bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals
Authors: Jiang Yujian , Song Lin , Zhang Junming , Song Yang , Yan Ming
Published: August 2022

Gesture recognition based on wearable devices is one of the vital components of human–computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models’ test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.
Time Series Forecasting of Motor Bearing Vibration Based on Informer
Authors: Yang Zhengqiang , Liu Linyue , Li Ning , Tian Junwei
Published: August 2022

Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach 10−2∼10−6.
Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework
Authors: Li Yuxuan , Yang Chunjie , Sun Youxian
Published: August 2022

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.
Event-Related Potential-Based Brain–Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern
Authors: Borirakarawin Manorot , Punsawad Yunyong
Published: August 2022

Herein, we developed an auditory stimulus pattern for an event-related potential (ERP)-based brain–computer interface (BCI) system to improve control and communication in quadriplegia with visual impairment. Auditory stimulus paradigms for multicommand electroencephalogram (EEG)-based BCIs and audio stimulus patterns were examined. With the proposed auditory stimulation, using the selected Thai vowel, similar to the English vowel, and Thai numeral sounds, as simple target recognition, we explored the ERPs’ response and classification efficiency from the suggested EEG channels. We also investigated the use of single and multi-loudspeakers for auditory stimuli. Four commands were created using the proposed paradigm. The experimental paradigm was designed to observe ERP responses and verify the proposed auditory stimulus pattern. The conventional classification method produced four commands using the proposed auditory stimulus pattern. The results established that the proposed auditory stimulation with 20 to 30 trials of stream stimuli could produce a prominent ERP response from Pz channels. The vowel stimuli could achieve higher accuracy than the proposed numeral stimuli for two auditory stimuli intervals (100 and 250 ms). Additionally, multi-loudspeaker patterns through vowel and numeral sound stimulation provided an accuracy greater than 85% of the average accuracy. Thus, the proposed auditory stimulation patterns can be implemented as a real-time BCI system to aid in the daily activities of quadratic patients with visual and tactile impairments. In future, practical use of the auditory ERP-based BCI system will be demonstrated and verified in an actual scenario.
Heuristic Method for Minimizing Model Size of CNN by Combining Multiple Pruning Techniques
Authors: Tian Danhe , Yamagiwa Shinichi , Wada Koichi
Published: August 2022

Network pruning techniques have been widely used for compressing computational and memory intensive deep learning models through removing redundant components of the model. According to the pruning granularity, network pruning can be categorized into structured and unstructured methods. The structured pruning removes the large components in a model such as channels or layers, which might reduce the accuracy. The unstructured pruning directly removes mainly the parameters in a model as well as the redundant channels or layers, which might result in an inadequate pruning. To address the limitations of the pruning methods, this paper proposes a heuristic method for minimizing model size. This paper implements an algorithm to combine both the structured and the unstructured pruning methods while maintaining the target accuracy that is configured by its application. We use network slimming for the structured pruning method and deep compression for the unstructured one. Our method achieves a higher compression ratio than the case when the individual pruning method is applied. To show the effectiveness of our proposed method, this paper evaluates our proposed method with actual state-of-the-art CNN models of VGGNet, ResNet and DenseNet under the CIFAR-10 dataset. This paper discusses the performance of the proposed method with the cases of individual usage of the structured and unstructured pruning methods and then proves that our method achieves better performance with higher compression ratio. In the best case of the VGGNet, our method results in a 13× reduction ratio in the model size, and also gives a 15× reduction ratio regarding the pruning time compared with the brute-force search method.
Application of Image Sensors to Detect and Locate Electrical Discharges: A Review
Authors: Riba Jordi-Roger
Published: August 2022

Today, there are many attempts to introduce the Internet of Things (IoT) in high-voltage systems, where partial discharges are a focus of concern since they degrade the insulation. The idea is to detect such discharges at a very early stage so that corrective actions can be taken before major damage is produced. Electronic image sensors are traditionally based on charge-coupled devices (CCDs) and, next, on complementary metal oxide semiconductor (CMOS) devices. This paper performs a review and analysis of state-of-the-art image sensors for detecting, locating, and quantifying partial discharges in insulation systems and, in particular, corona discharges since it is an area with an important potential for expansion due to the important consequences of discharges and the complexity of their detection. The paper also discusses the recent progress, as well as the research needs and the challenges to be faced, in applying image sensors in this area. Although many of the cited research works focused on high-voltage applications, partial discharges can also occur in medium- and low-voltage applications. Thus, the potential applications that could potentially benefit from the introduction of image sensors to detect electrical discharges include power substations, buried power cables, overhead power lines, and automotive applications, among others.
Direct Conversion X-ray Detector with Micron-Scale Pixel Pitch for Edge-Illumination and Propagation-Based X-ray Phase-Contrast Imaging
Authors: Pil-Ali Abdollah , Adnani Sahar , Scott Christopher C. , Karim Karim S.
Published: August 2022

