Background Information Research Articles

Research on an Edge Detection Method Applied to Fire Localization on Storage Racks in a Warehouse

When a fire occurs on storage racks in a warehouse, it is not advisable to find the location of the fire point accurately, because there are a large number of goods on the storage rack, and many interference factors such as light will disturb the precise location of the fire. In response to the above problems, and thanks to the high-speed growth of deep learning technology, we propose an edge detection method and apply it in fire locations successfully. We adopt VGG-16 as our backbone and introduce an attention module to suppress background information and eliminate interference. We test the proposed method on our collected dataset, and the results show that our proposed model can extract the shelf edges more completely and locate the fire point accurately. In terms of detection speed, our method can achieve 0.188 s per image, which meets the requirements of real-time detection. Our approach lays a good foundation for the precise extinguishing of fire that occurs on storage racks.

University Students’ Attitudes Affecting Their engrossments in Physical Education Activities in a University in China

Recent advances in technology have dramatically changed students' lifestyles, diverting their attention away from physical activity. This study examines students' attitudes towards physical activity and its impact on engrossments in a Chinese university. Using a description-comparation-correlation study design, a questionnaire was used, adapted from previous studies by Zeng et al. and Khan et al. The sample consisted of selected first-year to third-year students from a Chinese university. Statistical analysis including percentage, weighted mean, standard deviation, T-test, ANOVA, and Pearson correlation were used to interpret the data. Through questionnaires, background information on students' gender, grade, age, and course choices were collected, as well as data on their attitudes and engagement with physical activity. The results showed that students' attitudes towards physical activity showed intermediate levels in terms of motivation, determination and goal setting, and these attitudes significantly influenced how engaged they were in physical activity. The study found that although most students have a moderate level of commitment to physical education activities, the importance, motivation and expectations they place on these activities have a significant impact on their participation. In particular, students' motivation and determination have a strong impact on their engagement in physical activity. In addition, gender, age, grade, and course choice did not significantly differ in student attitudes and engagement. Based on these findings, the study makes recommendations such as strengthening physical education teacher training, adopting interactive games and team-building activities to increase student engagement, and encouraging cooperation and teamwork among students. These strategies aim to improve students' attitudes and participation in physical activity, thereby promoting their physical health and overall development.

Symptom-based survey diagnoses may serve to identify more homogenous sub-groups of fatigue and postviral diseases

ABSTRACT Background and objective A range of diagnostic criteria are applied for the heterogenous patient group with fatigue or postviral fatigue syndromes with or without Post Exertional Malaise (PEM). We explored whether DePaul Symptom Questionnaires (DSQ) based symptoms reported in an open online survey called MECOV, served to identify more homogenous sub-groups. Method Patients living in Norway were invited to participate in an open online survey in 2022. The questionnaire covered diagnostic history, SARS-CoV-2, general health, RAND-36 and DSQ symptoms, treatments and background information. Results 2362 patients responded to the survey. 1904 respondents had Fatigue or Postviral disease. 1026 fulfilled CCC criteria with multi-dimensional PEM and 14 h recovery period or ICC criteria for ME. 384 fulfilled IOM/NICE or CCC criteria with only less rigid PEM, while 494 respondents fulfilled only broad fatigue and Fukuda criteria. Self-reported health status, number of treatments tried, and reported effect of activity-based treatments varied significantly across the three groups. Conclusion DSQ symptom-based survey diagnoses served to identify more homogenous subgroups of patients with Fatigue or Postviral diseases (ME and CFS) and may serve as a valid supplement to standard medical examinations. Symptoms, treatment, management strategies and further research may gain from being tailored to the three sub-groups.

TLTNet: A novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation

Extracting global and local feature information is still challenging due to the problems of retinal blood vessel medical images like fuzzy edge features, noise, difficulty in distinguishing between lesion regions and background information, and loss of low-level feature information, which leads to insufficient extraction of feature information. To better solve these problems and fully extract the global and local feature information of the image, we propose a novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation, which consists of an encoder and a decoder and is connected between the encoder and decoder by a transscale transformer cascade module. Among them, the encoder consists of a local–global transscale transformer module, a multi-head layered transscale adaptive embedding module, and a local context(LCNet) module. The transscale transformer cascade module learns local and global feature information from the first three layers of the encoder, and multi-scale dependent features, fuses the hierarchical feature information from the skip connection block and the channel-token interaction fusion block, respectively, and inputs it to the decoder. The decoder includes a decoding module for the local context network and a transscale position transformer module to input the local and global feature information extracted from the encoder with retained key position information into the decoding module and the position embedding transformer module for recovery and output of the prediction results that are consistent with the input feature information. In addition, we propose an improved cross-entropy loss function based on the difference between the deterministic observation samples and the prediction results with the deviation distance, which is validated on the DRIVE and STARE datasets combined with the proposed network model based on the dual transformer structure in this paper, and the segmentation accuracies are 97.26% and 97.87%, respectively. Compared with other state-of-the-art networks, the results show that the proposed network model has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.

