bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles
Authors: Li Fuyang , Wu Zhiguo , Li Jingyu , Lai Zhitong , Zhao Botong , Min Chen
Published: December 2021

This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity. In recent years, target detection models dependent on deep learning and pose estimation methods relying on a single RGB image have made significant progress. Based on these advanced algorithms, this paper proposes a method for measuring the actual angles of landing gears in two-dimensional images. A single RGB image of an aircraft is inputted to the target detection module to obtain the key points of landing gears. The vector field network votes the key points of the fuselage after extraction and scale normalization of the pixels inside the aircraft prediction box. Knowing the pixel position of the key points and the constraints on the aircraft, the angle between the landing gear and fuselage plane can be calculated even without depth information. The vector field loss function is improved based on the distance between pixels and key points, and synthetic datasets of aircraft with different angle landing gears are created to verify the validity of the proposed algorithm. The experimental results show that the mean error of the proposed algorithm for the landing gears is less than 5 degrees on the light-varying dataset.
Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
Authors: Bhattacharyya Susmita
Published: December 2021

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.
SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
Authors: Choi Jaehyeop , Lee Chaehyeon , Lee Donggyu , Jung Heechul
Published: December 2021

Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision.
Dissolved Organic Carbon Source Attribution in the Changjiang Outflow Region of the East China Sea
Authors: Zhang Xiaoyu , Du Yong , Mao Zhihua , Bi Lei , Chen Jianyu , Jin Haiyan , Ma Shuchang
Published: December 2021

The variable optical properties of chromophoric dissolved organic matter (CDOM) under the complicated dynamic marine environment make it difficult to establish a robust inversion algorithm for quantifying the dissolved organic carbon (DOC). To better understand the main factors affecting the relationship between the DOC and the CDOM when the Changjiang diluted water (CDW) interacts with the marine currents on the wide continental shelf, we measured the DOC concentration, the absorption, and the fluorescence spectra of the CDOM along the main axis and the northern boundary of the CDW. The sources of DOC and their impacts on the relationship between the optical properties of the DOC and CDOM are discussed. We reached the following conclusions: There are strong positive correlations between the absorptive and fluorescent properties of the DOC and the CDOM as a whole. The dilution of the terrestrial DOC carried by the CDW through mixing with saline sea water is the dominant mechanism controlling the characteristics of the optical properties of the CDOM. CDOM optical properties can be adopted to establish inversion models in retrieving DOC in Changjiang River Estuary. It is concluded that the introduction of extra DOC from different sources is the main factor causing the regional optical complexity leading to the bias of DOC estimation rather than removal mechanism. As whole, the input of polluted water from Huangpujiang River with abnormally high a(355) and Fs(355) will induce the overestimation of DOC. In the main axis of CDW, the impact from autochthonous DOC input to the correlation between DOC and CDOM can be neglected in comparison with conservative dilution procedure. The relationship between the DOC and the CDOM on the northern boundary of the CDW is more complicated, which can be attributed to the continuous input of terrestrial material from the Old Huanghe Delta by the Subei Coastal Current, the input of materials from the Yellow sea by the Yellow Sea Warm Western Coastal Current, and the input of materials from the Changjiang Basin by the CDW. The results of this study suggest that long-term observations of the regional variations in the DOM inputs from multiple sources in the interior of the CDW are essential, which is conducive to assess the degree of impact to the DOC estimation through the CDOM in the East China Sea.
Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics
Authors: Lee Yoonjae , Jo Minho , Cho Gyoujin , Joo Changbeom , Lee Changwoo
Published: December 2021

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data.
Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging
Authors: Queirós Pokee Diana , Barbosa Pereira Carina , Mösch Lucas , Follmann Andreas , Czaplik Michael
Published: December 2021

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.
Real-Time Detection of Concealed Threats with Passive Millimeter Wave and Visible Images via Deep Neural Networks
Authors: Yang Hao , Zhang Dinghao , Qin Shiyin , Cui Tie Jun , Miao Jungang
Published: December 2021

