bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Fusion of Knowledge Graphs into Cognitive Modular Production
Authors: Jaryani Soheil , Yitmen Ibrahim , Sadri Habib , Alizadehsalehi Sepehr
Published: September 2023

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line. Empirical data were collected through a computerized self-administered questionnaire (CSAQ) survey, with a specific emphasis on exploring the potential benefits of incorporating KGs into CMP. The quantitative analysis findings underscore the effectiveness of integrating KGs into CMP, particularly through the utilization of visual representations that depict the relationships between diverse components and subprocesses within a virtual environment. This fusion facilitates the real-time monitoring and control of the physical production process. By harnessing the power of KGs, CMP can attain a comprehensive understanding of the manufacturing process, thereby supporting interoperability and decision-making capabilities within modular production systems in the industrialized building industry.
Molecular Catalysts for OER/ORR in Zn–Air Batteries
Authors: Rebrov Evgeny V. , Gao Peng-Zhao
Published: September 2023

Zn–air batteries are becoming the promising power source for small electronic devices and electric vehicles. They provide a relatively high specific energy density at relatively low cost. This review presents exciting advances and challenges related to the development of molecular catalysts for cathode reactions in Zn–air batteries. Bifunctional electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) play the main role in improving performance of reversible fuel cell and metal–air batteries. The catalyst development strategies are reviewed, along with strategies to enhance catalyst performance by application of magnetic field. Proper design of bifunctional molecular ORR/OER catalysts allows the prolongment of the battery reversibility to a few thousand cycles and reach of energy efficiencies of over 70%.
Myoglobin in Brown Adipose Tissue: A Multifaceted Player in Thermogenesis
Authors: Aboouf Mostafa A. , Gorr Thomas A. , Hamdy Nadia M. , Gassmann Max , Thiersch Markus
Published: September 2023

Brown adipose tissue (BAT) plays an important role in energy homeostasis by generating heat from chemical energy via uncoupled oxidative phosphorylation. Besides its high mitochondrial content and its exclusive expression of the uncoupling protein 1, another key feature of BAT is the high expression of myoglobin (MB), a heme-containing protein that typically binds oxygen, thereby facilitating the diffusion of the gas from cell membranes to mitochondria of muscle cells. In addition, MB also modulates nitric oxide (NO•) pools and can bind C16 and C18 fatty acids, which indicates a role in lipid metabolism. Recent studies in humans and mice implicated MB present in BAT in the regulation of lipid droplet morphology and fatty acid shuttling and composition, as well as mitochondrial oxidative metabolism. These functions suggest that MB plays an essential role in BAT energy metabolism and thermogenesis. In this review, we will discuss in detail the possible physiological roles played by MB in BAT thermogenesis along with the potential underlying molecular mechanisms and focus on the question of how BAT–MB expression is regulated and, in turn, how this globin regulates mitochondrial, lipid, and NO• metabolism. Finally, we present potential MB-mediated approaches to augment energy metabolism, which ultimately could help tackle different metabolic disorders.
Obatoclax Rescues FUS-ALS Phenotypes in iPSC-Derived Neurons by Inducing Autophagy
Authors: Castillo Bautista Cristina Marisol , Eismann Kristin , Gentzel Marc , Pelucchi Silvia , Mertens Jerome , Walters Hannah E. , Yun Maximina H. , Sterneckert Jared
Published: September 2023

Aging is associated with the disruption of protein homeostasis and causally contributes to multiple diseases, including amyotrophic lateral sclerosis (ALS). One strategy for restoring protein homeostasis and protecting neurons against age-dependent diseases such as ALS is to de-repress autophagy. BECN1 is a master regulator of autophagy; however, is repressed by BCL2 via a BH3 domain-mediated interaction. We used an induced pluripotent stem cell model of ALS caused by mutant FUS to identify a small molecule BH3 mimetic that disrupts the BECN1-BCL2 interaction. We identified obatoclax as a brain-penetrant drug candidate that rescued neurons at nanomolar concentrations by reducing cytoplasmic FUS levels, restoring protein homeostasis, and reducing degeneration. Proteomics data suggest that obatoclax protects neurons via multiple mechanisms. Thus, obatoclax is a candidate for repurposing as a possible ALS therapeutic and, potentially, for other age-associated disorders linked to defects in protein homeostasis.
Importance of Early Detection of Wire Syndrome: A Case Series Illustrating the Main Stages of the Clinical Gradient
Authors: Charavet Carole , Israël Nathan , Vives France , Dridi Sophie-Myriam
Published: September 2023

