bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Chinese Address Parsing in Low-Resource Scenarios through In-Context Learning
Authors: Ling Guangming , Mu Xiaofeng , Wang Chao , Xu Aiping
Published: July 2023

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and Generative Pre-trained Transformer (GPT) modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, which mitigates interference from unnecessary variations. We introduce the REB–KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model’s effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.
Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification
Authors: Gkikas Dimitris C. , Theodoridis Prokopis K. , Theodoridis Theodoros , Gkikas Marios C.
Published: July 2023

This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.
Deflection-Based Approach for Flexible Pavement Design in Thailand
Authors: Sawangsuriya Auckpath , Svasdisant Tunwin , Jitareekul Poranic
Published: July 2023

The Department of Highways (DOH), Thailand, has adopted both empirical and mechanistic approaches for flexible pavement analysis and design. Recently, the deflection-based design approach has been comprehensively reviewed by the DOH for the possible adoption of national design standards and practices. One of the key reasons is that Thailand’s road authorities, i.e., the DOH and the Department of Rural Roads (DRR), have considered the falling weight deflectometer (FWD) for the new construction and rehabilitation of road pavements. In addition, the FWD is widely accepted as the non-destructive test for deflection measurement and structural capacity evaluation. Ultimately, the implication of FWD deflections for in-house pavement analysis and design shall be developed and proposed to Thailand’s road authorities. Therefore, this study presents the deflection-based approach of flexible pavement design in Thailand. The FWD and a standard Thai truck were selected as the main loading applications in this study. A typical FWD loading stress of 700–800 kPa was practically adopted by the DOH and compared with a standard 10-wheel 25-ton truck with a tandem axle-dual wheel configuration with a tire pressure of 690 kPa. The layered elastic analysis was performed to calculate the pavement responses. The results suggest that the flexible pavement design based on a deflection-based approach is simple, practical, and conservative.
The Ladybird Beetles (Coleoptera: Coccinellidae) of La Palma
Authors: Romanowski Jerzy , Ceryngier Piotr , Vĕtrovec Jaroslav , Szawaryn Karol
Published: July 2023

This paper provides new data on the ladybird beetles (Coccinellidae) of La Palma, one of the western islands of the Canarian archipelago. The field survey of 54 study sites resulted in recording 2494 ladybird individuals belonging to 26 species. Seven of the species recorded were new to La Palma, including two, Harmonia quadripunctata (Pontoppidan) and Nephus reunioni (Fürsch), which were not registered so far on any of the Canary Islands. Novius conicollis (Korschefsky) is synonymized with N. cruentatus (Mulsant). Taking our survey and literature reports into account, a total of at least 35 species of Coccinellidae have so far been recorded on La Palma. This richness in species is lower compared to that of the central islands of the Canarian archipelago, Gran Canaria (42 species) and Tenerife (41 species), but higher than that of the remaining four islands (between 22 and 27 species). The detection of two alien species new to La Palma, Nephaspis bicolor Gordon and Nephus reunioni (Fürsch), confirms earlier observations that colonization of the Canary Islands by ladybird species of exotic origins seems to be a frequent phenomenon.
Transcriptomic Analysis of Starvation on the Silkworm Brain
Authors: Li Yi , Wang Xin , Dong Haonan , Xia Qingyou , Zhao Ping
Published: July 2023

Starvation imposes significant stress on animal survival and development, resulting in organ damage within the organism. The brain, being one of the most vital organs in animals, plays a crucial role in coordinating the physiological functions of other organs. However, performing brain experiments on the human body is challenging. In this work, we selected the silkworm, a model Lepidoptera organism, due to its favorable characteristics. A comprehensive transcriptome analysis was conducted on the brain of silkworm subjected to starvation treatment. The analysis of differentially expressed genes revealed significant alterations in 330 genes following the period of starvation. Through an enrichment analysis, we successfully identified pathways associated with metabolism, hormones, immunity, and diseases. Our findings highlight the transcriptional response of the brain to starvation, providing valuable insights for comprehending the impact of starvation stress in other animals.
Effectiveness of Pulmonary Rehabilitation for Chronic Obstructive Pulmonary Disease Therapy: Focusing on Traditional Medical Practices
Authors: Tonga Katrina O. , Oliver Brian G.
Published: July 2023

