Original Form Research Articles

A food quality detection method based on electronic nose technology

Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of convolutional neural network combined with wavelet scattering network (CNN-WSN) and improved seahorse optimizes kernel extreme learning machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of CNN are fused with the scattering features of WSN, and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the seahorse optimisation algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the KELM model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

Assumed strain methods in micromechanics, laminate composite voxels and level sets

AbstractThis work deals with the composite voxel method, which—in its original form—furnishes voxels containing more than one material with a surrogate material law accounting for the heterogeneity in the voxel. We show that the laminate composite voxel technique naturally arises as an assumed strain method, that is, the general framework introduced by Simo‐Rifai, for a specific choice of enhanced strain field. As a consequence, laminate composite voxels may be regarded as a kinematic assumption within a discretization scheme rather than a part of material modeling, as suggested originally. We discuss how to seamlessly integrate composite voxels into the framework of a level‐set description of the microstructure, in particular the accurate and efficient computation of normals and cut‐volume fractions. In contrast to more traditional strategies based on subvoxelizations, the introduced method avoids systematic errors when computing composite voxel properties. We demonstrate the applicability of the developed technology for a number of relevant computational examples.

Diabetes Insipidus can Increase Patient Security: A Review

Diabetes insipidus (DI) is a condition marked by polydipsia (drinking more than three liters of fluid per day) and a high hypotonic urine production (greater than 50 milliliters per kilogram of body weight per 24 hours). The hypothalamus or pituitary gland's insufficient production of arginine vasopressin (AVP) and its consequent inadequate secretion cause central DI. In addition to central DI, different primary forms (of renal origin) or secondary forms of polyuria may be the cause of additional underlying etiologies of DI. These variations are all part of the Polyuria Polydipsia Syndrome. This is crucial since different treatment plans have different effects, and using the incorrect one can be harmful. Since its discovery in 1942, diabetes insipidus has been linked to pregnancy complications in at least 4 out of every 100,000 patients.The regulatory mechanisms behind CDI and NDI are then discussed, with an emphasis on the water channel molecule aquaporin 2 (AQP2) and the vasopressin receptor 2 (V2R) regulatory axis. (DI) is a complicated pregnancy condition that can affect about 1 in 30,000 pregnancies. It is a heterogeneous illness that most commonly manifests as polyuria and polydipsia. The pathophysiology of DI during pregnancy determines the course of treatment; DI without AVP can be managed with desmopressin (DDAVP); DI with AVP resistance necessitates investigation of the underlying reasons. Previously known as diabetes insipidus (DI), arginase vasopressin disorder is a disease condition that causes electrolyte imbalances by either reducing the secretion of antidiuretic hormone (ADH, also known as vasopressin or AVP) or by reducing the body's response to ADH. Both arginine vasopressin deficiency (AVP-D; formerly known as central DI) and arginine vasopressin resistance (AVP-R; formerly known as nephrogenic DI) are forms of arginine vasopressin disease, with both congenital and acquired origins. Lithium increased the amount of lipid peroxidation (LPO) and reactive oxygen species (ROS) in the kidney.

Multisource High-Resolution Remote Sensing Image Vegetation Extraction with Comprehensive Multifeature Perception

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network’s generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity.

Biology teaching that develops the metacognitive aspect of learning: How to learn competence

Higher-order thinking and metacognition are closely related and are part of learning to develop competence. The goal of this research was to determine the practice of teaching biology that develops metacognition and evaluate whether teachers require specific professional training for this purpose or if broader cognitive-focused training can also enhance metacognitive development in students. The original Teaching Observation Form (TOF) has been adapted for research objectives. A survey was designed to capture the subjective perspectives of 292 students and their teachers regarding metacognition development. Additionally, the research involved six biology teachers who were professionally trained in a program focused on higher-order thinking. The survey results indicate a self-assessed good teaching practice for the development of metacognition. However, analysis of the first lesson's video recordings showed that some components had been eliminated from biology classes which hindered students' ability to develop metacognition. Although higher-order thinking and metacognition are interconnected phenomena, professional development training focuses solely on higher-order thinking whose impact we established through analysis of other lessons does not induce the necessary positive changes for the comprehensive development of metacognition in students. In a nutshell, explicit professional development programs aimed at fostering metacognitive awareness among teachers need to be designed. These programs should instruct teachers on how to model the development of metacognition in students through their teaching.