(1) Context and Objective: Wire syndrome (WS) refers to dental displacements which can be qualified as aberrant, unexpected, unexplained, or excessive of teeth still contained by an intact orthodontic retainer wire without detachment or fracture, leading to evolving aesthetic and/or functional consequences, both dental and periodontal. The clinical diagnosis of WS in severe cases is often easy. On the other hand, emerging cases must be detected early to stop this evolutionary process as soon as possible, as well as to effectively manage unwanted dental displacements and associated dento-periodontal tissue repercussions. The aim of this retrospective study was to understand the challenges and importance of early diagnosis, highlight the clinical gradient of WS, and clarify the key elements of diagnosis for many practitioners confronted with this type of problem. (2) Materials and Methods: Three cases of increasing complexity were described: the history of wire syndrome, a description of the key elements of its diagnosis, and the final diagnosis itself. (3) Results: Different types and locations of wire syndrome have been observed, from early form to terminal wire syndrome. The three main stages of the clinical gradient are described as follows. In the first case, wire syndrome starting on tooth 41, called the “X-effect” type, was suspected. X-effect wire syndrome on 21, X-effect wire syndrome on 41, and Twist-effect wire syndrome on 33 were diagnosed in the second case, which can be classified as an intermediate case. In the extreme clinical situation of the last case, severe and terminal wire syndrome on tooth 41, the X-effect type, was observed. (4) Conclusions: This case series presents the main stages of the clinical gradient of WS. Although in the case of early WS it is very difficult to identify and/or differentiate it from movements related to a classical relapse phenomenon, the diagnosis of terminal WS is very easy. The challenge for the practitioner is therefore to detect WS as early as possible to stop the iatrogenic process and propose a personalized treatment depending on the severity of clinical signs. The earlier WS is detected, the less invasive the treatment.
Evaluation of Sleep Behavior and the Use of Sleep Aids among Adults Living in Saudi Arabia: A Cross-Sectional Study
Authors: Aldhafiri Ahmed , Almutairi Nawaf , Alharbi Mohammed , Aloufi Abdullah , Hakeem Abdulaziz , Kattan Abdulmalik , Alzahrani Fahad
Published: September 2023

A negative attitude toward sleep has greatly affected sleep habits. In addition to contributing to physical and metabolic disorders, poor sleep quality may cause emotional disturbances. This study aimed to measure sleep behavior and factors contributing to poor sleep quality in the Madinah region, Saudi Arabia. We also assessed whether the use of sleeping aids improved peoples sleep. Three hundred and ninety-nine adults in the Madinah region of Saudi Arabia participated in this cross-sectional study. Three data domains were collected using an online questionnaire between 30 January and 26 April 2022. In the first domain, the characteristics of participants were discussed. In the second domain, questions about sleep behavior were asked. In the third domain, we examined the types, frequency, and impact of sleep aid use. Out of the 399 participants, 154 (38.59%) reported sleep problems. A total of 64.94% of the 154 participants blamed stress as the leading cause of their sleep disorders, and 74.68% of those with sleep problems reported reduced productivity. Among those who reported having sleep problems, 46.10% used sleep aids, with Panadol night (antihistamine) being the most used, 49.30%, followed by Melatonin at 39.44%. Sleep quality improved by 67.6% among those who used sleep aids. A total of 71.8% of the participants think it is not safe to use sleep aids in the long term. Our findings suggest that sleep problems are a prevalent concern in Madinah, Saudi Arabia, and even though the use of sleep aids improved sleep quality, it should be considered an emerging and important public health objective in Saudi Arabia. Further studies are needed to evaluate sleep quality and the level of sleep aid usage among other Saudi Arabian regions.
Polyalphaolefin Oil/MgO-20 Nanofluids Coating Shows Corrosion Resistance, High Moisture Resistance, and Water Resistance for Electrical and Electronic Equipment
Authors: Son Ha Huu , Long Nguyen Phi , Thanh Nguyen Van , Ngoc Nguyen Thi Hong , Thuy Dang Minh , Pham Le Quoc , Tuynh Luu Van
Published: September 2023

