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Predictors of implant failure: A comprehensive analysis of risk factors in oral implant restoration for patients with partial defects of dentition

Implant failure remains a significant challenge in oral implantology, necessitating a deeper understanding of its risk factors to improve treatment outcomes. This study aimed to enhance the clinical outcomes of oral implant restoration by investigating the factors contributing to implant failure in patients with partial dentition defects within two years of treatment. Additionally, the study sought to develop an early risk prediction model for implant failure. A retrospective analysis was conducted on 300 patients with partial dentition defects, dividing them into two groups: a failed implant group and a successful implant group, based on the occurrence of implant failure within two years. General clinical data and condition-specific clinical information were compared between the groups. Multivariate binary logistic regression analysis was used to identify influencing factors, while the predictive effectiveness of the model was assessed using a receiver operating characteristic (ROC) curve. The analysis revealed that factors such as gender, post-implant smoking, oral hygiene status at the second-year follow-up, tooth position, number of implants, timing of loading, width of keratinized mucosa, and bone quantity significantly influenced the likelihood of implant failure (P<0.05). Among these, post-implant smoking and tooth position were identified as independent risk factors. The area under the curve (AUC) for tooth position was 0.695, indicating low predictive performance. Although tooth position was determined to be an independent risk factor for implant failure within two years, its predictive performance was limited.

Multi-dimensional failure risk load prediction of electric motor based on physical model and data-driven approach

Dynamic prediction of failure risk loads is a critical prerequisite for system damage monitoring and service life evaluation. As the failure mechanism of electric vehicle drive motors under multi-source load excitations is complex, and the risk load poses challenges in rapid acquisition, a physics-based and data-driven method for multi-dimensional failure risk load prediction of motors is proposed. Based on actual operational data collected from users, we analyze the failure risk loads of various components of a motor. A physical simulation model of the motor is established to extract multi-dimensional failure risk load time history, and the effectiveness of the simulation model is verified using data from bench tests. Moreover, using the physical model simulation data as a foundation, a two-stage data-driven model is constructed. In the first stage, the genetic algorithm optimized back propagation neural network (GA-BPNN) model is implemented for multi-dimensional loss prediction of the motor, and serves as the primary input for the second stage model, which integrates prediction accuracy and training efficiency. In the second stage, by comprehensively considering the spatial and temporal correlation features with the input layer reconstructed using correlation matrix weights, an improved convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM) model is employed to predict multi-scale loads. Compared with traditional models, the proposed method exhibits significantly improved prediction accuracy, with R2 greater than 0.95. Furthermore, the damage error between the predicted load and the expected load is within 10%, which provides technical support for reliability evaluation and life prediction of electric motor.

Eliminating osmotic stress during cryoprotectant loading: A mathematical investigation of solute-solvent transport.

Osmotic stresses during cryoprotectant loading induce changes in cellular volume, leading to membrane damage or even cell death. Appropriate model-guided mitigation of these osmotic gradients during cryoprotectant loading is currently lacking, but would be highly beneficial in reducing viability loss during the loading process. To address this need, we reformulate the two-parameter formalism described by Jacobs and Stewart for cryoprotectant loading under the constraint of constant cell volume. We then derive simple, concise, analytic solutions to these equations, showing the transient extracellular permeating and nonpermeating cryoprotectant concentrations required to load a cell at constant volume, thus eliminating osmotic stresses during cryoprotectant loading. Additionally, we show analytic approximations of both ramp (linear) as well as step-wise loading and how one can use the hydraulic conductivity Lp, membrane permeability Ps, cell volume Vo, and osmotically inactive fraction to derive cryoprotectant loading protocols that minimize osmotic stress. We also present timescales for water and cryoprotectant transport which can be used to estimate loading times as well as Lp and Ps. We discuss how previous optimized loading strategies are inherently sensitive to parameter uncertainties and biological variability, increasing the likelihood of exceeding critical osmotic limits. By contrast, the proposed protocol provides a larger buffer against deviations, offering a safer and more robust solution to CPA loading. Importantly, we demonstrate that the volume-loss-free CPA loading protocols outlined in this paper occur on the same timescale as conventional and step-loading methods, suggesting that these protocols could be a safer, more efficient alternative for CPA loading.

