Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Fine-Grained Distributed Routing and Data Scheduling for Interplanetary Data Transfers

Interplanetary networks (IPNs) are complex communication infrastructures used for data exchange among spacecrafts, rovers and ground stations. Due to the significant delay and uncertainty in communications, efficient routing and data scheduling of interplanetary data transfer (IP-DT) becomes crucial. With the increase of deep space (DS) exploration missions, it would be difficult for existing IPNs to cope with the growing of IP-DT demands. In this work, to improve the performance of IP-DTs in IPNs, we formulate an integer linear programming (ILP) model and design an effective fine-grained distributed routing and data scheduling (FD-RDS) algorithm based on it. We prove that the proposed algorithm is a polynomial-time 2-approximation algorithm for solving the ILP model. Extensive simulations show that our proposals can significantly improve the efficiency and reliability of IPNs. Specifically, our proposals outperforms known benchmarks in terms of both the delivery ratio and E2E latency of IP-DTs.
Pedophilia – Definitions of the Disorder. Selected Clinical, Legal, and Social Issues

The concept of pedophilia is very broad and extremely complex. Thus, when examining this disorder, it is important to take into account a wide-ranging debate on the definitione of pedophilia itself, the type of definition, and elements that constitute it. The fact that there are so many definitions of pedophilia, which at the same time determine the ways it is responded to, shows how important this issue is. The way the term ‘pedophilia’ is used causes many misunderstandings and controversies, primarily between clinicians and lawyers. Further confusion as to the meaning of the concept arises when representatives of other professions join in these discussions. The article attempts to define pedophilia in clinical, legal, and social terms.
DEVELOPMENT OF THE MESHLESS LOCAL PETROV-GALERKIN METHOD TO ANALYZE THREE-DIMENSIONAL TRANSIENT INCOMPRESSIBLE LAMINAR FLUID FLOW

In this paper, a numerical algorithm is presented to simulate the three-dimensional transient incompressible flow using a meshless local Petrov-Galerkin (MLPG) method. In the proposed algorithm, the forward finite difference (FFD) and MLPG methods are employed for discretization of time derivatives and solving the Poisson equation of the pressure, respectively. The moving least-square (MLS) approximation is considered for interpolation, while the Gaussian weight function is used as a test function. Furthermore, the penalty approach is applied to satisfy the boundary conditions. Moreover, in two examples, the accuracy and efficiency of this approach is compared with the exact solutions.
Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems

This paper presents the design of decentralized robust controllers for a network of inverter-based distributed generation systems with LC filters in the scale of a nanogrid. Using overlapping decomposition, the network of inverters is clustered into several subnetworks such that all inverters within a subnetwork are strongly coupled and there is no or a weak coupling effect between any two inverters from different subnetworks. For the inverters within the same subnetwork, decentralized robust controllers are designed sequentially in the μ-synthesis framework. In addition, all controllers are designed to be robust against ±10% variations in the LC filter parameters. To assess the performance of the proposed sequentially-designed controllers and compare it to that of the benchmark independently-designed ones, the distances between two neighboring inverters from the same and different subnetworks are considered to be 200 (m) and 800 (m), respectively. In this case, time-response and robustness analysis results illustrate the superiority of the proposed sequentially-designed controllers in the overlapping decomposition framework over the benchmark independently-designed ones. Moreover, transient overload and nonlinear load analyses demonstrate that the proposed sequentially-designed decentralized controllers are able to keep the load voltage within ±10% of the nominal value and the harmonic voltage distortions to less than 4%.
INTERVENTION RECOMMENDATION WEB APPLICATION BASED ON USER INTERESTS FOR MENTAL WELL-BEING

