bluuebunny/crossref_metadata_2025 huggingface dataset:
Underwater Object Detection Using TC-YOLO with Attention Mechanisms
Authors: Liu Kun , Peng Lei , Tang Shanran
Published: February 2023

Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The proposed TC-YOLO network was developed based on YOLOv5s. Transformer self-attention and coordinate attention were adopted in the backbone and neck of the new network, respectively, to enhance feature extraction for underwater objects. The application of optimal transport label assignment enables a significant reduction in the number of fuzzy boxes and improves the utilization of training data. Our tests using the RUIE2020 dataset and ablation experiments demonstrate that the proposed approach performs better than the original YOLOv5s and other similar networks for underwater object detection tasks; moreover, the size and computational cost of the proposed model remain small for underwater mobile applications.
Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques
Authors: Gundu Sireesha , Syed Hussain
Published: February 2023

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.
Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges
Authors: Kao Szu-Pyng , Chang Yung-Chen , Wang Feng-Liang
Published: February 2023

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.
Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches
Authors: Baloi Aurel , Costea Carmen , Gutt Robert , Balacescu Ovidiu , Turcu Flaviu , Belean Bogdan
Published: February 2023

Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones.
Online SFRA for Reliability of Power Systems: Characterization of a Batch of Healthy and Damaged Induction Motors for Predictive Maintenance
Authors: Bucci Giovanni , Ciancetta Fabrizio , Fioravanti Andrea , Fiorucci Edoardo , Mari Simone , Silvestri Andrea
Published: February 2023

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique’s performance. The results show that the online SFRA could be of interest for monitoring induction motors’ health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.
Ambient Electromagnetic Radiation as a Predictor of Honey Bee (Apis mellifera) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency
Authors: Kulyukin Vladimir A. , Coster Daniel , Tkachenko Anastasiia , Hornberger Daniel , Kulyukin Aleksey V.
Published: February 2023

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, UT, USA. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable.
Aligned Matching: Improving Small Object Detection in SSD
Authors: Kang Seok-Hoon , Park Joon-Sang
Published: February 2023

Although detecting small objects is critical in various applications, neural network models designed and trained for generic object detection struggle to do so with precision. For example, the popular Single Shot MultiBox Detector (SSD) tends to perform poorly for small objects, and balancing the performance of SSD across different sized objects remains challenging. In this study, we argue that the current IoU-based matching strategy used in SSD reduces the training efficiency for small objects due to improper matches between default boxes and ground truth objects. To address this issue and improve the performance of SSD in detecting small objects, we propose a new matching strategy called aligned matching that considers aspect ratios and center-point distance in addition to IoU. The results of experiments on the TT100K and Pascal VOC datasets show that SSD with aligned matching detected small objects significantly better without sacrificing performance on large objects or requiring extra parameters.
An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)
Authors: Rashid Kanwal , Saeed Yousaf , Ali Abid , Jamil Faisal , Alkanhel Reem , Muthanna Ammar
Published: February 2023

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network’s performance has improved because training and testing time do not increase when we include more nodes in the network.
Utilization of Carrier-Frequency Offset Measurements in UWB TDoA Positioning with Receiving Tag
Authors: Krška Josef , Navrátil Václav
Published: February 2023

High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate their position based on differences in the time of arrival of those messages. However, the drift of the tag clock causes systematic errors at a sufficiently high magnitude to effectively deny the positioning, if left uncorrected. Previously, the extended Kalman filter (EKF) has been used to track and compensate for the clock drift. In this article, the utilization of a carrier frequency offset (CFO) measurement for suppressing the clock-drift related error in anchor-to-tag positioning is presented and compared to the filtered solution. The CFO is readily available in the coherent UWB transceivers, such as Decawave DW1000. It is inherently related to the clock drift, since both carrier and timestamping frequencies are derived from the identical reference oscillator. The experimental evaluation shows that the CFO-aided solution performs worse than the EKF-based solution in terms of accuracy. Nonetheless, with CFO-aiding it is possible to obtain a solution based on measurements from a single epoch, which is favorable especially for power-constrained applications.
A Sol-Gel/Solvothermal Synthetic Approach to Titania Nanoparticles for Raman Thermometry
Authors: Pretto Thomas , Franca Marina , Zani Veronica , Gross Silvia , Pedron Danilo , Pilot Roberto , Signorini Raffaella
Published: February 2023

