All Nations University

Despite the potency of Financial Technology (FinTech) in facilitating financial inclusion, the determinants of the diffusion in Small and Medium-Sized Enterprises (SMEs) remain intricate. Consequently, the study assesses the determinants of the diffusion of FinTech Payment Services (mobile money, card, and online payments) by SMEs in the context of Ghana. We sample 407 registered SMEs with the Association of Ghanaian Industries (AGI) and employ hierarchical logistic regression models to explore the multiplicative effects of SMEs Chief Executive Officers Characteristics (CEOC), Business Characteristics (BUSC), and FinTech Payment Service Characteristics (FPSC) on the Diffusion of FinTech Payment Services (DFPS). Consistent with the technology diffusion theories, the finding reveals that CEOC, BUSC, and FPSC altogether determines the DFPS in Ghanaian SMEs. Thus, the combined effects of human, business, and technology actors drive the DFPS in SMEs. Therefore, the optimal design of FinTech services is critical for mass diffusion by SMEs in emerging economies.

Purpose This study aims to contribute to the existing understanding of export practices in sub-Saharan African firms with a contextual focus on Ghanaian exporters operating in business-to-business (B2B) markets. Underpinned by resource-based theory and its association with the relational view, it examines how the interplay between various decision makers’ international experience, export commitment and distribution adaptation decisions influence firms’ performance. Design/methodology/approach The study uses a mixed methods approach, using survey data from 116 internationalising Ghanaian businesses across three sectors, supplemented with qualitative insights from 18 follow-up interviews. Findings The study establishes a full mediation effect of export commitment on the association between international experience and export performance; also, the moderating effect of distribution adaptation on export commitment – performance relationships. Unique insights are provided into the perceived role of trustworthy, intermediaries as “stakeholders” that add to a respective firm’s resource base; that is, in building capabilities in overseas markets and informing evolving business model decisions to overcome potential export barriers. Originality/value The insights from sub-Saharan African firms provide contextual value given the relatively under-represented existing research from the region. Original insights highlight ways in which decision makers build capabilities and that they do not always follow a forward moving internationalisation process, so use different measures of performance regarding B2B product-market ventures over time.

Strategic decisions like supply chain integration and interfirm value co-creation are significant to SMEs’ performance. Therefore, this paper aims to find out the relationships between supply chain integration, interfirm value co-creation, and firm performance in Ghanaian SMEs. We employed a structural equation model (SEM) to estimate the responses of 473 SMEs registered with the Association of Ghanaian Industries (AGI) to find the nexus between supply chain integration, interfirm value co-creation, and the performance of Ghanaian SMEs. Further, we test for the mediating role of innovation capability and stakeholder pressure in the relationships between supply chain integration and firm performance and the relationship between supply chain integration and interfirm value co-creation, respectively. We found a positive significant relationship between the variables. Innovation capability mediates the positive relationship between supply chain integration and firm performance. Interfirm value co-creation has a negative relationship with the innovation capabilities of SMEs. Therefore, Ghanaian SMEs can invest in technologies, which promote collaborations with external parties to create value while minimizing cost.

Purpose As global concerns for sustainability have gained traction in all sectors of every economy including agribusiness, the need to investigate the critical barriers that could hamper this novelty has also risen. In that regard, this study presents a comprehensive overview of the dominant barriers encountered by agribusinesses to ensure long-term success through the lenses of a literature review. Design/methodology/approach The study used a systematic literature review (SLR) of 43 relevant articles. The study applies content analysis to identify and analyze the selected articles. The conceptual framework underlines the three principal barriers to sustainable agribusinesses. Findings The results from the SLR demonstrates that inadequate financial support, excessive post-harvest loss, gender inequality, non-climate-smart policies and weak institutional controls constitute the major challenges to the sustainability of agribusinesses. Research limitations/implications The study is limited in scope to barriers to the sustainability of agribusiness only not the broad spectrum of the concept of agriculture. Originality/value This study's uniqueness is twofold. First, it provides a checklist for practice with the goal of addressing problems that hamper the sustainability of agribusinesses. Second, the findings and research gaps in this study are important to support future studies.

