Miracle-dz/UniFAR-FTUs huggingface dataset:
xApp for Traffic Steering and Load Balancing in the O-RAN Architecture

Traffic steering is an essential aspect of the radio access network (RAN). The O-RAN Alliance architectural framework provides an environment for intelligent traffic steering using different AI/ML techniques. In this work, we present an xApp in the RAN intelligence controller (RIC) for traffic steering and load balancing to ensure that the user equipment (UE) achieves an acceptable throughput. We present a K-means learning clustering technique on the UEs based on the throughput and quality of service metrics. The clustering technique is used to determine UEs experiencing low throughput for handover. Cell throughput prediction is then performed using long-short-term memory (LSTM) to predict the average cell throughput generated by the individual cells. A steering algorithm is developed to select UEs for handover. A Handover request message is generated and then sent to the E2 nodes. The achieved results demonstrate the effectiveness of our proposed algorithm with a significant gain in throughput and a fair distribution of UEs among cells.
Fault zone architecture, microstructures, deformation mechanisms and frictional behaviour of seismogenic, shallow-crustal, lithologically heterogeneous faults

Earthquakes that rupture the Earth’s surface are typically the most damaging and highlight the need for us to better constrain the style of deformation and frictional behaviour of fault zones in the shallow crust. This thesis presents two studies of natural, seismogenic, shallow crustal fault zones: 1) the Gubbio fault zone, which has been exhumed from 2.5-3 km depth and deforms a mixture of limestone and phyllosilicate-rich marly limestone; and 2) the Masada fault zone, which deforms near-surface, poorly lithified lake sediments. Field studies were complemented by low- and high-velocity rotary shear experiments to constrain the frictional behaviours of the naturally observed fault gouges under representative conditions. In addition, microstructural analyses of both naturally- and experimentally-produced fault rocks were performed in order to constrain the deformation mechanisms operating during fault slip. Our results show that the dominant deformation mechanisms operating within a fault zone, which are highly variable depending on environmental conditions such as depth, fault rock composition, fluid presence and composition, and strain-rate, will control: 1) fault zone architecture and therefore the distribution of seismicity; and 2) slip zone processes, which can subsequently affect the frictional behaviour of a fault, and also determine whether or not signatures of seismic slip are produced during rupture propagation. These are useful tools for geologists when trying to decipher the seismic history of natural faults. Frictional behaviour, in terms of the likelihood of rupture propagation through the shallow crust, is also found to vary significantly as a function of the aforementioned environmental conditions. A fuller knowledge of spatial, and possible temporal, variations in fault zone properties is therefore essential if more accurate earthquake forecasting models and assessments of their associated hazards are to be produced.
Joint Resource Allocation and Relay Selection in Wireless-Powered FD Relay Networks with Imperfect CSI

In this paper, we investigate the robust resource allocation and relay selection in wireless-powered full-duplex (FD) multi-relay networks, where a self-energy recycling relay node is selected from $N$ candidates to assist the transmission from the source node to the destination node. With imperfect channel state information (CSI), an optimization problem is formulated to minimize the outage probability, and the optimal values of time and power allocation coefficients are obtained. Moreover, on the basis of the optimal time and power allocation coefficients, a two-stage relay selection scheme is proposed. In addition, the analytical expressions of outage probability and diversity order are derived to characterize the performance of the proposed scheme. Finally, simulation results are provided to verify the theoretical analysis and demonstrate the effectiveness of the proposed scheme in different scenarios.
Agronomic Behaviour and Chemical Composition of Three Varieties of Pennisetum sp in the Peruvian Tropics

