Miracle-dz/UniFAR-FTUs huggingface dataset:
Error Performance of RIS-Assisted NOMA Networks with Imperfect Channel State Information

This paper investigates the impact of imperfect channel state information (ipCSI) on the performance of RIS-assisted NOMA vehicular networks while considering the effect of imperfect successive interference cancellation (ipSIC). Moreover, we present novel closed-form pairwise error probability (PEP) expressions with arbitrary L users. The PEP is used to evaluate the union bound on the bit error rate of NOMA users. Finally, the analysis is supported by numerical and Monte Carlo simulation results. We show that the impact of ipCSI with ipSIC on each user’s error rate performance is great at the high Ps. We also confirm that the error rate performance of the system decreases with the increase of RUs.
Real-time route planning based on network coverage for connected vehicles

Advances in wireless technologies will allow future vehicles to rely on wireless networks and infrastructure using vehicle-to-everything (V2X) communications. Therefore, vehicular route selection will not only depend on the distance, traffic level, or toll cost, but it will also depend on the quality of service (QoS) of the network. To address this issue, this article introduces a solution to efficiently calculate the best route for the user based on their personal preferences while calculating a score that represents the network’s QoS prediction of a specific route.
Neural Network Based Node Prioritization for Efficient Localization

Optimizing the resource utilization is essential for efficiently providing reliable location awareness in complex wireless environments. This paper presents a data-driven approach to node prioritization for efficient localization based on neural networks. We develop a node prioritization strategy for power allocation consisting of offline training and online operation. In the offline phase, we train a neural network to approximate a mapping of node prioritization decisions obtained via model-based optimization. In the online phase, the trained neural network is employed to determine the resource allocation. A case study validates the proposed approach and compares it against conventional methods based on uniform power allocation.
TDANet: An Efficient Solution For Short-Term Mobile Traffic Forecasting

Mobile traffic forecasting is crucial for optimizing the network configuration and improving Quality-of-Service (QoS); accurate mobile traffic predictions can help the network operators better configure the network and adapt to the trend of mobile demand. However, improving mobile traffic forecasting is challenging as traffic patterns exhibit strong periodicity while also retaining a certain level of randomness; this makes it difficult to model the sequence correlation. To obtain better mobile traffic predictions, we proposed a deep learning-based predictor called Temporal Dynamics Aware Network (TDANet). TDANet is carefully designed to extract both the global and the local temporal patterns employing recurrent and attention components; a linear module is employed to make the predictions more sensitive to the magnitude of mobile traffic, which makes the model better able to capture fast dynamics. Extensive experiments are conducted on a real-world mobile traffic dataset, and the results show that TDANet outperforms all the baseline models on all evaluation metrics, including both accuracy and complexity.
Transcatheter Interventions for Neonates with Congenital Heart Disease: A Review

Newborns with congenital heart disease often require interventions linked to high morbidity and mortality rates. In the last few decades, many transcatheter interventions have become the first-line treatments for some critical conditions in the neonatal period. A catheter-based approach provides several advantages in terms of procedural time, length of hospitalization, repeatability and neurodevelopmental issues (usually related to cardiopulmonary bypass). The main transcatheter procedures will be reviewed, as they are now valid alternatives to conventional surgical management.
Deciphering the Hazardous Effects of AFB1 and T-2 Toxins: Unveiling Toxicity and Oxidative Stress Mechanisms in PK15 Cells and Mouse Kidneys