W-FENet: Wavelet-based Fourier-Enhanced Network Model Decomposition for Multivariate Long-Term Time-Series Forecasting

Accurately predicting the future trend of a time series holds immense importance for decision-making and planning across various domains, including energy planning, weather forecasting, traffic warning, and other practical applications. Recently, deep learning methods based on transformers and time convolution networks (TCN) have achieved a surprising performance in long-term sequence prediction. However, the attention mechanism for calculating global correlation is highly complex, and TCN methods do not fully consider the characteristics of time-series data. To address these challenges, we introduce a new learning model named wavelet-based Fourier-enhanced network model decomposition (W-FENet). Specifically, we have used trend decomposition and wavelet transform to decompose the original data. This processed time-series data can then be more effectively analyzed by the model and mined for different components in the series, as well as capture the local details and overall trendiness of the series. An efficient feature extraction method, Fourier enhancement-based feature extraction (FEMEX), is introduced in our model. The mechanism converts time-domain information into frequency-domain information through a Fourier enhancement module, and the obtained frequency-domain information is better captured by the model than the original time-domain information in terms of periodicity, trend, and frequency features. Experiments on multiple benchmark datasets show that, compared with the state-of-the-art methods, the MSE and MAE of our model are improved by 11.1 and 6.36% on average, respectively, covering three applications (i.e. ETT, Exchange, and Weather).

Lost in translation: questioning the role of European evaluations in environmental policy change

In the EU, evaluations are built into most modern laws, creating windows for policy feedback and new interpretations accounting for a constantly shifting society and environment. One issue often addressed by evaluators is whether a given policy should be retained in its original form, especially when new information is inconsistent and may upend former policy compromises. To what extent, then, are EU evaluations a legitimate opportunity for learning, adaptation and transformation? This article shows how evaluators engage in an activity known as transcoding, which involves translating expert knowledge into actionable recommendations to maintain a functioning policy system. Based on a study of the Water Framework Directive and its two evaluations – Water Blueprint (2012) and Fitness Check (2019) – we argue that transcoding has employed conflict-averse strategies, sidestepping difficult implementation dilemmas. Such strategies may have contributed to the development of lock-ins, that is, a persistence of the status quo.

Integrable discretization of recursion operators and unified bilinear forms to soliton hierarchies

In this paper, we give a procedure for discretizing recursion operators by utilizing unified bilinear forms within integrable hierarchies. To illustrate this approach, we present unified bilinear forms for both the AKNS hierarchy and the KdV hierarchy, derived from their respective recursion operators. Leveraging the inherent connection between soliton equations and their auto-B\"acklund transformations, we discretize the bilinear integrable hierarchies and derive discrete recursion operators. These discrete recursion operators exhibit convergence towards the original continuous forms when subjected to a standard limiting process.

Balanced transformer: efficient classification of glioblastoma and primary central nervous system lymphoma