Sentiment Analysis for Movie Database Management System Using Naive Bayes Algorithm

Abstract: According to F. Furtado and A. Singh (2021), movie recommendation systems aim to assist viewers by suggesting movies to watch, thereby avoiding the time-consuming and complex process of selecting from a vast library of movies, which can number in the thousands or even millions. This article seeks to reduce human effort by recommending movies based on user interests. To address these challenges, we propose a technique that combines content-based and collaborative approaches. This method produces more precise results than systems that rely solely on content-based methods. Content-based recommendation systems are limited to individual preferences and do not recommend items outside the user’s immediate interests, restricting exploration. To overcome these limitations, we developed a system that addresses these concerns. The proposed movie recommendation system uses Cosine Similarity to suggest movies like the one chosen by the user. While existing algorithms generate recommendations, they often fail to answer the critical question, “Is this a film worth watching?” To enhance the overall experience, our system incorporates sentiment analysis of selected movie reviews using machine learning techniques. This study employs two supervised learning approaches, Naïve Bayes (NB) and Support Vector Machine (SVM), to improve accuracy and efficacy. A comparison of the two methods reveals that SVM achieved an accuracy score of 98.63%, while NB scored 97.33%, as reported by N. Pavitha et al. (2022). The project integrates a sentiment analysis module into the movie management system to enhance decision-making and user experience. The Naïve Bayes approach is used to classify and evaluate user reviews and feedback into positive, negative, and neutral categories. This analysis provides insights into audience preferences, overall satisfaction, and movie reception. The system utilizes this information to refine movie recommendations, analyze trends, and enhance content selection, all of which contribute to a more personalized user experience. The system is evaluated using the ISO 25010 framework, focusing on functionality, reliability, usability, effectiveness, and robustness. Findings show that respondents actively participated in the review process across multiple domains. Key results indicate high satisfaction levels in terms of ease of use, interface design, and system performance. Feedback also highlighted areas for improvement, such as optimizing loading times and enhancing specific features for a smoother user experience. Overall, the findings reflect positive engagement with the system, demonstrating its effectiveness in meeting user needs while identifying opportunities for future enhancements.

Optimization strategies for bioethanol production from sweet lime peel waste: a study on simultaneous saccharification and fermentation with co-cultures

In the present study, sweet lime peel (SLP) is tested for the production of bioethanol. The acid-catalysed steam-pretreated SLP was studied for simultaneous saccharification and fermentation (SSF). Cellulase, pectinase and beta-glucosidase enzymes were used for the saccharification of pretreated SLP. SSF studies were performed using these enzymes and a co-culture of Saccharomyces cerevisiae, Zymomonas mobilis and Escherichia coli. The process variables such as peel loading (5–15%w/v), temperature (25–50 °C), pH (4–6) and fermentation time (24–72 h) were optimized for bioethanol using central composite design, and the effect of these variables on ethanol concentration was analysed. Ethanol concentration was maximum with a peel loading of 10% (w/v) fermented using the co-culture of S. cerevisiae, Z. mobilis and E. coli at a temperature of 37.5 °C and initial pH 5 for 48 h; 16.5 g/L of ethanol was produced at the optimum conditions with a fermentation yield of 89.6%.

Kaolin-supported molybdovanado phosphoric acid as green catalyst for fluoride ion removal in water

Using the impregnation approach, molybdovanado phosphoric acid (MVPA) has been immobilized onto kaolin resulting in a green heterogeneous catalyst (MVPA@Kaolin). A comprehensive physicochemical analysis was performed on the catalyst. Because of its uses in fine chemicals and medicines, this hybrid material is meant to be used as a sustainable catalyst. To investigate the catalyst, a variety of physicochemical characterization approaches were used. Through the adsorption of fluoride, the performance of the material was evaluated with temperature, pH, catalyst load, and contact time. The results demonstrated rapid and efficient fluoride removal, with a removal efficiency of more than 85 % in a little contact time. The optimum pH obtained as 1 with fluoride removal efficiency of 84 %, catalyst load 5 g/L (85 %), and temperature 50 oC (85 %). Additionally, composite material regeneration and reusability were investigated. The results aid in the creation of effective and environmentally sustainable fluoride-in water treatments.

