bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preventing comorbidity between distress and suicidality: a network analysis
Authors: Junus Alvin , Yip Paul S. F. 
Published: March 2023

AbstractSuicidality among individuals between 10 and 35 years of age may be poised to exert massive burdens on society through decreased economic productivity and increased incidence of chronic physical conditions in the individuals’ later years, thereby necessitating early prevention of suicide. While research suggests that the pathway to suicidality may begin from episodes of psychological distress, such pathway may involve complex interplays between intermediary psychiatric symptoms and external stimuli that are not easily delineated through conventional means. This study applies the network approach to psychopathology to elucidate this complexity. Comorbidity between psychological distress and suicidality in 1968 community-dwelling individuals is analyzed with regularized partial correlation networks to identify their bridge symptoms and links. Temporal relationships between symptoms are analyzed through temporal symptom network formed from 453 individuals who completed subsequent follow-up surveys. Network analysis shows that feelings of hopelessness and the presence of suicidal ideation are the strongest bridge symptoms in the comorbidity symptom network, and form the only prominent link that bridges psychological distress and suicidality. Effects of sleep troubles, anxiety, and poor social relationships on suicidal ideation appear to be mediated by hopelessness. The same observations hold among individuals with and without diagnoses of psychiatric disorders, as well as young people (10–24 year-olds) and young adults (25–35 year-olds). The edge between hopelessness and suicidal ideation remains the strongest bridge link after controlling for effects of symptoms from the previous time point. Findings here provide an evidence base for both professional training in caregiving professions as well as gatekeeper training in community members to emphasize more on how to effectively recognize hopelessness, and instill hope, in young people and young adults for various types of distress.
A review on learning to solve combinatorial optimisation problems in manufacturing
Authors: Zhang Cong , Wu Yaoxin , Ma Yining , Song Wen , Le Zhang , Cao Zhiguang , Zhang Jie 
Published: March 2023

AbstractAn efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state‐of‐the‐art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges.
A dual‐balanced network for long‐tail distribution object detection
Authors: Gong Huiyun , Li Yeguang , Dong Jian 
Published: March 2023

AbstractObject detection on datasets with imbalanced distributions (i.e. long‐tail distributions) dataset is a significantly challenging task. Some re‐balancing solutions, such as re‐weighting and re‐sampling have two main disadvantages. First, re‐balancing strategies only utilise a coarse‐grained global threshold to suppress some of the most influential categories, while overlooking locally influential categories. Second, very few studies have specifically designed algorithms for object detection tasks under long‐tail distribution. To address these two issues, a dual‐balanced network for fine‐grained re‐balancing object detection is proposed. Our re‐balancing strategies are both in proposal and classification logic, corresponding to two sub‐networks, the Balance Region Proposal Network (BRPN) and the Balance Classification Network (BCN). The BRPN sub‐network equalises the number of proposals in the background and foreground by reducing the sampling probability of simple backgrounds, and the BCN sub‐network equalises the logic between head and tail categories by globally suppressing negative gradients and locally fixing the over‐suppressed negative gradients. In addition, the authors advise a balance binary cross entropy loss to jointly re‐balance the entire network. This design can be generalised to different two‐stage object detection frameworks. The experimental mAP result of 26.40% on this LVIS‐v0.5 dataset outperforms most SOTA methods.
Selective feature fusion network for salient object detection
Authors: Sun Fengming , Yuan Xia , Zhao Chunxia 
Published: March 2023

AbstractFully convolutional neural networks have achieved great success in salient object detection, in which the effective use of multi‐layer features plays a critical role. Based on this advantage, many saliency detectors have emerged in recent years, and most of them designed a series of network structures to integrate the multi‐level features generated by the backbone network. However, information in different layer play different roles in saliency object detection, how to integrate them effectively is still a great challenge. In this article, a selective feature fusion network which consists of a selective feature fusion module (SFM) and an attention‐guide hierarchical feature emphasis module (AEM) is proposed. Most of the previous works mainly integrate multi‐level feature by addition and concatenation, as a difference, SFM adaptively selects the important information from the input features in the fusion, which effectively avoids introducing too much redundant information. Besides, AEM combines spatial attention and channel attention to enhance features simply and effectively by hierarchical iteration, and further improve the accuracy of salient object detection. Experiments on five datasets show that the proposed selective feature fusion method achieve satisfactory results when comparing to other state‐of‐the‐art salient object detection approaches.
Review of condition monitoring and defect inspection methods for composited cable terminals
Authors: Li Shuaibing , Cao Binglei , Li Jin , Cui Yi , Kang Yongqiang , Wu Guangning 
Published: March 2023

