Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent progress on metal-based nanomaterials for cancer radiosensitization

Radiotherapy is a mainstay treatment for malignant tumors in clinical. However, enhancing radiation damage to tumor cells meanwhile sparing normal tissues is still a great challenge in radiotherapy. Nanomaterials with high atomic number (Z) values are promising radiosensitizers by promoting the radiation energy deposition in irradiated tumor cells, thus enhancing the therapeutic ratio of radiotherapy. In this review, we described the mechanisms of high-Z element radiosensitizers and systematically summarized the recent progress on metal-based nanomaterials, including high-Z metal nanoparticles, metalorganic frameworks (MOFs) and other high-Z-containing nanomaterials. Finally, further potential and challenges in this field
Comparison of two synthesis methods for birefringent metasurfaces

Birefringent metasurfaces are two-dimensional structures capable of independently controlling the amplitude, phase, and polarization of orthogonally polarized incident waves. In this work, we propose an in-depth discussion on the mathematical synthesis of such metasurfaces. We compare the two methods, one that is rigorous and based on the exact electromagnetic fields involved in the transformation and one that is based on approximate reflection and transmission coefficients. We next validate the synthesis technique in metasurfaces performing the operations of a half- and quarter-wave plates, polarization beam splitting, and orbital angular momentum multiplexing and present the corresponding microwave experimental demonstrations.
Farmers’ Variety Naming and Crop Varietal Diversity of Two Cereal and Three Legume Species in the Moroccan High Atlas, Using DATAR

Local agrobiodiversity in remote areas such as the Moroccan High Atlas is poorly studied, despite being of great importance for the sustainability and resilience of mountainous populations. This includes important species such as wheat (Triticum spp.), barley (Hordeum vulgare), fava beans (Vicia faba), peas (Pisum sativum), and alfalfa (Medicago sativa). This study aimed to better understand varietal naming by farmers and the traits they use for assessing the current diversity of the five species, in 22 locations, distributed across three hubs of the High Atlas. The data were provided by 282 Amazigh informants during focus-group discussions, household surveys, and market surveys, with the support of the Diversity Assessment Tool for Agrobiodiversity and Resilience (DATAR). The use of local terminology for variety names and systematically collected morphological, ecological, and use descriptors appears to be a valuable way to assess local intraspecific diversity, and further comparisons with genomic results are recommended. Furthermore, the results also indicate low diversity at the household level, which contrasts with the greater diversity at the community level. Larger areas are still planted with landraces compared to areas planted with modern varieties, although the levels of richness (number) of both landraces and modern varieties are equivalent overall. Many factors influence this diversity: the biophysical characteristics of the sites, the socio-economic and management practices of farmers, and the availability of varietal diversity and of modern varieties or landraces. Although selection processes have reduced the local diversity available for economically important crops, we found that farmers still rely greatly on landraces, which present traits and variability that allow them to adapt to local conditions.
Research on 5G Network Slicing Strategy for Urban Complex Environment

Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.
Comparison of the Micronaut-AM System and the EUCAST Broth Microdilution Reference Method for MIC Determination of Four Antifungals against Aspergillus fumigatus

The Antifungal Susceptibility Testing method of the European Committee on Antimicrobial Susceptibility Testing (EUCAST-AFST) is a reference technique for the determination of the Minimum Inhibitory Concentration (MIC) of antifungals for Aspergillus fumigatus. However, it is time-consuming and requires expertise. Micronaut-AM (M-AM) is a fast, simple, time-saving, and ready-to-use new colorimetric method using an indicator (resazurin) to facilitate the visual reading. The aim of this retrospective study was to evaluate the performance of the M-AM system and compare it with the EUCAST broth microdilution reference method to determine the susceptibility of 77 A. fumigatus clinical strains to amphotericin B, itraconazole, voriconazole, and posaconazole. Overall, the essential agreements within ±2 dilutions were 100%, 62%, 58%, and 30% and the categorical agreements were 100%, 97%, 91%, and 87% for amphotericin B, itraconazole, voriconazole, and posaconazole, respectively. No categorical discrepancy was found for amphotericin B, but several categorical discordances were observed with azole antifungals. However, only 2 of the 16 azole-resistant strains confirmed by the cyp51A sequencing would have been misclassified by M-AM. The use of M-AM is probably suitable for the determination of the MICs of amphotericin B, but further evaluations are needed to confirm its usefulness for the determination of the MICs of azoles for A. fumigatus.
Conditional abstraction trees for sample-efficient reinforcement learning

