bluuebunny/crossref_metadata_2025 huggingface dataset:
Are Neural Networks the Right Tool for Process Modeling and Control of Batch and Batch-like Processes?
Authors: Rashid Mustafa , Mhaskar Prashant
Published: February 2023

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence of artificial intelligence techniques for clustering and classification applications, has increasingly motivated the exploration of the applicability of deep learning for modeling and feedback control of batch and batch-like processes. To this end, the present study seeks to evaluate the viability of artificial intelligence in general, and neural networks in particular, toward process modeling and control via a case study. Nonlinear autoregressive with exogeneous input (NARX) networks are evaluated in comparison with subspace models within the framework of model-based control. A batch polymethyl methacrylate (PMMA) polymerization process is chosen as a simulation test-bed. Subspace-based state-space models and NARX networks identified for the process are first compared for their predictive power. The identified models are then implemented in model predictive control (MPC) to compare the control performance for both modeling approaches. The comparative analysis reveals that the state-space models performed better than NARX networks in predictive power and control performance. Moreover, the NARX networks were found to be less versatile than state-space models in adapting to new process operation. The results of the study indicate that further research is needed before neural networks may become readily applicable for the feedback control of batch processes.
Divorce: Experiential and Structural Elements: Cases from Papua New Guinea and Africa
Authors: Stewart Pamela J. , Strathern Andrew J.
Published: February 2023

Divorce emerges as a phenomenon in counterpoint to marriage, both terms representing processes or phases of interaction punctuated by moments of completion and transition to further phases. We can make an initial distinction between divorce, viewed as undoing of preceding phases, and marriage, viewed as prospective of moving into a new relationship. Both divorce and marriage may carry different meanings depending on the wider culture in which they occur. Where marriage comes into being via a series of reciprocal transactions of wealth objects, divorce correspondingly consists of the undoing of such transactions, with the aim of creating a new order of relationships. This process can, in turn, itself vary as it turns on emotional manifestations between the parties involved, sometimes connected with the presence of offspring, as in the case of the Nuer people of South Sudan, among whom a wife does not shift to her husband’s settlement place until the couple have a child. The question of transactions goes with the significance of the wider kin networks in which marriages and divorces are regulated. All in all, our paper examines a counterpoint between legal and emotional aspects of both marriage and divorce, raising issues about what a marriage is and what constitutes a divorce, together with nuances of ritual processes that mark pathways between these categories. We draw on ethnography from Pacific cultures, especially Papua New Guinea, and from Africa, to explore these processes.
Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers
Authors: Piontek Dennis , Bugliaro Luca , Müller Richard , Muser Lukas , Jerg Matthias
Published: February 2023

The newest and upcoming geostationary passive imagers have thermal infrared channels comparable to those of more established instruments, but their spectral response functions still differ significantly. Therefore, retrievals developed for a certain type of radiometer cannot simply be applied to another imager. Here, a set of spectral band adjustment factors is determined for MSG/SEVIRI, Himawari-8/AHI, and MTG1/FCI from a training dataset based on MetOp/IASI hyperspectral observations. These correction functions allow to turn the observation of one sensor into an analogue observation of another sensor. This way, the same satellite retrieval—that has been usually developed for a specific instrument with a specific spectral response function—can be applied to produce long time series that go beyond one single satellite/satellite series or to cover the entire geostationary ring in a consistent way. It is shown that the mean uncorrected brightness temperature differences between corresponding channels of two imagers can be >1 K, in particular for the channels centered around 13.4 μm in the carbon dioxide absorption band and even when comparing different imager realizations of the same series, such as the four SEVIRI sensors aboard MSG1 to MSG4. The spectral band adjustment factors can remove the bias and even reduce the standard deviation in the brightness temperature difference by more than 80%, with the effect being dependent on the spectral channel and the complexity of the correction function. Further tests include the application of the spectral band adjustment factors in combination with (a) a volcanic ash cloud retrieval to Himawari-8/AHI observations of the Raikoke eruption 2019 and a comparison to an ICON-ART model simulation, and (b) an ice cloud retrieval to simulated MTG1/FCI test data with the outcome compared to the retrieval results using real MSG3/SEVIRI measurements for the same scene.
A Compact High-Isolation Four-Element MIMO Antenna with Asymptote-Shaped Structure
Authors: Wu Aiting , Tao Yingxiang , Zhang Pengquan , Zhang Zhonghai , Fang Zhihua
Published: February 2023