AbstractComposited cable terminals are critical for reliable power delivery of traditional and traction power supply systems. During its operation, the cable terminal continually undergoes complex circumstances where humidity, temperature, field strength, mechanical stress and other factors continuously change at different levels. In extreme cases, defects or even failures may occur in the cable terminals, leading to catastrophic accidents. This makes the condition monitoring and defect detection of composited cable terminals become an imperative need for the power industry. The “state‐of‐the‐art” technology of cable terminal condition assessment is focussed and a systematic review of the current research progress of cable terminal condition assessment from the operation circumstances to failure mechanisms is provided. It covers both online/offline condition monitoring methods and defects detection approaches. In addition, challenges and future research directions for cable terminal condition assessment are also addressed. It is concluded that the non‐destructive and non‐intrusive methods like terahertz imaging and ultrasonic testing, and multi‐source information fusion methods as well as the digital twin technology have been gaining popularity for cable terminal defect inspection. It is expected the presented work can provide a global field of vision for further advancement of both scholars and industrial engineers in this field.
Design of 3D stacked wideband Wilkinson power divider based on rectangular micro‐coaxial process
Authors: Xu Li‐Jie , Xu Lang , Zhang Cong‐Tian , Wu Wen‐Jie , Zhang Hong‐Lin , Lyu Yunpeng , Li Bo 
Published: March 2023

AbstractBased on transmission line theory, a 4‐way wideband coupled‐line Wilkinson power divider is designed by the authors, operating from 6 to 18 GHz. It is established by using a novel manufacturing technology with 3D stacked micro‐coaxial line, realising attractive advantages of low insertion (IL) loss and compact size. The simulated result shows that the proposed power divider of 23.5 × 14.46 × 0.9 mm obtains a maximum IL of 0.42 dB over the entire operating band. Measured results are obtained by connecting this micro‐coaxial power divider with printed circuit board, showing that the proposed structure is suitable for microwave communication systems.
A new clustering‐based optimised energy approach for fog‐enabled IoT networks
Authors: Essalhi Salah Eddine , Raiss El Fenni Mohammed , Chafnaji Houda 
Published: March 2023

AbstractThe Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.
A power switch open‐circuit fault‐tolerant scheme using dc current injection for dual active bridge converters in cascaded h‐bridge solid‐state transformer
Authors: Zang Jiajie , Wang Jiacheng , Zhang Jianwen , Zhou Jianqiao , Shi Gang , Feng Xin , Zhang Yixin 
Published: March 2023

AbstractDue to its excellent modularity and scalability, the cascaded H‐bridge solid‐state transformer (CHB SST) is a prominent candidate for interconnecting different grids in future hybrid ac/dc distribution systems. However, the CHB SST's large number of power switches are potential sources of faults that can disrupt the system's normal operation. Among other failure modes, a power switch open‐circuit fault (OCF) in a dual active bridge (DAB) module in the SST can result in overcurrent and previously ignored DAB high‐frequency transformer (HFT) saturation issues. The fault is generally addressed by installing redundant modules or additional devices, which would increase the cost, size, and complexity of the system. Based on the OCF analysis presented in this paper, a fault‐tolerant scheme using dc current injection and phase shift ratio adjustment is proposed to maintain the CHB SST operation with maximum remaining bidirectional power transfer capability of the faulty DAB. The overcurrent and HFT saturation issues are also eliminated. The proposed scheme features less cost and volume as compared with module redundancy methods. Moreover, it does not require modifications in the configuration and implementation of the SST. The feasibility and effectiveness of the proposed scheme are verified through a 4.8 kVA CHB SST experimental platform.
Enhancing satellite‐to‐ground communication using quantum key distribution
Authors: Kasliwal Keshav , P N Jayanthi , Jain Adarsh , Bahl Rajesh Kumar 
Published: March 2023

AbstractClassical Cryptography has been in use for a long time. It has been the only way of securing people's communication. However, there are some flaws observed during the execution of classical cryptography. One of them being the staunch belief that the number composed of multiplication of two large prime numbers cannot be factorised easily, which is under a threat thanks to the computational power of the quantum computers. The next flaw is the non‐detection of the hacker, both of which can be eliminated by using quantum mechanical principles for encryption purposes, which is known as quantum cryptography. Quantum Key Distribution, which provides an information‐theoretically safe solution to the key exchange problem, is the most well‐known example of quantum cryptography. The benefit of quantum cryptography is that it makes it possible to perform a variety of cryptographic operations that have been demonstrated or are hypothesised to be impractical when using solely traditional (i.e., non‐quantum) communication. Free‐space quantum communication has been successfully demonstrated across 300 m by the Indian Space Research Organization (ISRO) in March 2021. With this, ISRO is trying to achieve the same using a satellite‐based communication mechanism, which would revolutionise the mode of modern communication. It is justified that the key generation rate depends on factors like the aperture diameter of the sender and receiver, distance between them, the quantum bit error rate, and many more. The results vary with the parameters in the discussion as explained in the upcoming sections. The avenue of different types of losses that occur while transmitting at large distances, such as Atmospheric Loss, Pointing Loss and Geometric Loss, is explored.
Geolocation tracking for human identification and activity recognition using radar deep transfer learning
Authors: Alkasimi Ahmad , Pham Anh‐Vu , Gardner Christopher , Funsten Brad 
Published: March 2023

AbstractHuman identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.
Supernova search with active learning in ZTF DR3
Authors: Pruzhinskaya M. V. , Ishida E. E. O. , Novinskaya A. K. , Russeil E. , Volnova A. A. , Malanchev K. L. , Kornilov M. V. , Aleo P. D. , Korolev V. S. , Krushinsky V. V. , Sreejith S. , Gangler E. 
Published: March 2023

