bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Characterisation of the Transcription Factor GsWRKY23 Gene from Glycine soja in Overexpressed Soybean Composite Plants and Arabidopsis under Salt Stress
Authors: Sun Shile , Liu Xun , Zhang Tianlei , Yang Hao , Yu Bingjun
Published: August 2023

WRKY proteins are a superfamily of transcription factors (TFs) that play multiple roles in plants’ growth, development, and environmental stress response. In this study, a novel WRKY gene called GsWRKY23 that is specifically upregulated in salt-tolerant Glycine soja accession BB52 seedlings was identified by transcriptomic analysis under salt stress. How the physiological functions and mechanisms of the GsWRKY23 gene affect salt tolerance was investigated using transformations of soybean hairy roots and Arabidopsis, including wild-type (WT) and atwrky23-mutant plants. The results showed that GsWRKY23 in the roots, stems, and leaves of BB52, along with its promoter in the cotyledons and root tips of GsWRKY23pro::GUS Arabidopsis seedlings, displayed enhanced induction under salt stress. GsWRKY23 localises to the nucleus and shows transcriptional activation ability in yeast cells. Compared to GsWRKY23-RNAi wild soybean hairy-root composite plants under salt stress, obvious improvements, such as superior growth appearance, plant height and fresh weight (FW), and leaf chlorophyll and relative water content (RWC), were displayed by GsWRKY23-overexpressing (OE) composite plants. Moreover, their relative electrolytic leakage (REL) values and malondialdehyde (MDA) contents in the roots and leaves declined significantly. Most of the contents of Na+ and Cl− in the roots, stems, and leaves of GsWRKY23-OE plants decreased significantly, while the content of K+ in the roots increased, and the content of NO3− displayed no obvious change. Ultimately, the Na+/K+ ratios of roots, stems, and leaves, along with the Cl−/NO3− ratios of roots and stems, decreased significantly. In the transgenic WT-GsWRKY23 and atwrky23-GsWRKY23 Arabidopsis seedlings, the salt-induced reduction in seed germination rate and seedling growth was markedly ameliorated; plant FW, leaf chlorophyll content, and RWC increased, and the REL value and MDA content in shoots decreased significantly. In addition, the accumulation of Na+ and Cl− decreased, and the K+ and NO3− levels increased markedly to maintain lower Na+/K+ and Cl−/NO3− ratios in the roots and shoots. Taken together, these results highlight the role of GsWRKY23 in regulating ionic homeostasis in NaCl-stressed overexpressed soybean composite plants and Arabidopsis seedlings to maintain lower Na+/K+ and Cl−/NO3− ratios in the roots and shoots, thus conferring improved salt tolerance.
Development of a Boron Nitride-Filled Dental Adhesive System
Authors: Kulanthaivel Senthilguru , Poppen Jeremiah , Ribeiro Cunha Sandra , Furman Benjamin , Whang Kyumin , Teixeira Erica C.
Published: August 2023

There is a dearth of adhesive systems capable of forming stable bonds between restorative materials and tooth surfaces. To address the concern, this study determined the effects of using methacrylate-functionalized boron nitride nanosheets (BNNSs) in a polymeric dental adhesive system. The bisphenol A glycidyl dimethacrylate (BisGMA):2 hydroxyethyl methacrylate (HEMA) (60:40) adhesive monomer blend with a photoinitiator was filled with 0 wt% (control), 0.1 wt%, and 1 wt% BNNSs and light cured. Fourier transform infrared spectroscopy was performed to determine the conversion degree of monomer double bonds (DoC). Water absorption and solubility were measured. Flexural strength and Youngs’s modulus were evaluated to determine the mechanical properties of the composite adhesive system. Finally, dentin bond strength degradation and fracture mode were quantified with a microtensile bond test to confirm the bonding ability of the developed adhesive system. Results showed that the incorporation of BNNSs increased DoC (9.8% and 5.4% for 0.1 and 1 wt%, respectively), but it did not affect water sorption (101.9–119.72 (µg/mm3)), solubility (2.62–5.54 (µg/mm3)), Young’s modulus (529.1–1716.1 MPa), or microtensile bond strength (46.66–54.72 MPa). Further studies are needed with varying BNNS loading percentages from 0.1 wt% to 1 wt% in order to more comprehensively determine the effect of BNNSs on dental adhesives.
Three-Dimensional Printing of Synthetic Core Plugs as an Alternative to Natural Core Plugs: Experimental and Numerical Study
Authors: Cruz-Maya Juan Antonio , Mendoza-de la Cruz José Luis , Martínez-Mendoza Luis Carlos , Sánchez-Silva Florencio , Rosas-Flores José Alfredo , Jan-Roblero Janet
Published: August 2023