Mastic: Private Weighted Heavy-Hitters and Attribute-Based Metrics

Insight into user experience and behavior is critical to the success of large software systems and web services. Gaining such insights, while preserving user privacy, is a significant challenge. Recent advancements in multi-party computation have made it practical to securely compute aggregates over secret shared data. Two such protocols have emerged as candidates for standardization at IETF: Prio (NSDI 2017) for general-purpose statistics; and Poplar (IEEE S&P 2021) for heavy hitters, where the goal is to compute the most popular inputs held by users without learning the inputs themselves. While each of these protocols is well-suited to certain applications, there remain a number of use cases identified by IETF for which neither Prio nor Poplar is practical. We introduce Mastic, a protocol for the following functionality: each of a large number of clients holds an input (e.g., a URL) and its corresponding weight (e.g., page load time); for a given candidate input (or prefix), a small number of non-colluding servers wish to securely aggregate the weights of clients that hold that input (or some input with that prefix), without learning the weights or which client holds which input. This functionality makes two new classes of applications possible. The first is a natural generalization of heavy hitters we call weighted heavy-hitters. The second is an enhancement of Prio-style metrics we call attribute-based metrics in which aggregates are grouped by hierarchical user attributes (e.g., their geographic location or software version). We demonstrate Mastic's practicality for these applications with a real-world example of each. We also compare our protocol with Prio and Poplar on a wide area network. Overall, we report over one order of magnitude performance improvement over Poplar for plain heavy-hitters and 1.5-2x improvement over Prio for attribute-based metrics.

Evaluation of Production Capability of Transportation Equipment and Loading Equipment in Limestone Mining at PT X, South Sulawesi

This study focuses on the evaluation of the production capacity of loading and hauling equipment in limestone mining operations at PT. X, South Sulawesi Province. The selection of this topic is based on the importance of operational efficiency in limestone mining which has a direct impact on the productivity and sustainability of the cement industry. The main objectives of this study are to determine the actual production of loading and hauling equipment, determine the number of equipment needed to meet production targets and identify compatibility factors between mechanical equipment at the mining site. The methods used include field data collection involving measurement of cycle time and production capacity of loading and hauling equipment. The data obtained are then processed using statistical methods to ensure the accuracy of the results. Data analysis is carried out by considering variables such as work efficiency, cycle time, and equipment capacity. The results show that Kobelco SK 480 LC has the highest production capacity (509.34 tons/hour), followed by Komatsu PC 400 (448.25 tons/hour) and PC 300 (417.05 tons/hour). To achieve the Crusher production target of 1450 tons/hour, a combination of loading equipment and dump trucks with a compatibility factor between 0.87 and 0.92 is required, indicating potential for increased efficiency. It is recommended to improve maintenance of equipment and mining roads to optimize productivity.

Heuristic-based vehicle arrangement for ro-ro ships

In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space–time scheduling strategy is proposed using the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios.

Quantifying performance gains of DirectStorage for the visualisation of time-dependent particle datasets

Abstract Visualisation is an essential tool for analysing unstructured particle data as they are produced, for instance, in molecular dynamics or astrodynamics simulations. Such simulations often comprise multiple time steps, and scientists produce datasets with more and more particles in each step. To handle the steadily increasing size of these simulations, many solutions based on organising the data efficiently or reducing their amount using compression have been proposed over the years. Recently, a new storage API called DirectStorage has been introduced on Xbox consoles and Windows. DirectStorage promises a dramatic reduction of loading times in games by making the transfer from NVMe drives to graphics memory more efficient. That begs the question of whether DirectStorage is also beneficial for visualising time-dependent particle data that have to be streamed from disc to the GPU and whether a potential improvement in throughput enables interactive streaming of frames that traditional APIs cannot handle. For that, we implemented a benchmarking application that supports different streaming methods. We report on the results of an extensive series of tests with varying parameters that influence the streaming performance, which show that the performance of DirectStorage is highly dependent on the choice of various parameters. Graphic Abstract We provide an empirical evaluation for the performance gains to be expected when using the DirectStorage API for rendering dynamic particle data set like the one on the right side. For that, we compare the overlapping DirectStorage-based I/O scheme depicted on at the bottom with traditional methods

IMPLEMENTATION OF PROPHET IN AMERICAN ELECTRICITY FORECASTING WITH AND WITHOUT PARAMETER TUNING

Prophet is one of the machine learning approximation methods that accommodate trends, seasonality, and holiday impacts in time series data. Generally, the performance of machine learning models can be improved by implementing hyperparameter tuning. This study investigates whether hyperparameter tuning can improve the model's performance. To show its effectiveness, the Prophet model constructed by parameter tuning is compared to the one with fixed parameter values (namely the default model) for both the original series and the Box-Cox transformed series in terms of mean absolute percentage error (MAPE). Based on the experimental results of the twenty-four daily electricity load time series in American Electric Power (AEP). This shows that parameter tuning successfully reduces the MAPE of the default model in the range of about 3-8% for training data. However, there is no guarantee for testing data. Although, in some cases, parameter tuning can reduce the MAPE value of the default model by up to 38%, in other cases, it actually increases the MAPE of the default model by almost 15%. The experiments on testing data also show that models built from transformed data do not necessarily produce more accurate forecast values than those built from the original data.

