Papua New Guinea University of Technology

By employing the statistical tools, the cointegration analysis and the adjustment matrices, the Vector error Correction models, it is established that there is a statistically significant long-run demand function for real narrow money in India. The sign of the coefficient of the real stock prices is positive and statistically significant corroborating the wealth effect. Similarly, for the real broad money demand function, the statistical tests results corroborate existence of a long-run demand for broad money. But, unlike the demand for narrow money, the real stock prices have negative sign and statistically significant in the VECM vector, indicating a substitution effect of reducing the demand for broad money when real stock prices increase. Apart from the capital and money markets, and the real GDP, the foreign exchange markets also have a role in determining the real money demand .Therefore, the money has not withered away despite financial innovations, and the monetary aggregates should be again recognized as important monetary policy indicator variables in India and other developing countries ..

This study introduces a novel, cost-effective, and combustion synthesis approach for synthesizing dysprosium-doped magnesium niobium oxide (Dy: MNbO) nanoparticles (NPs) via a solution combustion method utilizing aloe vera gel as a green fuel. The use of aloe vera gel not only simplifies the synthesis process but also enhances the ecocompatibility of the method, making it a significant advancement over conventional techniques. Advanced spectral techniques were employed to characterize the Dy: MNbO NPs. PXRD analysis revealed that the average crystalline size of the NPs was approximately 45 nm. The energy band gap of the synthesized Dy: MNbO NPs was determined to be in the range of 4-5 eV. SEM analysis showed the presence of distinctly agglomerated, lump-like structures. The photocatalytic performance of Dy: MNbO NPs was evaluated for the degradation of industrial dyes, specifically direct green (DG) dye, under UV light irradiation. Among different doping concentrations, the 4 mol% Dy: MNbO NPs exhibited the highest photocatalytic efficiency, achieving an 82% degradation. In comparison, the degradation rates for other doping concentrations were 58% for 2 mol%, 62% for 6 mol%, and 75% for 8 mol%. Electrochemical analyses using 4 mol% Dy: MNbO NPs as a modified electrode were performed in a 0.1 M HCl electrolyte solution. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) confirmed the reversibility of the electrode reaction. The sample demonstrated excellent performance in electrochemical sensing applications, specifically for detecting ibuprofen and glucose molecules.

Fused deposition modeling (FDM) is emerging as a promising technique for manufacturing bioresorbable stents (BRS), particularly for coronary artery disease treatment. Polycaprolactone (PCL) has emerged as a favored material due to its biocompatibility, controlled degradation rate and mechanical properties. This review provides a comprehensive analysis of the effects of key FDM printing parameters on the quality aspects of PCL-based BRS, focusing on morphological, mechanical and biological characteristics. This review also highlights inconsistencies in previous studies, particularly in the impact of these parameters on stent dimensions and mechanical properties, emphasizing the need for standardization in experimental methodologies. Additionally, the current gaps in research related to the mechanical and biological performances of PCL-based BRS are discussed, with a call for further studies on long-term effects. This review aims to guide future research by offering insights into optimizing FDM parameters for improving the overall performance and clinical outcomes of PCL-based BRS.

This study investigates the electrochemical properties of pure bamboo activated carbon (pure BAC), and its nanocomposite used as a electrode materials in supercapacitor applications. BAC was synthesized using a two-step potassium hydroxide (KOH) activation process under a nitrogen atmosphere, resulting in an enhanced surface area and increased porosity. X-ray diffraction (XRD) analysis revealed crystallinity percentages of 75.31% for pure BAC and 86.87% for the BAC nanocomposite. The BAC nanocomposite demonstrated improved conductivity compared to pure BAC. From this scanning electronic microscopy (SEM) image at depth of 100 μm, it is found that BAC has abundant pores represented by thick pore walls and circular pores. This image reveals the porous nature of BAC. The ruthenium oxide (RuO2)nanoshells were linked, resulting in porous surface morphology. Cyclic voltammetry (CV) measurements indicated that the BAC nanocomposite achieved a specific capacitance of 241.59 F/g, compared to 146.78 F/g for pure BAC at a scan rate of 2 mV/s. In terms of capacitance retention, pure BAC exhibited 74.70% capacitance retention, while the BAC nanocomposite achieved 82.49% after 2500 cycles. These results highlight the potential of BAC and its nanocomposite as promising, sustainable electrode materials for advanced supercapacitor applications.

