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Self-motion perception is a multi-sensory process that involves visual, vestibular, and other cues. When perception of self-motion is induced using only visual motion, vestibular cues indicate that the body remains stationary, which may bias an observer’s perception. When lowering the precision of the vestibular cue by for example, lying down or by adapting to microgravity, these biases may decrease, accompanied by a decrease in precision. To test this hypothesis, we used a move-to-target task in virtual reality. Astronauts and Earth-based controls were shown a target at a range of simulated distances. After the target disappeared, forward self-motion was induced by optic flow. Participants indicated when they thought they had arrived at the target’s previously seen location. Astronauts completed the task on Earth (supine and sitting upright) prior to space travel, early and late in space, and early and late after landing. Controls completed the experiment on Earth using a similar regime with a supine posture used to simulate being in space. While variability was similar across all conditions, the supine posture led to significantly higher gains (target distance/perceived travel distance) than the sitting posture for the astronauts pre-flight and early post-flight but not late post-flight. No difference was detected between the astronauts’ performance on Earth and onboard the ISS, indicating that judgments of traveled distance were largely unaffected by long-term exposure to microgravity. Overall, this constitutes mixed evidence as to whether non-visual cues to travel distance are integrated with relevant visual cues when self-motion is simulated using optic flow alone.
Protocol for conducting advanced cyclic tests in lithium-ion batteries to estimate capacity fade
(2024)
Using advanced cyclic testing techniques improves accuracy in estimating capacity fade and incorporates real-world scenarios in battery cycle aging assessment. Here, we present a protocol for conducting cyclic tests in lithium-ion batteries to estimate capacity fade. We describe steps for implementing strategies for accounting for variations in rest periods, charge-discharge rates, and temperatures. We also detail procedures for validating tests experimentally within a climate-controlled chamber and for developing an empirical model to estimate capacity fading under various testing objectives. For complete details on the use and execution of this protocol, please refer to Mulpuri et al.1.
The lattice Boltzmann method (LBM) stands apart from conventional macroscopic approaches due to its low numerical dissipation and reduced computational cost, attributed to a simple streaming and local collision step. While this property makes the method particularly attractive for applications such as direct noise computation, it also renders the method highly susceptible to instabilities. A vast body of literature exists on stability-enhancing techniques, which can be categorized into selective filtering, regularized LBM, and multi-relaxation time (MRT) models. Although each technique bolsters stability by adding numerical dissipation, they act on different modes. Consequently, there is not a universal scheme optimally suited for a wide range of different flows. The reason for this lies in the static nature of these methods; they cannot adapt to local or global flow features. Still, adaptive filtering using a shear sensor constitutes an exception to this. For this reason, we developed a novel collision operator that uses space- and time-variant collision rates associated with the bulk viscosity. These rates are optimized by a physically informed neural net. In this study, the training data consists of a time series of different instances of a 2D barotropic vortex solution, obtained from a high-order Navier–Stokes solver that embodies desirable numerical features. For this specific text case our results demonstrate that the relaxation times adapt to the local flow and show a dependence on the velocity field. Furthermore, the novel collision operator demonstrates a better stability-to-precision ratio and outperforms conventional techniques that use an empirical constant for the bulk viscosity.
The paper investigates the nature of Kenya's entrepreneurship education ecosystem (EEE) through a comparative analysis of three entrepreneurship education programs and an examination of how the institutions foster a favourable entrepreneurial environment. This study looks at the entrepreneurship education ecosystem through the lens of universities, NGO's and private institutes in Kenya.
A systemic analysis of EEE is provided by utilizing the Actiotope Model as a conceptual framework. The exploratory research adopts a pragmatic mixed-method methodological approach best suited to understand the research problem.
The results reveal that entrepreneurship education at higher education institutions was primarily theoretical and relied on traditional forms of entrepreneurship education. Recurring rigid patterns show minimal personalization of content and learning styles within the University, with more personalization reported in the Mully Model of education and the more specialized entrepreneurship program of the Identity Projects.
The adaptation of the Actiotope Model provided a new and unique approach to analyzing entrepreneurship ecosystems. The person-centred approach of the model provides valuable insights to learners and to entrepreneurship education institutions and researchers.
Enhanced collaboration between the different entrepreneurial education stakeholders could be a more effective short to medium-term solution to addressing the gaps in entrepreneurial education at tertiary institutions.