BACKGROUND: The association between subjective age and social activity has been reported in the extant literature, but whether this association is mediated by information technology ability and its domains (i.e., internet use assessment, packaged software use assessment, and innovativeness attitude) has not been examined. AIM: To assess the association between subjective age and social activity and to ascertain whether this association is mediated by information technology ability. METHODS: This study adopted a cross-sectional design characterising sensitivity analyses and common methods bias. The participants were 895 community-dwelling older adults aged 60 years or higher in Accra, Ghana. We measured subjective age, information technology ability, and social activity with previously validated Likert scales, each of which was internally consistent at a Cronbach's α ≥0.7. The data were analysed with partial least squares structural equation modelling (PLS-SEM) and hierarchical linear regression (HLR) analysis. RESULTS: Subjective age was positively associated with social activity, and this association was partially mediated by information technology ability but none of the three domains of information technology ability mediated this relationship. Subjective age was positively associated with information technology ability and its three domains. Information technology ability (but not its domains) was positively associated with social activity. CONCLUSION: Older subjective age was associated with higher social activity through information technology ability. Social activity and information technology ability levels among older adults depend on subjective age, which has implications for ageing and gerontology as reported in this paper.

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence‐based forest fire warning system to prevent major disasters. This work aims to present an overview of vision‐based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception‐v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision‐makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN‐RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no‐fire photos, the proposed RBFN‐RAISR fire detection approach achieves 97.55% accuracy, 93.33% F‐Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one‐of‐a‐kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

Face recognition (FR) is a technique for recognizing individuals through the use of face photographs. The FR technology is widely applicable in a variety of fields, including security, biometrics, authentication, law enforcement, smart cards, and surveillance. Recent advances in deep learning (DL) models, particularly convolutional neural networks (CNNs), have demonstrated promising results in the field of FR. CNN models that have been pretrained can be utilized to extract characteristics for effective FR. In this regard, this research introduces the GWOECN‐FR approach, a unique grey wolf optimization with an enhanced capsule network‐based deep transfer learning model for real‐time face recognition. The proposed GWOECN‐FR approach is primarily concerned with reliably and rapidly recognizing faces in input photos. Additionally, the GWOECN‐FR approach is preprocessed in two steps, namely, data augmentation and noise reduction by bilateral filtering (BF). Additionally, for feature vector extraction, an expanded capsule network (ECN) model can be used. Additionally, grey wolf optimization (GWO) combined with a stacked autoencoder (SAE) model is used to identify and classify faces in images. The GWO algorithm is used to optimize the SAE model’s weight and bias settings. The GWOECN‐FR technique’s performance is validated using a benchmark dataset, and the results are analyzed in a variety of aspects. The GWOECN‐FR approach achieved a TST of 0.03 s on the FEI dataset, whereas the AlexNet‐SVM, ResNet‐SVM, and AlexNet models achieved TSTs of 0.125 s, 0.0051 s, and 0.0062 s, respectively. The experimental results established that the GWOECN‐FR technology outperformed more contemporary approaches.

