bluuebunny/crossref_metadata_2025 huggingface dataset:
Support Needs of Parents of Adolescents Abusing Substances in Selected Hospitals in Limpopo Province
Authors: Hlahla Lina Sebolaisi , Ngoatle Charity , Mothiba Tebogo Maria
Published: March 2023

Parents with adolescents who abuse substances need support. They have high stress levels and low quality of life compared to other parents. This is because they have unmet support needs, do not know what to do to help their adolescents, and are distressed. Most studies focus on the support needs of adolescents. Less is known about the specific support needs of their parents. This study explores the support needs of parents of adolescents abusing substances and being treated in five hospitals in Limpopo Province. A qualitative research approach was applied with an explorative, descriptive, and contextual design using semi-structured interviews to understand parents’ views. Data saturation was reached at the 14th parent. Data were analyzed using the Tesch method. The parents mainly wanted informational and emotional support. The study identified specific parent-related support needs and adolescent-related support needs. This study is the first to explore the support needs of parents of adolescents abusing substances in a South African rural context.
Epilepsy Phenotypes of Vitamin B6-Dependent Diseases: An Updated Systematic Review
Authors: Mastrangelo Mario , Gasparri Valentina , Bernardi Katerina , Foglietta Silvia , Ramantani Georgia , Pisani Francesco
Published: March 2023

Background: Vitamin B6-dependent epilepsies include treatable diseases responding to pyridoxine or pyridoxal-5Iphosphate (ALDH7A1 deficiency, PNPO deficiency, PLP binding protein deficiency, hyperprolinemia type II and hypophosphatasia and glycosylphosphatidylinositol anchor synthesis defects). Patients and methods: We conducted a systematic review of published pediatric cases with a confirmed molecular genetic diagnosis of vitamin B6-dependent epilepsy according to PRISMA guidelines. Data on demographic features, seizure semiology, EEG patterns, neuroimaging, treatment, and developmental outcomes were collected. Results: 497 published patients fulfilled the inclusion criteria. Seizure onset manifested at 59.8 ± 291.6 days (67.8% of cases in the first month of life). Clonic, tonic-clonic, and myoclonic seizures accounted for two-thirds of the cases, while epileptic spasms were observed in 7.6%. Burst-suppression/suppression-burst represented the most frequently reported specific EEG pattern (14.4%), mainly in PLPB, ALDH7A1, and PNPO deficiency. Pyridoxine was administered to 312 patients (18.5% intravenously, 76.9% orally, 4.6% not specified), and 180 also received antiseizure medications. Pyridoxine dosage ranged between 1 and 55 mg/kg/die. Complete seizure freedom was achieved in 160 patients, while a significant seizure reduction occurred in 38. PLP, lysine-restricted diet, and arginine supplementation were used in a small proportion of patients with variable efficacy. Global developmental delay was established in 30.5% of a few patients in whom neurocognitive tests were performed. Conclusions: Despite the wide variability, the most frequent hallmarks of the epilepsy phenotype in patients with vitamin B6-dependent seizures include generalized or focal motor seizure semiology and a burst suppression/suppression burst pattern in EEG.
Less-Invasive Approach to Early-Onset Scoliosis—Surgical Technique for Magnetically Controlled Growing Rod (MCGR) Based on Treatment of 2-Year-Old Child with Severe Scoliosis
Authors: Grabala Pawel , Helenius Ilkka J. , Chamberlin Kelly , Galgano Michael
Published: March 2023

Background: Spinal deformities in children can be caused by various etiologies, such as congenital, syndromic, neuromuscular, or idiopathic. Early-onset scoliosis (EOS) is diagnosed before the age of ten years, and when the curvature continues to progress and exceeds a Cobb angle of 60–65 degrees, surgical treatment should be considered. Initial minimally invasive surgery and the implantation of magnetically controlled growing rods (MCGRs) allows for the noninvasive distraction of the spine, growing, and avoids multiple operations associated with the classic distractions of standard growing rods. Case presentation: A 2-year-old girl was admitted to our clinic with rapidly progressive thoracic scoliosis. The major curve of the thoracic spine Cobb angle was 122° at 30 months. No congenital deformities were detected. The surgical technique was the less-invasive percutaneous and subfascial implantation of MCGRs, without long incisions on the back and the non-invasive ambulatory lengthening of her spine over the next 4 years. Conclusions: MCGR is a safe procedure for EOS patients. It is extremely effective at correcting spinal deformity; controlling the growth and curvature of the spine as the child develops during growth; reducing the number of hospitalizations and anesthesia; and minimizing the physical and mental burden of young patients, parents, and their families.
Mediterranean Influence on the Climatic Regime over the Balkan Peninsula from 1901–2021
Authors: Peneva Elisaveta , Matov Mirna , Tsekov Milen
Published: March 2023