The PAO/MgO nanofluids-based dielectric fluid DF(MgO-20) has significantly increased corrosion resistance as a coating. Electrochemical studies show that the DF(MgO-20) coating has protection efficiency of up to 99% for steel, copper, and aluminum. This coating is capable of providing corrosion protection for steel samples for up to 120 h in salt spray tests, and printed circuit boards (PCBs) for more than 20 days in salt spray tests in accordance with the ASTM B117 standard. The DF(MgO-20) coating fully meets the moisture resistance and fungal resistance standards required by the MIL-1-46058 C standard. The coating also demonstrates water displacement, meeting the requirements of the MIL-PRF-81309G standard. The DF(MgO-20) coating is able to protect electronic equipment working in underwater environment for up to 20 days. The aforementioned outstanding protection properties are achieved thanks to the nanofluid effect of the DF(MgO-20) dielectric fluid with the presence of MgO nano-additives that increase its overflow ability. The coating penetrates deeply and adheres tightly to the metal substrates, helping to separate them from moisturizing agents and corrosive agents. The research results aim to apply this coating to protect electronic equipment working in the tropical marine climate of Vietnam.
Incorporation of Ca, P, Mg, and Zn Elements in Ti-30Nb-5Mo Alloy by Micro-Arc Oxidation for Biomedical Implant Applications: Surface Characterization, Cellular Growth, and Microorganisms’ Activity
Authors: Cardoso Giovana Collombaro , Barbaro Katia , Kuroda Pedro Akira Bazaglia , Imperatori Luca , De Bonis Angela , Teghil Roberto , Curcio Mariangela , Innocenzi Elisa , Grigorieva Victoria Yu. , Vadalà Gianluca , Grandini Carlos Roberto , Rau Julietta V.
Published: September 2023

Micro-arc oxidation (MAO) is a surface modification technique used to improve the surface properties of titanium alloys, such as corrosion, wear resistance, and osseointegration. In addition to promoting the growth of a porous oxide coating on the sample’s surface, it is also possible to incorporate bioactive elements into this coating, such as calcium, phosphorus, and magnesium, as well as elements with antimicrobial action, such as zinc. Thus, this study aimed at the surface modification of the β Ti-30Nb-5Mo alloy by the MAO method, incorporating calcium, phosphorus, magnesium, and zinc to improve osseointegration and promote bactericidal character in the produced coating. The results showed that the porosity, roughness, and crystallinity of the coating tend to increase with increasing Zn concentration in the electrolyte, while the contact angle decreases. The antimicrobial activity was promoted against the E. faecalis and P. aeruginosa bacteria strains and the C. albicans fungus. Incorporating Zn on the surface also did not negatively affect adiposetissue-derived mesenchymal stem cell differentiation, and promoted more significant growth of these cells on the samples’ surface.
Study on the Relationship between Process Parameters and theFormation of GTAW Additive Manufacturing of TC4 Titanium Alloy Using the Response Surface Method
Authors: Liu Huanyu , Feng Tianting , Chen Chao , Chen Hongrui
Published: September 2023

The geometric parameters of the deposited layer include the width, height, and penetration depth of the deposited layer. The welding current, wire feeding speed, and torch travel speed during the additive manufacturing process of TC4 titanium alloy have the greatest impact on the geometric parameters of the deposited layer. In order to study how the deposition layer width, deposition layer height, and penetration depth are affected by the welding current, wire feeding speed, and torch travel speed, this article uses Design Expert 8.0.6 software for Box−Behnken design response surface experiments. During the experimental design, the welding current, wire feeding speed, and torch travel speed are used as input variables. The deposition layer width, deposition layer height, and penetration depth are selected as the responses. We designed 17 response surface experiments that were conducted using GTAW-AM. The results show that as the welding current increases, the penetration depth and width of deposition layer gradually increase, and the deposition layer height gradually decreases. As the wire feeding speed increases, the deposition layer height and penetration depth gradually increase, and the wire feeding speed has a minimal effect on the deposition layer width. As the torch travel speed increases, the penetration depth, width and height of deposition layer gradually decrease. The response surface method experimental design can also optimize the matching of three process parameters: welding current, wire feeding speed, and torch travel speed, thereby obtaining the optimal matching range of process parameters. Within the optimized matching range of process parameters, a welding current of 90 A, a wire feeding speed of 900 mm/min, and a torch travel speed of 200.18 mm/min were selected to prepare TC4 titanium alloy thin-walled part. The microstructure of the top, middle and bottom are all basketweave structure. The α phase gradually becomes coarse from the top to the bottom. The microhardness of the top, middle, and bottom of the thin-walled parts is 362.7 HV, 352.7 HV, and 340.5 HV, respectively. The horizontal tensile strength is 926.1 MPa, with an elongation of 12.22%, and the vertical tensile strength is 938.1 MPa, with an elongation of 14.41%.
A DFT + U Study on the Stability of Small CuN Clusters (N = 3–6 Atoms): Calculation of Phonon Frequencies
Authors: Alcalá-Varilla Luis A. , Ponnefz-Durango Rafael E. , Seriani Nicola , Araujo-Lopez Eduard , Montoya Javier A.
Published: September 2023

Despite the interest in copper clusters, a consensus on their atomic structure is still lacking. The experimental observation of isolated clusters is difficult, and theoretical predictions vary widely. The latter is because one must adequately describe the closed shell of d electrons both in its short- and long-range effects. Herein, we investigate the stability of small copper clusters (CuN, N = 3–6 atoms) using spin-polarized DFT calculations under the GGA approximation, the Hubbard U correction, and the van der Waals forces. We found that the spin-polarized and vdW contributions have little effect on the binding energies of the isomers. The inclusion of U represents the most relevant contribution to the ordering of the CuN isomers, and our calculated binding energies for the clusters agreed with the experimental values. We also found that atomic relaxations alone are not enough to determine the stability of small copper clusters. It is also necessary to build the energy landscape or calculate the vibrational frequencies of the isomers. We found that the vibrational frequencies of the isomers were in the THz range and the normal modes of vibration were discrete. This approach is relevant to future studies involving isolated or supported copper clusters.
The Role of the Spleen and the Place of Splenectomy in Autoimmune Hemolytic Anemia—A Review of Current Knowledge
Authors: Cvetković Zorica , Pantić Nikola , Cvetković Mirjana , Virijević Marijana , Sabljić Nikica , Marinković Gligorije , Milosavljević Vladimir , Pravdić Zlatko , Suvajdžić-Vuković Nada , Mitrović Mirjana
Published: September 2023

Autoimmune hemolytic anemia (AIHA) is a rare, very heterogeneous, and sometimes life-threatening acquired hematologic disease characterized by increased red blood cell (RBC) destruction by autoantibodies (autoAbs), either with or without complement involvement. Recent studies have shown that the involvement of T- and B-cell dysregulation and an imbalance of T-helper 2 (Th2) and Th17 phenotypes play major roles in the pathogenesis of AIHA. AIHA can be primary (idiopathic) but is more often secondary, triggered by infections or drug use or as a part of other diseases. As the location of origin of autoAbs and the location of autoAb-mediated RBC clearance, as well as the location of extramedullary hematopoiesis, the spleen is crucially involved in all the steps of AIHA pathobiology. Splenectomy, which was the established second-line therapeutic option in corticosteroid-resistant AIHA patients for decades, has become less common due to increasing knowledge of immunopathogenesis and the introduction of targeted therapy. This article provides a comprehensive overview of current knowledge regarding the place of the spleen in the immunological background of AIHA and the rapidly growing spectrum of novel therapeutic approaches. Furthermore, this review emphasizes the still-existing expediency of laparoscopic splenectomy with appropriate perioperative thromboprophylaxis and the prevention of infection as a safe and reliable therapeutic option in the context of the limited availability of rituximab and other novel therapies.
Determination of the Diagnostic Performance of Laboratory Tests in the Absence of a Perfect Reference Standard: The Case of SARS-CoV-2 Tests
Authors: Hartnack Sonja , Nilius Henning , Jegerlehner Sabrina , Suter-Riniker Franziska , Bittel Pascal , Jent Philipp , Nagler Michael
Published: September 2023

Background: Currently, assessing the diagnostic performance of new laboratory tests assumes a perfect reference standard, which is rarely the case. Wrong classifications of the true disease status will inevitably lead to biased estimates of sensitivity and specificity. Objectives: Using Bayesian’ latent class models (BLCMs), an approach that does not assume a perfect reference standard, we re-analyzed data of a large prospective observational study assessing the diagnostic accuracy of an antigen test for the diagnosis of SARS-CoV-2 infection in clinical practice. Methods: A cohort of consecutive patients presenting to a COVID-19 testing facility affiliated with a Swiss University Hospital were recruited (n = 1465). Two real-time PCR tests were conducted in parallel with the Roche/SD Biosensor rapid antigen test on nasopharyngeal swabs. A two-test (PCR and antigen test), three-population BLCM was fitted to the frequencies of paired test results. Results: Based on the BLCM, the sensitivities of the RT-PCR and the Roche/SD Biosensor rapid antigen test were 98.5% [95% CRI 94.8;100] and 82.7% [95% CRI 66.8;100]. The specificities were 97.7% [96.1;99.7] and 99.9% [95% CRI 99.6;100]. Conclusions: Applying the BLCM, the diagnostic accuracy of RT-PCR was high but not perfect. In contrast to previous results, the sensitivity of the antigen test was higher. Our results suggest that BLCMs are valuable tools for investigating the diagnostic performance of laboratory tests in the absence of perfect reference standard.
The Value of Ultrasonic Elastography in Detecting Placental Stiffness for the Diagnosis of Preeclampsia: A Meta-Analysis
Authors: Su Shanshan , Huang Yanyan , Luo Weiwen , Li Shaohui
Published: September 2023

This meta-analysis evaluated the diagnostic value of ultrasonic elastography in detecting placental stiffness in the diagnosis of preeclampsia (PE). A systematic search was conducted in the EMBASE, Web of Science, Cochrane Library, Scopus database, and PubMed databases to identify studies published before June 2023 using ultrasonic elastography to diagnose PE. The sensitivity, specificity, and diagnostic odds ratio of ultrasonic elastography for diagnosing PE were calculated, and a summary receiver operating characteristic curve model was constructed. The degree of heterogeneity was estimated using the I2 statistic, and a meta-regression analysis was performed to explore its sources. A protocol was determined previously (PROSPERO: CRD42023443646). We included 1188 participants from 11 studies, including 190 patients with PE and 998 patients without PE as controls. Overall sensitivity and specificity of ultrasonic elastography in detecting placental stiffness for the diagnosis of PE were 89% (95% CI: 85–93) and 74% (95% CI: 51–89), respectively. The I2 values for sensitivity and specificity were 59% (95% CI: 29–89) and 96% (95% CI: 95–98), respectively. The area under the receiver operating characteristic curve was 0.90 (95% CI: 0.87–0.92). The meta-regression analysis showed no significant heterogeneity. Ultrasonic elastography exhibits good diagnostic accuracy for detecting placental stiffness and can serve as a non-invasive tool for differentially diagnosing PE.
Reduced Left Ventricular Twist Early after Acute ST-Segment Elevation Myocardial Infarction as a Predictor of Left Ventricular Adverse Remodelling
Authors: Lazăr Mihai-Andrei , Ionac Ioana , Luca Constantin-Tudor , Petrescu Lucian , Vacarescu Cristina , Crisan Simina , Gaiță Dan , Cozma Dragos , Sosdean Raluca , Arnăutu Diana-Aurora , Cozlac Alina-Ramona , Luca Slivia-Ana , Gurgu Andra , Totorean Claudia , Mornos Cristian
Published: September 2023

Background: The left ventricular (LV) remodelling process represents the main cause of heart failure after a ST-segment elevation myocardial infarction (STEMI). Speckle-tracking echocardiography (STE) can detect early deformation impairment, while also predicting LV remodelling during follow-up. The aim of this study was to investigate the STE parameters in predicting cardiac remodelling following a percutaneous coronary intervention (PCI) in STEMI patients. Methods: The study population consisted of 60 patients with acute STEMI and no history of prior myocardial infarction treated with PCI. The patients were assessed both by conventional transthoracic and ST echocardiography in the first 12 h after admission and 6 months after the acute phase. Adverse remodelling was defined as an increase in LVEDV and/or LVESV by 15%. Results: Adverse remodelling occurred in 26 patients (43.33%). By multivariate regression equation, the risk of adverse remodelling increases with age (by 1.1-fold), triglyceride level (by 1.009-fold), and midmyocardial radial strain (mid-RS) (1.06-fold). Increased initial twist decreases the chances of adverse remodelling (0.847-fold). The LV twist presented the largest area under the receiver operating characteristic (ROC) curve to predict adverse remodelling (AUROC = 0.648; 95% CI [0.506;0.789], p = 0.04). A twist value higher than 11° has a 76.9% specificity and a 72.7% positive predictive value for reverse remodelling at 6 months.
SmrtSwarm: A Novel Swarming Model for Real-World Environments
Authors: Bhamu Nikita , Verma Harshit , Dixit Akanksha , Bollard Barbara , Sarangi Smruti R.
Published: September 2023