Passive millimeter wave has been employed in security inspection owing to a good penetrability to clothing and harmlessness. However, the passive millimeter wave images (PMMWIs) suffer from low resolution and inherent noise. The published methods have rarely improved the quality of images for PMMWI and performed the detection only based on PMMWI with bounding box, which cause a high rate of false alarm. Moreover, it is difficult to identify the low-reflective non-metallic threats by the differences in grayscale. In this paper, a method of detecting concealed threats in human body is proposed. We introduce the GAN architecture to reconstruct high-quality images from multi-source PMMWIs. Meanwhile, we develop a novel detection pipeline involving semantic segmentation, image registration, and comprehensive analyzer. The segmentation network exploits multi-scale features to merge local and global information together in both PMMWIs and visible images to obtain precise shape and location information in the images, and the registration network is proposed for privacy concerns and the elimination of false alarms. With the grayscale and contour features, the detection for metallic and non-metallic threats can be conducted, respectively. After that, a synthetic strategy is applied to integrate the detection results of each single frame. In the numerical experiments, we evaluate the effectiveness of each module and the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms the existing methods with 92.35% precision and 90.3% recall in our dataset, and also has a fast detection rate.
The Role of Different Lanthanoid and Transition Metals in Perovskite Gas Sensors
Authors: Alharbi Abdulaziz , Junker Benjamin , Alduraibi Mohammad , Algarni Ahmad , Weimar Udo , Bârsan Nicolae
Published: December 2021

Beginning with LaFeO3, a prominent perovskite-structured material used in the field of gas sensing, various perovskite-structured materials were prepared using sol–gel technique. The composition was systematically modified by replacing La with Sm and Gd, or Fe with Cr, Mn, Co, and Ni. The materials synthesized are comparable in grain size and morphology. DC resistance measurements performed on gas sensors reveal Fe-based compounds solely demonstrated effective sensing performance of acetylene and ethylene. Operando diffuse reflectance infrared Fourier transform spectroscopy shows the sensing mechanism is dependent on semiconductor properties of such materials, and that surface reactivity plays a key role in the sensing response. The replacement of A-site with various lanthanoid elements conserves surface reactivity of AFeO3, while changes at the B-site of LaBO3 lead to alterations in sensor surface chemistry.
Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry
Authors: Li Youwei , Jin Huaiping , Dong Shoulong , Yang Biao , Chen Xiangguang
Published: December 2021

Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes. However, labeled data are often scarce in many real-world applications, which poses a significant challenge when building accurate soft sensor models. Therefore, this paper proposes a novel semi-supervised soft sensor method, referred to as ensemble semi-supervised negative correlation learning extreme learning machine (EnSSNCLELM), for industrial processes with limited labeled data. First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM). Then, with NCLELM as the base learning technique, a multi-learner pseudo-labeling optimization approach is proposed, by converting the estimation of pseudo labels as an explicit optimization problem, in order to obtain high-confidence pseudo-labeled data. Furthermore, a set of diverse semi-supervised NCLELM models (SSNCLELM) are developed from different enlarged labeled sets, which are obtained by combining the labeled and pseudo-labeled training data. Finally, those SSNCLELM models whose prediction accuracies were not worse than their supervised counterparts were combined using a stacking strategy. The proposed method can not only exploit both labeled and unlabeled data, but also combine the merits of semi-supervised and ensemble learning paradigms, thereby providing superior predictions over traditional supervised and semi-supervised soft sensor methods. The effectiveness and superiority of the proposed method were demonstrated through two chemical applications.
Non-Invasive Inspections: A Review on Methods and Tools
Authors: Alotaibi Mubarak , Honarvar Shakibaei Asli Barmak , Khan Muhammad
Published: December 2021

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis.
Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment
Authors: Mohammadi Roozbeh , Roncoli Claudio
Published: December 2021

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.
A Dual-Polarimetric SAR Ship Detection Dataset and a Memory-Augmented Autoencoder-Based Detection Method
Authors: Hu Yuxin , Li Yini , Pan Zongxu
Published: December 2021

