Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-Morphology and Mechanical Function of the Trichoideum Sensillum in Nabis rugosus (Linnaeus, 1758) (Insecta: Heteroptera: Cimicomorpha)

Simple Summary Nabis rugosus is a representative of the Nabidae family belonging to the heteropteran group. It is a predator of tiny insects and has different sensory receptors that detect environmental changes. The present study focuses on the antennal sensilla of N. rugosus, mainly on the trichoideum sensillum as mechanoreceptors for detecting various tactile factors surrounding the insect. The morphology of trichoideum mechanosensillum in N. rugosus was modelled as a three-dimensional structure from the derived data sets using SEM and TEM. Specific inner features of the sensillum are also presented, which will be useful to build a biosensor for detecting physical environmental parameters. Abstract The present study aims to investigate the morphological features of the antennal sensilla by using SEM and TEM. The construction of a 3D model of trichoideum sensillum using Amira software is presented in this paper. Five sensillum types, namely trichoideum, chaeticum, campaniformium, coeloconicum, and basiconicum, were recorded. This model exhibits the mechanosensillum components, including the embedded hair in a socket attached by the joint membrane and the dendrite connected to the hair base passing through the cuticle layers. TEM images present the dendrite way, micro-tubules inside the dendritic sheath, and terminal structure of the tubular dendrite body and so-called companion cells included in the receptor, e.g., tormogen and trichogen. The parameters noted for the external structure and ultrastructure of the mechano-receptor indicate that they are specific to a particular type of sensillum and would be useful in developing the model for a biosensor. Results show that bio-inspired sensors can be developed based on morphological and ultrastructural studies and to conduct mechanical studies on their components.
Zooplankton composition links to climate and salmon survival in a northern temperate fjord

A zooplankton time series from the eastern Strait of Juan de Fuca in the Salish Sea was explored for its relationships to climate variability and survival of Puget Sound salmon populations. Vertical net tows were conducted ~ monthly over the upper 40 m from 2003 to 2018. Species with warm‐water associations that occur year‐round in Puget Sound, and species with cold‐water associations that dominate on the continental shelf during summer upwelling, were important members of the community at different times of year. Shifts in advection from Puget Sound and the coastal ocean were indicated by the abundance of Puget Sound‐dominant copepods Ditrichocorycaeus anglicus and Paracalanus spp. in summer through mid‐fall. In contrast, the coastal upwelling species Pseudocalanus mimus, Calanus marshallae, and Acartia longiremis were most abundant in fall through early spring, despite being the seasons they are least abundant on the continental shelf. Axis 1 of a Nonmetric Multidimensional Scaling ordination of the copepod community revealed clear seasonal cycles in community structure and longer‐term variance that correlated with the Pacific Decadal Oscillation and local temperatures. Axis 2 correlated to smolt‐to‐adult survival of Puget Sound coho salmon, but not with measured environmental factors. Copepod size composition and biogeographic affinities shifted along that axis. The mechanisms driving the shifts are not clear, but may indicate changes in trophic dynamics, or cryptic responses to environmental changes. These patterns suggest the importance of large‐scale climate influences and local environmental processes that affect estuarine circulation, population growth, and links to economically important fisheries.
Economic Dispatch for Grid-Connected Wind Power With Battery-Supercapacitor Hybrid Energy Storage System

This study demonstrates an effective dispatching scheme of utility-scale wind power at one-hour increments for an entire day with a hybrid energy storage system consisting of a battery and a supercapacitor (SC). Accurate forecasting of wind power is crucial for generation scheduling and economic operation. Here, wind speed is predicted by one hour ahead of time using a multilayer perceptron Artificial Neural Network, which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, an adaptive neuro-fuzzy inference system is employed to devise a state of charge (SOC) controller to accurately estimate the grid reference power (P$_\mathrm{Grid,ref}$) for each one-hour dispatching period. This type of desired P$_\mathrm{Grid,ref}$ estimation is critical to ensure the energy storage system (ESS) completes each dispatching period with its starting SOC and has adequate capacity available for next-day operation. Also, the particle swarm optimization technique is implemented to optimize the life cycle cost of the ESS based on its depth of discharge usage, which is vital for minimizing the cost of a dispatchable wind power scheme. The actual wind speed data of four different days as a representative of each season recorded at Oak Ridge National Laboratory are utilized in the simulations to provide a realistic economic assessment for dispatching the wind power.
Actuator Fault Diagnosis for a Class of Nonlinear Uncertain Systems via Deterministic Learning