The demand for high-speed wireless communication systems has led to the development of ultrawide-band (UWB) antennas with a compact size and high performance. In this paper, we propose a novel four-port multiple-input multiple-output (MIMO) antenna with an asymptote-shaped structure that overcomes the limitations of existing designs for UWB applications. The antenna elements are placed orthogonally to each other for polarization diversity, and each element features a stepped rectangular patch with a tapered microstrip feedline. The unique structure of the antenna significantly reduces its dimensions to 42 × 42 mm2 (0.43λ×0.43λ@ 3.09GHz), making it highly desirable for use in small wireless devices. To further enhance the antenna’s performance, we use two parasitic tapes on the ground plane at the back as decoupling structures between adjacent elements. The tapes are designed in a windmill shape and a rotating extended cross shape, respectively, to further improve the isolation. We fabricated and measured the proposed antenna design on a single-layer substrate (FR4) with a dielectric constant of 4.4 and a thickness of 1 mm. The measured results show that the impedance bandwidth of the antenna is 3.09–12 GHz, with an isolation of −16.4 dB, an envelope correlation coefficient (ECC) of 0.02, a diversity gain (DG) of 9.991 dB, an average total effective reflection coefficient (TARC) of −20 dB, an overall group delay value less than 1.4 ns, and a peak gain of 5.1 dBi. Although there may be some antennas that have better performance in one or two specific aspects, our proposed antenna has an excellent trade-off among all the antenna characteristics including bandwidth, size, and isolation. The proposed antenna also exhibits good quasi-omnidirectional radiation properties, making it well-suited for a range of emerging UWB-MIMO communication systems, particularly in small wireless devices. In summary, the compact size and ultrawide-band capabilities of the proposed MIMO antenna design, coupled with its improved performance compared to other recent UWB-MIMO designs, make it a promising candidate for 5G and next-generation wireless communication systems.
The Empty-Nest Power User Management Based on Data Mining Technology
Authors: Li Jing , Yang Jiahui , Cai Hui , Jiang Chi , Jiang Qun , Xie Yue , Lu Zimeng , Li Lingzhi , Sun Guanqun
Published: February 2023

With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.
A Study on the Gas/Humidity Sensitivity of the High-Frequency SAW CO Gas Sensor Based on Noble-Metal-Modified Metal Oxide Film
Authors: Yang Haiyang , Shen Bin , Liu Xinlei , Jin Chunbo , Zhou Tianshun
Published: February 2023

In order to improve the response characteristics of the surface acoustic wave (SAW) sensor to trace gases, a SAW CO gas sensor based on a Pd–Pt/SnO2/Al2O3 film with a high-frequency response performance is proposed in this paper. The gas sensitivity and humidity sensitivity of trace CO gas are tested and analyzed under normal temperatures and pressures. The research results show that, compared with the frequency response of the Pd–Pt/SnO2 film, the CO gas sensor based on a Pd–Pt/SnO2/Al2O3 film has a higher frequency response performance, and the sensor has high-frequency response characteristics to CO gas with a concentration in the range of 10–100 ppm. The average response recovery time of 90% ranges from 33.4 s to 37.2 s, respectively. When the CO gas with a concentration of 30 ppm is tested repeatedly, its frequency fluctuation is less than 5%, indicating that the sensor has good stability. In the range of relative humidity (RH) from 25% to 75%, it also has high-frequency response characteristics for CO gas with a 20 ppm concentration.
PCNN Model Guided by Saliency Mechanism for Image Fusion in Transform Domain
Authors: Liu Liqun , Huo Jiuyuan
Published: February 2023