Livestock production in the Peruvian tropics is mainly based on grazing and cutting grasses, which have variable productive and nutritional characteristics according to the variety of grasses, agronomic management, and climatic conditions. The objective of this research was to compare the agronomic behaviour and chemical composition of three varieties of Pennisetum sp (Pennisetum purpureum Schum cv. Cameroon or Cameroon grass, Pennisetum sp. or Maralfalfa, and Pennisetum purpureum × Pennisetum typhoides or King grass). The experiment was carried out at the Estación Experimental Agraria “Campo Verde,” Ucayali, Peru, under a randomized complete block design (RCBD) with a 3 (Pennisetum sp. variety) × 3 (cutting age) factorial arrangement and 4 replicates each. Thirty-six subplots were installed, and treatments were randomly assigned, using vegetative seed with three buds each. Measurements of agronomic behaviour and chemical analysis were carried out in the 8th, 12th, and 16th week. The data were subjected to ANOVA and multiple comparisons with the Duncan and Kruskal–Wallis tests ( p < 0.05) and correlations using Pearson coefficients ( p < 0.05). The agronomic behaviour did not vary between Pennisetum sp. varieties, but according to cutting age, Cameroon grass produced greater plant height, biomass, dry matter, and Mg content at the 8th week. The highest concentrations of N (2.38 ± 0.11%), P (0.18 ± 0.01%), and Ca (0.70 ± 0.03%) were recorded at the 8th week, Zn (27.33 ± 1.91 mg/kg) at the 12th week, and Mg (0.26 ± 0.02%) at the 16th week. A negative correlation was found between biomass and the stem-leaf ratio at the 12th week because the first leaves of plants died, but the stems increased in size. It is concluded that, at an early cutting age in varieties of Pennisetum sp. , lower biomass and dry matter result, but higher content of N, P, Ca, and Fe; and at a late cutting age, it shows greater biomass, dry matter, Cu, and Mg content.
Automatic Segmentation and Classification of Skin Cancer Cells using Thresholding and Deep Learning based Techniques

The proposed manuscript derives its origin from the fact that annually about 57 million people die due to skin cancer. Skin biopsy is the most widely practiced method to detect skin cancer. Fear of bleeding, and infection raises the concern of patients to agree for skin biopsy. Also, its' high cost, and requirement of specific infrastructure is a limitation in its use. The proven potential of artificial intelligence motivated us to resolve the above-mentioned challenges and propose an accurate diagnosis method for skin cancer. In this research, we apply different preprocessing methods for removing rulers and hair from the images in the dataset. Also, we employ different segmentation techniques, such as color based segmentation, thresholding, edge detection, and region-based segmentation to segment skin cancer images. Next, we compare the performance of these techniques by implementing them on the same testing dataset. We use PSNR, SSIM, entropy, and brisque score to evaluate the quality of segmented images. The mean entropy of 0.52 bits/pixels, mean PSNR of 38.75dB, mean SSIM of 0.82 and brisque score of 119.09 on the testing dataset prove the efficacy of the proposed model. Further, finetuning the VGG-16 model for classifying the testing dataset comprising 617 images improve the performance and reported 98% accuracy. The high accuracy and good quality of segmented images justify the performance of the model.
Bidirectional Double-Spring Pneumatic Artificial Muscle With Inductive Self-Sensing

This study proposes a bidirectional double-spring pneumatic artificial muscle (PAM) with inductive self-sensing for wearable robotic devices. The actuator structure, composed of commercially available latex and springs, enables a low-cost and consistent manufacturing process. Springs, which serve as the skeleton of the actuator, were used to increase the stability and stiffness of the structure, as well as to embed a self-sensing ability using the inductance of the spring. The actuator design parameters were selected through simulations to prevent buckling while achieving a notable actuation performance. The bidirectional force characteristics of the actuator were evaluated through quasi-static experiments, and the inductance-based length-sensing performance was validated during both contraction and elongation. The need for self-sensing in bidirectional actuation was emphasized through closed-loop length control. The proposed actuator can potentially enable the development of lightweight and efficient wearable robotic devices with improved sensing and actuation capabilities.
Heterogeneous Signcryption Scheme With Group Equality Test for Satellite-Enabled IoVs