Chronic obstructive pulmonary disease (COPD) is a complex and serious disease that is characterized by dyspnea, fatigue, decreased exercise tolerance, peripheral muscle dysfunction, and mood disorders. These manifestations are successfully treated with pulmonary rehabilitation, a comprehensive intervention and holistic approach designed to improve the physical and psychological condition of people with COPD. Exercise is a big component of pulmonary rehabilitation programs, but the efficacy of non-traditional forms of exercise as used in alternative medicine is poorly understood. Here, we aim to address this gap in knowledge and summarize the clinical evidence for the use of traditional exercise regimens in the pulmonary rehabilitation of COPD patients.
Hematological Indices Are Useful in Predicting Complications of Liver Cirrhosis
Authors: Glisic Tijana , Popovic Dusan D. , Lolic Iva , Toplicanin Aleksandar , Jankovic Katarina , Dragasevic Sanja , Aleksic Marko , Stjepanovic Mihailo , Oluic Branislav , Matovic Zaric Vera , Radisavljevic Mirjana M. , Stojkovic Lalosevic Milica
Published: July 2023

Background: Liver cirrhosis is the final stage of chronic liver disease. We aimed to evaluate non-invasive scores as predictors of complications and outcome in cirrhotic patients. Methods: A total of 150 cirrhotic patients were included. Models for end-stage liver disease (MELD), albumin-bilirubin (ALBI) score, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MoLR), and neutrophil-lymphocyte-albumin ratio (NLA) scores were tested in relation to the development of complications and mortality using receiver operating characteristic (ROC) curves. Results: The ROC curve analysis showed (area under the curve) AUC values of NLR, NLA, ALBI, and MELD of 0.711, 0.730, 0.627, and 0.684, respectively, for short-term mortality. MELD, ALBI, and NLA scores showed a statistically significant correlation with hepatic encephalopathy (p = 0.000 vs. 0.014 vs. 0.040, respectively), and the MELD cut-off value of 16 had a sensitivity of 70% and a specificity of 52% (AUC: 0.671, 95% CI (0.577–0.765)). For the assessment of the presence of ascites, the AUC values for NLA and MoLR were 0.583 and 0.658, respectively, with cut-offs of 11.38 and 0.44. Conclusions: MELD, ALBI, and NLA are reliable predictors of hepatic encephalopathy. NLA and MoLR showed a significant correlation with the presence of ascites, and MELD, ALBI, NLR, and NLA have prognostic value to predict 30-day mortality in cirrhotic patients.
Determination of Mandibular Position and Mouth Opening in Healthy Patients and Patients with Articular and/or Muscular Pathology: A Pilot Study with 3D Electromagnetic Articulography and Surface Electromyography
Authors: Cerda Camila , Lezcano María Florencia , Marinelli Franco , Alarcón Josefa , Fuentes Ramón
Published: July 2023

Temporomandibular disorders (TMDs) are a group of pathologies that affect the temporomandibular joint and its related structures, producing intracapsular and muscular pathologies. The aim of this study is to describe, by electromagnetic articulography (EMA) and simultaneous electromyography (sEMG), the mandibular postural position and mouth opening in healthy patients and with articular and/or muscular pathology. Materials and methods: A pilot study was conducted with a sample of sixteen participants aged 18 years or older who attended the TMDs and Orofacial Pain Polyclinic of the University of La Frontera due to TMDs. The physiological inoculation space was evaluated from the mandibular postural position (MPP) with swallowing command and without command, in both healthy patients and patients with articular, muscular, and mixed TMDs, measured simultaneously with EMA and sEMG. An angular measurement of the oral opening was also performed with the data obtained. Results: The physiological inoculation space was obtained from the determination of the MPP through the procedures with swallowing command and without command, and different mouth opening degrees were evaluated. Conclusions: Simultaneous position and sEMG records can be produced from EMA, and different characterization variables such as the vertical distance, Euclidean distance, and angle can be obtained.
Acute Effects of Blood Flow Restriction Training on Movement Velocity and Neuromuscular Signal during the Back Squat Exercise
Authors: García-Sillero Manuel , Maroto-Izquierdo Sergio , Galván-García María , Benitez-Porres Javier , Vargas-Molina Salvador , Jurado-Castro Jose Manuel
Published: July 2023

