cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2025.53
title: Vehicle routing problem in cold chain based on sweep algorithm with load distance

description: Due to the current epidemic situation that is COVID-19, most people are changing behavior to be the new normal that is mostly staying at home and making us turn to online platforms to order and deliver. including ordering fresh food and ready-to-eat food. That makes us interested in the way how to deliver products to customers by using vehicle routing problems to organize the shortest route for the delivery which has a requirement to deliver fresh food products that should keep at a low temperature to maintain the temperature and reduce the rotten product before it reaches to the customer. That brings us to the vehicle routing problem in cold chain systems, this paper creates a mathematical model with the goal of minimize total distance. A vehicle routing problem with adding load in each node based on the sweep algorithm with load distance that designs solutions to solve the problem and plan which route should select to routing to reduce the total distance.Computational results on the dataset by excel solver compare with CPLEX solver in terms of problem-solving ability and find the optimal route for delivery Python.
DOI: 10.14457/tu.the.2025.54
title: Anti-spoofing using ResNet50 with linear discriminant analysis for automatic speaker verification

description: Deep-learning-based models have shown significant potential in speech spoof detection, which is crucial to ensuring the authenticity of speech signals. This work aims to expand the knowledge about deep learning-based spoof detection by integrating ResNet50 with linear discriminant analysis (LDA) to reduce the dimensionality. Using the logical access (LA) subset from the ASVspoof 2019 dataset, we generated mel-spectrogram and gammatone spectrogram representations of the speech signals. ResNet50 was used to extract deep features from these spectrograms, and subsequently LDA was applied to reduce feature dimensionality and improve classification accuracy. Our method significantly outperformed the baseline ResNet50 model by reducing the equal error rate (EER) by 43.55% and increasing balanced accuracy by 48.59% for duplicated mel-spectrogram tensor, 8.95% and 15.52% for differentiated mel-spectrogram tensor, and 44.14% and 44.77% for differentiated gammatone spectrogram tensor, respectively. These results demonstrate the effectiveness of combining ResNet50 with gammatone spectrograms and LDA, providing a more robust solution for audio spoof detection.To further investigate our approach, we extended the evaluation by applying traditional classifiers such as Random Forest (RF), k-Nearest Neighbors (KNN), and Naïve Bayes (NB) on the deep features extracted by ResNet50 and reduced by LDA or PCA. Among all combinations, the LDA-reduced features paired with Naïve Bayes classifier achieved the best result, reaching 88.18% balanced accuracy and 2.80% EER. These findings confirm that our proposed framework not only improves spoof detection performance under a threshold-based scheme but is also compatible with various machine learning classifiers, making it a flexible and effective solution for audio spoof detection tasks.
DOI: 10.14457/tu.the.2025.55
title: Delivery optimization via heterogeneous fleet

description: This research optimizes tire distribution operations through a heterogeneous fleet vehicle routing problem with time window (HFVRPTW) solved with Google OR- Tools. The model minimizes total transportation costs, including fixed and distance- based variable costs, while meeting customer demands from a central depot. A heterogeneous fleet with varying capacities and cost structures is considered, enabling efficient vehicle allocation and allowing multiple trips per vehicle. By addressing key constraints such as vehicle capacity, demand, and route distances, the model determines optimal routes and vehicle assignments. Results show that a heterogeneous fleet provides greater flexibility and significant cost savings compared to a uniform fleet, offering practical insights for logistics optimization and efficiency improvement in distribution networks.
DOI: 10.14457/tu.the.2025.57
title: Development of hybrid machine learning models for enhanced quality control and optimization of resveratrol-loaded polymeric nanoparticles