Levofloxacin-loaded silicone contact lenses materials for ocular drug delivery

Silicone contact lenses (SCL), as an emerging ocular drug delivery system, achieve controlled drug release. However, the existing drug loading methods have limitations such as low drug uptake, complicated operation process, poor welling rate and transmittance of the lens after drug loading. In this study, an effective microemulsion soaking method was proposed to increase the drug-loading capacity of silicone contact lenses. Levofloxacin (LVF) was encapsulated into the microemulsion by direct agitation, then the microemulsion was loaded into silicone contact lenses using the immersion method. The adsorption capacity of levofloxacin and its effect on drug release kinetics were explored. The results showed that the particle size of the microemulsion was approximately 160 nm. The levofloxacin microemulsion soaking method (LVF-ME-SCL) significantly enhanced the drug loading of levofloxacin in the silicone contact lenses, achieving a maximum drug loading of 216.32 ± 1.15 μg/lens ( p > 0.05). The total release rate of levofloxacin was 95.96% when the sustained release time was 10 h, and the drug leakage observed after 10 h was negligible. The survival rate of E. coli and S. aureus in LVF-ME-SCL-1 (LVF concentration was 4.8 mg/mL) group was 0 and 19.33 ± 0.02% ( p < 0.0001), with a significant difference, indicating that the drug-loaded silicone contact lenses exhibited effective bactericidal properties against E. coli and S. aureus. Following the addition of maximum levofloxacin, the surface contact angle of silicone contact lenses decreased significantly to 32.88 ± 1.19° ( p > 0.05), while the swelling, mechanical properties, and oxygen permeability remained relatively unchanged. There was no significant decrease in the transmittance of the contact lenses after the addition of levofloxacin, which remained above 95%. In conclusion, these results show that the microemulsion impregnation method effectively improves the drug loading and sustained release time of levofloxacin, and maintains lens performance stability before and after drug loading, so it is expected to be used in ophthalmic treatment.

Automated External Defibrillator: Is Internet Education Reliable

Automatic external defibrillators (AED) are life-saving devices whose prevalence is increasing day by day and are designed to be used easily by everyone. In this study, we evaluated the videos about AEDs on "YouTube" in terms of accuracy and compliance with the guidelines. AED videos on YouTube were evaluated based on the AED section of ILCOR 2015. While the videos were evaluated, the uploader, video duration, number of views, and who the AED was applied to were also recorded. Each video was evaluated by applying a score between 0-9 points. Three hundred videos were evaluated in this study. Of these, 215 were excluded from the study according to the exclusion criteria and 85 were included in the study. When these videos were evaluated, it was found that 36 of them were uploaded by private companies for educational purposes, the average number of views was 19836 (min. 7 – max. 254318) and the median duration was 5.46 seconds or minutes (min. 0.24 – max. 59.1). The highest number videos were uploaded in 2014 (17) and a mannequin was used for demonstration in 68 videos. Only 3 videos (3.5%) received full marks. It was observed that there was a significant relationship between the institution that uploaded the videos and the reliability of the views (p≤0.05). No correlation was found between reliability and the person to whom AED was applied, video duration and loading time (p=0.218, p=0.491 and p=0.324, respectively). As a result of our study, we saw that only 3 out of 85 published under the name 'automatic external defibrillator' on YouTube received full scores, and 23 videos received scores above the average score, therefore, YouTube does not appear to be a reliable source of education for AED.