This paper proposes three-dimensional (3D) additive fabrication of synthetic core plugs for core flooding experiments from spheres and grains of Berea Sandstone using a digital particle packing approach. Samples were generated by systematically combining the main textural parameters of the rock reservoir to design synthetic core plugs Numerical flow simulation was per-formed using the lattice Boltzmann method (LBM) to verify the flow distribution and permeability for comparison with the experimentally measured permeability and to that obtained from correlations in the literature. The digital porosity of the sample was compared to the porosity measured using an HEP-P helium porosimeter. The numerical and experimental results for permeability and porosity differed by a maximum of 18%.
A Method for Defining Sedimentary Characteristics and Distributions and Its Application in Qinnan Depression, Bohai Bay Basin
Authors: Zhang Zehua , Xu Chunqiang , Wang Chenjie , Li Hong , Zhu Wensen , Wang Hongliang , Liang Kaixuan , Su Yong
Published: August 2023

A new method incorporating geophysical analysis and geological analysis is proposed to define the sedimentary characteristics and distributions in basins with few drilling wells to promote the exploration of reservoirs. This method is applied to a study, through which its principles, closed-loop workflow and technologies are introduced in detail and the sedimentary characteristics and distributions of the study area are accurately defined. During the application process of the method, a compatible geological model is established, based on which the seismic data are interpreted and the results derived from the interpretation are further verified via seismic forward modeling. The study results exhibit a successive sand-rich deposition from the retrogradational gully-filling gravity flow deposition including near-shore fans, slope fans and basin-floor fans delimited by different slope break belts in transgressive sequences to the progradational delta deposition in a retrogressive sequence including braided river deltas with a long extension distance and fan deltas developed along a steep slope belt. And the potential reservoirs are located at the point-out sites of sand bodies with lower average P-wave velocities than those of muddy sediments. The proposition and application of this method are of great significance for oil and gas exploration.
An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function
Authors: Zhang Fangfang , Li Yebin , Shan Dongri , Liu Yuanhong , Ma Fengying
Published: August 2023

Due to the complex underground environment, pumping machines are prone to producing numerous failures. The indicator diagrams of faults are similar to a certain degree, which produces indistinguishable samples. As the samples increase, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. To accurately and quickly judge the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the Rectifying linear unit activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared to the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity and can improve the generalization performance of the deep learning model and the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well-embedded into other neural networks.
The Co-Design of an Embodied Conversational Agent to Help Stroke Survivors Manage Their Recovery
Authors: Richards Deborah , Miranda Maciel Paulo Sergio , Janssen Heidi
Published: August 2023

