Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence of Some Pathogenic Bacteria in Fast Food Sandwiches

In the current study, a grand total of 100 random samples of fast food sandwiches including; Shawerma, Kibda (liver), kofta (mined meat) and beef burger (25 of each) were collected from different street vendors at Alexandria Province. The collected samples were examined bacteriologically for detection of some pathogenic bacteria including Salmonellae, Shigella, Staphylococcus aureus and Bacillus cereus. The obtained results revealed that the incidence of Salmonellae in sandwiches of Shawerma, Kibda, kofta and beef burger was 8, 4, 4 and 8%, respectively and serotyping of the obtained isolates revealed the presence of S. Enteritidis, S. Typhimurium and S. Dublin. In addition, the incidence of Shigella was 16, 12, 8 and 20% in the examined samples, respectively and the identification of the obtained isolates revealed the presence of S. dysenteriae, S. flexneri and S. sonnei. Moreover, the incidence of Staph. aureus in the examined sandwiches was 20, 12, 16 and 12%, respectively. Finally, the incidence of B. cereus was 24, 16, 8 and 12% in the examined sandwiches, respectively. It was clear that the examined sandwiches of Shawerma and beef burger were highly contaminated by the identified bacteria compared to the examined sandwiches of Kibda and kofta. Based on results obtained in this study, it was indicated that consuming sandwiches of street vended meat products as ready to- eat (RTE) foods, sold in many locations of Alexandria may possess a serious challenge to the public health authorities and hazardous food for the consumer so the recommended measures to overcome these microorganisms in street vended meat products sandwiches and improve their quality must be followed.
Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada

The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.
Modeling the Effects of Global and Diffuse Radiation on Terrestrial Gross Primary Productivity in China Based on a Two-Leaf Light Use Efficiency Model

Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in China. However, the effects on GPP of variation in solar radiation because of changes in diffuse radiation are unclear. In this study, national global radiation in conjunction with other meteorological data and remotely sensed data were used as input into a two-leaf light use efficiency model (TL-LUE) that simulated GPP separately for sunlit and shaded leaves for the period from 1981 to 2012. The results showed that the nationwide annual global radiation experienced a significant reduction (2.18 MJ m−2 y−1; p < 0.05) from 1981 to 2012, decreasing by 1.3% over this 32-year interval. However, the nationwide annual diffuse radiation increased significantly (p < 0.05). The reduction in global radiation from 1981 to 2012 decreased the average annual GPP of terrestrial ecosystems in China by 0.09 Pg C y−1, whereas the gain in diffuse radiation from 1981 to 2012 increased the average annual GPP in China by about 50%. Therefore, the increase in canopy light use efficiency under higher diffuse radiation only partially offsets the loss of GPP caused by lower global radiation.
Classification Strategies for Unbalanced Binary Maps: Finding Ponderosa Pine (Pinus ponderosa) in the Willamette Valley

Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler conventional methods. The goal of this classification was to identify contiguous stands of ponderosa pine (Pinus ponderosa Douglas ex Lawson) against a mix of forest and non-forest background in the southern Willamette Valley, Oregon. The study area is approximately 816,600 ha, considerably larger than most study areas used for presenting techniques for tree species classification. To achieve the objective, we used two classification procedures, one parametric and one non-parametric. For the parametric method, we selected the maximum likelihood (ML) algorithm, whereas for the non-parametric method we chose the random forest (RF) algorithm. To identify ponderosa pine, we used 1 m spatial resolution red-green-blue-infrared (RGBI) aerial images supplied by the U.S. National Agriculture Imagery Program (NAIP) and 1 m spatial resolution canopy height models (CHMs) provided by the Oregon Department of Geology and Mineral Industries (DOGAMI). We tested four data variations for each method: Aerial imagery, CHM-masked aerial imagery, aerial imagery with an additional CHM band, and CHM-masked aerial imagery with a CHM band. The parametric classifications of aerial imagery alone reached an average kappa coefficient of 0.29, which increased to 0.51 when masked with CHM data. The incorporation of CHM data as a fifth band resulted in a similar improvement in kappa (0.47), but the most effective parametric method was the incorporation of CHM data as both a fifth band and a post-classification mask, resulting in a kappa coefficient of 0.89. The non-parametric classification of aerial imagery achieved a mean validation kappa coefficient of 0.85 collectively and 0.90 individually, which only increased by approximately 0.01 or less when the CHM masks were applied. The addition of the CHM band increased the kappa value to 0.91 for both individual and collective tile classifications. The highest kappa of all methods was achieved through five-band non-parametric classification with the addition of the CHM band (0.94) for both collective and individual classifications. Our results suggest that parametric methods, when enhanced with a CHM mask, could be suitable for large-area, small-scale classifications based on RGBI imagery, but a non-parametric classification of fused spectral and height data will generally achieve the highest accuracy for large, unbalanced datasets.
Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data - A Review

Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.
Netzgesteuertes adaptives Videostreaming über Mobilfunknetze

Today’s widespread end-to-end video streaming, to some extent, adapts itself to the prevailing conditions in the network without any assistance from the network itself. The benefit of this solution is that it can be used in any heterogenous network constellation. The transmission data rate must be estimated by the end systems. However, the limits of this approach are the swiftness and precision of the estimate, which can be harmful either to the quality of the video stream or the efficiency of the resource utilization. To get rid of the latter drawbacks, a networkcontrolled adaptation can be applied, which can work simultaneously and in conjunction with the end-to-end adaptation process. This thesis proposes and examines an embedded network-controlled video streaming adaptation in a mobile network. The concept consists of two components. One component makes an adaptation decision with knowledge of the network conditions and capabilities of the end systems and transmits an adaptation recommendation to the video streaming server. In the temporal phases of scarce transmission resources where video data rate adaptation is not possible, the second component applies a proactive video-specific discard of video data, based on its quantitative importance. This changes the video data loss structure, resulting in less video frame loss from the application’s point of view. The proposed techniques will be examined using a specifically developed experimental video streaming environment. To generalize the results, 27 compressed video sequences with different video content and structures are used, generated using three different compression methods. For a vivid assessment of the video quality, a new diagram type developed for this work is proposed. The results of the procedure for determining the adaptation recommendations show a fast and precise adaptation of the video data rate. The proactive video-specific discarding of video data based on their quantitative importance effects a significant reduction of the video frame loss rate in the temporal phases of scarce transmission resources.
Predictive and cooperative online motion planning: a contribution to networked mobile robot navigation in industrial applications

Current trends in intra-logistics and industrial manufacturing demand for fleets of mobile robots, or automated guided vehicles (AGVs), respectively, with high degrees of navigation autonomy to realize flexible and adaptive material transportation systems. Thereby, instead of operating in separated cells, the mobile robots are supposed to navigate autonomously in open workspaces, perform reactive obstacle avoidance and interact with pedestrian workers, human-controlled vehicles and other mobile robots. In the context of digitization, industrial environments are becoming more and more networked, raising the question how fleets of mobile robots in these environments can benefit from networked architectures to enhance their navigation capabilities regarding cooperative solutions. For that matter, this work particularly focuses on the aspect of online motion planning, a key component for autonomous mobile robot navigation. The main objective of this work is to develop and implement an online motion planning approach that enables cooperative trajectory optimization when multiple mobile robots are operating in close proximity to each other. In path-crossing scenarios, using a networked navigation architecture, the robots’ local motion planners should be able to cooperatively resolve predicted collisions and obtain smooth transitions with respect to path and velocity profiles for each involved mobile robot. In contrast, current AGV systems usually handle such path-crossing scenarios by implementing static right of way regulations where one or several mobile robots need to stop to let another robot pass. First of all, this thesis proposes a general concept of a networked system architecture for cooperative multi-robot navigation in industrial applications. Based on this concept, a concrete motion planning architecture is derived that enables cooperative optimization in multi-robot path-crossing scenarios by sharing predicted trajectories at runtime between the involved mobile robots. For this purpose, this work introduces a novel online motion planning approach for mobile robots to perform predictive trajectory planning in dynamic environments. In terms of reactive obstacle avoidance, the approach is able to incorporate predicted trajectories of dynamic obstacles. Vice versa, the planning approach considers the individual kinematic and kinodynamic constraints of the mobile robot in order to reliably predict its movement over time. Using the networked architecture, trajectories predicting the robot’s current motion are shared at runtime with other mobile robots to enable cooperative collision avoidance in path-crossing scenarios. The concepts and algorithms presented in this work have been successfully implemented in corresponding software components and have been demonstrated using a set of industrial mobile service robots. Based on this implementation, practical feasibility and performance of the proposed approach are validated and evaluated in a series of singleand multi-robot experiments.
De Poisson à Lazarsfeld en passant chez Quetelet, Bertillon, Galton et Pearson

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. De Poisson à Lazarsfeld en passant chez Quetelet, Bertillon, Galton et Pearson Jean-Jacques Droesbeke, Gilbert Saporta
Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.
Malnutrition Prevalence according to the GLIM Criteria in Head and Neck Cancer Patients Undergoing Cancer Treatment