In China, animal feeds are frequently contaminated with a range of mycotoxins, with Aflatoxin B1 (AFB1) and T-2 toxin (T-2) being two highly toxic mycotoxins. This study investigates the combined nephrotoxicity of AFB1 and T-2 on PK15 cells and murine renal tissues and their related oxidative stress mechanisms. PK15 cells were treated with the respective toxin concentrations for 24 h, and oxidative stress-related indicators were assessed. The results showed that the combination of AFB1 and T-2 led to more severe cellular damage and oxidative stress compared to exposure to the individual toxins (p < 0.05). In the in vivo study, pathological examination revealed that the kidney tissue of mice exposed to the combined toxins showed signs of glomerular atrophy. The contents of oxidative stress-related indicators were significantly increased in the kidney tissue (p < 0.05). These findings suggest that the combined toxins cause significant oxidative damage to mouse kidneys. The study highlights the importance of considering the combined effects of mycotoxins in animal feed, particularly AFB1 and T-2, which can lead to severe nephrotoxicity and oxidative stress in PK15 cells and mouse kidneys. The findings have important implications for animal feed safety and regulatory policy.
THE EFFECT OF ENVIRONMENTAL COSTS, THE FOREIGN OF OWNERSHIP AND THE SIZE OF COMPANY ON RETURN ON ASSETS & CORPORATE SOCIAL RESPONSIBILITY AS INTERVENING VARIABLE

This objective of this study was to determine the effect of the environment, foreign ownership of company size, and corporate social responsibility (CSR) on performance (ROA), and to find out whether environmental costs, foreign ownership, and company size on company performance (ROA) through corporate social responsibility (CSR) as Intervening variables. This research was included in the comparative causal research. The population of this study were mining companies listed on the Indonesia Stock Exchange in 2016-2019. Using purposive sampling method, there were 40 companies that meet the criteria with a total was160 data. The data analysis technique used was multiple linear regression analysis using SmartPLS software. The results of this study indicate that the variables of environmental costs, the foreign ownership, the size of company and the corporate of social responsibility there was no the direct or indirect effect on ROA.
Nonbinary American English speakers encode gender in vowel acoustics

Encoding of gender in speech is a well-researched phenomenon, especially as it concerns men and women. Men tend to produce certain acoustic characteristics in certain ways (low fundamental frequency (F0), lower formant frequencies, lower center of gravity for [s] in English) compared to women, though these characteristics also differ based on other group memberships (e.g. race, sexuality, etc). Those who are more feminine, regardless of categorical gender, have been found to produce an increase in F0 and a larger vowel space. However, these previous studies used largely cisgender women and men or only examined encoding of binary gender in speech and did not consider encoding of “other” or nonbinary gender in speech. This study recruited American English nonbinary speakers to record 400 utterances and correlated acoustic characteristics with multidimensional gender. Masculine, feminine, and “other” gender are significantly correlated with vowel acoustics.
HER2 Oncogene as Molecular Target in Uterine Serous Carcinoma and Uterine Carcinosarcoma

Simple Summary There are several types of uterine cancer. Two of the more rare types are uterine serous carcinoma and carcinosarcoma, which are both very aggressive forms. These rare, aggressive types often have mutations that allow them to be treated with specific therapies that are targeted. One such mutation causes an increase in proteins named HER2 on the tumor cell surfaces. This article summarizes the evidence behind all of the different HER2-targeting therapies for uterine serous carcinoma and uterine carcinosarcoma. Abstract Uterine serous carcinoma (USC) and uterine carcinosarcoma (UCS) are two rare histologic variants of uterine carcinoma, with distinct molecular profiles and aggressive metastatic potential. As the effectivity of traditional platinum-based chemotherapy for USC and UCS is low, and there are high rates of resistance and recurrence, the development of novel targeted therapeutics is needed. Human epidermal growth factor receptor 2 (HER2) has proven to be an oncogene of increasing interest in these cancers, as HER2 protein overexpression and/or c-ERBB2 gene amplification ranges from ~30 to 35% in USC, and between ~15 and 20% in UCS. This review summarizes the existing clinical and preclinical evidence, as well as ongoing clinical trials of HER2-targeting therapeutics, and identifies potential areas of further development and inquiry.
DPC-Inspired Beamforming Design for Integrated Sensing and Communications