In heterogeneous image fusion problems, different imaging mechanisms have always existed between time-of-flight and visible light heterogeneous images which are collected by binocular acquisition systems in orchard environments. Determining how to enhance the fusion quality is key to the solution. A shortcoming of the pulse coupled neural network model is that parameters are limited by manual experience settings and cannot be terminated adaptively. The limitations are obvious during the ignition process, and include ignoring the impact of image changes and fluctuations on the results, pixel artifacts, area blurring, and the occurrence of unclear edges. Aiming at these problems, an image fusion method in a pulse coupled neural network transform domain guided by a saliency mechanism is proposed. A non-subsampled shearlet transform is used to decompose the accurately registered image; the time-of-flight low-frequency component, after multiple lighting segmentation using a pulse coupled neural network, is simplified to a first-order Markov situation. The significance function is defined as first-order Markov mutual information to measure the termination condition. A new momentum-driven multi-objective artificial bee colony algorithm is used to optimize the parameters of the link channel feedback term, link strength, and dynamic threshold attenuation factor. The low-frequency components of time-of-flight and color images, after multiple lighting segmentation using a pulse coupled neural network, are fused using the weighted average rule. The high-frequency components are fused using improved bilateral filters. The results show that the proposed algorithm has the best fusion effect on the time-of-flight confidence image and the corresponding visible light image collected in the natural scene, according to nine objective image evaluation indicators. It is suitable for the heterogeneous image fusion of complex orchard environments in natural landscapes.
Potentiometric Chloride Ion Biosensor for Cystic Fibrosis Diagnosis and Management: Modeling and Design
Authors: la Grasta Annabella , De Carlo Martino , Di Nisio Attilio , Dell’Olio Francesco , Passaro Vittorio M. N.
Published: February 2023

The ion-sensitive field-effect transistor is a well-established electronic device typically used for pH sensing. The usability of the device for detecting other biomarkers in easily accessible biologic fluids, with dynamic range and resolution compliant with high-impact medical applications, is still an open research topic. Here, we report on an ion-sensitive field-effect transistor that is able to detect the presence of chloride ions in sweat with a limit-of-detection of 0.004 mol/m3. The device is intended for supporting the diagnosis of cystic fibrosis, and it has been designed considering two adjacent domains, namely the semiconductor and the electrolyte containing the ions of interest, by using the finite element method, which models the experimental reality with great accuracy. According to the literature explaining the chemical reactions that take place between the gate oxide and the electrolytic solution, we have concluded that anions directly interact with the hydroxyl surface groups and replace protons previously adsorbed from the surface. The achieved results confirm that such a device can be used to replace the traditional sweat test in the diagnosis and management of cystic fibrosis. In fact, the reported technology is easy-to-use, cost-effective, and non-invasive, leading to earlier and more accurate diagnoses.
Enhanced Terahertz Fingerprint Sensing Mechanism Study of Tiny Molecules Based on Tunable Spoof Surface Plasmon Polaritons on Composite Periodic Groove Structures
Authors: Zhao Ruiqi , Feng Yu , Ling Haotian , Zou Xudong , Wang Meng , Lu Guizhen
Published: February 2023

Highly sensitive detection of enhanced terahertz (THz) fingerprint absorption spectrum of trace-amount tiny molecules is essential for biosensing. THz surface plasmon resonance (SPR) sensors based on Otto prism-coupled attenuated total reflection (OPC-ATR) configuration have been recognized as a promising technology in biomedical detection applications. However, THz-SPR sensors based on the traditional OPC-ATR configuration have long been associated with low sensitivity, poor tunability, low refractive index resolution, large sample consumption, and lack of fingerprint analysis. Here, we propose an enhanced tunable high-sensitivity and trace-amount THz-SPR biosensor based on a composite periodic groove structure (CPGS). The elaborate geometric design of the spoof surface plasmon polaritons (SSPPs) metasurface increases the number of electromagnetic hot spots on the surface of the CPGS, improves the near-field enhancement effect of SSPPs, and enhances the interaction between THz wave and the sample. The results show that the sensitivity (S), figure of merit (FOM) and Q-factor (Q) can be increased to 6.55 THz/RIU, 4234.06 1/RIU and 629.28, respectively, when the refractive index range of the sample to measure is between 1 and 1.05 with the resolution 1.54×10−5 RIU. Moreover, by making use of the high structural tunability of CPGS, the best sensitivity (SPR frequency shift) can be obtained when the resonant frequency of the metamaterial approaches the biological molecule oscillation. These advantages make CPGS a strong candidate for the high-sensitivity detection of trace-amount biochemical samples.
Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation
Authors: Meng Jie , Xiao Hanbiao , Jiang Liyu , Hu Zhaozheng , Jiang Liquan , Jiang Ning
Published: February 2023

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy—a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method—a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.
Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling
Authors: Vasile Floriana , Vizziello Anna , Brondino Natascia , Savazzi Pietro
Published: February 2023

Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients’ health. In this work, a novel method of analyzing EDA signals is proposed with the ultimate goal of helping caregivers assess the emotional states of autistic people, such as stress and frustration, which could cause aggression onset. Since many autistic people are non-verbal or suffer from alexithymia, the development of a method able to detect and measure these arousal states could be useful to aid with predicting imminent aggression. Therefore, the main objective of this paper is to classify their emotional states to prevent these crises with proper actions. Several studies were conducted to classify EDA signals, usually employing learning methods, where data augmentation was often performed to countervail the lack of extensive datasets. Differently, in this work, we use a model to generate synthetic data that are employed to train a deep neural network for EDA signal classification. This method is automatic and does not require a separate step for features extraction, as in EDA classification solutions based on machine learning. The network is first trained with synthetic data and then tested on another set of synthetic data, as well as on experimental sequences. In the first case, an accuracy of 96% is reached, which becomes 84% in the second case, thus demonstrating the feasibility of the proposed approach and its high performance.
Development of a Disposable Polyacrylamide Hydrogel-Based Semipermeable Membrane for Micro Ag/AgCl Reference Electrode
Authors: Andriukonis Eivydas , Butkevicius Marius , Simonis Povilas , Ramanavicius Arunas
Published: February 2023

Currently, Ag/AgCl-based reference electrodes are used in most electrochemical biosensors and other bioelectrochemical devices. However, standard reference electrodes are rather large and do not always fit within electrochemical cells designed for the determination of analytes in low-volume aliquots. Therefore, various designs and improvements in reference electrodes are critical for the future development of electrochemical biosensors and other bioelectrochemical devices. In this study, we explain a procedure to apply common laboratory polyacrylamide hydrogel in a semipermeable junction membrane between the Ag/AgCl reference electrode and the electrochemical cell. During this research, we have created disposable, easily scalable, and reproducible membranes suitable for the design of reference electrodes. Thus, we came up with castable semipermeable membranes for reference electrodes. Performed experiments highlighted the most suitable gel formation conditions to achieve optimal porosity. Here, Cl− ion diffusion through the designed polymeric junctions was evaluated. The designed reference electrode was also tested in a three-electrode flow system. The results show that home-built electrodes can compete with commercial products due to low reference electrode potential deviation (~3 mV), long shelf-life (up to six months), good stability, low cost, and disposability. The results show a high response rate, which makes in-house formed polyacrylamide gel junctions good membrane alternatives in the design of reference electrodes, especially for these applications where high-intensity dyes or toxic compounds are used and therefore disposable electrodes are required.
Fiber Optic Sensors for Harsh and High Radiation Environments in Aerospace Applications
Authors: Rovera Alberto , Tancau Alexandru , Boetti Nadia , Dalla Vedova Matteo D. L. , Maggiore Paolo , Janner Davide
Published: February 2023

In the upcoming space revolutions aiming at the implementation of automated, smart, and self-aware crewless vehicles and reusable spacecraft, sensors play a significant role in the control systems. In particular, fiber optic sensors, with their small footprint and electromagnetic immunity, represent a great opportunity in aerospace. The radiation environment and the harsh conditions in which these sensors will operate represent a challenge for the potential user in the aerospace vehicle design and the fiber optic sensor specialist. We present a review that aims to be a primer in the field of fiber optic sensors in radiation environments for aerospace. We review the main aerospace requirements and their relationship with fiber optics. We also present a brief overview of fiber optics and sensors based on them. Finally, we present different examples of applications in radiation environments for aerospace applications.
Centroid Optimization of DNN Classification in DOA Estimation for UAV
Authors: Wu Long , Zhang Zidan , Yang Xu , Xu Lu , Chen Shuyu , Zhang Yong , Zhang Jianlong
Published: February 2023

Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time.
Robot Programming from a Single Demonstration for High Precision Industrial Insertion
Authors: Wang Kaimeng , Fan Yongxiang , Sakuma Ichiro
Published: February 2023

We propose a novel approach for robotic industrial insertion tasks using the Programming by Demonstration technique. Our method allows robots to learn a high-precision task by observing human demonstration once, without requiring any prior knowledge of the object. We introduce an Imitated-to-Finetuned approach that generates imitated approach trajectories by cloning the human hand’s movements and then fine-tunes the goal position with a visual servoing approach. To identify features on the object used in visual servoing, we model object tracking as the moving object detection problem, separating each demonstration video frame into the moving foreground that includes the object and demonstrator’s hand and the static background. Then a hand keypoints estimation function is used to remove the redundant features on the hand. The experiment shows that the proposed method can make robots learn precision industrial insertion tasks from a single human demonstration.
Battery-Less Industrial Wireless Monitoring and Control System for Improved Operational Efficiency
Authors: Hidalgo-Fort Eduardo , Gómez-Galán Juan Antonio , González-Carvajal Ramón , Sánchez-Cárdenas Pedro , Clemente-Maya Carlos
Published: February 2023

