Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic-scale cryogenic electron microscopy imaging of self-assembled peptoid nanostructures

Amphiphilic polypeptoids with defined sequences, versatile in forming various nanostructures, are ideal for mimicking biomacromolecular structures. The predictive design of nanostructures depends on our understanding of the relationship between molecular structure and the locations of atoms in the nanostructure. Factors of importance include chain conformation, crystal motifs, and the arrangement of the molecules within the nanostructure. This review introduces the cryogenic transmission electron microscopy (cryo-TEM) method, sorting and averaging unit cells in nanosheets for resolution enhancement and identifying structural heterogeneity. The resulting atomic-scale images reveal the presence of two types of crystal motifs. The impact of processing conditions, capping group chemistry, and side chain chemistry on structural heterogeneity and crystal motifs can be quantified. The 3D reconstruction of nanosheets, wherein atomic-scale corrugations were revealed, is introduced in this review. New developments in cryo-TEM, such as phase retrieval reconstruction, hold great promise for atomic-scale imaging of soft nanostructures.
ANALISIS PENGARUH PROMOSI, HARGA DAN INOVASI TERHADAP KEPUTUSAN PEMBELIAN PRODUK SASIRANGAN MENGGUNAKAN REGRESI LOGISTIK ORDINAL

Purchasing decision is a very important thing to create an optimal marketing plan. Promotion, price and innovation are factors that influence the decision to buy a product. Home industry craftsmen must be able to determine the right strategy to increase consumer purchasing decisions for a product. This is a problem that requires a solution by identifying the factors that influence purchasing decisions for sasirangan fabric in one of the home industries in Kalimantan Province using the ordinal logistic regression analysis method. Ordinal logistic regression analysis is a regression analysis in which the response variable is in the form of categories. The results of this study obtained one significant predictor variable, namely the price variable with the OR value obtained 1322 times. Assessment of the decision making process of buyer interest for the price of Sasirangan products is mostly in the high category, 1322 times compared to the low category
Flexible Time-Aware Shaper Scheduling System for a Multitenancy Environment

The realization of Cyber-Physical Systems (CPSs) is awaited. CPS analyzes various real-world sensing data in cyberspace and utilizes the analysis results to control real-world systems. This is expected to revolutionize various fields such as industrial automation and remote vehicle control. These usecases require analysis of large amounts of data and periodic real-time communication. Effective ways to meet these requirements include distributed processing with edge data centers (EdgeDCs) and guaranteed jitter with Time-Sensitive Networking (TSN). In EdgeDCs, servers and networks are virtualized and shared by multiple users in a multitenancy architecture, which is beneficial for cost reduction. In such a multitenancy environment, various users can add or remove applications at any time, and the number of applications is expected to change from moment to moment. In contrast, Time-Aware Shaper (TAS), the standard for time-division transmission in TSN, requires network-wide configuration in advance for each application. If TAS is applied in EdgeDCs, network-wide reconfiguration occurs frequently. It takes more time to allocate applications, and configuration traffic increases. Moreover, the traffic classes used in TAS cannot accommodate a large number of applications. In this paper, we propose a flexible TAS scheduling system. Our system does not require reconfiguration of relay devices when applications are added or deleted and enables communicating without depending on the traffic class. We implemented the proposed system into Linux kernel and extension plugins for Kubernetes, which is assumed to be the infrastructure for EdgeDC. We also evaluated bandwidth utilization and the number of reconfigurations through simulations.
An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer

Simple Summary Magnetic resonance imaging (MRI) has a pivotal role in accurately staging cervical cancer and has been formally incorporated into the 2018 FIGO staging system. MRI can accurately assess tumour size and local and distant invasion as well as lymph node involvement, which is essential for triaging patients into surgical or chemotherapeutic management. In this review, we highlight key MRI findings and pitfalls pertaining to the updated FIGO stages and their implications for treatment selection into surgery or chemoradiation. Abstract Cervical cancer is the fourth most common cancer in women worldwide and the most common gynaecological malignancy. The FIGO staging system is the most commonly utilised classification system for cervical cancer worldwide. Prior to the most recent update in the FIGO staging in 2018, the staging was dependent upon clinical assessment alone. Concordance between the surgical and clinical FIGO staging decreases rapidly as the tumour becomes more advanced. MRI now plays a central role in patients diagnosed with cervical cancer and enables accurate staging, which is essential to determining the most appropriate treatment. MRI is the best imaging option for the assessment of tumour size, location, and parametrial and sidewall invasion. Notably, the presence of parametrial invasion precludes surgical options, and the patient will be triaged to chemoradiotherapy. As imaging is intrinsic to the new 2018 FIGO staging system, nodal metastases have been included within the classification as stage IIIC disease. The presence of lymph node metastases within the pelvis or abdomen is associated with a poorer prognosis, which previously could not be included in the staging classification as these could not be reliably detected on clinical examination. MRI findings corresponding to the 2018 revised FIGO staging of cervical cancers and their impact on treatment selection will be described.
Constrained Bipartite Graph Learning for Imbalanced Multi-Modal Retrieval

Real-worldmulti-modal retrieval tasks always encounter modal imbalance scenarios. The scales of instances from different modalities are inconsistent and unaligned with each other. Though several methods alleviate the issue by establishing miscellaneous representations of multi-modal data, they still suffer from difficulties like laborious human-being annotations and complex common-space optimization. In the research, we present Constrained Bipartite Graph Learning (CBGL) for imbalanced correlations, where a size-flexible correlation graph is learned from instances' representations. To guide the graph learning, we take advantage of prior side information, including positive pairs and negative pairs, which readily express intra-modality affinity and inter-modality discrepancy. Accordingly, both positive and negative correlations are propagated over instances, and a similarity graph with satisfactory neighbors is achieved. Benefiting from the probabilistic similarities, a query graph is then naturally constructed that directly achieves multi-modal retrieval. To validate the effect, we build a Music-Video-Image (MVI) dataset in regard to music and images with imbalanced scales. Experimental results reported on MVI dataset and three benchmarks demonstrate our prominent superiority over ten representative competitors in multi-modal retrieval.
An Efficient Distributed Intrusion Detection System in IoT: GAN-Based Attacks and a Countermeasure

This paper designs an efficient distributed intrusion detection system (DIDS) for Internet of Things (IoT) data traffic. The proposed DIDS has been implemented at IoT network gateways and edge sites to detect and alarm on anomalous traffic data. We implement different machine learning (ML) algorithms to classify the traffic as benign or malicious. We perform an in-depth parametric study of the models using multiple real-time IoT datasets to enable the model deployment to be consistent with the demands of the specific IoT network. Specifically, we develop a decentralized method using federated learning (FL) for collecting data from IoT sensor nodes to address the data privacy issues associated with centralizing data at the gateway DIDS. We propose two poisoning attacks on the perception layer of these IoT networks that use generative adversarial networks (GAN) to determine how the threats of unpredictable authenticity of the IoT sensors can be triggered. To address such attacks, we design an appropriate defence algorithm that is implemented at the gateways to help separate anomalous from benign data and preserve the system's robustness. The suggested defence algorithm successfully classifies anomalies with high accuracy, exhibiting the system's immunity against poisoning attacks. We confirm that the Random Forest classifier performs the best across all ML key performance indicators (KPIs) and can be implemented at the edge to reduce false alarm rates.
Genome-wide association analysis of Monilinia fructicola lesion in a collection of Spanish peach landraces