: The sensitive topic of ‘mental health’ is increasingly gaining more recognition and respect among people of all age groups. Starting from children to senior citizens, everyone needs aid that will help them keep their mental state healthy. The seemingly fast-paced world and rising competition have contributed to disturbing one’s mental condition to an extent. The Coronavirus pandemic has accelerated the decline in the mental health of the majority of people. In such a condition, it becomes both - necessary and beneficial - to use the power of technology in helping people maintain a positive state of mind. With the vast range of advantages that machine learning offers, it becomes easy and effective to cater to individuals as per their choices and preferences. Machine learning is used widely in the healthcare sector because of the benefits it provides by equipping the ability to train models to perform specific tasks based on the information supplied. Similarly, machine learning can be used to help people improve their mental state by learning their favorite activities to engage in to feel better and generate recommendations for activities accordingly. In such a way, it can help uplift a person’s mood by giving him a range of options for tasks to perform based on his liking. In an attempt to help people of all age groups improve their mental health, an application was developed which takes into consideration the user’s likings and current mental state and recommends intervening activities to participate in.
The Cdk5 inhibitor β‐butyrolactone impairs reconsolidation of heroin‐associated memory in the rat basolateral amygdala

The persistence of maladaptive heroin‐associated memory, which is triggered by drug‐related stimuli that remind the individual of the drug's pleasurable and rewarding effects, can impede abstinence efforts. Cyclin‐dependent kinase 5 (Cdk5), a neuronal serine/threonine protein kinase that plays a role in multiple neuronal functions, has been demonstrated to be involved in drug addiction and learning and memory. Here, we aimed to investigate the role of cdk5 activity in the basolateral amygdala (BLA) in relapse to heroin seeking, using a self‐administration rat model. Male rats underwent 10 days of heroin self‐administration training, during which an active nose poke resulted in an intravenous infusion of heroin that was accompanied by a cue. The rats then underwent nose poke extinction for 10 days, followed by subsequent tests of heroin‐seeking behaviour. We found that intra‐BLA infusion of β‐butyrolactone (100 ng/side), a Cdk5 inhibitor, administered 5 min after reactivation, led to a subsequent decrease in heroin‐seeking behaviour. Further experiments demonstrated that the effects of β‐butyrolactone are dependent on reactivated memories, temporal‐specific and long‐lasting on relapse of heroin‐associated memory. Results provide suggestive evidence that the activity of Cdk5 in BLA is critical for heroin‐associated memory and that the specific inhibitor, β‐butyrolactone, may hold potential as a substance for the treatment of heroin abuse.
Statistical Modeling of Atlantic Hurricane Activity using Atmospheric Reanalyses and IPCC Simulations and Projections

Title of Document: STATISTICAL MODELING OF ATLANTIC HURRICANE ACTIVITY USING ATMOSPHERIC REANALYSES AND IPCC SIMULATIONS AND PROJECTIONS Kye-Hwan Kim, Doctor of Philosophy, 2013 Directed By: Professor Sumant Nigam, Department of Atmospheric and Oceanic Science Atlantic hurricane activity has increased in recent decades leading to extensive investigation of its links with rising greenhouse gases (GHG). The International Panel on Climate Change (IPCC) is investigating how global-regional climate will change in the 21st century (21C) in response to GHG emissions but the deployed climate model grids are too coarse to resolve hurricanes. Projections of hurricane activity must thus be obtained, indirectly, from regional downscaling of climate using statistical and dynamical approaches; the former is adopted here. Hurricane counts are reconstructed in 20C and projected in 21C from statistical modeling with Atlantic Sea-Surface Temperature (SST), static-stability, and zonal-wind shear as predictors. Optimal predictor definitions, including geographical domains, are identified from observational analysis (1958-2005). The viability of the statistical approach is demonstrated from the successful reconstruction of hurricane counts in both training and independent periods, with reconstruction-observation correlations (0.720.86) higher than Kim and Webster’s (2010; the best statistical model to date). Statistical models for counts were also developed for each of the five analyzed IPCCthe-fifth-Assessment (AR5) models, based on their 1958-2005 simulations. The long-term trend in counts was modeled using multivariate linear regression with predictors from the ensemble-mean simulation. On the other hand, predictors from several ensemble members were used via the best subset regression technique when modeling the interannual-to-decadal count variability. Focusing on the observationally rich 1958-2005 period provided critical evaluation and a basis for selection of a credible model-subset. The 21C projections of hurricane counts by this model subset will be deemed more trustworthy than the average of all AR5-based models. Modeling of Atlantic hurricane activity with IPCC-AR5 predictors shows a stronger count-trend in the 21C, principally, from increasing SSTs; AR5-models disagree on the trend in zonal-wind shear which is a less influential predictor in the AR5-based models compared to observations. Decadal predictions of hurricane activity for the independent but observationsavailable 2006-2010 period show the promise of the best subset regression models, especially with predictors from the IPCC-Decadal (2005 ocean-initialized) experiments. According to this model, the 2013 hurricane season will be slightly more active (+1 count) but not as much as NOAA’s forecast (+3 counts). STATISTICAL MODELING OF ATLANTIC HURRICANE ACTIVITY USING ATMOSPHERIC REANALYSES AND IPCC SIMULATIONS AND PROJECTIONS
Political Instability and Investment Behaviour in Pakistan

