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We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze’elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze’elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze’elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment.

Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.

During the past three decades, the Dead Sea (DS) water level has dropped at an average rate of ~1 m/year, resulting in the formation of thousands of sinkholes along its coastline that severely affect the economy and infrastructure of the region. The sinkholes are associated with gradual land subsidence, preceding their collapse by periods ranging from a few days to about five years. We present the results of over six years of systematic high temporal and spatial resolution interferometric synthetic aperture radar (InSAR) observations, incorporated with and refined by detailed Light Detection and Ranging (LiDAR) measurements. The combined data enable the utilization of interferometric pairs with a wide range of spatial baselines to detect minute precursory subsidence before the catastrophic collapse of the sinkholes and to map zones susceptible to future sinkhole formation. We present here four case studies that illustrate the timelines and effectiveness of our methodology as well as its limitations and complementary methodologies used for sinkhole monitoring and hazard assessment. Today, InSAR-derived subsidence maps have become fundamental for sinkhole early warning and mitigation along the DS coast in Israel and are incorporated in all sinkhole potential maps which are mandatory for the planning and licensing of new infrastructure.

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features.

Swarms of self-propelled micromotors can mimic the processes of natural systems and construct artificial intelligent materials to perform complex collective behaviors. Compared to self-propelled Janus micromotors, the isotropic colloid motors, also called micromotors or microswimmers, have advantages in self-assembly to form micromotor swarms, which are efficient in resistance to external disturbance and the delivery of large quantity of cargos. In this minireview, we summarize the fundamental principles and interactions for the assembly of isotropic active particles to generate micromotor swarms. Recent discoveries based on either catalytic or external physical field-stimulated micromotor swarms are also presented. Then, the strategy for the reconstruction and motion control of micromotor swarms in complex environments, including narrow channels, maze, raised obstacles, and high steps/low gaps, is summarized. Finally, we outline the future directions of micromotor swarms and the remaining challenges and opportunities.

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.

Earthquake early warning systems aim to reduce the potential danger by providing a warning in the seconds before strong ground shaking occurs. In this study, we implemented EPIC, an early warning algorithm for Bursa province and its surroundings, which is a seismically active region. We replayed 104 earthquakes of M ≥ 3.5, which occurred in and around the study area between 2012 and 2021. We derived period and amplitude-based magnitude-scaling relationships using peak displacement amplitude (Pd) and predominant period (Tpmax) parameters of the first 4 s of P-wave arrivals. We investigated the performance of magnitude-scaling relationships through testing with real-time data. We observed an improvement when comparing the magnitude estimates made with the newly developed equations with the default equations used for California. We have also found that magnitude estimation with Pd gives better results than Tpmax for estimating the accurate final magnitude. We aim to adapt the EPIC early warning system, implemented for Bursa province and its surroundings, specifically for each region of Türkiye where the earthquake risk is high.

Background: previous worldwide reports indicated a substantial short-term reduction in various respiratory infections during the early phase of the SARS-CoV-2 pandemic. Aims: exploring the long-term impact of the COVID-19 pandemic on respiratory pathogens. Methods: retrospective analysis of bacterial and viral positivity rate in respiratory samples, between 1 January 2017–30 June 2022 in a tertiary hospital in Jerusalem, Israel. Results: A decline in overall respiratory tests and positivity rate was observed in the first months of the pandemic. Respiratory isolations of Hemophilus influenza and Streptococcus pneumoniae were insignificantly affected and returned to their monthly average by November 2020, despite a parallel surge in COVID-19 activity, while Mycoplasma pneumoniae was almost eliminated from the respiratory pathogens scene. Each viral pathogen acted differently, with adenovirus affected only for few months. Human-metapneumovirus and respiratory-syncytial-virus had reduced activity for approximately a year, and influenza A virus resurged in November 2021 with the elimination of Influenza-B. Conclusions: After an immediate decline in non-SARS-CoV-2 respiratory infections, each pathogen has a different pattern during a 2-year follow-up. These patterns might be influenced by intrinsic factors of each pathogen and different risk reduction behaviors of the population. Since some of these measures will remain in the following years, we cannot predict the timing of return to pre-COVID-19 normalcy.

Lip reading has attracted increasing attention recently due to advances in deep learning. However, most research targets English datasets. The study of Chinese lip-reading technology is still in its initial stage. Firstly, in this paper, we expand the naturally distributed word-level Chinese dataset called ‘Databox’ previously built by our laboratory. Secondly, the current state-of-the-art model consists of a residual network and a temporal convolutional network. The residual network leads to excessive computational cost and is not suitable for the on-device applications. In the new model, the residual network is replaced with ShuffleNet, which is an extremely computation-efficient Convolutional Neural Network (CNN) architecture. Thirdly, to help the network focus on the most useful information, we insert a simple but effective attention module called Convolutional Block Attention Module (CBAM) into the ShuffleNet. In our experiment, we compare several model architectures and find that our model achieves a comparable accuracy to the residual network (3.5 GFLOPs) under the computational budget of 1.01 GFLOPs.