With the growing popularization of the Internet of Vehicles (IoVs), the combination of satellite navigation system and IoVs is also in a state of continuous improvement. In this article, we present a heterogeneous signcryption scheme with group equality test for IoVs (HSC-GET), which avoids the adversaries existing in the insecure channels to intercept, alter or delete messages from satellite to vehicles. The satellite is arranged in an identity-based cryptographic (IBC) system to ensure safe and fast transmission of instruction, while the vehicles are arranged in certificateless cryptosystem (CLC) to concern the security of the equipment. In addition, the group granularity authorization is integrated to ensure the cloud server can only execute the equality test on ciphertext generated by the same group of vehicles. Through rigorous performance and security analyses, we observe that our proposed construction reduces the equality test overhead by about 63.96%, 81.23%, 80.84%, and 54.98% in comparison to other competitive protocols. Furthermore, the confidentiality, integrity and authenticity of messages are guaranteed.
Understanding the Role of Parent‒Child Relationships in Conscientiousness and Neuroticism Development among Chinese Middle School Students: A Cross-Lagged Model

The parent‒child relationship is a crucial factor in promoting adolescent mental health. However, the current evidence on the relationship between parent‒child relationships and adolescent conscientiousness and neuroticism, as well as the directionality of these relationships, remains limited. In particular, there is a lack of analysis focusing on Chinese middle school students. Based on a sample of 8437 students from the China Education Panel Survey (CEPS) database, this study empirically examined the bidirectional relationships between parent‒child relationships, conscientiousness and neuroticism among Chinese middle school students, with specific emphasis on the significant role of parent‒child relationships in the development of conscientiousness and neuroticism. Descriptive statistical results indicated that during the seventh and eighth grades of Chinese middle school students, the closeness of their parent‒child relationships with both parents decreased, while the level of conscientiousness showed a slight decrease, and neuroticism showed an increasing trend. Correlational results demonstrated a significant positive correlation between parent‒child relationships and conscientiousness and a significant negative correlation between parent‒child relationships and neuroticism. Further analysis using cross-lagged models revealed that parent‒child relationships significantly positively predicted subsequent conscientiousness development, and conscientiousness significantly positively predicted subsequent parent‒child relationships. Parent‒child relationships significantly negatively predicted subsequent neuroticism development, and neuroticism levels also significantly negatively predicted subsequent parent‒child relationships. Based on these findings, we believe that there is a need to strengthen parent‒child relationships and to recognize the important role that both mothers and fathers play in the healthy development of their children. Both parents should actively contribute to their children’s upbringing and take responsibility for their family education.
Methylliberine Ingestion Improves Various Indices of Affect but Not Cognitive Function in Healthy Men and Women

This study assessed the acute effects of oral methylliberine (DynamineTM) supplementation on cognitive function and indices of well-being. This was a double-blind, randomized, within-subject crossover trial. In total, 25 healthy men and women (33.5 ± 10.7 yr, 172.7 ± 8.6 cm, 73.3 ± 11.0 kg) underwent pretesting before ingesting methylliberine (100 mg) or a placebo (PLA) for 3 days. On the fourth day, the participants were tested before their fourth dose (baseline) and every hour post-ingestion for 3 h. After a one-week washout period, the participants repeated testing with the alternate investigational product. The testing battery consisted of vitals, Stroop test, Trail Making Test-B, and visual analog scales that assessed various indices of well-being. Mixed factorial ANOVAs with repeated measures were used to assess all variables. There were significant (p ≤ 0.050) interactions in terms of concentration, motivation, and mood. Methylliberine improved concentration at 1 and 3 h, motivation at 3 h, and mood at 1, 2, and 3 h (p ≤ 0.050). Methylliberine improved energy, sustained energy, and mood in all participants to a greater extent than PLA at 1 h and 3 h relative to baseline (p ≤ 0.050). PLA improved motivation at 1 and 2 h and mood at 2 h (p ≤ 0.050). Methylliberine improved concentration, well-being, and the ability to tolerate stress to a greater extent than PLA at 3 h relative to baseline (p ≤ 0.050). Women observed elevations in sustained energy at 1 and 3 h (p ≤ 0.050) with methylliberine vs. PLA. Methylliberine had a negligible influence on cognitive function and vitals (p > 0.050), and no adverse events were reported. Methylliberine significantly improved subjective feelings of energy, concentration, motivation, and mood, but not cognitive function. PLA improved motivation and mood at hours 1 and 2, while methylliberine sustained these benefits for longer. Methylliberine also improved concentration, well-being, and the ability to tolerate stress to a greater degree than PLA, while having no detrimental effects on vital signs. Methylliberine also seemed to have a positive impact on sustained energy in women.
CRUSTAL EVOLUTION OF CHAGAI RASKOH ARC TERRANE, BALOCHISTAN, PAKISTAN.