Drone swarms have gained a lot of popularity in recent times because, as a group, drones can perform highly intelligent tasks. Drone swarms are strongly inspired by the flocking behavior of birds, insects, and schools of fish, where all the members work in a coordinated manner to achieve a common goal. Since each drone is an independent entity, automating the control of a swarm is difficult. Previous works propose various swarming models with either centralized or distributed control. With distributed control, each drone makes its own decisions based on a small set of rules to accomplish swarm behavior, whereas in centralized control, one drone acts as the leader, who knows the final destination and the path to follow; it specifies the trajectories and velocities for the rest of the drones. Almost all the work in the area of swarming models follows Reynolds’ model, which has three basic rules. For GPS-aided settings, state-of-the-art proposals are not mature enough to handle complex environments with obstacles where primarily local decisions are taken. We propose a new set of rules and a game-theoretic method to set the values of the hyperparameters to design robust swarming algorithms for such scenarios. Similarly, the area of realistic swarming in GPS-denied environments is very sparse, and no work simultaneously handles obstacles and ensures that the drones stay in a confined zone and move along with the swarm. Our proposed solution SmrtSwarm solves all of these problems. It is the first comprehensive model that enables swarming in all kinds of decentralized environments regardless of GPS signal availability and obstacles. We achieve this by using a stereo camera and a novel algorithm that quickly identifies drones in depth maps and infers their velocities and identities with reference to itself. We implement our algorithms on the Unity gaming engine and study them using exhaustive simulations. We simulate 15-node swarms and observe cohesive swarming behavior without seeing any collisions or drones drifting apart. We also implement our algorithms on a Beaglebone Black board and show that even in a GPS-denied setting, we can sustain a frame rate of 75 FPS, much more than what is required in practical settings.
A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs
Authors: Yan Jichen , Zhang Xiaoguang , Shen Siyang , He Xing , Xia Xuan , Li Nan , Wang Song , Yang Yuxuan , Ding Ning
Published: September 2023

Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.
Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France
Authors: Jari Abdessamad , Khaddari Achraf , Hajaj Soufiane , Bachaoui El Mostafa , Mohammedi Sabine , Jellouli Amine , Mosaid Hassan , El Harti Abderrazak , Barakat Ahmed
Published: September 2023

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.
Teachers’ Agency in Technology for Education in Pre- and Post-COVID-19 Periods: A Systematic Literature Review
Authors: Pathiranage Anne , Karunaratne Thashmee
Published: September 2023

Technology has become an indispensable element in education that challenges conventional teaching. The pandemic significantly forced a paradigm shift in education from traditional methods to digital platforms. Emerging technology expanded the teacher’s role faster than predicted, and technology has become a significant criterion in defining 21st-century teachers. Teachers had to upgrade education and act as change agents in creating and managing technology-enhanced learning environments requiring teachers to be digitally literate. Considering teachers as significant stakeholders, this paper investigates how literature contributes to the knowledge of their perceptions of digital literacy in education by systematically investigating 59 research articles searched in EBSCO discovery services, covering commonly included 80 different databases in the default search. The review focuses on how teachers use technology, their challenges, and what teachers expect from successful technology integration. Results revealed a contradiction between expectations for technology integration into education before the pandemic and the experience after the lockdown. Even though teachers are confident using basic technology, many have observed a disconnect between technology and pedagogy that emphasises digital literacy’s need. Teachers struggle with technology integration due to lack of knowledge, accessibility, cost, disconnection, infrastructure, time, workload, and technology anxiety Therefore, teachers expect institutions to play a significant role in integrating technology by assisting them in mitigating challenges that require defining the institutional role of a technology-integrated learning environment.
Order and Crash: Multilingual Ecology and Language Planning in Sino-Foreign Cooperative Education Institutions
Authors: Hu Wenting
Published: September 2023

As preconditions and consequences of the globalization of higher education, multilingualism has reconfigured language ecology, language policy and planning, and multilingual management in the contemporary world. However, studies of multilingualism focusing on real-language scenarios in Sino-foreign cooperative education institutions (SCEIs) are rare. This study explores the multilingual ecology, language policy and planning, and language management of three leading SCEIs in China. It also investigates the attitudes of SCEIs’ stakeholders toward language use and language policy in multilingual contexts. A case study was conducted by analyzing a significant number of language policy documents, semi-structured interviews with stakeholders, and partial participant observations. The research results reveal the main features of multilingual coexistence and intense competition in the three leading SCEIs by identifying the micro, meso, and macro issues of language management. The effects of language policy and planning mechanisms are also identified in SCEIs of higher education. Potential conflicts in multilingual environments are interpreted from the perspective of global cooperation. Last, recommendations are offered for the advancement of the language evolution of SCEIs in China.
Impact of Digital Contexts in the Training of University Education Students
Authors: Cebrián-Robles Violeta , Ruíz-Rey Francisco José , Raposo-Rivas Manuela , Cebrián-de-la-Serna Manuel
Published: September 2023