Brown rot, caused by the Monilinia spp. , is the disease that causes the greatest losses in stone fruit worldwide. Currently, M. fructicola has become the dominant species in the main peach production area in Spain. The fruit cuticle is the first barrier of protection against external aggressions and may have a key role in the susceptibility to brown rot. However, information on the role of skin fruit on the resistance to brown rot in peach is scarce. Previous genetic analyses in peach have demonstrated that brown rot resistance is a complex and quantitative trait in which different fruit parts and resistance mechanisms are involved. To search for genomic areas involved in the control of the cultivar susceptibility to brown rot and to elucidate the role of fruit skin against this infection, we have studied, for two consecutive seasons (2019 and 2020), the fruit susceptibility to M. fructicola, together with fruit cuticle thickness (CT) and density (CD), in a collection of 80 Spanish and 5 foreign peach cultivars from the National Peach Collection at CITA (Zaragoza, Spain). Brown rot incidence, lesion diameter, and severity index were calculated after 5 days of inoculation on non-wounded fruit. The peach collection has also been genotyped using the new peach SNP chip (9 + 9K). Genotypic and phenotypic data have been used to perform a genome-wide association analysis (GWAS). Phenotyping has shown a wide variability on the brown rot susceptibility within the Spanish germplasm as well as on CD and CT. The GWAS results have identified several significant SNPs associated with disease severity index (DSI), CD, and CT, five of which were considered as reliable SNP-trait associations. A wide protein network analysis, using 127 genes within the regions of the reliable SNPs and previously identified candidate genes (169) associated with Monilinia spp. resistance, highlighted several genes involved in classical hypersensitive response (HR), genes related to wax layers as ceramidases and lignin precursors catalyzers, and a possible role of autophagy during brown rot infection. This work adds relevant information on the complexity resistance mechanisms to brown rot infection in peach fruits and the genetics behind them.
Establishment of Functional PCR-Based Markers against Bacterial Leaf Blight Disease in Rice Landraces of Yunnan Province of China

Bacterial leaf blight is a devastating disease of rice worldwide. The resistant genes are routinely transferred from landraces to cultivated varieties through backcross breeding along with marker-assisted selection. In the present study, we use the gene-specific markers to screen the rice landraces in Yunnan Province of China. We collected 404 representative samples of 24 different rice landraces from Yunnan Province of China. The initial PCR-based screening suggested that the leaf blight resistance was not evenly distributed in Yunnan Province. Our results indicate that there is a complete loss of resistance for landraces based on xa5 and xa13 genes. On the other hand, landraces harboring Xa7 and Xa21 showed a high level of resistance. Using gene-specific PCR-based data, we were able to identify the resistant, susceptible and heterozygous populations across Yunnan Province. The widely used Xa21 gene alone showed a remarkable level of resistance throughout the province, indicating its potential to develop broad-spectrum resistance in rice germplasm. The key aspects of bacterial blight spread according to local sites in Yunnan Province and the resistance conferred by different landraces due to the presence of different resistance genes are discussed.
Multiscale Template Matching for Multimodal Remote Sensing Image

Multimodal matching remains a difficult and pressing problem in the imaging processing community. Accurate and robust multimodal matching is important for the performance of applications, such as registration and fusion. Traditional image matching algorithms cannot effectively handle multimodal images with severe nonlinear radiometric distortion (NRD). In this article, a novel multiscale template matching algorithm for multimodal image matching is proposed to address this problem. We propose a novel frequency-domain convolutional map based on the wavelet transform and phase congruency to construct a feature description map that significantly reduces the NRD between multimodal images. The development of omnidirectional aggregated feature vectors with rotational invariance also helped to achieve robustness on rotated images. Finally, a multiscale template matching strategy improved the matching performance on multimodal images with displacement and scale variations. To improve the time efficiency of the algorithm, most of the complex computations in this article are performed in the frequency domain. According to the experimental findings on six multimodal image datasets, the method can obtain accurate and robust matching results between multimodal images. Through qualitative and quantitative evaluations, the method outperforms several mainstream multimodal image matching algorithms in terms of matching accuracy, success rate, and time consumption.
Thin Receiver Freeform Lenslet Concentrator Array for LiFi