The aim of this study was to verify the effects of blood flow restriction on movement velocity and muscle activity during the back squat exercise. Methods: Twenty-four university students participated in this study. In two randomized sessions 72 h apart, participants performed a 4-set protocol consisting of 30-15-15-15 repetitions performed at 30% of their one-repetition maximum in the back squat exercise. In both sessions, neuromuscular function was monitored by surface electromyography (EMG) and movement velocity (mean propulsive velocity (MPV), peak concentric velocity (Vmax), and the effort index (EI)). Blood flow restriction (BFR) was applied during exercise in one of the experimental sessions with 80% of full arterial occlusion pressure over lower limbs. Results: The BFR condition showed higher (p < 0.05) EI, peak, and rooted mean square normalized EMG in Set 1 compared to Set 2. Similar MPV and Vmax were observed in each set for both the BFR and control conditions. No significant differences were observed between conditions in any set. Conclusions: BFR did not imply changes in neuromuscular performance during low-intensity resistance training, but it might induce greater intra-series velocity loss and less excitation of the muscles involved.
Adenomyosis: An Updated Review on Diagnosis and Classification
Authors: Moawad Gaby , Fruscalzo Arrigo , Youssef Youssef , Kheil Mira , Tawil Tala , Nehme Jimmy , Pirtea Paul , Guani Benedetta , Afaneh Huda , Ayoubi Jean Marc , Feki Anis
Published: July 2023

Adenomyosis is a commonly diagnosed benign condition characterized by the presence of ectopic endometrial glands within the underlying myometrium. The most common presenting signs and symptoms are abnormal uterine bleeding, chronic pelvic pain, and infertility. The clinical relevance of this condition is evident in both medical and surgical care. Histopathology and imaging studies are used for the diagnosis and classification of adenomyosis, which are hallmarks of the advancement of our ability to diagnose adenomyosis. Importantly, the diagnosis and classification of adenomyosis lacks standardization due to the nature of imaging techniques, features of adenomyosis, and the clinical spectrum of adenomyosis. We reviewed the literature to summarize the available classification systems for adenomyosis and highlight the different imaging approaches and histologic criteria used in diagnosis. Despite the high prevalence of the disease, there is no clear consensus on one classification system. We provide a review of some of the classification systems available and discuss their strengths and limitations.
Myeloid Sarcoma as a Presentation of Acute Myeloid Leukemia and Blastic Phase in the Course of Chronic Myeloid Leukemia: A Case Report and Literature Review
Authors: Giordano Ugo , Sawicki Mateusz , Pilch Justyna , Mizera Jakub , Borkowski Artur , Zduniak Krzysztof , Dybko Jarosław
Published: July 2023

Background: Myeloid sarcoma (MS), also known as granulocytic sarcoma or chloroma, is a rare disease entity characterized by the emergence of an extramedullary tumor, which may be antecedent, coexisting, or manifest secondarily to an ongoing malignancy of lymphoid origin. Owing to its low prevalence, scientific reports addressing this matter comprise mainly retrospective studies with a limited number of participants, rather low-quality research, and only few case reports. Despite MS’s rarity, the need for enhancing their diagnostic tools and refinement of their therapeutic regimens is broadly recognized among physicians. Case summary: In this case series, we present the clinical histories of two patients diagnosed with MS. The former (Case 1) exhibited MS of the sternum alongside chronic myeloid leukemia (CML), while in case of the latter (Case 2) MS presented as the initial manifestation of a current acute myeloid leukemia (AML). Treatment for both patients included chemotherapy (CHTH) and radiation (RT); however, patient 1 with CML died due to cardiorespiratory insufficiency secondary to an infection, while patient 2 is in clinical remission (CR) for 16 months since their MS diagnosis. Furthermore, a comprehensive analysis of previously reported cases was conducted which incorporated MS in patients with AML and CML. Conclusion: The objective of this report was to emphasize the heterogeneity among the clinical manifestations of MS, to underline the relevance of the histopathological and molecular diagnostic tools in opting for the appropriate therapy, and that, in spite of it occurring rather uncommonly, physicians should think of MS in the presence of pathological masses in patients under risk of hematological malignancies.
Test Experience, Direct Instruction, and Their Combination Promote Accurate Beliefs about the Testing Effect
Authors: Rivers Michelle L.
Published: July 2023