An investigation on novel Kulkual fibers that were derived from Ethiopia was carried out in this work. An open-mold casting approach was employed to manufacture a lightweight composite comprising chopped Kulkual fibers and titanium diboride (TiB₂) particles. The primary objective of this study was to scrutinize the interfacial dynamics of the composites upon inclusion of the reinforcements, focusing on compression, hardness, and water absorption characteristics. The incorporation of both TiB₂ and Kulkual fibers markedly augmented the inherent properties of the epoxy matrix, evident in compression testing. Notably, composites containing 5 vol% of fibers exhibited a significantly higher modulus of 87 MPa, while those with 5 vol% of fibers demonstrated an impressive strength of 90 MPa. Vickers hardness assessments revealed composites containing 5 vol% of fibers displaying a superior hardness value of 45 HV. Subsequent water absorption tests with different types of water unveiled a Fickian behavior, characterized by an initial exponential increase in the absorption rate within the first 50 h. The incorporation of Kulkual fibers amplified this intake rate, particularly evident at the 10 vol% level, which eventually reached saturation after 200 h. Collectively, these findings underscore the optimal efficacy of fiber addition up to 5 vol% in enhancing composite properties, suggesting a threshold beyond which further increments may not yield proportional benefits.

Graphitic carbon nitride (g-C3N4) is utilized across various fields, including catalysis, hydrogen production, and biosensing, due to its basic surface sites. However, g-C3N4 often shows limited efficiency in such applications, mainly due to challenges related to absorption and low conductivity. This study aimed to synthesize S-doped g-C3N4/CuO/ZrO2-based semiconducting ternary nanocomposites (NCs) for catalytic applications using a chemical precipitation method. A straightforward gas-templating technique was applied to achieve one-step nano-structuring of S-doped g-C3N4 at 550 °C. The interactions between cubical CuO structures, monoclinic ZrO2, and flower-like S-doped g-C3N4 morphologies were investigated using a range of analytical techniques, including X-ray diffraction (XRD), high-resolution X-ray photoelectron spectroscopy (HR-XPS), high-resolution scanning electron microscopy (HR-SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, ultraviolet-visible (UV-Vis) diffuse reflectance spectroscopy (DRS), thermogravimetric analysis (TGA), differential thermal analysis (DTA), Brunauer–Emmett–Teller (BET) surface area analysis, and photoluminescence (PL) studies. The superior performance of CuO/ZrO2@2S-doped g-C3N4 (30%) nanocomposite is attributed to its smaller crystalline size, optimized band gap energy, and large surface area. These features collectively enhance electron–hole pair separation efficiency and increase the number of active sites for adsorption and reaction. Such improvements not only enhance sensitivity but also boost sensor efficiency through better charge carrier generation, selective detection capabilities, and facilitated charge transfer. These enhancements also contribute to reducing recombination losses and accelerating signal generation and g-C3N4 transport. These synthesized ternary NCs were applied to bisphenol A (BPA) detection. The electrocatalytic behaviour of the modified carbon paste electrode (CPE) was assessed by measuring BPA via cyclic voltammetry (CV) under ideal conditions (pH 5). The CuO/ZrO2@S-doped g-C3N4 (30%)/CPE system demonstrated high electrocatalytic performance, achieving reproducibility with a 1.7 μM detection limit and 2.1 μM limit of quantification. Overall, this research highlights the potential of synthetic ternary NCs as versatile materials for environmental remediation applications.

The efficient management and prediction of urban traffic flow are paramount in the age of beyond 5G smart cities and advanced transportation systems. Traditional methods often fail to handle the nonlinear and dynamic nature of traffic data, necessitating more advanced solutions. This paper introduces NeuroSync, a novel neural network architecture designed to leverage the strengths of spiking neuron layers and gated recurrent units (GRUs) combined with temporal pattern attention mechanisms to effectively forecast traffic patterns. The architecture is specifically tailored to address the complexities inherent in nonstationary urban traffic datasets, capturing both spatial and temporal relationships within the data. NeuroSync not only outperforms traditional forecasting models such as ARIMA and exponential smoothing but also shows significant improvement over contemporary neural network approaches like LSTM, CNN, Seq2Seq, RNN, GRU, Transformer, and Autoencoder in terms of mean squared error (MSE) and mean absolute error (MAE). The model's efficacy is demonstrated through extensive experiments with real‐world traffic data, underscoring its potential to enhance urban mobility management and support the infrastructure of intelligent transportation systems.

