Search Results (35)

This article presents a comprehensive review of short text clustering using state-of-the-art methods: Bidirectional Encoder Representations from Transformers (BERT), Term Frequency-Inverse Document Frequency (TF-IDF), and the novel hybrid method Latent Dirichlet Allocation + BERT + Autoencoder (LDA + BERT + AE). The article begins by outlining the theoretical foundation of each technique and its merits and limitations. BERT is critiqued for its ability to understand word dependence in text, while TF-IDF is lauded for its applicability in terms of importance assessment. The experimental section compares the efficacy of these methods in clustering short texts, with a specific focus on the hybrid LDA + BERT + AE approach. A detailed examination of the LDA-BERT model’s training and validation loss over 200 epochs shows that the loss values start above 1.2 and quickly decrease to around 0.8 within the first 25 epochs, eventually stabilizing at approximately 0.4. The close alignment of these curves suggests the model’s practical learning and generalization capabilities, with minimal overfitting. The study demonstrates that the hybrid LDA + BERT + AE method significantly enhances text clustering quality compared to individual methods. Based on the findings, the study recommends the optimum choice and use of clustering methods for different short texts and natural language processing operations. The applications of these methods in industrial and educational settings, where successful text handling and categorization are critical, are also addressed. The study ends by emphasizing the importance of the holistic handling of short texts for deeper semantic comprehension and effective information retrieval. Full article

Concrete is popular in construction due to its strong performance and low maintenance. However, some structures become unsafe over time due to poor maintenance and design flaws. As demand for maintenance grows, restoring older structures is a cost-effective option. Advanced repair techniques aim to extend service life and improve concrete properties, with a focus on eco-friendly solutions. Recent trends have highlighted the potential of incorporating polymers into repair methods, but the use of glycoluril–formaldehyde, a polymeric material known for its hydrogen bonding capacity, remains unexplored in repairing existing structures. This research investigates the effects of glycoluril–formaldehyde in simple matrices like cement paste and mortar to understand its impact. By examining the chemical reactions between glycoluril–formaldehyde with cement paste, this study delves into the fresh, mechanical, and microstructural characteristics. To evaluate the influence of glycoluril–formaldehyde, cement paste specimens were subjected to various tests, including consistency, initial and final setting time, and miniature slump flow tests. Cement mortar specimens were then subjected to compression strength tests conducted at various ages. The results demonstrate that a 3% addition of glycoluril–formaldehyde in concrete offers optimum performance, ensuring improved mechanical strength and microstructure. The microstructural investigation using optical microscopy, an X-ray diffraction, and SEM analysis confirms the polymerization of glycoluril–formaldehyde and the formation of a denser microstructure. The thermogravimetric (TG) and differential thermogravimetric (DTG) analysis provides crucial insights into the thermal stability of the cementitious system, aiding its characterization for high-temperature applications. Full article

Over recent years, location-routing problems have become popular, since they tackle multiple major decisions in supply chains. With the focus now on sustainable supply chains, the problem has become sought-after, with the emphasis on complying with goals set by world leaders such as complying with environmental rules and social equity. As a result, this has opened multiple research directions within the location-routing problem. In the literature, the focus has merely been on two of the sustainability pillars, the environmental and economic pillars, with no integration of the social pillar. In this article, the aim is to integrate the social pillar alongside the environmental and economic ones by modeling the system as a capacitated two-echelon location-routing problem tackling multiple scenarios. Under the umbrella of optimization technology, the algorithm used to solve the problem is a genetic algorithm. This article also demonstrates the process of designing the experimentation phase and selecting variables aiming to fine-tune the models. Multiple parent selection methods and crossover methods were tested, among other variables. The algorithm has proven its success in finding a near-optimum value when compared to the benchmark solution, with an error less than 0.05%. Tournament has performed better as a parent selection method in contrast to stochastic universal sampling, and has proved to be more stable in the face of the stochastic noise induced in the models. This study shows that the social pillar, like the other two pillars, can be integrated in the location-routing problem at an extensive level, beyond what is normally implemented. Full article

Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea. Full article

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm’s convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT’s average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT. Full article

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs. Full article

Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources. Full article

Anaerobic digestion (AD) involves a set of microbiological reactions and physio-chemical processes to generate biogas, a mixture of predominantly CH₄ and CO₂. It is commercialized globally; however, AD has limited commercial applications in the U.S. compared to other regions of the world. The main objective of this article is to review different studies on socio-economic and environmental aspects and policies of biogas/biomethane production and to focus on resource availability. The key outcome from this review shows that the anaerobic digestion of food waste and animal manure has great potential to achieve economic and environmental benefits compared to other waste management techniques such as landfilling or conventional manure management. The 12 life cycle assessment (LCA) studies reviewed showed lower impacts for biogas systems and indicated a need for standardization of methodology so that alternative production concepts can be objectively compared. Similarly, economic analyses showed higher profitability for a biogas combined heat and power facility compared to a biomethane facility. By considering a review of the sustainability of biogas, we presented a new multi-criteria sustainable assessment framework that includes three domains: i. resource availability and logistics, ii. process modeling, and iii. impact assessment with primary application to the optimum location and installation of sustainable biogas/biomethane plants in the U.S. Full article