Practice testing is a highly robust learning strategy that promotes long-term retention, especially in comparison to more passive strategies such as restudying—a finding referred to as the testing effect. However, learners do not always appreciate the memorial benefits of practice testing over restudying, which could limit their use of practice testing during self-regulated learning. The current investigation explored the extent to which learners’ metacognitive judgments about the testing effect can be improved via test experience, direct instruction, or a combination of both techniques. Prolific participants underwent two learning cycles. In the first cycle, participants were randomly assigned to either (a) experience a testing effect in their own memory performance (i.e., study unrelated word pairs, practice half the pairs through restudying and half through testing with correct-answer feedback, complete a critical test on the pairs, and receive feedback regarding their performance after using each strategy); (b) imagine they had to learn word pairs and read a passage on the purported benefits of practice testing; or (c) undergo both procedures. In the second cycle, all participants learned a novel set of word pairs. Across both learning cycles, participants estimated memory performance for material learned through testing versus restudying. Both test experience and direct instruction—independently and in combination—led to more accurate memory estimates across learning cycles, but no technique was more effective than the other. In summary, people can learn about the memorial benefits of practice testing when they experience a testing effect on their own memory performance and/or when they receive instruction about its benefits.
Mechanical and Microstructure Characterisation of the Hypoeutectic Cast Aluminium Alloy AlSi10Mg Manufactured by the Twin-Roll Casting Process
Authors: Neuser Moritz , Schaper Mirko , Grydin Olexandr
Published: July 2023

Multi-material designs (MMD) are more frequently used in the automotive industry. Hereby, the combination of different materials, metal sheets, or cast components, is mechanically joined, often by forming joining processes. The cast components mostly used are high-strength, age-hardenable aluminium alloys of the Al–Si system. Here, the low ductility of the AlSi alloys constitutes a challenge because their brittle nature causes cracks during the joining process. However, by using suitable solidification conditions, it is possible to achieve a microstructure with improved mechanical and joining properties. For this study, we used the twin-roll casting process (TRC) with water-cooled rollers to manufacture the hypoeutectic AlSi10Mg for the first time. Hereby, high solidification rates are realisable, which introduces a microstructure that is about four times finer than in the sand casting process. In particular, it is shown that a fine microstructure close to the modification with Na or Sr is achieved by the high solidification rate in the TRC process without using these elements. Based on this, the mechanical properties increase, and especially the ductility is enhanced. Subsequent joining investigations validate the positive influence of a high solidification rate since cracks in joints can be avoided. Finally, a microstructure-property-joint suitability correlation is presented.
Innovative Process Strategies in Powder-Based Multi-Material Additive Manufacturing
Authors: Setter Robert , Hafenecker Jan , Rothfelder Richard , Kopp Sebastian-Paul , Roth Stephan , Schmidt Michael , Merklein Marion , Wudy Katrin
Published: July 2023

Multi-material additive manufacturing (AM) attempts to utilize the full benefits of complex part production with a comprehensive and complementary material spectrum. In this context, this research article presents new processing strategies in the field of polymer- and metal-based multi-material AM. The investigation highlights the current progress in powder-based multi-material AM based on three successfully utilized technological approaches: additive and formative manufacturing of hybrid metal parts with locally adapted and tailored properties, material-efficient AM of multi-material polymer parts through electrophotography, and the implementation of UV-curable thermosets within the laser-based powder bed fusion of plastics. Owing to the complex requirements for the mechanical testing of multi-material parts with an emphasis on the transition area, this research targets an experimental shear testing set-up as a universal method for both metal- and polymer-based processes. The method was selected based on the common need of all technologies for the sufficient characterization of the bonding behavior between the individual materials.
A Numerical Investigation of the Dynamic Interaction between the Deep-Sea Mining Vehicle and Sediment Plumes Based on a Small-Scale Analysis
Authors: Liu Shihang , Yang Jianmin , Lu Haining , Sun Pengfei , Zhang Bei
Published: July 2023

