of maintenance records/logbook and by conducting personal meetings with maintenance executives of the plant. The results obtained in the paper helped them to plan maintenance strategies accordingly to get optimal system availability.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

The purpose of this paper is to propose and evaluate a novel tool for the assessment and selection of rapid prototyping (RP)/manufacturing (RM) systems as alternative processes for low‐volume production in the machinery and equipment design sector. By analysing previous RP/RM selectors, this research addresses the necessary factors that a knowledge‐based engineering (KBE) system must include for the analysis, comparison and ranking of candidate technologies.

This research starts with the analysis of previous KBE solutions for RP/RM process selection, then a new KBE tool is proposed through the integration of artificial intelligence tools such as fuzzy logic, artificial neural networks (ANNs) and relational databases. Three case studies, provided by a Spanish machinery design centre, are used in order to measure the suitability of the proposed system for the assessment of real designs of special purpose mechanical parts.

The paper reports several improvements based on case studies which include a more suitable logic for process selection according to the designer's criteria and improvements in the overall parts cost estimation when compared to conventional parametric methods.

The newly proposed KBE system has proven useful especially in cases where non‐experts or students need to select a RP/RM process according to an initial product design specification. The cost estimation module based on ANNs provides a practical tool which may be used by academics but also practitioners who wish to automate product costing calculations.

Unlike previous solutions, the proposed system provides a straightforward means for RP/RM selection by an overall ranking of candidate processes, part cost estimation and materials selection. The main contribution is the modular design and logical planning, that overcomes the dilemma: material‐or‐process first.