With the development of imaging and space-borne satellite technology, a growing number of multipolarized SAR imageries have been implemented for object detection. However, most of the existing public SAR ship datasets are grayscale images under single polarization mode. To make full use of the polarization characteristics of multipolarized SAR, a dual-polarimetric SAR dataset specifically used for ship detection is presented in this paper (DSSDD). For construction, 50 dual-polarimetric Sentinel-1 SAR images were cropped into 1236 image slices with the size of 256 × 256 pixels. The variances and covariance of both VV and VH polarization were fused into R,G,B channels of the pseudo-color image. Each ship was labeled with both a rotatable bounding box (RBox) and a horizontal bounding box (BBox). Apart from 8-bit pseudo-color images, DSSDD also provides 16-bit complex data for readers. Two prevalent object detectors R3Det and Yolo-v4 were implemented on DSSDD to establish the baselines of the detectors with the RBox and BBox respectively. Furthermore, we proposed a weakly supervised ship detection method based on anomaly detection via advanced memory-augmented autoencoder (MemAE), which can significantly remove false alarms generated by the two-parameter CFAR algorithm applied upon our dual-polarimetric dataset. The proposed advanced MemAE method has the advantages of a lower annotation workload, high efficiency, good performance even compared with supervised methods, making it a promising direction for ship detection in dual-polarimetric SAR images. The dataset is available on github.
Aligning Patient’s Ideas of a Good Life with Medically Indicated Therapies in Geriatric Rehabilitation Using Smart Sensors
Authors: Timmermann Cristian , Ursin Frank , Predel Christopher , Steger Florian
Published: December 2021

New technologies such as smart sensors improve rehabilitation processes and thereby increase older adults’ capabilities to participate in social life, leading to direct physical and mental health benefits. Wearable smart sensors for home use have the additional advantage of monitoring day-to-day activities and thereby identifying rehabilitation progress and needs. However, identifying and selecting rehabilitation priorities is ethically challenging because physicians, therapists, and caregivers may impose their own personal values leading to paternalism. Therefore, we develop a discussion template consisting of a series of adaptable questions for the patient–physician encounter based on the capability approach. The goal is to improve geriatric rehabilitation and thereby increase participation in social life and well-being. To achieve this goal, we first analyzed what is considered important for participation on basis of the capability approach, human rights, and ethics of care. Second, we conducted an ethical analysis of each of the four identified dimensions of participation: political, economic, socio-cultural, and care. To improve compliance with rehabilitation measures, health professionals must align rehabilitation measures in an open dialogue with the patient’s aspiration for participation in each dimension. A discussion template based on the capability approach allows for a proactive approach in patient information and stimulates a critical assessment of treatment alternatives while reducing the risk of imposing personal values.
Design of a New Seismoelectric Logging Instrument
Authors: Zhang Liangchen , Ju Xiaodong , Lu Junqiang , Men Baiyong , He Weiliang
Published: December 2021

To increase the accuracy of reservoir evaluation, a new type of seismoelectric logging instrument was designed. The designed tool comprises the invented sonde-structured array complex. The tool includes several modules, including a signal excitation module, data acquisition module, phased array transmitting module, impedance matching module and a main system control circuit, which are interconnected through high-speed tool bus to form a distributed architecture. UC/OS-II was used for the real-time system control. After constructing the experimental measurement system prototype of the seismoelectric logging detector, its performance was verified in the laboratory. The obtained results showed that the consistency between the multi-channel received waveform amplitude and benchmark spectrum was more than 97%. The binary phased linear array transmitting module of the instrument can realize 0° to 20° deflection and directional radiation. In the end, a field test was conducted to verify the tool’s performance in downhole conditions. The results of this test proved the effectiveness of the developed seismoelectric logging tool.
Recent Advances in Aptasensor for Cytokine Detection: A Review
Authors: Kim Jinmyeong , Noh Seungwoo , Park Jeong Ah , Park Sang-Chan , Park Seong Jun , Lee Jin-Ho , Ahn Jae-Hyuk , Lee Taek
Published: December 2021

