Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent DRL Approach for Energy-Efficient Resource Allocation in URLLC-Enabled Grant-Free NOMA Systems

Grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising access technology for the fifth generation and beyond wireless networks that enable ultra-reliable and low-latency communications (URLLC) to ensure low access latency and high connectivity density. Furthermore, designing energy-efficient (EE) resource allocation strategies is a crucial aspect of future cellular system development. Taking these goals into account, this paper proposes an EE sub-channel and power allocation strategy for URLLC-enabled GF-NOMA (URLLC-GF-NOMA) systems based on multi-agent (MA) deep reinforcement learning (MADRL). In particular, the URLLC-GF-NOMA methods using MA dueling double deep Q network (MA3DQN), MA double deep Q network (MA2DQN), and MA deep Q network (MADQN) techniques are designed to enable users to select the most appropriate sub-channel and transmission power for their communications. The aim is to build an efficient MADRL-based solution, ensuring rapid convergence with small signaling overhead, to maximize the network EE while fulfilling the URLLC requirements of all users. Simulation results show that the MADQN and MA2DQN methods, which have lower complexity than MA3DQN, are more appropriate for the URLLC-GF-NOMA systems under consideration. Moreover, our proposed methods exhibit superior convergence characteristics, a reduction in signaling overhead, and enhanced EE performance compared to other benchmark strategies.
Clinical Results of Transarterial Radioembolization (TARE) with Holmium-166 Microspheres in the Multidisciplinary Oncologic Treatment of Patients with Primary and Secondary Liver Cancer

Holmium-166 microspheres are used for the transarterial radioembolization (TARE) treatment of primary and secondary liver cancers. In this study, its efficacy regarding local tumor control and integration into the oncological treatment sequence of the first 20 patients treated in our institution were examined. A total of twenty-nine 166Ho-TARE procedures were performed to treat hepatocellular carcinoma (HCC, fourteen patients), metastatic colorectal cancer (mCRC, four patients), intrahepatic cholangiocarcinoma (ICC, one patient), and hemangioendothelioma of the liver (HE, one patient). In eight patients, 166Ho-TARE was the initial oncologic treatment. In patients with HCC, the median treated-liver progression-free survival (PFS), overall PFS, and overall survival after 166Ho-TARE were 10.3, 7.3, and 22.1 months; in patients with mCRC, these were 2.6, 2.9, and 20.6 months, respectively. Survival after 166Ho-TARE in the patients with ICC and HE were 5.2 and 0.8 months, respectively. Two patients with HCC were bridged to liver transplantation, and one patient with mCRC was downstaged to curative surgery. In patients with HCC, a median treatment-free interval of 7.3 months was achieved. In line with previous publications, 166Ho-TARE was a feasible treatment option in patients with liver tumors, with favorable clinical outcomes in the majority of cases. It was able to achieve treatment-free intervals, served as bridging-to-transplant, and did not prevent subsequent therapies.
Mathematical modelling of the viable epidermis: impact of cell shape and vertical arrangement

In-silico methods are valuable tools for understanding the barrier function of the skin. The key benefit is that mathematical modelling allows the interplay between cell shape and function to be elucidated. This study focuses on the viable (living) epidermis. For this region, previous works suggested a diffusion model and an approximation of the cells by hexagonal prisms. The work at hand extends this in three ways. First, the extracellular space is treated with full spatial resolution. This induces a decrease of permeability by about 10%. Second, cells of tetrakaidecahedral shape are considered, in addition to the original hexagonal prisms. For both cell types, the resulting membrane permeabilities are compared. Third, for the first time, the influence of cell stacking in the vertical direction is considered. This is particularly important for the stratum granulosum, where tight junctions are present.
Ottimizzazione termica di dissipatori per convertitori di potenza per applicazioni in ambienti ostili

