Miracle-dz/UniFAR-FTUs huggingface dataset:
Metamaterial‐based wideband circularly polarised antenna with rotated V‐shaped metasurface for small satellite applications

This Letter explains the design and development of a wideband circularly polarised (CP) antenna based on composite metamaterial (MTM) structures which are applicable for use in small satellite applications. The presented antenna is based on a modified co-planar waveguide feeding technique. The proposed antenna unit cell offers compact size due to its MTM property with dimensions of 0. 088λ 0 × 0. 19λ 0 at 2.4 GHz. The CP radiation is obtained by placing two composite right/left-handed transmission line unit cells orthogonally and exciting two orthogonally-polarised modes simultaneously. A new 4 × 4 unit cell artificial magnetic conductor metasurface is designed with a compact size at the same operating frequency of MTM-based CP antenna as a ground plane for the proposed antenna, which is found to be increased bandwidth, improved gain, and wider axial ratio bandwidth without altering the antenna compactness. The final antenna obtains an overall dimension of 0. 37λ 0 × 0. 37λ 0 × 0. 18λ 0 at 2.5 GHz. The measured result shows a wider impedance bandwidth of 860 MHz (33.07%), improved axial ratio bandwidth of 410 MHz (15.92%), and a good gain of 5.76 dBic is obtained which are adequate for the use in modern small satellite applications.
An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images

Simple Summary This study presents an AI system for the automatic diagnosis of lung cancer based on lung nodule images from CT scans. Lung cancer is the leading cause of cancer-related deaths in Taiwan, and early detection can improve the survival rate of patients. The system uses a 3D interpretable hierarchical semantic convolutional neural network (HSNet) that can recognize different features of lung nodules, such as calcification, margin, texture, sphericity, and malignancy. The system achieves better performance than previous methods, with high accuracy and interpretability. Abstract Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) computer-aided diagnosis system can be developed for the automatic recognition of CT images of patients, creating a new form of intelligent medical service. For many years, lung cancer has been the leading cause of cancer-related deaths in Taiwan, with smoking and air pollution increasing the likelihood of developing the disease. The incidence of lung adenocarcinoma in never-smoking women has also increased significantly in recent years, resulting in an important public health problem. Early detection of lung cancer and prompt treatment can help reduce the mortality rate of patients with lung cancer. In this study, an improved 3D interpretable hierarchical semantic convolutional neural network named HSNet was developed and validated for the automatic diagnosis of lung cancer based on a collection of lung nodule images. The interpretable AI model proposed in this study, with different training strategies and adjustment of model parameters, such as cyclic learning rate and random weight averaging, demonstrated better diagnostic performance than the previous literature, with results of a four-fold cross-validation procedure showing calcification: 0.9873 ± 0.006, margin: 0.9207 ± 0.009, subtlety: 0.9026 ± 0.014, texture: 0.9685 ± 0.006, sphericity: 0.8652 ± 0.021, and malignancy: 0.9685 ± 0.006.
The biology and conservation of wild canids

PART I REVIEWS: 1. Dramatis personae 2. Ancestry 3. Population genetics 4. Society 5. Management 6. Infectious disease 7. Tools PART II CASE STUDIES 8. Arctic foxes 9. Island foxes 10. Swift foxes 11. Blanford's foxes 12. Red foxes 13. Raccoon dogs 14. Bat-eared foxes 15. Patagonian foxes 16. Jackals 17. Coyotes 18. Grey wolves - Isle Royale 19. Grey wolves - Yellowstone 20. Ethiopian wolves 21. Dholes 22. African wild dogs CONCLUSIONS 23. Conservation REFERENCES
Omega-3 fatty acid ameliorates bisphenol F-induced testicular toxicity by modulating Nrf2/NFkB pathway and apoptotic signaling