Cytokines are proteins secreted by immune cells. They promote cell signal transduction and are involved in cell replication, death, and recovery. Cytokines are immune modulators, but their excessive secretion causes uncontrolled inflammation that attacks normal cells. Considering the properties of cytokines, monitoring the secretion of cytokines in vivo is of great value for medical and biological research. In this review, we offer a report on recent studies for cytokine detection, especially studies on aptasensors using aptamers. Aptamers are single strand nucleic acids that form a stable three-dimensional structure and have been receiving attention due to various characteristics such as simple production methods, low molecular weight, and ease of modification while performing a physiological role similar to antibodies.
Assessing Post-Driving Discomfort and Its Influence on Gait Patterns
Authors: Cvetkovic Marko M. , Soares Denise , Baptista João Santos
Published: December 2021

Professional drivers need constant attention during long driving periods and sometimes perform tasks outside the truck. Driving discomfort may justify inattention, but it does not explain post-driving accidents outside the vehicle. This study aims to study the discomfort developed during driving by analysing modified preferred postures, pressure applied at the interface with the seat, and changes in pre- and post-driving gait patterns. Each of the forty-four volunteers drove for two hours in a driving simulator. Based on the walking speed changes between the two gait cycles, three homogeneous study groups were identified. Two groups performed faster speeds, while one reduced it in the post-steering gait. While driving, the pressure at the interface and the area covered over the seat increased throughout the sample. Preferred driving postures differed between groups. No statistical differences were found between the groups in the angles between the segments (flexed and extended). Long-time driving develops local or whole-body discomfort, increasing interface pressure over time. While driving, drivers try to compensate by modifying their posture. After long steering periods, a change in gait patterns can be observed. These behaviours may result from the difficulties imposed on blood circulation by increasing pressure at this interface.
Thermal Drift Correction for Laboratory Nano Computed Tomography via Outlier Elimination and Feature Point Adjustment
Authors: Liu Mengnan , Han Yu , Xi Xiaoqi , Tan Siyu , Chen Jian , Li Lei , Yan Bin
Published: December 2021

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully.
Resource Prediction-Based Edge Collaboration Scheme for Improving QoE
Authors: Park Jinho , Chung Kwangsue
Published: December 2021

Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers’ computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.
Optimized and Validated DBS/MAE/LC–MS Method for Rapid Determination of Date-Rape Drugs and Cocaine in Human Blood Samples—A New Tool in Forensic Analysis
Authors: Stelmaszczyk Paweł , Gacek Ewa , Wietecha-Posłuszny Renata
Published: December 2021

The aim of this work was to develop a new method for the determination of selected substances from the date-rape drugs group: ketamine, benzodiazepines and cocaine. The method is based on the dried blood spot method which seems to be a suitable tool in the analysis of tested substances. The extraction process based on microwave-assisted extraction was optimized to enable optimal conditions for the isolation of a wide range of analytes from blood samples collected on DBS cards. The extraction with ethyl acetate with a buffer of pH = 9 carried out at a temperature of 50 °C for 15 min ensured high extraction efficiency of the tested analytes. The optimized method was validated. Limits of detection (LOD = 4.38–21.1 ng/mL) and quantification (LOQ = 14.6–70.4 ng/mL), inter- and intra-day precision (CV = 1.37–13.4% and 3.39–14.8%, respectively), recovery (RE = 93.0–112.4%) and matrix effect (ME = 98.4–101.6%) were determined. The validation results indicate the possibility of using the proposed method in the analysis of real blood samples collected from victims of sexual assault.
Unpacking the Role of Neoliberalism on the Politics of Poverty Reduction Policies in Ontario, Canada: A Descriptive Case Study and Critical Analysis
Authors: Gill Jessica K.
Published: December 2021

