Search Results (118)

The performance of the UAVs while executing various mission profiles greatly depends on the selection of planning algorithms. Reinforcement learning (RL) algorithms can effectively be utilized for robot path planning. Due to random action selection in case of action ties, the traditional Q-learning algorithm and its other variants face the issues of slow convergence and suboptimal path planning in high-dimensional navigational environments. To solve these problems, we propose an improved deep Q-network (DQN), incorporating an efficient tie-breaking mechanism, prioritized experience replay (PER), and L2-regularization. The adopted tie-breaking mechanism improves the action selection and ultimately helps in generating an optimal trajectory for the UAV in a 3D cluttered environment. To improve the convergence speed of the traditional Q-algorithm, prioritized experience replay is used, which learns from experiences with high temporal difference (TD) error and avoids uniform sampling of stored transitions during training. This also allows the prioritization of high-reward experiences (e.g., reaching a goal), which helps the agent to rediscover these valuable states and improve learning. Moreover, L2-regularization is adopted that encourages smaller weights for more stable and smoother Q-values to reduce the erratic action selections and promote smoother UAV flight paths. Finally, the performance of the proposed method is presented and thoroughly compared against the traditional DQN, demonstrating its superior effectiveness. Full article

Road transport plays a significant role in the economic growth of a country. Conventional internal combustion engines (ICEs) are widely used in automobiles, with an efficiency range of 25% to 35%, while the remaining energy is lost through cooling and exhaust gases. Additionally, two parasitic loads—the alternator and the air conditioning (AC) compressor—are driven by the ICE via a belt, further reducing efficiency. In this paper, energy and exergy analysis of the waste heat of exhaust gases has been performed for automobiles equipped with ICEs, i.e., R06A, F8B, K10B, 2NZ-FE, and 2ZR-FE, to evaluate their potential to drive these parasitic loads. The working cycles of these ICE models were simulated using a zero-dimensional MATLAB model based on fundamental governing equations. The results indicate that approximately 10–40 kW of energy is lost through exhaust gases under varying operating conditions for the examined ICEs. The average exhaust gas temperature and mass flow rate for these ICEs are approximately 900 K and 0.016 kg/s, respectively. Based on these findings, an E-turbine retrofit system is proposed to operate under these conditions, recovering exhaust energy to power the alternator and AC compressor. The results showed that the E-turbine generated 6.8 kW of mechanical power, which was converted into 4 kW of electrical power by the generator. This electrical power was used to supply the parasitic loads, thereby enhancing the overall efficiency of ICE. Full article

A tapered roller bearing (TRB) is a specialized type of bearing with a high load-to-volume ratio, designed to support both radial and axial loads. Lubrication plays a crucial role in TRB operation by reducing friction and dissipating heat generated during rotation. However, it can also negatively impact TRB performance due to the viscous and inertial effects of the lubricant. Extensive research has been conducted to examine the role of lubrication in TRB performance. Lubrication primarily influences the frictional characteristics, thermal behavior, hydraulic losses, dynamic stability, and contact mechanics of TRBs. This paper aims to collect and classify the scientific literature on TRB lubrication based on these key aspects. Specifically, it explores the scope of research on the use of Newtonian and non-Newtonian lubricants in TRBs. Furthermore, this study analyzes research based on TRB size and type, considering both oil and grease as lubricants. The findings indicate that both numerical and experimental studies have been conducted to investigate Newtonian and non-Newtonian lubricants across various TRB sizes and types. However, the results highlight that limited research has focused on non-Newtonian lubricants in TRBs with an Outer Diameter (OD) exceeding 300 mm, i.e., those typically used in wind turbines, industrial gearboxes, and railways. Full article

Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical for optimizing productivity. This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. The Agricultural Production Systems sIMulator was calibrated to simulate multiple traits across the growth season based on geo-tagged wheat field ground information. Reflectance and spectral indices were processed for the geo-tagged fields across temporal observations to enable real-time, spatially explicit monitoring. Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. The performance evaluation revealed that the Long Short-Term Memory (LSTM) model achieved superior accuracy for the integrated parameters (RMSE = 250.68 kg/ha, MAE = 193.76 kg/ha, and R² = 0.84), followed by traits alone. The Random Forest model followed the LSTM model, with an RMSE = 293.56 kg/ha, MAE = 230.68 kg/ha, and R² = 0.78 for integrated parameters, and an RMSE = 291.73 kg/ha, MAE = 223.17 kg/ha, and R² = 0.78 for crop traits. The superior prediction demonstrated the dominant role of multiple crop traits with satellite-derived reflectance metrics to develop robust CYM frameworks capable of capturing intra- and inter-field yield variability. Full article