The Internet is an important source of information and is a real ecosystem for learning that has provided important advances in education, although it has also generated problems, especially in terms of data security, identity theft, and cyber-plagiarism. During the COVID-19 pandemic, we had the opportunity to check levels of development in terms of infrastructures and digital competence, and subsequently detected serious problems in online assessment. In particular, the behaviour and digital competence of future teachers are essential, as they will inevitably be role models for their students. The present study analyses academic cyber-plagiarism derived from digital ecosystems during the pandemic in the academic work of pre-service teachers, advancing studies and warnings regarding Artificial Intelligence as a new learning ecosystem. A validated online questionnaire is used that considers the digital context surrounding training both during and after the pandemic. For the factor analysis, both descriptive and correlational, 324 responses from three Spanish universities are analysed. The confirmatory factor analysis reports four factors: the digital context of the pandemic as an opportunity for plagiarism, the response of the educational community to plagiarism, and both the unconscious and intentional misuse of sources. It is concluded that the digital context of the pandemic provided an opportunity for academic plagiarism, conscious or unconscious, with a clear distinction according to gender and the degree of reference. Finally, it is recommended to promote students’ digital skills to avoid risks such as cyber-plagiarism or authorship theft, using institutional repositories that can provide students with prominence by safely and ethically publishing their intellectual creations. Similarly, national policies are required to address advances in AI in education.
What Matters in Leadership Practices among Estonian Upper Secondary School Principals?
Authors: Tamkivi Kaisa , Eisenschmidt Eve
Published: September 2023

High standards are required to succeed in the state upper secondary school system and many challenges have to be met. School principals with their leadership experience and practices are key actors to ensure the achievement of these goals. The study aims to find out Estonian state- operated upper secondary school principals’ understandings of their leadership practices and supporting factors of their work. The data were collected with semi-structured interviews with eight school leaders. They were analysed with directed content analysis and leadership practices were analysed in four dimensions: setting directions; building relationships and developing people; developing the organisation to support desired practices; improving instructional programmes. An inductive approach was used to analyse data to explore the supporting factors. The findings showed that principals followed all dimensions of successful leadership practices. An important dimension appeared to be building relationships with students and teachers. The main concern in their work regarding school improvement challenges was teachers’ workload. Principals saw the strength in having trustworthy and supporting relationships with the operator and being part of the state upper secondary school network. It would be recommended to systematically assess and develop the leadership competencies of school leaders along the dimensions of successful leadership and to closely address the professionalism and support of the school operator, because it was identified by school leaders as an important factor influencing their work.
New Multicell Switched-Inductor Quasi-Z-Source Inverter
Authors: Gong Renxi , Tang Jing , Wan Xingyuan , Wu Hao
Published: September 2023

To address the problems of quasi-Z-source inverters with limited boosting ability and high voltage stress, a novel class of multicell switched-inductor quasi-Z-source inverters is proposed. The new inverter is based on the quasi-Z-source inverter in which the inductors in the impedance source network are replaced by a multicell switched-inductors. In this paper, a detailed description of the topology and operating principle of the new inverter is first made. Then, a deep comparison of the proposed topology with the existing topologies is performed by selecting the appropriate number of switched-inductor units. Finally, to verify the feasibility and superiority of the proposed new topology, a great number of simulations and experiments are conducted. The results demonstrate that, compared to the original quasi-Z-source inverter and existing modified topologies, the boosting capacity of the proposed new inverter increases significantly with the number of cascaded switched-inductor units, and its voltage stress across the capacitors and current ripple through the inductors are effectively reduced.
Active STARS-Assisted Rate-Splitting Multiple-Access Networks
Authors: Xie Jin , Yue Xinwei , Han Zhihao , Liu Xuliang , Xiang Wei
Published: September 2023

The active simultaneously transmitting/reflecting surface (ASTARS) is considered a promising technique to achieve full spatial coverage and overcome multiplicative fading caused by cascaded paths. This paper investigates the performance of ASTARS-assisted rate-splitting multiple-access networks (ASTARS-RSMA) with multiple transmission users (TUs) and reflection users (RUs). The energy-splitting configurations of ASTARS and the effects of imperfect/perfect successive interference cancellation (SIC) on ASTARS-RSMA networks are considered in the analysis. We derive new exact and asymptotic expressions of the outage probability with imperfect/perfect SIC for TUs and RUs. On this basis, we further calculate the diversity orders of TUs and RUs. Moreover, the system throughput and energy efficiency (EE) of ASTARS-RSMA are evaluated in the delay-limited mode. The simulation results confirm the accuracy of the theoretical expressions and show that (i) the outage probability and system throughput with imperfect/perfect SIC of ASTARS-RSMA exceed that of passive simultaneously transmitting/reflecting surface (PSTARS)-assisted RSMA when the number of elements is not too large; (ii) although ASTARS increases power consumption compared to PSTARS, it can bring further EE improvements to RSMA networks.
FAC: A Fault-Tolerant Design Approach Based on Approximate Computing
Authors: Balasubramanian Padmanabhan , Maskell Douglas L.
Published: September 2023