Objective. Primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) are malignant primary brain tumors with different biological characteristics. Great differences exist between the treatment strategies of PCNSL and GBM. Thus, accurately distinguishing between PCNSL and GBM before surgery is very important for guiding neurosurgery. At present, the spinal fluid of patients is commonly extracted to find tumor markers for diagnosis. However, this method not only causes secondary injury to patients, but also easily delays treatment. Although diagnosis using radiology images is non-invasive, the morphological features and texture features of the two in magnetic resonance imaging (MRI) are quite similar, making distinction with human eyes and image diagnosis very difficult. In order to solve the problem of insufficient number of samples and sample imbalance, we used data augmentation and balanced sample sampling methods. Conventional Transformer networks use patch segmentation operations to divide images into small patches, but the lack of communication between patches leads to unbalanced data layers. Approach. To address this problem, we propose a balanced patch embedding approach that extracts high-level semantic information by reducing the feature dimensionality and maintaining the geometric variation invariance of the features. This approach balances the interactions between the information and improves the representativeness of the data. To further address the imbalance problem, the balanced patch partition method is proposed to increase the receptive field by sampling the four corners of the sliding window and introducing a linear encoding component without increasing the computational effort, and designed a new balanced loss function. Main results. Benefiting from the overall balance design, we conducted an experiment using Balanced Transformer and obtained an accuracy of 99.89%, sensitivity of 99.74%, specificity of 99.73% and AUC of 99.19%, which is far higher than the previous results (accuracy of 89.6% ∼ 96.8%, sensitivity of 74.3% ∼ 91.3%, specificity of 88.9% ∼ 96.02% and AUC of 87.8% ∼ 94.9%). Significance. This study can accurately distinguish PCNSL and GBM before surgery. Because GBM is a common type of malignant tumor, the 1% improvement in accuracy has saved many patients and reduced treatment times considerably. Thus, it can provide doctors with a good basis for auxiliary diagnosis.

Контрольная сумма как элемент достоверности доказательства, содержащего электронную информацию, в уголовном процессе

The emergence of electronic information in criminal proceedings has contributed to the creation of new elements regulating criminal procedural relations. There are three directions of using electronic information in criminal proceedings. Particular attention is paid to the copying of electronic information during the investigative action. There is a need to fix the checksum as an additional guarantee of the reliability of the evidence containing electronic information. Also, its importance is noted in the criminal process for resolving the issue of the immutability of the original electronic information in the case of the creation of a backup copy by the investigator for ongoing work with evidence; when evaluating the conclusion of a forensic computer examination; when restoring a criminal case or its materials. In conclusion, the author’s concept of checksum of electronic information in criminal proceedings and an addition to Article 164.1 of the Criminal Procedure Code of Russia are proposed.

Image Hash Layer Triggered CNN Framework for Wafer Map Failure Pattern Retrieval and Classification

Recently, deep learning methods are often used in wafer map failure pattern classification. CNN requires less feature engineering but still needs preprocessing, e.g., denoising and resizing. Denoising is used to improve the quality of the input data, and resizing is used to transform the input into an identical size when the input data sizes are various. However, denoising and resizing may distort the original data information. Nevertheless, CNN-based applications are focusing on studying different feature map architectures and the input data manipulation is less attractive. In this study, we proposed an image hash layer triggered CNN framework for wafer map failure pattern retrieval and classification. The motivation and novelty are to design a CNN layer that can play as a resizing, information retrieval-preservation method in one step. The experiments proved that the proposed hash layer can retrieve the failure pattern information while maintaining the classification performance even though the input data size is decreased significantly. In the meantime, it can prevent overfitting, false negatives, and false positives, and save computing costs to a certain extent.

Reconfigurable Growth of Engineered Living Materials.

The growth of multicellular organisms is a process akin to additive manufacturing where cellular proliferation and mechanical boundary conditions, among other factors, drive morphogenesis. Engineers have limited ability to engineer morphogenesis to manufacture goods or to reconfigure materials comprised of biomass. Herein, a method that uses biological processes to grow and regrow magnetic engineered living materials (mELMs) into desired geometries is reported. These composites contain Saccharomyces cerevisiae and magnetic particles within a hydrogel matrix. The reconfigurable manufacturing process relies on the growth of living cells, magnetic forces, and elastic recovery of the hydrogel. The mELM then adopts a form in an external magnetic field. Yeast within the material proliferates, resulting in 259 ± 14% volume expansion. Yeast proliferation fixes the magnetic deformation, even when the magnetic field is removed. The shape fixity can be up to 99.3 ± 0.3%. The grown mELM can recover up to 73.9 ± 1.9% of the original form by removing yeast cell walls. The directed growth and recovery process can be repeated at least five times. This work enables ELMs to be processed and reprocessed into user-defined geometries without external material deposition.