The study investigates the impact of political instability on FDI and private investment in Pakistan from 1990-2019. Control variables include trade openness, inflation, real GDP, and interest rate differential. We have employed Zivot and Andrews stationarity test. The study makes use of the recently introduced dynamic ARDL simulation framework. The long-run results of our first object show the negative association between FDI and political instability. They concern the factors we used as controls, trade openness, real GDP, and interest rate differential boost the FDI inflows, whereas inflation remains insignificant. The short-run result infers that the rate of adjustment is about 25%. As for our second objective, the dynamic ARDL model's long-term findings indicate that political instability deteriorates private investment. As for as control variables are concerned, trade openness and real GDP are positively associated with private investment, whereas interest rates dampen it. Inflation remains insignificant in affecting private investment. Short-run results show that the speed of adjustment is 53% annually. It is suggested that the government is responsible for creating an atmosphere promoting domestic and foreign investment. Investor confidence will be boosted through greater governance, higher institutional quality, and political stability.
Fault Diagnosis for Oil Immersed Transformer Using Certainty Factor

Traditional dissolved gas analysis (DGA) methods for incipient fault diagnosis of oil immersed power transformers often give contradictory results. Due to the increased availability of methods, and procedures for fault diagnosis a reliable method is needed to reach a conclusion. The certainty factor (CF) method gives promising results where the available knowledge base and techniques are unable to arrive at the same conclusion. In this work, a traditional method based on a gas ratio method, two graphical methods, and a key gas (KG) method are applied to DGA samples. When the fault diagnosis by the traditional methods is contradictory, a computational method based on evidence and CF is used. The combined certainty factor (CCF) is applied to the CF results incrementally for confirming fault type. There is a special provision for partial discharge (PD) type of fault. The proposed method is checked on samples used in literature where the actual fault is known. The goal is to make available a reliable method that scientifically combines the diagnosis of three traditional DGA methods and confirms with an increased level of responsibility the diagnosis of the type of incipient fault. The proposed method may be used by utilities to reach a common consensus in case of contradictory diagnosis by traditional DGA methods.
Threatened fauna protections compromised by agricultural interests in Australia

Australia is a global leader in land clearing and biodiversity loss. The overwhelming majority of land clearing within Australia and, globally, is driven by agricultural conversion. The importance of agricultural lands also leads to the concentration of habitat protection in landscapes that do not support productive land uses, which might contribute to species conservation in marginal habitat. Using an integrated agricultural capability map and threatened vertebrate fauna range maps, we show that observed biases in protected area location have varied impacts at the species level. Specifically, threatened vertebrate fauna with habitat capable of supporting high‐value productive lands received less protection and experienced greater habitat loss. Similarly, almost all species assessed received protection in the portions of their ranges less conducive to productive land uses. Finally, we identify regions of Australia at risk of future land clearing and the species likely to bear the brunt of the impacts. Our results demonstrate the importance of protecting land capable of supporting productive uses to conserve the most affected threatened species.
Adversarial Attacks on Medical Image Classification

