Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Location Algorithm Based on WIFI Location Fingerprint

With the development of Internet of Things technology, more and more devices are connected to the Internet, which enables people to obtain the necessary information and services anytime and anywhere. The commonly used WIFI (Wireless Fidelity) location fingerprint indoor positioning algorithm is mainly based on the received signal strength (RSSI) to calculate user positions. Due to the presence of obstacles in buildings and interference between different channels, the accuracy of RSSI is often low, resulting in inaccurate positioning results. This study aims to solve the problems of existing WIFI location fingerprint indoor positioning algorithms. It introduces a location fingerprint positioning algorithm based on Weighted Nearest Neighbor Network (WNN). This algorithm collects the arrival time and intensity information of WIFI signals, and processes this information to achieve user location fingerprint extraction and indoor positioning. In order to verify the effectiveness of the position fingerprint localization algorithm based on weighted noise network in indoor position localization, the final experimental results show that the average positioning accuracy of the position fingerprint localization algorithm based on weighted noise network is 93.19%, and the average indoor positioning time is 4.32 seconds. Based on experimental data, it can be concluded that the position fingerprint localization algorithm based on weighted noise network is superior to traditional indoor localization algorithms in indoor localization.
Box Huckleberry Roots at Highest Percentages From Softwood Stem Cuttings

Box huckleberry [Gaylussacia brachycera (Michx.) Gray] is a slow-growing, dwarf evergreen woody groundcover that is native to both the mountains and coastal plains of eight states in the mid-Atlantic region of the United States. It grows as a low, dense, spreading mat and is affected by few major disease or insect pests. While uncommon in the wild, it has potentially high ornamental value as an evergreen groundcover, growing well in dry shade and acidic soil. Unfortunately, knowledge on the optimal asexual propagation of box huckleberry is lacking. In order to identify optimal propagation timing, we took stem cuttings from five genotypes of box huckleberry every month for two years. Although some genotypes differed, on average 90% or more of the cuttings rooted for those taken during May to July, 30% or fewer for September to December cuttings, and 40 to 70% for cuttings taken in other months. Cuttings generally formed roots faster in months with a higher rooting percentage. Based on these results, the be.
MLOps in Data Science Projects: A Review

Data Science (DS) has gained increased relevance due to the potential to extract useful insights from data. Quite commonly, this involves the utilization of Machine Learning (ML). The challenging pursuit of developing and productionizing ML models can be supported and automated through MLOps, a specialization of the DevOps paradigm from software development. Therefore, MLOps offers significant potential for DS projects, which are suffering from notable failure rates. Accordingly, this literature review focuses on examining the current state-of-the-art of the publications in this area. Most importantly, the analysis showed that the current MLOps approaches in the literature predominantly emphasize model development and deployment, while organizational aspects (business understanding, evaluation) in a DS project are neglected. As DS project success is not exclusively dependent on technical matters, advancing the MLOps field by bridging the gap between business objectives and the modeling perspective through appropriate frameworks should be pursued in future research.
Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes

Background The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions.
Ready for the Big Game: How FanDuel Boosts Conversions and Revenue at Scale with a Real-Time Recommender System for Daily Fantasy Sports

Daily fantasy sports allow sports fans to participate in the matches they are watching. The daily fantasy sports landscape is competitive and evolves fast. To see off their competitors, some operators now personalize their offerings to improve the engagement of their users and increase their revenues. This paper presents the development and implementation of a scaled and real-time recommender system at FanDuel for its Daily Fantasy Sports (DFS) users. The system aims to recommend contests to users based on their past contest entries, leveraging historical data and machine learning techniques.In this paper, we propose a scalable contest recommender model that was built on real-time data pipelines and is re-trained every hour. We identify key factors that influence user preferences and contest selection. The recommender system analyzes users’ historical data, identifies similar users based on their participation behavior, and suggests contests that align with their interests. On average, we recommend contests to 180K users daily and have recommended over 300 million contests to 2 million users since the model’s inception. Notably, we saw over 1000% lift in user conversion rates and 4.43% lift in overall entry fees, peaking at 15.6% in April.
Prediction-Based Virtual Array Beamforming