Allergic conjunctivitis, an inflammation of the conjunctiva, is commonly treated with conventional drugs such as mast cell stabilizers, corticosteroids, antihistamines, NSAIDs, and others; these drugs associated lots of side effects including dryness, redness, and blurred vision. Nelumbo nucifera Gaertn., known as Indian lotus, is traditionally used for various ailments. This study aimed at assessing the antiallergic conjunctivitis effects of the hydroalcoholic extract of the rhizome of N. nucifera (HNN) and their isolated constituents (phytosphingosine and betulinic acid) in rats. Acute oral toxicity tests followed OECD Guideline No. 423, revealing no mortality up to 2000 mg/kg for HNN and 300 mg/kg for isolated constituents, indicating their safety. Rats were induced with allergic conjunctivitis and treated with varying doses of HNN extract and isolated constituents. The results showed a significant reduction in allergic signs, eye-scratching behavior, and eosinophil count in conjunctival tissues in a dose-dependent manner. This demonstrates the potential of HNN and its constituents in combating allergic conjunctivitis safely and effectively. Further research is warranted to explore their clinical applications.

This review provides a comprehensive overview of current progress in catalytic technologies for converting CO2 to ethanol, emphasizing the importance of sustainable and environmentally friendly alternatives. A range of methodologies is explored, including thermodynamic analysis, thermocatalytic, electrocatalytic, and photocatalytic approaches, while discussing fundamental reaction mechanisms and catalyst design strategies. Significant advancement has been made in the thermocatalytic hydrogenation of CO2, with mixed metal and metal oxide catalysts achieving selectivities exceeding 90%. However, challenges remain in optimizing catalyst performance for enhanced selectivity and conversion rates. Electrocatalytic reduction offers a promising pathway, focusing on alkaline electrolytes and innovative catalyst designs such as Cu/Au and Al‐Cu/Cu2O. Meanwhile, photocatalytic systems harness solar energy, with various novel photocatalysts showing potential for high efficiency. This review aims to elucidate the current landscape and future perspectives on CO2‐to‐ethanol conversion technologies, highlighting their potential role in sustainable energy solutions.

In recent decades, poorly insulated windows have increased the energy consumption of heating and cooling systems, thus contributing to excessive carbon dioxide emissions and other related pollution issues. From this perspective, the electrochromic (EC) windows could be a tangible solution as the indoor conditions are highly controllable by these smart devices even at a low applied voltage. Literally, vanadium pentoxide (V2O5) is a renowned candidate for the EC application due to its multicolor appearance and substantial lithium insertion capacity. Despite the growing interest in V2O5 thin films, only limited literature study is available for V2O5 films specifically the annealing effects of these films at lower temperatures (< 300 °C). It is noteworthy that a low temperature is advantageous for glass-based EC devices, as it prevents deformation, cracking, and structural damage to the transparent conductive glass. In this study, V2O5 thin films were fabricated using the sol–gel spin coating technique prior to annealing over the temperature range of 100–300 °C. Subsequently, V2O5 thin films were assembled into a device form to analyze their EC characteristics. The V2O5 device, featuring thin film annealed at 200 °C, demonstrated excellent EC performance with high optical contrast of 42.32%, high coloration efficiency (CE) of 34.93 cm²/C, as well as rapid coloring and bleaching times of 0.4 s and 3 s, respectively. These results shed light on the importance of annealing temperature control towards the EC performance of V2O5 devices for future applications.

Electrochromic devices (ECDs) are devices that change their optical properties in response to a low applied voltage. These devices typically consist of an electrochromic layer, a transparent conducting substrate, and an electrolyte. The advancement in solid-state ECDs has been driven by the need for improved durability, optical performance, and energy efficiency. In this study, we investigate varying the temperature to the casting solution for polymethylmethacrylate (PMMA)-based electrolytes for solid-state ECDs with a structure of glass/ITO/WO3/PMMA electrolyte/ITO/glass. The electrochromic layer, composed of WO3, was deposited using the sol-gel method, while the electrolyte, comprising lithium perchlorate (LiClO4) in propylene carbonate (PC) with PMMA, was prepared via solution casting. Various electrolyte samples were heated at different temperatures of 25, 40, 60, 80, and 100 °C to analyze the impact on the devices’ performance. Our findings indicate that the devices with electrolytes at 25 °C exhibited superior anodic and cathodic diffusion. An increase in heating temperature corresponded with an increase in switching time. Notably, the sample heated at higher temperatures (60, 80, and 100 °C) demonstrated exceptional cycle stability. Nevertheless, samples with higher temperatures displayed a decrease in optical modulation. Additionally, the 100 °C sample exhibited the highest coloration efficiency compared to other samples at lower temperatures. This research highlights the potential of varying the temperature of solution casting on PMMA-based electrolytes in optimizing the performance of solid-state ECDs, particularly regarding coloration efficiency and durability.

Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. Additionally, relevancy factor is estimated for each input parameter to assess the relative effects of input parameters on the saturation exponent. The sensitivity analysis indicates that resistivity index and true resistivity have direct correlation with the saturation exponent while porosity, absolute permeability and water saturation is inversely related with saturation exponent. In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).