Emerging optical wireless communication (OWC) and LiFi require an ever-increasing bandwidth and decreasing size of the receiver modules compatible with mobile communication. This leads to using arrayed photodetector (PD) solutions for the receiver. However, the classical imaging solutions (e.g., convex lenses) struggle to achieve the required optical concentration at mobile form factors. In comparison, the non-imaging solutions (e.g., compound parabolic concentrators (CPCs)) provide poor uniformity of the irradiance distribution at the PD plane that can impair the link performance in the arrayed PD receivers. This paper proposes a novel approach using a freeform lenslet concentrator array to achieve an enhanced gain at a 5-degree field-of-view (FoV) compared to existing standard imaging optics solutions. The design also maintains a better irradiance uniformity and smaller refracted beam angle than the non-imaging solutions. Making it a promising optical solution that can help bridge the gap between imaging and non-imaging optics for high bandwidth arrayed PD receivers for mobile LiFi and OWC applications.
Queueing Model for Intermittent Communication and Computing of Battery-Less Edge Computing

Battery-less intermittent IoT system supports reliable computing without a battery or stable energy supply but with the drawback of low computation capacity. To overcome this drawback, intermittent edge computing systems are proposed to integrate Multi-access edge computing (MEC) with intermittent devices through wireless connections. The key challenge of such a system is determining the offloading timing: offloading could be inefficient when it is interrupted by power failures. An accurate estimation model of the offloading efficiency is the key to determining the optimal offloading decisions. We proposed a queueing-based estimation model to estimate the offloading and local computing efficiency of a Bluetooth Low Energy (BLE) intermittent edge computing system. The proposed Discrete Time Markov Chain (DTMC) model takes the energy arrival and task creation processes into account to capture the uncertainty in the battery-less devices. The simulation results suggested that the proposed model provides more accurate estimations and achieves the lowest system latency compared with the state-of-the-art model and baseline algorithms.
Detecting bedform migration from high-resolution multibeam bathymetry in Portsmouth Harbor, New Hampshire, USA

A study was undertaken to quantify dune migration in Portsmouth Harbor, New Hampshire, USA from repeat high-resolution multibeam echosounder (MBES) surveys. Repeat MBES surveys were conducted in June 2007 and July 2008 over periods ranging from 6 hours to 7 days in order to capture the response of dune morphology to ebb-flood and spring-neap tidal cycles. A new technique was developed for detecting bedform migration. This approach utilizes a fingerprint-detection algorithm (Bishnu et al., 2002) to convert the bathymetric surface to a binary map of bedform crests, which are subsequently tracked using a spatial crosscorrelation technique (Duffy and Hughes-Clarke, 2005). Acoustic current-meter observations from July 2008 provided context for the observed bedform-migration patterns, and were subsequently used to compute bed shear stress. Results indicate that dune migration occurred over periods as short as 6 hours. Dune migration distances in excess of 2 m were observed over 6and 7-day periods.
Spatio‐Temporal Variations in Sediment Delivery as a Response to Rapid Quaternary Climate Change in the Lake Malawi Rift, East Africa

The interplay of rapid climate change and tectonics drives landscape development, sediment routing, and deposition in early‐stage continental rift systems. The Lake Malawi Rift, in the Western Branch of the East African Rift, is an archetype of a juvenile rift and an ideal natural laboratory for evaluating lacustrine source‐to‐sink systems on orbital or shorter timescales. We examine the interplay of these processes over the past 140 kyr using observations from nested seismic reflection data sets tied to scientific drill cores, which calibrate numerical forward models of this closed sedimentary system. Fault slip rates measured from seismic data drive tectonic displacements in the model. Satellite‐derived precipitation maps constrain modern precipitation and are scaled to previous hydrologic balance studies to reconstruct past climates. Our model reproduces known sediment thicknesses across the rift and accounts for 96% of the estimated siliciclastic sediment deposited over the past 140 kyr. The results demonstrate that the onset of arid climate conditions (140–95 kyr BP) causes extreme drainage adjustments downstream and the formation of mega‐catchments that flow axially into a shallow restricted paleo‐lake. Sedimentation rates during this time are twice the present values due to increased sediment focusing via these axial systems into a much smaller, hydrologically closed lake. As the climate became wetter (95–50 kyr BP), the lake rapidly expanded, decreasing both erosion and sedimentation rates across the rift. This closed‐loop approach allows us to evaluate the role of high‐frequency climate change in modulating basin physiography as well as sediment fluxes in juvenile rift systems.
Balancing on a Rolling Contact