Introduction Bisphenol F (BPF) has been shown to disrupt testicular functions via perturbation of testicular redox balance, while omega-3 fatty acid (O3FA) has been established to exert antioxidant and anti-inflammatory activities. Therefore, this study focused on the role and associated molecular mechanism of O3FA in BPF-induced testicular dysfunction in male Wistar rats. Methods Twenty-four (24) rats were randomly grouped after two weeks of acclimatization into four (4) groups (n=6/group); the vehicle-treated control group, BPF treated group received 30 mg/kg of BPF, and the intervention groups received 30 mg/kg BPF + 100 mg/kg O3FA (BPF+O3FA-L) and 30 mg/kg BPF + 300 mg/kg of O3FA (BPF+O3FA-H). All treatment lasted for 28 days. Results Low and high doses of O3FA ameliorated BPF-impaired sperm quality, and induced hormonal imbalance, accompanied by a distortion in testicular histology and elevated testicular injury markers. Furthermore, co-administration of BPF with both doses of O3FA blunted BPF-induced redox imbalance, inflammatory response, and apoptosis. Discussions In conclusion, our present findings show that O3FA improves testicular functions in BPF-treated rats by improving sperm quality and reproductive hormones via the maintenance of testicular redox balance.
MFSANet: Zero-Shot Side-Scan Sonar Image Recognition Based on Style Transfer

Side-scan sonar (SSS) is attracting increasing attention in ocean exploration for its utility and stability on autonomous underwater vehicles (AUVs). Existing SSS image recognition methods mainly employ deep neural networks (DNNs) for various tasks. However, the effectiveness of DNN-based approaches is limited in situations with zero samples. In this letter, a multifeature fusion self-attention network (MFSANet) is proposed to generate SSS images of novel categories, transforming this problem into a conventional supervised learning problem. Specifically, optical-acoustic image pairs are used as inputs to the network to synthesize pseudo-SSS images. First, the shallow and deep features of the input images are extracted by different layers of the encoder. Then, the long-range dependencies of the acoustic images are efficiently modeled with the proposed simplified self-attention module (SSAM). Finally, the acoustic features are appropriately aggregated to each position of the optical features to efficiently generate pseudo-SSS samples for training the classification network. In addition, a novel contrastive loss is proposed to optimize the cross-modal feature space distribution. Experimental results demonstrate that our method can efficiently generate high-quality pseudo-SSS samples, which improves the accuracy of zero-shot SSS image recognition.
Uncertainties in cloud-radiative heating within an idealized extratropical cyclone - Response to Reviewers

Cloud radiative heating (CRH) within the atmosphere affects the dynamics and predictability of extratropical cy-clones. However, CRH is uncertain in numerical weather prediction and climate models, and this could affect model predictions of extratropical cyclones. In this paper, we present a systematic quantification of CRH uncertainties. To this end, we study an idealized extratropical cyclone simulated at a convection-permitting resolution of 2.5 km , and combine large-eddy simulations at 300 m resolution with offline radiative transfer calculations. We quantify four factors contributing to the CRH uncertainty 5 in different regions of the cyclone: 3D cloud radiative effects, parameterization of ice-optical properties, cloud horizontal heterogeneity, and cloud vertical overlap. The two last factors can be considered to be essentially resolved at 300 m but need to be parametrized at 2.5 km resolution. Our results indicate that the parameterization of ice-optical properties and the cloud horizontal heterogeneity are the two factors contributing most to the uncertainty in CRH. On the other hand, 3D cloud radiative effects are much smaller, especially for stratiform clouds within the warm conveyor belt of the cyclone. Our analysis in 10 particular highlights the potential to improve the simulation of CRH by better representing ice-optical properties. Future work should in particular address how uncertainty in ice-optical properties affects the dynamics and predictability of extratropical cyclones.
Evaluation des performances de l'analyse statistique et physique d'images hyperspectrales de Mars. Application au capteur multi-angulaire CRISM