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases. Several interesting algorithms are proposed to focus on this dilemma, such as the Self-Clocked Rate Adaptation for Multimedia (SCReAM) designed for interactive real-time video streaming applications. One of the main issues of SCReAM is the high design complexity due to the large size of its documentation and coding. Furthermore, there is a considerable number of parameters that can be adjusted to accomplish the desired performance. This study proposes a guided parameters’ tuning approach to assess and optimize the SCReAM algorithm in an emulated 5G environment through a detailed exploration of its parameters. The proposed approach consists of three phases, namely, the initialization phase, the standalone experimentation phase, and the hybrid experimentation phase. In the first phase, we illustrate the method of initializing and implementing the environment, followed by specifying the investigated parameters’ settings, testing, and validation. The second phase aims to investigate SCReAM parameters in isolation to identify the effect on the performance in relation to network queue delay, smoothed Round Trip Time (sRTT), and throughput. The final phase discusses the possibility of achieving the optimum performance by combining various sets to provide researchers with clear and explicit guidelines to establish an adequate SCReAM behavior for the desired application. To the best of our knowledge, this is the first study that proposes a preliminary and comprehensive analysis of the SCReAM algorithm. Based on the proposed approach, when L4S/ECN is disabled, we reduced the network queue delay by 63.36% and increased the network throughput by 48.6% as compared to the results generated by the original design. In L4S/ECN-enabled mode, the network queue delay is reduced by 16.17% while the network throughput increased by 93%. Full article

In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets yield consistent estimates from conditional logit estimators for binary response panel data models. When faced with a missing covariates problem, researchers adopt various imputation techniques to complete the data and without loss of generality; consistent estimates still suffice asymptotically. For maximum likelihood estimates of the parameters for logistic regression in cases of imputed covariates, the optimal choice of an imputation technique that yields the best estimates with minimum variance is still elusive. This paper aims to examine the behaviour of the Hessian matrix with optimal values of the imputed covariates vector, which will make the Newton–Raphson algorithm converge faster through a reduced absolute value of the product of the score function and the inverse fisher information component. We focus on a method used to modify the conditional likelihood function through the partitioning of the covariate matrix. We also confirm that the positive moduli of the Hessian for conditional estimators are sufficient for the concavity of the log-likelihood function, resulting in optimum parameter estimates. An increased Hessian modulus ensures the faster convergence of the parameter estimates. Simulation results reveal that model-based imputations perform better than classical imputation techniques, yielding estimates with smaller bias and higher precision for the conditional maximum likelihood estimation of nonlinear panel models. Full article

To address the problem of the expensive and time-consuming annotation of high-resolution remote sensing images (HRRSIs), scholars have proposed cross-domain scene classification models, which can utilize learned knowledge to classify unlabeled data samples. Due to the significant distribution difference between a source domain (training sample set) and a target domain (test sample set), scholars have proposed domain adaptation models based on deep learning to reduce the above differences. However, the existing models have the following shortcomings: (1) insufficient learning of feature information, resulting in feature loss and restricting the spatial extent of domain-invariant features; (2) models easily focus on background feature information, resulting in negative transfer; (3) the relationship between the marginal distribution and the conditional distribution is not fully considered, and the weight parameters between them are manually set, which is time-consuming and may fall into local optimum. To address the above problems, this study proposes a novel remote sensing cross-domain scene classification model based on Lie group spatial attention and adaptive multi-feature distribution. Concretely, the model first introduces Lie group feature learning and maps the samples to the Lie group manifold space. By learning features of different levels and different scales and feature fusion, richer features are obtained, and the spatial scope of domain-invariant features is expanded. In addition, we also design an attention mechanism based on dynamic feature fusion alignment, which effectively enhances the weight of key regions and dynamically balances the importance between marginal and conditional distributions. Extensive experiments are conducted on three publicly available and challenging datasets, and the experimental results show the advantages of our proposed method over other state-of-the-art deep domain adaptation methods. Full article

Modern heating furnaces use combined modes of heating the charge. At high heating temperatures, more radiation heating is used; at lower temperatures, more convection heating is used. In large heating furnaces, such as pusher furnaces, it is necessary to monitor the heating of the material zonally. Zonal heating allows the appropriate thermal regime to be set in each zone, according to the desired parameters for heating the charge. The problem for each heating furnace is to set the optimum thermal regime so that at the end of the heating, after the material has been cross-sectioned, there is a uniform temperature field with a minimum temperature differential. In order to evaluate the heating of the charge, a mathematical model was developed to calculate the heat fluxes of the moving charge (slabs) along the length of the pusher furnace. The obtained results are based on experimental measurements on a test slab on which thermocouples were installed, and data acquisition was provided by a TERMOPHIL-stor data logger placed directly on the slab. Most of the developed models focus only on energy balance assessment or external heat exchange. The results from the model created showed reserves for changing the thermal regimes in the different zones. The developed model was used to compare the heating evaluation of the slabs after the rebuilding of the pusher furnace. Changing the furnace parameters and altering the heat fluxes or heating regimes in each zone contributed to more uniform heating and a reduction in specific heat consumption. The developed mathematical heat flux model is applicable as part of the powerful tools for monitoring and controlling the thermal condition of the charge inside the furnace as well as evaluating the operating condition of such furnaces. Full article