An industrial wireless monitoring and control system, capable of supporting energy-harvesting devices through smart sensing and network management, designed for improving electro-refinery performance by applying predictive maintenance, is presented. The system is self-powered from bus bars, and features wireless communication and easy-to-access information and alarms. With cell voltage and electrolyte temperature measurements, the system enables real-time cell performance discovery and early reaction to critical production or quality disturbances such as short-circuiting, flow blockages, or electrolyte temperature excursions. Field validation shows an increase in operational performance of 30% (reaching 97%) in the detection of short circuits, which, thanks to a neural network deployed, are detected, on average, 10.5 h earlier compared to the traditional methodology. The developed system is a sustainable IoT solution, being easy to maintain after its deployment, and providing benefits of improved control and operation, increased current efficiency, and decreased maintenance costs.
Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques
Authors: Mitrea Delia-Alexandrina , Brehar Raluca , Nedevschi Sergiu , Lupsor-Platon Monica , Socaciu Mihai , Badea Radu
Published: February 2023

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. Computerized methods are due to achieve a noninvasive, accurate HCC detection process based on medical images. We developed image analysis and recognition methods to perform automatic and computer-aided diagnosis of HCC. Conventional approaches that combined advanced texture analysis, mainly based on Generalized Co-occurrence Matrices (GCM) with traditional classifiers, as well as deep learning approaches based on Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE), were involved in our research. The best accuracy of 91% was achieved for B-mode ultrasound images through CNN by our research group. In this work, we combined the classical approaches with CNN techniques, within B-mode ultrasound images. The combination was performed at the classifier level. The CNN features obtained at the output of various convolution layers were combined with powerful textural features, then supervised classifiers were employed. The experiments were conducted on two datasets, acquired with different ultrasound machines. The best performance, above 98%, overpassed our previous results, as well as representative state-of-the-art results.
Determination of 27 Glucocorticoids in Urine by Ultra-High-Performance Liquid Chromatography-Tandem Mass Spectrometry Using UniSprayTM Source
Authors: Wicka Mariola , Grucza Krzysztof , Drapała Aleksandra , Konarski Patryk , Kwiatkowska Dorota
Published: February 2023

Glucocorticoids (GCs) are a group of the most important and commonly used anti-inflammatory, antiallergenic, and immunosuppressive drugs. Like narcotics, they can be addictive if taken at increasing doses to achieve greater analgesic effects. The purpose of the study was to develop initial and confirmation testing analytical methods that would allow for the identification of glucocorticoid class substances in human urine to be used for routine analyses for the purposes of prosecution in the case of abuse, for clinical toxicology, medical jurisprudence, as well as for routine testing of athletes for the presence of prohibited substances in sports by means of ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) using a new generation ionization source UniSprayTM (U.S). This new method allows for the simultaneous detection of 27 glucocorticoids in human urine using LC-MS/MS. The tests conducted yielded relatively low LOD and LOQ values, ranging from 0.06 ng/mL to 0.14 ng/mL and 0.75 ng/mL for LOD and LOQ, respectively.
Computational Techniques for Analyzing Women’s Social Change in Saudi Newspapers
Authors: Almaghlouth Shrouq , Almujaiwel Sultan
Published: February 2023

This study utilized computational techniques for a reliable analysis of discourse. These techniques were adopted to analyze the progress of Saudi social change in terms of women’s empowerment within the Saudi transformation program. The data from open source 2021–2022 Saudi newspaper archives were automatically crawled using cutting-edge computational techniques and structured according to the sections of the Saudi newspapers: front page, economy, international, sports, society, culture and religion. The analysis was based on computing the minimally uneven distribution of the relative frequencies of the occurrence of the central word (the Arabic forms of woman and women) from the years 2021 to 2022. This produced two samples of text data, each of which represented the respective years. Calculating the normalized and adjusted frequencies of the central word from each section in the data from each year was important to avoid unbalanced absolute frequencies in the qualitative analysis stage. In addition, dispersion measures showed that the amount of variance in terms of the lexical dispersion of the central word was not high. The observable facts from the quantitative analysis produced a more accurate observational sample of citations, which we qualitatively analyzed. The results of the latter showed a considerable ascending change in favor of empowering women as a consequence of Saudi Vision 2030.
The Cultural Roots of Violence against Women: Individual and Institutional Gender Norms in 12 Countries
Authors: Lomazzi Vera
Published: February 2023