Inspired by Dirty Paper Coding (DPC), in this paper we propose a design approach of transmit beamforming for the integrated sensing and communications (ISAC) system that offers simultaneous sensing services via radar and information delivery to users. Specifically, we shape the channel matrix into an equivalent lower triangular matrix. Under this strategy, the communicating users are able to eliminate the multi-user interference (MUI) by using successive interference cancellation (SIC). The corresponding transmit beamforming optimization problem subject to radar constraints is formulated and then solved by a manifold optimization approach. Compared to the conventional design, the idea behind our proposal is to reduce partial burden of beamforming towards interference suppression while taking advantage of low-complexity detection at the receiver to achieve better performances. The simulation results show that our proposed scheme has lower BER than the conventional scheme at low-SNR regime. As for sensing function, the beam pattern of proposed scheme has only a small performance loss compared with the desired beam pattern.
A Hybrid Approach to Header Size and Forwarding Table Optimization in Segment Routing

Segment Routing is a source routing paradigm that simplifies packet forwarding and management across networks. Two major challenges of segment routing are the limitation of header stack size and the size of the Forwarding Table specific to each node. It has been observed that there is a trade-off between these two factors. In this letter, we have addressed this trade-off and presented a mechanism to minimize the stack size as well as the forwarding table. Instead of individual Segment IDs used in traditional segment routing, unique Path IDs are considered. This reduces the size of the header stack to one irrespective of the number of segments in the path. To reduce the size of the forwarding table, a forwarding technique based on matrix operations is used. Our approach also works on the reliability issues in forwarding and guarantees 100% successful packet forwarding.
The GLAS Algorithm Theoretical Basis Document for Laser Footprint Location (Geolocation) and Surface Profiles

This ATBD summarizes (and links with other ATBDs) the elements used to obtain the geolocated GLAS laser spot location, with respect to the Earth Center of Mass. Because of the approach used, the reference frame used to express the geolocation is linked to the reference frame used for POD and PAD, which are related to the ITRF. The geolocated spot coordinates (which includes the elevation or height, with respect to an adopted reference ellipsoid) is the inferred position of the laser spot, since the spot location is not directly measured. This document also summarizes the GLAS operation time periods.
Reflective Practices Among Secondary School Computer Science Teachers: Their Point of View

Reflective practice is an essential catalyst through which the benefits of teaching and learning can be reaped. Through it, weaknesses and strengths can be identified in a way that helps raise the level of addressing challenges that may arise as well as overcome them. This paper presents the critical reflective practices among computer science secondary school teachers from their point of view in Riyadh, Saudi Arabia. To this extent, the study aims to determine the degree of critical reflective practices among computer science secondary school teachers in Riyadh from their perspective. The paper also seeks to investigate the effects of variables such as gender, qualifications and experience on the perceptions of the aforementioned teachers, towards the critical reflective practices among computer science secondary school teachers. The study tool is a questionnaire which consisted of two dimensions and was distributed to a population of 739 participants. From this, the study sample comprised (223) computer science teachers working in secondary school in Riyadh. The findings revealed that there is no significant difference in the estimation degree concerning the critical reflective practices due to the gender. From the results, it was also established that there is no significant difference in the degree of estimation in relation to the critical reflective practices due to educational qualification variables. On the contrary however, there is a significant difference in the degree of estimation in regard to the critical reflective practices due to the years of experience variable. These differences were evident in a group of those with more than 10 years of experience. The other findings produced by the study highlight that the participants are in agreement about the importance of critical reflective practices. The degree of reflective practice, which is from the participants’ point of view, is considered to be of a high value. The majority of the subjects opted to agree with the practice of reflection after a training session. It was determined from the results that some of the most common strategies favored by practitioners involved the communal practice of mind reflection with individuals from outside the school.
PeanutNeRF: 3D Radiance Field for Peanuts