Une nouvelle generation de spectrometres imageurs emerge dans le domaine de l'exploration spatiale par l'ajout d'une dimension supplementaire de mesure, la dimension angulaire. L'imagerie spectroscopique multi-angulaire est concue pour fournir une caracterisation plus precise des materiaux planetaires et permet une meilleure separation des signaux provenant de l'atmosphere et la surface. Le capteur Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) a bord de la sonde Mars Reconnaissance Orbiter est une camera hyperspectrale qui fonctionne systematiquement dans le mode multi-angulaire depuis l'orbite. Neanmoins, les images multi-angulaires hyperspectrales posent certains problemes de manipulation, de visualisation et d'analyse en raison de leur taille et de leur complexite. Dans ce cadre, cette these propose des algorithmes statistiques et physiques pour analyser les images acquises par l'instrument CRISM de maniere efficace et robuste. Premierement, je propose une chaine de post-traitement visant a ameliorer la qualite radiometrique des donnees CRISM et a generer des produits ameliores, ces dernieres donnees etant concues pour permettre une analyse fine de la planete Mars. Deuxiemement, je m'interesse a la correction atmospherique des images CRISM en exploitant les capacites multi-angulaires de cet instrument. Un algorithme innovant, a base physique est mis en oeuvre pour compenser les effets atmospheriques afin d'estimer la reflectance de surface. Cette approche est particulierement utilisee dans cette these pour deduire les proprietes photometriques des materiaux qui coexistent dans un site specifique de Mars, le cratere de Gusev. Troisiemement, j'effectue une comparaison d'une selection des meilleurs techniques existantes, visant a realiser une deconvolution spectrale des donnees acquises par l'instrument CRISM. Ces techniques statistiques se sont averees utiles lors de l'analyse d'images hyperspectrales de maniere non supervise, c'est a dire, sans aucun a priori sur la scene. Une strategie originale est proposee pour discriminer les techniques les plus appropriees pour l'exploration de Mars, a partir de donnees independantes provenant d'autres capteurs d'imagerie haute resolution afin de construire une verite de terrain.
Expander Recovery Performance of Bipartite Graphs With Girth Greater Than 4

Expander recovery is an iterative algorithm designed to recover sparse signals measured with binary matrices with linear complexity. In the paper, we study the expander recovery performance of the bipartite graph with girth greater than 4, which can be associated with a binary matrix with column correlations equal to either 0 or 1. For such a graph, expander recovery is proved to achieve the same performance as the traditional basis pursuit recovery, as the signal is dissociated. Compared to random graphs widely used for expander recovery, the graph we study tends to present better empirical performance. Furthermore, its special structure enables reducing the iteration number of expander recovery from <inline-formula><tex-math notation="LaTeX">$O(n\log k)$</tex-math></inline-formula> times to exactly <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> times in serial recovery, and from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\log k)$</tex-math></inline-formula> times to exactly one time in parallel recovery.
The Eﬀect of Flood Exposure on Insurance Adoption among US Households

Despite increasing exposure to ﬂooding and associated ﬁnancial damages, estimates suggest more than two-thirds of ﬂood-exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the ﬁnancial solvency of the United States National Flood Insurance Program (NFIP). We study whether repeated exposure to ﬂood events, especially disaster-scale ﬂoods expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take-up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of ﬂood events over time, we quantify how ﬂood events impact local insurance demand. We ﬁnd that a ﬂood disaster declaration in a given year increases the take-up rate of insurance by 7% in the following year, but the eﬀect diminishes in subsequent years and is gone after ﬁve years. This eﬀect is more short-lived in counties in inland states that do not border the Gulf and Atlantic coasts. The eﬀect of a ﬂood on takeup is substantially larger if there was also a ﬂood in the previous year. We also ﬁnd that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non-primary residences. Our results provide a more comprehensive understanding of the salience eﬀect of ﬂooding on insurance demand compared to previous studies. Overall, these ﬁndings suggest that relying on households to self-adapt to increasing ﬂood risks in a changing climate is insuﬃcient for closing the insurance protection gap.
Narrative competence and underlying mechanisms in children with pragmatic language impairment

This study investigated narrative competence in children with pragmatic language impairment (PLI) and the extent to which it is related to impairments in theory of mind and executive functioning (EF). Narrative competence was assessed using a retelling design in a group of 77 children with PLI and a control group of 77 typically developing children, aged 5. The children with PLI showed an overall poorer narrative competence as apparent in measures of narrative productivity, organization of content, and cohesion. Some of these differences could be attributed to language impairments. The remaining differences could be partly interpreted as pragmatic deficits. In typically developing children, narrative productivity skills were related to both theory of mind and EF, but only theory of mind explained unique variance once language ability was added to the model. In the PLI group, however, narrative productivity skills were solely related to EF, over and above language abilities. Organization of story content and cohesion were not related to any of the cognitive measures for either group. The results indicate that children with PLI show narrative deficits and that these deficits are related to EF.
Building and Sustaining Ethnically, Racially, and Gender Diverse Software Engineering Teams: A Study at Google