The discharge of sediment plumes, which occurs mainly in the two depth zones, has a critical impact on assessing the deep-sea environment. Therefore, it is necessary to establish the corresponding physical oceanography for the evolution of these sediment plumes. For a more accurate evolution estimation of the plumes, the model in this research is concerned with the dynamic interaction between the deep-sea mining vehicle (DSMV) and the sediment plumes on small scales (t ≤ 2 s), contributing to a focus on the vital physical mechanics of controlling the extent of these plumes. The sediment concentration and particle trajectories of the plume emissions were determined using the Lagrangian discrete phase model (DPM). The results show that (1) the wake structure of the DSMV wraps the plume vortex discharged from the rear of the vehicle and inhibits the lateral diffusion of the plume, (2) the length of the entire wake (Lw) increases exponentially as the relative discharge velocity of the plume (U*) increases, where U* is defined as the dimensionless difference between the traveling velocity of the DSMV and the discharge velocity of the plume, and (3) at the same traveling speed of the DSMV and U* less than 0.75, the dispersion of the sediment particles in the early discharge stage of the plume does not vary with the plume discharge rate. This will be beneficial for the more accurate monitoring of ecological changes in deep-sea mining activities and provide theoretical guidance for the green design of DSMVs.
Three-Dimensional Ultrasonic Reverse-Time Migration Imaging of Submarine Pipeline Nondestructive Testing in Cylindrical Coordinates
Authors: Peng Daicheng , Cheng Fei , She Xiaoyu , Zheng Yunpeng , Tang Yongjie , Fan Zhuo
Published: July 2023

Submarine pipelines are a safe and energy-efficient mode of gas transport. However, due to the complex manufacturing process and harsh operating environment, submarine pipelines are subject to fatigue cracks under long-term cyclic loading. A comprehensive and high-precision characterization strategy for submarine pipelines can effectively prevent potential safety hazards and have significant economic and social repercussions. As a matter of fact, pipeline defects cannot be reliably detected with current traditional 2D methods. On the other hand, in ultrasonic testing, cylindrical geometry increases the complexity of the 3D wave field in the submarine pipeline space and significantly influences the accuracy of the detection results. In this paper, we put forward a novel method for 3D ultrasonic image testing that is suitable for cylindrical coordinates. In order to accurately simulate the ultrasonic signal received from pipelines, we generalize the 3D staggered-grid finite-difference method from Cartesian coordinates to cylindrical ones and simulate the full wave field in the 3D pipeline space. Then, signal processing is performed on the ultrasound simulation records, and 3D reverse-time migration imaging of submarine pipeline defects can be effectively achieved using the reverse-time migration method and cross-correlation imaging conditions. The results obtained from simulations and real field data show that the proposed method provides high-quality 3D imaging of defects in pipelines, taking into account multiple scattering and mode conversion information at the bottom of the defects.
Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning
Authors: Liu Shijing , Qian Cheng , Tu Xueying , Zheng Haojun , Zhu Lin , Liu Huang , Chen Jun
Published: July 2023

Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. To solve these problems, we proposed a new method for identifying large yellow croaker based on the CycleGAN (cycle generative adversarial network) and transfer learning. This method constructs source sample sets and target sample sets by acquiring large yellow croaker images in controllable scenes and actual farming conditions, respectively. The CycleGAN was used as the basic framework for image transformation from the source domain to the target domain to realize data amplification in the target domain. In particular, IDF (identity foreground loss) was used to optimize identity loss judgment criteria, and MMD (maximum mean discrepancy) was used to narrow the distribution between the source domain and target domain. Finally, transfer learning was carried out with the expanded samples to realize the identification of large yellow croaker under varying conditions. The experimental results showed that the proposed method achieved good identification results in both the controlled scene and the actual culture scene, with an average recognition accuracy of 96.9% and 94%, respectively. These provide effective technical support for the next steps in fish behavior tracking and phenotype measurement.
A Review of Marine In Situ Sensors and Biosensors
Authors: Liu Yiqun , Lu Hailong , Cui Yue
Published: July 2023

The exploration of the ocean is essential for the exploitation of marine resources and the sustainable development of human society. In order to assess both the health and the resources of the marine environment, a variety of chemical and biological sampling is needed. Traditionally, marine samples are collected on site and transported to a laboratory for analysis. Analytical methods are often tedious, and it is difficult to know the in situ real-time status. This review provides a comprehensive summary of the development of in situ chemical and biological sensors for the typical compounds in the ocean, including methane, radon, ferrous ion, carbon dioxide, microorganisms, pollutants, nutrients and seafood. Different types of sensors for each compound are highlighted, such as novel electrochemical and optical sensors. Commercial status of different sensors is introduced, and performance of representative sensors is compared and discussed deeply. The advantages and disadvantages of each sensing technique are analyzed and evaluated in detail. Finally, future prospects and work directions are presented, such as the deployment of these in situ sensors on fixed and/or moving platforms, development of microfluidic sensors and exploration of new antifouling materials and methods. This paper could serve as a resource for developing more advanced in situ chemical sensors and biosensors for marine scientific research, as well as related practical applications for monitoring marine resource exploration and exploitation and for environmental protection.
Machine-Learning-Enabled Foil Design Assistant
Authors: Kostas Konstantinos V. , Manousaridou Maria
Published: July 2023

