Types Of Features Research Articles

Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test.

Background and Objectives: In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. Methods: This study included 129 older adults attending a dementia outpatient clinic. We obtained information on the diagnosis of dementia and CDT data from medical records and quantified 12 types of drawing features according to the Freedman scoring system. Based on the dementia diagnosis information, participants were assigned to two groups: 58 in the dementia diagnosis group and 71 in the non-diagnosis group. Using Boruta, an iterative feature selection algorithm, and a support vector machine, a machine learning method, we analyzed the drawing features contributing to dementia discrimination and evaluated discrimination performance. Results: Five types of drawing features were selected as contributors to discrimination, including "numbers in the correct position," "minute target number indicated," and "hand in correct proportion." These features exhibited a discriminating sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for detecting dementia. Conclusion: This study demonstrated a method for identifying individuals likely to be diagnosed with dementia among patients attending a dementia outpatient clinic using drawing features. The knowledge of drawing features contributing to dementia differentiation may assist healthcare practitioners in clinical reasoning and provide novel insights for clinical practice. In the future, we plan to develop a primary screening for dementia based on machine learning using CDT.

Models and applications of stochastic programming with decision‐dependent uncertainty in power systems: A review

AbstractStochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision‐dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi‐source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision‐dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two‐stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

New physics opportunities in triangle singularity

In this paper, we show that loop-induced processes involving new physics particles can readily satisfy Landau Equation and trigger triangular singularities at high-energy colliders, leading to fully visible Standard Model final states. In addition, a t-channel triangular singularity can also occur during particle scattering processes that may extend to low momentum exchange. We discuss several typical scenarios in supersymmetric and extended Higgs models, and three types of final-state kinematic features at the collider. Four-particle vertices in new physics can play a significant role in high energy diagrams. We identify an “everything on shell” triangular singularity diagram only involving bosonic couplings, which has the potential to completely avoid large virtuality suppression. We further show that such a virtuality-free diagram is missing in the Standard Model at electroweak scale, and it becomes available in new physics models.

Tire wear monitoring using feature fusion and CatBoost classifier

Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.

A Depth Awareness and Learnable Feature Fusion Network for Enhanced Geometric Perception in Semantic Correspondence.

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object's structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed.

Distribution and Controlling Factors of the Contourites on the Northern Continental Slope of the South China Sea

ABSTRACTThe northern continental margin of the South China Sea (SCS) is an important component of deep‐water circulation, providing excellent conditions for studying bottom currents in a marginal sea. Seismic data were employed to discern the sedimentary patterns prevalent in the deep‐water continental slope sediments on the northern continental margin of the SCS, encompassing gravity flow, contourite and mixed depositional systems. The contourite depositional system includes various types of deposits (such as separated mounded drifts, patch or channel‐related drifts, deformed sheeted drifts, composite drifts, bottom current sediment waves, plastered contourite drifts) and various morphologic erosional features eroded by the bottom current (such as moats, non‐depositional surfaces, troughs and scarps). These contourite features are related to the continental slope's morphology and its sources. The Dongsha slope exhibits distinctive characteristics marked by intense bottom current erosion and deposition, featuring separated mounded drifts and deformed sheeted drifts along its lower slope. The lower slope of the Pearl River showcases a spectrum of bottom current‐induced features, including sediment wave fields, erosion fields and contourite drifts. The southern flank of the Shenhu slope is characterised by a bottom current erosion field, a non‐depositional surface, a sediment wave field and isolated mounded drifts. On the Yingqiong slope, the contourite drifts are limited to its southern flank where gravity flow action is absent, and the complex geomorphology interacts with the bottom current, forming a complex contourite depositional system. The results of this study serve as a foundational framework for further global research on bottom current circulation and hydrodynamics.

Void detection in structural adhesive joints using a k-Nearest Neighbors model with features from Electromechanical Impedance Spectroscopy

Electromechanical Impedance Spectroscopy (EMIS) is a promising Structural Health Monitoring (SHM) method which allows the early detection of defects by analyzing the structure response to an AC electrical signal swept through a range of high frequencies. In this work, EMIS measurements of pristine and damaged adhesive joints were performed, and features were extracted from the experimental measurements. These features were inputted to a k-Nearest Neighbors (kNN) model for damage detection. Results show that only one type of features is enough for damage detection. Furthermore, the use of the Manhattan Distance in the kNN enables a better classification.