M. Lazzaroni, M. Citterio, S. Latorre, A. Lanza, P. Cova, N. Delmonte, F. Giuliani (1) Dip. di Fisica, Università degli Studi di Milano, Via Celoria, 1620133 Milano, Italy (2) INFN Milano, via Celoria, 16, 20133 Milano, Italy (3) INFN Pavia, via Agostino Bassi, 6, 27100 Pavia, Italy (4) Dipartimento Ingegneria dell'Informazione, University of Parma, v.le Usberti 181/A, Parma, Italy mail autore di riferimento: massimo.lazzaroni@unimi.it
Blessing of randomness against the curse of dimensionality

Modern hyperspectral images, especially acquired in remote sensing and from on‐field measurements, can easily contain from hundreds of thousands to several millions of pixels. This often leads to a quite long computational time when, eg, the images are decomposed by Principal Component Analysis (PCA) or similar algorithms. In this paper, we are going to show how randomization can tackle this problem. The main idea is described in detail by Halko et al in 2011 and can be used for speeding up most of the low‐rank matrix decomposition methods. The paper explains this approach using visual interpretation of its main steps and shows how the use of randomness influences the speed and accuracy of PCA decomposition of hyperspectral images.
AWC‐NIDS: Attack‐wise customized network intrusion detection system using machine learning, concurrency, and distributed systems

With digitization and modern network applications, information security has gained a tremendous importance. Therefore, accurate and efficient detection systems are crucial for maintaining proactive security in computer networks. Machine learning (ML) has shown great potential as a promising solution since it can teach a machine to distinguish malicious and normal network activities. However, recently proposed methods are suffering from at least one of the following: detection accuracy, false alarm rate, and computational complexity issues. The main reason behind this problem is the complexity of the model in terms of attack types. From the ML perspective, intrusion detection is a classification problem where each attack type is identified by a set of different features, and features are used for classifying network activities. Thus, training an ML algorithm to detect more than one attack type leads to a more complex model; the increasing number of used features contributes positively to the model complexity, and may result in relatively lower detection accuracy or a higher false positive rate. To tackle this problem, this study proposes an attack‐wise customized network intrusion detection system (AWC‐NIDS) based on ML, concurrency, and distributed systems to achieve accurate and efficient network‐wide intrusion detection. Since CICIDS2017 contains many modern attacks, it was used for model development and performance evaluation. The experimental results showed that the proposed methodology achieved high classification performance for all datasets with a small number of features. However, it was observed that the lowest accuracy was achieved for the comprehensive dataset (which contains all attack types); for the single attack‐type datasets, the obtained accuracy was above 99%. This finding proves the concept of attack‐wise customization for intrusion detection and shows the significance of the proposed methodology. In conclusion, this framework is promising for implementing robust and accurate cybersecurity systems for traditional and modern networking.
Lens-Based Switched-Beam Antenna for a 5G Smart Repeater

In this letter, a 16-switched-beam lens-based antenna with a coverage area of <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>30° in the horizontal plane and from 0 to <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>20° in the vertical plane has been built and tested. The intended use of the antenna is as part of a smart repeater servicing the coverage area of a 5G wireless communication system operating in the n258 band of the 5G Frequency Range 2, from 24.25 to 27.5 GHz. The antenna has been built using state-of-the-art materials and components in order to assess the performance of this antenna concept. The overall antenna losses including the feeding network are of the order of 8 dB and the realized gain in the <inline-formula><tex-math notation="LaTeX">$80$</tex-math></inline-formula>% of the coverage area is above 11 dB.
Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps

The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM.
Assessing the role of hydrogen in Fermi-level pinning in chalcopyrite and kesterite solar absorbers from first-principles calculations