Whilst the use of digital interventions to assist patients with self-management involving embodied conversational agents (ECA) is emerging, the use of such agents to support stroke rehabilitation and recovery is rare. This iTakeCharge project takes inspiration from the evidence-based narrative style self-management intervention for stroke recovery, the ‘Take Charge’ intervention, which has been shown to contribute to significant improvements in disability and quality of life after stroke. We worked with the developers and deliverers of the ‘Take Charge’ intervention tool, clinical stroke researchers and stroke survivors, to adapt the ‘Take Charge’ intervention tool to be delivered by an ECA (i.e., the Taking Charge Intelligent Agent (TaCIA)). TaCIA was co-designed using a three-phased approach: Stage 1: Phase I with the developers and Phase II with people who delivered the original Take Charge intervention to stroke survivors (i.e., facilitators); and Stage 2: Phase III with stroke survivors. This paper reports the results from each of these phases including an evaluation of the resulting ECA. Stage 1: Phase I, where TaCIA V.1 was evaluated by the Take Charge developers, did not build a good working alliance, provide adequate options, or deliver the intended Take Charge outcomes. In particular, the use of answer options and the coaching aspects of TaCIA V.1 were felt to conflict with the intention that Take Charge facilitators would not influence the responses of the patient. In response, in Stage 1: Phase II, TaCIA V.2 incorporated an experiment to determine the value of providing answer options versus free text responses. Take Charge facilitators agreed that allowing an open response concurrently with providing answer options was optimal and determined that working alliance and usability were satisfactory. Finally, in Stage 2: Phase III, TaCIA V.3 was evaluated with eight stroke survivors and was generally well accepted and considered useful. Increased user control, clarification of TaCIA’s role, and other improvements to improve accessibility were suggested. The article concludes with limitations and recommendations for future changes based on stroke survivor feedback.
Task-Based Configuration Synthesis of an Underactuated Resilient Robot
Authors: Zhang Tan , Zhang Dan , Zhang Wenjun
Published: August 2023

A resilient robot can recover its original function after partial damage of the system. This paper develops an underactuated resilient robot that utilizes a combination of passive joints, active joints, adjustable links, and passive links. A novel method based on the genetic algorithm was proposed to determine the goal configuration of a partially damaged robot. The novelty of the method lies in the integration of both discrete and continuous variables. This model is illustrated by a 3-DOF robot manipulator in the simulation.
Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations
Authors: Zhang Hongwei , Ni Jiacheng , Li Kaiming , Luo Ying , Zhang Qun
Published: August 2023

Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown superior potential in the reconstruction of nonsparse scenes. However, many existing compressed sensing (CS) methods with sparse representation cannot obtain an optimal sparse basis and only apply to the sensing matrix obtained by exact observation, resulting in a low image quality occupying more storage space. To reduce the computational cost and improve the imaging performance of nonsparse scenes, we formulate a deep learning SAR imaging method based on sparse representation and approximated observation deduced from the chirp-scaling algorithm (CSA). First, we incorporate the CSA-derived approximated observation model and a nonlinear transform function within a sparse reconstruction framework. Second, an iterative shrinkage threshold algorithm is adopted to solve this framework, and the solving process is unfolded as a deep SAR imaging network. Third, a dual-path convolutional neural network (CNN) block is designed in the network to achieve the nonlinear transform, dramatically improving the sparse representation capability over conventional transform-domain-based CS methods. Last, we improve the CNN block to develop an enhanced version of the deep SAR imaging network, in which all the parameters are layer-varied and trained by supervised learning. The experiments demonstrate that our proposed two imaging networks outperform conventional CS-driven and deep-learning-based methods in terms of computing efficiency and reconstruction performance of nonsparse scenes.
A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data
Authors: Xu Yi , Wang Tiejun , Skidmore Andrew K. , Gara Tawanda W.
Published: August 2023

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.
Detector Consistency Research on Remote Sensing Object Detection
Authors: Zhang Yuanlin , Jin Haiyan
Published: August 2023

Remote Sensing Image processing is a traditional research field, where RSI object detection is one of the most important directions. This paper focuses on an inherent problem of multi-stage object detection frameworks: the coupling error transmitting problem. In brief, because of the coupling method between the classifier and the regressor, the traditional multi-stage Detection frameworks tend to be fallible when encountering coarse object proposals. To deal with this problem, this article proposes a novel deep learning-based multi-stage object detection framework. Specifically, a novel network head architecture with a multi-to-one coupling method is proposed to avoid the coupling error of the traditional network head architecture. Moreover, it is found that the traditional network head architecture is more efficient than the novel network architecture when encountering fine object proposals. Considering this phenomenon, a proposal-consistent cooperation mechanism between the network heads is proposed. This mechanism makes the traditional network head and the novel network head develop each other’s advantages and avoid the disadvantages. Experiments with different backbone networks on three publicly available data sets have shown the effectiveness of the proposed method since mAP is proposed as 0.7% to 12.3% on most models and data sets.
Combining the Back Propagation Neural Network and Particle Swarm Optimization Algorithm for Lithological Mapping in North China
Authors: Dong Yanqi , Ma Zhibin , Xu Fu , Su Xiaohui , Chen Feixiang
Published: August 2023

Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features.
Fire and Smoke Segmentation Using Active Learning Methods
Authors: Marto Tiago , Bernardino Alexandre , Cruz Gonçalo
Published: August 2023

This work proposes an active learning (AL) methodology to create models for the segmentation of fire and smoke in video images. With this model, a model learns in an incremental manner over several AL rounds. Initially, the model is trained in a given subset of samples, and in each AL round, the model selects the most informative samples to be added to the training set in the next training session. Our approach is based on a decomposition of the task in an AL classification phase, followed by an attention-based segmentation phase with class activation mapping on the learned classifiers. The use of AL in classification and segmentation tasks resulted in a 2% improvement in accuracy and mean intersection over union. More importantly, we showed that the approach using AL achieved results similar to non-AL with fewer labeled data samples.
Optofluidic Sensor Based on Polymer Optical Microresonators for the Specific, Sensitive and Fast Detection of Chemical and Biochemical Species
Authors: Keriel Nolwenn-Amandine , Delezoide Camille , Chauvin David , Korri-Youssoufi Hafsa , Lai Ngoc Diep , Ledoux-Rak Isabelle , Nguyen Chi-Thanh
Published: August 2023

The accurate, rapid, and specific detection of DNA strands in solution is becoming increasingly important, especially in biomedical applications such as the trace detection of COVID-19 or cancer diagnosis. In this work we present the design, elaboration and characterization of an optofluidic sensor based on a polymer-based microresonator which shows a quick response time, a low detection limit and good sensitivity. The device is composed of a micro-racetrack waveguide vertically coupled to a bus waveguide and embedded within a microfluidic circuit. The spectral response of the microresonator, in air or immersed in deionised water, shows quality factors up to 72,900 and contrasts up to 0.9. The concentration of DNA strands in water is related to the spectral shift of the microresonator transmission function, as measured at the inflection points of resonance peaks in order to optimize the signal-over-noise ratio. After functionalization by a DNA probe strand on the surface of the microresonator, a specific and real time measurement of the complementary DNA strands in the solution is realized. Additionally, we have inferred the dissociation constant value of the binding equilibrium of the two complementary DNA strands and evidenced a sensitivity of 16.0 pm/µM and a detection limit of 121 nM.
Electrochemical DNA Biosensors with Dual-Signal Amplification Strategy for Highly Sensitive HPV 16 Detection
Authors: Yang Yuxing , Liao Yazhen , Qing Yang , Li Haiyu , Du Jie
Published: August 2023

Cervical cancer is an important topic in the study of global health issues, ranking fourth among women’s cancer cases in the world. It is one of the nine major cancers that China is focusing on preventing and treating, and it is the only cancer that can be prevented through vaccination. Systematic and effective screening for human papilloma (HPV) infection, which is closely linked to the development of cervical cancer, can reduce cervical cancer incidence and mortality. In this paper, an electrochemical sensor was designed to detect HPV 16 using dual-signal amplification. An APTES-modified glassy carbon electrode was used for improved stability. Gold nanoparticles and a chain amplification reaction were combined for signal amplification. The limit of detection (LOD) of this electrochemical sensor was 1.731 × 10−16 mol/L, and the linear response of the target detector range was from 1.0 × 10−13 mol/L to 1.0 × 10−5 mol/L (R2 = 0.99232). The test of serum sample recovery showed that it has good anti-interference, and the performance of all aspects was improved to different degrees compared with the previous research from the team. The designed sensor is centered around the principles of low cost, high sensitivity and stability, which provides new ideas for the future development of cervical cancer prevention and electrochemical biosensors.
Assessing the Global Cognition of Community-Dwelling Older Adults Using Motor and Sensory Factors: A Cross-Sectional Feasibility Study
Authors: Kostic Emilija , Kwak Kiyoung , Kim Dongwook
Published: August 2023