To discuss the cultural roots of violence against women (VAW), this study focuses on individual gender norms, prescriptive gender role expectations, moral justification of VAW, and institutional gender norms that define gender cultures, that provide opportunities for VAW, and legitimize roles and behaviors. We used indicators of gender norms related to VAW from different sources to provide an overview of 12 countries (Armenia, Cyprus, Czechia, Germany, Greece, The Netherlands, Romania, Russia, Serbia, Slovakia, Turkey, and Ukraine). The indicators include individual gender role attitudes and justification of wife beating from the World Values Survey; information on national legislation and institutional discrimination from the Social Institution Gender Index from the OECD; and each country’s position on the Council of Europe Convention on Preventing and Combating Violence Against Women and Domestic Violence—a transnational platform with relevant transformative power that has been opposed by anti-Europeanists. Although situations vary significantly in the different countries, this explorative study suggests that eradicating the cultural roots of VAW is more difficult in societies in which rigid traditional gender roles and a strongly patriarchal culture in legislation and institutions are supported by moral views legitimizing violence as a form of punishment for challenging prescribed gender roles.
Analysis of Activated Materials of Disposed Medical Linear Accelerators according to Clearance Level for Self-Disposal
Authors: Jang Young Jae , Kwon Na Hye , Park Seong Hee , Choi Yona , Kim Kum Bae , Kim Dong Wook , Bhang Suk Ho , Choi Sang Hyoun
Published: February 2023

In Korea, when replacing or discarding parts of a medical linear accelerator (linac), self-disposal is required in the consideration of the activity, but there is no standard regulation to manage radioactive waste. The aim of this study is to check the activity of each part to determine the disposal time according to the clearance level for self-disposal. The results of measuring the components of the linac head parts of the disposed Varian, Elekta, and Siemens equipment were reflected in the Monte Carlo simulation to confirm the radionuclide change according to the presence or absence of impurities. To confirm the degree of activation of the linac, the main radionuclides according to the time after the linac shutdown, considering the workloads of 40/80 Gy/day of 10/15 MV linac irradiated with beams for 10 years in the results of the simulation of the linac parts, and the radionuclide concentration was confirmed. As a result of applying the clearance level for self-disposal in the notice of the Korean Nuclear Safety (KINS) to each linac head part, most parts of the 10 MV linac could be dismantled after 1 month, and 15 MV target and primary collimators were stored after a long period of time before being dismantled. Although additional radionuclides were identified according to the presence or absence of impurities, the disposal timing for each part did not change significantly. In this study, the clearance level for self-disposal for each radionuclide was applied to activated parts by three manufacturers to confirm the self-disposal timing and predict the timing at which workers are not exposed to radiation during dismantling/disposal.
Skill Needs for Sustainable Agri-Food and Forestry Sectors (II): Insights of a European Survey
Authors: Ramalho Ribeiro Ana , Goodburn Billy , Mayor Luis , Lindner Line F. , Knöbl Christoph F. , Trienekens Jacques , Rossi Daniel , Sanna Francesca , Berruto Remigio , Busato Patrizia
Published: February 2023

The agri-food and forestry sectors are in transition towards more sustainable, green, and innovative systems tackling several challenges posed by globalization, governance, and consumers’ demands. This transition to novel processes, markets, and businesses requires skills and competences to prepare the new generations and upskill the actual workforce. The purpose of this paper was to assess the skills and knowledge needs of future professionals in the agri-food and forestry sectors, from European stakeholders’ perspectives, by using a European questionnaire. Overall, respondents highlighted the importance of improving sustainability and soft and digital skills. In particular, food safety management and control; quality management and assurance of processes and product; efficient use of resources and organization; and planning, visioning, and strategic thinking skills ranked higher. In almost all countries, respondents had the perception that neither formal nor non-formal training covered training needs, though formal training was more suited to address education requirements. Both for organizations and individuals, it is far more relevant to have skills to perform than to have training recognition. The outcomes also provide findings that can be used to help develop updated curricula that meet the sector’s needs.
Relationship Recognition between Knowledge and Ability Based on the Modularity of Complex Networks
Authors: Zou Qingyu , Sun Xu , Zhou Zhenxiong
Published: February 2023