Understanding the impact of impurities in solar absorbers is critical to engineering high-performance in devices, particularly over extended periods of time. Here, we use hybrid functional calculations to explore the role of hydrogen interstitial (Hi) defects in the electronic properties of a number of attractive solar absorbers within the chalcopyrite and kesterite families to identify how this common impurity may influence device performance. Our results identify that Hi can inhibit the highly p-type conditions desirable for several higher-band gap absorbers and that H incorporation could detrimentally affect the open-circuit voltage (Voc) and limit device efficiencies. Additionally, we find that Hi can drive the Fermi level away from the valence band edge enough to lead to n-type conductivity in a number of chalcopyrite and kesterite absorbers, particularly those containing Ag rather than Cu. We find that these effects can lead to interfacial Fermi-level pinning that can qualitatively explain the obser.
Uncertainty of noise prediction in case of flow over a forward-facing step

The demand of development and application of computational aeroacoustic (CAA) prediction tools for turbulence related noise is increasing to overcome the future requirements of low noise design. Fast and accurate prediction methods are necessary to be integrated into early design processes. Several numerical and experimental studies show that the complex flow physics even of simple geometries is still not fully understood. A great interest is the identification and quantification of noise sources. Hence, immense effort has to be spent for a detailed investigation of the capability of experimental and numerical methods with respect to industrial applications. In this context the uncertainty analysis of aeroacoustic computation is a key issue for the evaluation of low-noise design.
Characterization of Antagonistic Bacteria Paenibacillus polymyxa ZYPP18 and the Effects on Plant Growth

Paenibacillus polymyxa is a plant growth–promoting rhizobacteria (PGPR) that has significant biocontrol properties. Wheat sheath blight caused by Rhizoctonia cerealis is a significant soil–borne disease of wheat that causes significant losses in wheat production, and the biological control against the disease has received extensive attention. P. polymyxa ZYPP18 was identified using morphological and molecular characterization. An antagonistic activity experiment verified that ZYPP18 inhibits the growth of R. cerealis on artificial growth media. A detached leaf assay verified that ZYPP18 inhibits the expansion of wheat sheath blight on the detached leaf. ZYPP18 has been found to possess plant growth–promoting properties, as well as the ability to solubilize phosphate and generate indole–3–acetic acid. Results from hydroponic experiments showed that wheat seedlings treated with ZYPP18 grew faster. Additionally, pot experiments and field experiments demonstrated that ZYPP18 effectively controls the occurrence of wheat sheath blight. ZYPP18 reduced the incidence of wheat sheath blight in wheat seedlings by 37.37% and 37.90%, respectively. The control effect of ZYPP18 on wheat sheath blight was 56.30% and 65.57%, respectively. These findings provide evidence that P. polymyxa ZYPP18 is an effective biological factor that can control disease and promote plant growth.
Plasma urea cycle metabolite levels and the risk of moyamoya disease

Background and purpose Urea cycle metabolites are expected to be the biomarkers for cerebrovascular diseases. However, the effects of circulating urea cycle metabolites on the risk of MMD and its subcategories remain unclear. The aim of this study was to prospectively investigate the association between plasma urea cycle metabolites and the risk of MMD and its subcategories. Methods We measured plasma urea cycle metabolite levels for 360 adult MMD patients and 89 matched healthy controls. Clinical and laboratory characteristics were obtained from the medical record. The study was conducted from July 2020 to December 2021. Results After multivariate adjustment, the risk of MMD increased with each increment in ornithine level (per natural log [ornithine] increment: OR, 3.893; 95% CI, 1.366–11.090). The risk of MMD decreased with each increment in arginine level (per natural log [arginine] increment: OR, 0.109; 95% CI, 0.028–0.427), urea level (per natural log [urea] increment: OR, 0.261; 95% CI, 0.072–0.940), and global arginine bioavailability ratio (GABR) level (per natural log [GABR] increment: OR, 0.189; 95% CI, 0.074–0.484). The addition of plasma arginine (integrated discrimination improvement: 1.76%, p = 0.021) or GABR (integrated discrimination improvement: 1.76%, p = 0.004) to conventional risk factors significantly improved the risk reclassification for MMD. Conclusion Plasma ornithine levels are positively associated with the risk of MMD. By contrast, the levels of arginine, urea, and GABR are inversely related to the risk of MMD. Plasma urea cycle metabolites might be potential biomarkers for the risk of MMD.
Chemical Weathering Intensity as a Reliable Indicator for Southwest Summer Monsoon Reconstruction: Evidence From Clay Minerals of Qionghai Lake Sediments Since the Last Glacial Maximum