Malnutrition is highly prevalent in people with head and neck cancer (HCN) and is associated with poorer outcomes. However, variation in malnutrition diagnostic criteria has made translation of the most effective interventions into practice challenging. This study aimed to determine the prevalence of malnutrition in a HNC population according to the Global Leadership Initiative on Malnutrition (GLIM) criteria and assess inter-rater reliability and predictive validity. A secondary analysis of data available for 188 patients with HNC extracted from two cancer malnutrition point prevalence studies was conducted. A GLIM diagnosis of malnutrition was assigned when one phenotypic and one etiologic criterion were present. Phenotypic criteria were ≥5% unintentional loss of body weight, body mass index (BMI), and subjective evidence of muscle loss. Etiologic criteria were reduced food intake, and presence of metastatic disease as a proxy for inflammation. The prevalence of malnutrition was 22.6% (8.0% moderately malnourished; 13.3% severely malnourished). Inter-rater reliability was classified as excellent for the GLIM criteria overall, as well as for each individual criterion. A GLIM diagnosis of malnutrition was found to be significantly associated with BMI but was not predictive of 30 day hospital readmission. Further large, prospective cohort studies are required in this patient population to further validate the GLIM criteria.
Clinical significance of CD28 gene‐related activating alterations in adult T‐cell leukaemia/lymphoma

Multiple oncogenic events are involved in the development of adult T‐cell leukaemia/lymphoma (ATL). Because CD28 plays a pivotal role in T‐cell activation, we focused on alterations of the CD28 gene in ATL. We found multiple genetic abnormalities related to CD28 among the 144 patients enrolled in the present study. These involved gene fusions with the cytotoxic T‐lymphocyte‐associated antigen 4 or the inducible T‐cell co‐stimulator in 14 patients (10%), CD28‐activating mutations in 3 (2%), and CD28 copy number variations in 34 (24%). Patients with such CD28 gene alterations were significantly younger than those without. In patients not receiving allogeneic haematopoietic stem cell transplantation, those with CD28 gene alterations tended to have a worse prognosis than those without. Finally, patients with chronic or smouldering ATL subtypes with CD28 gene alterations had a significantly worse prognosis than those without. These findings indicate that ATL, especially chronic or smouldering subtypes, have a more aggressive clinical course and are more refractory to conventional chemotherapies or mogamulizumab if they harbour CD28 gene alterations, likely because of continuous, prolonged, and enhanced CD28 activatory signalling. Novel treatment strategies to overcome the effects of these CD28 gene alterations are warranted.
The role of culture as a stimulator of transforming industrial cities into creative ones. Case study of Lodz, Poland

Paper presents the impact of culture for urban development in different stages, referring to the process of transforming urban economies from industrial to "creative". Case study presents contemporary changes in third largest Polish post-industrial city – Lodz. The role of culture and people's creativity was compared here with the results of the survey made among inhabitants of Lodz. Research revealed that despite many economic struggles, city is able to transform cultural offer to today's needs, referring to its territorial specificity. However, also some problems and challenges were identified, to which cultural policy must respond.
Effect of Carboxymethyl Chitosan on Quality of Fermented Pan Bread

Carboxymethyl chitosan (CMC) was added to pan bread, prepared by the straight-dough method, and its effects on shelf-life, retrogradation, antimicrobial activity, water activity, and sensory quality of pan bread were evaluated. The results of differential scanning calorimetry (DSC) and thermoconstanter analysis for retrogradation indicated that shelf-life of pan bread was significantly extended by addition of CMC. Growth of microorganisms, including bakery molds (Aspergillus, Penicillium, etc.) was significantly inhibited by adding CMC to pan bread. Sensory color, off-flavor and overall acceptability of pan bread were stable during storage. However, sensory texture of control was significantly different from that of sample with added CMC.
Organic Electrochemical Transistors (OECTs) Toward Flexible and Wearable Bioelectronics

Organic electronics have emerged as a fascinating area of research and technology in the past two decades and are anticipated to replace classic inorganic semiconductors in many applications. Research on organic light-emitting diodes, organic photovoltaics, and organic thin-film transistors is already in an advanced stage, and the derived devices are commercially available. A more recent case is the organic electrochemical transistors (OECTs), whose core component is a conductive polymer in contact with ions and solvent molecules of an electrolyte, thus allowing it to simultaneously regulate electron and ion transport. OECTs are very effective in ion-to-electron transduction and sensor signal amplification. The use of synthetically tunable, biocompatible, and depositable organic materials in OECTs makes them specially interesting for biological applications and printable devices. In this review, we provide an overview of the history of OECTs, their physical characterization, and their operation mechanism. We analyze OECT performance improvements obtained by geometry design and active material selection (i.e., conductive polymers and small molecules) and conclude with their broad range of applications from biological sensors to wearable devices.