TUT DIGITAL OPEN REPOSITORY

Innovation is now globally considered a key factor in driving economic growth and development (Eggink, 2012). This is strongly supported by Scerri (2020c), who states that innovation is one of the important elements for development and economic growth. However, the critical issue in this regard is whether the innovation approaches and models that the countries adopt serve to promote inclusive innovation and development. In South Africa, more specifically with consideration of the historical background of the apartheid regime, the country’s adopted innovation approach and model has always narrowly focused on quantitative measures such as science, technology, research and development (Kahn, 2013). This also covers the years of segregation after colonialism and apartheid, which tended to ignore the consideration that the innovation system (IS) could be national, regional, local or sectoral. In other words, the persistent and uneven distribution of the innovation actors’ capabilities could also be identified across sectors, countries and regions.

This study aimed to assess the developmental effects of economic integration on SADC member countries using the system of innovation approach. In order to achieve this aim, the following research objectives were formulated: ▪ To evaluate the degree of regional trade integration among SADC member states ▪ To examine the level of institutional integration among SADC member states ▪ To assess the national systems of innovation of SADC member states and the SADC regional system of innovation ▪ To determine the degree of alignment between national systems of innovation of SADC member states and the SADC regional system of innovation. A qualitative research approach which combined a systematic literature review with document analysis using content analysis was adopted for this study. The critical literature review yielded contradictory results, with some studies finding that regional integration had positive developmental effects and others finding that regional integration had negative developmental effects. Some did not find any effect. Document analysis revealed, firstly, that SADC member countries had mainly failed to meet the targets of regional trade integration set out in the original SADC RISDP 2005-2020. Secondly, national economies of SADC countries remained undiversified, dominated mainly by the agricultural and mining sectors. The study found that there was still a low possibility of establishing a viable SADC regional system of innovation.

South Africa is home to numerous indigenous and locally developed sheep (Nguni Pedi, Zulu, and Namaqua Afrikaner, Afrino, Africander, Bezuidenhout Africander, Damara, Dorper, Döhne Merino, Meat Master, South African Merino, South African Mutton Merino, Van Rooy, and Dorper), goat (SA veld, Tankwa, Imbuzi, Bantu, Boer, and Savanna) and cattle (Afrigus, Afrikaner, Bolowana, Bonsmara, Bovelder, Drakensberger, South African Angus, South African Dairy Swiss, South African Friesland, South African Red, and Veld Master) animals. These breeds require less veterinary service, feed, management efforts, provide income to rural and or poor owners. However, most of them are under extinction risks and some with unknown status hence, require immediate conservation intervention. To allow faster genetic progress on the endangered animals, it is important to generate productive animals while reducing wastages and this can be achieved through sex-sorted semen. Therefore, this systematic review is aimed to evaluate the prospects of X and Y-sexed semen in ruminant livestock and some solutions that can be used to address poor sex-sorted semen and its fertility. This review was incorporated through gathering and assessing relevant articles and through the data from the DAD-IS database. The keywords that were used to search articles online were pre-gender selection, indigenous ecotypes, fertility, flow cytometry, artificial insemination, conservation, and improving sexed semen. Following a careful review of all articles, PRISMA guidelines were used to find the articles that are suitable to address the aim of this review. Sex-sorted semen is a recently introduced technology gaining more attention from researchers particularly, in the conservation programs. Preselection of semen based on the sex chromosomes (X- and or Y-bearing chromosomes) is of paramount importance to obtain desired sex of the offspring and avoid animal wastage as much as possible. However, diverse factors can affect quality of semen of different animal species especially after sex-sorting. Flow cytometry is a common method used to select male and female sperm cells and discard dead and abnormal sperm cells during the process. Thus, sperm sexing is a good advanced reproductive technology (ART) however, it is associated with the production of oxidative stress (OS) and DNA fragmentation (SDF). These findings, therefore, necessitates more innovation studies to come up with a sexing technology that will protect sperm cell injuries during sorting in frozen-thawed.

Sows are selected for their prolificacy. Therefore, assessing difficulties that can be associated with large litters is crucial. This review aims to highlight the factors that can affect reproductive performance such as breed, parity, litter size, environment, and nutrition. Understanding these factors can help producers make informed decisions about sow management and other aspects. Higher within-litter variation is the result of a high number of piglets born. It has been determined that large litter sizes pose a risk to animal welfare in the pig industry. Moreover, large litters bring more piglets with low birth weight leading to high pre-weaning losses. Lighter littermates struggle to get enough colostrum due to the competition with heavy littermates, which lowers their chances of survival causing high mortality. Larger litters may cause greater stress and discomfort of the sow. Therefore, applying stress-free environment and treating the sow with anti-inflammatory compounds may enhance the sow performance and consequent survival of piglets. The litter size is positively correlated with the variation in birth weight within a litter, while the average weight at birth is negatively related to litter size. Producers should therefore focus on potential management strategies that can improve piglet performance, survival and welfare such as proper supervision and adequate colostrum intake. This review highlights the historical development of the pig industry and literature on the variation in piglet weight at birth within a litter, factors influencing weight at birth, and the potential effects on the piglet performance before weaning. Also, the difficulties amongst large litters and possible strategies for managing survival in large litters were also reviewed.

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT.