Search Results (371)

Musculoskeletal (MSK) diseases present major health and economic challenges globally. Advancing age, diseases like osteoarthritis (OA), osteoporosis (OP), fracture and other conditions significantly reduce the quality of life (QOL) of these patients. Current pharmaceutical approaches are able to manage symptoms for some of these; however, they do not provide long-term solutions. Surgeries which are usually the final resort, present an added layer of challenges with the risk of post-surgical complications. The last couple of decades have observed an increase in the use of tissue engineering and regenerative medicine (TERM) for bone tissue engineering (BTE) applications. With the advent of artificial intelligence (AI), there will inevitably be an intersection of AI with TERM for MSK conditions. As of 2025, AI is already in use for small-scale applications in BTE including data extraction, image analysis, scaffold design and fabrication using three-dimensional (3D) printing techniques. This review outlines the convergence of these three fields and discusses the potential of their intersection. The author describes the need for this convergence, a brief update of TERM in MSK in the last decade, followed by the potential of AI in MSK-TERM. The review concludes on the challenges and future directions of the emerging field and hopes to encourage bold and ambitious collaborations between industry, academia, hospitals and health-care start-ups to realize the potential of this unique intersection. Full article

Dual-fuel, diesel–LPG (LPG being Liquified Petroleum Gas, e.g., propane) compression-ignition engines reduce CO₂ and particulate emissions compared to diesel-only operation but are prone to knock at high load due to charge homogeneity and increased ignition delay. AdBlue port injection (API) was evaluated as a combustion stabilisation strategy for a diesel–LPG engine and compared with water port injection (WPI). Experiments were performed on a 2.0 L diesel–LPG engine operated at 2000 RPM, BMEP ≈ 9 bar, λ ≈ 1.27 and LPG substitution of 72%. Knock intensity was quantified using knock-induced signal energy (KISE) derived from the oscillatory component of the in-cylinder pressure over a knock-sensitive crank angle window. Characterisation of combustion was done through HRR analyses, MFB analyses and FFT-based frequency characterisation. Baseline operation exhibited severe knock with a peak HRR ≈ 200 J/°CA and mean KISE of 307.2 bar². WPI at a water mass ratio WMR of 130% reduced the peak HRR by 56% and mean KISE by 88%, but decreased the peak pressure, BMEP and BTE. API at an AdBlue mass ratio AMR of 130% reduced the peak HRR by 37% and KISE by 82.6% while maintaining BMEP and BTE within baseline variability. Both strategies attenuated the dominant ~19.8 kHz (1,2) mode. NOx emissions decreased with WPI but increased at a high AMR. Full article

Biodiesel’s application in compression–ignition engines is mostly limited by the type of methyl esters it contains rather than the total amount of feedstocks. In order to modify the fatty acid methyl ester (FAME) profile for better combustion and emissions, cottonseed (CSOME), neem (NOME), and orange peel oil methyl esters (OPOMEs) were carefully mixed. Fuel chemistry was examined using Gas Chromatography–Mass Spectrometry (GC-MS) and Fourier Transform Infrared (FTIR), which confirmed variations in oxygenated functional groups, saturation levels, and volatility. In a single-cylinder CI engine, diesel, single, binary, and ternary biodiesel mixes were tested over 25–100% load at compression ratios of 17 and 18, both with and without 10% EGR. The ester-optimized ternary blend HBO70 delivered the best overall performance at CR 18 with EGR, exhibiting only a 0.61% reduction in BTE while achieving significant reductions in smoke (44%), PM (51%), NO_x (30%), HC (11%), CO (10%), and specific fuel consumption (SFC) (6.8%). Regression analysis confirmed a temperature- and oxygen-controlled NO_x–PM trade-off, demonstrating that ester-profile optimization is an excellent way to achieve cleaner and more efficient CI engine operation. Full article

This research examines the feasibility of using waste cooking oil (WCO) as a substitute for traditional diesel fuel in internal combustion engines, with a focus on biodiesel production. The aim of this research is to evaluate the effects of WCO–diesel blends on engine performance, with particular emphasis on critical metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The study utilizes artificial neural networks (ANNs) to model and forecast the performance and emission characteristics of engines operating with different fuel combinations. The study employs a methodology that involves conducting experiments to evaluate the mixtures of waste cooking oil (WCO) and diesel fuel in diesel engines. Furthermore, artificial neural networks (ANNs) are employed to develop models for predicting engine performance. The analysis focuses on critical metrics, including BSFC and BTE, under various operating conditions. This research aims to improve sustainable energy solutions by demonstrating the benefits of alternative fuels and advanced artificial intelligence (AI) prediction models in automotive applications. Full article

The skeletal and immune systems are intricately linked, forming a dynamic interface that regulates both bone homeostasis and immune function. This bidirectional relationship, central to the field of osteoimmunology, highlights how bone and immune cells interact via shared progenitors and signaling pathways. Osteoclasts and osteoblasts not only coordinate bone remodeling but also influence hematopoietic and immune functions within the bone marrow microenvironment. The concept of the “bone immune system” underscores this crosstalk, particularly in pathological and regenerative contexts. Despite progress, contradictory findings complicate our understanding of cytokine activity. Pro-inflammatory mediators such as TNF-α and IL-17 are typically associated with bone loss, yet under certain conditions, they paradoxically promote repair by stimulating osteoblast differentiation. Conversely, anti-inflammatory cytokines like IL-10 and TGF-β are generally protective, but their effects vary depending on local context, sometimes even impairing regeneration. These inconsistencies highlight unresolved questions and gaps in mechanistic insight into immune–bone interactions. Bone tissue engineering (BTE) has advanced through biomimetic scaffolds, osteogenic cells, and bioactive molecules, offering hope for large defect repair. However, clinical translation remains limited, largely because immune modulation is not fully integrated into scaffold design. Current preclinical models often fail to capture the complexity of immune–skeletal interplay, reducing predictive value. Addressing these gaps requires improved models and systematic evaluation of immunoregulatory biomaterials, paving the way for more effective and personalized regenerative therapies. Full article

Hybrid electric vehicles (HEVs) frequently cycle their internal combustion engines (ICE), potentially cooling the three-way catalyst (TWC). This challenges the use of compressed natural gas (CNG), as methane (CH₄) requires high temperatures for TWC oxidation. This study experimentally investigates the performance, engine-out emissions (CO, NO_x, CH₄, NMHC, CO₂), and catalyst temperatures of a Toyota RAV4 hybrid vehicle on gasoline (G), CNG, and dual fuel (MIX) during the WLTC. Engine-out emissions were measured upstream of the TWC. Results showed similar engine work output (~17.8 kWh/100 km), while CNG significantly reduced fuel mass consumption (−18.7%) and CO₂ emissions (−27.5%) compared to gasoline, driven by both its higher LHV and higher average BTE. CO (−32.3%) and NO_x (−34.0%) emissions were lower with CNG, linked to leaner operation and significantly retarded ignition timing for NO_x control. However, CH₄ emissions drastically increased with CNG. This study reveals a synergy between the same retarded ignition timing strategy used to successfully control engine-out NO_x (−34.0%) and created a positive secondary effect, raising pre-TWC temperatures by 4.5%. Higher thermal condition is essential for the aftertreatment of chemically stable methane, highlighting a direct link between the engine’s NO_x control logic and the potential to mitigate methane slip. Full article

To address the dual challenges of fossil fuel depletion and environmental pollution, developing clean, renewable alternative fuels is an urgent need. Biomass gas, including biomass syngas and biogas, offers significant potential as an internal combustion engine alternative fuel due to its widespread availability and carbon-neutral properties. This review summarizes research on biomass gas application in dual-fuel diesel engines. Firstly, biosyngas and biogas production methods, characteristics, and purification needs are detailed, highlighting gas composition variability as a key factor impacting engine performance. Secondly, dual-fuel diesel engine operating modes and their integration with advanced low-temperature combustion technologies are analyzed. The review focuses on how biomass gas affects combustion characteristics, engine performance, and emissions. Results indicate dual-fuel mode effectively reduces diesel consumption, emissions, while its carbon-neutrality lowers life-cycle CO₂ emissions and generally suppresses NO_x formation. However, challenges include potential BTE reduction and increased CO and HC emissions at low loads. Future research should prioritize gas quality standardization, intelligent combustion system optimization, and full-chain techno-economic evaluation to advance this technology. Overall, this review concludes that dual-fuel operation with biomass gases can achieve high diesel substitution rates, significantly reducing NO_x and particulate matter emissions. However, challenges such as decreased brake thermal efficiency and increased CO and HC emissions under low-load conditions remain. Future efforts should focus on gas composition standardization, intelligent combustion control, and system-level optimization. Full article

Roadway anti-icing requires low-carbon alternatives to chloride salts and electric heating. This study evaluated a seasonal thermal energy storage system that couples a geothermal hydronic heated pavement (HHPS-G) with borehole thermal energy storage (BTES), operated without auxiliary heat. A coupled transient HHPS-G–BTES model was developed and validated against independent experimental data. A continuous cycle was then simulated, consisting of three months of summer pavement heat harvesting and BTES, followed by three months of winter heat discharge. A parametric analysis varied borehole depth (10, 20, and 40 m) and number of units (1, 2, and 4). Results indicated that depth is consistently more effective than unit number. Deeper fields produced larger summer pavement surface cooling with less long-term drift and yielded more persistent winter anti-icing performance. The 40 m 4-unit case lowered the end-of-summer surface temperature by 3.8 °C relative to the no-operation case and kept the surface at or above 0 °C throughout winter. In contrast, the 10 m–1-unit case was near 0 °C by late winter. A depth-first BTES design, supplemented by spacing or edge placement to limit interference, showed practical potential for anti-icing without auxiliary heat. Full article

The Bagley–Torvik equation (BTE) is an important model in mathematical physics and mechanics, but obtaining its analytical solution remains challenging. For its numerical treatment, the presence of composite functions in the generalized BTE poses additional difficulties, and efficient approaches for handling nonlinear terms are still lacking in the literature. This study proposes an improved numerical method for the fractional BTE with integral boundary conditions. By employing an integration technique, the original problem is transformed into a weakly singular Fredholm–Hammerstein (F–H) integral equation of the second kind. To address the nonlinear terms, an enhanced piecewise Taylor expansion scheme is developed to construct the discrete form, while the uniqueness of the solution is proven using the contraction mapping theorem in Banach spaces. The convergence and error analyses are rigorously carried out, and numerical experiments confirm the accuracy and efficiency of the proposed approach. Full article

This study examines the effects of injection timing and cerium oxide (CeO₂) nanoparticle (NP) size on NOx emissions and brake thermal efficiency (BTE) in a compression ignition engine, contributing to Sustainable Development Goals 7 and 13. Experiments were conducted at four load conditions (25–100%) using NP sizes of 10 nm, 30 nm, and 80 nm. An artificial neural network integrated with multi-objective particle swarm optimization (ANN-PSO) was employed to identify optimal operating parameters. The optimized configurations improved BTE and reduced NOx emissions across all loads; for example, at 75% load, BTE increased from 30.38% (average) to 32.13% (optimum), while simultaneously reducing the NOx emissions from 1322 ppm (average) to 1272 ppm (optimum). Analysis of variance (ANOVA) confirmed load as the most significant factor (p < 0.001), followed by injection timing and NP size. The model predictions closely matched experimental results, validating the optimization approach. The optimization suggests an interpolated optimal NP size of approximately 45 nm, highlighting the potential for further exploration. This integrated experimental and computational approach offers a promising framework for improving combustion efficiency and reducing emissions, thereby advancing cleaner and more sustainable fuel technologies. Full article

The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods for calculating the average temperature of the storage unit. This limitation hinders accurate assessment of the thermal charging and discharging states. Furthermore, some models involve complex computations and exhibit low operational efficiency, failing to meet the practical engineering demands for rapid prediction and response. To address these challenges, this study first develops a thermal response model for the average temperature of the storage unit based on the finite line source theory and further proposes a simplified engineering algorithm for predicting the storage unit temperature. Subsequently, two-dimensional discrete convolution and Fast Fourier Transform (FFT) techniques are introduced to accelerate the solution of the storage unit temperature distribution. Finally, the model’s accuracy is validated against practical engineering cases. The results indicate that the single-point temperature engineering algorithm yields a maximum relative error of only 0.3%, while the average temperature exhibits a maximum relative error of 1.2%. After employing FFT, the computation time of both single-point and average temperature engineering algorithms over a 10-year simulation period is reduced by more than 90%. When using two-dimensional discrete convolution to calculate the temperature distribution of the storage unit, expanding the input layer from 200 × 200 to 400 × 400 and the convolution kernel from 25 × 25 to 51 × 51 reduces the time required for temperature superposition calculations to approximately 0.14–0.82% of the original time. This substantial improvement in computational efficiency is achieved without compromising accuracy. Full article

In this study, cottonseed oil biodiesel and waste lard biodiesel were produced through a transesterification process and blended with conventional diesel at different ratios (B10 and B20). The performance and emission characteristics of these fuels were systematically evaluated in a single-cylinder, four-stroke, water-cooled diesel engine operating at speeds of 1000–1800 rpm under a constant 50% load. The physicochemical properties of the fuels were analyzed, and engine parameters including brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), and emissions of carbon monoxide (CO), hydrocarbon (HC), carbon dioxide (CO₂), and nitrogen oxides (NOx) were measured. The results demonstrated that, compared with diesel, biodiesel blends significantly reduced CO, HC, and CO₂ emissions. At 1800 rpm, the LB20 blend showed reductions of 31.03% in CO, 47.06% in HCs, and 19.14% in CO₂ relative to diesel. These reductions are mainly attributed to the higher oxygen content and lower hydrogen-to-carbon ratio of biodiesel, which promote more complete combustion. However, all biodiesel blends exhibited higher NOx emissions than diesel, with the increase being more pronounced at higher blend ratios. At 1800 rpm, the LB20 blend recorded the highest NOx emissions, which were 20.63% higher than those of diesel under the same condition. In terms of performance, biodiesel blends showed higher BSFC and lower BTE compared with diesel, mainly due to their lower calorific value and higher viscosity. The lowest BTE and the highest BSFC were both observed with the LB20 blend, at 22.64% and 358.11 g/kWh, respectively. Full article

The growing dependence on fossil fuels has raised concerns over energy security, resource depletion, and environmental impacts, driving the need for renewable alternatives. Coffee husk, a widely available agro-industrial residue, represents an underutilized feedstock for biodiesel production. In this study, biodiesel was synthesized from coffee husk oil using a two-step transesterification process to address its high free fatty acid content (21%). Physicochemical analysis showed that Coffee Husk Oil Methyl Ester (CHOME) possessed a density of 863 kg m⁻³, viscosity of 4.85 cSt, and calorific value of 33.51 MJ kg⁻¹, compared to diesel with 812 kg m⁻³, 2.3 cSt, and 42.4 MJ kg⁻¹. FTIR analysis confirmed the presence of ester carbonyl and C–O functional groups characteristic of CHOME, influencing its combustion behavior. Engine tests were then conducted using B0, B10, B30, B50, and B100 blends under different loads, both with and without fuel preheating. Results showed that neat CHOME (B100) exhibited 11.8% lower brake thermal efficiency (BTE) than diesel, but preheating at 95 °C improved BTE by 5%, with preheated B10 slightly surpassing diesel by 0.5%. Preheating also reduced brake-specific fuel consumption by up to 7.75%. Emission analysis revealed that B100 achieved reductions of 6.4% CO, 8.3% HC, and 7.0% smoke opacity, while NOx increased only marginally (2.86%). Overall, fuel preheating effectively mitigated viscosity-related drawbacks, enabling coffee husk biodiesel to deliver competitive performance with lower emissions, highlighting its potential as a sustainable waste-to-energy fuel. Full article

This work provides a systematic evaluation of the performance and regulated emissions of binary n-pentanol–diesel blends under steady-state conditions, thereby clarifying condition-dependent efficiency–emission trade-offs across multiple loads and speeds. A single-cylinder, air-cooled diesel engine was operated at two speeds (1700 and 2700 rpm) and four brake mean effective pressure (BMEP) levels (0.25–0.49 MPa) using commercial diesel (D100) and three n-pentanol–diesel blends at volume ratios of 10%, 30%, and 50% (designated D90P10, D70P30, and D50P50, respectively). Brake thermal efficiency (BTE), brake specific energy consumption (BSEC), and brake specific fuel consumption (BSFC) were measured alongside exhaust emissions of nitrogen oxides (NO_x), carbon monoxide (CO), hydrocarbon (HC), carbon dioxide (CO₂), and smoke opacity. The results show that due to a lower cetane number, high latent heat of vaporization, and reduced heating value, n-pentanol blends incur efficiency and fuel consumption penalties at light to moderate loads. However, these disadvantages diminish or reverse at high loads and speeds: D50P50 surpasses D100 in BTE and matches or improves BSEC and BSFC at 2700 rpm and 0.49 MPa. Emission data reveal that the blend’s fuel-bound oxygen and enhanced mixing provide up to 16% NO_x reduction; 35% and 45% reductions in CO and HC, respectively; and a 74% reduction in smoke opacity under demanding conditions, while CO₂ per unit work output aligns with or falls below D100 at high load. These findings demonstrate that optimized n-pentanol–diesel blends can simultaneously improve efficiency and mitigate emissions, offering a practical pathway for low-carbon diesel engines. Full article

Without changing any of its constituents, tyre pyrolysis oil energy (TPOE) has frequently been subjected to Diesel-RK (D-RK) analyses in diesel engines in an effort to serve as a substitute for diesel fuel. Environmentally beneficial TPOE features, such as biodegradability, renewability, and ease and safety of handling, are highly sought after. In addition to its beneficial aspects, TOPE also has drawbacks. The BTE and SFC of performance metrics, as well as the smoke and NO_x of emission parameters of alternative fuel, do not meet the emission limits specified by regulatory authorities. Nano-additions have been shown to be effective for boosting fuel quality for improved performance and production characteristics. In this study, TPOE–diesel blends are blended with ceramic oxide (CeO₂ of 50 and 100 ppm) nanoparticles and subjected to a performance and production investigation of engine working physiognomies in diesel engines. For the blend TPOE10CDF80 + D, the numerical results show a positive outcome of a 1.0% rise in BTE, a 2.0% decrease in SFC, a 17.7% decrease in smoke emission, and an 18.2% increase in NO_x emission as compared to diesel fuel (CDF). Full article