The Chagai-Raskoh arc terrane occurs in the western part of Pakistan. In the regional geotectonic scenario this arc terrance belongs to an ancient Tethan convergence zone. Which was initiated probably during Early Jurassic, due to an intra-oceanic convergence in the Ceno Tethys. The are terrane exhibits spectacular field exposures of interstraified volcanic and sedimentary rocks, intruded by large plutons. The sedimentary rock formations from the late Early Jurassic to Recent arc well preserved, which represent excellent manifestations of sedimentary evolution from late Early Jurassic pelagic sedimens, through diversified varieties of Cretaceous to Oligocene shallow marine limestone, shale and sandstone deposits and Miocene onwards terrestrial sediments. The sedimentation in the arc terrane is interrupted by several episodes of magmatism that occurred during Cretaceous to quaternary. The Cretaceous and Paleocene volcanism is dominated by vbasaltic-andesites with minor basalts and andesites with their volcaniclastic counterparts, whereas Eocene onwards volcanism is generally dominated by andesites with minor dacites and their volcaniclastic equivalents. The mafic mineralogy (augite-biotite), anorthite contents (An68-15) and phenocrysts to groundmass ratios (20:80-45:55) in the volcanics rocks generally show progressive evolution towards younger volcanics.
Managing digital transformation in a global environment: The role of national culture

In today's fast-changing business environment, digital transformation has become an essential strategic imperative for companies seeking to maintain a competitive edge. However, research on the effect of national culture on digital transformation is lacking. This study seeks to address this gap by investigating the effect of national culture on digital transformation, with a focus on the mediating role of digital competencies and information system management practices, as well as the moderating effect of leadership. 250 managers and employees with digital transformation experience across various industries in Saudi Arabia were surveyed. Structural equation modeling (SEM) was employed to analyze and test the research hypotheses. The findings demonstrate that national culture significantly affects digital transformation, with digital competencies and information system management practices acting as mediators. Furthermore, the study discovered that leadership plays a moderating role in the relationship between national culture and digital transformation. The positive impact of national culture on digital transformation is amplified when there is a transformational leadership style in place. These insights hold significant implications for organizations embarking on digital transformation initiatives, emphasizing the importance of fostering a culture encourages digital innovation and risk-taking, investing in digital capabilities and effective information system management, and nurturing transformational leadership to drive successful digital transformation efforts.
Content-Aided IoT Traffic Anomaly Detection

Most traffic anomaly detection systems for the Internet of Things (IoT) build detection models using features extracted from the packet header. The performance of such models may fluctuate when network conditions change over time, because some information in the packet header, e.g., the packet arrival interval, reflects the current network condition. To improve the performance of IoT anomaly detection models, we propose a content-aided approach that leverages the payload of packets, i.e., the content carried by the packets, to build a machine learning model. This approach is based on the observation that IoT devices, unlike general-purpose computers, are usually used for very specific tasks, e.g., a smart camera mainly carries video information. Our content-aided approach considers both packet header as well as payload information and ensembles two machine learning models, one built with information from packet headers and the other with information from packet payload, to obtain the final detection results. Experimental results demonstrate that our content-aided method can achieve consistent detection results of about 99% of true positive rate and about 4% false positive rate, in the presence of significant network condition changes.
Optimized Circulation Management In Hospitals During COVID-19