In many real-world problems, the learning agent needs to learn a problem’s abstractions and solu-tion simultaneously. However, most such abstractions need to be designed and refined by hand for different problems and domains of application. This paper presents a novel top-down approach for constructing state abstractions while carrying out reinforcement learning (RL). Starting with state variables and a simulator, it presents a novel domain-independent approach for dynamically computing an abstraction based on the dispersion of temporal difference errors in abstract states as the agent continues acting and learning. Extensive empirical evaluation on multiple domains and problems shows that this approach automatically learns semantically rich abstractions that are finely-tuned to the problem, yield strong sample efficiency, and result in the RL agent significantly outperforming existing approaches.
Explanation Framework for Optimization-Based Scheduling: Evaluating Contributions of Constraints and Parameters by Shapley Values

Although automated planning and scheduling systems based on optimization models are increasingly being adopted into socially responsible tasks, the derived plan is often counter-intuitive under complicated considerations. Users will claim the right to know the reason for “Why did the optimal plan include something or not include something else (that I would have chosen)? ” Explanations of constraints and parameters that cause the unexpected plan derivation can play an important role in building trust between users and the scheduling system. However, existing approaches require an assumption of a specific problem setting, and have not addressed quantitative analysis for multiple types of factors. In this paper, we propose a general explanation framework to quantitatively evaluate the effect of constraints and parameters on the plan derivation by applying the concept of Shapley values, which satisfy the desirable axioms for explanations. The coalitional game based on optimization models is formulated to calculate the contributions of these factors to the ful-fillment of values or conditions in which users are interested. Through numerical experiments of the typical personnel assignment problem, we show that our framework can identify the major causes efficiently under various parameter settings and provide directly understandable explanations compared to the basic contrastive explanations.
Guest editorial: Social media in business-to-business interaction, engagement, co-creation, and communication

This special issue provides new insights into social media’s (SM) use and integration into business-to-business (B2B) relationships and networks, particularly the areas of B2B marketing communications, engagement, interaction and co-creation. Many of the papers in this special issue are extended studies which were presented at the 37th Industrial Marketing and Purchasing (IMP) Group Conference, held virtually in Ireland and co-hosted by the business schools in University College Cork and Southeast Technology University. For the most part, the papers use the IMPGroup’s (see IMPgroup.org; Industrial Marketing and Purchasing Group) core paradigmatic assumptions of interaction and interdependence to understand the use of SM in business markets. It is clear that SM is changing the industrial marketing landscape (Cortez and Johnston, 2017; Tiwary et al., 2021). Prior research has suggested that industrial marketing researchers have been slow to study SM’s impacts on business relationships and networks in comparison to their consumer marketing counterparts (Hofacker et al., 2020; Quinton and Wilson, 2016). This is no longer the case. Rather, we have witnessed an explosion of research on implementing SM in the B2B domain, with both conceptual and empirical studies emerging from recent literature calls (Cartwright et al., 2021a; Salo, 2017). This research has evolved from SM being viewed and analysed as a media tool within marketing communications (Swani et al., 2014) to a tool for enhancing; sales and customer engagement processes (Agnihotri, 2020; Bocconcelli et al., 2017), information sharing and problemsolving (Leek et al., 2016), and collaboration across business relationships (Wang et al., 2016; Drummond et al., 2022). Whilst there are many SM platforms and each has its own characteristics, the inability of any one particular firm to control the media and its inherent network interactions makes their study potentially unique when compared to more traditional single actor led communications. SM can add richness to information exchange and be part of the interactive resource structure of actors at both a relationship and network level. It can play a role and theoretically alter the balance in important relationship dynamics such as power and trust. For many entrepreneurial firms, it helps them in their initial reach out to new customers beyond their formative base of social contacts (Fraccastoro et al., 2021a). SM is also providing new opportunities for business marketing engagement and cocreation as it shifts marketing boundaries from the focal actor to between the actors involved in the network (Drummond et al., 2020). Indeed, SM may offer opportunities beyond its classification as another media within marketing communications, but it is only starting to be researched within this space, for example, as a strategic and tactical tool (Cartwright et al., 2021b; Drummond et al., 2020; Iankova et al., 2018; Felix et al, 2017), and in the sales process (Agnihotri and John-Mariadoss, 2022; Agnihotri, 2020).
Students’ Attitude to Online Emergency Remote Teaching (ERT) at Nikola Vaptsarov Naval Academy and Faculty of Maritime Studies Split during COVID-19 crisis