The Load Cycle Amplitude Model: An Efficient Time-Domain Extrapolation Technique for Non-Stationary Loads in Agricultural Machinery

In traditional time-domain extrapolation methods, the peak over threshold (POT) model is unable to accurately identify large load cycles in the load time history, resulting in distorted extrapolation results, particularly when addressing non-stationary loads. To address this problem, this paper proposes a time-domain extrapolation method based on the load cycle amplitude (LCA) model. The core of the method involves using load cycle amplitude features extracted from the measured loads as the basis for modelling, rather than extreme turning points based on threshold extraction. This approach prevents the load’s time-domain characteristics from compromising the accuracy of the extrapolation results. The case analysis results demonstrate that the extrapolation method based on the LCA model achieves more reliable results with both non-stationary and stationary loads. Furthermore, the streamlined modelling process results in reductions of 10.63% and 20.84% in the average computing time for the algorithm when addressing stress and vibration loads, respectively. The LCA model proposed in this paper further facilitates the integration of time-domain extrapolation methods into reliability analysis software.

Removal of Chromium (VI) from Aqueous Environment Using Modified Parkia Biglobosa (Locust Bean) Shell

The different metal complexing ligands containing synthetic and natural adsorbents have been described in literature for the removal of heavy metal ions from aqueous solutions. However, the removal of Chromium (IV) ion using Parkia biglobosa is yet to be reported in literature. A new method was developed to obtain adsorption capacity using modified Parkia biglobosa shells as a natural Chromium (IV) heavy metal ion adsorbent from aqueous solution at a certain pH. Agricultural waste: Carob husks (a product derived from carob beans) were modified with HNO3 and citric acid, and their effectiveness on the removal of Cr (VI) from aqueous solutions was examined in a batch experiment. The core sizes used are 150 µm and 300 µm. The effects of different parameters such as pH, Cr (VI) concentration, adsorbent loading, and contact time were determined. The optimum pH values for the two dimensions were found to be 3 and 1, respectively. The contact time for maximum adsorption is 30 minutes. The Freundlich isotherm describes the equilibrium between liquid and solid phases. Parkia biglobosa can be said to be a good adsorbent for the removal of Chromium (VI) heavy metal ion from aqueous solutions depending on the pH of the solution.

Leveraging Deep Learning for Robust Structural Damage Detection and Classification: A Transfer Learning Approach via CNN

Transfer learning techniques for structural health monitoring in bridge-type structures are investigated, focusing on model generalizability and domain adaptation challenges. Finite element models of bridge-type structures with varying geometry were simulated using the OpenSeesPy platform. Different levels of damage states were introduced at the midspans of these models, and Gaussian-based load time histories were applied at mid-span for dynamic time-history analysis to calculate acceleration data. Then, this acceleration time-history series was transformed into grayscale images, serving as inputs for a Convolutional Neural Network developed to detect and classify structural damage states. Initially, it was trained and tested on datasets derived from a Single-Source Domain structure, achieving perfect accuracy (1.0) in a ten-label multi-class classification task. However, this accuracy significantly decreased when the model was sequentially tested on structures with different geometry without retraining. To address this challenge, it is proposed that transfer learning be employed via feature extraction and joint training. The model showed a reduction in accuracy percentage when adapting from a Single-Source Domain to Multiple-Target Domains, revealing potential issues with non-homogeneous data distribution and catastrophic forgetting. Conversely, joint training, which involves training on all datasets except the specific Target Domain, generated a generalized network that effectively mitigated these issues and maintained high accuracy in predicting unseen class labels. This study highlights the integration of simulation data into the Deep Learning-based SHM framework, demonstrating that a generalized model created via Joint Learning utilizing FEM can potentially reduce the consequences of modeling errors and operational uncertainties unavoidable in real-world applications.

Thermomigration Microstructure and Properties of Ni Nanoparticle-Reinforced Sn58Bi Composite Solder/Cu Solder Joint

A Sn58Bi composite solder reinforced by Ni nanoparticles was prepared using a mechanical mixing technique, and the thermomigration microstructure and properties of the solder joints were studied. The findings indicate that incorporating an appropriate quantity of Ni nanoparticles can enhance the microstructure of the composite solder and mitigate the coarsening of Bi-phase segregation. At 0.75 weight percent Ni nanoparticle content, the composite solder’s tensile strength is 59.7 MPa and its elongation is 54.6%, both of which are noticeably greater than those of the base solder. When the thermal loading time is 576 h, the shear strength of the composite solder joint is 25.5 MPa, which is 30.1% higher than that of the base solder joint. This study reveals that the shear fracture path shifts from the boundary region between the solder seam and the IMC layer to the IMC layer itself. Concurrently, the fracture mode evolves from a mix of brittle–ductile fracture, characterized by quasi-cleavage, to a predominantly brittle fracture, marked by numerous “rock candy-like” cross-sectional features and secondary cracking. Adding Ni nanoparticles to the Sn58Bi composite solder/Cu solder junction can significantly extend its service life.