The accurate determination of the local temperature is one of the most important challenges in the field of nanotechnology and nanomedicine. For this purpose, different techniques and materials have been extensively studied in order to identify both the best-performing materials and the techniques with greatest sensitivity. In this study, the Raman technique was exploited for the determination of the local temperature as a non-contact technique and titania nanoparticles (NPs) were tested as nanothermometer Raman active material. Biocompatible titania NPs were synthesized following a combination of sol-gel and solvothermal green synthesis approaches, with the aim of obtaining pure anatase samples. In particular, the optimization of three different synthesis protocols allowed materials to be obtained with well-defined crystallite dimensions and good control over the final morphology and dispersibility. TiO2 powders were characterized by X-ray diffraction (XRD) analyses and room-temperature Raman measurements, to confirm that the synthesized samples were single-phase anatase titania, and using SEM measurements, which clearly showed the nanometric dimension of the NPs. Stokes and anti-Stokes Raman measurements were collected, with the excitation laser at 514.5 nm (CW Ar/Kr ion laser), in the temperature range of 293–323 K, a range of interest for biological applications. The power of the laser was carefully chosen in order to avoid possible heating due to the laser irradiation. The data support the possibility of evaluating the local temperature and show that TiO2 NPs possess high sensitivity and low uncertainty in the range of a few degrees as a Raman nanothermometer material.
Deep Learning-Based Motion Style Transfer Tools, Techniques and Future Challenges
Authors: Akber Syed Muhammad Abrar , Kazmi Sadia Nishat , Mohsin Syed Muhammad , Szczęsna Agnieszka
Published: February 2023

In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators strive to computationally process human motion in a way that the motions appear realistic in animated applications. Motion style transfer is an attractive technique that is widely used to create realistic motions in near real-time. motion style transfer approach employs existing captured motion data to generate realistic samples automatically and updates the motion data accordingly. This approach eliminates the need for handcrafted motions from scratch for every frame. The popularity of deep learning (DL) algorithms reshapes motion style transfer approaches, as such algorithms can predict subsequent motion styles. The majority of motion style transfer approaches use different variants of deep neural networks (DNNs) to accomplish motion style transfer approaches. This paper provides a comprehensive comparative analysis of existing state-of-the-art DL-based motion style transfer approaches. The enabling technologies that facilitate motion style transfer approaches are briefly presented in this paper. When employing DL-based methods for motion style transfer, the selection of the training dataset plays a key role in the performance. By anticipating this vital aspect, this paper provides a detailed summary of existing well-known motion datasets. As an outcome of the extensive overview of the domain, this paper highlights the contemporary challenges faced by motion style transfer approaches.
Exploiting Mobile Gamification to Foster Physical Activity: A Remotely-Managed Field Study
Authors: Olivas Martinez Giorgio , Orso Valeria , Bettelli Alice , Gamberini Luciano
Published: February 2023

Physical inactivity is a plague for public health, especially in Western Countries. Among the countermeasures, mobile applications promoting physical activity seem particularly promising, thanks to the spread and adoption of mobile devices. However, the dropout rates of users are high, thereby calling for strategies to increase retention rates. Moreover, user testing can be problematic, because it is typically conducted in a laboratory, leading to a limited ecological validity. In the present research, we developed a custom mobile app to promote physical activity. Three versions of the app were implemented, each featuring a different pattern of gamification elements. Moreover, the app was designed to work as a self-managed experimental platform. A remote field study was conducted to investigate the effectiveness of the different versions of the app. Behavioral log data of physical activity and interaction with the app were collected. Our results show the feasibility of using a mobile app running on personal devices as an independently managed experimental platform. Moreover, we found that gamification elements per se do not ensure higher retention rates, rather it emerged that the richer combination of gamified elements was effective.
Evolution of Portable Sensors for In-Vivo Dose and Time-Activity Curve Monitoring as Tools for Personalized Dosimetry in Molecular Radiotherapy
Authors: Strigari Lidia , Marconi Raffaella , Solfaroli-Camillocci Elena
Published: February 2023