The Balkan Peninsula is a geographical region under various large-scale climatic influences, one of the most significant being the Mediterranean Sea in the southwest and the continent in the northeast. The novelty of this study is that the border between the zones with prevailing maritime or continental climate conditions is clearly identified by the month with the highest precipitation during the year. We use the gridded data product TS_4.06 of the Climatic Research Unit for monthly precipitation to identify the timing of the maximum rainfall at different locations. The grid boxes with highest precipitation in the cold part of the year (October to March) are considered to be under prevailing Mediterranean influences and, on the contrary, the ones with the highest precipitation are in the warm part of the year (April to September); these are climates with prevailing continental characteristics. In general, this border separates the zones with Cs and Dw types of climates. Its spatial variability at a decadal time-scale is discussed for the period from 1901–2021 and a general weakening of the Mediterranean influence over the Balkan Peninsula is found. However, for the last three decades, from 1991–2021, the tendency is the opposite. A periodicity of ~20 years is identified, suggesting that during the decade of 2020–2030, the Mediterranean influence in this region will dominate.
Microstructure, Mechanical Properties, Wear and Erosion Performance of a Novel High Entropy Nitride (AlCrTiMoV)N Coating Produced by Cathodic Arc Evaporation
Authors: Lothrop Alex , Yang Qi , Huang Xiao
Published: March 2023

(AlCrTiMoV)N high entropy nitride film was prepared on 17-4PH stainless steel substrate using cathodic arc evaporation (CAE). The composition, microstructure, and thermal stability were investigated using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). Mechanical properties such as hardness and Young’s modulus and coating performance against wear and erosion were also evaluated. The results show that the (AlCrTiMoV)N coating assumes a single-phased solid solution B1 FCC structure. It has excellent thermal stability and retains its structure with no decomposition observed up to a temperature of 1000 °C. The hardness and elastic modulus are measured as 21.3 GPa and 304 GPa, respectively. The coating contains some metallic droplets. As a potential protective coating, the (AlCrTiMoV)N coating has a lower wear rate but a higher erosion rate when compared to the TiN baseline coating.
Investigation of the Dependence of Electrocatalytic Activity of Copper and Palladium Nanoparticles on Morphology and Shape Formation
Authors: Petriev Iliya , Pushankina Polina , Glazkova Yuliya , Andreev Georgy , Baryshev Mikhail
Published: March 2023

A synthesis strategy for the manufacture of inexpensive highly efficient nanostructured catalysts has been developed. The developed unique nonplatinoid copper-based catalysts with different surface morphology were investigated as a functional layer with high activity in the ethanol oxidation in alkaline media. A modifying layer with controlled morphology, composition, and excellent electrocatalytic activity was synthesized by electrochemical deposition by varying such synthesis parameters as deposition temperature and time, concentration of structure-forming additives, and electrodeposition current. The dependence of the samples’ electrocatalytic activity on the shaping factors was established. According to the electrochemical study results, the highest current density peak of up to 33.01 mA cm−2, and hence the highest catalytic activity in comparison to other samples, were possessed by a catalyst with a regular cubic particle shape. A catalyst consisting of plate-like nanoparticles with a certain percentage of disclinations had similar, but slightly less activity, with a current density peak of up to 31.59 mA cm−2. The samples’ activity values are 8 times higher for cubic particles and 7.5 times higher for particles with a triangular plate shape than for an unmodified smooth copper film. The developed samples can be considered as quite competitive to platinoid catalysts, which significantly outperform copper analogues.
Research Progress in Capping Diamond Growth on GaN HEMT: A Review
Authors: Wang Yingnan , Hu Xiufei , Ge Lei , Liu Zonghao , Xu Mingsheng , Peng Yan , Li Bin , Yang Yiqiu , Li Shuqiang , Xie Xuejian , Wang Xiwei , Xu Xiangang , Hu Xiaobo
Published: March 2023