In the long term, the study recommends adopting practical-based and goal-oriented entrepreneurship teaching models.
Social businesses have a great positive impact on communities and are a sustainable way to do business today and in the future. This impact can be amplified through the means of digitalization. In the past, traditional for-profit business models have been used to understand the structures of business operations. However, the underlying business model of digital social businesses has not yet been explored. This study presents a building block analysis of business models and a subsequent typology. Digital and social business models are identified via a literature review. The building block analysis encompasses an assessment of the individual business activities contained in the business models. The typology is developed from existing literature utilizing a matrix for the evaluation of digital social businesses. Additionally, five semi-structured expert interviews are conducted to inform, extend, or content the findings of this study. To this end, an inductive coding procedure is applied to the transcribed interviews for the detection of themes within the text. This study contributes to social business model research by providing a first insight into the unique building blocks of digital social business models. It also creates a typology tool based on two parameters, which enables the comparison of digital social businesses.
Mobile technologies have evolved into the means of gaining access to information for learning. Its application in higher education is still a novel concept, particularly in underdeveloped countries. This study is aimed at exploring the views of doctoral students regarding their learning experiences with mobile technologies. Student focus group interviews of 24 doctoral students from 3 different academic institutions were interviewed. The participants’ responses were recorded, transcribed, and analyzed to make conclusions. According to the findings of this study, mobile devices play an important part in the learning experiences of doctoral students. The participating students engaged in collaborative learning using mobile technologies. Given the benefits of adopting mobile technologies for learning activities, academic institutions should focus on teaching faculty members to use this to involve students in their learning process. The implications of this study call for the continued advancement of mobile technologies to facilitate effective learning experience for the multitude of mobile learners in developing countries. Another implication is that academic institutions with collaboration with libraries should see the need to develop user friendly mobile app that is linked to the library management system. Such an application would allow the students to optimally use their smartphones and tablets to search the library’s resources from their mobile devices. Training should be offered to the teaching faculty members to come to terms with the benefits of mobile technologies for learning activities.
The access to electricity and water in rural areas in Côte d’Ivoire as well as in large parts of Africa is limited. According to Ivorian government sources, the national coverage rate of drinkable water and electricity was about 80% in 2020, whereas there are differences between rural and urban regions. The coverages are lower in rural areas that are situated far from the governmental infrastructures. The poor supply of electricity also hinders education, since petroleum lamps are often the only source of light for learning after sunset. Besides, increasing demand for electricity is predicted in Côte d’Ivoire due to economic growth. The economic power is also affected by the poor supply of electricity, so only a limited production of goods is possible. A further big concern in Côte d’Ivoire is the employability of graduate students, as the educational system has a strong theoretic character, not yet taking enough into account practice orientation. Scientific public universities in Côte d'Ivoire often offer only subjects such as mathematics, physics, or chemistry but hardly any engineering.
TREE Jahresbericht 2021/2022
(2023)
Das Institut TREE freut sich, ihnen den Jahresbericht der Jahre 2021 und 2022 präsentieren zu können. Blicken sie mit uns zurück auf zwei herausfordernde Jahre.
Unser neuer Doppel-Jahresbericht 2021/2022 enthält viele, interessante, Beiträgen unserer spannenden, interdisziplinären Forschungprojekte der Bereiche Energie, Modellbildung Simulation, Drohnenforschung, Materialien und Prozesse und Technikkommunikation.
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
Das Potenzial digitaler Informations- und Schulungsangebote, deren Ausgestaltung zum klassischen Aufgabenportfolio der Teaching Library gehört, hat sich im Zuge der Digitalisierung sowohl technisch als auch didaktisch stark ausdifferenziert. Darüber hinaus übernehmen Bibliotheken die Entwicklung und Betreuung zentraler Service-Strukturen für das digitale Lehren und Lernen an Hochschulen und entwickeln sich so zu E-Learning-Zentren weiter. Welche Chancen diese strategische Neuausrichtung für die Positionierung von Bibliotheken als Ganzes hat, wie sich dies auf die Gestaltung bereits bestehender Schulungsangebote auswirken kann und welcher sich wandelnde Kompetenzbedarf daraus hervorgeht, wird im Folgenden anhand eines praktischen Lehrbeispiels zur Vermittlung wissenschaftlicher Schreibkompetenz aufgezeigt.