Accurate phenotypic analysis can help plant breeders efficiently identify and analyze suitable plant traits to enhance crop yield. While 2D images from RGB cameras are easily accessible, their trait estimation performance is limited due to occlusion and the absence of depth information. On the other hand, 3D data from LiDAR sensors are noisy and limited in their ability to capture very thin plant parts such as peanut plant pegs. To combine the merits of both the 2D and 3D data analysis, the 2D images were used to capture thin parts in peanut plants, and deep learning-based 3D reconstruction using captured 2D images was performed to obtain 3D point clouds with information about the scene from different angles. The neural radiance fields were optimized for implicit 3D representation of the plants. The trained radiance fields were queried for 3D reconstruction to achieve point clouds for a 360-degree view and frontal view of the plant. With frontal-view reconstruction and the corresponding 2D images, we used Frustum PVCNN to perform 3D detection of peanut pods. We showed the effectiveness of PeanutNeRF on peanut plants with and without foliage: it showed negligible noise and a chamfer distance of less than 4 × 10−4 from a manually cleaned version. The pod detection showed a precision of around 0.7 at the IoU threshold of 0.5 on the validation set. This method can assist in accurate plant phenotypic studies of peanuts and other important crops.
ODIN: An OmniDirectional INdoor dataset capturing Activities of Daily Living from multiple synchronized modalities

We introduce ODIN (the OmniDirectional INdoor dataset), the first large-scale multi-modal dataset aimed at spurring research using top-view omnidirectional cameras in challenges related to human behaviour understanding. Recorded in real-life indoor environments with varying levels of occlusion, the dataset contains images of participants performing various activities of daily living. Along with omnidirectional images, additional synchronized modalities of data are provided. These include (1) RGB, infrared, and depth images from multiple RGB-D cameras, (2) egocentric videos, (3) physiological signals and accelerometer readings from a smart bracelet, and (4) 3D scans of the recording environments. To the best of our knowledge, ODIN is also the first dataset to provide camera-frame 3D human pose estimates for omnidirectional images, which are obtained using our novel pipeline. The project is open sourced and available at https://odin-dataset.github. io.
Collaborative Computing Services at Ground, Air, and Space: An Optimization Approach

Multi-access edge computing (MEC)-enabled integrated space-air-ground networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). They could make it possible for battery-powered Internet of Things (IoT) devices to offload their computation tasks to MEC-enabled unmanned aerial vehicles (UAVs) assisted aerial networks and low earth orbit (LEO) satellites and thus reduce their energy consumption and allow them to complete the execution of tasks on time. However, due to the limited computation capacity of the MEC servers at UAVs and satellites, an efficient offloading decision and computation resource allocation scheme is essential. Therefore, this paper investigates the problem of minimizing the latency experienced by the wireless devices in the MEC-enabled integrated space-air-ground network by optimizing the offloading decision while assuring the energy constraints of both devices and UAVs. The problem is proved to be a non-convex problem, and the block successive upper-bound minimization (BSUM) method is proposed as a solution. Finally, extensive simulation results are presented to exhibit the effectiveness of the BSUM algorithm in solving the proposed problem.
Diurnal Variation in and Optimal Time to Measure Holter-Based Late Potentials to Predict Lethal Arrhythmia after Myocardial Infarction