Studying the evolution history of the southwest summer monsoon (SSM) throughout geological time, particularly during its strongest period in the Holocene, can improve our understanding of its variation and driving mechanisms, and even help predict future climate changes, due to its significant social and economic implications. Here, we reconstructed the history of chemical weathering intensity since the Last Glacial Maximum (LGM) based on clay mineral proxies [(illite/smectite)/(illite + chlorite) and illite crystallinity] obtained from Qionghai Lake sediments and examined its response to paleoclimate and SSM. Our findings indicate that the intensity of chemical weathering generally aligned with changes in paleoclimate, exhibiting strong chemical weathering intensity during warm and humid climate conditions. In addition, the intensity of chemical weathering basically tracks the evolution of the SSM since the LGM. Our results support the view that the highest SSM intensity occurred during the early‐middle Holocene, followed by gradual weakening during the late Holocene, with Northern Hemisphere summer insolation being the primary driver of the SSM evolution. The variations of the SSM and the corresponding intensity of chemical weathering were also influenced by the cumulative effects of glacier boundary conditions, North Atlantic climate fluctuations, and Intertropical Convergence Zone migrations.
Centralized fixed cost allocation for generalized two-stage network DEA

ABSTRACT Many studies have dealt with the problem of fixed cost allocation by using data envelopment analysis. However, existing models allocate the fixed cost by treating the decision-making units (DMUs) as black-boxes and ignore the internal production structures of DMUs. To our knowledge, only a few work has considered the fixed cost allocation problem for an elementary two-stage production structure without external inputs and outputs. This paper deals with the fixed cost allocation problem for a general two-stage network production structure, in which both external inputs and outputs exist. Specifically, additive two-stage models are first presented to evaluate the performance for each DMU when allocating the fixed cost. Then, by introducing the concepts of satisfaction degree and fairness degree, we propose an approach to obtain an optimal allocation plan under the control of the centralized authority. Finally, an application to 27 banks is utilized to illustrate the proposed approach.
Fermentation of Gracilaria verrucosa to Reduce Insoluble Non-Starch Polysaccharide (iNSP) Using Cellulolytic Bacteria Pseudomonas stutzeri (ISO2) for a Dietary Ingredient of Golden Rabbitfish, Siganus guttatus

A series of experiments were conducted to optimize P. stutzeri (ISO2) fermentation in reducing the non-starch polysaccharide (NSP) of G. verrucosa as golden rabbitfish feed ingredient. A completely randomized experimental design in triplicates was used to optimize the pH of CMC media and the duration of fermentation. Using a 3 × 3 factorial design with two factors and triplicates, the optimum substrate-inoculum interaction was determined. Nine levels combination of G. verrucosa (G1 = 50 g; G2 = 100 g; G3 = 150 g) and P. stutzeri inoculum (P1 = 5%; P2 = 10%; P3 = 15%) were investigated. Cellulolytic activity (CA) of P. stutzeri was measured using UV-vis spectrophotometer, meanwhile chemical compound and physical characteristics of fermented G. verrucosa were evaluated using Fourier Transform Infra Red (FTIR) and Scanning Electron Microscope (SEM) respectively. Data on fermentation optimization were analyzed statistically using ANOVA. The result indicated pH, duration of fermentation and substrate-inoculum ratio had a significant effect on bacteria growth and CA (P<0.05). P. stutzeri grew well at pH 8, the fermentation process was ideally running in four days with (150 g-10%) G:P level. Hemicellulose was the most biodegradable NSP compared to cellulose and lignin. Fermented G. verrucosa (FG) had FTIR vibrations which indicated the conversion of the cell wall to reducing sugar. The fermentation process resulted in surface structure changes of G. verrucosa based on SEM photos.
Robust PCA , Subspace Learning , and Tracking *

