Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Model for Predicting Alzheimer's Disease and Disease Stages with CNN and Transformer Models

Alzheimer’s disease, a prevalent form of dementia, is a progressive, long-term and irreversible neurological disorder. With the growing elderly population, there has been a significant rise in the number of individuals affected by Alzheimer’s disease, which will bring serious social and economic burdens to countries all over the world. Therefore, it is crucial to identify and diagnose Alzheimer’s disease and its current disease stage promptly and accurately to facilitate prompt intervention and treatment. This paper proposes an ensemble model that combines the convolutional neural network (ResNet50, DenseNet121, EfficientNet_B0) and Transformer architectures (Vision Transformer, TransFG) to predict Alzheimer’s disease patients and their current disease stage. The ensemble model exhibits impressive classification accuracy, with 98.91% accuracy, a Micro Area Under the Curve (micro-AUC) of 0.9996, and a macro-AUC of 0.9995. And the ensemble model accurately differentiates Alzheimer’s disease cases and their current disease stages. The proposed ensemble model exhibits classification accuracies of 99.06%, 99.11%, 97.78%, and 100% for healthy control, very mild, mild, and moderate cases respectively. By utilizing this ensemble model, healthcare professionals can rely on a more accurate prediction tool for Alzheimer’s disease and its current disease stage, which enables them to devise personalized treatment plans and nursing measures for affected patients.
Multi-scale Fusion Dynamic Graph Neural Network For Traffic Flow Prediction

As a cornerstone of intelligent transportation systems, traffic flow prediction has garnered extensive research attention. However, traffic flow data exhibits pronounced spatio-temporal dynamics, rendering accurate traffic flow prediction a challenging endeavor. Existing methodologies compartmentalize the capture of temporal and spatial dependencies through distinct modules, overlooking latent spatio-temporal heterogeneity within traffic data. Furthermore, they often solely consider spatio-temporal correlations between adjacent historical time steps, neglecting the extraction of temporal dependencies across different time intervals. To address these issues, we propose a novel Multi-scale Fusion Dynamic Graph Neural Network (MFDGNN). MFDGNN employs Temporal Convolutional Networks (TCNs) to generate time series on multiple scales, subsequently extracting spatio-temporal features from these sequences to construct mutually independent fusion graphs. By leveraging fusion graphs to extract features from time series on multiple scales, our model adeptly captures spatio-temporal dependencies of traffic nodes across various time dimensions, thereby achieving enhanced precision in traffic flow prediction. Experimental results conducted on four publicly available datasets demonstrate the superiority of our model compared to other baseline approaches.
Organizational Network Border Router Policy Device Internal Router Unused but Routable Addresses Packets to Unused / Unreachable Addresses Routing / Security Policy Violations Remote Host Unreachable Internal & External Hosts Contacting Unused Addresses Incoming PacketsOutgoing Packets

Internet traffic destined for unused or unreachable addresses provides critically important information on malicious and misconfigured activity. Since Internet address allocation and policy information is distributed across many devices, applications, and administrative domains, constructing a comprehensive map of unused and unreachable (“dark”) addresses is challenging. In this paper, we present an architecture that automates the process of discovering these dark addresses by actively participating with allocation, routing, and policy systems. Our approach is to adopt a local perspective revealing unreachable external addresses and unused private and local addresses, and enabling the detection of threats coming into and out of a network. To validate the approach, we construct a prototype system called the Dark Oracle that uses internal and external routing data and host configuration information, such as DHCP logs, to automatically discover dark addresses. We experimentally evaluate the prototype using data from a large enterprise network, and a regional ISP, and from deployment of the Dark Oracle on a large academic network.
OF CONSENSUS-BASED SEDIMENT EFFECT CONCENTRATIONS FOR POLYCHLORINATED BIPHENYLS

Sediment-quality guidelines (SQGs) have been published for polychlorinated biphenyls (PCBs) using both empirical and theoretical approaches. Empirically based guidelines have been developed using the screening-level concentration, effects range, effects level, and apparent effects threshold approaches. Theoretically based guidelines have been developed using the equilibriumpartitioning approach. Empirically-based guidelines were classified into three general categories, in accordance with their original narrative intents, and used to develop three consensus-based sediment effect concentrations (SECs) for total PCBs (tPCBs), including a threshold effect concentration, a midrange effect concentration, and an extreme effect concentration. Consensus-based SECs were derived because they estimate the central tendency of the published SQGs and, thus, reconcile the guidance values that have been derived using various approaches. Initially, consensus-based SECs for tPCBs were developed separately for freshwater sediments and for marine and estuarine sediments. Because the respective SECs were statistically similar, the underlying SQGs were subsequently merged and used to formulate more generally applicable SECs. The three consensus-based SECs were then evaluated for reliability using matching sediment chemistry and toxicity data from field studies, dose-response data from spiked-sediment toxicity tests, and SQGs derived from the equilibrium-partitioning approach. The results of this evaluation demonstrated that the consensus-based SECs can accurately predict both the presence and absence of toxicity in field-collected sediments. Importantly, the incidence of toxicity increases incrementally with increasing concentrations of tPCBs. Moreover, the consensus-based SECs are comparable to the chronic toxicity thresholds that have been estimated from dose-response data and equilibrium-partitioning models. Therefore, consensus-based SECs provide a unifying synthesis of existing SQGs, reflect causal rather than correlative effects, and accurately predict sediment toxicity in PCB-contaminated sediments. Keywords—Polychlorinated biphenyls Mixtures Sediment quality guidelines Sediment Toxicity
Avoiding growing pains in reproductive trait databases: the curse of dimensionality