Making workable knowledge for asylum decisions: on tinkering with country of origin information

ABSTRACT As the number of asylum seekers grew, and flight stories became more complex, many Western governments deployed national research units, tasked with producing reliable Country of Origin Information (COI) to assist officials, judges and policy-makers in decision-making. Building on ethnographic research at Staatendokumentation, the COI unit at the Austrian Federal Office for Immigration and Asylum, the paper argues that country research practices can best be understood as ‘tinkering’ – e.g. making use of know-how, equipment, material sources at disposal to produce workable COI in conditions of uncertainty. The concept of tinkering is derived from science and technology studies (STS) and brings into view how the research professionals cobble together a workable version of reality with the methodologies and materials at hand. Moreover, it highlights how country research involves continuous modification and adjustments to satisfy the needs of the unwitting case officer as the end-user of COI reports. Finally, using insights from feminist science and technology studies, the paper shows how country experts foster care for some things – i.e. the workload of case officers – at the expense of others – i.e. the experience of the asylum seeker.

REMOVAL OF OIL FROM PRODUCED WATER USING SUGARCANE BAGASSE: EQUILIBRIUM AND KINETIC STUDIES

The improper disposal of produced water poses a significant environmental challenge, necessitating effective treatment measures to mitigate its harmful impact. This study explores the potential of sugarcane bagasse (SB) as a biosorbent, both in its original form and modified state for removal of oil from produced water. The SB samples were collected, washed, dried, and characterized for functional groups, surface morphology, and elemental composition using FT-IR, SEM and EDS equipment respectively. The analysis identified various elements in both modified and unmodified SB. The impact of biosorbent dose, contact time, pH, and temperature on oil removal from produced water was investigated. The results showed a rapid increase in oil uptake by modified SB with increase in dosage, reaching a maximum of 97.80%. Unmodified SB exhibited a gradual increase in oil uptake, leveling off at 81.32%. Modified SB demonstrated a shorter contact time compared to unmodified one. Isotherm studies revealed that the Langmuir isotherm best fit the data for both modified and unmodified SB, with R2 value of 1 and 0.997, respectively. On the other hand, the equilibrium parameter RL were found to be 0.00001 and 0.0194 for modified and unmodified SB as biosorbent respectively. Biosorption kinetics were tested using pseudo-first order and pseudo-second-order kinetic models. The results indicated that the biosorption process followed the pseudo-second-order model, with R2 values of 0.998 and 0.999 for modified and unmodified SB, respectively. In conclusion, both modified and unmodified sugarcane bagasse demonstrate promising potential as effective materials for oil removal from produced water.

PENDUSTAAN MANUSIA TERHADAP ALLAH

Human’s lying and insulting to God is inappropriate because human lives at God’s mercy. God is the most perfect being in which He is the only being (one), does not need any other existence and has dominion over All things. The Qur’an and hadith carry various news in which it tells about the greatness of Allah and his power that sometimes still doubted by some people such as people who doubt life after death even though before creation, the condition of man who was still in the form of origin (land) was a form of the first death of human. Among the main points in this article is talk about faith and monotheism, where the statement that Allah has broken His promises is a lie and the accusation that he took a son is an insult. With the Qur’an and hadith as the foundation for breaking every accusation that is thrown to Allah, it is impossible to think that it can be in God’s substance, and when someone can understand it as well, it can have a big impact on their faith and deliver a big love to their only one God. This research was conducted using the qualitative method, which entailed gathering data from various sources, connecting the data, and then using the data as a foundation to deny all lying and amplifying that thrown to Allah and as a foundation that Allah was the truth, and and only God all of time.

IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation

In this paper, we propose an Iterative Greedy-Universal Adversarial Perturbations (IGUAP) approach based on an iterative greedy algorithm to create universal adversarial perturbations for acoustic prints. A thorough, objective account of the IG-UAP method is provided, outlining its framework and approach. The method leverages a greedy iteration approach to formulate an optimization problem for solving acoustic universal adversarial perturbations, with a new objective function designed to ensure that the attack has higher accuracy in terms of minimizing the perceptibility of adversarial perturbations and increasing the accuracy of successful attacks. The perturbation generation process is described in detail, and the resulting acoustic universal adversarial perturbation is evaluated in both target-attack and no-target-attack scenarios. Experimental analysis and testing were carried out using comparable techniques and dissimilar target models. The findings reveal that the acoustic generality adversarial perturbation produced by the IG-UAP method can obtain effective attack results even when the audio training data sample size is minimal, i.e., one for each category. Moreover, the human ear finds it difficult to detect the loss of original data information and the addition of adversarial perturbation (for the case of a target attack, the ASR values range from 82.4% to 90.2% for the small sample data set). The success rates for untargeted and targeted attacks average 85.8% and 84.9%, respectively.

Submesoscale Short-Lived Eddies in the Southwestern Taiwan Strait Observed by High-Frequency Surface-Wave Radars

Surface currents obtained from the high-frequency surface-wave radars (HFSWRs) installed along the coast of Fujian Province are utilized to characterize submesoscale eddies in the southwestern Taiwan Strait from 29 January to 26 March 2013. The algorithm based on vector geometry (VG) has been applied to datasets and a total of 414 (161 anticyclonic and 253 cyclonic eddies) were obtained. Eddies with both rotations had a relatively short lifespan (≤3.7 h), and their radii were in the range of 5–22.5 km. Eddies with a lifespan of over 30 minutes were more likely to occur north of the Taiwan Strait shoals and move eastward or northeastward. The deviation of moving directions of eddies with a moving distance of more than 20 km was within 18°. Moreover, eddies could hardly hold their original forms with cyclones extending in the east-west and compressing in the north-south direction, and anticyclones were the opposite. The vorticity and strain rate were proportional to the square of the energy intensity (EI). This study shows that the array HFSWRs have a strong capability to observe short-lived submesoscale eddies.

Ref-MEF: Reference-Guided Flexible Gated Image Reconstruction Network for Multi-Exposure Image Fusion.

Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to the inherent inflexibilities of neural network structures, presenting difficulties in dynamically handling an unpredictable amount of exposure inputs. In response to this challenge, we introduce Ref-MEF, a method for color image multi-exposure fusion guided by a reference image designed to deal with an uncertain amount of inputs. We establish a reference-guided exposure correction (REC) module based on channel attention and spatial attention, which can correct input features and enhance pre-extraction features. The exposure-guided feature fusion (EGFF) module combines original image information and uses Gaussian filter weights for feature fusion while keeping the feature dimensions constant. The image reconstruction is completed through a gated context aggregation network (GCAN) and global residual learning GRL. Our refined loss function incorporates gradient fidelity, producing high dynamic range images that are rich in detail and demonstrate superior visual quality. In evaluation metrics focused on image features, our method exhibits significant superiority and leads in holistic assessments as well. It is worth emphasizing that as the number of input images increases, our algorithm exhibits notable computational efficiency.

Multiparameter Remote Contact Force Sensor With Embedded Bend Sensing for Tendon-Driven Hand Robots

Integrating multiple sensors without increasing the structural complexity and their original form factor is a major concern in robotic applications, particularly in tendon-driven systems. This study proposes a multiparameter contact force sensor with bend sensing by utilizing an improved Bowden cable design. The proposed sensor consists of an end tip with an elastomer membrane, a Bowden cable consisting of a sheath and dual inner wires, and a remotely separated sensing component. The electrical wirings are decoupled from the mechanical Bowden cable system. The displacement difference between the force-sensing and bend-sensing wires is transmitted through the sheath, indicating the contact force on the end tip. The modeling and experiments verify that the contact force applied on the end tip is linearly related to the sensor output signal and is reliable after repeated measurements. The proposed sensor, utilizing the flexible Bowden cable as its sensing modality, can be integrated into a diverse range of tendon-driven robotic applications. These applications include a robotic hand with tactile and angle sensing capabilities, as well as a soft wearable robot for the hand (Exo-glove Poly II). The compact form factor of the proposed sensor enables geometric sensing without increasing structural complexity for multiparameter sensing in robotic applications.