This review discusses the significant progress made in the development of CNT/GO-based biosensors for disease biomarker detection. It highlights the specific applications of CNT/GO-based biosensors in the detection of various disease biomarkers, including cancer, cardiovascular diseases, infectious diseases, and neurodegenerative disorders. The superior performance of these biosensors, such as their high sensitivity, low detection limits, and real-time monitoring capabilities, makes them highly promising for early disease diagnosis. Moreover, the challenges and future directions in the field of CNT/GO-based biosensors are discussed, focusing on the need for standardization, scalability, and commercialization of these biosensing platforms. In conclusion, CNT/GO-based biosensors have demonstrated immense potential in the field of disease biomarker detection, offering a promising approach towards early diagnosis. Continued research and development in this area hold great promise for advancing personalized medicine and improving patient outcomes.

Two-dimensional hexagonal boron nitride nanosheets (h-BNNS) were synthesized on silver (Ag) substrates via a scalable, room-temperature atmospheric pressure plasma (APP) technique, employing borazine as a precursor. This approach overcomes the limitations of conventional chemical vapor deposition (CVD), which requires high temperatures (>800 °C) and low pressures (10⁻² Pa). The h-BNNS were characterized using FT-IR spectroscopy, confirming the presence of BN functional groups (805 cm⁻¹ and 1632 cm⁻¹), while FESEM/EDS revealed uniform nanosheet morphology with reduced particle size (80.66 nm at 20 min plasma exposure) and pore size (28.6 nm). XRD analysis demonstrated high crystallinity, with prominent h-BN (002) and h-BN (100) peaks, and Scherrer calculations indicated a crystallite size of ~15 nm. The coatings exhibited minimal disruption to UV-VIS reflectivity, maintaining Ag’s optical properties. Crucially, Vickers hardness tests showed a 39% improvement (38.3 HV vs. 27.6 HV for pristine Ag) due to plasma-induced cross-linking and interfacial adhesion. This work establishes APP as a cost-effective, eco-friendly alternative for growing h-BNNS on temperature-sensitive substrates, with applications in optical mirrors, corrosion-resistant coatings, energy devices and gas sensing.

The coconut industry has deep historical and economic importance in Sri Lanka, but coconut palms are vulnerable to water stress exacerbated by environmental challenges. This study explored using Sunn hemp (Crotalaria juncea L.) in major coconut-growing soils in Sri Lanka to improve resilience to water stress. The study was conducted at the Coconut Research Institute of Sri Lanka to evaluate the growth of Sunn hemp in prominent coconut soils—gravel, loamy, and sandy—to determine its cover crop potential. Sunn hemp was planted in pots with the three soil types, arranged in a randomized, complete design with 48 replicates. Growth parameters like plant height, shoot/root dry weight, root length, and leaf area were measured at 2, 4, 6, and 8 weeks after planting. Soil type significantly impacted all growth parameters. After 8 weeks, sandy soil showed the highest plant height and root length, while loamy soil showed the highest shoot/root dry weight and leaf area, followed by sandy and gravel soils. Nitrogen content at 6 and 8 weeks was highest in loamy soil plants. In summary, Sunn hemp produces more biomass in sandy soils, while loamy soils promote greater nutrient accumulation and growth. This suggests the suitability of Sunn hemp as a cover crop across major coconut-growing soils in Sri Lanka, improving resilience.

The number of accidents at level railway crossings, especially crossings without gate barriers/attendants, is still very high due to technical problems, driving culture, and human error. The aim of this research is to provide road maps application based on ergonomic visual displays design that can increase awareness level for drivers before crossing railway crossings. The double awareness driving (DAD) map information system was built based on the waterfall method, which has 4 steps: defining requirements, system and software design, unit testing, and implementation. User needs to include origin-destination location, geolocation, distance & travel time, directions, crossing information, and crossing notifications. The DAD map application was tested using a usability test to determine the ease of using the application used the System Usability Scale (SUS) questionnaire and an Electroencephalogram (EEG) test to determine the increase in concentration in drivers before and immediately crossing a railway crossing. Periodically, the application provides information on the driving zone being passed; green zone for driving distances > 500 m to the crossing, the yellow zone for distances 500m to 100m, and the red zone for distances < 100 m. The DAD map also provides information on the position and speed of the nearest train that will cross the railway crossing. The usability test for 10 respondents giving SUS score = 97.5 (satisfaction category) with a time-based efficiency value = 0.29 goals/s, error rate = 0%, and a success rate of 93.33%. The cognitive ergonomic testing via Electroencephalogram (EEG) produced a focus level of 21.66%. Based on the results of DAD map testing can be implemented to improve the safety of level railroad crossings in an effort to reduce the number of driving accidents.

As digital technologies continue to shape the economy, countries are faced with increasing scrutiny in the use of digital transformation to aid productivity and improve performance. In South Africa, the COVID-19 pandemic accelerated Small and medium-sized businesses’ (SMEs’) uptake of digital technologies, as many businesses had to shift their operations online and adopt new digital tools and technologies to solve the challenges posed by the pandemic. This has led to an increased focus on digital transformation mechanisms among South African firms. Therefore, the study examines the effect of digital transformation on the productivity of firms using cross-sectional data from the World Bank Enterprise Survey (WBES) (2020). The survey was based on firms and is a representative sample of the private sector in the South African economy and covers a wide variety of business environment themes, such as infrastructure, competitiveness, access to finance, and performance indicators. We found that digital transformation improved productivity of South African firms. Furthermore, empirical findings are reassuring robust to the IV-2SLS and quantile regression model, size of business, sectoral and provincial analysis. Finally, we recommend that policy makers should develop and implement initiatives to improve digital infrastructure, including high-speed internet access and reliable connectivity, especially in rural and underserved areas.

Governments intervene in the housing market via implementing various monetary, fiscal, foreign exchange and credit policies. By this, the housing market undergoes cycles of boom and bust as well as significant swings in value added and housing prices. Therefore, the main goal of this research is to consider the effect of the government’s change on the monetary and financial policy’s impact on the business cycles of the housing sector during the period of 1978–2020. On the other hand, we estimate the impact of monetary and fiscal policies on housing business cycles concerning government’s change. To calculate housing business cycles (boom and busts), the housing value added were initially de-trended using the Hodrick–Prescott filter. This paper takes a novel use of the threshold regression model with government’s change as threshold variable. According to the study’s findings, there are three threshold effects (two threshold levels or three regimes) of monetary and fiscal policy on housing business cycles. For instance, the money supply coefficient in the first regime was −1.68, indicating that the effect of monetary policy in this regime is countercyclical. in the second and third regimes, it was 0.19 and 0.03, respectively; indicating its alignment with the housing business cycle. Regarding the estimated models, we may derive several interesting conclusions. In first regime, the money supply is countercyclical and government expenditure is pro-cyclical. This means that monetary policy exacerbates recession and fiscal policy weakens it. in the second and third regimes, the money supply is pro-cyclical and government expenditure is countercyclical. As a result, while formulating their monetary policies, governments should give the housing sector more consideration. Additionally, when putting this policy into practice, the housing sector has to be carefully examined.