Background and Objectives: Holter-based late potentials (LPs) are useful for predicting lethal arrhythmias in organic cardiac diseases. Although Holter-based LPs exhibit diurnal variation, no studies have evaluated the optimal timing of LP measurement over 24 h for predicting lethal arrhythmia that leads to sudden cardiac death. Thus, this study aimed to validate the most effective timing for Holter-based LP testing and to explore factors influencing the diurnal variability in LP parameters. Materials and Methods: We retrospectively analyzed 126 patients with post-myocardial infarction (MI) status and 60 control participants who underwent high-resolution Holter electrocardiography. Among the 126 post-MI patients, 23 developed sustained ventricular tachycardia (VT) (the MI-VT group), while 103 did not (the MI-non-VT group) during the observation period. Holter-based LPs were measured at 0:00, 4:00, 8:00, 12:00, 16:00, and 20:00, and heart rate variability analysis was simultaneously performed to investigate factors influencing the diurnal variability in LP parameters. Results: Holter-based LP parameters showed diurnal variation with significant deterioration at night and improvement during the day. Assessment at the time with the longest duration of low-amplitude signals < 40 μV in the filtered QRS complex terminus (LAS40) gave the highest receiver operating characteristics curve (area under the curve, 0.659) and the highest odds ratio (3.75; 95% confidence interval, 1.45–9.71; p = 0.006) for predicting VT. In the multiple regression analysis, heart rate and noise were significant factors affecting the LP parameters in the MI-VT and control groups. In the non-VT group, the LP parameters were significantly influenced by noise and parasympathetic heart rate variability parameters, such as logpNN50. Conclusions: For Holter-based LP measurements, the test accuracy was higher when the LP was measured at the time of the highest or worst value of LAS40. Changes in autonomic nervous system activity, including heart rate, were factors influencing diurnal variability. Increased parasympathetic activity or bradycardia may exacerbate Holter-based LP parameters.
An Environmental Impact Assessment of Largemouth Bass (Micropterus salmoides) Aquaculture in Hangzhou, China

With the rapid increase in aquaculture production, its role in food safety and nutritional security has become increasingly important, but this has also given rise to environmental problems that cannot be ignored. The largemouth bass (Micropterus salmoides) has become a widely cultivated and highly economic freshwater farmed species since it was introduced to China in 1983; however, the environmental impacts of its freshwater pond aquaculture process have not yet been elucidated. Here, life cycle assessment (LCA), a decision-making tool that can evaluate and identify environmental issues during production processes, was used to evaluate the environmental performance of the largemouth bass freshwater pond aquaculture process, and a large-scale, commercial company was selected as an example in Hangzhou, China. The results showed that the pond-farming stage and marine aquatic ecotoxicity potential (MAETP) had the largest environmental impacts on the entire aquaculture process. An environmental contribution analysis indicated that electricity (48%) and emissions (23%) were two key factors in the seed-rearing stage, and electricity (60%) and feeds (26%) were two main impact contributors in the pond-farming stage. Improvement measures based on emerging technologies in aquaculture were discussed, namely, clean energies, industrial pond farming, and intelligent feeding strategies, to help with decision making for continuous improvement in the environmental performance of largemouth bass pond farming. Moreover, suggestions for further aquaculture LCA studies in China were summarized, as they will provide a useful reference for promoting the development of China’s aquaculture LCA research and the enrichment of the world’s aquaculture life cycle inventory databases.
Changes in sodium adsorption ratios following revegetation of coal mine spoils in southeastern Montana. Forest service research paper

The magnitude of reductions in sodium adsorption ratios of coal mine spoils following revegetation at the Decker coal mine in southeastern Montana indicates improvement in both the chemical and physical quality of sodic spoils within a 5- to 7-year period. This reduction is sufficient to eliminate sodium as a serious obstacle in future vegetal development. Increases in biomass production resulting from revegetation produced further decreases in a relative proportion of sodium ions to other cations in sodic spoils at the Decker coal mine.
A Novel High Dynamic Image Fusion Method via an Unsupervised End-to-End Framework

For the sake of high-quality images of the high dynamic range (HDR) scenes, it is effective means to fuse the multiexposure sequences for the same HDR scene. However, the fused images using the existing fusion methods are prone to detail loss or block effect. Aiming at these problems, a novel unsupervised end-to-end framework is developed to provide solutions for the multiexposure image fusion. Instead of conventional manual setting, the optimal image weight coefficients of the multiexposure images are learned automatically, which makes this model more suitable for application. Most importantly, a customized loss function is designed to enhance the network achievement and automatically learn the parameters in the direction of optimal fusion image. According to the quantitative and qualitative results of a large number of experiments, it is demonstrated that the proposed framework performs its superiority and effectiveness compared with the state-of-the-art approaches.