With the increased power density of gallium nitride (GaN) high electron mobility transistors (HEMTs), effective cooling is required to eliminate the self-heating effect. Incorporating diamond into GaN HEMT is an alternative way to dissipate the heat generated from the active region. In this review, the four main approaches for the integration of diamond and GaN are briefly reviewed, including bonding the GaN wafer and diamond wafer together, depositing diamond as a heat-dissipation layer on the GaN epitaxial layer or HEMTs, and the epitaxial growth of GaN on the diamond substrate. Due to the large lattice mismatch and thermal mismatch, as well as the crystal structure differences between diamond and GaN, all above works face some problems and challenges. Moreover, the review is focused on the state-of-art of polycrystalline or nanocrystalline diamond (NCD) passivation layers on the topside of GaN HEMTs, including the nucleation and growth of the diamond on GaN HEMTs, structure and interface analysis, and thermal characterization, as well as electrical performance of GaN HEMTs after diamond film growth. Upon comparing three different nucleation methods of diamond on GaN, electrostatic seeding is the most commonly used pretreatment method to enhance the nucleation density. NCDs are usually grown at lower temperatures (600–800 °C) on GaN HEMTs, and the methods of “gate after growth” and selective area growth are emphasized. The influence of interface quality on the heat dissipation of capped diamond on GaN is analyzed. We consider that effectively reducing the thermal boundary resistance, improving the regional quality at the interface, and optimizing the stress–strain state are needed to improve the heat-spreading performance and stability of GaN HEMTs. NCD-capped GaN HEMTs exhibit more than a 20% lower operating temperature, and the current density is also improved, which shows good application potential. Furthermore, the existing problems and challenges have also been discussed. The nucleation and growth characteristics of diamond itself and the integration of diamond and GaN HEMT are discussed together, which can more completely explain the thermal diffusion effect of diamond for GaN HEMT and the corresponding technical problems.
Packing Motifs in [M(bpy)2X2] Coordination Compounds (bpy = 2,2′-bipyridine; X = F, Cl, Br, I)
Authors: Constable Edwin C. , Housecroft Catherine E.
Published: March 2023

Packing motifs within structurally characterized cis-[M(bpy)2X2] (M = any metal, bpy = 2,2′-bipyridine, X = F, Cl, Br, I) coordination compounds have been investigated using data from the Cambridge Structural Database. Compounds fall into two classes: non-solvated cis-[M(bpy)2X2] moieties and those with additional lattice molecules (solvent or other molecules). A recurring packing motif is a dimeric unit involving intermolecular face-to-face π-stacking of bpy ligands and CHbpy...X contacts, although in several cases, slippage of the stacked bpy units reduces the effectiveness of the face-to-face interaction leaving the CHbpy...X contacts as the dominant crystal-packing interaction. The prevalence of the dimeric unit versus the assembly of 1D-chains in the solid state is described.
Genetic Evidence for Indo-Western Pacific Olive Ridley Sea Turtles in Mexican Waters
Authors: Martín-del-Campo Rodolfo , Ortega-Ortiz Christian D. , Abreu-Grobois Alberto , Enríquez-Paredes Luis M. , Petatán-Ramírez David , García-Gasca Alejandra , Quijano-Scheggia Sonia I.
Published: March 2023

The olive ridley sea turtle is predominantly an epipelagic species with no apparent migration corridors. Research in feeding areas in other sea turtle species has identified aggregations of individuals from diverse backgrounds; however, no specific feeding areas have been identified for the olive ridley. We used mtDNA control region sequencing to identify the haplotype composition of 85 olive ridley turtles (adult and immature turtles from both sexes) captured and released in Central Mexican Pacific waters. Amplified fragments of the control region (751 bp) revealed the presence of 17 haplotypes (h = 0.5877 ± 0.0622 and π = 0.001698 ± 0.001185), and 16 of them were phylogenetically grouped within the lineage of the Eastern Pacific, but the haplotype Lo37 showed close evolutionary relationships with the lineage of the East Coast of India. Lagrangian drifter data showed that West–East transport from the Indo-Pacific to the tropical Eastern Pacific might be possible through passive drift. These results highlight the importance of more genetic studies offshore and the role of the ocean currents in the migratory behavior of olive ridleys, as well as the need to establish multinational strategies for the conservation and management of the species in international waters.
Photodynamic Therapy Used to Treat an HIV Patient with an Efflorescence of Viral Warts after HAART Therapy
Authors: Ambrogio Francesca , Meduri Alexandre Raphael , Infante Giovanni , Poli Melita Anna , Romita Paolo , Bonamonte Domenico , Filotico Raffaele , Ingravallo Giuseppe , Cazzato Gerardo , Laface Carmelo , De Marco Aurora , Foti Caterina
Published: March 2023

Healing from viral warts lesions can be hard to achieve in immunocompromised subjects like HIV-positive patients. The therapeutic target in immunocompetent subjects can be reached using different methods, including topical ointments, cryotherapy, laser therapy, imiquimod, and photodynamic therapy (PDT). We present a case of a male HIV-positive patient who came to the Dermatology department with multifocal wart lesions on his face, auricular, and retro-auricular areas after treatment with highly active antiretroviral therapy (HAART). In our case, surprisingly, only one session of PDT proved to induce complete regression of lesions which, despite their thickness, had a much more robust response to treatment than we could have possibly expected. After a brief review of the literature, it is possible to state that PDT revealed itself to be a valid option in immunocompromised patients who have a major risk of relapse.
YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection
Authors: Zhu Ronghua , Cui Yansong , Huang Jianming , Hou Enyu , Zhao Jiayu , Zhou Zhilin , Li Hao
Published: March 2023

Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.
Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening
Authors: Spadaccini Marco , Massimi Davide , Mori Yuichi , Alfarone Ludovico , Fugazza Alessandro , Maselli Roberta , Sharma Prateek , Facciorusso Antonio , Hassan Cesare , Repici Alessandro
Published: March 2023

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams
Authors: Kalbhor Madhura , Shinde Swati
Published: March 2023

Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams. In this paper, ColpoClassifier, a hybrid framework for the classification of cervigrams, is proposed, which consists of feature extraction followed by classification. This paper uses a Gray-level co-occurrence matrix (GLCM), a Gray-level run length matrix (GLRLM), and a histogram of gradients (HOG) for feature extraction. These features are combined to form a feature fusion vector of the form GLCM + GLRLM + HOG. The different machine learning classifiers are used for classification by using individual feature vectors as well as feature fusion vectors. The dataset used in this paper is compiled by downloading images from the WHO website. Two variants of this dataset are created, Dataset-I contains images of the aceto-whitening effect, green filter, iodine application, and raw cervigram while Dataset-II only contains images of the aceto-whitening effect. This paper presents the classification performance on all kinds of images with the individual as well as hybrid feature fusion vector and concludes that hybrid feature fusion vectors on aceto-whitening images have given the best results.
New 6-Minute-Walking Test Parameter—Distance/Desaturation Index (DDI) Correctly Diagnoses Short-Term Response to Immunomodulatory Therapy in Hypersensitivity Pneumonitis
Authors: Lewandowska Katarzyna B. , Sobiecka Małgorzata , Boros Piotr W. , Dybowska Małgorzata , Barańska Inga , Jędrych Małgorzata E. , Gładzka Agata , Tomkowski Witold Z. , Szturmowicz Monika
Published: March 2023

The six-minute-walking test (6MWT) is an easy-to-perform, cheap and valuable tool to assess the physical performance of patients. It has been used as one of the endpoints in many clinical trials investigating treatment efficacy in pulmonary arterial hypertension and idiopathic pulmonary fibrosis. However, the utility of 6MWT in patients diagnosed with hypersensitivity pneumonitis (HP) is still under investigation. The aim of the present retrospective study was to assess the value of different 6MWT parameters, including the newly developed distance-desaturation index (DDI), to evaluate immunomodulatory treatment outcomes in HP patients. Methods: 6MWT parameters (distance, initial saturation, final saturation, desaturation, distance-saturation product (DSP), and DDI) were analyzed at baseline and after 3 to 6 months of treatment with corticosteroids alone or in combination with azathioprine. Results: 91 consecutive HP patients diagnosed and treated in a single pulmonary unit from 2005 to 2017 entered the study. There were 44 (48%) males and 52 (57%) patients with fibrotic HP (fHP). Sixty-three patients (69%) responded to treatment (responders) and 28 (31%) did not respond (non-responders). In the responders group, all parameters assessed during 6MWT significantly improved, whereas in non-responders, they worsened. Medians (95% CI) of best indices were post-treatment DDI/baseline DDI—1.67 (1.85–3.63) in responders versus 0.88 (0.7–1.73) in non-responders (p = 0.0001) and change in walking distance–51 m (36–72 m) in responders, versus 10.5 m (−61.2–27.9) in non-responders (p = 0.0056). The area under the curve (AUC) of receiver operating characteristics (ROC) for post-treatment DDI/baseline DDI was 0.74 and the optimal cut-off was 1.075, with 71% of specificity and 71% of sensitivity. Conclusions: 6MWT may be used as a tool to assess and monitor the response to immunomodulatory therapy in HP patients, especially if indices incorporating both distance and desaturation are used. Based on the present study results, we recommend 6MWD and DDI use, in addition to FVC and TL,co, to monitor treatment efficacy in patients with interstitial lung diseases.
Current Guidelines for Diagnosing PCOS
Authors: Christ Jacob P. , Cedars Marcelle I.
Published: March 2023

