Miracle-dz/UniFAR-FTUs huggingface dataset:
Do high recapture rates indicate representative sampling? The relationship between recapture probability, risk-taking, and personality

ABSTRACT Context. Monitoring programs provide valuable information on wildlife populations, thereby underpinning strategies for conservation and control. For threatened species, where every animal represents a substantial portion of the population, representative sampling is vital. One fundamental challenge during sampling is understanding drivers of survey bias; for instance, behavioural heterogeneity in trap response. Methods such as capture–mark–recapture have long been used to estimate capture and recapture heterogeneity; yet, this method, like many others, is able to gather data only from the trappable and re-trappable portion of the population; a problem that presents a particular challenge for small or vulnerable populations. A greater understanding of why biases arise can result in improved survey methods, more reliable survey data and increased modelling accuracy. Aims. We focus on an endangered species with unusually high recapture probabilities (0.78–0.92), namely, the mountain pygmy-possum (Burramys parvus). Specifically, we examine whether, within a single trapping session, a recapture bias exists either as a function of past trapping experience or personality. Methods. We tested whether recapture probability differs among cohorts with different capture histories (‘known’ animals captured during trapping sessions in previous years vs ‘new’ animals trapped for the first time in this study). We also tested for individual personality, general risk-taking behaviour during foraging, and subsequent links to recapture probability. Key results. Recapture probability was significantly affected by cohort. New animals had lower probabilities of recapture and took fewer risks during foraging than did known animals. Although personality did not significantly influence recapture probability, it did influence risk-taking during foraging. Conclusions. Despite high recapture probability within the populations, captures were significantly skewed towards a subset of the population, likely being due to different perceptions of risk among individuals. Implications. Understanding potential sources of bias during live-capture surveys is the initial step towards modifying and improving surveys to reduce sampling biases and to ensure representative population sampling.
Identification of a Five Immune Term Signature for Prognosis and Therapy Options (Immunotherapy versus Targeted Therapy) for Patients with Hepatocellular Carcinoma

Background Immune microenvironment implicated in liver cancer development. Nevertheless, previous studies have not fully investigated the immune microenvironment in liver cancer. Methods The open-access data used for analysis were obtained from The Cancer Genome Atlas (TCGA-LIHC) and the International Cancer Genome Consortium databases (ICGC-JP and ICGC-FR). R program was employed to analyze all the data statistically. Results First, the TCGA-LIHC, ICGC-FR, and ICGC-JP cohorts were selected for our analysis, which were merged into a combined cohort. Then, we quantified 53 immune terms in this combined cohort with large populations using the ssGSEA algorithm. Next, a prognostic approach was established based on five immune principles (CORE.SERUM.RESPONSE.UP, angiogenesis, CD8.T.cells, Th2.cells, and B.cells) was established, which showed great prognostic prediction efficiency. Clinical correlation analysis demonstrated that high-risk patients could reveal higher progressive clinical features. Next, to examine the inherent biological variations in high- and low-risk patients, pathway enrichment tests were conducted. DNA repair, E2F targets, G2M checkpoints, HEDGEHOG signaling, mTORC1 signaling, and MYC target were positively correlated with the risk score. Examination of genomic instability revealed that high-risk patients may exhibit a higher tumor mutation burden score. Meanwhile, the risk score showed a strong positive correlation with the tumor stemness index. In addition, the Tumor Immune Dysfunction and Exclusion outcome indicated that high-risk patients could be higher responsive to immunotherapy, whereas low-risk patients may be higher responsive to Erlotinib. Finally, six characteristic genes DEPDC1, DEPDC1B, NGFR, CALCRL, PRR11, and TRIP13 were identified for risk group prediction. Conclusions In summary, our study identified a signature as a useful tool to indicate prognosis and therapy options for liver cancer patients.
Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning

The ways in which people dress, as well as the styles that they prefer for different contexts and occasions, are part of their identity. Every day, blind people face limitations in identifying and inspecting their garments, and dressing can be a difficult and stressful task. Taking advantage of the great technological advancements, it becomes of the utmost importance to minimize, as much as possible, the limitations of a blind person when choosing garments. Hence, this work aimed at categorizing and detecting the presence of stains on garments, using artificial intelligence algorithms. In our approach, transfer learning was used for category classification, where a benchmark was performed between convolutional neural networks (CNNs), with the best model achieving an F1 score of 91%. Stain detection was performed through the fine tuning of a deep learning object detector, i.e., the mask R (region-based)-CNN. This approach is also analyzed and discussed, as it allowed us to achieve better results than those available in the literature.
Nanomaterials and Devices for Harvesting Ambient Electromagnetic Waves

This manuscript presents an overview of the implications of nanomaterials in harvesting ambient electromagnetic waves. We show that the most advanced electromagnetic harvesting devices are based on oxides with a thickness of few nanometers, carbon nanotubes, graphene, and molybdenum disulfide thanks to their unique physical properties. These tiny objects can produce in the years to come a revolution in the harvesting of energy originating from the Sun, heat, or the Earth itself.
Anti-Muslim Surveillance: Canadian Muslims’ Experiences with CSIS

