Search Results (64)

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Reusing cached data is a widely adopted technique for improving network and system performance. Future Internet architectures such as Named Data Networking (NDN) leverage intermediate nodes—such as proxy servers and routers—to cache and deliver data, reducing latency and alleviating load on original data sources. However, a fundamental challenge of this approach is the lack of trust in intermediate nodes, as users cannot reliably identify and verify them. To address this issue, many systems adopt data-oriented verification rather than sender authentication, using Merkle Hash Trees (MHTs) to enable users to verify both the integrity and authenticity of received data. Despite its advantages, MHT-based authentication incurs significant redundancy: identical hash values are often recomputed, and witness data are repeatedly transmitted for each segment. These redundancies lead to increased computational and communication overhead, particularly in large-scale data publishing scenarios. This paper proposes a novel scheme to reduce such inefficiencies by enabling the reuse of previously verified node values, especially transmitted witnesses. The proposed scheme improves both computational and transmission efficiency by eliminating redundant computation arising from repeated calculation of identical node values. To achieve this, it stores and reuses received witness values. As a result, when verifying 2ⁿ segments (n > 8), the proposed method achieves more than an 80% reduction in total hash operations compared to the standard MHT. Moreover, our method preserves the security guarantees of the MHT while significantly optimizing its performance in terms of both computation and transmission costs. Full article

Across the centuries, trees have been recognised as one of the oldest lifeforms on earth, witnessing and subject to the passage of time on a scale that far exceeds human life, telling us who we are in the world. This paper explores the intricate nature of human interactions with trees across a broad chronological and conceptual range, and the cultural, symbolic, and ecological meaning of “iconic” trees, drawing upon a selection of case studies to explore and analyse the relationship between the tree as a living organism and its cultural, textual, and mnemonic meaning. In conducting this, it reflects upon the cultural geographies of presence and absence, and the role of emblematic trees as places of memory and structures of belief. The relationship between human life and the life of trees is shown to be symbiotic; multiple cultural values and symbolic forms are ascribed to trees, and those same trees shape the physical, ecological, and human environment. The social and cultural construction of the landscape and sites of memory is presented as a key component in the formation of narratives and mentalities that define the relationship between humans and iconic trees, material and imagined. Physical, ecological, and cultural erosion, it is suggested, have the capacity of memorialising forgetfulness and creating a space in which the absence of presence and the presence of absence co-exist. The iconic image of the fallen tree, in its presence and absence, exposes the extent to which trees are also human objects, constructed and understood in human terms, and subject to a range of personal, political, and pragmatic impulses. A tree can be iconic not simply because of what it was but because of what it was believed to be, integrating a physical, historical, memory, and ecological or cultural space into our relationship with the natural world. Full article

The centennial olive trees of Tunisia serve as enduring symbols of resilience, having withstood the test of time while witnessing the effects of climate change, rising temperatures, water scarcity, and the emergence of new diseases. Presently, there is a notable lack of research on the genomic analysis of ancient trees. This study investigates the genetic diversity of twenty-eight ancient olive specimens collected from archeological sites in nine governorates from the north to the south of Tunisia. Using nine highly polymorphic microsatellite markers, these ancient olive trees were compared with twenty-five local Tunisian cultivars and sixty olive varieties from other Mediterranean countries (Greece, Italy, and Spain). The ancient olive trees were revealed to have a high genetic diversity, with 67 alleles and a Shannon index of 1.68. The key findings identify the ancient trees M25, M1, M28, and M24 as synonyms for local olive cultivars, while “M10” is noted as a first-generation migrant from Tunisian olives. Cluster analysis methods, including structure, neighbor-joining (NJ), and principal coordinates (PCoA), show that these ancient trees share a common genetic background and ancestry with varieties from Tunisia, Italy, Spain, and Greece. The conservation and evaluation of these genotypes will increase the genetic diversity available for breeding programs and strengthen the resilience of agriculture, which is currently facing unprecedented pressure worldwide. Full article