Background: The aging process contributes to the decline in physical capacity that leads to loss of independence in performing life activities. Immobility and instability are the most significant predictors and indicators of physical disability and dependence. As a result, a variety of assistive devices exist to address immobility and instability in older adults, including walkers, canes, crutches, wheelchairs and handrails. Sit-to-stand (STS) transitions are the most common transitions in daily mobility activities. The ability to perform STS transitions successfully is therefore one of the most important activities to focus attention on. As a result of physical deterioration, older adults will sooner or later be faced with their physical limitations, and in particular, will not be able to provide enough torque at critical body joints to make the STS transition. Aim: This paper suggests employing two-arm assistance using two handles located symmetrically in the body’s sagittal plane. During the aging process, people are faced with varying levels of muscle deterioration and body constraints and consequently require different levels of assistance to complete the transition successfully. This paper aims to develop a tool to find the optimum handle location for people based on their body constraints to reduce knee torque (identified as the critical joint in the STS transition). These findings are also used to measure the effects of assistive device handle position on the biomechanics of the two-arm assisted STS transition. Methods: For this purpose, a theoretical tool was developed by integrating human body kinetics with a multi-objective genetic algorithm to find the optimum hand force required at the seat-off point for a set of potential handle locations. The tool was set to achieve the minimum knee torque within the defined body constraints and assumptions. In line with the physics of the STS transition, the “seat-off point”, when subjects lose their seat support, was chosen as the most challenging point of the task. This was coupled with the “nose over toes” posture recommended to older adults by occupational therapists. Results and Discussion: The schematic of the developed tool shows that the best handle locations requiring the minimum torques at the body joints are positioned in handle zone 2, where the handles are placed vertically above the knee and below the hip joints and horizontally located ahead of the hip and behind the knee joints. Within this handle zone, both components of the hand forces (vertical downward and horizontal backward) provide assisting torque to all the body joints and consequently reduce the torques required at body joints. Full article

The 2021 WHO and UNICEF Estimates of National Immunization Coverage (WUENIC) reported approximately 25 million under-vaccinated children in 2021, out of which 18 million were zero-dose children who did not receive even the first dose of a diphtheria-tetanus-pertussis-(DPT) containing vaccine. The number of zero-dose children increased by six million between 2019, the pre-pandemic year, and 2021. A total of 20 countries with the highest number of zero-dose children and home to over 75% of these children in 2021 were prioritized for this review. Several of these countries have substantial urbanization with accompanying challenges. This review paper summarizes routine immunization backsliding following the COVID-19 pandemic and predictors of coverage and identifies pro-equity strategies in urban and peri-urban settings through a systematic search of the published literature. Two databases, PubMed and Web of Science, were exhaustively searched using search terms and synonyms, resulting in 608 identified peer-reviewed papers. Based on the inclusion criteria, 15 papers were included in the final review. The inclusion criteria included papers published between March 2020 and January 2023 and references to urban settings and COVID-19 in the papers. Several studies clearly documented a backsliding of coverage in urban and peri-urban settings, with some predictors or challenges to optimum coverage as well as some pro-equity strategies deployed or recommended in these studies. This emphasizes the need to focus on context-specific routine immunization catch-up and recovery strategies to suit the peculiarities of urban areas to get countries back on track toward achieving the targets of the IA2030. While more evidence is needed around the impact of the pandemic in urban areas, utilizing tools and platforms created to support advancing the equity agenda is pivotal. We posit that a renewed focus on urban immunization is critical if we are to achieve the IA2030 targets. Full article

In this study, a scatterometry-based monitoring system designed for tracking the quality and reproducibility of laser-textured surfaces in industrial environments was validated in off-line and real-time modes. To this end, a stainless steel plate was structured by direct laser interference patterning (DLIP) following a set of conditions with artificial patterning errors. Namely, fluctuations of the DLIP process parameters such as laser fluence, spatial period, and focus position are introduced, and also, two patterning strategies are implemented, whereby pulses are deliberately not fired at both deterministic and random positions. The detection limits of the system were determined by recording the intensities of the zero, first, and second diffraction order using a charge-coupled device (CCD) camera. As supported by topographical measurements, the system can accurately calculate spatial periods with a resolution of at least 100 nm. In addition, focus shifts of 70 µm from the optimum focus position can be detected, and missing patterned lines with a minimum width of 28 µm can be identified. The validation of this compact characterization unit represents a step forward for its implementation as an in-line monitoring tool for industrial laser-based micropatterning. Full article