In recent years, with the rapid development of deep learning, the requirements for the performance of the corresponding real-time recognition system are getting higher and higher. However, the rapid expansion of data volume means that time delay, power consumption, and cost have become problems that cannot be ignored. In this case, the traditional neural network is almost impossible to use to achieve productization. In order to improve the potential problems of a neural network facing a huge number of datasets without affecting the recognition effect, the model compression method has gradually entered people’s vision. However, the existing model compression methods still have some shortcomings in some aspects, such as low rank decomposition, transfer/compact convolution filter, knowledge distillation, etc. These problems enable the traditional model compression to cope with the huge amount of computation brought by large datasets to a certain extent, but also make the results unstable on some datasets, and the system performance has not been improved satisfactorily. To address this, we proposed a structured network compression and acceleration method for the convolutional neural network, which integrates the pruned convolutional neural network and the recurrent neural network, and applied it to the lip-recognition system in this paper.

In the past decade, aluminum scandium nitride (AlScN) with a high Sc content has shown ferroelectric properties, which provides a new option for CMOS-process-compatible ferroelectric memory, sensors and actuators, as well as tunable devices. In this paper, the ferroelectric properties of Al0.7Sc0.3N grown on different metals were studied. The effect of metal and abnormal orientation grains (AOGs) on ferroelectric properties was observed. A coercive field of approximately 3 MV/cm and a large remanent polarization of more than 100 μC/cm2 were exhibited on the Pt surface. The Al0.7Sc0.3N thin film grown on the Mo metal surface exhibited a large leakage current. We analyzed the leakage current of Al0.7Sc0.3N during polarization with the polarization frequency, and found that the Al0.7Sc0.3N films grown on either Pt or Mo surfaces have large leakage currents at frequencies below 5 kHz. The leakage current decreases significantly as the frequency approaches 10 kHz. The positive up negative down (PUND) measurement was used to obtain the remanent polarization of the films, and it was found that the remanent polarization values were not the same in the positive and negative directions, indicating that the electrode material has an effect on the ferroelectric properties.

Photovoltaic (PV) panels are widely adopted and set up on residential rooftops and photovoltaic power plants. However, long-term exposure to ultraviolet rays, high temperature and humid environments accelerates the oxidation of PV panels, which finally results in functional failure. The traditional fault detection approach for photovoltaic panels mainly relies on manual inspection, which is inefficient. Lately, machine vision-based approaches for fault detection have emerged, but lack of negative samples usually results in low accuracy and hinders the wide adoption of machine vision-based approaches. To address this issue, we proposed a semi-supervised anomaly detection model based on the generative adversarial network. The proposed model uses the generator network to learn the data distribution of the normal PV panel dataset during training. When abnormal PV panel data are put into the model in the test phase, the reconstructed image generated by the model does not equal the input image. Since the abnormal PV panel data do not obey the data distribution learned by the generator, the difference between the original image and its reconstructed image exceeds the given threshold. So, the model can filter out the fault PV panel by checking the error value between the original image and its reconstructed image. The model adopts Gradient Centralization and SmoothL1 loss function to improve its generalization performance. Meanwhile, we use the convolutional block attention module (CBAM) to make the model pay more attention to the defective area and greatly improve the performance of the model. In this paper, the photovoltaic panels dataset is collected from a PV power plant located in Zhejiang, China. We compare the proposed approach with state-of-the-art semi-supervised and unsupervised approaches (i.e., AnoGAN (Anomaly Detection with Generative Adversarial Networks), Zhao’s method, GANomaly, and f-AnoGAN), and the result indicates that the Area Under Curve (AUC) increases by 0.06, 0.052, 0.041 and 0.035, respectively, significantly improving the accuracy of photovoltaic panel fault detection.