Adaptive beamforming using the sample covariance matrix inversion (SMI) method suffers from severe performance degradation in the presence of steering vector (SV) mismatches. Classical robust algorithms based on the sample covariance matrix (SCM) sacrifice the capability of interference suppression to improve main-lobe robustness. In this paper, a prediction-based adaptive beamforming algorithm for virtual arrays is proposed, which can suppress spatial interference while ensuring a distortionless main-lobe response. First, the virtual sensor is constructed, and its observation data are estimated using a prediction algorithm that has noncumulative errors and real array observation data. Then, the interference-plus-noise covariance matrix (INCM) of the extended array is reconstructed within the airspace sector, and finally, the extended SV is estimated by the eigenvector corresponding to the largest eigenvalue. Simulation results indicate that the proposed algorithm can reduce the impact of interference on the output while improving main-lobe robustness, thereby improving the output signal-to-interference-plus-noise ratio (SINR).
SLM Printed Dual-Band Dual-Polarized Shared-Aperture Filtering Antenna Array for Millimeter-Wave Integrated Sensing and Communication

A 3-D-printed dual-band dual-polarized (DBDP) shared-aperture $1\times8$ antenna array with filtering response is proposed for millimeter-wave (mm-Wave) integrated sensing and communication (ISAC) systems. The stepped horn is designed first to achieve shared-aperture DBDP radiation. To realize fixed beam radiation for communication purposes, an integrated 1-to-8 filtering network is designed to feed the horns in the high band (HB). To enable a phased scanning array for detection in the low band (LB) with excellent out-of-band suppression, a compact spoof surface plasmon polaritons (SSPPs) filter is studied to feed the individual radiating horn. Selective laser melting (SLM) is employed to monolithically fabricate a $1\times8$ prototype for demonstration. The measured results demonstrate the operating bandwidths (return loss > 10 dB) of 23.7–26.6 GHz and 28.0–30.4 GHz, respectively, with cross-band isolation higher than 60 dB. The maximum gains of 18.2 and 20.5 dBi are achieved within the operating band, respectively, and the out-of-band gain decays more than 20 dB/GHz. A beam-scanning range of ±47° is achieved in the LB with vertical polarization (VP), while a fixed beam radiation is realized in the HB with horizontal polarization (HP). The proposed design shows great potential for application in future mm-Wave ISAC systems.
Mathematical Model Analysis of Photovoltaic-Induced Land Use Transition

Recent policy initiatives aimed at advancing re-newable energy have spurred the proliferation of small-scale photovoltaic power generation installations, with a particular concentration in Japan’s mountainous terrain. These installations predominantly occupy fallow farmland. As explained herein, we embark on the modeling of land use transitions using population dynamics as the overarching framework, encompassing agricultural land and photovoltaic power generation land as important variables. The effectiveness of our proposed model is evaluated rigorously using empirical data. Our numerical simulations vividly depict the potential for harmonious land use patterns between agriculture and photovoltaic power generation, as our model demonstrates. The mathematical model further presents its utility for forecasting the amalgamation and cessation of diverse sites and infrastructure ventures slated for rural landscapes, while concurrently serving as a valuable tool for assessing associated policy initiatives.
An Analysis of the Palestinian Foreign Trade During the Period 1995–2022: A Gravity Model Approach

This research investigates the determinants of Palestinian foreign trade by conducting a comprehensive analysis of various factors influencing both export and import volumes, using the gravity model as the analytical framework. The considered determinants include per capita GDP, Consumer Price Index (CPI), trade agreements, Israeli closure of commercial borders, and Geographical Distance. In terms of exports, the results demonstrate a statistically significant positive relationship with per capita GDP, affirming the hypothesis that heightened economic growth is associated with increased exports. On the import side, the Consumer Price Index (CPI) reveals a statistically significant positive association, showing that increased inflation is linked to a decline in imports, likely due to reduced purchasing power. Contrastingly, trade agreements show a complex relationship with both positive and negative potential impacts on both exports and imports. The complex nature of its impact necessitates further investigation. The Israeli closure of the commercial borders significantly hampers both import and export volumes, underscoring the challenges imposed by Israeli restrictions. Geographical Distance does not emerge as a significant factor in either exports or imports, challenging established economic theories regarding the impact of proximity on trade volumes. The research findings highlight the profound impact of Israeli occupation policies on the Palestinian economy, resulting in significant economic distortions and impeding economic development over several decades. Furthermore, the research shows the inability of the Palestinian Authority to implement independent economic policies due to the dominant role of Israel in decision-making processes. This inability, combined with the significant economic distortions caused by Israeli occupation policies, hinders further economic growth and development, as the Palestinian Authority cannot undertake crucial economic initiatives.
Mutation of BAM2 rescues the sunn hypernodulation phenotype in Medicago truncatula, suggesting that a signaling pathway like CLV1/BAM in Arabidopsis affects nodule number