Teams that build software are largely demographically homogeneous. Without diversity, homogeneous perspectives dominate how, why, and for whom software is designed. To understand how teams can successfully build and sustain diversity, we interviewed 11 engineers and 9 managers from some of the most gender and racially diverse teams at Google, a large software company. Qualitatively analyzing the interviews, we found shared approaches to recruiting, hiring, and promoting an inclusive environment, all of which create a positive feedback loop. Our findings produce actionable practices that every member of the team can take to increase diversity by fostering a more inclusive software engineering environment.
Entity-Relationship Modeling : Historical Events , Future Trends , and Lessons Learned

This paper describes the historial developments of the ER model from the 70’s to recent years. It starts with a discussion of the motivations and the environmental factors in the early days. Then, the paper points out the role of the ER model in the Computer-Aided Software Engineering (CASE) movement in the late 80’s and early 90’s. It also describes the possibility of the role of author’s Chinese culture heritage in the development of the ER model. In that context, the relationships between natural languages (including Ancient Egyptian hieroglyphs) and ER concepts are explored. Finally, the lessons learned and future directions are presented.
EEG-based classification of natural sounds reveals specialized responses to speech 1 and music

: 20 Humans can easily distinguish many sounds in the environment, but speech and music are 21 uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the 22 brain that respond selectively for speech and music. Yet there is little evidence that brain 23 responses are larger and more temporally precise for human-specific sounds like speech and 24 music, as has been found for responses to species-specific sounds in other animals. We recorded 25 EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By 26 classifying each sound based on the EEG response, we found that speech, music, and impact 27 sounds were classified better than other natural sounds. But unlike impact sounds, the 28 classification accuracy for speech and music dropped for synthesized sounds that have identical 29 “low-level” acoustic statistics based on a subcortical model, indicating a selectivity for higher-30 order features in these sounds. Lastly, the trends in average power and phase consistency of the 31 two-second EEG responses to each sound replicated the patterns of speech and music selectivity 32 observed with classification accuracy. Together with the classification results, this suggests that 33 the brain produces temporally individualized responses to speech and music sounds that are 34 stronger than the responses to other natural sounds. In addition to highlighting the importance of 35 speech and music for the human brain, the techniques used here could be a cost-effective and 36 efficient way to study the human brain’s selectivity for speech and music in other populations.
Formulasi dan Uji Penetrasi In Vitro Sediaan Gel Transfersom Mengandung Kofein sebagai Antiselulit

Transfersom merupakan vesikel nano yang bersifat deformabel sehingga mampu berpenetrasi sampai ke lapisan kulit yang lebih dalam. Kofein memiliki khasiat sebagai antiselulit. Penelitian ini bertujuan untuk formulasi dan mengetahui kemampuan daya penetrasi gel transfersom kofein dibandingkan dengan gel kofein tanpa transfersom. Empat formula transfersom dibuat dengan konsentrasi kofein yang berbeda (1; 2; 3; 5%) menggunakan metode hidrasi lapis tipis. Suspensi formula 4 dengan ukuran partikel 202,35 nm, indeks polidipersitas 0,1090 dan efisiensi jerapan 58,91% dipilih untuk sediaan gel. Uji penetrasi dilakukan secara in vitro dengan sel difusi Franz. Hasil yang diperoleh adalah jumlah kumulatif kofein yang terpenetrasi dari gel transfersom lebih tinggi yaitu 1218,34+358,71 μg cm-2, persentase jumlah kumulatif 8,53+2,55% dan fluks 360,91+86,50 μg cm-2 jam-1. Sementara gel kofein tanpa transfersom memiliki jumlah kumulatif terpenetrasi 369,29+231,57 μg cm-2, persentase jumlah kumulatif 2,61+1,37% dan fluks 70,96+73,39 μg cm-2 jam-1. Dapat disimpulkan bahwa gel transfersom kofein menghasilkan daya penetrasi yang lebih baik dibandingkan gel kofein tanpa transfersom.
Learned Monotone Minimal Perfect Hashing