Aim: Reproductive output features prominently in many trait databases, but the met - rics describing it vary and are often untethered to temporal and volumetric dimensions (e.g., fecundity per bout). The use of such ambiguous reproductive measures to make broad-scale comparisons across taxonomic groups will be meaningful only if they show a 1:1 relationship with a reproductive measure that explicitly includes both a volumetric and a temporal component (i
Comprehensive Characterization of Genes Required for Protein Folding in the Endoplasmic Reticulum

Protein folding in the endoplasmic reticulum is a complex process whose malfunction is implicated in disease and aging. By using the cell's endogenous sensor (the unfolded protein response), we identified several hundred yeast genes with roles in endoplasmic reticulum folding and systematically characterized their functional interdependencies by measuring unfolded protein response levels in double mutants. This strategy revealed multiple conserved factors critical for endoplasmic reticulum folding, including an intimate dependence on the later secretory pathway, a previously uncharacterized six-protein transmembrane complex, and a co-chaperone complex that delivers tail-anchored proteins to their membrane insertion machinery. The use of a quantitative reporter in a comprehensive screen followed by systematic analysis of genetic dependencies should be broadly applicable to functional dissection of complex cellular processes from yeast to human.
Ribonucleicacid interference or small molecule inhibition of Runx1 in the border zone prevents cardiac contractile dysfunction following myocardial infarction

Abstract Aims Myocardial infarction (MI) is a major cause of death worldwide. Effective treatments are required to improve recovery of cardiac function following MI, with the aim of improving patient outcomes and preventing progression to heart failure. The perfused but hypocontractile region bordering an infarct is functionally distinct from the remote surviving myocardium and is a determinant of adverse remodelling and cardiac contractility. Expression of the transcription factor RUNX1 is increased in the border zone 1-day after MI, suggesting potential for targeted therapeutic intervention. Objective This study sought to investigate whether an increase in RUNX1 in the border zone can be therapeutically targeted to preserve contractility following MI. Methods and results In this work we demonstrate that Runx1 drives reductions in cardiomyocyte contractility, calcium handling, mitochondrial density, and expression of genes important for oxidative phosphorylation. Both tamoxifen-inducible Runx1-deficient and essential co-factor common β subunit (Cbfβ)-deficient cardiomyocyte-specific mouse models demonstrated that antagonizing RUNX1 function preserves the expression of genes important for oxidative phosphorylation following MI. Antagonizing RUNX1 expression via short-hairpin RNA interference preserved contractile function following MI. Equivalent effects were obtained with a small molecule inhibitor (Ro5-3335) that reduces RUNX1 function by blocking its interaction with CBFβ. Conclusions Our results confirm the translational potential of RUNX1 as a novel therapeutic target in MI, with wider opportunities for use across a range of cardiac diseases where RUNX1 drives adverse cardiac remodelling.
PROCESSING, NEURAL NETWORK-BASED MODELING OF BIOMONITORING STUDIES DATA AND VALIDATION ON REPUBLIC OF MOLDOVA DATA

This paper suggests an approach to process and model the data obtained in biomonitoring studies. The approach is validated on data obtained from biomonitoring studies performed in the Republic of Moldova in 2015. Using the preliminary data, the decomposition on the basis of the pollution spectrum for the most polluted and cleanest sites is first carried out. The deviations of model predictions from the actual measurements are considered. A correlation analysis is next performed to evidence the correlation of two geographical coordinates with chemical elements. Factor analysis and regression analysis are applied to highlight the nonlinear mechanisms specific to the obtained data. A multilayer neural network-based model is derived to describe the relationship of the pollution rank to the geographic coordinates. The predictive capabilities of the model are represented graphically
on User