Treatment personalization in Molecular Radiotherapy (MRT) relies on pre- and post-treatment SPECT/ PET-based images and measurements to obtain a patient-specific absorbed dose-rate distribution map and its evolution over time. Unfortunately, the number of time points that are available per patient to investigate individual pharmacokinetics is often reduced by limited patient compliance or SPECT or PET/CT scanner availability for dosimetry in busy departments. The adoption of portable sensors for in-vivo dose monitoring during the entire treatment could improve the assessment of individual biokinetics in MRT and, thus, the treatment personalization. The evolution of portable devices, non-SPECT/PET-based options, already used for monitoring radionuclide activity transit and accumulation during therapy with radionuclides (i.e., MRT or brachytherapy), is presented to identify valuable ones, which combined with conventional nuclear medicine imaging systems could be effective in MRT. External probes, integration dosimeters and active detecting systems were included in the study. The devices and their technology, the range of applications, the features and limitations are discussed. Our overview of the available technologies encourages research and development of portable devices and dedicated algorithms for MRT patient-specific biokinetics study. This would represent a crucial advancement towards personalized treatment in MRT.
Influences of CNT Dispersion Methods, W/C Ratios, and Concrete Constituents on Piezoelectric Properties of CNT-Modified Smart Cementitious Materials
Authors: Jannat Tofatun , Huang Ying , Zhou Zhi , Zhang Dawei
Published: February 2023

In order to achieve effective monitoring of concrete structures for sound structural health, the addition of carbon nanotubes (CNTs) into cementitious materials offers a promising solution for fabricating CNT-modified smart concrete with self-sensing ability. This study investigated the influences of CNT dispersion method, water/cement (W/C) ratio, and concrete constituents on the piezoelectric properties of CNT-modified cementitious materials. Three CNT dispersion methods (direct mixing, sodium dodecyl benzenesulfonate (NaDDBS) and carboxymethyl cellulose (CMC) surface treatment), three W/C ratios (0.4, 0.5, and 0.6), and three concrete constituent compositions (pure cement, cement/sand, and cement/sand/coarse aggregate) were considered. The experimental results showed that CNT-modified cementitious materials with CMC surface treatment had valid and consistent piezoelectric responses to external loading. The piezoelectric sensitivity improved significantly with increased W/C ratio and reduced progressively with the addition of sand and coarse aggregates.
Performance-Enhanced Static Modulated Fourier Transform Spectrometer with a Spectral Reconstruction
Authors: Cho Ju Yong , Lee Seunghoon , Jang Won Kweon
Published: February 2023

A static modulated Fourier transform spectrometer has been noted to be a compact and fast evaluation tool for spectroscopic inspection, and many novel structures have been reported to support its performance. However, it still suffers from poor spectral resolution due to the limited sampling data points, which marks its intrinsic drawback. In this paper, we outline the enhanced performance of a static modulated Fourier transform spectrometer with a spectral reconstruction method that can compensate for the insufficient data points. An enhanced spectrum can be reconstructed by applying a linear regression method to a measured interferogram. We obtain the transfer function of a spectrometer by analyzing what interferogram can be detected with different values of parameters such as focal length of the Fourier lens, mirror displacement, and wavenumber range, instead of direct measurement of the transfer function. Additionally, the optimal experimental conditions for the narrowest spectral width are investigated. Application of the spectral reconstruction method achieves an improved spectral resolution from 74 cm−1 when spectral reconstruction is not applied to 8.9 cm−1, and a narrowed spectral width from 414 cm−1 to 371 cm−1, which are close to the values of the spectral reference. In conclusion, the spectral reconstruction method in a compact static modulated Fourier transform spectrometer effectively enhances its performance without any additional optic in the structure.
RunScribe Sacral Gait Lab™ Validation for Measuring Pelvic Kinematics during Human Locomotion at Different Speeds
Authors: Ruiz-Malagón Emilio J. , García-Pinillos Felipe , Molina-Molina Alejandro , Soto-Hermoso Víctor M. , Ruiz-Alias Santiago A.
Published: February 2023