In this work, supervised Machine Learning (ML) techniques were employed to solve the forward and inverse problems of airfoil and hydrofoil design. The forward problem pertains to the prediction of a foil’s aerodynamic or hydrodynamic performance given its geometric description, whereas the inverse problem calls for the identification of the geometric profile exhibiting a given set of performance indices. This study begins with the consideration of multivariate linear regression as the base approach in addressing the requirements of the two problems, and it then proceeds with the training of a series of Artificial Neural Networks (ANNs) in predicting performance (lift and drag coefficients over a range of angles of attack) and geometric design (foil profiles), which were subsequently compared to the base approach. Two novel components were employed in this study: a high-level parametric model for foil design and geometric moments, which, as we will demonstrate in this work, had a significant beneficial impact on the training and effectiveness of the resulting ANNs. Foil parametric models have been widely used in the pertinent literature for reconstructing, modifying, and representing a wide range of airfoil and hydrofoil profile geometries. The parametric model employed in this work uses a relatively small number of parameters, 17, to describe uniquely and accurately a large dataset of profile shapes. The corresponding design vectors, coupled with the foils’ geometric moments, constitute the training input from the forward ML models. Similarly, performance curves (lift and drag over a range of angles of attack) and their corresponding moments make up the input for the models used in the inverse problem. The effect of various training datasets and training methods in the predictive power of the resulting ANNs was examined in detail. The use of the best-performing ML models is then demonstrated in two relevant design scenarios. The first scenario involved a software application, the Design Foil Assistant, which allows real-time evaluation of foil designs and the identification of designs exhibiting a set of given aerodynamic or hydrodynamic parameters. The second case benchmarked the use of ML-enabled, performance-based design optimization against traditional foil design optimization carried out with classical computational analysis tools. It is demonstrated that a user-friendly real-time design assistant can be easily implemented and deployed with the identified models, whereas significant time savings with adequate accuracy can be achieved when ML tools are employed in design optimization.
Improving Urban Habitat Connectivity for Native Birds: Using Least-Cost Path Analyses to Design Urban Green Infrastructure Networks
Authors: MacKinnon Maggie , Pedersen Zari Maibritt , Brown Daniel K.
Published: July 2023

Habitat loss and fragmentation are primary threats to biodiversity in urban areas. Least-cost path analyses are commonly used in ecology to identify and protect wildlife corridors and stepping-stone habitats that minimise the difficulty and risk for species dispersing across human-modified landscapes. However, they are rarely considered or used in the design of urban green infrastructure networks, particularly those that include building-integrated vegetation, such as green walls and green roofs. This study uses Linkage Mapper, an ArcGIS toolbox, to identify the least-cost paths for four native keystone birds (kererū, tūī, korimako, and hihi) in Wellington, New Zealand, to design a network of green roof corridors that ease native bird dispersal. The results identified 27 least-cost paths across the central city that connect existing native forest habitats. Creating 0.7 km2 of green roof corridors along these least-cost paths reduced cost-weighted distances by 8.5–9.3% for the kererū, tūī, and korimako, but there was only a 4.3% reduction for the hihi (a small forest bird). In urban areas with little ground-level space for green infrastructure, this study demonstrates how least-cost path analyses can inform the design of building-integrated vegetation networks and quantify their impacts on corridor quality for target species in cities.
Tactical Urbanism Interventions for the Urban Environment: Which Economic Impacts?
Authors: Rossitti Marco , Oppio Alessandra , Torrieri Francesca , Dell’Ovo Marta
Published: July 2023