Optimizing the placement of new wells and well spacing are vital issues in oilfield development. In recent years, the use of particle swarm algorithm (PSA) in many reservoir applications has gained wide acceptance. More importantly, the applications of PSA in determining optimal well placement and well spacing facilitate subsurface development in oil and gas fields. Due to the quest for hydrocarbons, there is the need to maximize oil recovery from petroleum reservoirs. Besides, drilling infill wells are one way to maximize oil recovery from reservoirs. However, the problem of infill well placement is very challenging. This is because many different well placement scenarios must be evaluated when undertaking the optimization program. Most often, the variables that affect the reservoir performance are nonlinearly correlated with some degree of uncertainty. Therefore, the use of computational algorithm has become increasingly common in handling well placement problems. In this paper, PSA has been efficiently used to determine optimal locations of infill wells and their spacings in a synthetic reservoir. The reservoir used in the optimization process is a two-dimensional implicit black-oil model. The objective function in this study is the net present value of the asset (reservoir). For optimal locations, 20-acre, 40-acre and 80-acre spacing were considered for maximization of the objective function. The spacing for optimal locations was varied between wells in the reservoir model. Multiple cases for infill well locations with six existing appraisal wells were considered. After various simulation runs, the optimum locations of infill wells, number of wells and the corresponding well spacings were determined. Consequently, 4 vertical infill wells located at 40-acre spacing predicted the optimum NPV of $3.973 × 109. Therefore, this infill design is recommended for field development. Pressure and saturation distribution maps were generated with the maximization of net present value as the objective function. The oil, water and gas productions from the reservoir after infill well drilling were also analyzed. The total oil production after implementation of infill drilling peaked at 44.0 MMSTB, representing 48.31% recovery. In addition, an uncertainty analysis was performed to evaluate the reservoir performance and its impact on economic parameters that directly affect the net present value. Probability estimates: P10, P50, and P90 were obtained from the uncertainty analysis to provide a basis to estimate the possible net present values and the options for evaluating the different reservoir development scenarios. The major contribution of this study is that a methodology for infill well design has been developed. This will be a useful tool to support petroleum engineers in deciding how to maximize the value of their asset—the petroleum reservoir.

Alzheimer's disease (AD) is a current public health challenge and will remain until the development of an effective intervention. However, developing an effective treatment for the disease requires a thorough understanding of its etiology, which is currently lacking. Although several studies have shown the association between oxidative damage and AD, only a few have clarified the specific mechanisms involved. Herein, we reviewed recent preclinical and clinical studies that indicated the significance of oxidative damage in AD, as well as potential antioxidants. Although several factors regulate oxidative stress in AD, we centered our investigation on apolipoprotein E and the gut microbiome. Apolipoprotein E, particularly apolipoprotein E-ε4, can impair the structural facets of the mitochondria. This, in turn, can minimize the mitochondrial functionality and result in the progressive build-up of free radicals, eventually leading to oxidative stress. Similarly, the gut microbiome can influence oxidative stress to a significant degree via its metabolite, trimethylamine N-oxide. Given the various roles of these two factors in modulating oxidative stress, we also discuss the possible relationship between them and provide future research directions.

Recent studies have focused on unsteady aerodynamics of an aircraft using computational fluid dynamics. It was found that a change in the density of air within the flight domain influences the aerodynamic performance of aircrafts in flight. This is as a result of the occurrence of turbulence and unsteadiness in the air velocity vectors. Limited work has focused on the impact of transient change in the density of air to lift, drag and moment coefficient acting on the aircraft fuselage during flight. This paper is aimed at determining how the unsteadiness in the density of air influences the aerodynamic performance of an aircraft with time. To determine the aerodynamic performance of an aircraft within this unsteady airflow condition, lift, drag coefficient and the wing design efficiency was used as the deterministic parameter for the investigation. A compressible simulation was performed for transonic flows with Mach number 0.84. The wing geometry was designed using SOLIDWORKS and the CFD simulation was performed using ANSYS Fluent version 18.1 software. A User Defined Function (UDF) was developed using a MATLAB code. This was aimed at, to introduce a time-dependent change in the density of air, in the Fluent environment. Results obtained showed that a 0.24 average change in air density caused a drop in lift coefficient by an amount 0.01014. The wing design efficiency achieved in this study was 56% and this value is low. Therefore, a change in the density of air will cause the aircraft to observe a poor aerodynamic behavior and eventually stall.