In this work, we investigate the potential of employing a direct conversion integration mode X-ray detector with micron-scale pixels in two different X-ray phase-contrast imaging (XPCi) configurations, propagation-based (PB) and edge illumination (EI). Both PB-XPCi and EI-XPCi implementations are evaluated through a wave optics model—numerically simulated in MATLAB—and are compared based on their contrast, edge-enhancement, visibility, and dose efficiency characteristics. The EI-XPCi configuration, in general, demonstrates higher performance compared to PB-XPCi, considering a setup with the same X-ray source and detector. However, absorption masks quality (thickness of X-ray absorption material) and environmental vibration effect are two potential challenges for EI-XPCi employing a detector with micron-scale pixels. Simulation results confirm that the behavior of an EI-XPCi system employing a high-resolution detector is susceptible to its absorption masks thickness and misalignment. This work demonstrates the potential and feasibility of employing a high-resolution direct conversion detector for phase-contrast imaging applications where higher dose efficiency, higher contrast images, and a more compact imaging system are of interest.
Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification
Authors: Torres Cesar , Gonzalez Claudia I. , Martinez Gabriela E.
Published: August 2022

Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images.
Distributed Agent-Based Orchestrator Model for Fog Computing
Authors: Liutkevičius Agnius , Morkevičius Nerijus , Venčkauskas Algimantas , Toldinas Jevgenijus
Published: August 2022

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed.
Transient 2D Junction Temperature Distribution Measurement by Short Pulse Driving and Gated Integration with Ordinary CCD Camera
Authors: Wang Zhiyun , Gong Honglin , Zhuang Peng , Fu Nuoyi , Zhu Lihong , Chen Zhong , Lu Yijun
Published: August 2022

The time resolution of the transient process is usually limited by the minimum exposure time of the high-speed camera. In this work, we proposed a method that can achieve nanosecond temporal resolution with an ordinary CCD camera by driving the LED under test with a periodic short-pulse signal and multiple-cycle superposition to obtain two-dimensional transient junction temperature distribution of the heating process. The temporal resolution is determined by the pulse width of the drive source. In the cooling process, the Boxcar gated integration principle is adopted to complete the two-dimensional transient junction temperature distribution with temporal resolution subject to the minimum exposure time of the CCD camera, i.e., 1 μs in this case. To demonstrate the validity of this method, we measured the two-dimensional transient junction temperature distribution of the blue LEDs according to the principle of thermoreflectance and compared it with the thermal imaging method.
Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors
Authors: Li Zhi , Fei Fei , Zhang Guanglie
Published: August 2022

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.
The Characteristics Analysis of a Microfluid-Based EGFET Biosensor with On-Chip Sensing Film for Lactic Acid Detection
Authors: Kuo Po-Yu , Chang Chun-Hung , Lai Wei-Hao , Wang Tai-Hui
Published: August 2022

In this research, a microfluid-based extended gate field-effect transistor (EGFET) biosensor with an on-chip sensing window (OCSW) was fabricated. The detection window was composed of six metal layers, and a ruthenium dioxide (RuO2) film was spattered on the surface and functionalized with lactase to detect lactic acid (LA). To detect LA in a more diversified way, a microfluidic system was integrated with the biosensor. Moreover, a special package was used to seal the sensing window and microfluidic tube and insulate it from other parts to prevent water molecule invasion and chip damage. The sensitivity analysis of the EGFET biosensor was studied by a semiconductor parameter analyzer (SPA). The static and dynamic measurements of the EGFET with sensing windows on a chip were analyzed. The sensing characteristics of the EGFET biosensor were verified by the experimental results. The proposed biosensor is suitable for wearable applications due to the advantages of its low weight, low voltage, and simple manufacturing process.
Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering
Authors: Zhu Xing-Hui , Zhou Yi , Yang Meng-Long , Deng Yang-Jun
Published: August 2022