In the last decades, the emergence of new social, environmental, and economic demands, exacerbated by the COVID-19 pandemic, has led urban planning to innovate its themes, methods, and approaches. In this context, temporary urbanism has emerged as a mainstream approach. How-ever, the impacts of temporary approaches to urban planning are far from being fully understood. In this light, this study focuses on one of the mainstream approaches to temporary urbanism, tactical urbanism, and tries to understand its economic impacts on contemporary cities. Indeed, despite the growing interest in tactical urbanism interventions and their value as an urban regeneration tool, there are no specific reflections focused on investigating their economic effects. Based on these premises, this paper focuses on different tactical urbanism experiences in the Italian context and tries to assess the economic impacts of tactical urbanism interventions by adopting the lens of real estate values as a suitable proxy when dealing with urban environments. The first obtained results show that the experiences of tactical urbanism, partly because of their temporary nature and their tendency toward minimal intervention, fail to trigger regeneration processes or produce significant economic impacts on the territory. Instead, such experiences can play a role in accelerating or consolidating urban regeneration processes already underway, and, in this sense, they contribute to the generation of economic impact on the territory.
Strategies for Green Space Management in Mountain Cities Based on the Habitat Suitability for Urban Birds Breeding
Authors: Zhu Xiongbin , Srirangam Sucharita , Mari TamilSalvi
Published: July 2023

The reproduction suitability of urban birds reflects the health status and service level of urban ecosystems. Therefore, studying the relationship between the nest density of urban birds and environmental factors has certain reference significance for guiding green space management. In this study, 67 green spaces of Liupanshui city in the Wumeng Mountain Area were selected as study sites. Through the statistics of the number of bird’s nests in the plots in 2020, a stepwise regression analysis was conducted on 10 environment-influencing factors. The results show that the nest density of birds in urban green space is not significantly correlated with the plot size, noise, light pollution, vegetation diversity, proportion of paved land, proportion of native plants, or distance from water body, but is significantly correlated with the canopy coverage of arbor, spatial openness, and distance from non-artificial environment to plot. The research identified the environmental factors affecting nest density, and frames a method to compare the density of bird’s nests in urban green space. Based on this, strategies for the construction and management of green space are put forward, so as to provide reference for ecological city construction and alleviate the correlation between the expansion of urban construction land and the deterioration of bird habitats.
Classification Confidence in Exploratory Learning: A User’s Guide
Authors: Salamon Peter , Salamon David , Cantu V. Adrian , An Michelle , Perry Tyler , Edwards Robert A. , Segall Anca M.
Published: July 2023

This paper investigates the post-hoc calibration of confidence for “exploratory” machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the “one-versus-all” approach (top-label calibration) must be used rather than the “calibrate-the-full-response-matrix” approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation using only the test set and the final model. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, may be performed using only the test dataset, and should be sanity-checked visually.
Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Inpainting
Authors: You Shaopei , Xu Jianlou , Fan Yajing , Guo Yuying , Wang Xiaodong
Published: July 2023

Image inpainting is a crucial task in computer vision that aims to restore missing and occluded parts of damaged images. Deep-learning-based image inpainting methods have gained popularity in recent research. One such method is the deep image prior, which is unsupervised and does not require a large number of training samples. However, the deep image prior method often encounters overfitting problems, resulting in blurred image edges. In contrast, the second-order total generalized variation can effectively protect the image edge information. In this paper, we propose a novel image restoration model that combines the strengths of both the deep image prior and the second-order total generalized variation. Our model aims to better preserve the edges of the image structure. To effectively solve the optimization problem, we employ the augmented Lagrangian method and the alternating direction method of the multiplier. Numerical experiments show that the proposed method can repair images more effectively, retain more image details, and achieve higher performance than some recent methods in terms of peak signal-to-noise ratio and structural similarity.
Activities with BBC micro:bit as a Foundation for Statistical Reasoning of Lower-Secondary Students
Authors: Fojtík Michal , Cápay Martin , Medová Janka , Valovičová Ľubomíra
Published: July 2023

Computational thinking is considered a set of skills necessary for living and working in today’s society. It has recently become part of mathematics curricula in several European countries. In this study, we offer a glimpse into the informal classroom where statistical reasoning and computational thinking are developed simultaneously. The student–teacher discussion was analysed and categorised. The dialogue dealt with both statistical and computational thinking. Implying from the nature of the activity, sources of errors and variation in data were discussed and ways to overcome the errors were discussed. The discussions related to computational thinking mainly addressed the work with BBC micro:bit, whether with the manipulation and hardware of the coding. A variable was used from both perspectives, as a mathematical concept and as an entity used to store the information. We identified the notion of a variable as the intersection between statistics and computational thinking.
A Modified Structured Spectral HS Method for Nonlinear Least Squares Problems and Applications in Robot Arm Control
Authors: Yunus Rabiu Bashir , Zainuddin Nooraini , Daud Hanita , Kannan Ramani , Abdul Karim Samsul Ariffin , Yahaya Mahmoud Muhammad
Published: July 2023