The study examined the effect of macroeconomic variables on exchange rate in Ghana using a multivariate modeling technique of the Vector Autoregression (VAR) and focusing on impact of broad money supply (M2), lending rate, inflation and real GDP on exchange rate, for 76 quarterly observations period of 2000-2019, in Ghana and to examine their effectiveness in managing exchange rate in Ghana. The study used only secondary sources of data from Bank of Ghana, World Development Indicators and Ghana Statistical Service. It was found that, real GDP granger causes exchange rate in Ghana. However, inflation, money supply and lending rate do not granger cause exchange rate in Ghana but they affect exchange rate indirectly. It was recommended that a sound exchange rate policy should take into account some considerations. The bank of Ghana should try to reduce the lending rate and money supply in order to lower inflation to create rooms for more investors to produce more to increase the GDP produced in the country, in order to depreciate the foreign currency.

Patients usually undergo repeated X-ray examinations after their initial X-ray radiographs are rejected due to poor image quality. This subjects the patients to an excess radiation exposure and extra cost and necessitates the need to investigate the causes of reject. The use of reject analysis as part of the overall quality assurance programs in clinical radiography and radiology services is vital in the evaluation of image quality of a well-established practice. It is shown that, in spite of good quality control maintained by the Radiology Department of a Teaching hospital in Ghana, reject analysis performed on a number of radiographic films developed indicated 14.1% reject rate against 85.9% accepted films. The highest reject rate was 57.1 ± 0.7% which occurs in cervical spine and the lowest was7.7 ± 0.5% for lumbar spine. The major factors contributing to film rejection were found to be over exposure and patient positioning in cervical spine examinations. The most frequent examination was chest X-ray which accounts for about 42.2% of the total examinations. The results show low reject rates by considering the factors for radiographic rejection analysis in relation to both equipment functionality and film development in the facility. Keywords: Radiation exposure, X-ray examination, Image quality, Film reject, Quality assurance

The growing microbial resistance against antibiotics and the development of resistant strains has shifted the interests of many scientists to focus on metallic nanoparticle applications. Although several metal oxide nanoparticles have been synthesized using green route approach to measure their antimicrobial activity, there has been little or no literature on the use of Eucalyptus robusta Smith aqueous leaf extract mediated zinc oxide nanoparticles (ZnONPs). The study therefore examined the effect of two morphological nanostructures of Eucalyptus robusta Sm mediated ZnONPs and their antimicrobial and antifungal potential on some selected pathogens using disc diffusion method. The samples were characterized using Scanning and Transmission Electron Microscopy, Energy-Dispersive Spectroscopy and Fourier Transform Infrared Spectroscopy. From the results, the two ZnO samples were agglomerated with zinc oxide nanocrystalline structure sample calcined at 400 °C (ZnO NS400) been spherical in shape while zinc oxide nanocrystalline structure sample calcined at 60 °C (ZnO NS60) was rod-like. The sample calcined at higher temperature recorded the smallest particle size of 49.16 ± 1.6 nm as compared to the low temperature calcined sample of 51.04 ± 17.5 nm. It is obvious from the results that, ZnO NS400 exhibited better antibacterial and antifungal activity than ZnO NS60. Out of the different bacterial and fungal strains, ZnO NS400 sample showed an enhanced activity against S. aureus (17.2 ± 0.1 mm) bacterial strain and C. albicans (15.7 ± 0.1 mm) fungal strain at 50 mg/ml. Since this sample showed higher antimicrobial and antifungal activity, it may be explored for its applications in some fields including medicine, agriculture, and aquaculture industry in combating some of the pathogens that has been a worry to the sector. Notwithstanding, the study also provides valuable insights for future studies aiming to explore the antimicrobial potential of other plant extracts mediated zinc oxide nanostructures.