This paper employs a descriptive case study method to analyze and critically review the emergence of the provincial poverty reduction strategy in Ontario, Canada which was implemented in 2008 and renewed in 2014. The purpose of this study is two-fold: first, it defines the principles of neoliberalism and explores the historical growth of neoliberal thought in Canada, and specifically within Ontario, beginning in the 1980s to the present-day. Drawing on a combination of primary, secondary and grey literature, this paper discusses the ways in which neoliberal ideologies and rhetoric became deeply rooted in political thought and discourse within the province. Employing a critical theory framework, the paper highlights the contrasting ways in which neoliberal values were adopted by the different political parties in power and the detrimental impact this espousal had on individuals living in poverty within Ontario. Second, the paper illustrates the powerful ways in which anti-poverty grassroots movements and social advocacy groups assembled to push for the creation of a provincial poverty reduction strategy. The analysis ends with a critique of the neoliberal influences on the strategy’s recommendations and the future outlook of the poverty reduction strategy based on the current political climate within the province.
Turbans vs. Helmets: A Systematic Narrative Review of the Literature on Head Injuries and Impact Loci of Cranial Trauma in Several Recreational Outdoor Sports
Authors: Spennemann Dirk H. R.
Published: December 2021

When in public, faith-based mandates require practising Sikh men to wear a turban which may not be covered by hats or caps. This makes it impossible for practising Sikhs to wear helmets and other protective headwear, mandatory in many countries and facilities for engagement in recreational pursuits (e.g., skiing) and on adventure outdoor recreation camps mandatorily run for school groups. The result is often social exclusion and ostracisation in the case of school children. Despite studies into the efficacy of protective helmets in some recreational outdoor activity settings, virtually nothing is known about the protective potential of turbans. This paper systematically reviews the extant literature on head injuries in several recreational outdoor activities and sports sectors (aerial, water, winter, wheeled and animal-based sports) and finds that the extant literature is of limited value when trying to understand the spatial distribution of trauma on the cranial surface. As the data do not permit to make inferences on the protective potential of turbans, future systematic, evidence-based epidemiological studies derived from hospital admissions and forensic examinations are required. Failure to do so perpetuates social exclusion and discrimination of religious grounds without an evidentiary basis for defensible public health measures.
Effects on Global Forests and Wood Product Markets of Increased Demand for Mass Timber
Authors: Nepal Prakash , Johnston Craig M. T. , Ganguly Indroneil
Published: December 2021

This study evaluated the effects on forest resources and forest product markets of three contrasting mass timber demand scenarios (Conservative, Optimistic, and Extreme), up to 2060, in twelve selected countries in Asia, Europe, North America, and South America. Analyses were carried out by utilizing the FOrest Resource Outlook Model, a partial market equilibrium model of the global forest sector. The findings suggest increases in global softwood lumber production of 8, 23, and 53 million m3 per year by 2060, under the Conservative, Optimistic, and Extreme scenarios, respectively, leading to world price increases of 2%, 7%, and 23%, respectively. This projected price increase is relative to the projected price in the reference scenario, altering prices, production, consumption, trade of forest products, timber harvest, forest growth, and forest stock in individual countries. An increase in softwood lumber prices due to increased mass timber demand would lead to the reduced consumption of softwood lumber for traditional end-use (e.g., light-frame construction), suggesting a likely strong market competition for softwood lumber between the mass timber and traditional construction industries. In contrast, the projected effect on global forest stock was relatively small based on the relatively fast projected biomass growth in stands assumed to be regenerated after harvest.
Why People Settle in Shrinking Communities: A Unified Framework of Psychological, Environmental, and Cognitive Factors
Authors: An Siyi , Aoki Toshiaki , Suzuki Atsushi
Published: December 2021

The interpretation of settlement behavior in shrinking areas can provide insights into sustainability strategies in shrinking communities. However, the choice to settle in areas undergoing shrinkage is hard to interpret when considering residents as rational decisionmakers. To attain a deeper understanding of this decision-making process, a framework of residential decision making (RDM) considering a subjective environmental evaluation dimension, psychological dimension, and cognitive dimension is proposed. This process was further validated by conducting a questionnaire survey in Japanese communities. The results of the structural equation modeling reveal that the RDM framework proposed is applicable to RDM in shrinking communities. By considering geographical differences, we further found that residents in suburban communities tend to consider overall satisfaction with their location when deciding whether to stay, whereas residents in mountainous communities value emotional satisfaction factors such as place attachment when considering continuous residence. Different residential preferences contributing to the formation of RDM factors were also revealed between communities. The results of this study imply that sustainable development strategies to assist shrinking communities should be tailored to their geographical characteristics. Further, a regional design that can enrich residential experiences and neighborhood communication is important for promoting population settlement.
The Effect of the Thermal Annealing Process to the Sensing Performance of Magnetoelastic Ribbon Materials
Authors: Samourgkanidis Georgios , Varvatsoulis Kostantis , Kouzoudis Dimitris
Published: December 2021