Impairments in gait, postural stability, and sensory functions were proved to be strongly associated with severe cognitive impairment such as in dementia. However, to prevent dementia, it is necessary to detect cognitive deterioration early, which requires a deeper understanding of the connections between the aforementioned functions and global cognition. Therefore, the current study measured gait, postural, auditory, and visual functions and, using principal component analysis, explored their individual and cumulative association with global cognition. The global cognitive function of 82 older Korean males was determined using the Montreal Cognitive Assessment. The motor and sensory functions were summarized into seven independent factors using factor analysis, followed by age and education-level-adjusted linear regression model analysis. The seven factors obtained using factor analysis were gait speed, gait stability, midstance, general auditory ability, auditory recognition, overall visual ability, and postural stability. The linear regression model included years of education, gait stability, postural stability, and auditory recognition, and was able to explain more than half of the variability in cognitive score. This shows that motor and sensory parameters, which are obtainable through wearable sensors and mobile applications, could be utilized in detecting cognitive fluctuations even in the early stages of cognitive deterioration.
Using System Dynamics Approach to Explore the Mode Shift between Automated Vehicles, Conventional Vehicles, and Public Transport in Melbourne, Australia
Authors: Chen Yilun , Stasinopoulos Peter , Shiwakoti Nirajan , Khan Shah Khalid
Published: August 2023

With the increasing use of automated vehicles (AVs) in the coming decades, government authorities and private companies must leverage their potential disruption to benefit society. Few studies have considered the impact of AVs towards mode shift by considering a range of factors at the city level, especially in Australia. To address this knowledge gap, we developed a system dynamic (SD)-based model to explore the mode shift between conventional vehicles (CVs), AVs, and public transport (PT) by systematically considering a range of factors, such as road network, vehicle cost, public transport supply, and congestion level. By using Melbourne’s Transport Network as a case study, the model simulates the mode shift among AVs, CVs, and PT modes in the transportation system over 50 years, starting from 2018, with the adoption of AVs beginning in 2025. Inputs such as current traffic, road capacity, public perception, and technological advancement of AVs are used to assess the effects of different policy options on the transport systems. The data source used is from the Victorian Integrated Transport Model (VITM), provided by the Department of Transport and Planning, Melbourne, Australia, data from the existing literature, and authors’ assumptions. To our best knowledge, this is the first time using an SD model to investigate the impacts of AVs on mode shift in the Australian context. The findings suggest that AVs will gradually replace CVs as another primary mode of transportation. However, PT will still play a significant role in the transportation system, accounting for 50% of total trips by person after 2058. Cost is the most critical factor affecting AV adoption rates, followed by road network capacity and awareness programs. This study also identifies the need for future research to investigate the induced demand for travel due to the adoption of AVs and the application of equilibrium constraints to the traffic assignment model to increase model accuracy. These findings can be helpful for policymakers and stakeholders to make informed decisions regarding AV adoption policies and strategies.
A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN
Authors: Yang Qiang , Ma Song , Guo Dequan , Wang Ping , Lin Meichen , Hu Yangheng
Published: August 2023

Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.
Morphology of Particulate Structures on a Fiber Array before and at Clogging Point of an Aerosol Filtration Process
Authors: Poggemann Lukas , King Benedikt , Meyer Jörg , Dittler Achim
Published: August 2023

Fundamental microscopic investigations are based on the interaction between filter fiber and particles. The fibrous filter theory is based on a single fiber of a fiber array, and experiments focus either on a single fiber or a fiber array. This study investigates the particle loading process of a fiber array and focuses on the microscopic development of particle structures on the fiber array. Gravimetric measurements and image analysis of the array were used to study the development of the particle structure and morphology. Microscopic analysis of the video data focused on the evolution of particle accumulation within the fiber spacing, the development of boundary lines of the particle structure, and the number/size of bridges and pores within the particle structure. For the tested flow velocities, an increase in mass was observed to increase the duration of the filtration process. An extreme increase in deposited mass was observed at the moment of complete blocking (clogging) of the fiber spacing at flow velocities of 0.65 m/s. The size of pores (hydraulic diameter) increased with the flow velocity during the loading procedure. However, the total number of pores was higher in tests with lower Stokes numbers due to the dendritic and more porous structure. This work provides insights into the growth kinetics and structural setup of the particle structure (on a microscopic level). This will improve the understanding of the change of the filtration process from the transition phase to the clogging phase in fibrous filters.
Psychosocial Traumatic Environments: A Clinical Case of Childhood Abuse
Authors: Amorim-Gaudêncio Carmen , Mendes Gisele Suminski , Silveira Lucas José Bacalhau
Published: August 2023

Traumatic psychosocial environments directly impact adult life. Compromised self-esteem, emotional self-regulation, and interpersonal relationships are observed, and a predisposition to the presence of mental and personality disorders is exacerbated in cases of sexual abuse. Science has presented quantitative and statistical studies on child abuse and a limited number of studies that have investigated the consequences and long-term impacts on the lives of individuals who suffered maltreatment in childhood and did not have access to forms of protection or social support after the identification of child abuse, especially in Brazil. Therefore, this case report is a qualitative study of an ideographic nature which aims to evaluate the “particular” form of abuse repercussions in the life of a 60-year-old woman, giving voice to the victim of child abuse through the process of the investigation and complex analysis of her life history and family relationships. The psychological assessment protocol included a semi-structured interview, active listening, a genogram, and a mental status examination. The results reveal the coexistence of a rigid and violent environment with the presence of physical punishment and child sexual abuse. Self-injurious episodes and suicide attempts began in adolescence. Changes in mood, changes in remote memory, negative thoughts, self-injurious behavior, suicidal ideation and attempts, clinical comorbidities, a lack of interest in romantic relationships, and the repetition of behaviors that perpetuate the cycle of abuse and violence (physical, psychological, and patrimonial) were identified. The diagnostic criteria for bipolar disorder with dependent and borderline personality traits have been established. The case requires a multi-disciplinary follow-up with the use of medication to control and remit the symptoms.
IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid
Authors: Qayyum Faiza , Jamil Harun , Iqbal Naeem , Kim Do-Hyeun
Published: August 2023

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.
Narrow-Linewidth Pr:YLF Laser for High-Resolution Raman Trace Gas Spectroscopy
Authors: Arachchige Charuka Muktha , Muller Andreas
Published: August 2023

Spontaneous Raman gas spectroscopy, which stands out as a versatile chemical identification tool, typically relies on frequency-doubled infrared laser sources to deliver the high power and narrow linewidth needed to achieve chemical detection at trace concentrations. The relatively low efficiency and high complexity of these lasers, however, can make them challenging to integrate into field-deployable instruments. Additionally, the frequency doubling prevents the utilization of circulating laser power for Raman enhancement. A diode-pumped Pr:YLF laser was investigated as an alternative narrow-band light source that could potentially realize a more portable Raman scattering system. When operated with an intracavity etalon, the laser realized a linewidth of 0.5 cm−1 with a green output power of 0.37 W and circulating power of 16 W when pumped with 3.1 W from a blue diode laser. Trace detection at atmospheric pressure with a high degree of spectral discrimination was demonstrated by resolving overlapping N2/CO and CO2/N2O Raman bands in air.
The In-Hexagon Borehole Layout for the Optimization of the Effective Radius of Gas Extraction
Authors: Zhang Luwei , Chu Yanyu , Zhou Yong , Ren Gaofeng , Ge Yongxiang , Liu Jun
Published: August 2023

This research was conducted in order to study the relationship between gas geology (initial gas pressure, initial permeability, and buried depth) and effective extraction radius and to achieve precise borehole layouts. Based on the in-hexagon borehole layout mode, the influence of geological factors on borehole effective extraction radius is quantitatively analyzed. Combined with gas geology, the precise borehole layout mode of gas extraction is constructed. The results show that: Based on the two evaluation indexes of borehole number and area redundancy rate, the optimal implementation scheme of the in-hexagon is selected; that is, when the effective extraction radius is R, the borehole spacing along the coal seam strike is 3R, and along the dip is 1.5R. Based on the four evaluation indexes of effective extraction space volume, relative gas emission, cost rate, and gas isobaric surface shape, the relationship between effective extraction radius and initial gas pressure, permeability, and burial depth is matched quantitatively. The effective extraction radius decreases with the initial gas pressure and buried depth and increases with the initial permeability. The effective extraction radius and initial gas pressure have a linear relationship R=aP+b, the effective extraction radius and initial permeability have a power function relationship R=akb, and the effective extraction radius and burial depth have a negative exponential relationship R =ae−bH. The response surface interaction model analysis shows that the buried depth has the strongest influence on the effective radius of gas extraction, followed by the initial gas pressure and the initial permeability. Based on the effective extraction radius as a function of gas geology, the precise borehole layout mode of gas extraction is constructed, which can provide a reference for the construction design of underground gas drilling in coal mines. This will provide a technical guarantee for the efficient mining of gas and promote the sustainable development of gas resources.
TomTom Data Applications for the Assessment of Tactical Urbanism Interventions: The Case of Bologna
Authors: Pozzoni Marco , Ceccarelli Giulia , Gorrini Andrea , Manenti Lorenza , Sanfilippo Luigi
Published: August 2023