Phage therapy is an experimental therapeutic approach used to target multidrug-resistant bacterial infections. A lack of reliable data with regard to its efficacy and regulatory hurdles hinders a broad application. Here we report, for the first time, a case of vancomycin-resistant Enterococcus faecium abdominal infection in a one-year-old, critically ill, and three times liver transplanted girl, which was successfully treated with intravenous injections (twice per day for 20 days) of a magistral preparation containing two Enterococcus phages. This correlated with a reduction in baseline C-reactive protein (CRP), successful weaning from mechanical ventilation and without associated clinical adverse events. Prior to clinical use, phage genome was sequenced to confirm the absence of genetic determinants conferring lysogeny, virulence or antibiotic resistance, and thus their safety. Using a phage neutralization assay, no neutralizing anti-phage antibodies in the patient’s serum could be detected. Vancomycin-susceptible E. faecium isolates were identified in close relation to phage therapy and, by using whole-genome sequencing, it was demonstrated that vancomycin-susceptible E. faecium emerged from vancomycin-resistant progenitors. Covering a one year follow up, we provide further evidence for the feasibility of bacteriophage therapy that can serve as a basis for urgently needed controlled clinical trials.

Moringa oleifera Lam. (moringa hereafter) is cultivated as a new summer super-forage field crop in Israel, yet no weed control protocol has been developed for it. The objective of the study was to develop an integrated weed management (IWM) practice for the moringa agro-system in arid and semi-arid regions like the Mediterranean basin. We tested various herbicides applied pre (PRE) and post (POST) crop emergence and cultivation methods for weed control, with an emphasis on crop safety. The PRE herbicides were the most effective and safe control mean. Their application resulted in minor (

Providencia spp. are emerging pathogens mainly in nosocomial infections. Providencia stuartii in particular is involved in urinary tract infections and contributes significantly to the high incidence of biofilm-formation in catheterized patients. Furthermore, recent reports suggested a role for multiple drug resistant (MDR) P. stuartii in hospital-associated outbreaks which leads to excessive complications resulting in challenging treatments. Phage therapy is currently one of the most promising solutions to combat antibiotic-resistant infections. However, the number of available phages targeting Providencia spp. is extremely limited, restricting the use of phage therapy in such cases. In the present study, we describe the isolation and characterization of 17 lytic and temperate bacteriophages targeting clinical isolates of Providencia spp. as part of the Israeli Phage Bank (IPB). These phages, isolated from sewage samples, were evaluated for host range activity and effectively eradicated 95% of the tested bacterial strains isolated from different geographic locations and displaying a wide range of antibiotic resistance. Their lytic activity is demonstrated on agar plates, planktonic cultures, and biofilm formed in a catheter model. The results suggest that these bacteriophages can potentially be used for treatment of antibiotic-resistant Providencia spp. infections in general and of urinary tract infections in particular.

A key element in phage therapy is the establishment of large phage collections, termed herein “banks”, where many well-characterized phages, ready to be used in the clinic, are stored. These phage banks serve for both research and clinical purposes. Phage banks are also a key element in clinical phage microbiology, the prior treatment matching of phages and antibiotics to specific bacterial targets. A worldwide network of phage banks can promote a phage-based solution for any isolated bacteria. Herein, we describe the Israeli Phage Bank (IPB) established in the Hebrew University, Jerusalem, which currently has over 300 phages matching 16 bacteria, mainly pathogens. The phage bank is constantly isolating new phages and developing methods for phage isolation and characterization. The information on the phages and bacteria stored in the bank is available online.

The demand for pesticide-free food has increased the need for sustainable organic farming. Onion (Allium cepa L.) is an important vegetable crop cultivated worldwide. The available weed control tools for intra-row weeds following onion emergence are limited. This study aimed to evaluate the potential use of liquefied petroleum gas (LPG) flaming as a pre- and post-emergence weed control method for both direct-seeded onion seedlings and transplanted onion bulbs. The safety of cross-row, where the flames are targeted to the intra-row area from both sides of the row, and broadcast flaming for bulb onion was compared. Cross-row flaming at twelve days after planting had no effect on onion dry weight, while broadcast flaming-treated plants’ dry weight was reduced by 36% as compared to controls. For the cross-row technique, the tested burners’ angle (45° and 30°) and inter-burner distances (30 and 40 cm) had no impact on weed control efficacy, and similar control levels, between 55% (15 cm) and 45% (10 cm), were observed 15 cm from both sides of the row-center. Direct-seeded onion cultivars were treated at various growth stages. The pre-crop-emergence stage was completely safe for the crop, and the second leaf stage exhibited a wide range of tolerance levels to flaming treatment across the different onion cultivars, with dry weights ranging between 39 and 117% compared to non-treated control in the flaming sensitive and tolerant cultivars, respectively. These results were validated under field conditions using the two most tolerant cultivars (Orlando and Browny); no yield reductions were observed for either cultivar when treated from the third leaf stage. In bulb onion, flaming had no impact on dry weight of shoots or roots when applied from four weeks after planting. This study demonstrates, for the first time, the potential of using flaming as a post-emergence weed control tool for direct-seeded and bulb onion, and at earlier time points than previously shown. Cross-row flaming proved effective for controlling intra-row weeds and can lower weeding costs. Future research should evaluate the safety of sequential applications and test complementary control methods for the initial growth stages.