description: The formulation of resveratrol-loaded polymeric nanoparticles (RES-PNPs) is hindered by resveratrol’s low solubility, instability, and rapid clearance. This study introduces an integrated hybrid machine-learning (ML), genetic-algorithm (GA), and reinforcement-learning (RL) framework to rationally design RES-PNPs and improve their anticancer performance. Hybrid ML models combining linear regression (LR), k-nearest neighbors (k-NN), and artificial neural networks (ANN) were constructed to predict particle size (PS), polydispersity index (PDI), zeta potential (ZP), and drug loading (%DL). The hybrid models substantially outperformed single learners (e.g., ANN RMSE: 69.19 for PS, 0.06 for PDI, 4.28 mV for ZP; k-NN RMSE: 6.69 for %DL), reducing error by 15–40% across endpoints. Final hybrid RMSE values were 55.12 (PS), 0.05 (PDI), 3.90 mV (ZP), and 5.21 (%DL), demonstrating strong predictive fidelity. Optimization employed a GA with a population of 100,000 individuals, 100 generations, and 10 RL-tuned episodes. Fitness was defined as f = –PS – PDI – ZP + %DL, with the best episodes achieving a terminal fitness of –1.025. RL adaptively refined GA parameters, converging on crossover = 0.10 and mutation = 0.18, which improved stability and reduced variance relative to fixed settings. The optimal solution identified (PAA = 0.30, GT = 0.13, P407 = 8.11, Hz = 11.56, Time = 12.50) produced PS = 80 nm, PDI = 0.31, ZP = –36.94 mV, and %DL = 68.02%, all within predefined feasibility limits. Experimental validation showed no significant differences between predicted and measured CQAs (p &gt; 0.05). This AI-augmented workflow establishes a robust design space aligned with ICH Q10 and demonstrates a powerful strategy for intelligent nanomedicine formulation and optimization.
DOI: 10.15468/dl.wa9jxt
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.78822700000001 24.216437, 88.711231 24.21086, 88.636151 24.194268, 88.564852 24.167073, 88.499105 24.129952, 88.44053599999999 24.083826, 88.390592 24.02984, 88.350497 23.969332, 88.32123199999999 23.9038, 88.303501 23.834864, 88.297724 23.764224, 88.304024 23.693622, 88.322226 23.624794, 88.351866 23.559432, 88.392201 23.499137, 88.442229 23.445388, 88.500714 23.399499, 88.566221 23.362591, 88.637145 23.335565, 88.711754 23.319082, 88.78822700000001 23.313542, 88.8647 23.319082, 88.93930899999999 23.335565, 89.010233 23.362591, 89.07574 23.399499, 89.134225 23.445388, 89.184253 23.499137, 89.224588 23.559432, 89.254228 23.624794, 89.27243 23.693622, 89.27873 23.764224, 89.272953 23.834864, 89.255222 23.9038, 89.22595699999999 23.969332, 89.185862 24.02984, 89.135918 24.083826, 89.077349 24.129952, 89.011602 24.167073, 88.940303 24.194268, 88.865223 24.21086, 88.78822700000001 24.216437))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006410-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2025.58
title: Inventory management under partial backlog and storage constraints using simulation optimization approach

description: This research develops a simulation-optimization framework for inventory management in a single-location system with partial backlog and storage constraints. Using empirical data from a Thai hospital, the study examines fast-moving, medium- moving, and slow-moving product categories with distinct demand patterns. The methodology employs a continuous-review (Q, R) inventory policy implemented through Microsoft Excel with VBA macros, making it accessible without specialized software. The simulation incorporates empirical demand distributions from 92 days of historical data, stochastic lead times, and dual storage constraints. A partial backlog policy assumes 50% of unfulfilled demand results in backorders while 50% becomes lost sales, reflecting realistic customer behavior during stockouts. Two optimization techniques are evaluated: grid search and golden section search. Grid search systematically evaluates all parameter combinations to identify the global optimum, while golden section search efficiently converges toward optimal solutions with reduced computational effort. Statistical validation determined 23 to 32 replications were required across product categories to achieve 95% confidence levels. Results show both methods successfully identify effective inventory policies. For fast-moving products, grid search achieved 1,678.10 THB versus 1,698.36 THB for golden section search, only 1.21% difference. Both methods converged to identical solutions for medium-moving and slow-moving products. Golden section search demonstrates significant computational efficiency, requiring 63 to 77% fewer iterations and 44 to 60% less computation time. Cost analysis reveals holding costs dominate total costs at 50 to 80%, indicating where inventory reduction initiatives yield greatest savings. This research integrates multiple realistic constraints: empirical demand distributions, stochastic lead times, partial backlog behavior, and dual storage constraints. The Excel-based implementation provides organizations a cost-effective optimization tool with actionable insights for inventory management.
DOI: 10.6084/m9.figshare.31132447
title: Data source of Xizang broder towns

description: Data source of Xizang broder towns
DOI: 10.6084/m9.figshare.31132447.v1
title: Data source of Xizang broder towns

description: Data source of Xizang broder towns
DOI: 10.14457/tu.the.2025.59
title: Applying machine learning to identify optimal file compression methods

description: This independent study addresses the inefficiency of using a single lossless compression algorithm for diverse file types, a common practice in financial reporting and other domains. We propose an adaptive framework that uses supervised machine learning to predict the most suitable compression method for each file. A dataset of approximately 120,000 real-world files (including text, tabular, and semi-structured formats) was created. Each file was compressed using six major algorithms (Zstd, LZ4, Brotli, LZMA, Bzip2, and zlib) to determine the "ground-truth" best method based on the lowest compression ratio achieved within a 30-second time limit. We extracted an initial set of 15 structural features for each file. A Sequential Feature Selection (SFS) technique was then employed to identify the most predictive subset of features. The final model predicts the optimal algorithm, achieving compression ratios close to the empirical optimum without the high cost of an exhaustive search. This model can be embedded into existing data pipelines to automatically reduce storage costs and data transfer times with minimal added latency.
DOI: 10.15468/dl.q9czjx
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oxytropis talassica"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006413-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2025.60
title: Simulated annealing for vehicle routing problem with time window

description: Nowadays. Vehicle routing problems (VRP) are an important component of logistics management. and is often used in transportation logistics and distribution within this article, we will look at the vehicle routing problem and present a solution using an integer linear programming model. The objective is to reduce the total load distance of transportation for each customer. For the problem, we considered the number of customers, vehicles, and the transportation distance to determine the best route for the vehicle to take from the warehouse to the customer. from the customer back to the warehouse to avoid unnecessary travel, if a shorter distance can be traveled to deliver goods, The delivery time will also be shorter, which will benefit both the customers and the transport companies. However, the use of integer linear programming is still limited. In order to resolve the issue, we developed a simulated annealing (SA) method to create delivery routes that can satisfy both requests simultaneously while reducing transportation costs and resolving a wider issue.
DOI: 10.15468/dl.sfmf5g
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-589.36283 -84.28656,63.42259 -84.28656,63.42259 87.31904,-589.36283 87.31904,-589.36283 -84.28656))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Stygiomedusa gigantea"
 ]
}

 The dataset includes 130 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006411-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2025.61
title: Peak load of electricity demand forecast using machine learning and electricity generation planning

description: Short-term load forecasting plays a critical role in power system planning, operational scheduling, and economic dispatch. However, forecasting accuracy often deteriorates under irregular calendar conditions, such as weekends, public holidays, and bridging holidays, when load behavior deviates from typical daily patterns. This study proposes a two-stage hybrid forecasting framework that integrates calendar-aware classification with machine learning regression to improve day-ahead load prediction across diverse operating conditions. The methodology incorporates Random Forest (RF) classification to segment historical data using Month of Year (MoY), Day of Week (DoW), holiday, and bridging-holiday indicators, followed by RF regression to predict the 48 half-hourly loads for Thailand and the 24-hourly loads for France from 2019 to 2021. A linear interpolation mechanism is introduced to address insufficient samples in rare calendar categories.Experimental results demonstrate that the proposed RF-RF framework consistently outperforms baseline methods, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Everyday classification, and Rule-based classification across both countries. For Thailand, the hybrid model achieves the lowest average MAPE of 4.03% and RMSE of 4.47%, effectively capturing nonlinear seasonal and calendar-driven variations. For France, characterized by strong winter heating demand, the proposed method also yields superior performance, with MAPE 3.01% and RMSE 4.49%, confirming its generalizability across different climatic and load-profile regimes. The improvements are most pronounced on holidays and bridging holidays, where traditional models typically suffer from instability due to irregular consumption patterns. Overall, this research demonstrates that integrating calendar-based segmentation with ensemble learning enhances pattern recognition, model robustness, and prediction accuracy. The proposed framework offers a scalable, interpretable solution for system operators seeking reliable short-term forecasting across diverse climatic contexts and complex calendar effects.
DOI: 10.6084/m9.figshare.29328799.v1
title: Mechanisms of color change and smart control strategies during carrot drying