The unique evolutionary adaptation of legumes for nitrogen-fixing symbiosis leading to nodulation is tightly regulated by the host plant. The autoregulation of nodulation (AON) pathway negatively regulates the number of nodules formed in response to the carbon/nitrogen metabolic status of the shoot and root by long-distance signaling to and from the shoot and root. Central to AON signaling in the shoots of Medicago truncatula is SUNN, a leucine-rich repeat receptor-like kinase with high sequence similarity with CLAVATA1 (CLV1), part of a class of receptors in Arabidopsis involved in regulating stem cell populations in the root and shoot. This class of receptors in Arabidopsis includes the BARELY ANY MERISTEM family, which, like CLV1, binds to CLE peptides and interacts with CLV1 to regulate meristem development. M. truncatula contains five members of the BAM family, but only MtBAM1 and MtBAM2 are highly expressed in the nodules 48 hours after inoculation. Plants carry mutations in individual MtBAMs, and several double BAM mutant combinations all displayed wild-type nodule number phenotypes. However, Mtbam2 suppressed the sunn-5 hypernodulation phenotype and partially rescued the short root length phenotype of sunn-5 when present in a sunn-5 background. Grafting determined that bam2 suppresses supernodulation from the roots, regardless of the SUNN status of the root. Overexpression of MtBAM2 in wild-type plants increases nodule numbers, while overexpression of MtBAM2 in some sunn mutants rescues the hypernodulation phenotype, but not the hypernodulation phenotypes of AON mutant rdn1-2 or crn. Relative expression measurements of the nodule transcription factor MtWOX5 downstream of the putative bam2 sunn-5 complex revealed disruption of meristem signaling; while both bam2 and bam2 sunn-5 influence MtWOX5 expression, the expression changes are in different directions. We propose a genetic model wherein the specific root interactions of BAM2/SUNN are critical for signaling in nodule meristem cell homeostasis in M. truncatula.
Therapeutic strategies targeting the NLRP3‑mediated inflammatory response and pyroptosis in cerebral ischemia/reperfusion injury (Review)

Ischemic stroke poses a major threat to human health. Therefore, the molecular mechanisms of cerebral ischemia/reperfusion injury (CIRI) need to be further clarified, and the associated treatment approaches require exploration. The NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome serves an important role in causing CIRI, and its activation exacerbates the underlying injury. Activation of the NLRP3 inflammasome triggers the maturation and production of the inflammatory molecules IL-1β and IL-18, as well as gasdermin-D-mediated pyroptosis and CIRI damage. Thus, the NLRP3 inflammasome may be a viable target for the treatment of CIRI. In the present review, the mechanisms of the NLRP3 inflammasome in the intense inflammatory response and pyroptosis induced by CIRI are discussed, and the therapeutic strategies that target the NLRP3-mediated inflammatory response and pyroptosis in CIRI are summarized. At present, certain drugs have already been studied, highlighting future therapeutic perspectives.
Security Algorithms to Detect and Prevent Advanced Persistent Threats in Cloud Computing: A Systematic Review

Cloud computing simplifies and improves access to data and services for organizations and individuals. The number of users and data in cloud computing increases, making it more susceptible to targeted attacks. The cloud is a complex IT environment that makes it harder to detect advanced persistent threats (APTs). Continuous monitoring and defensive systems have proven efficient in identifying and blocking these attacks, although they have limits due to their reliance on known attack signatures. Thus, this paper proposes a security model for protecting cloud data from these attacks and overcoming the limitations of existing approaches. Our security model uses Deep Packet Inspection (DPI), AUSPEX, Domain-Generating Algorithm (DGA), and Pearson Correlation Coefficient (PCC) to detect and prevent APTs attacks. This model simplifies classification by identifying complex relationships between database features by intercepting and correlating operating system events with network events. Moreover, this paper reviews existing algorithms used to prevent APTs in cloud computing. Thereafter, we present the limitations of these existing algorithms. This comprehensive study serves as a guideline for everyone interested in developing security techniques for preventing APTs in cloud computing. Finally, the paper presents the conclusion and future work.
Quantitatively Evaluating the Validity of Contrastive Generators for Recourse

Explanations of classifiers can provide recourse to those impacted, i.e., the ability to facilitate those impacted to understand and potentially change the classification. Contrastive explanations provide this recourse by producing an alternative input close to the original such that the label is changed to the desired, implying actions in the difference between the original and contrastive inputs. However, quantitatively evaluating contrastive explanations remains a challenging task. In particular, some state-of-the-art contrastive explanation algorithms for decisions made by deep neural networks can produce inputs that are out-of-distribution or adversarial and are thus either infeasible for a user to achieve or do not change the label according to the underlying data distribution, respectively. Past work has termed contrastive examples valid if they cross the decision boundary of the classifier. However, this definition does not encompass these failure modes and thus is not suitable to evaluate contrastive methods when used for recourse. In this paper, we define a new type of validity, called distributional validity, that checks for these failure modes. We experiment with the distributional validity of state-of-the-art contrastive explanation methods and find that the best contrastive method depends on the architecture of the classification model.
Hide My Payload: An Empirical Study of Antimalware Evasion Tools