The purpose of formal education is to increase students’ abilities, and its content is to impart knowledge through various courses. Thus, it is essential to accurately identify the relationship between knowledge and students’ ability increment to ensure the quality of education and the sustainable development of education. Currently, this relationship is mainly established based on previous educational data and teachers’ experience, which is often imprecise. This paper proposes a framework for knowledge and ability recognition based on the structural characteristics of complex network modules. The proposed framework utilizes a knowledge cognitive-interdependent network model (KCIN) as its object. First, the key knowledge nodes are identified via cognitive convergence flow of knowledge nodes in KCIN. Subsequently, the module structure of the knowledge network is identified by taking the key knowledge nodes as the core. Finally, the relationship between knowledge and ability is established by identifying the similar attributes of nodes in complex network modules. To validate the framework, we use teaching process data on the Data Structure course, which is a fundamental course for Information majors. The results show that the framework can effectively optimize the knowledge–ability relationship acquired from previous data and teacher experience.
The Preparation of Prussian Blue in a Mortar: An Example to Teach Sustainable Chemistry with Mechanochemical Reactions
Authors: Kraft Alexander
Published: February 2023

Solvent-free mechanochemical reactions represent an important path towards sustainable chemistry. The preparation of Prussian blue from solid iron and hexacyanoferrate compounds by the simple use of a mortar and pestle is an easy, inexpensive, and fast method to teach mechanochemical reactions. The course of the reaction can be followed very well visually via the color change of the solid mixture towards blue. With this communication, earlier publications on Prussian blue and mechanochemistry in the field of chemical education are updated and extended.
Statistical Inference of the Half Logistic Modified Kies Exponential Model with Modeling to Engineering Data
Authors: Alghamdi Safar M. , Shrahili Mansour , Hassan Amal S. , Gemeay Ahmed M. , Elbatal Ibrahim , Elgarhy Mohammed
Published: February 2023

The half-logistic modified Kies exponential (HLMKEx) distribution is a novel three-parameter model that is introduced in the current work to expand the modified Kies exponential distribution and improve its flexibility in modeling real-world data. Due to its versatility, the density function of the HLMKEx distribution offers symmetrical, asymmetrical, unimodal, and reversed-J-shaped, as well as increasing, reversed-J shaped, and upside-down hazard rate forms. An infinite linear representation can be used to represent the HLMKEx density. The HLMKEx model’s fundamental mathematical features are obtained, such as the quantile function, moments, incomplete moments, and moments of residuals. Additionally, some measures of uncertainty as well as stochastic ordering are derived. To estimate its parameters, eight estimation methods are used. With the use of detailed simulation data, we compare the performance of each estimating technique and obtain partial and total ranks for the accuracy measures of absolute bias, mean squared error, and mean absolute relative error. The simulation results demonstrate that, in contrast to other competing distributions, the proposed distribution can actually fit the data more accurately. Two actual data sets are investigated in the field of engineering to demonstrate the adaptability and application of the suggested distribution. The findings demonstrate that, in contrast to other competing distributions, the provided distribution can actually fit the data more accurately.
A New Adaptive Accelerated Levenberg–Marquardt Method for Solving Nonlinear Equations and Its Applications in Supply Chain Problems
Authors: Li Rong , Cao Mingyuan , Zhou Guoling
Published: February 2023

In this paper, a new adaptive Levenberg–Marquardt method is proposed to solve the nonlinear equations including supply chain optimization problems. We present a new adaptive update rule which is a segmented function on the ratio between the actual and predicted reductions of the objective function to accept a large number of unsuccessful iterations and avoid jumping in local areas. The global convergence and quadratic convergence of the proposed method are proved by using the trust region technique and local error bound condition, respectively. In addition, we use the proposed algorithm to test on the symmetric and asymmetric linear equations. Numerical results show that the proposed method has good numerical performance and development prospects. Furthermore, we apply the algorithm to solve the fresh agricultural products supply chain optimization problems.
Carbon Reduction Countermeasure from a System Perspective for the Electricity Sector of Yangtze River Delta (China) by an Extended Logarithmic Mean Divisia Index (LMDI)
Authors: Chen Jianfeng , Jia Junsong , Wang Lin , Zhong Chenglin , Wu Bo
Published: February 2023