Background: Orthodontic problems can affect vital quality of life functions, such as swallowing, speech sound production, and the aesthetic effect. Therefore, it is important to diagnose and treat these patients precisely. The main aim of this study is to introduce new classification methods for skeletal class I occlusion (SCIO) and skeletal class II malocclusion (SCIIMO) among Arab patients in Israel. We conducted hierarchical clustering to detect critical trends within malocclusion classes and applied machine learning (ML) models to predict classification outcomes. Methods: This study is based on assessing the lateral cephalometric parameters from the Center for Dentistry Research and Aesthetics based in Jatt, Israel. The study involved the encoded records of 394 Arab patients with diagnoses of SCIO/SCIIMO, according to the individualized ANB of Panagiotidis and Witt. After clustering analysis, an ML model was established by evaluating the performance of different models. Results: The clustering analysis identified three distinct clusters for each skeletal class (SCIO and SCIIMO). Among SCIO clusters, the results showed that in the second cluster, retrognathism of the mandible was less severe, as represented by a lower ANB angle. In addition, the third cluster had a lower NL-ML angle, gonial angle, SN-Ba angle, and lower ML-NSL angle compared to clusters 1 and 2. Among SCIIMO clusters, the results also showed that the second cluster has less severe retrognathism of the mandible, which is represented by a lower ANB angle and Calculated_ANB and a higher SNB angle (p < 0.05). The general ML model that included all measurements for patient classification showed a classification accuracy of 0.87 in the Random Forest and the Classification and Regression Tree models. Using ANB angle and Wits appraisal only in the ML, an accuracy of 0.78 (sensitivity = 0.80, specificity = 0.76) was achieved to classify patients as SCIO or SCIIMO. Conclusions: The clustering analysis revealed distinguished patterns that can be present within SCIO and SCIIMO patients, which can affect the diagnosis and treatment plan. In addition, the ML model can accurately diagnose SCIO/SCIIMO patients, which should improve precise diagnostics. Full article

The combination of global climate change and the urban heat island effect has given rise to a deterioration in the livability of residential districts within cities, posing challenges to enhancing the health quality of urban environments. Meanwhile, the intensification of daytime changes in temperature and humidity in residential districts has rendered the sensory representation of the urban heat island effect more pronounced. This study selects the residential districts in Fuzhou City as the research case area, which have witnessed a discernible warming trend in recent years, and acquires temperature and humidity parameter data at three time periods (early morning, noon, and evening) to represent the daytime temperature and humidity change phase. Through aerial photography and field research, three types of spatial morphological indicators (buildings I, vegetation II, and the combination of buildings and vegetation II) of residential districts are quantified to represent the three-dimensional spatial form of the case study area. The analysis results show the following: ➀ Residential districts experience two phases of daytime changes in temperature and humidity: a warming and drying phase (WDP) in the morning and a cooling and humidifying phase (CHP) in the afternoon. The characteristics of changes in temperature and humidity show a spatial correlation with each other. ➁ The impact of urban three-dimensional morphology on changes in temperature and humidity in WDP is minor, whereas, in CHP, it is influenced by Class II and Class III indicators. The two types of urban morphology exert a synergistic regulatory effect on changes in temperature and humidity. ➂ Vegetation has a significant regulatory effect on temperature and humidity variations in residential areas through changes in its three-dimensional form. Enlarging the area of individual trees while reducing their canopy volume can restrain the warming and dehumidification of residential districts and promote cooling and humidification. In contrast to only planting trees, a vegetation configuration combining trees, shrubs, and grass can bring a more obvious cooling effect to residential districts. The research results can provide a reference for urban planners in the planning and design of residential areas as well as the optimization and improvement of urban living environments. Full article

The past couple of decades have witnessed an increasing application of tree ring observations to assess forest carbon (C) balance and its historical dynamics. To address the growing need for understanding long-term forest C sequestration dynamics through tree rings, we developed a new desktop tool (TR-SNP v1.0) that estimates the annual aboveground biomass increment (AABI) of trees from tree ring width (TRW). Users can easily process and convert TRW into AABI using either the built-in dataset or by uploading local TRW data. TR-SNP offers methods for correcting potential bias from unmeasured initial core width, converting TRW to diameter at breast height (DBH), and estimating AABI using species-specific allometric relationships. We provide examples from specific sites to demonstrate how TR-SNP functions and its potential for identifying bias sources of AABI estimation. We anticipate that TR-SNP will streamline the analysis of tree ring data and advance our understanding of forest biomass increment dynamics. Full article

