Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

The roasting process of Gari (Gelatinized cassava mash), a shelf-stable cassava product, is energy-intensive. Due to a lack of information on thermal characteristics and scarcity/rising energy costs, heat and mass transfer calculations are essential to optimizing the traditional gari procedure. The objective of this study was to determine the proximate, density, and thermal properties of traditionally processed de-watered cassava mash and gari at initial and final processing temperatures and moisture contents (MC_wb). The density and thermal properties were determined using proximate composition-based predictive empirical models. The cassava mash had thermal conductivity, density, specific heat capacity, and diffusivity of 0.34 to 0.35 W m⁻¹ °C⁻¹, 1207.72 to 1223.09 kg m⁻³, 2849.95 to 2883.17 J kg⁻¹ °C, and 9.62 × 10⁻⁸ to 9.76 × 10⁻⁸ m² s⁻¹, respectively, at fermentation temperatures and MC_wb of 34.82 to 35.89 °C and 47.81 to 49%, respectively. The thermal conductivity, density, specific heat capacity and diffusivity of gari, ranged from 0.27 to 0.31 W m⁻¹ °C⁻¹, 1490.07 to 1511.11 kg m⁻³, 1827.71 to 1882.61 J kg⁻¹ °C and 9.64 × 10⁻⁸ to 1.15 × 10⁻⁸ m² s⁻¹, respectively. Correlation of all the parameters was achieved, and the regression models developed showed good correlation to the published models developed based on measuring techniques.

In March 2020, the World Health Organization (WHO) proclaimed the novel coronavirus 2019 (COVID‐19) a global pandemic with no therapy/vaccination or universally accepted therapeutic modalities. This resulted in the implementation of containment measures such as lockdowns, which hindered economic activities and, as a result, hampered global demand, consumption, and product transportation. The global crude oil supply chain was one of the most affected. As a result, this study was done to examine the effects of COVID‐19 on global crude oil markets and its consequences in sub‐Saharan Africa (SSA), using Kenya and Tanzania as case studies. Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA)‐based content analysis approaches were utilized to analyze data from multiple sources, and the COVID‐19‐induced oil price crisis was compared to previous global crude oil gluts from 1970 to 2020. It has been determined that global crude oil prices dropped by 65.96% from around $69.25/barrel in January 2020 to $23.57/barrel in June 2020, with West Texas Intermediate (WTI) reaching −$36.98/barrel in March 2020 (one of the highest in recent times). As a result, petroleum prices in Kenya and Tanzania fell by 30%. This, however, did not result in long‐term energy price stability in the SSA due to a lack of logistical and storage capabilities. More research on energy storage, consumption, analysis, modeling, and preparedness for COVID‐19‐like disasters are suggested.

While much research has been published on the Compost Heat Recovery Systems (CHRs), little has been documented on the design and performance evaluation of the Hydronic compost heat exchangers using numerical and computational methods, occasionally resulting in compost process inhibition. A CHRs (0.036 m³/7.2 m²) Hydronic-type heat exchanger and 12.43 m²/2.83 m³, compost reactor (CR), was designed and developed with the main objective of evaluating the design and its performance. The numerical design and performance evaluation was achieved by using Kern’s and the effectiveness and Number of Transfer Units methods (ε-NTU), respectively. Empirically, data were captured by using the Polytetrafluoroethylene (PTFE) thermocouples connected to the TC-8 Picolog Data loggers. Data validation (empirical and mathematical), was achieved by modifying a free computer-based software developed by the Chemical Engineering Calculations (CHECAL), into a Hydronic Compost Heat Exchanger design and performance evaluation software (HYDROCOHE). Between the HYDROCOHE and numerical, and between empirical and HYDROCOHE, R² values of 0.99938–0.9995, and R² of 0.99269–0.9432 with the effectiveness of 0.4853–0.4848 were achieved with 0.99 kW-empirical and 2.10 kW-HYDROCOHE, respectively. The power disparity may be ascribed to the compost reactor’s insufficient thermal insulation. Counterflow arrangement was more effective (0.4766) than crossflow (0.4622) and parallelflow (0.4430) setups. Parallelflow heat exchanger system, therefore, has the potential to extract heat steadily, minimizing the composting cycle inhibition. Further work on the impact of various flowrates on the direction of flow and heat extraction is recommended.