Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Prediction of Neighboring Vehicles via Periodic Beaconing with Inaccurate GPS Data

Predicting trajectories of neighboring vehicles accurately is essential for safe driving in Advanced Driving Assistance Systems (ADAS). Conventional approaches for predicting trajectory rely upon an expensive image sensor (i.e., lidar). However, high cost but limited sensing range of lidar makes installing a trajectory prediction system on existing vehicles impractical. Using two functionalities provided by a smartphone, i.e., periodic beaconing and GPS positioning could address issues of conventional approaches. However, modules for wireless communications and GPS are limited in achieving accurate prediction in their current form: transmission error and inaccurate GPS data. In this paper, we propose a novel scheme for predicting neighbors’ trajectories using periodic beaconing and GPS, and show that our scheme is feasible to trajectory prediction. To address the limitations of the wireless and GPS modules, our scheme integrates Long Short-Term Memory (LSTM) with two strategies: 1) Tailoring Multi-band Transmission to Prediction (TMTP) and 2) Environment-Aware Positioning (EAP). To improve prediction accuracy even with the imperfect input, our scheme employs interactive LSTM model. To our knowledge, this is the first to enable predicting the neighbors’ trajectories using periodic beaconing with inaccurate GPS data. Experimental results using real traces demonstrate that our scheme is accurate in predicting neighbors’ trajectories.
The Declining Profile of Physical Education Programme in Educational Institutions in Nigeria

Since the early days of Nigerian history, Physical Education has always been considered as an integral part of educational system as obtained in several nations of the world. The awareness of the values of physical activities and sports, gave a boost to the prominence given to the practical and pedagogical aspects of physical education in all segments of educational system in Nigeria, between 1950s and 1980s. Physical Education was a compulsory subject in the curriculum of primary and teacher education, while the National Policy on Education of 1970s and 1980s accommodated the programme for secondary education. However, it is rather disturbing to note that there has been a systematic decline in the fortune of physical education in Nigerian schools since 1990 to date. This paper therefore, examines the acceptability and recognition of physical education programmes in Nigerian schools, and the inherent factors responsible for the steady decline of its profile over the years.
Teaching small groups

Group discussion plays a valuable role in the all-round education of students, whether in problem based learning and team projects or in the more traditional academic scenario of the tutorial or seminar. When it works well, discussion can allow students to negotiate meanings, express themselves in the language of the subject, and establish closer contact with academic staff than more formal methods permit. Discussion can also develop the more instrumental skills of listening, presenting ideas, persuading, and working as part of a team. But perhaps most importantly, discussion in small groups can or should give students the chance to monitor their own learning and thus gain a degree of self direction and independence in their studies. “By separating teaching from learning, we have teachers who do not listen and students who do not talk” Based on Palmer P ( The Courage to Teach . Jossey Bass, 1998) #### Problems associated with leading effective small groups All these worthy aims require active participation and the ready expression of ideas. Yet it frequently doesn't work out this way. Indeed many tutors too readily fall back on their reserve positions of authority, expert, and prime talker. Many of the problems associated with leading small groups effectively are likely to be exacerbated with larger groups. So how can we avoid the common traps? If you are leading a group discussion you will need to consider both the configuration of the group and your own behaviour. …
Teaching Machine Learning with Industrial Projects in a Joint Computer Science Master Course: Experiences, Challenges, Perspectives

Teaching machine learning is not only a balancing act between an appropriate amount of theory and practice. Beside relevant classic methods, it further needs to mind the state-of-the-art in a rapidly evolving field. Project-based learning approaches can aid translation of theory into practice, and serve as playground for modern methods. In addition, they foster advancement of various skills, crucial for working in teams. Ideally, projects topically align with study programs they are offered to, and address current and relevant problems. Joint courses offered to different programs thus display a demanding environment. The paper at hand presents insights into an implemented approach for a respective course environment, involving industrial projects of small extent with differentiated topics for various computer science Master programs. Students get in touch with industrial partners and apply in vogue methods, while traversing the Cross-Industry Standard Process for Data Mining (CRISP-DM), respectively large parts of its counterpart for Machine Learning with Quality Assurance Methodology (CRISP-ML(Q)). Gathered experiences from student surveys from two iterations are reported, and challenges associated with conduction of the approach and project fulfillment are outlined. Further, perspectives on future course and approach adjustments are presented. A discussion states limitations in regards to its implementation and potential scope, and addresses emerging trends to mind. It concludes that once implemented, the approach aids program-specific teaching, proves scalable regarding provision to further programs, and can adapt to current progress in the field.
TECHNOLOGY DISSEMINATION AMONG SMALL-SCALE FARMERS IN MERU CENTRAL DISTRICT OF KENYA: IMPACT OF GROUP PARTICIPATION