In this article, we investigate the actuator fault detection (AFD) problem for a class of closed-loop systems with nonlinear uncertain dynamics. Two AFD schemes are developed via the deterministic learning method (DLM). In the first scheme, knowledge of the system dynamics and actuator fault dynamics is extracted from state signals by using DLM. The learned knowledge is utilized to construct a bank of estimators. When the residual norm of a fault estimator becomes smaller than that of the nominal estimator, the occurrence of the actuator fault is deduced. In the second scheme, a learning controller is constructed to identify the feedback control dynamics under normal and fault conditions. The knowledge of the feedback control dynamics is utilized to construct a bank of controllers, which are embedded in a bank of estimators. When the residual norm of the estimator embedded with the matched controller becomes smaller than that of the estimator embedded with the nominal controller, the actuator fault can be detected. Finally, the properties of constant neural networks and the persistent excitation condition are exploited to derive the detectability conditions for diagnosis schemes. The attraction of this article is that two knowledge-bank-based AFD schemes are developed, in which the knowledge extracted from state and controller signals will enhance the sensitivity to actuator faults. Simulation results are also included to illustrate the effectiveness of these schemes.
Trust Region Methods

Preface 1. Introduction Part I. Preliminaries: 2. Basic Concepts 3. Basic Analysis and Optimality Conditions 4. Basic Linear Algebra 5. Krylov Subspace Methods Part II. Trust-Region Methods for Unconstrained Optimization: 6. Global Convergence of the Basic Algorithm 7.The Trust-Region Subproblem 8. Further Convergence Theory Issues 9. Conditional Models 10. Algorithmic Extensions 11. Nonsmooth Problems Part III. Trust-Region Methods for Constrained Optimization with Convex Constraints: 12. Projection Methods for Convex Constraints 13. Barrier Methods for Inequality Constraints Part IV. Trust-Region Mewthods for General Constained Optimization and Systems of Nonlinear Equations: 14. Penalty-Function Methods 15. Sequential Quadratic Programming Methods 16. Nonlinear Equations and Nonlinear Fitting Part V. Final Considerations: Practicalities Afterword Appendix: A Summary of Assumptions Annotated Bibliography Subject and Notation Index Author Index.
The Affordances of Multivariate Elo-Based Learner Modeling in Game-Based Assessment

Previous research and experiences have indicated the potential that games have in educational settings. One of the possible uses of games in education is as game-based assessments (GBA), using game tasks to generate evidence about skills and content knowledge that can be valuable. There are different approaches in the literature to implement the assessment machinery of these GBA, all of them having strengths and drawbacks. In this article, we propose using multivariate Elo-based learner modeling, as we believe it has a strong potential in the context of GBA for three aims: first, to simultaneously measure students competence across several knowledge components in a game; second, to predict task performance; and finally, to estimate task difficulty within the game. To do so, we present our GBA Shadowspect, which is focused on solving geometry puzzles, and we depict our implementation using data collected from several high schools across the USA. We obtain high-performing results (AUC of 0.87) and demonstrate that the model enables analysis of how each student's competency evolves after each puzzle attempt. Moreover, the model provides accurate estimations of each task's difficulty, enabling iterative improvement of the game design. This study highlights the potential that multivariate Elo-based learner modeling has within the context of GBA, sharing lessons learned, and encouraging future researchers in the field to consider this algorithm to build their assessment machinery.
Classification of transition temperature behavior in ferroelectric PbTiO3-Bi(Me'Me )O3 solid solutions

The ferroelectric transition temperature (Tc) behavior of perovskite solid solutions based on PbTiO3–Bi(Me′Me″)O3 (Me′=Fe3+, Zn2+, Sc3+, In3+, Mg2+, Ni2+, etc., and Me″=Ti4+, Nb5+, W6+) was considered. Trends in the Tc compositional dependence near the PbTiO3 end member could be described with a geometrical polynomial expression. Three main cases were observed: Case 1, a continued increase in transition temperature above the end member PbTiO3 (495°C); case 2, an increase and then decrease of the transition temperature; and case 3, a continuous decrease in the transition temperature with Bi(Me′Me″)O3 additions. It was noted that for all case 2 examples the enhancement of ΔTc=Tc(max)−Tc(PT) increased as the distribution of B-site ionic radii increased. A correlation was therefore proposed between the maximum enhancement in transition temperature and the spread of tolerance factor (Δt) and∕or variance in B-site ionic radius (σ2). Finally, it was proposed that these observations are consistent with random-fie.
Bolt: Scalable and Cost-Efficient Multistring Pattern Matching With Programmable Switches

Multi-string pattern matching is a crucial building block for many network security applications and thus of great importance. Since every byte of a packet has to be inspected by a large set of patterns, it often becomes a bottleneck of these applications and dominates the performance of an entire system. Many existing studies have been devoted to alleviating this performance bottleneck either by algorithm optimization or hardware acceleration. However, neither one provides the desired scalability and costs that keep pace with the drastic increase in network bandwidth and traffic today. To address these issues, in this paper, we present BOLT, a scalable and cost-efficient multi-string pattern matching system leveraging the capability of emerging programmable switches. BOLT combines the following techniques: (1) an efficient state encoding scheme to fit a large number of strings into the limited memory on a programmable switch; (2) a variable $k$ -stride transition mechanism to increase the throughput significantly with the same level of memory cost; and (3) a compact pattern2rule mapping method to accommodate multiple co-existing strings in one rule. We implement a prototype of BOLT and make its source code publicly available. Extensive evaluations demonstrate that BOLT can provide multi-hundred Gbps throughput and scales well with various pattern sets and workloads.
Hearing Loss in Stickler Syndrome: An Update

Stickler syndrome is a connective tissue disorder characterized by ocular, skeletal, orofacial and auditory manifestations. Its main symptoms are high myopia, retinal detachment, joint hypermobility, early osteoarthritis, cleft palate, midfacial hypoplasia, micrognathia and hearing loss. Large phenotypical variability is apparent and partly explained by the underlying genetic heterogeneity, including collagen genes (COL2A1, COL11A1, COL11A2, COL9A1, COL9A2, COL9A3) and non-collagen genes (BMP4, LRP2, LOXL3). The most frequent type of Stickler syndrome (COL2A1) is characterized by a rather mild high-frequency sensorineural hearing loss in about half of the patients. COL11A1- and COL11A2-related Stickler syndrome results in more frequent hearing loss, being moderate and involving all frequencies. Hearing loss in the rarer types of Stickler syndrome depends on the gene expression in the cochlea, with moderate to severe downsloping hearing loss for Stickler syndrome caused by biallelic type IX collagen gene mutations and none or mild hearing loss for the non-collagen genes. Inherent to the orofacial manifestations, middle ear problems and temporary conductive hearing loss, especially at young age, are also prevalent. Consequently, hearing loss should be actively sought for and adequately treated in Stickler syndrome patients given its high prevalence and the concomitant visual impairment in most patients.
Attitude Analysis of Agriculture Graduates towards Agriculture Education

The present study was conducted to analyze the attitude of agriculture graduates towards agriculture education. It is very important to analyze the attitude of agriculture studies towards agriculture education, as this is the one of important sector to serve the farming community in future. The study was conducted in Orissa University of Agriculture and Technology, Bhubaneswar with 60 numbers of respondents by random sampling method. It has been observed that most of the respondents belong to general category, rural back ground, having good academic performance, interested to work as agriculture scientist and neutrally favorable towards agriculture education.
Coherent Targets Parameter Estimation for EVS-MIMO Radar

As an emerging technique for detection, electromagnetic vector sensor multiple-input multiple-output (EVS-MIMO) radar has attracted extensive interest recently. This paper focuses on the coherent targets issue in EVS-MIMO radar, and a spatial smoothing estimator is developed to estimate the multiple parameters. It first recovers the rank of the array data via forward spatial smoothing. Then, it estimates the elevation angles via the rotational invariance technique. Combined with the vector cross-product method, the azimuth angles are obtained. Thereafter, with the previously achieved direction angles, the polarized parameters are acquired by using the least squares technique. Unlike the existing polarization smoothing techniques, the proposed estimator is able to estimate the two-dimensional direction parameter. Furthermore, it can provide a polarized parameter of the target. In addition, the proposed estimator is computationally efficient, since it offers closed-form and automatically paired solutions to all the parameters. Numerical experiments are carried out to show its superiority and effectiveness.
Cancer-Related Bone Pain: Patients’ Satisfaction with Analgesic Pain Control

Background: Management of cancer-related bone pain (CRBP) with analgesics poses a substantial challenge. This study aimed to determine the correlation between pain control satisfaction and prescribed analgesics. Methods: This 12-month descriptive prospective study included 96 adults who were consecutively sampled and followed up daily for 5 days. Eligible patients had histopathologically confirmed tumor, osseous lesion(s) on radiographs, and cognitive capacity to rank pain on the Numerical Rating Scale. Data were collected using questionnaires drafted from the Brief Pain Inventory. Self-assessed pain scores within the past 24 hours were reported on a scale of 0 (no pain) to 10 (worst pain). Pain control satisfaction was assessed as a single response question (satisfied, not sure, or dissatisfied). Associations were examined in multiple logistic regression models. Ethical approval and informed consent were obtained. Results: The median age was 57 (range, 19–90) years, and more males (52.1%) than females were recruited. The proportion of CRBP ranged from 83.3% to 86.5%, whereas that of moderate to severe pain ranged from 57.3% to 69.8%. Overall, 70.8% were satisfied with their pain control. Patients prescribed opioids (adjusted odds ratio, 0.027; p=0.041) had an increased likelihood of pain control satisfaction. Conclusion: Although a high percentage of patients experienced moderate to severe CRBP, a majority were satisfied with their analgesic pain control. Prescription opioids were associated with higher satisfaction. Policies that sustain ready accessibility of analgesics, particularly opioids, should be implemented.
Building Information Modelling (BIM) and Energy Performance of Building - A Review

Nowadays, the advancement of technologies in the building industry may give a significant impact to the environment. The building sector contributes to the greenhouse gas emissions which may harm the environment and the high consumption of energy required high demand in burning the fossil fuel. These may effects the environment where, the carbon dioxide (CO2) has been significantly released to the atmosphere and harm the environment which may lead to global warming. In order to reduce the impact of greenhouse gas emissions and energy consumption, there is a need to develop a solution which may account these environmental problems. Conserving the energy of the building is one of the strategies to reduce the impact of greenhouse gas emissions to the environment. Recently, linking the building information modelling (BIM) to analyse the energy performance of the building has been spread widely. BIM has been recognized as a suitable tool to perform a building analysis in the early design stage which helps to model the energy use, flow of thermal, patterns of lighting and many more sustainable measures. BIM is believed able to help a designer to choose the best building design due to its concept of parametric change that assist the AEC industry to implement the sustainable design in an effective way and significantly improve the energy efficiency of building.
PROMO: PROactive MObility-support model for task scheduling in fog computing

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling.
SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration

Increased capabilities, such as recognition and self-adaptability, are now required from Internet-of-Things (IoT) applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus, optimized ML capabilities and data transfers should be integrated into the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g., image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This article presents SamurAI, a versatile IoT node bridging this gap in processing and energy by leveraging two on-chip sub-systems: a low-power, clock-less, event-driven always-responsive (AR) part and an energy-efficient on-demand (OD) part. AR contains a 1.7-MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207-ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3-TOPS/W machine learning (ML) for more complex tasks up to 36 GOPS. This architecture partitioning achieves the best-in-class versatility metrics, such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to $3.5\times $ , compared to cloud-based processing, and, thus, extended battery lifetime.
Non-Viral Delivery of CRISPR/Cas Cargo to the Retina Using Nanoparticles: Current Possibilities, Challenges, and Limitations

The discovery of the CRISPR/Cas system and its development into a powerful genome engineering tool have revolutionized the field of molecular biology and generated excitement for its potential to treat a wide range of human diseases. As a gene therapy target, the retina offers many advantages over other tissues because of its surgical accessibility and relative immunity privilege due to its blood–retinal barrier. These features explain the large advances made in ocular gene therapy over the past decade, including the first in vivo clinical trial using CRISPR gene-editing reagents. Although viral vector-mediated therapeutic approaches have been successful, they have several shortcomings, including packaging constraints, pre-existing anti-capsid immunity and vector-induced immunogenicity, therapeutic potency and persistence, and potential genotoxicity. The use of nanomaterials in the delivery of therapeutic agents has revolutionized the way genetic materials are delivered to cells, tissues, and organs, and presents an appealing alternative to bypass the limitations of viral delivery systems. In this review, we explore the potential use of non-viral vectors as tools for gene therapy, exploring the latest advancements in nanotechnology in medicine and focusing on the nanoparticle-mediated delivery of CRIPSR genetic cargo to the retina.
Might black holes reveal their inner secrets

Black holes harbor a spacetime singularity of infinite curvature, where classical spacetime physics breaks down, and current theory cannot predict what will happen. However, the singularity is invisible from the outside because strong gravity traps all signals, even light, behind an event horizon. In this essay we discuss whether it might be possible to destroy the horizon, if a body is tossed into the black hole so as to make it spin faster and/or have more charge than a certain limit. It turns out that one could expose a "naked" singularity if effects of the body's own gravity can be neglected. We suspect however that such neglect is unjustified.
Shadow Removal of Hyperspectral Remote Sensing Images With Multiexposure Fusion

Shadow removal is a challenging problem in hyperspectral remote sensing images due to its spatial-variant properties and diverse patterns. In this work, a shadow removal framework with multiexposure fusion is proposed for hyperspectral remote sensing images, which consists of three major steps. First, a color space conversion method is exploited to detect the shadow regions. Second, the principle of the intrinsic decomposition model is utilized to generate a set of differently exposed hyperspectral images (HSIs), i.e., multiexposure images. Third, the generated multiexposure images and the original HSIs are fused together with a two-stage image fusion method so as to remove the shadows in hyperspectral remote sensing images effectively. Experiments performed on three real hyperspectral datasets confirm that the performance of the proposed method outperforms other state-of-the-art shadow removal approaches.