description: This study elucidated coupled surface-interior color evolution mechanisms, examined the interrelationship between external and internal chromatic variations and proposed an intelligent control strategy for carrot drying processes. Four drying techniques—hot air drying (HAD), infrared drying (IRD), microwave vacuum drying (MVD), and freeze drying (FD)—were comparatively analyzed in terms of their effects on surface color, internal browning, and pigment stability. Both enzymatic and non-enzymatic browning were examined by monitoring browning degree (BD), 5-hydroxymethylfurfural (5-HMF) levels, and the activities of polyphenol oxidase (PPO) and peroxidase (POD) throughout the drying stages. Dynamic analyses revealed enzymatic reactions dominated high-moisture stages (MC &gt; 0.6 g/g w.b.), while Maillard reactions prevailed at MC &lt; 0.6 g/g w.b. To enable nondestructive, real-time monitoring, a hybrid detection system integrating near-infrared spectroscopy (NIR) and low-field nuclear magnetic resonance (LF-NMR) was developed. BD prediction models were constructed using partial least squares (PLS), support vector machine (SVM), and back-propagation artificial neural network (BP-ANN) algorithms. Among these, the BP-ANN model exhibited superior predictive performance (<i>R</i><sup>2</sup> = 0.9220, RMSE = 0.0128). In the practical application of dry browning control and burn prevention, the browning degrees of carrots were 0.168 and 0.274, respectively, with the efficiency improvements of 28% and 37%. This work provides critical insights into spatiotemporal browning dynamics and demonstrates the viability of AI-driven quality control in industrial drying. First multimodal NIR-LF-NMR sensing platform enabling synchronous analysis of surface-interior browning dynamics.Constructed high-accuracy BP-ANN models for predicting browning degree using fusion spectral–moisture features.Proposed an intelligent MVD control strategy that reduced drying time by 37% while preserving color quality. First multimodal NIR-LF-NMR sensing platform enabling synchronous analysis of surface-interior browning dynamics. Constructed high-accuracy BP-ANN models for predicting browning degree using fusion spectral–moisture features. Proposed an intelligent MVD control strategy that reduced drying time by 37% while preserving color quality.
DOI: 10.6084/m9.figshare.29377159
title: A study on gas diffusion through unsaturated fibrous porous materials based on fractal multi-scale model

description: The drying process of fibrous porous materials, such as wood and paper, is fundamentally governed by the dynamics of gas transport within their multi-scale pore networks under unsaturated conditions. Accurately modeling gas diffusion behavior is critical for improving the drying performance of fibrous porous materials. To address this, fractal dimensions are introduced to quantitatively characterize multi-scale pore structures, and a gas diffusion model integrating molecular diffusion and Knudsen diffusion mechanisms is developed based on capillary bundle framework. The explicit relationship between gas diffusivity and microstructural parameters, including porosity, pore and tortuosity fractal dimensions, liquid saturation, and Knudsen number, is proposed. And finite element method is also performed on gas–liquid two-phase flow in anisotropic fibrous material to reveal the sensitivity of relative diffusivity to these structural parameters. The present fractal model is validated by comparing with available experiment data for gas diffusivity. It has been shown that porosity and liquid saturation serve to promote and inhibit gas diffusion, respectively. Furthermore, effective diffusivity exhibits positive and negative correlations with the pore and tortuosity fractal dimensions, respectively. The proposed model provides a robust theoretical framework to predict the gas diffusion properties of unsaturated fibrous porous materials, offering a foundation for refining mathematical models of drying process and optimizing drying techniques.
DOI: 10.6084/m9.figshare.29377159.v1
title: A study on gas diffusion through unsaturated fibrous porous materials based on fractal multi-scale model

description: The drying process of fibrous porous materials, such as wood and paper, is fundamentally governed by the dynamics of gas transport within their multi-scale pore networks under unsaturated conditions. Accurately modeling gas diffusion behavior is critical for improving the drying performance of fibrous porous materials. To address this, fractal dimensions are introduced to quantitatively characterize multi-scale pore structures, and a gas diffusion model integrating molecular diffusion and Knudsen diffusion mechanisms is developed based on capillary bundle framework. The explicit relationship between gas diffusivity and microstructural parameters, including porosity, pore and tortuosity fractal dimensions, liquid saturation, and Knudsen number, is proposed. And finite element method is also performed on gas–liquid two-phase flow in anisotropic fibrous material to reveal the sensitivity of relative diffusivity to these structural parameters. The present fractal model is validated by comparing with available experiment data for gas diffusivity. It has been shown that porosity and liquid saturation serve to promote and inhibit gas diffusion, respectively. Furthermore, effective diffusivity exhibits positive and negative correlations with the pore and tortuosity fractal dimensions, respectively. The proposed model provides a robust theoretical framework to predict the gas diffusion properties of unsaturated fibrous porous materials, offering a foundation for refining mathematical models of drying process and optimizing drying techniques.