This paper presents an empirical analysis of antimalware evasion techniques. A total of 29504 tests generated by 16 antimalware evasion tools were run against 100 machines protected by either Microsoft Windows Defender or Symantec Endpoint Protection. The configuration of each test was mapped to a categorization framework with 11 evasion techniques. Out of the executed tests, 54% evaded the antimalware scans and 5% provided interactive shells (i.e., remote control of victims). Out of the studied evasion techniques, using a packer, encryption (AES or RC4), or storing the payload loader in a common data format (as compared to a script or executable) had the greatest impact on evading antimalware scans, while application of custom encryption and code injection had the greatest impact on gaining shells. The results also showed that several evasion techniques curiously increased the likelihood of detection.
Effects of different levels of iron, iodine and vitamin B12supplementation on egg quality and antioxidant capacity of eggs at different times of storage in laying hens

The experiment was carried out to evaluate the effects of adding different levels of iron salts, iodine and vitamin B12 on yield, quality traits and antioxidant capacity of eggs in laying hens. A total of 320 laying hens (HyLine Variety White, 28 wk old) in a 2x2x2 factorial experiment were randomly assigned to 8 groups, and each group consisting of 40 hens (5 replicates of 8 hens each) for a period of approximately 13 weeks in a completely randomized design. Hens in the control group received a basal diet with no supplementation whereas hens in treat 2 received basal diet supplemented with 400 mg Fe/kg as FeSO4, treat 3 (basal diet + 450 mg I/kg as KI), treat 4 (basal diet + 0.1 mg B12vit/kg), treat 5 (basal diet + 400 mg Fe/kg as FeSO4 + 450 mg I/kg as KI), treat 6 (basal diet + 400 mg Fe/kg as FeSO4 + 0.1 mg B12Vit/kg), treat 7 (basal diet + 450 mg I/kg as KI + 0.1 mg B12Vit/kg), treat 8 (basal diet + 400 mg Fe/kg as FeSO4 + 450 mg I/kg as KI + 0.1 mg B12Vit/kg), respectively. The results showed that the use of iodine and vitamin B12 increased the shell thickness of eggs (P<0.05). The use of iron, iodine and vitamin B12 supplements increased the antioxidant capacity of eggs compared to the control group (P<0.05). The highest antioxidant capacity of eggs (0.77) was observed when these three supplements were used together in the diet of laying hens. The length of storage had significant effects on the weight of albumin and yolk and antioxidant content of eggs (P<0.05). The highest weight of albumin and antioxidant content was at the beginning of storage, while the highest weight of yolk was obtained in 4 weeks after storage, with increasing of storage time to 8 weeks, all three attributes of albumin weight, yolk weight and antioxidant content of eggs decreased (P<0.05). The related effects of interval of the storage and the experimental supplementation, the use of iron, iodine and vitamin B12 supplements increased the antioxidant capacity of eggs, so that the highest amount of antioxidants in eggs in 8 weeks after storage and by supplementing the diets with all three additives (P<0.05). According to the present experiment, the use of iron, iodine and vitamin B12 additives in the diet of laying hens, improves the performance, the content of iron and iodine elements in eggs and maintains the suitable condition of eggs during storage.
Model development and initial characterization of Escherichia coli in the shellfish-producing area of Butrinti Lagoon

The purpose of this research was to estimate the correlation between hydrochemicals and Escherichia coli contamination in Mytilus galloprovincialis by using multi-linear regression and statistically processing the monthly mean results. This study was conducted in a traditional cultivation of M. galloprovincialis, sampled and analyzed (n=136) for E. coli microbial analysis with ISO 16649-3. From 2015 to 2017, seawater was measured with a multiparameter apparatus, where four variables [dissolved oxygen (n=115), temperature (n=127) and pH (n=115), salinity (n=127), and local area rainfall monitoring (n=23)] were taken into consideration. The results were compared and shown to have a significant correlation, allowing for the quantification of the impact resulting from adjustments made to the monthly mean computation. During the study period, statistical performance for each year was estimated R2=94.4% (2015), R2=46.8%, and R2=97.5% (2017).