Simple Summary As we increasingly rely on advanced imaging for medical diagnosis, it’s vital that our computer programs can accurately interpret these images. Even a single mistaken pixel can lead to wrong predictions, potentially causing incorrect medical decisions. This study looks into how these tiny mistakes can trick our advanced algorithms. By changing just one or a few pixels on medical images, we tested how various computer models handled these changes. The findings showed that even small disruptions made it hard for the models to correctly interpret the images. This raises concerns about how reliable our current computer-aided diagnostic tools are and underscores the need for models that can resist such small disturbances. Abstract Due to the growing number of medical images being produced by diverse radiological imaging techniques, radiography examinations with computer-aided diagnoses could greatly assist clinical applications. However, an imaging facility with just a one-pixel inaccuracy will lead to the inaccurate prediction of medical images. Misclassification may lead to the wrong clinical decision. This scenario is similar to the adversarial attacks on deep learning models. Therefore, one-pixel and multi-pixel level attacks on a Deep Neural Network (DNN) model trained on various medical image datasets are investigated in this study. Common multiclass and multi-label datasets are examined for one-pixel type attacks. Moreover, different experiments are conducted in order to determine how changing the number of pixels in the image may affect the classification performance and robustness of diverse DNN models. The experimental results show that it was difficult for the medical images to survive the pixel attacks, raising the issue of the accuracy of medical image classification and the importance of the model’s ability to resist these attacks for a computer-aided diagnosis.
Recent advances in self-interference incoherent digital holography

Self-interference holography is a common technique to record holograms of incoherently illuminated scenes. In this review, we survey the main milestones in the topic of self-interference incoherent digital holography from two main points of view. First, we review the prime architectures of optical hologram recorders over more than 50 years. Second, we discuss some of the key applications of these recorders in the field of imaging in general, and for 3D super-resolution imaging, fluorescence microscopy, partial aperture imaging, seeing through a scattering medium, and spectral imaging in particular. We summarize this overview with a general perspective on this research topic and its prospective directions.
Interaction Analysis Reveals Complex Genetic Associations with Alzheimer’s Disease in the CLU and ABCA7 Gene Regions

Sporadic Alzheimer’s disease (AD) is a polygenic neurodegenerative disorder. Single-nucleotide polymorphisms (SNPs) in multiple genes (e.g., CLU and ABCA7) have been associated with AD. However, none of them were characterized as causal variants that indicate the complex genetic architecture of AD, which is likely affected by individual variants and their interactions. We performed a meta-analysis of four independent cohorts to examine associations of 32 CLU and 50 ABCA7 polymorphisms as well as their 496 and 1225 pair-wise interactions with AD. The single SNP analyses revealed that six CLU and five ABCA7 SNPs were associated with AD. Ten of them were previously not reported. The interaction analyses identified AD-associated compound genotypes for 25 CLU and 24 ABCA7 SNP pairs, whose comprising SNPs were not associated with AD individually. Three and one additional CLU and ABCA7 pairs composed of the AD-associated SNPs showed partial interactions as the minor allele effect of one SNP in each pair was intensified in the absence of the minor allele of the other SNP. The interactions identified here may modulate associations of the CLU and ABCA7 variants with AD. Our analyses highlight the importance of the roles of combinations of genetic variants in AD risk assessment.
The Role of Novel Imaging and Biofluid Biomarkers in Traumatic Axonal Injury: An Updated Review