PCA is one of the most widely used dimension reduction techniques. A related easier problem is “subspace learning” or “subspace estimation”. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA). For long data sequences, if one tries to use a single lower dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of robust subspace learning and tracking. In particular solutions for three problems are discussed in detail: RPCA via sparse+lowrank matrix decomposition (S+LR), RST via S+LR, and “robust subspace recovery (RSR)”. RSR assumes that an entire data vector is either an outlier or an inlier. The S+LR formulation instead assumes that outliers occur on only a few data vector indices and hence are well modeled as sparse corruptions.
Microwave Metalens Antennas

Recently, there has been growing interest in the use of metamaterial (MTM)-based lenses, also known as metalenses, as innovative antenna technology. Increasingly widespread applications of metalenses in modern microwave communication and sensing systems have been found, following the development of the first microwave artificial lens in the 1940s based on the concept of an artificial dielectric, which was later broadly termed an “MTM. ” This article examines the evolution of metalens antennas over the past 80 years and introduces the principles and technologies underlying their design. It then focuses on the latest progress in the research on and applications of MTMs and metasurface (MTS)-based metalens antennas. The principles and basic structures of transmissive focusing metalens antennas in the microwave band are elaborated. Selected metalens antennas are introduced chronologically, starting with metallic waveguide lens antennas, followed by metallic or metal–dielectric Fresnel zone plate lens antennas, transmitarray lens antennas, MTS lens antennas, and flat transformation-optics-based MTM Luneburg lens antennas. The technical merits, challenges, applications, trends, and future research of each type of metalens antenna are also addressed. The information presented in this article will benefit the research, development, and application of metalens antennas in fifth- and beyond-generation communications, Wi-Fi 6 short-range connections, and next-generation microwave sensing and imaging systems.
Perbandingan Algoritma Cosine Similarity dan Euclidean Distance pada Sistem Rekomendasi Film dengan Metode Item-Based Collaborative Filtering

Sistem rekomendasi saat ini semakin dibutuhkan seiring dengan semakin banyaknya film yang ada, terutama di media digital. Item-Based Collaborative Filtering adalah salah satu dari sekian banyak metode dalam sistem rekomendasi. Metode Item-Based Collaborative Filtering menentukan film yang direkomendasikan berdasarkan kemiripan dengan film lainnya berdasarkan film-film lain yang telah diberi rating. Pada serangkaian proses yang ada dalam Item-Based Collaborative Filtering, terdapat satu tahapan dengan algoritma untuk menentukan similaritas atau kemiripan antar item. Penelitian ini membandingkan dua algoritma untuk menentukan kemiripan antar item. Algoritma yang dibandingkan yaitu Cosine Similarity dan Euclidean Distance. Kedua algoritma tersebut diterapkan dan dilakukan pengujian pada sistem rekomendasi film dengan metode Item-Based Collaborative Filtering pada data rating film MovieLens. Perbandingan dilakukan dengan menghitung nilai Mean Absolute Error dan Root Mean Square Error untuk mengevaluasi hasil akurasi pada tiap algoritma yang digunakan. Pada percobaan dengan menggunakan algoritma Cosine Similarity menghasilkan nilai akurasi dengan Mean Absolute Error sebesar 2,21 serta nilai Root Mean Square Error sebesar 2,51. Sedangkan pada percobaan dengan menggunakan algoritma Euclidean Distance menghasilkan nilai akurasi dengan Mean Absolute Error sebesar 2,24 serta nilai Root Mean Square Error sebesar 2,55. Dari hasil penelitian yang telah dilakukan, algoritma Cosine Similarity memiliki tingkat akurasi lebih baik dibandingkan dengan algoritma Euclidean Distance. Hal tersebut dapat dilihat dari nilai Mean Absolute Error dan juga Root Mean Square Error pada algoritma Cosine Similarity yang lebih memiliki nilai lebih kecil dari algoritma Euclidean Distance.