A Classification of Features for Interpersonal Disconnectivity in Digital Media: Block, Unfriend, Unfollow, Mute, Withhold, and Eject

This article presents for the first time a classification of, and lexicon for, features for dissolving interpersonal ties in digital environments: blocking, unfriending, unfollowing, muting, withholding, and ejecting. There are two main motivations and two main contributions. The first motivation is that analyses of social media features have not included treatment of features for interpersonal disconnectivity; the second is that analyses of interpersonal disconnectivity have not included reference to the features that enable it. The two main contributions are the disambiguation of a confusing terminological field, thus making it possible to analytically distinguish between types of feature, and an intervention in the question of whether the features described in the article concern filtering <em>information</em> or avoiding <em>people</em>. Data were collected from 37 social media platforms and other services that enable digitally-mediated communication. Features for interpersonal disconnectivity were documented and then the features were grouped based on five questions about their use, where A is the person using the feature, and B is the target of A’s use of the feature: (a) Does it affect A’s feed?; (b) does it affect B’s feed?; (c) is A still connected to B?; (d) is B still connected to A?; (e) can A and B see each other’s profile?

Compression-based inference of network motif sets.

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs.

Deep reinforcement learning driven trajectory-based meta-heuristic for distributed heterogeneous flexible job shop scheduling problem

As the production environment evolves, distributed manufacturing exhibits heterogeneous characteristics, including diverse machines, workers, and production processes. This paper examines a distributed heterogeneous flexible job shop scheduling problem (DHFJSP) with varying processing times. A mixed integer linear programming (MILP) model of the DHFJSP is formulated with the objective of minimizing the makespan. To solve the DHFJSP, we propose a deep Q network-aided automatic design of a variable neighborhood search algorithm (DQN-VNS). By analyzing schedules, sixty-one types of scheduling features are extracted. These features, along with six shaking strategies, are used as states and actions. A DHFJSP environment simulator is developed to train the deep Q network. The well-trained DQN then generates the shaking procedure for VNS. Additionally, a greedy initialization method is proposed to enhance the quality of the initial solution. Seven efficient critical path-based neighborhood structures with three-vector encoding scheme are introduced to improve local search. Numerical experiments on various scales of instances validate the effectiveness of the MILP model and the DQN-VNS algorithm. The results show that the DQN-VNS algorithm achieves an average relative percentage deviation (ARPD) of 3.2%, which represents an approximately 88.45% reduction compared to the best-performing algorithm among the six compared, with an ARPD of 27.7%. This significant reduction in ARPD highlights the superior stability and performance of the proposed DQN-VNS algorithm.

Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.

The purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning. We retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model. The deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p<0.05). Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles. We utilized GNNs to process multiple pathological slides as cell-level graphs, comparing their performance with XGBoost and Random Forest classifiers. The analysis included two feature types: multi-stain profiles and Radiomic features. Dimensionality reduction techniques such as UMAP and t-SNE were applied to optimize the feature space, and graph connectivity was based on spatial and feature closeness. Integrating Radiomic features into spatially connected graphs significantly improved classification accuracy over traditional models. The application of UMAP further enhanced the performance of GNNs, particularly in classifying diseases with similar pathological features. The GNN model outperformed baseline methods, demonstrating its robustness in handling complex histopathological data. Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study's findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectiveness.

Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy

ObjectivesRepeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).MethodsNine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.ResultsFirst-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42–0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70–0.84 vs 0.14–0.64). Features with smaller bin widths had higher repeatability (median ICC of 8–128: 0.65–0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).ConclusionsApplying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.Critical relevance statementThe exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.Key PointsThe repeatability of RFs impacts the generalizability of the radiomic model.The high-repeatable RFs safeguard the cross-institutional generalizability of the model.Smaller bin width helps improve the repeatability of RFs.Graphical

From technology opportunities to solutions generation via patent analysis: Application of machine learning-based link prediction

Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises. Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices. Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