This paper proposes a modification to the Hestenes-Stiefel (HS) method by devising a spectral parameter using a modified secant relation to solve nonlinear least-squares problems. Notably, in the implementation, the proposed method differs from existing approaches, in that it does not require a safeguarding strategy and its Hessian matrix is positive and definite throughout the iteration process. Numerical experiments are conducted on a range of test problems, with 120 instances to demonstrate the efficacy of the proposed algorithm by comparing it with existing techniques in the literature. However, the results obtained validate the effectiveness of the proposed method in terms of the standard metrics of comparison. Additionally, the proposed approach is applied to address motion-control problems in a robotic model, resulting in favorable outcomes in terms of the robot’s motion characteristics.
Integral Representations of a Generalized Linear Hermite Functional
Authors: Costas-Santos Roberto S.
Published: July 2023

In this paper, we find new integral representations for the generalized Hermite linear functional in the real line and the complex plane. As an application, new integral representations for the Euler Gamma function are given.
The Delay Time Profile of Multistage Networks with Synchronization
Authors: Barron Yonit
Published: July 2023

The interaction between projects and servers has grown significantly in complexity; thus, applying parallel calculations increases dramatically. However, it should not be ignored that parallel processing gives rise to synchronization constraints and delays, generating penalty costs that may overshadow the savings obtained from parallel processing. Motivated by this trade-off, this study investigates two special and symmetric systems of split–join structures: (i) parallel structure and (ii) serial structure. In a parallel structure, the project arrives, splits into m parallel groups (subprojects), each comprising n subsequent stages, and ends after all groups are completed. In the serial structure, the project requires synchronization after each stage. Employing a numerical study, we investigates the time profile of the project by focusing on two types of delays: delay due to synchronization overhead (occurring due to the parallel structure), and delay due to overloaded servers (occurring due to the serial structure). In particular, the author studies the effect of the number of stages, the number of groups, and the utilization of the servers on the time profile and performance of the system. Further, this study shows the efficiency of lower and upper bounds for the mean sojourn time. The results show that the added time grows logarithmically with m (parallelism) and linearly with n (seriality) in both structures. However, comparing the two types of split–join structures shows that the synchronization overhead grows logarithmically undr both parallelism and seriality; this yields an unexpected duality property of the added time to the serial system.
Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System
Authors: Yu Yun , Wang Jinhao , Zhou Xiao , Wang Chengyou , Bai Zhiquan , Ye Zhun
Published: July 2023

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.
Pattern-Multiplicative Average of Nonnegative Matrices Revisited: Eigenvalue Approximation Is the Best of Versatile Optimization Tools
Authors: Logofet Dmitrii
Published: July 2023

Given several nonnegative matrices with a single pattern of allocation among their zero/nonzero elements, the average matrix should have the same pattern, too. This is the first tenet of the pattern-multiplicative average (PMA) concept, while the second one suggests the multiplicative (or geometric) nature of averaging. The original concept of PMA was motivated by the practice of matrix population models as a tool to assess the population viability from long-term monitoring data. The task has reduced to searching for an approximate solution to an overdetermined system of polynomial equations for unknown elements of the average matrix (G), and hence to a nonlinear constrained minimization problem for the matrix norm. Former practical solutions faced certain technical problems, which required sophisticated algorithms but returned acceptable estimates. Now, we formulate (for the first time in ecological modeling and nonnegative matrix theory) the PMA problem as an eigenvalue approximation one and reduce it to a standard problem of linear programing (LP). The basic equation of averaging also determines the exact value of λ1(G), the dominant eigenvalue of matrix G, and the corresponding eigenvector. These are bound by the well-known linear equations, which enable an LP formulation of the former nonlinear problem. The LP approach is realized for 13 fixed-pattern matrices gained in a case study of Androsace albana, an alpine short-lived perennial, monitored on permanent plots over 14 years. A standard software routine reveals the unique exact solution, rather than an approximate one, to the PMA problem, which turns the LP approach into ‘’the best of versatile optimization tools”. The exact solution turns out to be peculiar in reaching zero bounds for certain nonnegative entries of G, which deserves modified problem formulation separating the lower bounds from zero.