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder of reproductive-aged women. Much of the confusion surrounding PCOS diagnosis stems from the broad heterogeneity of symptomology experienced by women with PCOS. The diverse features of the syndrome have led to a number of diagnostic criteria over the years. This manuscript describes each of the current composite criteria and individually breaks down each component. The importance of accurate diagnosis for both clinical care and research is emphasized.
The Possible Effect of the Long-Term Use of Glucagon-like Peptide-1 Receptor Agonists (GLP-1RA) on Hba1c and Lipid Profile in Type 2 Diabetes Mellitus: A Retrospective Study in KAUH, Jeddah, Saudi Arabia
Authors: Ajabnoor Ghada M. A. , Hashim Kamal Talat , Alzahrani Mohammed Meshari , Alsuheili Abdullah Zeid , Alharbi Abdullah Fahad , Alhozali Amani Matook , Enani Sumia , Eldakhakhny Basmah , Elsamanoudy Ayman
Published: March 2023

(1) Background: Type 2 diabetes (T2DM) is a chronic metabolic disease with serious health complications. T2DM is associated with many chronic illnesses, including kidney failure, cardiovascular diseases (CVD), vision loss, and other related diseases. Obesity is one of the major factors associated with insulin resistance and dyslipidemia. Recently, the development of GLP-1 Receptor agonist (GLP-1RA) showed great therapeutic potential for T2DM. Aim: To retrospectively investigate the association of the long-term use of GLP-1RA therapy in T2DM patients with HbA1c levels and dyslipidemia. (2) Methods: Retrospective data collection and analysis of demographic, clinical records, and biochemical parameters were carried out for 72 T2DM taking GLP-1RA treatments for six months. (3) Results: A total of 72 T2DM patients with a mean age = 55 (28 male and 44 female) were divided into two groups. Group 1 received statins (n = 63), and group 2 did not receive statins (n = 9). The GLP-1RA effect on BMI was significantly decreased in group 1 (p < 0.01). A significant effect was observed for HbA1c in both groups for six months of treatment duration (p < 0.05). The AST levels significantly decreased in group 2 from 25.2 to 19.4 U\L (p = 0.011). (4) Conclusions: GLP-1RA treatments were associated with weight reduction and improved glycemic control for T2DM patients. Moreover, it is suggested that it has anti-inflammatory and hepatoprotective effects. However, no direct association was found with the lipid profile in all groups of T2DM.
A Cephalometric Analysis Assessing the Validity of Camper’s Plane to Establishing the Occlusal Plane in Edentulous Patients
Authors: Sharab Lina , Jensen David , Hawk Gregory , Kutkut Ahmad
Published: March 2023

Complete denture fabrication requires multiple clinical and laboratory steps. One of the most critical clinical steps is establishing an anatomical occlusal plane based on hard and soft tissue references. The aim of this study was to determine whether age or gender affects the level of the Ala-Tragus plane to establish which reference point on the Tragus should be used when fabricating the occlusal plane in edentulous patients. Clinical photographs and lateral cephalometric radiographs with complete dentitions were taken from 58 volunteers at the DMD clinic at the University of Kentucky. Each photograph was superimposed over its corresponding cephalometric image. An analysis was conducted to establish the angle of the occlusal plane relative to the Ala-Tragus landmarks; this data was then grouped according to age and gender. The analysis shows that age and gender did not significantly affect where the Camper’s plane should be approximated for complete denture treatment. However, it was found that the most parallel line to the occlusal plane was Ala’s inferior border to the ‘Tragus’s inferior border. It should be noted that the volunteers’ skeletal classification was significantly related to a Cl III malocclusion tendency. Still, with this new information, functionality and esthetics can be more adequately addressed for patients undergoing complete denture treatment. Given our results, we suggest redefining the ‘Camper’s plane with a line extending from ‘Ala’s inferior border to the ‘Tragus’s inferior border instead of the superior border. Further consideration should be taken if the patient is a skeletal CL III malocclusion.
Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Zhao Honggang , Liu Mingyue , Li Mingyong
Published: March 2023

Zero-shot sketch-based image retrieval (ZS-SBIR) is an important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage. Because sketches are extremely abstract, the commonly used backbone networks (such as VGG-16 and ResNet-50) cannot handle both sketches and photos. Semantic similarities between the same features in photos and sketches are difficult to reflect in deep models without textual assistance. To solve this problem, we propose a novel and effective feature embedding model called Attention Map Feature Fusion (AMFF). The AMFF model combines the excellent feature extraction capability of the ResNet-50 network with the excellent representation ability of the attention network. By processing the residuals of the ResNet-50 network, the attention map is finally obtained without introducing external semantic knowledge. Most previous approaches treat the ZS-SBIR problem as a classification problem, which ignores the huge domain gap between sketches and photos. This paper proposes an effective method to optimize the entire network, called domain-aware triplets (DAT). Domain feature discrimination and semantic feature embedding can be learned through DAT. In this paper, we also use the classification loss function to stabilize the training process to avoid getting trapped in a local optimum. Compared with the state-of-the-art methods, our method shows a superior performance. For example, on the Tu-berlin dataset, we achieved 61.2 + 1.2% Prec200. On the Sketchy_c100 dataset, we achieved 62.3 + 3.3% mAPall and 75.5 + 1.5% Prec100.
How to Hedge against Inflation Risk in Vietnam
Authors: Thi Thanh Binh Nguyen
Published: March 2023