Optoelectronic motion capture systems are considered the gold standard for measuring walking and running kinematics parameters. However, these systems prerequisites are not feasible for practitioners as they entail a laboratory environment and time to process and calculate the data. Therefore, this study aims to evaluate the validity of the three-sensor RunScribe Sacral Gait Lab™ inertial measurement unit (IMU) in measuring pelvic kinematics in terms of vertical oscillation, tilt, obliquity, rotational range of motion, and the maximum angular rates during walking and running on a treadmill. Pelvic kinematic parameters were measured simultaneously using an eight-camera motion analysis system (Qualisys Medical AB, GÖTEBORG, Sweden) and the three-sensor RunScribe Sacral Gait Lab™ (Scribe Lab. Inc. San Francisco, CA, USA) in a sample of 16 healthy young adults. An acceptable level of agreement was considered if the following criteria were met: low bias and SEE (<0.2 times the between-subject differences SD), almost perfect (r > 0.90), and good reliability (ICC > 0.81). The results obtained reveal that the three-sensor RunScribe Sacral Gait Lab™ IMU did not reach the validity criteria established for any of the variables and velocities tested. The results obtained therefore show significant differences between the systems for the pelvic kinematic parameters measured during both walking and running.
An Analysis into Physical and Virtual Power Draw Characteristics of Embedded Wireless Sensor Network Devices under DoS and RPL-Based Attacks
Authors: Przybocki Patryk , Vassilakis Vassilios G.
Published: February 2023

Currently, within the world, cybercrime is becoming increasingly rampant—often targeting civil infrastructure like power stations and other critical systems. A trend that is being noticed with these attacks is their increased use of embedded devices in denial-of-service (DoS) attacks. This creates a substantial risk to systems and infrastructures worldwide. Threats to embedded devices can be significant, and network stability and reliability can suffer, mainly through the risk of battery draining or complete system hang. This paper investigates such consequences through simulations of excessive loads, by staging attacks on embedded devices. Experimentation within Contiki OS focused on loads placed on physical and virtualised wireless sensor network (WSN) embedded devices by launching DoS attacks and by exploiting the Routing Protocol for Low Power and Lossy Networks (RPL). Results from these experiments were based on the metric of power draw, mainly the percentage increase over baseline and the pattern of it. The physical study relied on the output of the inline power analyser and the virtual study relied on the output of a Cooja plugin called PowerTracker. This involved experiments on both physical and virtual devices, and analysis of the power draws characteristics of WSN devices with a focus on embedded Linux platforms and Contiki OS. Experimental results provide evidence that peak power draining occurs with a malicious-node-to-sensor device ratio of 13-to-1. Results show a decline in power usage with a more expansive 16-sensor network after modelling and simulating a growing sensor network within the Cooja simulator.
Guidelines for Renewal and Securitization of a Critical Infrastructure Based on IoT Networks
Authors: Villar Miguelez Cristina , Monzon Baeza Victor , Parada Raúl , Monzo Carlos
Published: February 2023

Global warming has increased uncertainty regarding managing traditional water supply systems. Unfortunately, there is a need for the smart management of water supply systems. This work aims to design a solution for renewing and securing critical infrastructure that supplies water and provides water purification inside the range of applications of Industry 4.0 for Smart Cities. Therefore, we analyze the renewal requirements and the applicable use cases and propose a solution based on IoT networks for critical infrastructure in the urban environment. We describe the architecture of the IoT network and the specific hardware for securing a water supply and wastewater treatment chain. In addition, the water level control process for the supply chain and the system that ensures the optimal level of chemicals for wastewater treatment are detailed. Finally, we present the guidelines for infrastructure operators to carry out this operation within Industry 4.0, constituting a development framework for future research on the design of Smart Cities.
What Makes People Abroad Satisfied? The Role of Cultural Intelligence, Cultural Identity, and Culture Shock
Authors: Jurásek Miroslav , Wawrosz Petr
Published: February 2023

If an individual who lives abroad feels satisfied, they are able to develop their ability to function in the new culture. The satisfaction with life abroad depends on cultural intelligence (CQ): people with higher CQ easily adapt to a new culture. Our article seeks to understand the relationship between CQ and satisfaction with life abroad using two new variables—culture shock and cultural identity. The direct and indirect relationships between all variables have been investigated using a sample of 194 predominantly international students studying at Czech universities and the PLS SEM statistical technique. The results have shown that CQ is a positive predictor of dependent variable satisfaction with life abroad. When two mediators—cultural identity and culture shock—were added, we found out that the CQ is a positive predictor of the former and a negative predictor of the latter. The analysis of multiple mediations has further confirmed the indirect influence of culture shock (but not cultural identity) on the direct relationship between cultural intelligence and satisfaction with life.
Vio-lense: A Model for Understanding How Violence and Senses Relate during Refugee Journeys in Europe, and How This in Turn Can Foster Collective Healing
Authors: Lewis Georgina
Published: February 2023