A Monotone Minimal Perfect Hash Function (MMPHF) constructed on a set S of keys is a function that maps each key in S to its rank. On keys not in S, the function returns an arbitrary value. Applications range from databases, search engines, data encryption, to pattern-matching algorithms. In this paper, we describe LeMonHash, a new technique for constructing MMPHFs for integers. The core idea of LeMonHash is surprisingly simple and effective: we learn a monotone mapping from keys to their rank via an error-bounded piecewise linear model (the PGM-index), and then we solve the collisions that might arise among keys mapping to the same rank estimate by associating small integers with them in a retrieval data structure (BuRR). On synthetic random datasets, LeMonHash needs 34% less space than the next larger competitor, while achieving about 16 times faster queries. On real-world datasets, the space usage is very close to or much better than the best competitors, while achieving up to 19 times faster queries than the next larger competitor. As far as the construction of LeMonHash is concerned, we get an improvement by a factor of up to 2, compared to the competitor with the next best space usage. We also investigate the case of keys being variable-length strings, introducing the so-called LeMonHash-VL: it needs space within 13% of the best competitors while achieving up to 3 times faster queries than the next larger competitor.
Beyond Smart Cities: How Cities Network, Learn and Innovate

1. Overview 2. The Slow Emergence of Learning Cities in an Urbanizing World 3. Cities as Collective Learners: What Do We Know? 4. A Gamut of Learning Types 5. Light on a Shadow Economy: City Learning in 53 Cities 6. Informal Learners - Turin, Portland and Charlotte 7. Technical Learning: Curitiba and City Think Tanks 8. Corporate Styles: Bilbao, Seattle and Others 9. Clouds of Trust in Style 10. Taking Stock: Why Some Cities Learn and Others Do Not 11. Turning the Learning World Upside Down - Pathways Forward in Policy and Research
In-Situ Sol-Gel Method of TiO 2 -reduced Graphene Oxide as Photocatalyst

Nanocomposites of TiO 2 -reduced Graphene Oxide were synthesized with graphene oxide (GO) loadings of 4, 8, 12, and 16 mg via in situ sol-gel method. The raw material of graphene oxide was coconut shell charcoal which was processed using a modiﬁed Hummer’s method by dissolving the graphite into the acid mixture of H 2 SO 4 and H 3 PO 4 . The rGO-TiO 2 composites were characterized by X-ray Diffraction (XRD), Inductance, Capacitance & Resistance Meter (LCR-Meter), and UV-Vis. LCR-meter results revealed that the highest electricalconductivityofthesampleis5.37 × 10 –6 Scm − 1 withaGOcomposition of 4 mg. XRD analysis results show that the sample was having both anatase and rutile phases with anatase as the dominant phase. The photocatalytic activity of the sample was investigated using a photocatalytic reactor under UV exposure using methylene blue as a pollutant. Composite with GO loading 4 mg resulted in the best percentage of degradation reaching 81.37% in 15 min.
The future of marine governance

[Extract] A remarkably simple lesson has emerged from recent research into preserving marine resources around the world: concerted human planning and effort are effective in preventing further decline of marine ecosystems. Fundamentally, the deterioration of marine ecosystems signals a crisis of governance, and the widespread degradation of our environment is a symptom of inadequate, dysfunctional, or missing institutions. ¹ Currently, most coastal and almost all oceanic fisheries lack effective property rights, leading to widespread depletion of stocks, while globalized markets pressure people to fish even harder. However, management of common-pool resources within the nearshore territorial waters of some countries has markedly improved in recent years, and some improvement has also been achieved at larger scales within the Exclusive Economic Zones (generally up to 200 nautical miles from the coast) of progressive nations. For example, Australia's rezoning of the Great Barrier Reef² and its designation of the adjoining Coral Sea as a new conservation zone represent a marked improvement in that country's marine governance and management. The major challenge globally, to overcome rampant free riding on the high seas, is to design new international agreements that reward cooperation and sanction violations.