The development and understanding of the behavior of construction materials is extremely complex due to the great variability of raw materials that can be used, which becomes even more challenging when functional materials, such as phase-change materials (PCM), are incorporated. Currently, we are witnessing an evolution of advanced construction materials as well as an evolution of powerful tools for modeling engineering problems using artificial intelligence, which makes it possible to predict the behavior of composite materials. Thus, the main objective of this study was exploring the potential of machine learning to predict the mechanical and physical behavior of mortars with direct incorporation of PCM, based on own experimental databases. For data preparation and modelling process, the cross-industry standard process for data mining, was adopted. Seven different models, namely multiple regression, decision trees, principal component regression, extreme gradient boosting, random forests, artificial neural networks, and support vector machines, were implemented. The results show potential, as machine learning models such as random forests and artificial neural networks were demonstrated to achieve a very good fit for the prediction of the compressive strength, flexural strength, water absorption by immersion, and water absorption by capillarity of the mortars with direct incorporation of PCM. Full article

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability. Full article

The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generates a series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier’s good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text. Full article

Objective: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission. Method: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared ($R^2$) were employed. Results: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and $R^2$ = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation. Conclusion: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue’s complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions. Full article

This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China. Full article

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological research on jujube. This review began by summarizing advancements in jujube genomics. Subsequently, we provided a comprehensive overview of the integrated application of genomics, transcriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress and discussed the transcriptional regulatory mechanisms underlying these traits. Furthermore, challenges in multi-omics research on jujube biological traits were outlined, and we proposed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic enhancement of jujube. Full article

In today’s digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users’ sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks. Due to the fact that ransomware encrypts user data and requests a ransom payment in exchange for the decryption key, it is one of the most devastating types of malicious software. The implications of ransomware attacks can range from a loss of essential data to a disruption of business operations and significant monetary damage. Artificial intelligence (AI)-based techniques, namely machine learning (ML), have proven to be notable in the detection of Android ransomware attacks. However, ensemble models and deep learning (DL) models have not been sufficiently explored. Therefore, in this study, we utilized ML- and DL-based techniques to build efficient, precise, and robust models for binary classification. A publicly available dataset from Kaggle consisting of 392,035 records with benign traffic and 10 different types of Android ransomware attacks was used to train and test the models. Two experiments were carried out. In experiment 1, all the features of the dataset were used. In experiment 2, only the best 19 features were used. The deployed models included a decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), ensemble of (DT, SVM, and KNN), feedforward neural network (FNN), and tabular attention network (TabNet). Overall, the experiments yielded excellent results. DT outperformed the others, with an accuracy of 97.24%, precision of 98.50%, and F1-score of 98.45%. Whereas, in terms of the highest recall, SVM achieved 100%. The acquired results were thoroughly discussed, in addition to addressing limitations and exploring potential directions for future work. Full article

Governments are faced with the unique challenge of implementing large-scale and targeted protection against the global decline of large old trees. Incorporating socio-cultural values and encouraging public involvement are important parts of conservation policy. However, current studies on the socio-cultural valuation of large old trees are still limited, and how rural residents perceive the human-related value of large old trees remains largely unknown. Using a quantitative, spatial analysis and the contingent valuation method (CVM), we tried to explore a multi-scale socio-cultural valuation and protection framework based on a case study of Baoding City and Xiongan New Area in North China. The results showed that (1) the scattered large old trees in the study area were generally at a relatively younger stage, showing normal growth performance but having poor living environments. Some 96.99% of the trees resided in the countryside. Their distribution showed an agglomerative pattern with several clusters. (2) The species richness was relatively lower than that reported in urban areas. The species diversity had an obvious high–low gradient from the mountain to plain areas. Most endemic species were found in habitats of the village fringe (VF) and government/community/institutional ground (GC). (3) The mean willingness to pay (WTP) for the socio-cultural value of scattered large old trees was CNY 132.48 per year per person (1 US dollar equals about 7.2 CNY) of all the respondents, and CNY 84.30 per year per person with regard to farmers, which is relatively higher than that reported in large cities. (4) Economic income, gender, age, education level, place of residence, diameter at breast height, and tree habitat were factors that significantly influenced the WTP, among which economic income was the most significant. (5) The importance ranking of socio-cultural value connotations perceived by rural residents was as follows: spiritual attachment and homesickness > fengshui > social bond > witnessing history > education > creative inspiration. (6) The annual gross value was estimated to be CNY 349 million in the study area, and CNY 169,500 for a single tree on average. Based on the case study, a conceptual framework for socio-cultural value assessment and multi-scale protection of large old trees was proposed, which can provide references for the improvement of current conservation policies from both quantitative and qualitative perspectives, and give insights into rural revitalization strategies in China. Full article