b. University of Split, Faculty of Maritime Studies, Split, Croatia COVID-19 pandemic has set new challenges to education systems in the Republic of Bulgaria and in the Republic of Croatia, particularly to higher education systems. In summer semester 2020/2021, emergency remote teaching (ERT) was imposed at Nikola Vaptsarov Naval Academy (NVNA) and at University of Split, Faculty of Maritime Studies (UoS FMS) under the circumstances of the COVID-19 pandemic. As researches related to the students’ satisfaction with learning in digital environment at the beginning Students’ Attitude to Online Emergency Remote Teaching (ERT) at Nikola Vaptsarov Naval Academy and Faculty of Maritime Studies Split during COVID-19 crisis
Finding Invariant Predictors Efficiently via Causal Structure

One fundamental problem in machine learning is out-of-distribution generalization. A method named the surgery estimator incorporates the causal structure in the form of a directed acyclic graph (DAG) to ﬁnd predictors that are invariant across target domains using distributional invari-ances via Pearl’s do-calculus. However, ﬁnding a surgery estimator can take exponential time as the current methods need to search through all possible predictors. In this work, we ﬁrst provide a graphical characterization of the identiﬁability of conditional causal queries. Next, we leverage this characterization together with a greedy search step to develop a polynomial-time algorithm for ﬁnding invariant predictors using the causal graph. Given the correct causal graph, our method is guaranteed to ﬁnd at least one invariant predictor, if it exists. We show that our proposed algorithm can signiﬁ-cantly reduce the run-time both in simulated and semi-synthetic data experiments and have predictive performance that is comparable to the existing work that runs in exponential time.
Stroke Risk Prediction: Comparing Different Sampling Algorithms

—Stroke is a serious disease that has a significant impact on the quality of life and safety of patients. Accurately predicting stroke risk is of great significance for preventing and treating stroke. In the past few years, machine learning methods have shown potential in predicting stroke risk. However, due to the imbalance of stroke data and the challenges of feature selection and model selection, stroke risk prediction still faces some difficulties.This article aims to compare the performance differences between different sampling algorithms and machine learning methods in stroke risk prediction. This study used the over-sampling algorithm (Random Over Sampling and SMOTE), the under-sampling algorithm (Random Under Sampling and ENN), and the hybrid sampling algorithm (SMOTE-ENN), and combined them with common machine learning methods such as K-Nearest Neighbors, Logistic Regression, Decision Tree and Support Vector Machine to build the prediction model.Through the analysis of experimental results, and found that the SMOTE combined with the LR model showed good performance in stroke risk prediction, with a high F1 score. In addition, this study found that the overall performance of the undersampling algorithm is better than that of the oversampling and hybrid sampling algorithms.These research results provide useful references for predicting stroke risk and provide a foundation for further research and application. Future research can continue to explore more sampling algorithms, machine learning methods, and feature engineering techniques to further improve the accuracy and interpretability of stroke risk prediction and promote its application in clinical practice.
M ODEL -B ASED A DVERSARIAL I MITATION L EARNING A S O NLINE F INE -T UNING

In many real world applications of sequential decision-making problems, such as robotics or autonomous driving, expert-level data is available (or easily obtainable) with methods such as tele-operation. However, directly learning to copy these expert behaviours can result in poor performance due to distribution shift at deployment time. Adversarial imitation learning algorithms alleviate this issue by learning to match the expert state-action distribution through additional environment interactions. Such methods are built around standard reinforcementlearning algorithms with both model-based and model-free approaches. In this work we focus on the model-based approach and argue that algorithms developed for online RL are sub-optimal for the distribution matching problem. We theoretically justify utilizing conservative algorithms developed for the offline learning paradigm in online adversarial imitation learning and empirically demonstrate improved performance and safety on a complex long-range robot manipulation task, directly from images.
Semi-Supervised Cell Instance Segmentation for Multi-Modality Microscope Images