During the COVID-19 pandemic, social distancing has been applied worldwide to reduce the risk of infection. In hospitals, this requires a new strategy for movement management in the corridors to avoid cross-overs and satisfy social distancing requirements. In this paper, we study the problem of routing and path finding for two flows of patients (COVID-19 and Non-COVID) in the hospital during the pandemic using as many disjoint paths as possible. We present two scenarios based respectively on an offline and an online method to model the routing and labeling of the hospital paths and compare them using a simulation tool. The simulation result shows the proposed solutions outperform the baseline which is based on the Dijkstra algorithm. This outcome can help increase safety by considering the guideline for COVID-19 and Non-COVID patients in healthcare centers during the pandemic.
The Generic Capacity of K User Symmetric Linear Computation Broadcast

The symmetric linear (over <tex>$\mathbb{F}_{p^{n}}$</tex>) computation broadcast (LCBC) problem considered in this work refers to a setting with <tex>$d$</tex> dimensional data stored at a central server, where <tex>$K$</tex> users, each with some <tex>$m^{\prime}$</tex> dimensional prior linear side-information, wish to retrieve various <tex>$m$</tex> dimensional linear combinations of the data. The goal is to determine the minimum amount of (potentially non-linear) coded information that must be broadcast to satisfy all the users. The reciprocal of the optimal broadcast cost is the capacity of LCBC. The capacity has been found previously for up to <tex>$K=3$</tex> users. Since LCBC includes index coding as a special case, LCBC settings with large number of users are at least as hard as the index coding problem. Instead of the general setting (all instances), here we make progress by focusing on the generic setting (almost all instances). For the LCBC with <tex>$d=4$</tex> dimensional data, and 1 dimensional demands and side-information <tex>$(m=m^{\prime}=1)$</tex>, we establish the generic capacity <tex>$C_{g}=\max(1/2,1/K)$</tex>, for any number of users <tex>$K$</tex>. This is the information theoretic capacity of almost all LCBC instances with <tex>$d=4, m=1, m^{\prime}=1$</tex> as <tex>$n\rightarrow\infty$</tex> for any field characteristic <tex>$p$</tex> and any number of users <tex>$K^{1}$</tex>
The IFTR Fading Model: Statistical Characterization and Empirical Validation

We introduce, characterize and validate the independent fluctuating two-ray (IFTR) fading model, which consists of two specular (dominant) components that fluctuate independently, plus a diffuse one resulting from the reception of multiple scattered waves. The IFTR model complements the fluctuating two-ray (FTR) model, in which the specular components are fully correlated and fluctuate jointly. The chief probability functions of the received SNR of this new model, including the PDF, CDF and MGF, are given in closed-form. The IFTR model is empirically validated using measured data from Line-Of-Sight (LOS) millimeter-wave channels, showing a better fit than the FTR model and other models previously used in these channels, and providing a remarkable improvement at 73 GHz.
Sustainable Application Support in Battery-Less IoT Sensing Network System

The future Internet-of-Everything (IoE), supported by the 6G and beyond wireless networks, will include battery-less intermittent computing (internet-of-things) IoT devices that use energy-harvesting power to support heterogeneous applications in diverse domains like smart-city, smart-infrastructure, environment-monitoring, and disaster scenarios. These devices need to operate sustainably in scarce-intermittent energy conditions and challenging environments. Energy buffers, implemented using small capacitors, support the execution of tasks like sense, send, receive, and control, that are associated with the concerned user-applications. In this article, we identify the end-user application support requirements and associated elements in intermittent computing IoT device network systems. We also discuss a multi-facet framework for sustainable operation of a battery-less IoT device network using application-aware federated charging controller based energy harvesting (AFCH) and energy-aware task scheduler with peripheral control (EASP). We describe the EASP with AFCH scheme for the battery-less IoT device operation to provide an improved application support. We conclude with a battery-less IoT device (with solar energy harvesting) case study that evaluates the peripheral (component) availability, initial-to-available time, and supported application rate in this context.