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies. Full article

Heat stress represents significant challenges for livestock, adversely affecting their production, reproduction, and overall welfare. This study aimed to explore the interrelationships between environmental and animal-related factors and the flank temperature (FT) and eye temperature (ET) recorded using IRT in dromedary camels. This study was conducted in the Cholistan Desert in 2023, and IRT images of the eyes and flanks were captured from 510 camels across 54 herds. During the image analyses, pictures taken from 499 camels were of good quality and included. The camels were of both sexes and of various ages (minimum 3 years, pubertal and adult stages), and they had diverse physiological statuses (breeding, immature, lactating, non-lactating, and pregnant). Before taking the IRT pictures, ambient temperature and humidity were registered using a weather station, and light intensity was recorded using a lux meter. The ET was associated only with physiological status (p < 0.05), with pregnant females showing the lowest values, while no effects of physiological status, sex, or age were found for FT. The environmental temperature showed a positive correlation with both ET (r = 0.7887) and FT (r = 0.6280), highlighting the sensitivity of camel thermoregulation to temperature fluctuations. As expected, a strong positive correlation between ET and FT (r = 0.6643) was found. Conversely, a significant negative correlation was observed between humidity and ET (−0.7444) and FT (−0.5519), indicating that higher humidity levels lead to decreased temperatures in both regions. Light intensity (lux) exhibited minimal influence on both temperatures, with correlations of 0.1019 for ET and 0.2650 for FT. This study contributes to the field of precision livestock farming by suggesting a possible application of IRT for detecting thermal stress in camels in pastoral settings. Full article

Hydrogen production is one of the best solutions to the growing energy concerns, owing to its clean and sustainable assets. The current review gives an overview of various hydrogen production technologies, highlighting solar water splitting as a promising approach for its sustainable production. Moreover, it gives a detailed mechanism of the water-splitting reaction and describes the significance of titania-based catalysts for solar water splitting. It further highlights diversified strategies to improve the catalytic efficiency of TiO₂ for the enhanced hydrogen production. These strategies include the doping of TiO₂, dye sensitization, and the addition of co-catalysts. Doping reduces the bandgap by generating new energy levels in TiO₂ and encourages visible-light absorption. Sensitization with dyes tunes the electronic states, which in turn broadens the light-absorption capacity of titania. Constructing heterojunctions reduces the charge recombination of TiO₂, while co-catalysts increase the number of active sites for an enhanced reaction rate. Thus, every modification strategy has a positive impact on the stability and photocatalytic efficiency of TiO₂ for improved water splitting. Lastly, this review provides a comprehensive description and future outlook for developing efficient catalysts to enhance the hydrogen production rate, thereby fulfilling the energy needs of the industrial sector. Full article

Significant progress has been made in the field of gene therapy, but effective treatments for brain tumors remain challenging due to their complex nature. Current treatment options have limitations, especially due to their inability to cross the blood-brain barrier (BBB) and precisely target cancer cells. Therefore options that are safer, more effective, and capable of specifically targeting cancer cells are urgently required as alternatives. This current study aimed to develop highly biocompatible natural biopolymeric chitosan nanoparticles (CNPs) as potential gene delivery vehicles that can cross the BBB and serve as gene or drug delivery vehicles for brain disease therapeutics. The efficiency of the CNPs was evaluated via in vitro transfection of Green Fluorescent Protein (GFP)-tagged plasmid in HEK293-293 and brain cancer MG-U87 cell lines, as well as within in vivo mouse models. The CNPs were prepared via a complex coacervation method, resulting in nanoparticles of approximately 260 nm in size. In vitro cytotoxicity analysis revealed that the CNPs had better cell viability (85%) in U87 cells compared to the chemical transfection reagent (CTR) (72%). Moreover, the transfection efficiency of the CNPs was also higher, as indicated by fluorescent emission microscopy (20.56% vs. 17.79%) and fluorescent-activated cell sorting (53% vs. 27%). In vivo assays using Balb/c mice revealed that the CNPs could efficiently cross the BBB, suggesting their potential as efficient gene delivery vehicles for targeted therapies against brain cancers as well as other brain diseases for which the efficient targeting of a therapeutic load to the brain cells has proven to be a real challenge. Full article