Traumatic brain injury (TBI) is a leading cause of disability worldwide. Traumatic axonal injury (TAI) is a subtype of TBI resulting from high-impact forces that cause shearing and/or stretching of the axonal fibers in white matter tracts. It is present in almost half of cases of severe TBI and frequently associated with poor functional outcomes. Axonal injury results from axonotomy due to mechanical forces and the activation of a biochemical cascade that induces the activation of proteases. It occurs at a cellular level; hence, conventional imaging modalities often fail to display TAI lesions. However, the advent of novel imaging modalities, such as functional magnetic resonance imaging and fiber tractography, has significantly improved the detection and characteristics of TAI. Furthermore, the significance of several fluid and structural biomarkers has also been researched, while the contribution of omics in the detection of novel biomarkers is currently under investigation. In the present review, we discuss the role of imaging modalities and potential biomarkers in diagnosing, classifying, and predicting the outcome in patients with TAI.
Influence of Temperament on the Acceptance of Two Conscious Sedation Techniques in Toddlers Undergoing Dental Treatment: A Randomised Cross Over Trial

Background Pediatric dentists often find it challenging to handle pediatric patients due to their fear, unease, and anxiety toward dental procedures. To address this, sedation agents such as intranasal midazolam and nitrous oxide are commonly used as pharmacological behavior management methods. A child's temperament affects their behavior in unfamiliar settings. Aim To study the effect of child temperament on the acceptance of the nasal mask and intranasal drug administration in children undergoing dental treatment. Methods Thirty-two anxious children aged three to five were randomly assigned to two groups. During the first visit, one group received intranasal midazolam sedation, while the other group received nitrous oxide administered through a mask. On the subsequent visit, the groups crossed over. The parent assessed the child's temperament, and the acceptance of the sedation methods was recorded. The Ohio State University Behavioral Rating Scale (OSUBRS) was employed to assess behavior during the administration of local anesthesia. Statistical analysis was carried out using the chi-square test and Mann–Whitney U test (p value <0.05). Results Children exhibited greater acceptance of the nasal mask compared to using the intranasal route for delivering midazolam during the induction process. A significant statistical influence of temperament was observed on the acceptance of the nasal mask and the intranasal atomisation device (p value <0.05). The mean OSUBRS scores did not show any statistically significant differences between the sedation groups (p = 0.14). Conclusion Most children demonstrated a more favorable acceptance of the nasal mask during the induction process; however, intranasal midazolam can serve as an effective alternative for anxious patients who struggle to keep the nitrous oxide mask on during the dental procedure. The adoption of these methods is influenced directly by the child's temperament.
The Mediating Impact of Goal–Role Clarity on the Relationship between Feedback–Seeking Behavior and Goal Orientations with Job Satisfaction Intrinsic Cognitions and Person–Organization Fit

This study aims to offer a design for the cognitive calibration of employees’ work behaviors. The study focuses on fostering sustainable behavioral patterns in the workplace by arguing that, to sustain the cognitive maintenance of workplace behaviors, the nexus between self-efficacy behaviors, ability beliefs, job–role clarity, and employees’ perceived alignment should be considered during human resource (HR) processes and events, such as feedback conversations and performance reviews. The tool used to collect our data was a questionnaire administered both in paper format and digitally through an online platform compliant with European data protection regulations, to 210 professionals from two IT companies in northeastern Romania (n = 116 for the first sample and n = 94 for the second sample). The series of questions included Likert scales addressing frequency, (dis)agreement, and clarity. The model was tested using SPSS v.27 and SMART-PLS v.4 software, and the study used partial least squares structural equation modeling to examine the proposed hypotheses. Findings for Study 1 indicated that role clarity partially mediated the relationship between feedback–seeking and job cognitions whilst fully mediating the effect on person–organization fit. Results from Study 2 indicated that role clarity partially mediated the relationship between goal orientations and job cognitions and fully mediated the effect on person–organization fit. Therefore, it is possible to aid employees’ in storing their employment narratives in coherent cognitive schemas while elevating their assessment of work values’ alignment and meaningfulness. On this basis, effective tools and methodologies should be made available to employees, at the individual and team level, to help them understand the cognitive processing endorsing workplace behavioral patterns, compatibility, and causality for them to sustainably transform their workplace behavior.