The electricity sector is a complex system, especially in the Yangtze River Delta (YRD) of China. Thus, the carbon dioxide (CO2) emission of YRD’s electricity sector during 2000–2020 was first calculated and then evaluated from two systematical dimensions of cross-region and the whole process (production, trade, transmission, and consumption) by an extended logarithmic mean Divisia index (LMDI). (1) During 2000–2020, the CO2 emission of YRD’s electricity sector increased from 228.12 Mt to 807.55 Mt, with an average annual growth rate of 6.52%. Compared to other regions, the YRD’s electricity mix effect had the strongest mitigation impact on CO2 growth. Therefore, it is important for YRD to build a low-carbon electricity system itself, including the de-carbonization of electricity production and the carbon reduction of the electricity-use process. (2) Nationally, electricity trade had an overall mitigating impact on emission growth during 2000–2020. This result means that cross-regional cooperation or trade in the electricity sector is beneficial to emission reduction. So, it is important to improve the national power grids to promote trade. (3) Jiangsu had the largest CO2 emissions, while Anhui had the fastest average annual growth rate (9.71%). Moreover, the economic activity effect was the most significant driver in all provinces, especially in Jiangsu and Anhui. Thus, Jiangsu and Anhui should strive to improve the quality of economic growth while vigorously cutting carbon emissions. (4) Electricity transmission loss had an overall driving impact on emission growth in each YRD province, especially in Zhejiang and Anhui. Meanwhile, electricity structure, electricity trade, and electricity intensity were the inhibiting factors. Particularly, the inhibiting effect of Shanghai’s electricity structure was notably weak (−2.17 Mt). So, Shanghai should try hard to increase the proportion of renewable energy, while Zhejiang and Anhui should upgrade their electricity transmission equipment.
Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
Authors: Bougas Ioannis D. , Papadopoulou Maria S. , Boursianis Achilles D. , Nikolaidis Spyridon , Goudos Sotirios K.
Published: February 2023

Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More particularly, we designed a dual-band RF to DC rectifier circuit at sub-6 GHz in the 5G bands, able to supply low-power sensors and microcontrollers used in agriculture, the military, or health services. The system operates at 3.5 GHz and 5 GHz in the 5G cellular network’s frequency band FR1. Numerical results reveal that the system provides maximum power conversion efficiency (PCE) equal to 53% when the output load (sensor or microcontroller) is 1.74 kΩ and the input power is 12 dBm.
Feasibility of [18F]FSPG PET for Early Response Assessment to Combined Blockade of EGFR and Glutamine Metabolism in Wild-Type KRAS Colorectal Cancer
Authors: Bae Seong-Woo , Wang Jianbo , Georgiou Dimitra K. , Wen Xiaoxia , Cohen Allison S. , Geng Ling , Tantawy Mohammed Noor , Manning H. Charles
Published: February 2023

Early response assessment is critical for personalizing cancer therapy. Emerging therapeutic regimens with encouraging results in the wild-type (WT) KRAS colorectal cancer (CRC) setting include inhibitors of epidermal growth factor receptor (EGFR) and glutaminolysis. Towards predicting clinical outcome, this preclinical study evaluated non-invasive positron emission tomography (PET) with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid ([18F]FSPG) in treatment-sensitive and treatment-resistant WT KRAS CRC patient-derived xenografts (PDXs). Tumor-bearing mice were imaged with [18F]FSPG PET before and one week following the initiation of treatment with either EGFR-targeted monoclonal antibody (mAb) therapy, glutaminase inhibitor therapy, or the combination. Imaging was correlated with tumor volume and histology. In PDX that responded to therapy, [18F]FSPG PET was significantly decreased from baseline at 1-week post-therapy, prior to changes in tumor volume. In contrast, [18F]FSPG PET was not decreased in non-responding PDX. These data suggest that [18F]FSPG PET may serve as an early metric of response to EGFR and glutaminase inhibition in the WT KRAS CRC setting.
Change in Air Quality during 2014–2021 in Jinan City in China and Its Influencing Factors
Authors: Guo Qingchun , He Zhenfang , Wang Zhaosheng
Published: February 2023

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014–2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014–2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.