Unveiling Courage

The authors review the children’s book Brave Every Day (2022) by Trudy Ludwig and illustrated by Patrice Barton, providing a brief summary, background information on Ludwig, and potential audiences and instructional strategies.

SREDet: Semantic-Driven Rotational Feature Enhancement for Oriented Object Detection in Remote Sensing Images

Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016).

Mass Attitudes about International Trade Agreements: Positive Messages and the Trans-Pacific Partnership

Abstract From the understanding that the mass public knows little about the benefits of international trade agreements but nonetheless opposes them with opposition representing a problem for domestic ratification, this research note explores if and what messages about the Trans-Pacific Partnership might increase popular support. The experimental results show that providing more background information alone does not increase support, but adding an economic message about how this agreement could lower prices or a security message about how it would help the United States counter China increases support. The economic message also encourages respondents to write a statement to their Senators urging them to vote for ratification. These positive effects appear even within groups that are more opposed, namely less educated citizens and Republicans. Our results address important questions about the formation of trade policy preferences, showing they can be shifted in directions contrary to one's egocentric interests and partisan predispositions.

Diagnosis Ability Evaluation of TMJ Condylar Disorders of Panoramic Radiographs in Senior Dental Students

Introduction: The temporomandibular joint is a unique and complex joint in the human body due to anatomical and functional reasons. The present study was conducted with the aim of investigating the knowledge of the senior dentistry students of Yazd Dental School about temporomandibular joint radiographic changes in panoramic images in 2022-2023. Methods: A cross-sectional descriptive analytical study was conducted with the participation of 59 senior dentistry students. After obtaining the consent of the students and collecting their background information, a questionnaire containing 14 questions was presented to the students with the aim of examining the students' ability to distinguish between normal, dislocation, hypoplasia, hyperplasia, osteophyte and condylar erosion appearance. Finally, the descriptive data were presented in the form of numbers and percentages in the form of a table. Data analysis was done with SPSS version16 and T-test. The significance level of the tests was considered less than 0.05. Results: The results showed that there was no significant difference in students' ability score according to their gender and type of entry (national-international) (P>0.05). The grade of students according to general point average (GPA) showed a significant difference (P<0.05). Conclusion: Based on the results of the study, and considering the importance and high prevalence of TMJ disorders, senior dentistry students in Yazd City did not have acceptable knowledge about radiographic changes in the TMJ joint.

Nexus of essay writing and computer-assisted language learning (CALL) in English language classroom

Purpose This study aims to explore computer-assisted language learning (CALL) activities to improve the English essay writing of Pakistani university students. It highlights the role of CALL in the language proficiency model cognitive academic language proficiency (CALP), which can enable second language learners to engage in more critical thinking skills effectively. Furthermore, it aims to explore English language teachers’ experience about the use of CALL-based activities in English language classrooms or labs. Design/methodology/approach This study followed a pre-post study design to examine whether the structure of an essay and computer-based activities helped to improve English language proficiency among non-native speakers. This design is adopted to investigate the effects of CALL activities on students’ English essays. This study used data from 198 students and after this, four English teachers were interviewed to know their opinions and observations about these CALL-based activities and students’ essay writings. Pre- and post-test data was analysed in percentages and further examined through a t-test. The findings of teachers’ interviews were further analysed through Nvivo software to develop an understanding of research questions through significant themes. Findings The pre-test results confirmed that students’ language proficiency is underdeveloped and informal. Students needed help constructing introductory paragraphs with the reader’s hook, background information, three-point thesis statement and transitional hook. Whereas, post-test results showed that students’ English language proficiency in essay writing was found improved with the use of technological tools and CALL-based activities. Furthermore, it was observed that structure and teacher feedback in essay writing are vital for English proficiency. Originality/value This study benefits English language teachers, administrators, language policymakers and syllabus designers at colleges and universities. The debate over how to improve English language proficiency and academic achievement presents diverse challenges across several countries. Non-native speakers, at an undergraduate level, can significantly improve academically and become proficient in English essay writing with the help of structural guidelines and the use of educational technology. This article demonstrates a two-way process to improve an objective definition of English language development, which is conceivable if the technology is adopted.

TRANSAKSI CRYPTOCURRENCY DITINJAU DALAM PERSPEKTIF HUKUM POSITIF DAN HUKUM ISLAM

The Globalization system has had a huge impact on modern society, especially in the fields of science, technology, information and communication, all of which are developing rapidly. The most famous currency is Bitcoin or cryptocurrency. Because there are now no clear boundaries and no recognition of the existence of cryptocurrencies in Indonesia, these cryptocurrencies are facing a number of polemics regarding their use throughout the world, including in that country. The debate over whether Bitcoin is halal or haram in Islam continues among scholars who study the Koran, hadith, ijma and other Islamic books. One important area of expertise in the world of information security is cryptography. Today, professionals in the fields of computer science and mathematics discovered a new application of encryption that could improve the quality of life of local communities: the buying and selling of digital currencies known as cryptocurrencies. Digital currencies that are not subject to government regulation are known as cryptocurrencies; this does not include official cash. This cryptocurrency is the forerunner to the creation of digital currency which is now known as Bitcoin currency, which can be used like other types of money. And the result of the discussion is that whether scholars allow cryptocurrency or not. This research uses a qualitative method where this method focuses on in-depth observation. Therefore, the use of qualitative methods in this research can produce a study of a phenomenon that is more comprehensive with literature research. Based on the background information provided previously, the author identifies problems that require further investigation. Among other things, what is the meaning of cryptocurrency in the current understanding and context and what is Islamic law regarding the use of cryptocurrency as a transaction tool.

Personal-Level Disposition as Predictors of Infant-Survival Skills among Nursing Mothers in Selected Primary Health Care Facilities in Sokoto State, Nigeria

Background information: Infant mortality is a major public health concern worldwide, especially in developing countries. It was reported that 5.2 million children die before the age of 5 years annually, and 47% of these deaths occur in sub- Saharan Africa alone. The Sustainable Development Goals (SDGs), especially Goal 3, aim to reduce under-five mortality to as low as 25 per 1000 live births by 2030. To achieve this goal, interventions that address the underlying factors contributing to infant mortality are necessary. Maternal health, including maternal knowledge, attitudes, and practices, is a crucial factor influencing infant survival, especially in low- and middle-income countries. This study investigated the Personal-level Disposition Factors Influencing survival skills among Nursing Mothers in Selected PHCs in Nigeria.  Methodology: The study design adopted for this research project was a cross-sectional study. Data were collected from 120 respondents who were conveniently selected from two primary health care facilities in Sokoto state with a semi- structured questionnaire (83% reliability). Data were analyzed using appropriate statistical tools.  Result: The results show that all female participants had similar characteristics at a full glance at some areas. The mean age of respondents was 28.32±6.49 years with an age range of 19 to 41 years of age. The Knowledge responses were grouped on a scale and the results showed that most of the respondents had a high level of knowledge regarding infant survival skills. The overall motivation score was moderate. There were significant correlations (at p < 0.05) between knowledge level (R = -0.201), Motivation (R = - 0.170), and Behavioural skills.  Conclusion: The report suggested that there should be an aggressive use of peer educators to expand rural health workers in areas with poor adoption of good health interventions, and low immunization coverage in Northwest of Nigeria.

Enhancing surgical internship experiences: The potential of a supporting digital curriculum

BackgroundCentralization of care jeopardizes interns' learning experiences and necessitates educational changes. Here we present the development and evaluation of a structured digital curriculum, offered in addition to the clinical internship, to address these challenges. MethodsThe structured digital curriculum was implemented in a the VUmc/Amsterdam UMC surgical internship program in the Netherlands. The curriculum used a modular format built around a skill or clinical condition. Each module included background information, digital elements like e-learnings and interactive vlogs, and self-assessments. From April 1st to June 30th, 2022, we conducted a mixed-methods evaluation comparing interns' experiences between the conventional and digital curriculum through surveys and interviews. ResultsThirty-nine interns (28.1 %) completed the survey, 17 (24.2 %) from the traditional curriculum and 22 (31.9 %) from the structured blended curriculum. Results from the interviews triangulated and complemented survey results. Interns appreciated both curricula (course marks 7.4 ± 2.0 vs. 8.1 ± 1.1, P = 0.207). The intervention cohort specifically appreciated the structured and comprehensive presentation of available study materials, which resulted in a sense of empowerment. ConclusionsIntegrating a structured digital curriculum to support clinical internships provides interns with comprehensive, readily accessible knowledge, refines their understanding of clinical topics, and results in feelings of empowerment. The combination of clinical and digital education ensures adequate exposure to subjects vital for future doctors, even if clinical exposure is limited. Thus, using a structured digital curriculum prepares the intern and helps the internship program to adequately navigate future medical challenges. Key messageCentralization of care jeopardizes interns' learning experiences and necessitates educational changes. A structured digital curriculum can empower interns in this scenario by providing readily accessible knowledge which refines their understanding of clinical topics.

Failure Causes Analysis of Circumferential Cracking on Gathering Pipeline in M Oil and Gas Field

The gathering and transportation pipeline experienced corrosion cracking failure after 2 years of operation. This paper conducted an analysis on the reasons for the pipeline failure by integrating background information on its usage, as well as observations, analyses, and detection of the morphology of failure samples. The results indicated that the fracture originated from the inner wall of the pipeline and extended to the outer wall along the wall thickness until complete fracture occurred. Based on microstructure analysis of the fracture and original microcracks at the top of the pipeline, it was determined that the fracture was a multi-source brittle fracture, spreading in both inter-granular and trans-granular forms with obvious radial quasi-cleavage fractures accompanied by secondary cracks. EDS analysis revealed that the element S was present in all zones related to fracture initiation, spreading, and transient zones. XRD analysis showed that corrosion products on the fracture surface were mainly composed of FeS, indicating the presence of H2S in the service environment leading to sulfide stress corrosion cracking characteristics in line with pipeline failure. It is recommended to confirm the source of H2S in the service medium and test residual stress within the same pipeline for potential risk assessment regarding cracking in other areas.

A power-efficient CMOS image sensor with current-mode 1-bit log-gradient feature extractor for always-on object detection

This paper presents a power-efficient CMOS image sensor (CIS) for always-on object detection by using 1-bit log-gradient feature extraction in analog current domain. Compared with traditional image sensors with high-resolution ADCs, the proposed CIS extracts gradients in ratio form and generates feature maps at 1-bit resolution, which eliminates unnecessary background information and illumination-related data, and reduces the data computation in the later detector. The system containing the proposed CIS and back-end ANN detector is modeled with python and the simulation achieves an accuracy of 93.2 % for person detection. The CIS employs current mode divider and comparator to compute the gradient ratios. An accurate and configurable muti-input and muti-output (MIMO) current divider is proposed, composed of two cascaded modified second generation current controlled conveyors (M-CCCIIs). The proposed CIS was fabricated in a 0.18 μm standard CMOS process and realized a frame rate of 170 fps and an energy efficiency of 337 pJ/pixel in a power consumption of 1.1 mW. Experimental results show that the proposed CIS can realize the function of extracting log-gradients.

Efficient data acquisition and reconstruction for air-coupled ultrasonic robotic NDE

Non-destructive evaluation of complex parts using surface scanning techniques, such as ultrasonic testing and eddy current testing, requires complex manipulation of such sensors to ensure quantitative results. A robotic arm may function as a complex manipulator for surface scanning, controlling the position and tilt between the probe and specimen’s surface. To ensure accuracy in probe manipulation, accurate geometric information of the specimen is required. This article explores a methodology that uses structured light for physical-to-virtual reconstruction, providing submillimeter scale and accurate surface geometries. Reconstruction aids in path planning through a novel ray-triangle intersection array algorithm, establishing movements for the NDE probe to orient itself on the specimen at a constant probe to specimen surface distance, or lift-off. The proposed technique is demonstrated and validated through experimental air-coupled ultrasonic inspection of automotive CFRP composite samples with simulated flaws such as interlaminar delamination. The proposed method employs guided waves and a pitch-catch configuration of air-coupled ultrasonic probes, enabling single-side access scans. A Fanuc 100ib robot arm was used to manipulate the ultrasonic probes along a sample reconstructed with a CR-Scan 01 structured light sensor. The probes were excited at 200khz from a SonoAir system, while also recovering defect vs background information synchronized with the probe’s orientation. Additionally, a framework for potential automation is proposed, with further details to be explored in future works.

Comparative Analysis of the Differences in the Education System in China vs Germany

This paper aims to analyze the education systems of China and Germany. While many papers solely focus on the individual nations' education systems, this study compares both countries. It offers comprehensive background information on both systems and statistical data is provided to show an understanding of their structures. As education has become more prevalent today, many discussions are going on about how it should be received. China and Germany are very distinct countries, not only in their history and culture but also in their educational framework. The study also evaluates the strengths and weaknesses of each education system. Furthermore, this study also explores the factors that shape these systems from different perspectives and how they impact societal values and individual aspirations. Moreover, the discrepancies between both education systems are identified, and a future-oriented solution is proposed. The study realizes the need for cross-cultural communication and mutual learning between both countries. Hence, it encourages an exchange of educational practices.