The French–Italian Concordia Research Station, situated on the Antarctic Polar Plateau at an elevation of 3233 m above sea level, offers a unique opportunity to study the presence and variation of microbes introduced by abiotic or biotic vectors and, consequently, appraise the amplitude of human impact in such a pristine environment. This research built upon a previous work, which explored microbial diversity in the surface snow surrounding the Concordia Research Station. While that study successfully characterized the bacterial assemblage, detecting fungal diversity was hampered by the low DNA content. To address this knowledge gap, in the present study, we optimized the sampling by increasing ice/snow collected to leverage the final DNA yield. The V4 variable region of the 16S rDNA and Internal Transcribed Spacer (ITS1) rDNA was used to evaluate bacterial and fungal diversity. From the sequencing, we obtained 3,352,661 and 4,433,595 reads clustered in 930 and 3182 amplicon sequence variants (ASVs) for fungi and bacteria, respectively. Amplicon sequencing revealed a predominance of Basidiomycota (49%) and Ascomycota (42%) in the fungal component; Bacteroidota (65.8%) is the main representative among the bacterial phyla. Basidiomycetes are almost exclusively represented by yeast-like fungi. Our findings provide the first comprehensive overview of both fungal and bacterial diversity in the Antarctic Polar Plateau’s surface snow/ice near Concordia Station and to identify seasonality as the main driver of microbial diversity; we also detected the most sensitive microorganisms to these factors, which could serve as indicators of human impact in this pristine environment and aid in planetary protection for future exploration missions.
The continuously increasing number of biomedical scholarly publications makes it challenging to construct document recommendation algorithms that can efficiently navigate through literature. Such algorithms would help researchers in finding similar, relevant, and related publications that align with their research interests. Natural Language Processing offers various alternatives to compare publications, ranging from entity recognition to document embeddings. In this paper, we present the results of a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. We aim to determine the best approach that resembles relevance without the need for further training. Specifically, we employ five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition, and state-of-the-art models based on BERT. To evaluate the document similarity obtained by these approaches, we utilize different evaluation metrics that account for relevance judgment, relevance search, and re-ranking of the relevance search. Our results demonstrate that the most promising approach is an in-house version of document embeddings, starting with word embeddings and using centroids to aggregate them by document.
Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.
The continuous increase of biomedical scholarly publications makes it challenging to construct document recommendation algorithms to navigate through literature, an important feature for researchers to keep up with relevant publications. Understanding semantic relatedness and similarity between two documents could improve document recommendations. The objective of this study is performing a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. Here we present our approach to compare five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition as well as state-of-the-art models based on BERT.
A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models.
Question Answering (QA) has gained significant attention in recent years, with transformer-based models improving natural language processing. However, issues of explainability remain, as it is difficult to determine whether an answer is based on a true fact or a hallucination. Knowledge-based question answering (KBQA) methods can address this problem by retrieving answers from a knowledge graph. This paper proposes a hybrid approach to KBQA called FRED, which combines pattern-based entity retrieval with a transformer-based question encoder. The method uses an evolutionary approach to learn SPARQL patterns, which retrieve candidate entities from a knowledge base. The transformer-based regressor is then trained to estimate each pattern’s expected F1 score for answering the question, resulting in a ranking ofcandidate entities. Unlike other approaches, FRED can attribute results to learned SPARQL patterns, making them more interpretable. The method is evaluated on two datasets and yields MAP scores of up to 73 percent, with the transformer-based interpretation falling only 4 pp short of an oracle run. Additionally, the learned patterns successfully complement manually generated ones and generalize well to novel questions.
Tourism in Rwanda is challenging. Since the country is small and hilly, it is difficult to tap the potential. As the country is blessed with diverse nature, the Rwandan government decided to combine ecotourism with high-end tourism, to exploit the full potential. This study aims to assess the extent to which these two types of tourism fit together, as well as if sustainability is a decisive argument in this upscale segment. In this context, ecotourism is characterized by its 3 core criteria: education, nature and sustainability. To evaluate the main question: to what extent can ecotourism projects help to promote the perception of Rwanda as a high-end tourist destination on the German market? As well as if sustainability is a decisive argument, interviews with stakeholder from the Rwandan tourism industry as well as German tour operators were conducted, to gain an understanding of both sites and then evaluate them according to the 3 ecotourism core criteria and the demands of high-end tourists. The results showed that there is a difference in the perception of the needs of high-end tourists. While the 3 core criteria seem to be too relevant while they are in booking decision with the tour operator. The high-end lodges in Rwanda state an interest in these three criteria. It is evident from the results that there is a limited active demand for sustainable tourist products, while nature and education are more relevant, but not yet fully exploited. However, all interviewees indicated that ecotourism, and in particular sustainability, is experiencing an increase in demand and will continue to grow in importance in the future. Accordingly, the results suggest the driving markets approach is relevant to further drive demand in that segment.