Vietnam has experienced galloping inflation and faced serious dollarization since its reform. To effectively control inflation for promoting price stability, it is necessary to find efficacious leading indicators and a hedging mechanism. Using monthly data over the period from January 1997 to June 2020, this study finds the predictive power and hedge effectiveness of both gold and the US dollar on inflation in the long-run and short-run within the asymmetric framework. Especially, the response of inflation to the shocks of gold price and the US dollar is quick and decisive, disclosing the sensitivity of inflation to these two variables.
Diversity in Software Design and Construction Teaching: A Systematic Literature Review
Authors: Castro Vitor de Souza , Oliveira Sandro Ronaldo Bezerra
Published: March 2023

Teaching in computing faces challenges concerning technological changes and strategies to encourage student engagement with the teaching–learning process. In software engineering, specifically in the construction of the solution, these challenges are even greater due to technological changes and the evolution of applications. Based on this context, the objective of this work is to present the results of a systematic literature review on the knowledge areas that are central to the software construction process, the Software Design and Construction. The methodology for developing the systematic literature review followed a review protocol in which 51 studies were selected out of 302 studies found by executing a search string in the Association for Computing Machinery and Institute of Electrical and Electronic Engineers databases. As a main result, the diversity of teaching strategies applied to the teaching of Software Design and Construction was identified. In addition, amongst the selected studies, we identified that active methodologies are more frequent in the literature.
Exploring STEM Teacher Learning in Out-of-School Settings: Sites for Learning about Relevance
Authors: Worsley Ti’Era , Heredia Sara
Published: March 2023

Decades of research on student’s science, technology, engineering, and mathematics (STEM) learning have highlighted the need to make science content more relevant for students. However, science teachers have described this as difficult, with large numbers of students in their classrooms and an administrative focus on achievement in high-stakes assessments. In this study, we explore the learning opportunities for two in-service science teachers when they participated in an out-of-school professional development (PD) program to design and implement a STEM after-school program (ASP) in partnership with a local university. We specifically look at how participation in the PD and the implementation of an ASP supported them in creating science content that was relevant to their students, as well as the activities and tools teachers highlighted as important for their learning. We provide a rich description through case study methodology of how both teachers learned to make the science content more relevant to students as they developed an ASP about water issues within the community. We argue that the ASP became a productive space for teachers to work with students in informal settings and acquaint themselves with their students in new and different ways to those afforded by their formal classroom.
Relations between Math Achievement, Math Anxiety, and the Quality of Parent–Child Interactions While Solving Math Problems
Authors: DiStefano Michela , Retanal Fraulein , Bureau Jean-François , Hunt Thomas E. , Lafay Anne , Osana Helena P. , Skwarchuk Sheri-Lynn , Trepiak Philip , Xu Chang , LeFevre Jo-Anne , Maloney Erin A.
Published: March 2023

In the current study, we used a multi-method approach to understand the quality of math homework-helping interactions between parents and their children and how parents’ and children’s own math achievement and math anxiety relate to the quality of the interaction. Forty Canadian parents and their children (ages 10–12 years; grades 5 to 7) completed self-report measures of math and general anxiety. Parents and children completed standardized assessments of math achievement and were then recorded as they engaged in a simulated math homework interaction. Coders assessed parent–child interaction quality during the interaction. Parent–child dyads generally performed well on the simulated math homework task. Nevertheless, task performance was correlated with the quality of the interaction, with high-quality interactions associated with high accuracy on the math task. Furthermore, the variability in the quality of the interaction was associated with parents’ and children’s math achievement and with the math anxiety of the children, but not the parents. Identifying the elements that influence parent–child interactions in math-related situations is essential to developing effective interventions to scaffold children’s math learning and attitudes.
Investigating Chemistry Teachers’ Assessment Knowledge via a Rubric for Self-Developed Tasks in a Food and Sustainability Context
Authors: Peretz Roee , Dori Dov , Dori Yehudit Judy
Published: March 2023