Many clinical and biological tasks depend on accurate cell instance segmentation. Currently, the rapid development of deep learning realizes the automation of cell segmentation, which significantly decreases the workload of clinicians and researchers. However, most existing cell segmentation frameworks are fully supervised and modality-specific. Towards this end, this paper proposes a semisupervised cell instance segmentation framework for multi-modality microscope images. Firstly, K-Means clustering is utilized to discriminate the image modality. Then, for phase contrast and differential interference contrast images, Cellpose is adopted. For brightfield images, we subdivide them into two sub-categories according to the cell diameter by K-Means and optimize a U-Net for the large ∗Ziyue Wang and Zijie Fang contributed equally. †Corresponding author 36th Conference on Neural Information Processing Systems (NeurIPS 2022). diameter group. For fluorescence images, we propose a semi-supervised learning strategy using CDNet. The leaderboard shows that our proposed framework reaches an F1 score of 0.8428 on the tuning set, which ranks 6th among all teams.
Rapid construction of Escherichia coli chassis with genome multi‐position integration of isopentenol utilization pathway for efficient and stable terpenoid accumulation

The isopentenol utilization pathway (IUP) is potential in terpenoids synthesis. This study aimed to construct IUP‐employed Escherichia coli chassis for stably synthesizing terpenoids. As to effectiveness, promotor engineering strategy was employed to regulate IUP expression level, while ribosome‐binding site (RBS) library of the key enzyme was constructed for screening the optimal RBS, followed by optimization of concentration of inducer and substrates, the titer of reporting production, lycopene, from 0.087 to 8.67 mg OD600−1. As about stability, the IUP expression cassette was integrated into the genome through transposition tool based on CRISPR‐associated transposases. Results showed that the strain with 13 copies produced 1.78‐fold lycopene titer that of the controlled strain with IUP‐harbored plasmid, and it exhibited stable expression after ten successions while the plasmid loss was observed in the controlled strain in the 3rd succession. This strategy provides valuable information for rapid construction of highly effective and stable chassis employing IUP for terpenoids production.
Minimal Convolutional Neural Networks for Temporal Anti Aliasing

Existing deep learning methods for performing temporal anti aliasing (TAA) in rendering are either closed source or rely on upsampling networks with a large operation count which are expensive to evaluate. We propose a simple deep learning architecture for TAA combining only a few common primitives, easy to assemble and to change for application needs. We use a fully-convolutional neural network architecture with recurrent temporal feedback, motion vectors and depth values as input and show that a simple network can produce satisfactory results. Our architecture template, for which we provide code, introduces a method that adapts to different temporal subpixel offsets for accumulation without increasing the operation count. To this end, convolutional layers cycle through a set of different weights per temporal subpixel offset while their operations remain fixed. We analyze the effect of this method on image quality and present different tradeoffs for adapting the architecture. We show that our simple network performs remarkably better than variance clipping TAA, eliminating both flickering and ghosting without performing upsampling.
Federated learning for competing risk analysis in healthcare

Performing survival analysis on distributed healthcare data is an important research problem as the existing privacy laws and emerging data-sharing regulations prohibit the sharing of sensitive patient data across multiple institutions. The distributed healthcare survival data is typically heterogeneous, non-uniformly censored, and comes from patients with multi-morbidities (competing events), which may lead to biased and inaccurate risk predictions. To address these challenges, we propose federated learning for survival analysis with competing events. Specifically, (a) we propose a simple algorithm to estimate consistent federated pseudo values for survival analysis with competing events and censoring; and (b) we introduce a novel and flexible federated pseudo-value-based deep learning framework named FedCRA, where we employ a transformer-based model; named TransPseudo, to enable subject-specific prediction of the marginal risk of an event while preserving the data privacy. Extensive experiments on two real-world distributed CRA datasets with non-IID and non-uniform censoring properties and on synthetic data with different censoring settings demonstrate that our FedCRA framework with the TransPseudo model performs better than the federated learning framework with state-of-the-art CRA models.
BULYYING BEHAVIOR IN YOUTH IN WEST SUMATRA PROVINCE

The problem of bullying has increased in recent years, especially for vocational students in the province of West Sumatra, this study will examine the problems in how the characteristics, factors, impacts, and efforts have been made by BK teachers and the school in overcoming bullying behavior. The type of research used is descriptive quantitative. The data of this research are counseling guidance teachers, students of Vocational Schools of West Sumatra Province. Data collection techniques, interviews, through questionnaires and research results, bullying that occurs in vocational students in the province of West Sumatra is in the moderate category with a percentage of 41%. Guidance and counseling has an important function in overcoming problems that occur, with various counseling guidance services expected to be able to prevent bullying from occurring in the school environment.