Impulsive emulsion formation, porous media wettability alteration, and interfacial tension (IFT) reduction are a list of advantages gained from the application of nanoparticles for enhanced oil recovery (EOR). However, low displacement efficiency (DE) coupled with in-effective mixing of the injected nanofluid to redeem the immobile volume of crude oil subsurface present a major challenge to the petroleum industry. The molecular chemistry of ethyl alcohol (ethanol) solvent enables the genesis of strong covalent bonds/mixing with the dense oil. As a result, the fastening converts the aromatic state of the crude oil to a lighter component to ease the flow. In this paper, we numerically investigated the potential for improving dead oil recovery in a heterogeneous rock setting using a blend of ethanol and silicon-based nanofluids to describe the EOR fluid. Herein, silica, silane and silicon carbide nanofluids were studied for their DE with ethanol as the co-solvent. A 2D heterogeneous pore-model was created and discretized to define the computational domain. Computational fluid dynamics code (ANSYS Fluent) facilitated the induction and analysis of interfacial property dynamics within the modelling space. The simulation was performed using the improved delayed detached-eddy simulation method, whereas the continuum surface-force equation with the Euler–Euler mixture multiphase method was used to model the fluid–wall and fluid–fluid adhesions at interfaces. Findings revealed that, silica nanofluid performed optimally compared to its counterpart. Furthermore, approximately 55.34% of the immobile oil was recovered using the optimal blend formulation comprising of equal proportion of silica nanofluid and ethanol. The increase in disjoining pressure and water molecules concentration at the fluid–wall​ contact, and the reduction in interfacial tension due to the evolution of in-situ surface acting agents (surfactants) from the chemical reaction kenetics accounts for this increase. In addition, the blend demonstrated good thermal stability for typical reservoir temperatures of around 400 K due to high intrinsic thermal resilence of silica nanoparticles present in the mixture.

With increasing global energy demand and the urgency to reduce carbon emissions, carbonated water injection has been proposed to tackle these pressing concerns. Carbonated water injection (CWI) in oil formations enhances oil production to support the global energy mix and tackle the issues of energy security, energy equity, and environmental sustainability. However, CWI in oil reservoirs and saline aquifers initiates multiple chemical reactions that promote carbon mineralization, cause formation damage problems, sea-bed subsidence, and reservoir compaction, increase the injection pressure requirement, and consequently affect the practicality of CWI for enhanced oil recovery (EOR) and CO2 storage. We therefore extensively reviewed the performance of CWI for EOR and CO2 storage and the implications of these interactions on EOR and CO2 storage. The analysis covered recent advancements in CWI, including its synergy with various EOR techniques where it was identified that combining CWI with surfactants, polymers, mutual solvents, and nanomaterials significantly improved oil recovery in tight formations (37–65%) compared to standalone CWI (35–36%), but the CO2 storage potential of the hybrid technique remains unexplored. Additionally, the complex geochemical interactions that occur during CWI, the influencing variables of these interactions, and their consequence on EOR and CO2 storage were discussed. The rigorous analysis revealed that the existing literature lacks consensus on the effects of CWI on pore structure. Geochemical interactions caused a −12% to +95% porosity change and a −96% to +417% alteration in permeability. Primarily, economic challenges including CO2 capture and transportation costs, carbonated water preparation, etc., and corrosion concerns hinder the large-scale implementation of CWI. Notwithstanding, the findings from the life cycle assessment of CWI suggested the economic viability of CWI and highlighted the importance of adopting the innovative CWI technique to achieve dual objectives of maximizing oil recovery and minimizing environmental footprints in the oil and gas industry. Multiple areas that require further investigation such as investigating the influence of condensable and incondensable contaminants on well integrity and safe CO2 storage among others were presented.

The study’s main objective was to evaluate the link between anti-money laundering (AML) regulations and financial sector development (FSD) in Africa and to test the nonlinearities in the AML regulations-FSD nexus. Panel data of 51 African countries from the World Bank’s indicators, the IMF, and the Basel Institute on Governance over the period 2012 to 2019 were used. The study employs the two-step system GMM and the dynamic panel threshold regression in estimating the model. The Hansen test and Arellano—Bond test for AR (2) were conducted to check for the robustness of the model specification. The study employed STATA 15 in analysing the study. The analysis shows that anti-money laundering regulation positively influences African financial sector development. However, the study found a significant positive coefficient for AML below the threshold value at a 1% significant level and a significant negative coefficient for AML above the threshold. This indicates that AML laws favour financial sector development below the threshold, but this link disappears for regimes with high AML requirements in Africa. This suggests that excessive AML structures might discourage financial sector development in Africa due to the cost associated with AML. Financial institutions in Africa should invest in technology solutions to support financial crime compliance efforts in combating criminal crimes involving digital payments, cryptocurrency, third parties and trafficking of proceeds and other crime-related activities such as drug trafficking, corruption, terrorism, arms dealing, confiscation of their illegal funds and bribery.