In recent decades, anthropogenic activity and climate changes have reshaped global weed dispersal and establishment in new territories. This study aimed to evaluate the effectiveness of propane flaming approach in the control of perennial invasive and native Mediterranean broadleaf and grass weeds. The invasive weeds, Cyperus rotundus, Sorghum halepense, and Ecballium elaterium, were treated multiple times with a single propane dose (2.5 kg propane km−1), using the broadcast technique. The local annual weeds, Sinapis arvensis, Lavatera trimestris, and Avena sativa, were treated once at five propane doses (0–2.5 kg propane km−1), using the cross-row technique. Dose-response analysis was performed. Three applications provided effective control (up to >90%) for all tested perennials, and affected seed and flower production in Sorghum halepense and Ecballium elaterium, respectively. However, the timing of the sequential application had a significant impact on the degree of control, in terms of dry weight reduction and seed production. Weed density had an impact on control efficacy but was only a significant determinant for Ecballium elaterium. Cross-row application was effective during early growth stages of broadleaf weeds (ED50 < 1.2 kg propane km−1), but was less effective during later growth stages (ED50 > 2.6 kg propane km−1). For grass weeds, both early and late application were ineffective (ED50 > 4.1 kg propane km−1). More research is needed to optimize this weed control tactic for various cropping systems and weed species. Implementation of this novel approach into integrated weed management programs will increase the control efficacy of invasive weed under the projected climate changes and reduce the evolution of herbicide-resistant weeds.

Effective control of the parasitic weed sunflower broomrape (Orobanche cumana Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively. The main challenge in developing such control practices for O. cumana lies in the fact that most of its life-cycle occurs in the soil sub-surface and by the time shoots emerge and become observable, the damage to the crop is irreversible. This paper approaches early O. cumana detection by hypothesizing that its parasitism already impacts the host plant morphology at the sub-soil surface developmental stage. To validate this hypothesis, O. cumana- infested sunflower and non-infested control plants were grown in pots and imaged weekly over 45-day period. Three-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level. Among the parameters estimated, height and first internode length were the earliest definitive indicators of infection. Furthermore, the detection timing of both parameters was early enough for herbicide post-emergence application. Considering the fact that 3-D morphological modeling is nondestructive, is based on commercially available RGB sensors and can be used under natural illumination; this approach holds potential contribution for site specific pre-emergence managements of parasitic weeds and as a phenotyping tool in O. cumana resistant sunflower breeding projects.

A transient, three-dimensional mathematical model has been developed to study the slag entrapment in a continuous casting mold. The unsteady turbulent flow is computed using the large eddy simulation (LES). The sub-grid scale structure is modeled by the Smagorinsky⁻Lilly model. The movements of discrete bubbles, as well as three continuous phases (air⁻slag⁻steel), are described by solving the coupled discrete particle model and volume of fraction (DPM+VOF) approach. The bubble transport inside different phases (steel and slag) and the escape near the air⁻slag interface are well studied. Good agreement is obtained by comparing with the plant observation of the slag eyes on the top surface of the mold. Three main mechanisms of slag entrapment are identified; vortex formation, shear-layer instability, and meniscus fluctuation. Four stages are observed for a slag entrapment: deformation, necking, breaking, and dragging in the mold. The model is helpful for understanding the formation of slag entrapment during continuous casting.

Duckweeds (Lemnaceae) are tiny plants that float on aquatic surfaces and are typically isolated from temperate and equatorial regions. Yet, duckweed diversity in Mediterranean and arid regions has been seldom explored. To address this gap in knowledge, we surveyed duckweed diversity in Israel, an ecological junction between Mediterranean and arid climates. We searched for duckweeds in the north and center of Israel on the surface of streams, ponds and waterholes. We collected and isolated 27 duckweeds and characterized their morphology, molecular barcodes ( atpF - atpH and psbK - psbI ) and biochemical features (protein content and fatty acids composition). Six species were identified- Lemna minor , L. gibba and Wolffia arrhiza dominated the duckweed populations, and together with past sightings, are suggested to be native to Israel. The fatty acid profiles and protein content further suggest that diverged functions have attributed to different haplotypes among the identified species. Spirodela polyrhiza , W. globosa and L. minuta were also identified but were rarer. S. polyrhiza was previously reported in our region, thus, its current low abundance should be revisited. However, L. minuta and W. globosa are native to America and Far East Asia, respectively, and are invasive in Europe. We hypothesize that they may be invasive species to our region as well, carried by migratory birds that disperse them through their migration routes. This study indicates that the duckweed population in Israel's aquatic environments consists of both native and transient species.