Hyperspectral image (HSI) clustering is a challenging task, whose purpose is to assign each pixel to a corresponding cluster. The high-dimensionality and noise corruption are two main problems that limit the performance of HSI clustering. To address those problems, this paper proposes a projected clustering with a spatial–spectral constrained adaptive graph (PCSSCAG) method for HSI clustering. PCSSCAG first constructs an adaptive adjacency graph to capture the accurate local geometric structure of HSI data adaptively. Then, a spatial–spectral constraint is employed to simultaneously explore the spatial and spectral information for reducing the negative influence on graph construction caused by noise. Finally, projection learning is integrated into the spatial–spectral constrained adaptive graph construction for reducing the redundancy and alleviating the computational cost. In addition, an alternating iteration algorithm is designed to solve the proposed model, and its computational complexity is theoretically analyzed. Experiments on two different scales of HSI datasets are conducted to evaluate the performance of PCSSCAG. The associated experimental results demonstrate the superiority of the proposed method for HSI clustering.
An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation
Authors: Li Zonglun , Fattah Alya , Timashev Peter , Zaikin Alexey
Published: August 2022

The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando’s model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando’s model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.
A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
Authors: Wu Wei , Peng Haipeng , Tong Fenghua , Li Lixiang , Xie Binzhu
Published: August 2022

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes. Therefore, BBNs have high requirements in energy efficiency, data security, and privacy protection. In this paper, we propose a secure and efficient data transmission method for sensor nodes within BBNs that is based on the perception of chaotic compressive sensing. This method can simultaneously accomplish data compression, encryption, and critical information concealment during the data sampling process and provide various levels of reconstruction qualities according to the authorization level of receivers. Simulation and experimental results demonstrate that the proposed method could realize data compression, encryption, and critical information concealment for images that are transmitted within BBNs. Specifically, the proposed method could enhance the security level of data transmission by breaking the statistical patterns of original data, providing large key space and sensitivity of the initial values, etc.
Bimodal Learning Engagement Recognition from Videos in the Classroom
Authors: Hu Meijia , Wei Yantao , Li Mengsiying , Yao Huang , Deng Wei , Tong Mingwen , Liu Qingtang
Published: August 2022

Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student’s learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasive classroom videos, first, a multi-cues classroom learning engagement database was constructed. Then, we introduced the power IoU loss function to You Only Look Once version 5 (YOLOv5) to detect the students and obtained a precision of 95.4%. Finally, we designed a bimodal learning engagement recognition method based on ResNet50 and CoAtNet. Our proposed bimodal learning engagement method obtained an accuracy of 93.94% using the KNN classifier. The experimental results confirmed that the proposed method outperforms most state-of-the-art techniques.
Metabolomic and Proteomic Profile of Dried Hop Inflorescences (Humulus lupulus L. cv. Chinook and cv. Cascade) by SPME-GC-MS and UPLC-MS-MS
Authors: Nezi Paola , Cicaloni Vittoria , Tinti Laura , Salvini Laura , Iannone Matteo , Vitalini Sara , Garzoli Stefania
Published: August 2022

Hop (Humulus lupulus L.) is grown mainly for the production of beer. The flowers of the female plant give it the bitter taste and pungent aroma. There are a large number of hop varieties differing in their α-acid content, essential oil levels and odor profiles. Aside from their use in brewing, more recently, hops have been used for the pharmacological properties of its derivatives that are of great importance to the pharmaceutical industry. Hop is known to have a fairly complex chemistry characterized by the presence of a variety of sesquiterpenoids, diterpenoids and triterpenoids, phytoestrogens and flavonoids. Additionally, considering the countless applications in the pharmacological sector in recent years, a chemical characterization of the different cultivars is essential to better identify the source of specific secondary metabolites. For this purpose, the dried inflorescences of two hop cultivars, Chinook and Cascade, were investigated using Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry and Liquid Chromatography-Tandem Mass Spectrometry (SPME-GC-MS and LC-MS-MS) to describe their metabolomic and proteomic profile. Furthermore, thanks to an in-depth statistical survey, it was possible to carry out a comparative study highlighting interesting implications deriving from this investigative study.
Ecofriendly Validated RP-HPTLC Method for Simultaneous Determination of the Bioactive Sesquiterpene Coumarins Feselol and Samarcandin in Five Ferula Species Using Green Solvents
Authors: Abdel-Kader Maged S. , Alqarni Mohammed H. , Baykan Sura , Oztürk Bintug , Salkini Mohammad Ayman A. , Yusufoglu Hasan S. , Alam Prawez , Foudah Ahmed I.
Published: August 2022