The targeting of Muslim communities through “the War on Terror” has given rise to a variety of schemes and tactics informed by Islamophobia and racializing narratives. Yet, there are few studies examining the specific intelligence practices deployed by governments as they engage in forms of racialized surveillance. This study analyses 95 in-depth interviews with Muslim community leaders in five Canadian cities to map the material structural practices employed by the Canadian Security Intelligence Services (CSIS) in its racialized surveillance of Muslim communities. This study documents how CSIS engages in the mass surveillance of Muslim communities, transforms Mosques into spaces of surveillance, creates a community of informants, and targets political activism. Moreover, we found that CSIS deploys illegal practices such as threatening citizenship and refugee status, intimidating people in their homes during the night and denying legal representation during interrogations. The article also explores how these state-led anti-Muslim surveillance tactics produce internal forms of community surveillance where individuals begin to self-regulate their own behavior. The level of CSIS surveillance of Muslim communities raises questions about the extent to which CSIS is overstepping its powers and engaging in illegal practices.
Multiple‐soliton solutions of Einstein’s equations

Using the Belinsky–Zakharov generating technique and a flat metric as a seed, two‐ and four‐soliton solutions of the Einstein vacuum equations for the cases of stationary axisymmetric, cylindrically symmetric, or plane symmetric gravitational fields are considered. Three‐ and five‐parameter classes of exact solutions are obtained, some of which are new.
Power-efficient carrier-depletion SOI Mach-Zehnder modulators for 4x25Gbit/s operation in the O-band

In this paper, we communicate on the design, fabrication, and testing of optical modulators for Silicon-based photonic integrated circuits (Si-PICs) in the O-band (1.31 μm), targeting the 100GBASE-LR4 norm (4 wavelengths at 25 Gbit/s). The modulators have been conceived to be later coupled with hybrid-III-V/Si lasers as well as echelle grating multiplexer, to create a hetero-integrated optical transmitter on a silicon-on-insulator (SOI) platform. The devices are based on a Mach-Zehnder Interferometer (MZI) architecture, where a p-n junction is implanted to provide optical modulation through carrier depletion. A detailed study focusing on the best doping scheme for the junction, aimed at optimizing the overall transmitter performance and power-efficiency is presented. In detail, the trade-off between low optical losses and high modulation efficiency is tackled, with a targeted CMOS-compatible voltage drive of 2.5 V. Process simulations of the junction are realized for the doping profile optimization. Modulators of different lengths are also investigated to study the compromise between extinction ratio, insertion losses and bandwidth. Furthermore, coplanar-strip (SGS) travelling-wave electrodes are designed to maximize the bandwidth, to reach the targeted bit rate of 25 Gbit/s. Measurements show modulation efficiencies up to 19 °/mm (or 2.4 V.cm) for a 2.5 V input voltage, with doping-related losses below 1 dB/mm, in line with theoretical estimates, and well-suited to enhance the Si-PIC transmission and power-efficiency. Finally, an electro-optical (EO) bandwidth at 1.25 V bias is measured above 28 GHz.
The conservation laws and integrals of motion for a certain class of equations in discrete models

We derive the conservation laws for a wide class of discrete equations, including the q-deformations of classical field-theoretic equations. The explicit expressions for the conserved currents are used in the construction of the integrals of motion. The procedure is applied to the nonlinear difference equation and to the q-wave equations in D=3 and D=4.
Performance Evaluation of Multi-Channel for 10×10 Mesh Wireless Network-on-Chip Architecture

Wireless Network-on-Chip (NoC) is envisioned as complementary to the conventional NoC due to its CMOS compatibility and architectural flexibility, which is advantageous as no wiring infrastructure is required for wireless transmission. On-chip wireless channels are used to actually minimize the communication latency between the distant processing cores because of its ability to communicate with long-distance communication processing cores in a single-hop. This paper investigates the effect of the single-, dual-, and triple-channels on the mesh-WiNoC architecture. Additionally, four and nine radio hubs are evenly distributed throughout the mesh-WiNoC topological structure to evaluate its global transmission latency, network throughput, and energy characteristics. The investigated architectures under test are simulated on the cycle-accurate systemC based Noxim simulator under a random traffic workload scenario for WiNoC performance evaluation. This study’s contribution is that it looks into the best number of wireless channels to use in a 10 × 10 mesh WiNoC architecture for 4 and 9 radio hub scenarios to get the best performance in transmission latency and energy consumption. Experimental results show that for both investigated number of radio hub on mesh-WiNoC architecture demonstrates nearly identical system performance in terms of transmission latency and throughput. However, the meshWiNoC architecture with 4 radio hub demonstrates better energy characteristics, saving 9.63% and 13.60% of energy, respectively, when compared to the architecture with 6 and 9 radio hub.
A heuristic optimization approach for ship block painting operation scheduling