Context. We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys.
Aims. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets.
Methods. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31, 2018 (58 194 ≤ MJD ≤ 58 483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers.
Results. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei.
Conclusions. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert.
Evidence of an age gradient along the line of sight in the nuclear stellar disc of the Milky Way
Authors: Nogueras-Lara F. , Schultheis M. , Najarro F. , Sormani M. C. , Gadotti D. A. , Rich R. M. 
Published: March 2023

Context. The nuclear stellar disc (NSD) is a flat dense stellar structure at the heart of the Milky Way. Recent work has shown that analogous structures are common in the nuclei of external spiral galaxies, where there is evidence of an age gradient that indicates that they form inside-out. However, the characterisation of the age of the NSD stellar population along the line of sight is still missing due to its extreme source crowding and the high interstellar extinction towards the Galactic centre.
Aims. We aim to characterise the age of the stellar population at different average Galactocentric NSD radii to investigate for the first time the presence of an age gradient along the line of sight.
Methods. We selected two groups of stars at different NSD radii via their different extinction and proper motion distribution. We analysed their stellar population by fitting their de-reddened Ks luminosity functions with a linear combination of theoretical models.
Results. We find significant differences in the stellar population at different NSD radii, indicating the presence of an age gradient along the line of sight. Our sample from the closest edge of the NSD contains a significant fraction (∼40% of its total stellar mass) of intermediate-age stars (2–7 Gyr) that is not present in the sample from stars deeper inside the NSD, in which ∼90% of the stellar mass is older than 7 Gyr. Our results suggest that the NSD age distribution is similar to the one found in external galaxies and they imply that bar-driven processes observed in external galaxies are similarly at play in the Milky Way.
Glycyrrhizin and Mannitol Nasal Therapy: Cytological and Clinical Outcomes in Chronic Rhinitis
Authors: Pagliuca Giulio , Clemenzi Veronica , Martellucci Salvatore , Gazia Francesco , Santarsiero Sara , Farina Luigi , Stolfa Andrea , Gallo Andrea 
Published: March 2023

Abstract
Introduction Chronic rhinitis (CR) represents a widespread inflammation with a high incidence in the general population. Although it is generally considered a benign condition, CR has a relevant impact on quality of life and requires a specific treatment approach.
Objective The aim of the present study was to investigate the efficacy of glycyrrhizin and mannitol intranasal treatment on chronic rhinitis using cytological analysis and subjective evaluation of symptoms.
Methods A total of 55 patients suffering from chronic rhinitis were enrolled in the present study, 34 with allergic rhinitis (AR) and 21 with nonallergic rhinitis (NAR). The severity of four different nasal symptoms was determined by using a visual analogue scale (VAS). Specimens obtained by nasal scraping were collected for cytological analysis. Data were acquired before and after a 30-day treatment with glycyrrhizin and mannitol nasal spray. Statistical analyses were performed.
Results The VAS scores for all four nasal symptoms considered in the present study, as well as for neutrophil cells, reduced significantly after therapy in both allergic and nonallergic patients. The number of eosinophils was not significantly lower in nonallergic patients.
Conclusion A 30-day topical treatment with glycyrrhizin and mannitol may improve nasal symptoms and reduce inflammatory cells in the nasal mucosa in patients with chronic rhinitis without significant contraindications. Further studies could support our results and would better clarify all the aspects of this treatment.
Comparative Study Between Single Layer and Double Layer Graft Technique in Tympanoplasty for Subtotal Tympanic Membrane Perforation
Authors: Aziz Ahmed Abdel Rahman Abdel , Hafez Montaser Abdel Salam , Mahmoud Ashraf Gamal , Hamad AbdelMoneim H. 
Published: March 2023

Abstract
Introduction Tympanoplasty is the main treatment of mucosal type of chronic suppurative otitis media.
Objective The aim of the present study was to compare clinical outcomes in terms of healing and audiological outcomes of two groups. The authors used single layer graft (perichondrium with cartilage island) graft in one group and double layer grafts (perichondrium cartilage island composite plus temporalis fascia) were used in the second group.
Methods Forty patients complained of chronic suppurative otitis media safe type with subtotal perforation subdivided into two groups. The first group author used single-layer perichondrium with cartilage island graft (composite graft) while in the second group authors used double graft in the form of perichondrium/cartilage island (composite graft) plus temporalis fascia.
Results There was no significant difference in the mean Air bone gap (ABG) between pre- and post-operative audiometry in subjects who had single layer tympanoplasty. There was a significant difference in the mean ABG between pre- and postoperative audiometry in subjects who had double layer tympanoplasty. There was a significant difference in in the mean ABG differences between subjects who had single layer tympanoplasty and double layer tympanoplasty. Also, there was a significant difference in the healing of the tympanic membrane between subjects who had single layer tympanoplasty and double layer tympanoplasty.
Conclusion Tympanoplasty by double graft (temporalis fascia and tragal cartilage/perichondrium) achieved a considerable improvement in healing of the tympanic membrane ™ with lower risk for residual perforation or graft rejection.