Survival Analysis on Lung Cancer Patients in Damaturu-Nigeria

Cancer is one of the leading causes of death by lung cancer in particular, is the leading cause of cancer death in both men and women, accounting for 23% of all cancer deaths in 2019 according to the Centers for Disease Control and Prevention. One particular problem with lung cancer is that it usually has a poor prognosis. With such a deadly disease, it is crucial to predict the survival likelihood of cancer patients. However, this is not an easy task due to the many factors affecting the disease progression. Survival time has become an essential outcome of clinical trial, which began to emerge among the latter half of the 20th century. A present study was carried out on the survival analysis for patients with lung cancer. The data was obtained from Yobe State Specialist Hospital, Damaturu where each sample was collected from the recipients of the treatment of radical prostatectomy. The Kaplan Meier method was used to obtain and estimate the survival function and median. The log-rank test was used to test the differences in the survival curves. The cox proportional hazard (PH) model provided an effective covariate on the hazard function. As a result of cox PH model, the influence of standard clinical prognostic factors is based on the hazard rate of lung cancer patients. We performed rigorous cross-examination on each feature's relationship and the model for each feature type using data analysis information and survival analysis models. For each feature type, we used one representative survival analysis model from semi-parametric methods (Cox proportional hazards model), one from non-parametric methods (Kaplan-Meier estimator), and one from machine learning approaches (random survival forests). Using the results obtained from these different methods, we identified the best feature types and model combinations to get the top performance for various follow-up periods. The best model is Cox proportional hazards model based on the AIC and log-likelihood functions respectively.

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information

Background and purposeRadiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF), despite advanced RT. This study integrates patient-specific information to develop learning-based models to predict the incidence of AF following NSCLC chemoradiotherapy (CRT) and evaluates these models using institutional and external datasets. Materials and methodsInstitutional and external patient cohorts consisted of 321 and 187 NSCLC datasets who received definitive CRT, including 17 and 6 AF incidences, respectively. The network input had 159 features with clinical, dosimetry, and diagnostic. The class imbalance was mitigated by synthetic minority oversampling technique. To handle various types of input features, machine learning-based model adopted an intervention technique that chose one feature with the largest weight at each dosimetry sub-group in feature selection process, while deep learning-based model employed a hybrid architecture assigning different types of networks to corresponding input paths. Performance was assessed by area under the curve (AUC). The key features were investigated for the machine and deep learning-based models. ResultsThe hybrid deep learning model outperformed the machine learning-based algorithm in internal validation (AUC: 0.817 vs. 0.801) and produced more consistent performance in external validation (AUC: 0.806 vs. 0.776). Importantly, maximum dose to heart and sinoatrial node (SAN) were found to be the key features for both learning-based models in external and internal validations. ConclusionsThe learning-based predictive models showed consistent prediction performance across internal and external cohorts, identifying maximum heart and SAN dose as key features for the incidence of AF.

The legal essence of tender under the legislation of the Republic of Kazakhstan

In modern conditions, tenders are a key source of income for government organizations and the business community, as it contributes to the effective organization of the procurement process and ensures transparen-cy and competitiveness in choosing a supplier. The authors of the article analyzed the key legislative acts regulating tenders, including the Law «On Public Procurement « in order to define the legal essence of the concept of «tender». As the analysis of industry legislation has shown, the concepts of «tender» and «contest» in most cases are identified and used as synonyms in the works of domestic and Russian scientists. The article also includes a comparative analysis with international practice, as well as a comparative analysis of the con-cept of the term «tender» in the current legislative acts of the United States of America, the People's Republic of China and Turkmenistan. This allowed the authors to identify potential areas for reforming the Kazakh tender system by including the relevant concept of «tender» in the current legislation. Within the framework of the article, the main legal aspects of tender procedures were considered, which represent an opportunity through free and open contest to conclude a mutually beneficial contract for contractors, as well as types of tenders, their significance and features in the field of public procurement. The methodological basis of the re-search consists of general and special methods, such as formal logical, scientific analysis and synthesis, his-torical and legal and comparative legal methods. The results obtained from the research allowed the authors to formulate proposals and recommendations for improving the legislative framework to ensure the effective-ness and fairness of tender processes.

Recent Works on Direct-Write Digital Holography

Abstract Direct-write digital holographic (DWDH) printing is a highly flexible technique for the generation of photoresist masters, which are required to produce the metallic shims used for the mass production of holograms in the security and packaging industries. Here we describe a new type of holographic feature, which can be combined with any other feature printable using DWDH: full-parallax, full-colour transmission masters containing limited animation. We will also describe a technique to print the fringe pattern of each hogel without using a reference beam. By programming the required fringe calculation algorithm on a graphics card using CUDA, we obtain acceptable calculation times. The advantage of using such direct fringe writing is that once again DWDH allows extra features to be written onto a master and combined with other features to produce a stronger security solution. Finally, we present results concerning the use of hogel image dithering to improve the grey-scale performance of DWDH printers.