Introduction: The ‘senses’—our sight, sound, taste, smell, and touch—are intrinsic components of our human experience. Trauma recovery practitioners afford the senses to foster healing and draw into awareness the sensations of the traumatised body. Therefore, if the senses are valuable in trauma recovery, then they must also be important in the initial traumatic acts—such as violence. Aim: This ongoing PhD project explores the role of senses in violence committed towards refugees and asylum seekers in Europe. Method: A sensorial model is developed through semi-structured interviews and key engagement with literature and research. Results: An excess and absence of senses are critical in understanding violence endured by refugees and asylum seekers, but also in how violence is witnessed and perceived by others. It is clear people on the move experience violence sensorially throughout their journey. Conclusions: The senses are evidently part of the violence. At times, violence is in sensorial excess: the sounds of camps; the smell of tear gas. Alternatively, a concealment or deprivation of senses can also be violent: camps on the periphery; violence out of sight. Notably, senses (in excess or through concealment) can also be vital components in the survival of violence. “Vio-lense” is a suggested model for considering ‘violence’ and ‘senses’ as being essentially interwoven rather than separate. This, in turn, is important for development of collective healing mechanisms and, more widely, defining and understanding violence.
Assessment of Forest Ecosystem Services in Burabay National Park, Kazakhstan: A Case Study
Authors: Kitaibekova Sara , Toktassynov Zhailau , Sarsekova Dani , Mohammadi Limaei Soleiman , Zhilkibayeva Elmira
Published: February 2023

The issues of forestry and the economic assessment of ecosystem services of forests, in the example of the national park “Burabay” in Kazakhstan, located in the northern part of the country, are investigated in this study. The relevance of forest ecosystem services, such as carbon fixation, oxygen emission, soil conservation from erosion and precipitation redistribution, are important environmental factors that contribute to the conservation of natural capital. Studies by domestic and international scientists show that the value of ecosystem services of forests is much higher than the cost of wood and its material products. Consequently, the ecosystem services of forests should be valued in monetary terms and considered in the context of the national wealth of the country. The main purpose of this research is to establish the value of non-market forest products while considering the prevailing natural and socio-economic conditions. The methods of the ecosystem approach for assessing the above ecosystem services of forests in value terms were implemented, and the real value of the multifunctional value of forests has been revealed. The results obtained can be used in planning measures to improve the sustainability of forests, ecotourism organizations and management decision-making.
External Sulfate Attack of Ambient-Cured One-Part Alkali-Activated Self-Consolidating Concrete
Authors: Kanaan Dima , Soliman Ahmed M. , Safhi Amine el Mahdi
Published: February 2023

The mechanism of sulfate attack on alkali-activated materials, particularly the alkali-activated self-consolidating concrete (AASCC), is complex and contradictory. This could be due to the wide range of precursor and activator materials used in the production of AASCC mixtures, which has called into question the reliability and validity of existing evaluation procedures and practices. This paper presents a systematic research effort on AASCC mixtures, based on granulated blast-furnace slag, prone to various sulfate attack scenarios that are thought necessary to establish a proposed criterion. The conducted experimental design demonstrated that single-, binary-, and ternary-precursor AASCC samples, activated with 1:1 Na2CO3 and MetaNa2SiO3, partially submerged in sodium, magnesium, and mixed sulfate solutions could experience a dual sulfate attack scheme. Sulfate attack can occur in the immersed section in sulfate solutions, while physical sulfate attack can occur in the portion above the solution level. The influence of physical sulfate attack on the concrete’s characteristics was not significant given that the damage was confined to the outer surface. However, the damage was primarily monitored by the AASCC different systems’ pore structure, which resulted in the leaching of ions from samples to solutions. It was found that maintaining the pH in the sulfate solutions increased the rate of damage of AASCC mixtures. Furthermore, binary, and ternary precursor blends partially replacing slag with SF, or FA resulted in decreased porosity, surface scaling, and AASCC deterioration caused by an expansion in the volume of very small diameter pores. Finally, in all AASCC systems, gypsum and ettringite were the primary degradation products of sulfate attack.
Effect of Nitrification Inhibitors on Photosynthesis and Nitrogen Metabolism in ‘Sweet Sapphire’ (V. vinifera L.) Grape Seedlings
Authors: Zhu Jingjing , Dou Feifei , Phillip Fesobi Olumide , Liu Gang , Liu Huaifeng
Published: February 2023

Nitrogen loss after urea application and the low nitrogen utilization rate of plants are major issues in fertilizer application. We therefore adopted a combination of urea and 3,4-dimethylpyrazole phosphate (DMPP) applications in order to investigate the response of DMPP in ‘sweet sapphire’ (V. vinifera L.) grape seedlings growth. Three combinations of DMPP and urea were tested to screen suitable DMPP application concentrations for grape seedlings’ growth. Transcriptome differential expression analysis was adopted to elucidate the regulation mechanism of DMPP. The results showed that the application of DMPP with urea significantly increased grape seedlings’ root dry weight, as well as the above-ground dry weight. The application of DMPP with urea significantly improved the total root length, surface area, volume, and root vigor. The application of urea nitrogen content with 1% of DMPP (T2) showed optimum effects. The application of DMPP can also significantly increase the net photosynthetic rate, photosynthetic pigments, and fluorescence intensity of grape leaves. Furthermore, the transcriptome differential expression analysis under T2 treatment revealed that members of the Nar (7) and NRT (12) gene families were up-regulated, which promotes nitrogen uptake and metabolism. Moreover, the LHC (11), Psa (7), Pet (4), and Psb (5) genes were up-regulated, which promotes photosynthesis.
Relevance and Role of Contemporary Architecture Preservation—Assessing and Evaluating Architectural Heritage as a Contemporary Landscape: A Study Case in Southern Italy
Authors: Ronsivalle Daniele
Published: February 2023

Since WWII to the 2000s, numerous masters of contemporary architecture have contributed to the construction of new landscapes with their works; therefore, these places have become part of a changing landscape and of the multifaceted process of landscape generation. Nevertheless, during this fifty-year period, capitalism has led to the destruction of many existing landscapes, and the policies of protection and preservation have often entailed a process of musealization. In 2000, the European Landscape Convention adopted a new common-grounded definition of landscape, integrating a wide set of cultural approaches and disciplinary topics. Starting from the assumption that contemporary architecture and urban projects can generate high-quality landscapes, this paper investigates the link between the architecture and the landscape, taking the opportunity to catalogue the second half of twentieth-century architecture and urban projects in Sicily as part of the national cataloguing activity “Ereditare il Presente” promoted by the Italian Ministry of Culture. Using the Ministry-proposed cataloguing procedure and adding a quality assessment methodology of buildings and urban projects, this study has produced a theoretical and applicative advancement on how architecture and urban projects of the second half of the twentieth century should be offered as a dynamic component of sustainable human settlement planning under SDG11 “sustainable cities and communities”.
Low-Carbon Community Regeneration in China: A Case Study in Dadong
Authors: Fang Kailun , Azizan Suzana Ariff , Wu Yifei
Published: February 2023

Low carbon sustainability has been addressed in China’s sustainable urbanism strategies as a response the impact of climate change. This study empirically examines the relationship between household factors and carbon emissions in the context of community regeneration through the use of correlational and regression analysis. The participants were drawn from the Dadong community in Guangzhou, China, who has been participating in low-carbon community regeneration. In total, 102 valid questionnaires were obtained from homeowners and the data were analyzed with SPSS and STATA software with an OLS estimate method, checking for BLUE to identify and decide upon the degree of correlation among the variables. The results reveal that family carbon emissions were primarily impacted by house area and income. This study found that larger houses tended to have higher carbon emissions, emphasizing the importance of using low-carbon materials and facilities in community regeneration. Additionally, households with higher incomes tended to have more household appliances, which can contribute to higher carbon emissions and potentially lead to conflicts between different actors involved in low-carbon community regeneration. To reduce household carbon emissions, low-carbon community regeneration develops through interactions and transformations among different actors. However, there has been a lack of research examining the mechanisms underlying the process of low-carbon community regeneration involving the various stakeholders. Using the reference of the actor–network theory (ANT), this research innovatively reveals the mechanisms related to key actors (community government) and multivocal obligatory passage points (OPP) with a synchronous process (problematization, interessement, enrollment, mobilization) through interactions and transformations made by different actors. Finally, the study highlights the need for further research on the low-carbon community or urban regeneration with innovative technological and self-regulation strategies.
The Impact of Digital Financial Inclusion on the Level of Agricultural Output
Authors: Xu Sheng , Wang Jingwen
Published: February 2023