Microplastic pollution has become a global concern, with potential negative impacts on various ecosystems and wildlife species. Among these species, ducks (Anas platyrhynchos) are particularly vulnerable due to their feeding habits and proximity to aquatic environments contaminated with microplastics. The current study was designed to monitor microplastic (MP) pollutants in the freshwater ecosystem of the Panjkora River, Lower Dir, Pakistan. A total of twenty (20) duck samples were brought up for four months and 13 days on the banks of the river, with no food intake outside the river. When they reached an average weight of 2.41 ± 0.53 kg, all samples were sacrificed, dissected, and transported in an ice box to the laboratory for further analysis. After sample preparation, such as digestion with 10% potassium hydroxide (KOH), density separation, filtration, and identification, the MP content was counted. A total of 2033 MP particles were recovered from 20 ducks with a mean value of 44.6 ± 15.8 MPs/crop and 57.05 ± 18.7 MPs/gizzard. MPs detected in surface water were 31.2 ± 15.5 MPs/L. The major shape types of MPs recovered were fragments in crop (67%) and gizzard (58%) samples and fibers in surface water (56%). Other types of particles recovered were fibers, sheets, and foams. The majority of these detected MP particles were in the size range of 300–500 µm (63%) in crops, and 50–150 µm (55%) in gizzards, while in water samples the most detected particles were in the range of 150–300 µm (61%). Chemical characterization by FTIR found six types of polymers. Low-density polyethylene (LDPE) had the greatest polymer detection rate (39.2%), followed by polyvinyl chloride (PVC) (28.3%), high-density polyethylene (HDPE) (22.7%), polystyrene (6.6%), co-polymerized polypropylene (2.5%), and polypropylene homopolymer (0.7%). This study investigated the presence of microplastics in the crops and gizzards of ducks, as well as in river surface water. The results revealed the significant and pervasive occurrence of microplastics in both the avian digestive systems and the surrounding water environment. These findings highlight the potential threat of microplastic pollution to wildlife and ecosystems, emphasizing the need for further research and effective mitigation strategies to address this pressing environmental concern. Full article

The intricate interplay between the environment and the economy entails numerous multifaceted factors that require thorough investigation. Civic activism, intergroup cohesion, and gender equality are among the pertinent factors that hold the potential to significantly impact CO₂ emissions in developing economies. However, these variables have not been explored to the extent that their importance warrants, leaving much to be studied and understood about their complex relationships with carbon emissions. Currently, developing nations find themselves more vulnerable and exposed to a plethora of environmental issues. In response to this pressing matter, the focus of this study is to expound upon the impact of various factors on the environment. To achieve this aim, this study utilizes annual data from 46 developing countries, spanning the extensive period from 1990 to 2014. Using the generalized method of moments and empirical Bayes methods, this study’s results emphasize the significant impact that civic activism, gender equality, intergroup cohesion, and financial development can have on increasing CO₂ emissions. However, civic activism reduces CO₂ emissions. These findings highlight the crucial importance of adopting a comprehensive approach that accounts for both economic and social cohesion indicators when tackling environmental challenges. Full article

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber–physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world. Full article

Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. Full article

The issue of microplastic (MP) pollution is one of the most pressing environmental problems faced today and for the future. Plastics are ubiquitous due to their exponential use and mismanagement, resulting in the accumulation of fragments across the world. Hence, the problem of MP pollution is aggravated when these plastic items disintegrate into smaller particles due to different physical, chemical, and environmental factors. The consumption of these MP pollutants by wildlife is a worldwide concern and a potentially crucial risk for all ecosystems. Consequently, MPs have caused a wide variety of problems for both public health and wildlife concerning vital life processes—specifically reproduction, which is critical to species’ survival in an ecosystem. Despite MPs’ detrimental effects on wildlife reproduction, it remains unclear how MPs can affect the hypothalamic–pituitary–gonadal (HPG) axis. This review highlights the significant reproductive toxicity of MPs in wildlife, with potentially devastating consequences for human health. The findings emphasize the urgency of developing effective solutions for mitigating the adverse effects of MP pollution on the reproductive systems of wildlife and preserving the integrity of aquatic and terrestrial habitats. Full article

Microplastics (MPs) are an emerging environmental health concern due to their widespread occurrence in food sources such as fish, meat, chicken, honey, sugar, salt, tea and drinking water, thereby posing possible risks to human health. This study aimed to observe the existence of MPs in the crop and gizzard of the farm chicken, a significant food source in Pakistan. Twenty-four chicken samples were taken from eight poultry farms across Punjab, Pakistan. A total of 1227 MP particles were found from 24 samples (crop and gizzards) originating from the 8 poultry farms. In all, 429 MP particles were found in 24 chicken crops, with a mean of 17.8 ± 12.1 MPs/crop. In contrast, 798 MP particles were found in 24 chicken gizzards, with a mean of 33.25 ± 17.8 MPs/gizzard. Comparatively larger particles, ranging between 300–500 µm, were more abundant (63%) than other considered sizes (300–150 µm [21%] and 150–50 µm [16%]). Additionally, fragments were the dominant type of shape in both sample types (crop [64%] and gizzard [53%]). The predominant colours of particles extracted from gizzards and crops were yellow (32%) and red (32%), respectively. Chemical characterisation of these particles detected four types of polymers: polyvinyl chloride (PVC) at 51.2%, followed by low-density polyethylene (LDPE) at 30.7%, polystyrene (PS) at 13.6% and polypropylene homopolymer (PPH) at 4.5%. In conclusion, we provide evidence for MPs in the gizzards and crops of farmed chickens which may originate from contaminated poultry feed. Only a few studies have been reported globally to assess MPs ingestion in chickens. The current study is the first report from Pakistan. It could be a valuable addition to support MPs literature to establish a relationship between MPs contamination and intake through the food chain. Full article