The magnetoelastic materials find many practical applications in everyday life like transformer cores, anti-theft tags, and sensors. The sensors should be very sensitive so as to be able to detect minute quantities of miscellaneous environmental parameters, which are very critical for sustainability such as pollution, air quality, corrosion, etc. Concerning the sensing sensitivity, the magnetoelastic material can be improved, even after its production, by either thermal annealing, as this method relaxes the internal stresses caused during manufacturing, or by applying an external DC magnetic bias field during the sensing operation. In the current work, we performed a systematic study on the optimum thermal annealing parameters of magnetoelastic materials and the Metglas alloy 2826 MB3 in particular. The study showed that a 100% signal enhancement can be achieved, without the presence of the bias field, just by annealing between 350 and 450 °C for at least half an hour. A smaller signal enhancement of 15% can be achieved with a bias field but only at much lower temperatures of 450 °C for a shorter time of 20 min. The magnetic hysteresis measurements show that during the annealing process, the material reorganizes itself, changing both its anisotropy energy and magnetostatic energy but in such a way such that the total material energy is approximately conserved.
Efficiency in Chinese Large Yellow Croaker Aquaculture: Implication for Sustainable Aquaculture in China
Authors: Liu Yiyang , Tveteras Sigbjorn , Xie Jinghua
Published: December 2021

Aquaculture supply from China has been a remedy to meet the growing global demand for seafood in the last decades. However, output growth has decreased dramatically in China in the 2000s. Previous literature focuses on the ecosystem problems arising in intensive farming in China. In this study, we used stochastic production analysis (SPA) to estimate the technical efficiency of Chinese large yellow croaker farming, which provides implications for impediments to the sustainable development of Chinese aquaculture. Data were collected from 430 large yellow croaker farmers in nine farming areas located along the coastline of southeastern China. The technical efficiency of large yellow croak farming is estimated to be 0.829, suggesting that farming is operated close to the production frontier with a maximal margin of 17% for improvement under the current technology. It further suggests that Chinese aquaculture growth is geared by conventional factors, expansion of fishing sites, and intensive farming, and is not sustainable under the constraint of farming areas and environmental problems in China. For the sustainable development of Chinese aquaculture, it is necessary to adopt new technology through innovation. The family-based farming model is a hinder to adopting new technology that requires systematic significant investment. Large-scale industrialized farming based on research and new technology development thus should be a modern trend in the future.
Top Management Team Stability and Corporate Social Responsibility: The Moderating Effects of Performance Aspiration Gap and Organisational Slack
Authors: Zheng Qiang , Lin Danming
Published: December 2021

Although studies on the impact of senior executives on corporate social responsibility (CSR) are relatively sufficient, they take a static perspective when investigating the different attitudes of senior executives towards fulfilling social responsibility. Few studies consider the impact of the dynamic changes in a top management team (TMT) on sustainable corporate development, especially social responsibility. We perform regression analysis on 2010–2019 data from Chinese listed firms to examine the relationship between TMT stability and CSR performance and consider the moderating roles of the performance aspiration gap and slack resources. We find that TMT stability has a positive impact on CSR performance and that the performance aspiration gap and slack resources negatively moderate the relationship between TMT stability and CSR performance. This study expands the current literature on the relationship between TMT characteristics and social responsibility, sheds light on what situations can cause agency problems, and provides practical guidance for the sustainable development of a firm and adequate performance of CSR.
Influence of Government Support on Proactive Environmental Strategies in Family Firms
Authors: Benito-Hernández Sonia , López-Cózar-Navarro Cristina , Priede-Bergamini Tiziana
Published: December 2021