As a developing economy, Rwanda has been exploring transitioning to being a technologically driven and sustainable economy. Moreover, research on economic growth have focused on the need to improve human capacity potential within increasing demands of climate change activists but there remains a theoretic and practical lacuna in including renewable energy resources in economic growth and expansion of electricity access. Therefore, it is necessary to study the impact of competent skill acquisition and graduate employment market on the interaction mix between economic growth and the expansion of energy access in Rwanda, particularly finding out the problems advancing the non-inclusiveness of engineering graduates, which result to high rate of unemployment and diversions, especially for the graduates specializing in energy fields. As a result, the following open questions were raised with variations 1; how did employees penetrate energy-sector labour market opportunity in Rwanda? 2; what influenced employee’s decision in pursuing a career in Rwanda’s labour market, 3; what were the specific employee competent skills that enabled smooth transition in energy-sector employment after graduation and the ones required to maintain their current positions? 4; what specific competent skills are required for inclusivity of today's engineering graduates in energy sector employment market? The study is qualitative and it uses the exploratory research design. It is based on the growth pole theory employing snowball/chain purposeful sampling technique, whereby key informants in Rwanda energy sector were located. Data was specifically collected from these primary sources through semi-structured interviews and documentary method. Interview data and text from documents were inductively analysed. The study generally recommended institution or program for connecting learning institutions, industry and employment market in the distributed and renewable energy resources to promote competent skills acquisition, competition and improve graduates’ inclusiveness in the expansion of electricity access, thereby leading to economic growth in Rwanda.
Rapid and sustained innovation in developed markets triggers the generation of innovative start-ups, some with disruptive innovations. However, when their offering faces a saturated market with satisfactory and widely available established traditional solutions, many innovative start-ups from these markets may fail. The literature on some start-ups that successfully brought their innovation to emerging markets shows how using leapfrogging traditional solutions to innovative solutions can offer survival and growth opportunities to these start-ups. However, a wide exploitation of leapfrogging processes in emerging markets for survival or business growth of innovative start-ups from developed markets is not yet theorized. To contribute to closing this gap, we propose a conceptual framework to assess the readiness of an emerging market to leapfrog to innovative solutions.
The design of the conceptual framework uses a scenario-planning like approach with two key factors, namely Context Readiness and Value Network Integration. To test and refine the proposed framework and show its relevance for coming to an informed expansion decision making, we used PAR (Participatory Action Research). For the illustration of the application of the proposed conceptual framework, the case of telehealth in Morocco is used.
„Ich kann jetzt nicht krank werden – ich muss arbeiten“. Diesen Satz hat wohl jeder schon einmal gehört oder sogar selbst gesagt. Dahinter steckt das Phänomen des Präsentismus. Präsentismus wird ganz allgemein bezeichnet als behaviour of working in the state of illhealth (Ruhle et al. 2020). Der Begriff entstand in Anlehnung an sein Gegenteil, den Absentismus, welcher das Fernbleiben von der Arbeit beschreibt (Hägerbäumer 2017). Die negativen langfristigen gesundheitlichen und arbeitsbezogenen Auswirkungen des Verhaltens, trotz Krankheit zu arbeiten, sind in der Literatur gut belegt (z.B. Gustafsson/Marklund 2011). Vor allem im Home-Office, welches während der Corona-Pandemie häufig genutzt wurde und nach der Corona-Pandemie in vielen Bereichen Normalität wurde, tritt Präsentismus aufgrund der geringeren Barrieren (z.B. keine Ansteckungsgefahr, kein Pendeln) noch verstärkter auf (Steidelmüller et al. 2020). Auch bei Studierenden, die im Rahmen von Online-Lehre öfter von zu Hause aus lernen, ist davon auszugehen, dass dies den Präsentismus begünstigt. Eine Auseinandersetzung mit dem Thema ist also von großer Relevanz. Es fehlt eine strukturierte Zusammenstellung geeigneter Gegenmaßnahmen, die zur Reduzierung von Präsentismus angewendet werden können. Darüber hinaus wurde vor allem Präsentismus mit dem Schwerpunkt Hochschule und damit den Zielgruppen Hochschulmitarbeitende und Studierende bisher kaum untersucht. Diese Forschungslücke soll der vorliegende Beitrag schließen und Präsentismus an Hochschulen beschreiben sowie mithilfe eines Literaturreviews mögliche Maßnahmenbereiche aufzeigen.