Abstract Current funding, mission, and organizational difficulties question the long‐run survival of extension and the Land‐Grant system. Consolidation of county extension offices is seen as a possible remedy for extension's problems. This paper estimates scale economies and inefficiencies in county extension offices in Kansas using parametric and nonparametric techniques. Large economies of scale and inefficiencies are found. Based on the empirical results, several simulations are performed to measure the short‐run cost savings associated with consolidation. The results reveal that while all counties would experience significant savings from consolidating, rural and less populated counties would experience the greatest per unit savings.

Steady airflow over the wing of an aircraft in-flight is critical to achieving maximum aerodynamic performance. However, commercial flight routes are most times characterized by fluctuations in the airflow properties. The unsteadiness in the air velocity vectors and mass flow directly affects the aerodynamic efficiency (AE) of the aircraft during flight and could lead to air accidents. These correlations between the variation in the airflow properties and the aerodynamic coefficients of a fixed-wing aircraft are not yet fully established. Therefore, this paper makes use of computational fluid dynamics code to study the link between these functions. Herein, a realistic wing model of the BOEING 737 aircraft was used for the investigation. Simulations were carried out at a Mach number of 0.84, with the nonzonal Hybrid RANS-LES method. The fluctuations in the airflow properties were modeled using the vortex fluctuation algorithm (vortex method) in Fluent software. The vortex number ,N formed in the flow field were varied in the range of 100–300. Findings from the numerical study revealed that the wing achieved an optimal AE of 95.1% for the steady case scenario. However, the wing’s AE was significantly reduced by 30% when the streamlined velocity was perturbed by the fluctuating velocity component of the flow. Also, a further decrease in the wing performance was observed with an increase in the divergence of the airflow velocity vectors which experienced a stalling effect after an 80 s flow period forN=300. Moreover, a static increase in the density of the airflow from 1.255 kg/m3 (15 °C) to 1.455 kg/m3 (−10 °C) contributed to approximately a 20% reduction in the lift and moment coefficients for N=300.

Differentiation between non-tuberculous mycobacteria (NTM) and Mycobacterium tuberculosis complex (MTBC) is crucial for case management with the appropriate antimycobacterials. This study was undertaken in three West and Central African countries to understand NTM associated with pulmonary tuberculosis in the sub-region. A collection of 503 isolates (158 from Cameroon, 202 from Nigeria and 143 from Ghana) obtained from solid and liquid cultures were analysed. The isolates were tested for drug susceptibility, and MTBC were confirmed using IS6110. All IS6110-negative isolates were identified by 65-kilodalton heat shock protein (hsp65) gene amplification, DNA sequencing and BLAST analysis. Overall, the prevalence of NTM was 16/503 (3.2%), distributed as 2/202 (1%) in Nigeria, 2/158 (1.3%) in Cameroon and 12/143 (8.4%) in Ghana. The main NTM isolates included 5/16 (31.3%) M. fortuitum, 2/16 (12.5%) M. intracellulare and 2/16 (12.5%) M. engbaekii. Eight (57.1%) of the 14 previously treated patients harboured NTM (odds ratio 0.21, 95% confidence interval 0.06–0.77; P=0.021). Three multi-drug-resistant strains were identified: M. engbaekii, M. fortuitum and M. intracellulare. NTM were mainly found among individuals with unsuccessful treatment. This highlights the need for mycobacterial species differentiation using rapid molecular tools for appropriate case management, as most are resistant to routine first-line antimycobacterials.