Scholars have dedicated significant efforts to understanding the factors that influence the environmental strategy of a firm, in order to mitigate the negative impacts on the ecosystem. Learning more about the factors that encourage environmental behavior allows managers and policy makers to improve action and advance correctly in this direction. Despite this academic interest, the literature regarding family business has undertaken limited attention over the issue. Hence, our paper aims to advance in this line of research, by empirically examining the relationship between government support in family firms and their investment in environmental protection. Specifically, we intend to analyze whether the family nature, and the government financial support, influence their environmental strategy. The empirical analysis is developed with a sample of 1802 manufacturing firms in Spain, using a binary logistic regression to evaluate the existence of dependency relationships between the analyzed variables. The results show this dependency, confirming family nature and government support as significant factors for a proactive environmental strategy, funding the idea that family character positively affects environmental performance in line with the socio-emotional wealth approach, and these differences are greater if the company operates mainly in a local area. Likewise, family firms that receive state direct and indirect funding, invest more in environmental issues to mitigate negative external impacts. The practical implications of the results obtained are especially useful for managers of family businesses as well as for central governments and local institutions as a matter of reflection. To reduce administrative processes and costs for family firms in terms of direct and indirect support is of great importance, as an inadequate process may become a barrier to develop environmental strategies.
An Analysis of the Dynamic Relationship between the Global Macroeconomy and Shipping and Shipbuilding Industries
Authors: Park Sunghwa , Kwon Janghan , Kim Taeil
Published: December 2021

Using time-series data from January 2006 to February 2021, this study analyzed the effect of macroeconomic shocks on the shipping and shipbuilding industries. The Granger causality test, recursive structural vector autoregressive models, impulse response analysis, historical decomposition, and local projections model were used to identify the dynamic relationships between the variables and their dynamic effects, based on the results of the theoretical model and previous research. First, the Granger causality test demonstrated that the macroeconomic variables have causal relations with the shipping and shipbuilding industries. Second, the recursive structural vector autoregressive estimation demonstrated that the direction of the shocks from macroeconomic variables is statistically significantly, consistent with the theoretical model. The same results were found in the recursive structural vector autoregressive model and local projection impulse response analysis. Finally, the historical decomposition identified the main causal variables affecting the shipping and shipbuilding industries by period. These findings can help policymakers, operators of shipping and shipbuilding companies, and investors evaluate and make policy-supporting decisions on industry conditions.
Change Management Indicators and Their Applications in Strengthening the Process and Learning Organization Levels
Authors: Mohd Hamzah Mohd Izham , Abd Rani Nurul Sahadila , Mohd Matore Mohd Effendi Ewan
Published: December 2021

(1) Background: This study is aimed at determining the strengthening process level, the learning organization level, and the indicator of change level among District Education Offices (PPDs’). This study aims to investigate the perceptions of the Head of District Education Officers and their use of change indicators in enhancing District Education Offices and their learning organization practices. (2) Methods: By using a mixed-method approach through an explanatory sequential design, a survey was conducted and 93 Head of District Education Officers throughout Malaysia were selected using the survey method through questionnaire. To identify the respondents, the study employed group sampling and strata random sampling techniques and the data analysis was conducted using the Statistical Packages for Social Sciences software. Then, the qualitative study involved five Head of District Education Officers and two Program Managers who were chosen through purposive and snowball sampling. Through semi-structured interviews, data were then collected, and themes were formulated and analyzed using Nvivo 11. (3) Results: The results of the descriptive analysis show that the strengthening process level, the learning organization level, and the change indicator level in the District Education Office, are all high. Thus, the research findings illuminate six theme of change indicators, i.e., namely, Head of District Education Officers Roles and Responsibilities, Program Manager Roles and Responsibilities, School Improvement Partner+ (SIP+) Roles and Responsibilities and School Improvement Specialist Coaches+ (SISC+) Roles and Responsibilities, Performance Dialogue, Provision Management, Key Performance Indicators (KPI), Dashboard and PPD Excellence Rating. (4) Conclusions: This study offers insights into how the PPDs’ can utilize, as a reference for monitoring, the implemented changes in the District Transformation Program, to see if they are on the right track, and also as treatment to deal with any conflicts or issues in the transformational programme.