While 14 % of the world's working-age population currently lives in sub-Saharan Africa (SSA), this figure will predictably be higher than the rest of the world combined by 2036. If this demographic group finds meaningful employment, Africa experiences an economic and social upswing. To tap this potential, the paper intends to answer the research question, "What are the prerequisites and how are they defined for the successful implementation of sustainable business model ideas in SSA?", by developing a top ten ranking consisting of previously identified sustainable business model ideas best suited for productive use. This achieves a novel approach to implementing future-oriented business models and contributes to current research on sustainable models. Since the geographical scope of SSA is pervasive, this paper focuses on Namibia, Rwanda, Senegal, and Uganda. An extensive literature review on these countries was conducted to gain a broader understanding of the situation in SSA. Additionally, research was carried out on the agricultural, energy, and information and communications technology (ICT) sectors to identify the most promising ideas. To contribute to current knowledge, experts were interviewed, and panel discussions were analyzed. Furthermore, the Business Model Canvas (BMC) was combined with the circular economy concept, which served as a framework for the business model ideas. Experts evaluated these ideas, which were subsequently ranked using fuzzy logic with artificial intelligence, based on the system for exploring country risks (CRISK-Explorer). The paper shows that skipping individual development processes opens up promising opportunities, such as the ICT-based business model e-crowd logistics or the renewable energy-based model e-Boda-Boda. Seven prerequisites for the successful implementation of these ideas were identified and defined: value delivery, promising customers, sufficient capital, presence of key resources, possibility to perform the key activities, sustainability, and profitability. The paper concludes by identifying limitations and suggesting avenues for future research.
Channels of distribution are important factors in the connection between goods and services produced for the final consumer and, therefore, determine the effectiveness with which they are delivered and ultimately availed to the final consumers. Globally, studies show that channels of distribution and sales play an essential role in building bonds between manufacturers, retailers, wholesalers and their consumers. The main purpose of this study is to examine the influence of distribution channels and networks on customer choice of fast-moving consumer goods (FCMG) in the Upper East Region of Ghana. The study adopted a quantitative approach and questionnaires were used to collect primary data from 110 customers of Unilever Ghana Limited in the Upper East Region of Ghana. The findings reveal that product-related factors, such as the price of products, perishability of products, size and weight of products, promote the effective distribution of Unilever goods and services, whilst consumer-related factors, such as the number of customers and increased consumer base, promote effective distribution channels. The study also established a positive influence of factors, such as incentives, receiving feedback and sales performance, on customer choice of fast-moving consumer goods (FMCG). Managers and producers in the FMCGs industry should implement reward and incentive programmes and policies to boost the sale and distribution of fast-moving consumer goods and services in the retail industry in Ghana.
Entrepreneurship is labelled as the panacea for graduate unemployment in Ghana. In the training process, students are mandatorily required to read a course in entrepreneurship, so as to be able to start their own businesses in the face of job adversities caused by the inadequacy of job opportunities created by government and lack of government drive to diversify the economy for more jobs to be created. This study, therefore aimed at investigating the critical precursors of entrepreneurial intentions among higher education students in Ghana. Using the analytical cross-sectional survey design, 250 respondents were recruited from public universities using probability sampling techniques (stratified-disproportionate and simple random) to participate in the survey. Respondents were required to respond to three constructs (entrepreneurial scaffolding, psychological capital, and entrepreneurial intentions). The data analyses were performed using multivariate regression. The study findings showed that entrepreneurial scaffolding and psychological capital were significant predictors of entrepreneurial intentions. The researchers concluded that students' convictions in succeeding or otherwise and planning to engage in entrepreneurial behaviours depended on proper entrepreneurial guidance and a positive mind-set. Therefore, it was recommended that higher education institutions in Ghana strengthened and included practical guides to entrepreneurial training. This will encourage higher education students to consider entrepreneurship, hence, reducing graduate unemployment in Ghana.