An environmentally friendly unreported rapid and simple reverse-phase high-performance thin-layer chromatography (RP-HPTLC) has been designed for the simultaneous determination of bioactive sesquiterpene coumarins feselol and samarcandin in the methanol extract of five Ferula species. The method was developed using glass plates coated with RP-18 silica gel 60 F254S and a green solvent system of ethanol–water mixture (8:2 v/v) as mobile phase. After development, the plates were quantified densitometrically at 254 for feselol and samarcandin. Feselol and samarcandin peaks from methanol extract of five Ferula species were identified by comparing their single band at Rf = 0.43 ± 0.02 and Rf = 0.60 ± 0.01, respectively. Valid linear relationships between the peak areas and concentrations of feselol and samarcandin in the range of 1000–7000 ng/band respectively were obtained. The method was subjected to the validation criteria of the international conference on harmonization (ICH) for precision, accuracy, and robustness. The new method provides an analytical tool to enumerate the therapeutic doses of feselol and samarcandin in herbal formulations and/or crude drugs. The obtained results indicated that F. drudeana was the richest species in the more active samarcandin, with 0.573% w/w, while F. duranii had the largest quantity of the less active feselol, 0.813% w/w. F. drudeana was superior to the other species in the sum of the two active compounds, 1.4552% w/w, and was consequently expected to be the most active aphrodisiac among the five studied species.
Extraction and Physicochemical Composition of Irvingiagabonensis Almond Oil: A Potential Healthy Source of Lauric-Myristic Oil
Authors: Koumba Ibinga Sidrine Kerthy , Cerny Muriel , Lacroux Eric , Fabre Jean-François , Valentin Romain , Merah Othmane , Bikanga Raphaël , Mouloungui Zéphirin
Published: August 2022

Irvingia gabonensis is a non-timber forest product, whose fruit contains an edible fat-rich kernel. This fat can be used not only in human food but also as a source of raw materials in the cosmetic, pharmaceutical and nutraceutical industries. This work aimed to provide a physicochemical description of components present in the almonds and butter of I. gabonensis. Oil was extracted by soxhlet and hot-pressing from almonds. Cryo-MEB analyses allowed the observation of oleosomes in which the triglycerides of almonds are located. The triglyceride profile and the fatty acids profile of the butter were determined by gas chromatography, and a statistical analysis was performed. The thermal properties of oil were analyzed by thermogravimetric analysis. The results revealed that oil bodies have sizes ranging from 30 to 60 µm. With a 63.8 ± 0.2% fat content, I. gabonensis is composed of 98.4% triglycerides. The hot-pressing yield is 47.9%. The main triglycerides are essentially made up of lauric (38.5 ± 0.1%) and myristic (51.9 ± 0.2%) acids. Thermogravimetric analysis showed that the butter melted at 43.4 °C and decomposed at 415.2 °C. These results show that I. gabonensis butter may be proposed as a good source of lauric acid for food and nutrition.
Death Threats and Attempted Femicide in the Context of Domestic Violence in Portugal
Authors: Neves Sofia , Silva Emília , Topa Joana , Borges Janete , Pereira Alice , Silva Estefânia
Published: August 2022

Domestic violence is a serious problem in Portugal, affecting mainly women. The significant number of femicides in the context of intimate partner violence has been showing not only the severity of the crime, but also the necessity to reinforce strategies to prevent and combat it. Although several studies were developed in the last decades to portray domestic violence, research on the threat of death and attempted femicide is scarce. This study aims to characterize death threats and attempts of femicide, in the light of the professionals’ perspectives from the Portugal National Support Network for Victims of Domestic Violence, contributing to informing best practices of risk assessment. Seventy-one professionals, 62 female (87.3%) and 9 male (12.7%), at an average age of 37.69 years, filled a questionnaire survey. Results suggest weaknesses in the application of current legislation and the adoption of effective measures in situations where victims experience death threats and attempted murders. Although there are criminal proceedings before these episodes, this does not seem to have effective consequences in terms of protection and security, whereby the development of strategies that safeguard victims from tragic endings such as femicide are necessary.
Recognition of Digital Content Needs for Inbound Marketing Solutions
Authors: Davidavičius Sigitas , Limba Tadas
Published: August 2022

The paper aims to investigate users’ behavior regarding inbound marketing while consuming content, in particular, to reveal the source of the reasons and triggers affecting content need in the case of long-consumption products. In the theoretical part of the article, the literature analysis is conducted in order to build a theoretical background. The variety of theories of content values as well as users’ decision-making processes are analyzed, and a conceptual view of the origins of content need is formed, which states that the need for a specific type of content emerges under the conditions of the consumer’s experienced gap of information or knowledge when in the stages of the buying model. In order to test this hypothesis, empirical research—the survey—was conducted. The main conclusion is that the decision-to-buy model makes a significant impact on the gap experienced by the consumer of the content and has the potential to be used to reveal the need for different content types in terms of its purposes.
Parental Partnership, Advocacy and Engagement: The Way Forward
Authors: Haworth Simon , Bilson Andy , Drayak Taliah , Mayes Tammy , Saar-Heiman Yuval
Published: August 2022

This article, written with parents as co-authors, has two aims: (1) to provide a critical view of the English child protection system based on parents’ views and to locate these views within contemporary child protection studies and (2) to present the transformative value of co-production in the context of child protection studies both as a form of critical scholarship and as a means to influence policy and practice. The current children’s social work system in England does not achieve good outcomes for families, and many children and parents frequently experience it as stigmatizing, inhumane, and harmful. The article presents the experience and recommendations for change produced by parents with a broad range of experience with child protection services in England. The Parents, Families and Allies Network worked with five allied organizations in which parents identified the extensive range of problems that the current system presents and ways forward to achieve more supportive, humane, and inclusive practice with families. Seven main themes emerged: a better definition of need and response to need; partnership, participation, and humane practice; improving legal representation and support in legal proceedings; better support in care proceedings; permanence that maintains links; a better response to domestic violence; and the lack of support for disabled children. The article discusses five features of the project that supported meaningful co-production: taking a political stance, choosing clear and feasible aims, incorporating a range of knowledge, the participation of parents with lived experience throughout all phases of the project, and not settling with just knowledge production.
A Web Model of Domestic Violence and Abuse in Muslim Communities—A Multi Perspective IPA Approach
Authors: Chowdhury Rahmanara , Winder Belinda
Published: August 2022

This paper brings together two qualitative studies exploring how domestic violence and abuse (DVA) manifests within UK Muslim communities. Study one was conducted with UK-based Muslim female survivors of DVA (n = 10). Study two was conducted with UK professionals working in a supportive capacity with both DVA victims/survivors and those perpetrating abuse within Muslim communities (n = 9). Through a multi-perspective interpretative phenomenological lens, the two data sets were analysed for overarching themes. These themes were subsequently used to develop a graphical representation of the findings. The resulting outcome was the web model of DVA. The model identifies the trajectories and interactions at four levels in relation to DVA in Muslim communities. It is argued that this model has increased capacity for understanding the extended nature of how DVA manifests for UK Muslim communities, with a particular emphasis on the active role of faith and additional nuances often missed by singular methodological approaches. The model is recommended for use by services as a means toward individually tailored client care. Recommendations are made in relation to inclusive and decolonial approaches within gender-related violence research relating to minority communities in the UK.
Poisson Extended Exponential Distribution with Associated INAR(1) Process and Applications
Authors: Maya Radhakumari , Chesneau Christophe , Krishna Anuresha , Irshad Muhammed Rasheed
Published: August 2022

The significance of count data modeling and its applications to real-world phenomena have been highlighted in several research studies. The present study focuses on a two-parameter discrete distribution that can be obtained by compounding the Poisson and extended exponential distributions. It has tractable and explicit forms for its statistical properties. The maximum likelihood estimation method is used to estimate the unknown parameters. An extensive simulation study was also performed. In this paper, the significance of the proposed distribution is demonstrated in a count regression model and in a first-order integer-valued autoregressive process, referred to as the INAR(1) process. In addition to this, the empirical importance of the proposed model is proved through three real-data applications, and the empirical findings indicate that the proposed INAR(1) model provides better results than other competitive models for time series of counts that display overdispersion.
The (Mis)Understanding of the Symbol Associated with Recycling on Plastic Containers in the US: A Brief Report
Authors: Latkin Carl A. , Dayton Lauren , Yi Grace , Balaban Ariel
Published: August 2022

Plastic containers are a major source of pollution and contribute to greenhouse gases. Many plastic containers are embossed with a small symbol of three arrows forming a triangle and a number in the middle of the triangle. As part of a US online study, we assessed the meaning of this symbol. Of the 808 respondents presented with a visual image of the three-arrow symbol, 81.3% reported (incorrectly) that the symbol indicated that the item could definitely be recycled and 16.3% reported that it could probably be recycled. The findings suggest that a large proportion of individuals have an inaccurate understanding of the symbol on plastics purported to indicate recycling. Plastic manufacturers should revise labels about recycling and not disseminate potentially deceptive information about the ability to recycle their products, and more effective methods such as extended producer responsibility legislation are needed to reduce plastic pollution.
A Holistic Overview of Studies to Improve Group-Based Assessments in Higher Education: A Systematic Literature Review
Authors: Tumpa Roksana Jahan , Skaik Samer , Ham Miriam , Chaudhry Ghulam
Published: August 2022

There is a soaring demand for work-ready graduates who can quickly adapt to an ever-challenging work environment. Group-based assessments have been widely recommended as a means to develop the skills required for the world of work. However, group-based assessments are perceived as challenging for both students and educators. This systematic literature review (SLR), based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), focuses on analyzing and synthesizing the existing literature on group-based assessments. A four-step approach was undertaken in order to conduct this research. The SLR identified 71 relevant articles, analyzed using thematic analysis with the aid of NVivo software. An open coding approach was adopted to generate codes. The validity of the SLR process and the reliability of the research tool were maintained through the use of trustworthiness. The findings identified dominant themes such as self- and peer evaluations, training students for group work assessments, group formation, group size, and the role of academics and technology in facilitating group processes. The outcomes of this review contribute significantly to the design and administration of group-based assessments in higher education by providing academics with practical guidelines to effectively facilitate group-based assessments which fit the purpose.
A Smart Campus Framework: Challenges and Opportunities for Education Based on the Sustainable Development Goals
Authors: Silva-da-Nóbrega Pedro Ivo , Chim-Miki Adriana Fumi , Castillo-Palacio Marysol
Published: August 2022

Although technology improvements boosted the digital transition of universities, which built a path for smart campuses, the smartization process is more than simply promoting digitalization. This research aims to identify the essential elements and the most significant deficiencies in the smart campus dimensions and its variables from the user’s viewpoint to offer a list of priorities for decision-makers. Through an importance-performance analysis (IPA) performed using IBP SPSS 26, we tested an integrative smart campus framework in a Brazilian university, previously validated with Latin American experts. This research confirmed that eight dimensions are important for a smart campus evaluation and provided a list of priorities for academic managers. The results indicated the main gaps among importance and performance. This research concluded that the smartization process could not rely on technology attributes only. Universities should meet the modern society’s present and emerging needs and the labor market in a sustainable, social, and technological manner. Smart campuses in developing countries may prioritize different components than developed countries, such as infrastructure. We propose that more studies should apply the framework in more universities.
Integrating Agroecological Food Production, Ecological Restoration, Peasants’ Wellbeing, and Agri-Food Biocultural Heritage in Xochimilco, Mexico City
Authors: Figueroa Fernanda , Puente-Uribe Martha B. , Arteaga-Ledesma David , Espinosa-García Ana C. , Tapia-Palacios Marco A. , Silva-Magaña Miguel A. , Mazari-Hiriart Marisa , Arroyo-Lambaer Denise , Revollo-Fernández Daniel , Sumano Carlos , Rivas Miguel I. , Jiménez-Serna Alaíde , Covarrubias Marco , Zambrano Luis
Published: August 2022

Chinampería, a jeopardized precolonial agricultural practice, persists in the Xochimilco wetland, Mexico City. Agroecological chinampa production is a recognized UNESCO World Heritage Site, and contributes to the sustainability of both the urban wetland and the city. The ‘chinampa-refuge’ model (CRM) is a transdisciplinary effort to strengthen traditional agroecological practices and ecological restoration. Through an inter/transdisciplinary research framework, we addressed the model’s role in the sustainability of this socio-ecosystem concerning four significant drivers of the wetland’s transformation. The CRM has improved water quality locally, increased the suitable habitat for native aquatic biodiversity, and supported traditional agroecological practices, thus improving the quality and safety of food products. However, there are clear challenges regarding production and commercialization, some of which may be addressed through the strengthening of the social organization and collective action. However, other challenges are beyond the reach of chinampa producers’ efforts and the CRM, but are decisive in changing the degradation trends. In order to address these challenges, urgent and participatory government actions are needed based on the recognition of the causes of wetland degradation and the role of traditional chinampa production in its sustainability.
Inclusive Assessment and Sustainability Dimensions: Pre-Service Teachers’ Ideas and Knowledge
Authors: Gallego-Noche Beatriz , García-González Esther , Jiménez-Fontana Rocío , Azcárate Pilar
Published: August 2022

The eco-social crisis urges the transformation of teacher training programmes. It is imperative for educators to be able to respond to global challenges. This transformation involves working from new perspectives that integrate assessment processes focused on eco-social justice and inclusion from a broad perspective. Bearing this in mind, a training process focused on the theoretical construction of assessment practice from the perspective of Education for Sustainability and Inclusive Education was designed. It is framed within the context of the Master’s Degree in Educational Research for Teachers’ Professional Development. The study presented, which is of a qualitative and interpretative nature, focuses on knowing the impact of the process in the progression of the participating students’ ideas and knowledge construction. The students’ group work, two questionnaires of a different nature and a teaching journal, were used to collect data. Likewise, an ad hoc analysis system was developed that allowed transforming and interpreting the data obtained. The results show progress in the ideas and knowledge of the students in aspects related to the socio-environmental and ethical dimension of the assessment, integrating the principles of Education for Sustainability and Inclusive Education in the design of assessment activities in the classroom. The results reflect that the developed training process has influenced the complexity of the ideas of the students with regard to the content worked on. The proposal could hence serve as a reference for other training processes that have similar characteristics.
Rural Geosystems’ Future in the Smartphone World: The Inception of Romanian Tourist Sites
Authors: Voda Mihai , Dumitrache Nicoleta Daniela , Negru Radu , Du Qingyun , Gan Chang
Published: August 2022

The present study aims to analyze the role of the advancement of smartphones in rural communities’ redefinition and future development opportunities. This paper explores how smartphone applications are transforming our cultural worlds as they are materially constituted in dynamic living Geosystems and virtually illustrated through collective stereotypical images. This research was carried out in Romania using Geomedia methodology, Google Earth datasets and unstructured interviews in order to assess the specific characteristics of analyzed locations and examine the impacts of technology’s evolution on rural communities. The results are as follows. Firstly, modern smartphone applications are providing the possibility for any rural Geosystem to sustainably adjust to modern technology, conserving its cultural values in the human–computer interaction. Secondly, many remote communities want to learn how to be found by worldly tourists within the touch of a smartphone display, and how to transform their traditional households in exquisite accommodations and share the daily life experiences for their families’ livelihood and communal wellbeing enhancement. Thirdly, there is an important tourism expertise deficiency and a considerable need for the valorization of local tourism potential in rural Romanian communities.
The History of Air Quality in Utah: A Narrative Review
Authors: Mitchell Logan E. , Zajchowski Chris A. B.
Published: August 2022

Utah has a rich history related to air pollution; however, it is not widely known or documented. This is despite air quality being a top issue of public concern for the state’s urban residents and acute episodes that feature some of the world’s worst short-term particulate matter exposure. As we discuss in this narrative review, the relationship between air pollution and the state’s residents has changed over time, as fuel sources shifted from wood to coal to petroleum and natural gas. Air pollution rose in prominence as a public issue in the 1880s as Utah’s urban areas grew. Since then, scientific advances have increased the understanding of air quality impacts on human health, groups of concerned citizens worked to raise public awareness, policy makers enacted legislation to improve air quality, and courts upheld rights to clean air. Utah’s air quality future holds challenges and opportunities and can serve as useful case for other urbanizing regions struggling with air quality concerns. Population growth and changing climate will exacerbate current air quality trends, but economically viable clean energy technologies can be deployed to reduce air pollution, bringing substantial public health and economic benefits to the state’s residents and other settings with similar public health concerns.