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TECHNOLOGY DISSEMINATION AMONG SMALL-SCALE FARMERS IN MERU CENTRAL DISTRICT OF KENYA: IMPACT OF GROUP PARTICIPATION By Kristin Elizabeth Davis August 2004 Chair: Nick Place Cochair: Peter Hildebrand Major Department: Agricultural Education and Communication The goal of this study was to examine the role of farmer groups in technology dissemination, and to assess what factors make groups effective in extending technologies among small-scale dairy-goat farmers in Meru Central District of Kenya. The theoretical framework for the study included insight from agricultural extension theory, farming systems research and extension, social capital, and group theory. A mixed-methods, multiple-stage approach was used to obtain data. Research techniques included participant observation, documentary analysis, semi-structured interviews, social mapping, group timelines, and structured questionnaires. Dairy-goat farmer groups (n = 46) and individual farmers (n = 88) were interviewed during the study. Qualitative data provided baseline information, and helped in the formation of research questions. Quantitative data were entered into the Statistical Package for Social
AI-Based Palm Print Recognition System for High-security Applications

In recent years, many studies have failed to implement an effective palm print recognition system for high-security applications. This study focuses on developing a novel palm print recognition system using novel data processing techniques. The study proposes an embedded zero-tree wavelet (EZW) and principal component analysis (PCA) feature extraction technique concerning palm print recognition. The database contains palm print image samples from right and left palm images. 200 images of 5 people were captured with each person, and 40 shots were used. 150 images were used in the SVM training, and 50 images were used in the SVM testing. The spectral feature extraction of the palm print image is processed by the EZW. The spatial feature extraction of the palm print image is processed by PCA. The minimum distance classifier is used for the comparison of results. Finally, the palm print images are trained and classified with Support Vector Machine (SVM). The researcher concluded that, when compared to the other evaluated approaches and classifiers, the palm print recognition system that combines EZW and PCA as a method of feature extraction is the most accurate. The overall testing results show that the proposed approach yields a maximum of 90.4% recognition accuracy.
Correlation Between Partial Discharge Parameters Measured by UHF, IEC and HFCT Methods

Partial discharge signal amplitudes measured using a UHF sensor are correlated with peak charge and average discharge power measured using the standard IEC and HFCT methods. Two common types of partial discharge, surface discharge and cavity discharge, are simulated in the laboratory with different insulation materials in air environment. The results show that for the surface and cavity discharge, there is strong correlation between the UHF signal amplitudes and the peak apparent charge and discharge power over an applied voltage between 1.1 and 1.6 times the partial discharge inception voltage, with a linear relation linking the discharge parameters. The pulse repetition frequency measured with the UHF method for the case of surface discharge shows a strong correlation with those measured with the IEC and HFCT methods, and is related to them through an exponential function, whereas a linear model can be used to link these discharge parameters for the case of cavity discharge. At higher voltages, the correlation between the UHF data and the data obtained by the IEC and HFCT methods becomes weaker, presumably due to different discharge processes occurring at these voltages.
Genetic diversity and antagonistic properties of Trichoderma strains from the crop rhizospheres in southern Rajasthan, India

There are fewer studies on Trichoderma diversity in agricultural �elds. The rhizosphere of 16 crops was analyzed for Trichoderma species in 7 districts of Rajasthan state of India. Based on DNA sequence of translation elongation factor 1 α (tef-1 α ), and morphological characteristics, 60 isolates were identi�ed as 11 species: Trichoderma brevicompactum, species in Harzianum clade identi�ed as T. afroharzianum, T. inhamatum, T. lentiforme, T. camerunense, T. asperellum, T. asperelloides, T. erinaceum, T. atroviride, T. ghanense, and T. longibrachiatum. T. brevicompactum is the most commonly occurring strain followed by T. afroharzianum. No new species were described in this study. T. lentiforme, showed its �rst occurrence outside the South American continent. The morphological and cultural characteristics of the major species were observed, described, and illustrated in detail. The isolates were tested for their antagonistic effect against three soilborne plant pathogens fungi: Sclerotium rolfsii, Rhizoctonia solani, and Fusarium verticillioides in plate culture assays. One of the most potent strains was T. afroharzianum BThr29 having a maximum in vitro inhibition of S. rolfsii (76.6%), R. solani (84.8%), and F. verticillioides (85.7%). Such information on crop selectivity, antagonistic properties, and geographic distribution of Trichoderma species will be bene�cial for developing e�cient Trichoderma-based biocontrol agents.
Machine Learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy

This article presents a comprehensive review of the machine learning methods used to model the service life of various products, which is a critical aspect of product development and production. With the recent advances in machine learning, it has become increasingly feasible to utilize these methods for modeling service life accurately. This review provides a detailed examination of the existing literature on machine learning applications for modeling service life, including a bibliometric analysis of the most frequently cited works. Furthermore, this review presents a taxonomy of the various machine learning methods employed in service life modeling, highlighting the fundamental methods such as Artificial neural networks, Support vector machine, and decision trees. The results of this review demonstrate the potential of machine learning methods for accurately modeling service life, while also emphasizing the need for further research in this field. Overall, this article provides a valuable resource for researchers and practitioners looking to apply machine learning methods for modeling service life.
The Flora of Lower Parts of Melet River (Ordu)

This study concerns the fl ora of the Melet River. According to grid system of Davis, the west side of Melet River is located within the A6 and east side is located within A7 square. At the end of study 190 taxa belong to 136 genera and 59 families were determined. The distribution of the taxa phytogeographical regions are as follows; Euro-Siberian elements 37 (19,38%), Mediterranean elements 5(2,63%), Irano-Turanien elements 7(3,68%) and pluriregionals 137(72,1%). Endemism rate is 0,031 (% 3,1) with 6 plants in the area.
Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient’s disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.
Droplet Probe for Characterization of Advancing and Receding Contact Angles of Single Fibers

Characterizing the wetting properties of fibers is crucial for many research and industry applications, including textiles for water-oil separation and composite materials. Those fibers are often soft, typically tens of micrometers in diameter but millimeters in length, making manipulation and characterization difficult. Contact angles of single fibers are usually determined by droplet shape analysis or force-based Wilhelmy method. However, these methods are unable to accurately measure contact angles above $60^{\circ}$ or ensure reliable control of the liquid-fiber interaction process, especially for soft fibers prone to bending. Consequently, reliable characterization of the advancing and receding contact angles of single fibers remains a challenge. Here we report a novel method for characterizing the advancing and receding contact angles of both soft and rigid single fibers using a millimeter-sized droplet probe affixed to a disk and a numerical model of the system. By analyzing side-view images, we extract key geometrical parameters of the disk-droplet-fiber system, which, when used in detailed simulations, allows estimating the contact angle of fibers ranging from $20^\circ$ to $140^\circ$. We applied this method to characterize three distinct micro-fibers: a highly hydrophilic rigid borosilicate glass fiber, a mildly hydrophilic soft PET fiber, and a rigid hydrophobic tungsten wire coated with a commercial super-repellent coating.
Physical Layer Security Against Passive Eavesdropper in Digital Twin-Enabler Power Grid: An IRS-Assisted Approach

The paper explores the issue of multiple-user fairness of intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in the digital twin (DT)-enabler power grid. Previous research works have focused on achieving secrecy rate fairness through beamforming or phase shift optimization. However, in the DT-enabler power grid, the secrecy rate is not available as the instantaneous channel state information (CSI) of the passive eavesdropper is unknown. To address these challenges, we apply an expression for secrecy outage probability, measured based on the statistical CSI of the eavesdropper for the scenario where multiple DT users are present. Using zero-forcing (ZF) precoding at the transmitter, we formulate the problem of achieving fairness in secrecy outage probability, and then solve it by optimizing the phase shift matrices. Simulation results demonstrate that the proposed methods can achieve higher fairness among users in comparison to existing IRS-assisted PLS schemes.
Distributed Radio Map Modeling Based on Feature Augmentation of Augmented Scatter Graph