This letter presents a controller for robots that balance in a vertical plane on a rolling contact on a flat horizontal surface. It is an extension of Featherstone's balance controller to the case of robots that balance on rounded feet or wheels. Simulation results demonstrate the ability of the new controller to balance an inverted double pendulum on a rolling contact and to balance a Segway-like wheeled robot and make it follow a motion command signal. Experimental validation is provided on an underactuated inverted double pendulum robot.
Influence of not homogeneous absorption on reverberation time

In present days, the indoor spaces need more and more attention on the design of acoustic proprieties of enclosed environments such as conference rooms, classrooms and offices. In order to obtain low internal noise levels and correct speech intelligibility, indoor sound field should be studied properly.In this work, a comparison of the reverberation time measured and calculated in two rooms using different model has been made. Results clearly show that calculated results are not in agreement and differ from measured values.
Joint Beamforming and Rate Splitting Scheme Design for Overloaded Industrial Private Networks

The fourth industrial revolution aims to realize industrial internet-of-things (IIoT) and automatic manufacturing systems. However, the stringent ultra-reliability low-latency communications (URLLC) requirements and severe mutual interference are two major challenges for industrial private networks. To achieve a broad range of industrial connections, we aim to investigate the method to realize the rate splitting multiple access (RSMA) for overloaded scenarios. A multi-antenna central controller (CC) is employed to serve multiple devices with the short blocklength regime. With the objective of maximizing weighted sum throughput over all devices, the cooperative beamforming and rate splitting schemes tailored for overloaded scenarios are joint optimized, subject to the constraints of resource allocation and diverse quality-of-service (QoS) requirements. To tackle the complexity of the fractional rate expression with channel dispersion, we propose a universal transform method, where tractable expressions for the original problem are efficiently obtained. The nonconvex optimization problem is solved iteratively using sequential convex approximation and interior-point methods. Simulation results confirm the effectiveness of the proposed method and show that RSMA can significantly improve performance in overloaded URLLC compared with other multi-antenna multiple access schemes.
Revisiting the seismic hazards of faults surrounding the 2022 Ms6.8 Luding earthquake, Sichuan, China

Abstract The occurrence of the Ms6.8 Luding earthquake on 5 September 2022 filled the seismic gap in the southeastern segment of the Xianshuihe fault zone and transferred stress to the surrounding faults, which attracted much more attention to the seismic hazard. In this study, we calculate the coseismic Coulomb stress changes caused by the Ms6.8 Luding earthquake and analyze its relationship with the relocated aftershocks, and further explore the stress evolution before the Luding earthquake and seismic hazards on the surrounding faults after it. The results show significant coseismic Coulomb stress increases in the northern Anninghe fault zone and the coseismic unruptured section of the southeastern Xianshuihe fault zone. Aftershocks are highly correlated with the positive coseismic Coulomb stress change, wherein eight out of eleven aftershocks of M ≥ 3.0 occurred in areas with significant stress increases ranging from 0.03 MPa to 0.44 MPa, indicating the triggering effect. The Moxi fault experiences co- and post-seismic Coulomb stress changes due to historical M ≥ 7.0 strong earthquakes since 1515, which help understand the seismogenic mechanism of the Luding earthquake. Moreover, the Luding earthquake advanced the potential earthquakes in the northern section of the Anninghe fault zone by approximately 60 years, further highlighting the potential seismic hazards.