This article introduces a new fault-tolerant design approach based on approximate computing, called FAC, for designing redundant circuits and systems. Traditionally, triple modular redundancy (TMR) has been used to ensure complete tolerance to any single fault or a faulty processing unit, where the processing unit may be a circuit or a system. However, TMR incurs more than 200% overhead in terms of area and power compared to a single processing unit. Alternative redundancy approaches have been proposed in the literature to mitigate these overheads associated with TMR, but they provide only partial or moderate fault tolerance. Among the alternatives, majority voting-based reduced precision redundancy (MVRPR) may be useful for error-resilient applications such as digital signal processing. While MVRPR guarantees only moderate fault tolerance, the proposed FAC is well-suited for error-resilient applications and ensures 100% tolerance to any single fault or a faulty processing unit, like TMR. In this work, we evaluate the performance of TMR, MVRPR, and FAC for a digital image processing application. The image processing results obtained demonstrate the effectiveness of FAC. Moreover, when the processing unit is implemented using a 28-nm CMOS technology, FAC achieves significant improvements over TMR, including a 15.3% reduction in delay, a 19.5% reduction in area, and a 24.7% reduction in power. Compared to MVRPR, FAC exhibits notable enhancements, with an 18% reduction in delay, a 5.4% reduction in area, and an 11.2% reduction in power. When considering the power-delay product, which reflects energy efficiency, FAC demonstrates a 36.2% reduction compared to TMR and a 27.2% reduction compared to MVRPR. When considering the power-delay-area product, which represents design efficiency, FAC achieves a 48.7% reduction compared to TMR and a 31.1% reduction compared to MVRPR.
Joint Dispatching and Cooperative Trajectory Planning for Multiple Autonomous Forklifts in a Warehouse: A Search-and-Learning-Based Approach
Authors: Zhang Tantan , Li Hu , Fang Yong , Luo Man , Cao Kai
Published: September 2023

Dispatching and cooperative trajectory planning for multiple autonomous forklifts in a warehouse is a widely applied research topic. The conventional methods in this domain regard dispatching and planning as isolated procedures, which render the overall motion quality of the forklift team imperfect. The dispatching and planning problems should be considered simultaneously to achieve optimal cooperative trajectories. However, this approach renders a large-scale nonconvex problem, which is extremely difficult to solve in real time. A joint dispatching and planning method is proposed to balance solution quality and speed. The proposed method is characterized by its fast runtime, light computational burden, and high solution quality. In particular, the candidate goals of each forklift are enumerated. Each candidate dispatch solution is measured after concrete trajectories are generated via an improved hybrid A* search algorithm, which is incorporated with an artificial neural network to improve the cost evaluation process. The proposed joint dispatching and planning method is computationally cheap, kinematically feasible, avoids collisions with obstacles/forklifts, and finds the global optimum quickly. The presented motion planning strategy demonstrates that the integration of a neural network with the dispatching approach leads to a warehouse filling/emptying mission completion time that is 2% shorter than the most efficient strategy lacking machine-learning integration. Notably, the mission completion times across these strategies vary by approximately 15%.
A Multi-Antenna Spectrum Sensing Method Based on CEEMDAN Decomposition Combined with Wavelet Packet Analysis
Authors: Li Suoping , Han Yuzhou , Gaber Jaafar , Yang Sa , Yang Qian
Published: September 2023

In many practical communication environments, the presence of uncertain and hard-to-estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distribution. This paper introduces a cutting-edge multi-antenna spectrum sensing methodology that synergistically integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet packet analysis, and differential entropy. Signal feature extraction commences by employing CEEMDAN decomposition and wavelet packet analysis to denoise signals collected by secondary antenna users. Subsequently, the differential entropy of the preprocessed signal observations serves as the feature vector for spectrum sensing. The spectrum sensing module utilizes the SVM classification algorithm for training, while incorporating elite opposition-based learning and the sparrow search algorithm with genetic variation to determine optimal kernel function parameters. Following successful training, a decision function is derived, which can obviate the need for threshold derivation present in conventional spectrum sensing methods. Experimental validation of the proposed methodology is conducted and comprehensively analyzed, conclusively demonstrating its remarkable efficacy in enhancing spectrum sensing performance.