We investigated the competence of in- and pre-service chemistry teachers and teacher mentors in designing sustainability- and systems-oriented online tasks for their students. Using a dedicated rubric, we evaluated their assessment knowledge (AK) as reflected in the tasks they had developed. The rubric is based on four attributes: integration of sustainability and chemistry, diversity of thinking skills, the variety of system aspects, and diversity of visual representations. Implementing a qualitative case study approach, we tracked the professional development of three purposefully sampled teachers in addition to using the rubric to score their tasks. Combining the rubric scorings and the qualitative investigation via feedback questionnaire revealed new insights. Besides the teachers’ content and pedagogical knowledge, the case studies’ context and relevance to the teachers were found central to their ability to assess learning. This research contributes to the theoretical understanding of AK of teachers with different backgrounds and professional experiences. The methodological contribution stems from the analysis of self-developed tasks based on a designated rubric, which should be further validated.
Interactive Learning with Student Response System to Encourage Students to Provide Peer Feedback
Authors: Sitthiworachart Jirarat , Joy Mike , Ponce Héctor R.
Published: March 2023

This study analyzed anonymous peer feedback among two groups of university students—a lower-performing class and a higher-performing class. Students used an audience response system to anonymously comment on each other’s work. Each peer feedback or comment was categorized into one of seven types: Praise+, Praise−, Criticism+, Criticism−, Combined Praise and Criticism, Opinion, and Irrelevant. The plus (+) and minus (−) signs were used to categorize the quality of the feedback. The learning performance of the two groups of students was also analyzed. The main result showed that the lower-performing class (based on the average midterm scores) provided more substantial Criticism+ and Opinion-type comments than the higher-performing students. Contrary to expectation, no significant difference was found between the two classes on the final exam, suggesting that anonymity allowed lower-performing students to express themselves more effectively than higher-performing students, leading them to improve their learning outcomes.
A Multi-Hop Graph Neural Network for Event Detection via a Stacked Module and a Feedback Network
Authors: Liu Liu , Ding Kun , Liu Ming , Liu Shanshan
Published: March 2023

Event detection is an important subtask of information extraction, aiming to identify triggers and recognize event types in text. Previous state-of-the-art studies using graph neural networks (GNNs) are mainly applied to obtain long distance features of text and have achieved impressive performance. However, these methods face the issues of over-smoothing and semantic feature destruction, when containing multiple GNN layers. For the reasons, this paper proposes an improved GNN model for event detection. The model first proposes a stacked module to enrich node representation to alleviate the over-smoothing. The module aggregates multi-hop neighbors with different weights by stacking different GNNs in each hidden layer, so that the representation of nodes no longer tends to be similar. Then, a feedback network is designed with a gating mechanism to retain effective semantic information in the propagation process of the model. Finally, experimental results demonstrate that our model achieves competitive results in many indicators compared with state-of-the-art methods.
Visual Attention Adversarial Networks for Chinese Font Translation
Authors: Li Te , Yang Fang , Song Yao
Published: March 2023

Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes to generate font images with complete and accurate strokes. Second, we propose a visual attention adversarial network. By using visual attention block, the network catches global and local features for constructing details of characters. Experiments demonstrate our method generates high-quality Chinese character images with great style diversity including calligraphy characters.
Small Signal Anti-Jamming Scheme Based on a DMA Linear Array under Strong Jamming
Authors: Wang Yuankai , Jin Liang , Lou Yangming , Hao Yinuo
Published: March 2023

Considering the difficulty of receiving small signals under strong electromagnetic jamming, this paper proposes a small-signal anti-jamming scheme based on a single dynamic metamaterial antenna (DMA). Our scheme uses the dynamic-adjustable characteristics of the DMA to perform spatial filtering at the antenna radio frequency (RF) front-end, to suppress strong jamming signals in advance and to improve the receiver’s ability to receive and demodulate small signals. Specifically, we take the maximization of signal-to-interference-plus-noise ratio (SINR) as the optimization goal, transform the fractional non-convex objective function model into a quasi-convex semi-definite relaxation (SDR) problem, and use the Charnes-Cooper (CC) transform algorithm to find the optimal DMA array-element codeword-state matrix. Simulation results show that DMA has better spatial-beamforming capability than traditional antenna arrays, and the proposed scheme can better resist strong jamming. DMA realizes the effect of digital beamforming at the back end of the traditional communication system, has the advantages of traditional digital-spatial filtering, and further improves the receiver’s ability to receive and demodulate small signals.
Research on an Ultra-Short-Term Working Condition Prediction Method Based on a CNN-LSTM Network
Authors: Tian Mengqing , Zhu Jijun , Xiong Huaping , Liu Wanwei , Liu Tao , Zhang Yan , Wang Shunzhi , Zhang Kejia , Liao Mingyue , Xu Yixing
Published: March 2023