Vehicle identification and analysis based on lightweight yolov5 on edge computing platform

Abstract With the accelerating process of urbanization, the role of intelligent transportation systems in urban traffic management has become more crucial. Nevertheless, traditional surveillance cameras only possess the ability to capture videos and lack the capacity for vehicle detection. Consequently, in order to enhance the intelligent capabilities of front-end surveillance cameras and achieve lightweight model deployment, this research has designed a lightweight detection model that can be deployed on embedded devices. Deploying detection models on embedded devices requires a high level of storage and system computing power, and there exist issues such as model speed, model accuracy, model size, and energy efficiency that are difficult to address in existing studies.Specifically, based on YOLOv5, we first use lightweight structures to replace the C3 module and introduce GSConv and Slim Neck structures to replace the Neck of YOLOv5 in order to reduce the model's parameters and computational complexity. After replacing the model structure, we use global channel pruning to remove redundant information, and use asymmetric quantization to convert model parameters from floating point numbers to fixed point numbers, significantly reducing the model's size and load time, and improving the inference speed. For the target tracking part, we combine the ByteTrack target tracking algorithm with the proposed model, effectively reducing the impact of target occlusion on detection and improving the correlation between video frames. In terms of data recording, detection information such as vehicle color, license plate number, and road blockage level can be uploaded to the database server, and the model's detection results can be encoded using the H.264 video encoding format and uploaded to the streaming media server through streaming media protocols. In the experimental part, we selected the embedded Rk3399pro as the deployment object and achieved 97.8% accuracy in the vehicle counting task, meeting the real-time performance requirements of 31FPS.

Evaluating Variables’ Influence on Forwarder Performance and Fuel Efficiency in Mountain Salvage Logging Using an Automatic Work-Element Detection Method

Extreme climate events are increasingly damaging forests, particularly in Europe’s Alps. These disturbances lead to more damaged timber, necessitating rapid salvage operations to preserve timber value and protect ecosystems. However, salvage logging, though essential, raises concerns about its environmental impact, especially on soil conservation and forest regeneration. To mitigate these effects, best practices such as leaving logging residues and avoiding wet soils are recommended. Nevertheless, fuel efficiency remains a critical concern. This study focuses on addressing gaps in understanding forwarder productivity in salvage logging, considering factors such as assortment number, extraction distance, and payload. Utilizing Automatic Work-Element Detection (AWED) for data collection, this study enhances fuel efficiency analysis. Findings show that the average cycle time was 27.4 min, with 4.9 L of fuel consumed per cycle. Each cycle covered 241.3 m, extracting 11.7 m3 of timber, yielding a productivity rate of 31.6 m3 per machine hour and a fuel efficiency of 0.4 L per m3 and per 100 m. Traveling was the most time- and fuel-intensive task. Assortment type significantly impacted loading time and fuel consumption, with short sawlogs requiring fewer crane cycles. Key factors influencing productivity and fuel efficiency were average log volume, distance, payload, and slope.

IoT Enabled Intelligent Load Balancing System for Railway Wagons through Artificial Intelligence

This research paper presents an innovative system that integrates IoT (Internet of Things) and AI (Artificial Intelligence) technologies to address the critical issue of load balancing in railway wagons. Traditional systems focus on post-event detection of overloading or under loading, which poses safety risks and inefficiencies. In contrast, our solution leverages IoT-enabled load cells to continuously monitor wagon loads in real-time and utilizes AI-driven algorithms such as Genetic Algorithms and Reinforcement Learning—to optimize load distribution dynamically. The system ensures balanced loading across wagons, preventing overloading and underutilization, improving safety, fuel efficiency, and operational effectiveness. The IoT sensors provide real-time data that is processed by AI models to predict and balance load allocation, thus minimizing the risk of infrastructural damage and enhancing wagon capacity utilization. Initial testing shows a 95%reduction in load imbalances, a 20% decrease in loading time, and improved fuel economy by 10%. This research contributes to railway freight logistics by offering an AI- powered solution for intelligent load management, ensuring safety, efficiency, and cost-effectiveness.