This work aims to evaluate how a temporary school square implemented in the city of Bologna under the principles of the tactical urbanism approach impacted on vehicular patterns through exploiting TomTom Floating Car Data (FCD) from before and after the intervention. Such data, passively collected by vehicles acting as moving sensors on the network, have been used for the analyses instead of data collected through usual methods. After statistical validation of available datasets through two-tailed paired Student’s t-tests, trend analyses have been performed on sample sizes and speed-related values to detect global variations in the first place, and more thoroughly among clusters of road segments based on graph-calculated distance from the intervention site. Results suggest that traffic flows have been relocated from segments directly affected by the intervention, where a decrease has been registered (−23.87%), towards adjacent streets or segments in a buffer area, which have recorded an increase (+3.51% and +3.50%, respectively), so the phenomenon of traffic evaporation did not take place as opposed to more widespread tactical urbanism interventions described in the literature. OD matrices per 15-min time fractions over the three selected peak time slots have been extracted in order to obtain reliable input data for a future development of traffic microsimulation models. The extraction method is based on least squares optimization problems solving systems of linear equations representing OD flows assigned to the observed link, after selecting a set of k¯ shortest paths through a Path Size Logit (PSL) model. Even though the availability of large amounts of data could not overcome typical underdetermination of the problem, due to the key issue of data dependence among traffic counts, the validation of retrieved matrices returned good results in terms of correlation between observed and estimated link flows. In the few cases where the quality of correlation fell, underlying causes have been investigated and the influence of outliers, amplified by the high fragmentation of the provided road graph, might represent the core problem.
Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network
Authors: Sun Weidong , Zhang Jialu , Mukhtar Yasir , Zuo Lili , Dong Shaohua
Published: August 2023

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline.
Exploring the Carbon-Mitigation Effect of High-Speed Railway and Its Underlying Mechanism
Authors: Gao Yake , Zhang Yawei , Wang Kelly Yujie , Yip Tsz Leung
Published: August 2023

Existing studies on high-speed railway (HSR) and carbon dioxide (CO2) emissions have focused on analyzing their net effect, paying less attention to HSR’s impact mechanism on CO2 emissions. This paper investigates the influence and underlying mechanism of HSR on CO2 emissions. We apply a time-varying difference-in-differences approach to panel data from 283 cities in China between 2003 and 2016 to analyze HSR’s carbon-mitigation effects. A causal three-path mediation model and bootstrap method are utilized to analyze how HSR contributes to CO2 mitigation. The results show that HSR operations have significant carbon-mitigation effects, especially in the eastern regions and large cities of China. Moreover, the carbon-mitigation effect of HSR tends to amplify over time. Mechanism analysis showed that HSR’s carbon-mitigation effects are realized through four pathways: transportation substitution, economic agglomeration, industrial structuring, and technological innovation, with the last one contributing the most. This paper provides practical policy recommendations for the next phase of carbon governance in China.
Logistics and Operations Modelling and Optimization for Sustainable Supply Chain
Authors: Weber Gerhard-Wilhelm , Goli Alireza , Tirkolaee Erfan Babaee
Published: August 2023

Optimization plays an important role in several branches of the engineering sciences [...]
Assessing the Effects of Conservation Tillage and In-Situ Crop Residue Management on Crop Yield and Soil Properties in Rice–Wheat Cropping System
Authors: Leharwan Munish , Kumar Yogesh , Kumar Rakesh , Kumar Saraswat Pankaj , Kumar Raj , Kumar Thaliyil Veetil Arun , Bhattacharjee Subhradip , Kumar Ankush , Kumar Sandeep
Published: August 2023

Rice–wheat cropping system (RWCS) is a dominant agricultural practice in the Indo-Gangetic plains, particularly in the North–Western states of India. The prevalent practice of open burning of rice residue, driven by the need for timely land preparation, poses severe environmental and health consequences, including nutrient loss, greenhouse gas emissions, high concentrations of particulate matter (PM), and disruption of the ecological cycle. This study focuses on implementing effective management practices in the RWCS through tillage-based crop establishment, residue retention, and incorporation methods. The objective is to improve crop yield and its attributes by enhancing soil health properties. A split-plot experimental design was practiced with four different treatments, zero-tillage with manual harvesting (ZT), Happy Seeder with combine harvester (HS), Happy Seeder with Mulcher and combine harvesting, and conventional tillage (CT). By evaluating soil nutrient content, including organic carbon (OC), N, P, and K, at a 0–10 cm depth, the study demonstrates the superiority of the mulcher with Happy Seeder (MHS), which significantly increased soil nutrient levels by 105, 59, 102, and 97%, respectively, compared to conventional tilled broadcasted wheat (CT). Furthermore, the MHS treatment exhibited the highest yield of 56.8 q ha−1, outperforming the yield of 43.6 q ha−1 recorded under conventional tilled broadcasted wheat. These findings underscore the critical role of surface residue retention with MHS in ensuring crop productivity and overall production sustainability of the RWCS in Haryana, India. Moreover, effective rice residue management holds long-term implications for agricultural resilience, farm economics, environmental conservation, and human health. It emphasizes the importance of adopting sustainable practices, prioritizing research efforts, and advocating for policies that ensure the prolonged sustainability and productivity of the RWCS while safeguarding environmental well-being.
Digital Influencers’ Attributes and Perceived Characterizations and Their Impact on Purchase Intentions
Authors: Pereira Manuel Joaquim de Sousa , Cardoso António , Canavarro Ana , Figueiredo Jorge , Garcia Jorge Esparteiro
Published: August 2023

Research into the role of digital influencers in marketing strategies is a rapidly developing area that has attracted the interest of researchers and organizations. In recent years, organizations have become increasingly interested in using digital influencers to promote their brands and disseminate advertising messages with a high impact on their target audience. Digital influencers are beginning to be used as models for sustainable consumption behavior (for example in the fashion, food, and health sectors) by promoting environmental and sustainable values. By promoting sustainable content and disseminating messages of environmental awareness, digital influencers can help achieve the Sustainable Development Goals (SDGs). This study aims to identify the attributes (attitude homophily, physical attractiveness, and social attractiveness) and perceived characterizations (trustworthiness, perceived expertise, and parasocial relationship) of digital influencers and their impact on purchase intention among a sample of Portuguese consumers. It also aims to identify the most relevant types of digital influencers according to their areas of influence (fashion, sports, beauty, and cinema/TV/music) and their impact on purchase intention. For data collection, an online questionnaire was developed and administered to a non-probabilistic convenience sample. Only respondents who had experience purchasing a product or service after watching a YouTuber’s advertisement (screening question) or following or searching for a digital influencer could complete the questionnaire. A total of 243 valid questionnaires were received. The main findings are that the attributes and perceived characterizations of digital influencers have a positive and significant impact on purchase intention. It was also found that digital influencers can enhance shopping experience and credibility, which has a strong impact on consumers’ purchase intentions. In terms of sector, the data show that the most important influencer in the ‘Fashion’ sector is Helena Coelho, in the ‘Sports’ sector is Cristiano Ronaldo, in the ‘Beauty’ sector is Sara Sampaio, and in the ‘Music, TV, Cinema’ sector is Ricardo Araújo Pereira. This study can help companies use digital influencers more effectively in their digital marketing strategies, as credibility, experience, and parasocial relationships have a strong impact on consumers’ purchase intention.