The workload of the block painting stage accounts for the largest proportion of the ship painting, so its operation scheduling directly affects the painting quality and efficiency. In this paper, the scheduling problem of ship block painting operation is investigated. The problem can be regarded as a variant of the flexible job-shop scheduling problem. The mathematical model is developed considering a variety of practical constraints, including space size constraints, multiple spray-painting constraints, and waiting time constraints between processes. To effectively solve the problem, we propose a hybrid meta-heuristic to combine the merits of genetic algorithm and tabu search. We conduct numerical experiments on actual production data instances of different scales. The results show that our hybrid approach has significant advantages over the baseline algorithms.
Accuracy of the scalar approximation in computations of diffuse and coherent backscattering by discrete random media

We use numerically exact computer simulations of multiple scattering in physically realistic models of sparse discrete random media to quantify the errors of the scalar approximation SA in computations of coherent backscattering CB assuming that the incident light is unpolarized. We show that while the SA errors in the diffuse backscattered intensity are often small, those in the CB enhancement factor can reach 25% and often exceed 20%. We attribute this to the fact that the computation of the enhancement factor involves all diagonal elements of the diffuse backscattering Stokes matrix rather than only its 1, 1 element. Therefore, the coherent enhancement of backscattered intensity appears to be the result of a complex interplay of various polarization effects involved in the process of multiple scattering. Thus our numerical data make a strong case against the use of the SA in theoretical computations of CB in the case of unpolarized incident light.
A Framework for Applying Global Learning to Improve Primary Health Care in the United States

Background and objectives: Lessons from global health have long informed efforts to improve primary health care (PHC) in the United States (US). Despite this history, no generalizable framework exists to guide US stakeholders in the identification and application of ideas from abroad related to the key PHC components of community engagement and service delivery. We sought to develop such a framework. Methods: We reviewed the experience of Global to Local, a community-based organization (CBO) founded with a mission to apply global health strategies to improve the health in vulnerable populations in the US, and examined the experience of care delivery organizations in the US that have successfully implemented global-to-local solutions. Based on that experience, and supported by the advice of an expert panel, we developed a framework for applying global learning to improve US PHC. Findings: The framework includes six change concepts under three broad categories. The first category focuses on the need to actively and intentionally incorporate a global perspective in organizational program design and improvement activities. The second category addresses approaches to identifying global solutions related to community engagement and to health service delivery. The third category focuses on adaptation and implementation of lessons from global health in domestic contexts by applying relevant insights from dissemination and implementation science and diffusion of innovation theory. Conclusions: In the absence of a robust literature providing implementation guidance to US health systems and CBOs open to adopting or adapting PHC strategies and practices from other countries, the proposed framework synthesizing the experience of organizations that have done so can inform efforts to apply lessons from global health to improve PHC in the US.
Interventions to support adolescents and young adults with the healthcare transition from paediatric to adult nephrology health services: A scoping review protocol

Background Due to technological advancements and improved medical management of adolescents and young adults (AYAs) living with renal disease, there has been an exponential increase noted in the number of patients advancing from the paediatric to the adult nephrology healthcare setting. Subsequently, more AYAs are required to undergo the process of healthcare transition from paediatric to adult healthcare services. This process can be a challenging period for young people and families and is often associated with a decline in physical and psychosocial health outcomes of AYAs with renal disorders. To ensure a successful transition, AYAs must develop the ability to manage their renal condition, including the medical and psychosocial aspects of their condition, independently. Despite significant research into the transition from paediatric to adult healthcare for this unique patient cohort, the transition period remains a challenge at times. This scoping review aims to map, explore, and understand the interventions that are currently available to offer positive perceptions and experiences of transition for both AYAs living with renal disorders and their families. Methods A systematic literature search will be conducted of PubMed, PsycInfo, CINAHL, ASSIA, EMBASE and Web of Science databases from the year 2000 to present. Two independent reviewers will screen the peer-reviewed literature obtained and assess them against the inclusion criteria to determine their inclusion eligibility. Data will be extracted and synthesised using a template refined by the authors. The scoping review will be undertaken in accordance with PRISMA-ScR guidelines. Data will undergo a formal critical appraisal using recognised appraisal tools. Conclusions Through mapping this knowledge, the scoping review will aim to identify interventions that are currently available and identify gaps within the literature. This evidence may support the development of transitional care interventions in the future, promote patient satisfaction, and improve patient outcome measures and experiences.
Graphene Nanofoam Based Nanomaterials: Manufacturing and Technical Prospects

This article fundamentally reviews progress in the design and manufacturing of three-dimensional (3D) graphene-based nanocomposites for technical applications. The 3D graphene nanostructures have been manufactured using techniques like the template method, chemical vapor deposition, sol-gel, freeze-drying, hydrothermal technique, and other approaches. The nanofoam has been reinforced in polymers to achieve superior structural, morphological, and physical characteristics of the ensuing polymer/graphene nanofoam nanocomposites. The polymer/graphene nanofoam nanocomposites have been manufactured using the approaches like direct template method, in situ technique, infiltration process, and other methods. The 3D nanofoam- and polymer-based nanostructures have shown high specific surface area, suppleness, electron transport, thermal conduction, mechanical resilience, and other physical properties. The technical applications of hierarchical graphene nanofoams have been observed in the fields of radiation shielding, solar cells, supercapacitors, fuel cells, and other applications.
Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloading-related issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybrid-based algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.
Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases.