Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Block-Based Color Constancy

Removing the effects of the light source from a color-casted image is a key step for numerous computer vision applications. Alongside introducing new color constancy algorithms, researchers also aim at improving the existing methods by utilizing different approaches. Using strategies that have not been investigated in the field of color constancy in detail might help us both to design cost-efficient simple yet effective algorithms and to improve the existing methods. Thereupon, we propose a color constancy method relying on surface orientations which utilizes block-based operations in scale space by only considering the salient regions of the image. According to the evaluations, our multi-scale color constancy method achieves state-of-the-art performance. Moreover, we demonstrate that the performance of several existing color constancy algorithms can be improved by benefiting from scale space.
C. elegans outside the Petri dish

The roundworm Caenorhabditis elegans has risen to the status of a top model organism for biological research in the last fifty years. Among laboratory animals, this tiny nematode is one of the simplest and easiest organisms to handle. And its life outside the laboratory is beginning to be unveiled. Like other model organisms, C. elegans has a boom-and-bust lifestyle. It feasts on ephemeral bacterial blooms in decomposing fruits and stems. After resource depletion, its young larvae enter a migratory diapause stage, called the dauer. Organisms known to be associated with C. elegans include migration vectors (such as snails, slugs and isopods) and pathogens (such as microsporidia, fungi, bacteria and viruses). By deepening our understanding of the natural history of C. elegans, we establish a broader context and improved tools for studying its biology. DOI: http://dx.doi. org/10.7554/eLife.05849. 001
Perception of Privacy Concerns in Using Instagram Among Students (Case Study: Sriwijaya State Polytechnic)

This study aims to investigate 3 things which include how the level of awareness and control regarding data privacy affects privacy concerns, how privacy concerns shape trust and risk, and investigate whether there is an influence of trust on risks arising from the use of Instagram among Informatics Management students at the State Polytechnic Sriwijaya. The data used in this research is collected from a questionnaire survey and analyzed using Structural Equation Modelling (SEM). The number of respondents was 301 obtained from the calculation of the Slovin formula. The results of the study show that there is a positive correlation between the level of awareness and control over user perceptions regarding privacy concerns and so do privacy concerns that affect the level of user trust and affect the risks that can occur from using Instagram. It's different from the perception of trust which has no influence on the perceived risk of users in using Instagram.
Estimation of Residence Time in a Shallow Back Barrier Lagoon, Hog Island Bay, Virginia, USA

Hog Island Bay, Virginia, is a shallow back barrier lagoon that is subject to seasonal inputs of inorganic nitrogen and related episodes of hypoxia. Numerical simulations were carried out to estimate the importance of physical flushing times relative to biochemical turnover times known to be a few days or less within the system. A 2D vertically averaged finite element hydrodynamic model, which was designed to accommodate regular flooding and dewatering of shallow flats and marshes, was coupled with a particle tracking model to estimate median lagoon residence time and the spatial distribution of local residence time in the lagoon. The model was forced with observed tidal elevations and winds from the end of the growing season when hypoxia tends to occur. The median residence time estimated by numerical modeling is on the order of weeks (358 hours), and variations in tidal stage, tidal range and wind produced deviations in median residence time on the order of days. Residence times near the inlets were very short, while those near the mainland were long, showing that (i) horizontal mixing in the Bay is insufficient to successfully apply integral methods to obtain residence times, and (ii) residence times near the mainland are long compared to timescales of biologically driven chemical transformations.
Diagnóstico ambiental de um trecho do Córrego Bonifácio, APA Jundiaí - SP

A crescente discussao sobre questoes ligadas a disponibilidade hidrica implica na necessidade de estudos e compreensao da dinâmica deste recurso natural, na escala de referencia fisico-territorial denominada bacia hidrografica. O objetivo do presente trabalho foi avaliar quais levantamentos expeditos de qualidade e quantidade de agua podera viabilizar a gestao dos recursos hidricos do Corrego Bonifacio, afluente do Rio Jundiai, atendendo as necessidades da irrigacao. Para a avaliacao da disponibilidade hidrica dessa microbacia hidrografica de 2,98 Km², foram realizadas medicoes de vazao por meio de um vertedouro triangular, situado na represa da Escola Tecnica Benedito Storani, pertencente ao Centro Paula Souza , no municipio de Jundiai- SP. Para caracterizar a qualidade da agua foram avaliados os seguintes parâmetros: pH, condutividade eletrica, temperatura, oxigenio dissolvido, turbidez, cor, coliforme total e fecal e as formas de nitrogenio – N Total, NO3 e NH3. Alguns desses parâmetros analisados extrapolaram os padroes previstos na Resolucao CONAMA 357/2005 para a classe do rio, especialmente coliformes totais e cor, provavelmente devido a ocupacao a montante do trecho avaliado. Foi tambem verificada elevada concentracao de nitrato nos periodos estudados. No estudo, o perfil topografico, a ocorrencia de mata ciliar e uma area de varzea natural na Escola Tecnica Benedito Storani, podem estar proporcionando a reducao da poluicao, como verificado nas remocoes de cor e turbidez. Palavras-chave: Bacia Hidrografica, Corrego Bonifacio, Qualidade da agua. ABSTRACT The growing discussion on topics related to water availability reveals the need of studies and understanding of the dynamics of this natural resource in a watershed scale. The main goal of this research was to evaluate which expedite assessments on water quality and quantity would provide a better management of the water resources of Bonifacio Creek, tributary of the Jundiai River, aiming irrigation demand. To evaluate the water availability of that microbasin (2,98 Km²) were carried out stream flow meter using triangular weir, located in the Technical School (ETE) reservoir. pH, electrical conductivity, water temperature, dissolved oxygen (OD), turbidity, color, total and fecal coliforms, and nitrogen (Total, NO3, NH3) were measured in four sites (ETE entrance, reservoir, after the vegetable garden, ETE exit) in twelve samplings. Results indicated that some parameters extrapolated the water standards defined for the Creek in CONAMA Resolution 357/2005, probably due the soil occupation in the basin. Also high concentrations of nitrates were verified during dry and wet seasons. However, the topographic profile, the riparian forest and a natural wetland in the Technical School sector of the basin are contributing for the water pollution abatement, as indicated by the readings on color and turbidity. Key words: watershed, Bonifacio Creek, water quality and quantity.
Game-Theoretic Flexible Resource Allocation for Handoff in Hybrid V2X Communication

In this study, we jointly investigate the connectivity probability and the number of required resource blocks (RBs) for load reduction during handoff (HO) decision for hybrid vehicle-to-everything (V2X) communication. Frequent handoffs (HOs) is a major issue in vehicular communication, causing various concerns such as loading problems in the overlapping area, signalling overhead, and decreasing the bandwidth efficiency of the network. To combat the loading problem due to HO in the overlapping area and the low convergence issues, we propose a game-theoretic approach to resolve the imbalance of resource allocation amongst the edge clusters. The proposed game-based Flexible Resource Allocation (FRA) approach optimally redistributes the resources to all clusters within a base station (BS). We obtain an approximate closed-form expression of the transmit power for a 5G network, besides analyzing the effects of connectivity probability and required resources for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) during HO. The analytical derivation has been verified through simulation results.
Deep Learning-Based Approach for Optical Microrobot Tracking, Pose Prediction and Trapping Points Localisation

Mobile micro-robots enables precise manipulation of micrometer scale biological objects, and offer new opportunities in biomedical applications. Optical microrobots are micron-sized structures actuated in a liquid medium by an external laser. This paper presents a deep learning-based approach for optical robots tracking, with the aim of implementing visual control-loops for task automatisation. The system is designed to allow the detection and tracking of robots, of trapping positions (TPs) and measuring the microrobot rotation angles under an optical microscope. YOLOv7 and Deep SORT algorithms was integrated and specially optimized for this specific environment. The proposed model demonstrated 3% higher mean average precision (mAP) at the 0.5:0.95 Intersection over Union (IoU) threshold for training and also $\sim3\%$ and $\sim 11\%$ increase in accuracy for both microrobot and TPs, respectively at the 0.95 IoU threshold in our test set. To validate the effectiveness of our TPs detection results, we compared them with potential circle detection methods, demonstrating a success rate of 99% in trapping events with zero false positive detections. Finally, our proposed rotation angle tracking results were verified successfully in two different case scenarios, involving deformed and undeformed microrobots at different speed profiles. Our analysis reveals that the error rate is significantly increased by velocity-induced drag force.
The Compositional Effect of Rigorous Teacher Evaluation on Workforce Quality

Improving public sector workforce quality is challenging in sectors such as education where worker productivity is difficult to assess and manager incentives are muted by political and bureaucratic constraints. In this paper, we study how providing improved information to principals about teacher effectiveness and encouraging them to use the information in personnel decisions affects the composition of teacher turnovers. Our setting is the Houston Independent School District, which recently implemented a rigorous teacher evaluation system. Prior to the new system, teacher effectiveness was negatively correlated with district exit and we show that the policy significantly strengthened this relationship, primarily by increasing the relative likelihood of exit for teachers in the bottom quintile of the quality distribution. Low-performing teachers working in low-achieving schools were especially likely to leave. However, despite the success, the implied change to the quality of the workforce overall is too small to have a detectable impact on student achievement.
. (2020). Treatment effects of reverse total shoulder arthroplasty: a simple method to measure outcomes at 6, 12, 24 and 60 months for each patient. BMC Musculoskeletal Disorders,

Background: Although shoulder arthroplasty is less common than knee or hip arthroplasty, the number of procedures being performed is increasing rapidly. The treatment effect is a simple method to measure outcome of joint replacement. The method was applied to measure results of total hip/knee arthroplasty but not yet for shoulder arthroplasty. Methods: Included were patients with unilateral cuff arthropathy (Hamada grades > = 2) treated with reversed total shoulder arthroplasty (RSA) in this prospective multicenter study. The patients were assessed with the ASES questionnaire. The treatment effects (TE) was calculated for each patient. TE = score reduction/baseline score. A positive TE means amelioration, TE = 0 unchanged, and a negative TE means worse. The primary aim was to calculate the TE ’ s for RSA at 6, 12, 24, and 60 months postoperatively. The secondary aim was to analyze the influence of confounders (preoperative Hamada grade, age, gender, dominance, side of the affected shoulder, general co-morbidities measured using ASA grade). Results: Two hundred three patients were included for this analysis of whom 183 patients had a complete 2 year follow up. Two years postoperatively the mean ASES score augmented significant from 20.5 to 78.7 ( p < 0.001). The 2 year TE ’ s ranged from 1 to 0.09. We had no patient with a negative TE. A higher Hamada grade was associated with better TE ’ s (Hamada grade 4+ vs. 2, p -value 0.042). For age and dominant side there were weak associations where those aged 80+ and dominant side had better TE ’ s. The patients with higher ASA grade had lower TE ’ s (ASA grade 4+ vs. 1, p -value 0.013). The mean TE ’ s were 0.77 at 6-months, 0.81 at 1 year, 0.76 at 2 years and 0.73 at 5 years. Conclusions: The outcome for reverse shoulder arthroplasty can be measured with the treatment effect method; the 2 years TE ’ s vary from 1 to 0.09. The mean treatment effects change little in the first five postoperative years (from 0.73 to 0.81). The confounders for better TE ’ s were: higher severity of cuff arthropathy (Hamada grade 3, 4 and 5), less co-morbidities (ASA Grade 1), higher age (80+) and dominant side. Gender did not influence the 2-year TE ’ s.
Thermal adaptation in butterflies: patterns, significance and mechanisms

The temperature-size rule (TSR), which states that in body size increases at lower developmental temperatures, appears to be a near universal law for ectotherms. Although recent studies seem to suggest that the temperature-size rule might be adaptive, the underlying developmental mechanisms are thus far largely unknown. Here, we investigate temperature effects on life -history traits, behaviour and physiology in the copper butterfly Lycaena tityrus in order to disentangle the mechanistic basis of the above rule. In Lycaena tityrus, a larger body size produced at a lower temperature was proximately due to a higher increase in mass, which was caused by both behavioural and physiological mechanisms: a much-increased food intake and a higher efficiency in converting ingested food into body matter. These mechanisms, combined with temperature-induced changes at the cellular level, may provide general explanations for the TSR. Body fat and protein content increased in butterflies reared at the higher temperature, indicating favourable growth conditions. As predicted from protandry theory, males showed reduced development times, caused by higher growth rates compared to females. The latter was itself related to a higher daily food consumption, while the total food consumption (due to the females’ longer developmental period) and assimilation was higher in females and may underly the sexual size dimorphism in body size.
Observation of Terahertz Spin Hall Conductivity Spectrum in Bulk GaAs at Room Temperature

We investigated ultrafast dynamics of light-induced anomalous Hall conductivity in a bulk semiconductor GaAs at room temperature using a circularly polarized near-infrared pump pulse and a terahertz (THz) probe pulse. Polarization rotation of the THz pulse was observed as a manifestation of the inverse spin Hall effect (ISHE) for optically injected spin-polarized carriers. The spin Hall conductivity spectrum is experimentally evaluated for the first time and shows excellent agreement with the microscopic theories of ISHE, demonstrating that the extrinsic impurity scattering dominates ISHE in the DC limit, whereas the intrinsic Berry-curvature mechanism becomes dominant for THz frequency. Our study paves a way toward ultrafast study of spin Hall effect with quantitative analysis of spin Hall conductivity spectrum.
Timely and Efficient Information Delivery in Real-Time Industrial IoT Networks

Enabling real-time communication in Industrial Internet of Things (IIoT) networks is crucial to support autonomous, self-organized and re-configurable industrial automation for Industry 4.0 and the forthcoming Industry 5.0. In this paper, we consider a SIC-assisted real-time IIoT network, in which sensor nodes generate reports according to an event-generation probability that is specific for the monitored phenomena. The reports are delivered over a block-fading channel to a common Access Point (AP) in slotted ALOHA fashion, which leverages the imbalances in the received powers among the contending users and applies successive interference cancellation (SIC) to decode user packets from the collisions. We provide an extensive analytical treatment of the setup, deriving the Age of Information (AoI), throughput and deadline violation probability, when the AP has access to both the perfect as well as the imperfect channel-state information. We show that adopting SIC improves all the performance parameters with respect to the standard slotted ALOHA, as well as to an age-dependent access method. The analytical results agree with the simulation based ones, demonstrating that investing in the SIC capability at the receiver enables this simple access method to support timely and efficient information delivery in IIoT networks.
Legitimation of Newness Challenges and Opportunities in the 6G Era

Mobile communication networks are the digitalization backbone of modern society. This study employs the futures scenario method to explore legitimation challenges and opportunities in the sixth-generation mobile communications (6G) era. Specifically, this conceptual paper discusses scenarios against the legitimation of newness and business models and their implications for research, practice, and policy. We conjecture that the most probable scenario in the future of 6G stems from industry legitimation, strong established stakeholders, and competition as a driver. Trust and privacy were found as the key challenges and limitations were related to adopted centralized platform business models in this scenario. The preferred scenario was driven by novel service performance innovations combined with sociopolitical sustainability goals. This scenario was seen to have challenges related to novel open business models and be limited by the traditional regulatory environment. The findings indicate that it is vital to bring together stakeholders within the ecosystem and pay special attention to identity-construction legitimation via an open, ecosystem-focused value configuration and decentralized poly-nodal power configurations.
Optimization of RF Frequencies in Dual-Frequency Capacitively Coupled Plasma Apparatus Using Genetic Algorithm (GA) and Plasma Simulation

As a method to optimize the power frequency of dual-frequency plasma, we propose an optimization method that combines genetic algorithm and plasma simulation. A two-dimensional plasma simulation model of Ar plasma was constructed with a fluid model. Combining this simulation model with a genetic algorithm, 300 cases of plasma conditions with high plasma density and small variation in electron density were calculated. As a result, the optimum conditions were 175 to 210 MHz for the high-frequency generator and 1.0 to 2.5 MHz for the low-frequency generator. Furthermore, two types of single objective functions that combine the electron density and the variation rate were proposed. The single objective functions with emphasis on electron density and with emphasis on variation rate were introduced, and each objective function was optimized.
Performance Analysis Based on Real-Field Data of a Method for Locating the Source of Voltage Dips

Voltage dips are one of the most recurrent types of power quality disturbances detectable in power systems. Root-cause analysis is important to identify opportunities for mitigation. In this paper, a recently proposed practical method to locate the source of voltage dips that requires only basic Power Quality measurements is refined, including, in particular, an improved solution for aggregating dip events and extended to the case of multi-level scenarios. The performance of this method is evaluated under different operating conditions using real-field data in a South African 66 kV/11 kV network.
Time-Series Prediction using Nature-Inspired Small Models and Curriculum Learning

As wireless cellular networks evolve from one generation to the next, the associated challenges in terms of compute, latency, and energy efficiency are continuously on the rise. However, traditional methods are unable to cope with the complexity of operation in next generation mobile networks, and the operational costs can become a significant hurdle in emerging generations. To address these issues, an AI-native wireless communication system is needed to support faster processing and optimization. Researchers have been exploring nature-inspired models for years to improve deep learning architectures, making them smaller, sparser, more efficient, and easily interpretable. In this study, we propose a nature-inspired Machine Learning architecture that leverages curriculum learning to predict cellular traffic on real wireless cellular network data. Our proposed architecture requires significantly fewer parameters to train, is 64% sparse and performs better than the standard state-of-the-art model. Here, sparsity refers to the fewer connections than the maximum possible number of connections within that network between each layer. Moreover, in terms of performance and carbon emissions, this sparsely connected architecture is better than traditional Machine Learning models.
Multi-label Ophthalmological Disease Classification Using Vision Transformers

Immediate diagnosis and timely treatment of fundus diseases are crucial in preventing permanent vision loss. Fundus imaging, a primary method in retinal imaging, effectively captures and investigates anatomical features and abnormalities in the human eye, and aids in observing and identifying various ophthalmological diseases. These diseases are indicated by variations in or surrounding structures such as the optic disk and the blood vessels. Given the complex nature of these diseases, it’s common for fundus images to show a patient having multiple conditions in one or both eyes. This work aims to develop and suggest various methods for detecting and classifying ophthalmological diseases using multiple deep learning vision transformers. The experiments were conducted and models tested using the ODIR-2019 dataset, which contains fundus images of both left and right eyes. The dataset includes eight categories, and the goal was to implement a transformer based approach for multi-label classification. The study used pre-trained Swin Transformer V2 and Data Efficient Image Transformer (DeiT) in its proposed approaches. After various preprocessing and fine-tuning techniques, the results showed that the transformer approach outperformed state-of-the-art CNN models in terms of accuracy. It achieved an accuracy of 93.1% and AUC of 94.4% for multi-label ophthalmological disease classification.