The development of digital inclusive finance has alleviated the problem that traditional finance cannot fully cover rural areas, provided convenient services for Chinese farmers, and solved the problem of “difficult and expensive loans” in agricultural development. This paper used the panel data of Beijing University’s Digital Inclusive Finance Index and 31 provinces and cities in China from 2011 to 2020, and adopted the double-fixed-effect and panel threshold model to study the impact of the development of digital inclusive finance on the level of agricultural output and its internal mechanism. The study found that digital inclusive finance can significantly improve the level of agricultural output, and there is a double threshold for the impact of digital inclusive finance on the level of agricultural output. The heterogeneity analysis showed that the coverage and depth of digital inclusive finance can significantly improve the level of agricultural output, and the depth of use plays a greater role. Digital inclusive finance has significantly improved the level of agricultural output in the midwest regions and major agricultural provinces, but its impact on the eastern regions and non-agricultural provinces is not significant. Finally, the mechanism analysis found that digital inclusive finance can improve the level of agricultural output by promoting the level of agricultural mechanization and improving farmers’ willingness to participate in insurance. Therefore, we should continue to promote the development of digital inclusive finance according to local conditions.
Risk Analysis under a Circular Economy Context Using a Systems Thinking Approach
Authors: AlMashaqbeh Sahar , Munive-Hernandez Jose Eduardo
Published: February 2023

Applying the circular economy (CE) concept is crucial for achieving sustainable development goals. A transition towards a CE requires new tools to clarify the interdependency among systems and assist policy-makers in their decisions, particularly in the risk assessment field. This paper analyzes the systemic effects and interdependencies of several risks in the context of a CE. The developed tool helps adopt proactive strategies that consider the four aspects of sustainability (economic, environmental, social, and technological). The adopted tool improves strategic thinking for a circular economy concept and supports organizations with respect to assessing risks. This paper aims to provide a comprehensive and novel model to quantify the priority weights of the sustainability risk indicators to provide guidelines for supporting the policy formulation process for decision-makers. In this paper, the taxonomy of various risk indicators has been proposed, and we have identified and adopted 40 risk indicators for the CE. This paper focuses on understanding how risks can be constructed and how they affect the performance of power plants over time in terms of availability, efficiency, and operational and maintenance cost. The causal loop diagram (CLD) model is built by deploying various risk quantifications, and the adopted tool was tested and validated to assess the CE risks relevant to the environmental perspective in power plants in the Middle East. The risk indicators under the concept of the CE model and the system thinking approach can help policy-makers in their strategic and operational decision-making process for achieving a better understanding of the risk assessment process. The taxonomy of risk categories and its linking with the system thinking approach will help in the successful and effective implementation of a CE in the energy sector in the long-term. The proposed model offers a tool for policy-makers to design policies when planning a CE.
Sustaining Construction Organisations in NZ: A Linear Regression Model Approach to Analysing Determinants of Their Performance
Authors: Alqudah Hamzah E. , Poshdar Mani , Oyewobi Luqman Oyekunle , Rotimi James Olabode Bamidele , Tookey John
Published: February 2023

The characteristics, strategies, capabilities, and resources of an organisation contribute to its competitive advantage and superior performance. A model to explain performance differences in the New Zealand context will be developed by examining the relationships between construction organisational performance and these constructs. The information was obtained using a questionnaire survey. A total of 101 organisations participated in the research. For the instrument used to elicit data, the literature was used to identify indicators associated with characteristics of organisational strategies for competition, resources and capabilities, and performance of the organisation. Analyses of descriptive, parametric, and linear regression were conducted to examine the effects of these constructs on organisational performance. The results suggest that organisational characteristics are significantly associated with internal business processes, learning, and the growth perspectives of an organisation’s performance, while competitive strategies, resources, and capability perspectives are significantly related to financial perspectives. As a result, these findings add to the current discourse regarding organisational performance differentials in the construction industry. The study demonstrates that it is critical to take into account the different organisational characteristics that are implemented within organisations and how they influence organisational performance beyond rational processes.