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Digital preservation of theses and dissertations (TDs) is crucial for the archiving (long-term preservation) of intellectual content of undergraduate and postgraduate students in universities. This paper aims to investigate the digitization of TDs in three selected academic libraries in Oyo State, south-west Nigeria.

Descriptive survey research design was used for this study. The instrument for data collection was the questionnaire. The study comprised 78 professionals and paraprofessional librarians in the three selected university libraries in Oyo State.

The study revealed that digital preservation of TDs in university libraries is at the developmental stage. Also, the study revealed that most participants strongly agreed that the main purpose for embarking on the digital preservation of TDs was to influence the web ranking of the institutions. Web archiving was the widely adopted method of digital preservation in university libraries. Poor information and communication technology infrastructure, absence of policy and poor funding were identified as major challenges militating against the effective digital preservation of theses in the libraries. Hence, it was recommended that the management of university libraries should be more committed to providing substantial funds to meet the financial obligation for digital preservation initiatives.

The study provides insight into a case study of digitization of theses and dissertation i academic libraries in three selected universities to serve as a basis for further research.

Rising energy expenditures due to more intensive use of energy in modern agriculture and increasing energy prices may affect rural households’ agricultural incomes, particularly the incomes of the rural poor in developing countries. However, the exact link between energy costs and income among the rural poor needs further empirical investigation. The purpose of this paper is to develop a deeper understanding of the relationship between energy use and family income, using household-level panel data collected from 500 potato farmers in a poor region of Northern China, where eliminating poverty by 2020 is now the top government priority.

The paper uses household survey data collected from six counties in a poor region in northern China in 2013 to measure the relationship between energy cost and family income. A fixed effect model is employed to estimate the relationship.

The findings indicate that potatoes play an important role in the surveyed families’ incomes, and that the energy costs of potato production have a significant negative relationship with family income. However, this negative relationship is only significant for farmers with low economic standing, such as those living below or just above the poverty line. The negative relationship between energy costs and family income is only significant for those cultivating a certain size of potato-sown area; it is insignificant for those cultivating smaller areas.

These findings indicate that, in general, reducing energy costs helps the poor increase their income but is not necessarily helpful to those with high economic standing or a relatively small potato-sown area. If rural development policies are to support poverty reduction and energy savings (at least in major potato production regions), interventions aimed at energy cost reduction may be effective only for the poor whose family income depends, to a relatively high degree, on potato production.

This study aims to introduce an effective optimization algorithm for finding the suitable penetration of renewable distributed generators (RDGs) in the power system. The target of this research is to minimize the total costs, including the cost of emissions, the cost of purchasing electricity from the primary grid and the cost of branch power losses.

Energy valley optimizer (EVO) is applied to solve the optimization problem for determining the integration of photovoltaic units and wind turbine units in the system. This study considers the time variation of load demand and output power of units to improve the quality of the found solution. Simulation results are collected for evaluating the performance of the used methods in the same conditions.

The results indicated the huge economic and technical benefits from adopting the optimal solution of placement and sizing of RDGs in the power system. Besides, the outstanding efficiency of EVO is also proven through comparison with sunflower optimizer, drawer algorithm, osprey optimization algorithm, genetic algorithm, particle swarm optimization and artificial bee colony algorithm.

The mathematical models for the objective function and the constraints are appropriately described for the optimization problem. A simulation program for applying the optimization algorithms to the above problem is developed in the MATLAB software. The simulation results demonstrated the excellent benefits not only economically but also technically for the hybrid distribution system.

Extreme weather events are on the rise around the globe. Nevertheless, it is unclear how these extreme weather events have impacted the supply chain sustainability (SCS) framework. To this end, this paper aims to identify and analyze the aspects and criteria to enable manufacturing firms to navigate shifts toward SCS under extreme weather events.

The Best-Worst Method is deployed and extended with the entropy concept to obtain the degree of significance of the identified framework of aspects and criteria for SCS in the context of extreme weather events through the lens of managers in the manufacturing firms of a developing country-Nigeria.

The results show that extreme weather preparedness and economic aspects take center stage and are most critical for overcoming the risk of unsustainable patterns within manufacturing supply chains under extreme weather events in developing country.

This study advances the body of knowledge by identifying how extreme weather events have become a significant moderator of the SCS framework in manufacturing firms. This research will assist decision-makers in the manufacturing sector to position viable niche regimes to achieve SCS in the context of extreme weather events for expected performance gains.