Radio map modeling based on the artificial neural network (ANN) has great prospects for applications in base station siting and network coverage optimization. However, the data to be processed contains a large amount of private user data, such as position and route. Moreover, it is difficult for field test users to have detailed information about the transmission environment, such as building heights and contours. In order to solve the above problems, this paper proposes a Distributed learning-based Radio map Modeling method with environment Feature Augmentation based on the Augmented Scatter graph of field test data (DRM-FAAS). In the proposed method, each local user augments the field test data with k-Nearest-Neighbor (KNN) and obtains 15-D augmented environmental features based on target detection of scatter graphs as network input to collaboratively predict the received signal strength (RSS) of all positions in the modeling region. The results on the test dataset show that the modeling accuracy of the proposed method is improved by 13.81 and 4.34 dB, respectively, over the traditional propagation model and the method without environmental feature augmentation. Besides, comparable accuracy is achieved with the modeling method with detailed environmental information.
Effect of drought stress on physiology ecology and yield of soybean

Aimed at the decrease of the crop yield caused by drought stress,this paper focused on the physiological ecological characteristics of soybean.The variations of soybean leaf chlorophyll,leaf area,photosynthetic rate,yield and quality are studied through control dry time and varying degrees of soil moisture treatments from the stage of flowering.The results show that the physiological and ecological characteristics of soybean will drop if the dry time is longer and it has recovery trend when the soybean is irrigated timely in a short period of drought time.The chlorophyll content and leaf area are maximum when the soil moisture content is 40% to 50%,and when soil water content is lower than 30%,they are minimum.The photosynthetic rate under control group is higher than that in time and water treatments,and the value of photosynthetic rate will increase if stop the processing.The yield of soybean will reduce with longer time stress and lower water stress,but the grain weight is contrary.The protein content will increase and the fat content will decrease.
Semi-Unbalanced Regularized Optimal Transport for Image Restoration

We consider in this paper the use of a penalty based on the theory of optimal transport (OT) in order to regularize inverse problems in imaging. The proposed approach is formulated in a variational setting and aims at promoting images whose patch distribution is close either to the one learned by a generative model, or to available uncorrupted reference patches. With the aid of numerical illustrations, we argue in favor of adopting an asymmetric form of unbalanced OT. We then provide details concerning the computation and the differentiation of the proposed penalty. Finally, we detail the application of our approach to a particular super-resolution setting.
Optimally Conditioned Channel Matrices in Precoding Enabled Non-Terrestrial Networks

This paper explores how the condition number of the channel matrix affects the performance of different precoding techniques in non-terrestrial network (NTN) communications. Precoding is a technique that can improve the signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) in massive multi-beam systems. However, the performance of precoding depends on the rank and condition number of the channel matrix, which measures how well-conditioned the matrix is for inversion. We compare three precoding techniques: zero-forcing (ZF), minimum mean square error (MMSE), and semi-linear precoding (SLP), and show that their performance degrades as the condition number increases. To mitigate this problem, we propose a user ordering approach that forms optimally conditioned channel matrices by selecting users with orthogonal channel vectors. We demonstrate that this approach improves the SINR and goodput of all the precoding techniques in full-frequency reuse NTN communications.
Cross-border Minorities as Cultural and Economic Mediators between China and Central Asia

Sino-Central Asian trade is for the most part in the hands either of large state-run enterprises, or of the Hans from Xinjiang—particularly the Xinjiang Production and Construction Corps—or lastly of private entrepreneurs from Zhejiang. The Uyghurs, Dungans, and China’s Central Asians minorities have therefore a limited room for trade and their competitiveness remains modest. However, these cross-border minorities play, have played, or might be called upon to play, a role in the development of Sino-Central Asian economic relations and in the cultural mediations between the two worlds. Even if this niche remains modest compared to the overall Sino-Central-Asian relations, it constitutes a key element in the social strategies of trans-nationalization being put into place by these minorities.
Deep-learning-based Accurate Beamforming Prediction Using LiDAR-assisted Network

Beamforming optimization can enhance the next-generation wireless networks. However, finding the optimal beamforming in real-time is hard due to the need for large beam training overhead. The problem can be more challenging in dynamic environments with small coherence channel time. In this paper, we propose an accurate beam prediction solution using light detection and ranging (LiDAR)-assisted radio frequency (RF) system. More specifically, we propose a deep-learning model based on long-sort term memory (LSTM) to predict future beam indices from a set of pre-defined beam steering codebook. In addition to solving the beamforming overhead problem, the proposed deep learning approach is a model-free approach that can be applied with no required knowledge of the channel state information. We compare the proposed model with the cross-validation results in addition to other benchmarks using the top-k accuracy metric. The numerical results show that the proposed scheme has achieved a top-1 accuracy of 84.6% compared to an 80.3% for cross-validation while the other benchmarks have achieved a 57.5% using the same data set.