Intelligent identification system of wild animals image based on deep learning in biodiversity conservation law

This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws.

SRFAD-Net: Scale-Robust Feature Aggregation and Diffusion Network for Object Detection in Remote Sensing Images

The significant differences in target scales of remote sensing images lead to remarkable variations in visual features, posing significant challenges for feature extraction, fusion, regression, and classification. For example, models frequently struggle to capture features of targets across all scales, inadequately consider the weights and importance of features at different scales during fusion, and encounter accuracy limitations when detecting targets of varying scales. To tackle these challenges, we proposes a Scale-Robust Feature Aggregation and Diffusion Network (SRFAD-Net) for remote sensing target detection. This model includes a Scale-Robust Feature Network (SRFN), an Adaptive Feature Aggregation and Diffusion (AFAD) module, and a Focaler-GIoU Loss. SRFN extracts scale-robust features by constructing a multi-scale pyramid. It includes a downsampling (ADown) module that combines the advantages of average pooling and max pooling, effectively preserving background information and salient features. This further enhances the network’s ability to handle targets of varying scales and shapes. The introduced Deformable Attention(DAttention) mechanism captures target features effectively through adaptive adjustment of the receptive field’s shape and size, reducing background clutter and substantially enhancing the model’s performance in detecting distant objects. In the feature fusion stage, we propose the AFAD module, which utilizes a dimension-adaptive perceptual selection mechanism and parallel depthwise convolutional operations to precisely aggregate multi-channel information. It then employs a diffusion mechanism to spread contextual information across various scales, greatly improving the network’s ability to extract and fuse features across multiple scales. For the detection head, we adopt the Focaler-GIoU Loss, leveraging its advantages in handling non-overlapping bounding boxes, effectively alleviating the difficulty of localization caused by scale variations. We have undertaken experiments on two widely utilized aerial target datasets: the Remote Sensing Scene Object Detection Dataset (RSOD) and NWPU VHR-10, which is a high-resolution object detection dataset from Northwestern Polytechnical University. The findings of these experiments clearly illustrate that SRFAD-Net surpasses the performances of mainstream detectors.

Current and Ongoing Developments in Targeting Clostridioides difficile Infection and Recurrence

Clostridioides difficile is a Gram-positive, spore-forming anaerobic bacterial pathogen that causes severe gastrointestinal infection in humans. This review provides background information on C. difficile infection and the pathogenesis and toxigenicity of C. difficile. The risk factors, causes, and the problem of recurrence of disease and current therapeutic treatments are also discussed. Recent therapeutic developments are reviewed including small molecules that inhibit toxin formation, disrupt the cell membrane, inhibit the sporulation process, and activate the host immune system in cells. Other treatments discussed include faecal microbiota treatment, antibody-based immunotherapies, probiotics, vaccines, and violet-blue light disinfection.

Cross-modal collaborative feature representation via Transformer-based multimodal mixers for RGB-T crowd counting

RGB-T crowd counting aims to utilize the thermal source data to compensate for the insufficient RGB feature representation under low-illumination conditions. However, how to efficiently use complementary information from different modalities is still a challenging issue for the target task. Existing CNN-based RGB-T crowd counting methods focus on the cross-modal feature representation via the local fixed-receptive-field convolutional operations and the transformer-based works mainly utilize the mutual attention, failing to realize the global cross-modal feature representation. To this end, this paper designs a multimodal counting network based on the multimodal transformer mixer to realize cross-modal collaborative feature representation. Firstly, we design a Transformer-based multimodal mixer to fully fuse the features from both modalities. And then we utilize the Transformer-based network with the multi-head self-attention layer replaced by the average pooling layer to build the two-stream backbone to extract rich multi-scale feature information of the two modalities. Meanwhile, the features extracted from the mixers are used to enhance the intermediate features to achieve the preservation of crowd detail information and suppression of background information. Finally, we design a pyramid regression head to aggregate the multi-scale feature maps to regress the density maps. Extensive experiments, as well as ablation studies, are conducted on challenging RGB-T crowd counting benchmark datasets (RGBT-CC and DroneRGBT), and the achieved competitive experimental results demonstrate the effectiveness of the proposed method on cross-modal collaborative feature representation for target task.