Affected by factors such as complex production operation data, high dimensions, and weak regularity, the existing ultra-short-term working condition prediction method struggles to guarantee the prediction accuracy and operation speed. Therefore, we propose an ultra-short-term working condition prediction method based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). Firstly, we use sliding window and normalized processing methods to carry out data processing, and use CNN to extract the characteristics of processed production operation data. Secondly, we then improve the LSTM gated structure and introduce L2 norm, learning the change law of the production operation data by means of the LSTM prediction layer, and then obtain the predicted value of the working condition. We use the Bayesian method to select the parameters of the CNN-LSTM model to improve the prediction accuracy. Finally, we apply our method to a real-world application to demonstrate that our ultra-short-term working condition prediction method achieves superior results for prediction accuracy and running speed when compared with other methods.
Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location
Authors: Li Weiwei , Zhang Zijing , Wang Mingyue , Chen Hang
Published: March 2023

In order to solve the problem of defect detection and to contour accurate segmentation of periodic texture fabric images, a fabric defect detection method based on saliency region and similarity location is proposed. Firstly, the image to be detected was processed by color space conversion, Gaussian filtering, and contrast enhancement, and a frequency-tuned (FT) salient region detection algorithm was used to estimate a saliency map of the enhanced image. The fabric image was divided into image blocks of the same size with overlapping areas through a sliding window, and then the statistical parameters of each image block were calculated. The outliers in the statistical parameters were filtered out using inter-quartile range (IQR). Through the positioning and processing of image defects, abnormal elimination was carried out, and the defect outline was finally obtained. The experimental results show that the method proposed in this paper has better performance in terms of qualitative characterization of Acc, Precision, Recall, and F1 score.
Active Disturbance Rejection-Based Performance Optimization and Control Strategy for Proton-Exchange Membrane Fuel Cell System
Authors: Wei Heng , Du Changqing
Published: March 2023

In this paper, to maximize the net output power and realize better performance optimization and control of the oxygen excess ratio, a complete dynamic model of the proton-exchange membrane fuel cell system is developed and an active disturbance rejection control strategy is proposed. The active disturbance rejection control drives the uncertainties and perturbations of the system to an extended state, which is predicted and eliminated by real-time input–output data. The simulation results indicate that, compared with the proportion–integral–differential and fuzzy proportion–integral–differential control, the active disturbance rejection control strategy can effectively improve the control performance with a lower control cost and less wear on the compressor, and the integral absolute error of the oxygen excess ratio control is reduced by up to 50%. In addition, the output voltage is improved and the power generation efficiency of the proton-exchange membrane fuel cell under the active disturbance rejection-based oxygen excess ratio control is 1.84% and 0.95% higher than that of the proportion–integral–differential and fuzzy proportion–integral–differential control, respectively. Moreover, the proposed optimal-reference control strategy increases the net power by up to 1.85% compared with the fixed-reference control strategy.
Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism
Authors: Peng Huiling , Yu Jia , Nie Yalin
Published: March 2023

Text recognition in natural scenes has been a very challenging task in recent years, and rich text semantic information is of great significance for the understanding of a scene. However, text images in natural scenes often contain a lot of noise data, which leads to error detection. The problems of high error detection rate and low recognition accuracy have brought great challenges to the task of text recognition. To solve this problem, we propose a text recognition algorithm based on natural scenes. First, the task of text detection and recognition is completed in an end-to-end way in a framework, which can reduce the cumulative error prediction and calculation caused by cascading, and has higher real-time and faster speed. In addition, we integrate a multi-scale attention mechanism to obtain attention features of different scale feature maps. Finally, we use the efficient deep learning network (EE-ACNN), which combines a convolutional neural network (CNN) with an end-to-end algorithm and multi-scale attention to enrich the text features to be detected, expands its receptive field, produces good robustness to the effective natural text information, and improves the recognition performance. Through experiments on text data sets of natural scenes, the accuracy of this method reached 93.87%, which is nearly 0.96–1.02% higher than that of traditional methods, and which proves the feasibility of this method.
A Network Traffic Anomaly Detection Method Based on Gaussian Mixture Model
Authors: Yu Bin , Zhang Yongzheng , Xie Wenshu , Zuo Wenjia , Zhao Yiming , Wei Yuliang
Published: March 2023

How can we learn the normal behavior of some communication processes and predict whether a single communication is under attack, with massive network traffic data representing the time costs of each stage in a single communication process? This paper introduces a statistical method for detecting network traffic anomalies using the Gaussian mixture model. There are two aspects to our contributions. First, we show how to learn the normal behavior of a communication process under the assumption that its time costs are generated from the Gaussian mixture model. Secondly, we show that with the learned Gaussian mixture model, we can predict whether a data point is under attack by computing the likelihood that the data point is drawn from the learned Gaussian distribution. The experimental results show that our method reached high accuracy in some cases, while in some other cases that are more complicated, the data point may have more factors and cannot be represented simply by only one Gaussian mixture model.