: The idea is to propose a new decentralized access control scheme for achieving security in data stored in clouds that supports anonymous authentication. In the proposed scheme, the cloud verifies the authenticity of the series without knowing the user’s identity before storing data in cloud. Here we also have added feature of access control so that only valid users are able to decrypt the data i.e. stored in cloud. The proposed scheme also prevents replay attacks and supports alteration of data and reading data stored in the cloud. We also address user revocation. Moreover, the proposed authentication and access control scheme is decentralized and robust, unlike the existing access control schemes that are centralized. So that here communication, computation, and storage overheads are comparable to centralized approaches.
The efficacy of an iterative “sequence of prevention” approach to injury prevention by a multidisciplinary team in professional rugby union

Objectives : Due to the complex-systems nature of injuries, the responsibility for injury risk management cannot lie solely within a single domain of professional practice. Interdisciplinary collaboration between technical/tactical coaches, strength and conditioning coaches, team doctors, physical therapists and sport scientists is likely to have a meaningful impact on injury risk. This study describes the application and efficacy of a multidisciplinary approach to reducing team injury risk in professional rugby union. Design: Observational longitudinal cohort study. Methods : Epidemiological injury data was collected from a professional rugby union team for 5 consecutive seasons. Following each season, these data informed multidisciplinary intervention strategies to reduce injury risk. The effectiveness of these strategies was iteratively assessed to inform future interventions. Specific examples of intervention strategies are provided.
IDENTIFICATION OF COMPOUNDS WITH THERAPEUTIC POTENTIAL FROM ETHANOLIC EXTRACT OF CENTELLA ASIATICA

flavonoids was carried out using the sodium nitrite method, and the evaluation of antioxidant activity was conducted using the DPPH IC50 method . Based on the obtained results, we can say that the ethanolic extract of Centella asiatica has a high content of polyphenols and flavonoids, demonstrating strong antioxidant activity.
Barycentric and Pl ¨ucker coordinates using projective Geometric algebra

: This paper presents the computation of the barycentric coordinates and Pl¨ucker co-ordinates using the projective extension of the Euclidean space and geometric algebra. Using the projective extension, it also presents a relationship between linear systems of equations Ax=b and Ax=0 using the projective extension. An application of the principle of duality enables solving dual problems efficiently. The given approach uses vector notation leading to efficient implementation on GPU or efficient use of SSE instructions. As the presented approach is based on projective notation, the division operation is postponed and the proposed method leads to higher computational robustness.
Inundation and the distribution of plant communities on Dutch river dikes

SUMMARY The relationship between the distribution of plant communities occurring on river dikes and the inundation by river water is described. The distribution of many species is restricted to the non-flooded upper part of the dike slope, while their lowest distribution limit coincides with the highest water-level for the growing season for the year preceding the recording of the vegetation. The phytosociological elements show the same distribution pattern in all five transects studied and can be clearly ordered by means of detrended correspondence analysis as well as by their relative inundation values: —hardly flooded: Koelerio–Corynephoretea, Festuco–Brometea and Mesobromion. —slightly flooded: Arrhenatheretum and Trifolion medii. —flooded: Arrhenatherion, Molinio–Arrhenatheretea, Artemisietea, Lolio-Cynosuretum and arable weeds. —considerably flooded: Ranunculo–Alopecuretum, Bidentetalia, Molinietalia, Phragmitetea and Lolio–Plantaginetum. Typical ‘fluviatile’ plant communities are strongly declining on river dikes. (Re) Introduction of these communities by means of special construction measures (e.g. the application of suitable soil) and special management practices is only possible above 2–2.30 m below ‘normative water level‘.
Perceived Quality on Junior High School Sex Education and its Sexual Knowledge and Attitudes among Senior High School Students in a Selected Private University in the National Capital Region

Comprehensive Sexual Education is often overlooked due to its sensitive nature in conservative society, resulting in higher teen pregnancy rates, unsafe sex practices, and an increase in sexually transmitted diseases. It also restricts preadolescents from learning about other equally valuable information such as sexual orientation and gender expression. This study utilized scales to measure its main four variables — Background Characteristics, Perceived Quality, Sexual Knowledge, and Attitude — and a descriptive correlational design to explore the relationships between them. The results showed that quality sexual education had positive contributions to sexual knowledge, and that students had a relatively positive sexual attitude. The study also revealed that respondents with higher religiosity had higher perceptions of comprehensiveness and the school environment in relation to their quality of JHS sex education.
SmallEvoTest: Genetically Created Unit Tests for Smalltalk

Evolutionary test generation techniques have emerged as a popular approach in recent years for enhancing the testing of software systems. However, while these techniques have proven to be efficient in programming languages that support static type annotations, dynamically typed programming languages have not received significant attention from the automatic test generation community. This paper introduces an approach aimed at automatically generating fully executable unit tests suitable for dynamically typed programming languages. In particular, our approach is tuned for dynamically-typed and class-based programming languages, and it is implemented in the Pharo and GToolkit programming languages. To address the absence of static type annotations, our approach uses a type profiling mechanism and employs a genetic algorithm to drive the evolution of the unit tests.
Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta

Abstract This study explored hyperspectral field and satellite-based remote sensing of soil salt content. Using Kenli County in the Yellow River Delta as the study area, in situ soil field spectra and satellite-based remote-sensing images were integrated with laboratory measurements of soil sample salinity to improve remote sensing-based soil salt estimation and inversion procedures. First, the narrow-band hyperspectral reflectance field data were used to model the wide-band reflectance data from Landsat 7. Second, the bands and spectral features sensitive to soil salt content were identified through correlation analysis and band combination. Stepwise multiple linear regression was used to find a best model, which was then inverted to predict soil salt content using remote-sensing images from Landsat 7 and Landsat 8. The applicability of the model was verified by ground-checking the inversion results. The results show that the bands sensitive to soil salinity are mainly in the visible and near-infrared (NIR) regions. Combining information from these bands can eliminate some background effects and significantly improve the correlation with salinity. The best model of soil salinity is y = 1.345 − 25.898 × gSWIR1 − 245.440 × gRed × (gRed − gNIR) − 0.252 × (gRed + gNIR)/(gRed − gNIR) − 19.563 × (gRed − gSWIR1). This model has a coefficient of determination (R2) of 0.896, a verification R2 of 0.867, a relative prediction deviation (RPD) of 2.135, and a root mean square error (RMSE) of 0.264. The model fits well and is highly stable. The inversion results based on Landsat 7 and Landsat 8 images are consistent with the actual situation of soil salinity in the study area. This study provides an effective and feasible method for the estimation of soil salt content in coastal regions based on field spectral measurements and remote-sensing inversion.
Differential Response of Female Deer Mice to Short Photoperiod 1

Individual male deer mice (Perotnyscus maniculatus} respond to inhibitory (short) photoperiod with gonadal responses that range from azoospermia to normal spermatogenesis. We undertook the present study to determine if female deer mice exhibit similar variation in reproductive response to inhibitory daylength. Following 8 wk exposure to short days, reproductive tract weights of 25% of all individual females did not differ from those displayed by mice housed on stimulatory (long) photoperiod; reproductive tracts of all remaining short day mice weighed significantly less. Short photoperiod also significantly reduced body weight, albeit only in those mice with regressed reproductive tracts. These results demonstrate that female deer mice respond differentially to the inhibitory effects of short photoperiod. Taken together with previous results, the present findings indicate that populations of deer mice are composed of subsets of males and females that differ in reproductive response to short daylength.
Guessing a tweet author’s political party using weighted n-gram models

Political parties and their candidates are increasingly using online channels for their electoral campaigns. This was, for instance, observable for the elections for the German parliament (Bundestag) in 2017. But even outside the campaigning time, politicians use Twitter to inform about their work and current topics. For an informed human it is usually easy to guess their political afﬁliation even if it is not explicitely stated in the tweets. In this paper we present a probabilistic classiﬁer for the political party of a tweet’s author and compare different weight conﬁgurations, either weighting by word frequency or by part of speech. In opposition to many existing systems that focus on the US and only work with two-party political systems, our model allows an arbitrary amount of parties. For the German election we included 9 political parties into the analysis and our system achieved an accuracy of 72 % when perusing all tweets published by an author in the speciﬁed time interval, or 36 % accuracy when using only one single tweet as input. A random guessing baseline system would have an expected accuracy of 11 % in both cases.
BLoB : Beating-based Localization for Single-antenna BLE Devices

Low-power wireless communication protocols based on synchronous transmissions have recently gained popularity. In such protocols, packets can be demodulated correctly even though several devices transmit at the same time, which results in high reliability and energy efficiency. A by-product of synchronous transmissions is the beating effect : a sinusoidal pattern of constructive and destructive interference across the received signal. In this paper, we leverage this beating to propose a new localization approach. Specifically, we present BLoB , a system in which multiple anchors transmit packets synchronously using the constant tone extension , an optional bit sequence introduced by BLE 5.1, whose signal is sent with constant amplitude and frequency. We let mobile tags sample the superimposed signal resulting from the synchronous transmissions, and extract peaks in the beating and signal spectrum. These peaks provide key insights about the anchors’ location that complement received signal strength information and allow BLoB to derive a tag’s position with sub-meter accuracy. A key property of BLoB is that both anchors and tags employ a single antenna , in contrast to state-of-the-art localization schemes based on angle of arrival/departure information that require costly and bulky antenna arrays to achieve sub-meter accuracy. We implement BLoB on off-the-shelf BLE devices and evaluate its performance experimentally in both static and mobile settings, and in different environments: office rooms, library, meeting room, and sports hall. Our results show that BLoB can distinguish several anchors in a single synchronous transmission and that it retains a sub-meter localization accuracy even in challenging indoor environments.