Use of Electromagnetic Radiation (EMR) in Preservation of Fruit Juices

This study shows the bactericidal effect of Electromagnetic Field on fruit juice microbes. Short shelf-life period of fruit juice caused by spoilage organisms has limiting factor for its economy value. The Eighteen microorganisms isolated from both fresh and spoilt fruit samples (Pineapple and Apple), and identified during the study include, twelve (12) bacteria and Six (6) fungi, out of which only the bacterial isolates were exposed to electromagnetic field of 0mG, 500mG, and 5000mG for thirty minutes. The bacteria species were Leuconostoc mesentroides, Bacillus species, Lactobacillus brevis, Microbacterium species, Clostridium species, Bacillus cereus, Acetobacter aceti, and Staphylococcus aureus. The Gram negative bacteria isolates were Erwinia carotovora, Erwinia ananas, and Proteus species. Exposure of the isolates to an electromagnetic field of 0mG, 500mG and 5000mG showed a decrease in some electromagnetic field magnitude. This study shows reduction in growth range among most bacterial species tested at 500mG electromagnetic radiation exposure, but the growth of many of these bacterial species were triggered at 5000mG electromagnetic radiation exposure. This may mean an initiation of: adaptation mechanism, growth mechanism in some microorganism, and sugar content of the fruit juice from which they are being isolated. The exposure of the bacteria to electromagnetic field elicited detectable responses therefore depends on the adaptation mechanism of each bacteria and sugar content of the fruit from which it is being isolated from. Thus, future research can be done to optimize the limits specified for target microbes that are strength and frequency of this EMF in diseases control.

Comparative Genomics Revealed Genus Specific Encoding of Amino Acids by Tri-Nucleotide SSRs in Human Pathogenic Streptococcus and Staphylococus Bacteria

Pathogenic bacteria use phase variation of surface molecules and other characteristics as a significant adaptation mechanism. Repetitive sequences made up of numerous identical repeat units can be found in many phase variable genes. Here, we investigated the frequency and distribution of long-SSRs in 15 human pathogenic Staphylococcus, Streptococcus, and Enterococcus bacteria. Long-SSRs were found to be distributed differently in the genic and intergenic sequences. In the genic sequences, 61.3 SSRs were discovered on average, while 16.2 SSRs were found in the intergenic regions. Staphylococci exhibited the highest frequency of SSRs, followed by Enterococcus, and Streptococci had the lowest frequency of SSRs. Higher A+T content was found to be the best predictor of long-SSR in these human pathogens. Tetranucleotide repeats predominated in intergenic regions, while trinucleotide repeats predominated in genic regions. In human pathogenic Streptococcus and Staphylococcus bacteria, genus-specific encoding of amino acids by tri-nucleotide SSRs was observed. A genetic relationship between these human pathogenic bacteria was derived based on the presence of SSRs in the housekeeping genes and compared to the phylogeny generated based on the 16S ribosomal RNA gene.

Phenotypic response to soil compaction varies among genotypes and correlates with plant size in sorghum

Aims Soil compaction is a major yield-reducing factor worldwide and imposes physico-chemical constraints to plant growth and development. Facing limitations, roots can adapt and compensate for loss of functioning through their plasticity. Being primarily a belowground challenge, tolerance to soil compaction needs to be associated with root phenotype and plasticity. It is therefore of importance to distinguish between size-related apparent and size-independent adaptive plasticity. We determined the above- and belowground plasticity of sorghum genotypes varying in overall plant size. Methods We quantified plasticity as the degree response (adaptive and apparent plasticity) to soil compaction and conducted two experiments with sorghum and two soil density levels (1.4 and 1.8 Mg m−3). First, we quantified the shoot biomass plasticity of 28 sorghum genotypes. Second, we studied the root plasticity of six genotypes varying in shoot size and tolerance to soil compaction. Results Plasticity was correlated with plant biomass with larger genotypes responding earlier and more intensely. Soil compaction affected roots more than shoots and plasticity was expressed foremost in nodal root number and fine root length. Impeded plants produced 35 and 47% less root mass and length, respectively. Conclusions Plasticity to soil compaction varies among genotypes, but less-sensitive lines are in general smaller-sized genotypes. The association between tolerance and plant biomass may pose challenges to crop production; however, vigorous genotypes with unresponsive shoots to soil compaction do exist. Maintaining shoot growth relatively stable while the root modifies its structure can be an important adaptation mechanism to soil compaction.

KATN

Leveraging sensor data of mobile devices and wearables, activity detection is a critical task in various intelligent systems. Most recent work train deep models to improve the accuracy of recognizing specific human activities, which, however, rely on specially collected and accurately labeled sensor data. It is labor-intensive and time-consuming to collect and label large-scale sensor data that cover various people, mobile devices, and environments. In production scenarios, on the one hand, the lack of accurately labeled sensor data poses significant challenges to the detection of key activities; on the other hand, massive continuously generated sensor data attached with inexact information is severely underutilized. For example, in an on-demand food delivery system, detecting the key activity that the rider gets off his/her motorcycle to hand food over to the customer is essential for predicting the exact delivery time. Nevertheless, the system has only the raw sensor data and the clicking "finish delivery" events, which are highly relevant to the key activity but very inexact, since different riders may click "finish delivery" at any time in the last-mile delivery. Without exact labels of key activities, in this work, we propose a system, named KATN, to detect the exact regions of key activities based on inexact supervised learning. We design a novel siamese key activity attention network (SAN) to learn both discriminative and detailed sequential features of the key activity under the supervision of inexact labels. By interpreting the behaviors of SAN, an exact time estimation method is devised. We also provide a personal adaptation mechanism to cope with diverse habits of users. Extensive experiments on both public datasets and data from a real-world food delivery system testify the significant advantages of our design. Furthermore, based on KATN, we propose a novel user-friendly annotation mechanism to facilitate the annotation of large-scale sensor data for a wide range of applications.

SCHEDULING WORK UNDER INTEGRATED URBAN DEVELOPMENT USING THE METHOD OF UNCERTAIN RESOURCE COEFFICIENTS

Introduction: Planning integrated development of a residential area involves determining the composition of the objects to be built and creating an appropriate integration mechanism, backed up by a generalized work schedule. The existing methods of forming integrated work schedules do not use a systemic approach, based on a universal mathematical model, to describe the organizational and technological aspects of construction. Methods: The present study uses the method of uncertain resource coefficients to demonstrate a mechanism for systemically describing organizational and technological construction processes. We present a way of adapting this method to forming a generalized construction schedule during integrated development. The proposed adaptation mechanism is based on managing schedule calculations by rationally influencing the elements of the linear equation system that describes the organizational and technological processes. Results and Discussion: The solutions presented in the paper are fully consistent with the calculations obtained by different flow methods of organizing construction, as well as with the critical path method used in project management programs. The method described in the paper has been implemented in well-known project management software, Microsoft Project, as a macro program in the Visual Basic for Applications programming language, making it possible to form, calculate, and optimize a schedule for integrated territory development using the unified software toolkit.

The C50 carotenoid bacterioruberin regulates membrane fluidity in pink-pigmented Arthrobacter species

Carotenoids have several crucial biological functions and are part of the cold adaptation mechanism of some bacteria. Some pink-pigmented Arthrobacter species produce the rare C50 carotenoid bacterioruberin, whose function in these bacteria is unclear and is found mainly in halophilic archaea. Strains Arthrobacter agilis DSM 20550T and Arthrobacter bussei DSM 109896T show an increased bacterioruberin content if growth temperature is reduced from 30 down to 10 °C. In vivo anisotropy measurements with trimethylammonium-diphenylhexatriene showed increased membrane fluidity and a broadening phase transition with increased bacterioruberin content in the membrane at low-temperature growth. Suppression of bacterioruberin synthesis at 10 °C using sodium chloride confirmed the function of bacterioruberin in modulating membrane fluidity. Increased bacterioruberin content also correlated with increased cell resistance to freeze–thaw stress. These findings confirmed the adaptive function of bacterioruberin for growth at low temperatures for pink-pigmented Arthrobacter species.

Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care

Background. In critical care, intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions, which has always been a challenging task. Recently, deep learning models such as recurrent neural networks (RNNs) have demonstrated their strong potential on predicting such events. However, in real deployment, the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate. Methods. In this study, we propose a novel self-correcting mechanism for RNN to fill in this gap. Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp, so that the model can “learn” from previous predictions. We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data. Results. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury (AKI) prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset. Conclusions. The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.

PERICLASE-SPINEL REFRACTORY MODIFIED ТІО2

Over the past decades, the development and improvement of refractory materials for lining high-temperature zones of rotary kilns continues. The main requirements for refractory products for lining rotary kilns for cement clinker roasting are: high density and ultimate compressive strength, low porosity and gas permeability, increased abrasion resistance, low thermal conductivity, high corrosion resistance and the ability to form a protective layer.Today, the main goal of modern researchers is to create a heat-resistant refractory with a flexible structure that ensures its integrity at high temperatures and mechanical loads, which have the ability to form a protective coating layer. In this work, a technological approach has been tested for introducing a vibro-milled modifier (briquette based on a high-alumina component and a titanium-containing additive) into the composition of the raw charge for periclase-spinel refractory in the form of a pre-synthesized product containing crystalline phases of the Al2O3 – TiO2 – FeO system. The basis for the production of periclase-spinel refractories modified with TiO2 is the four-component system MgO – Al2O3 – FeO – TiO2, on the basis of thermodynamic calculations of which the content of individual components of the charge was selected and the operational characteristics were predicted. The interrelation of physical and mechanical properties with the content of individual components in the initial charge warehouses is shown, and the directions of solid-phase processes with their participation are noted. The features of the microstructure of the sample material are noted in relation to the formation of an optimal set of properties. It is shown that the nature of the organization of micropores is favorable for increasing the thermal stability of the material, which complements the phase adaptation mechanism also with the structural effect of damping mechanical stresses during thermal cycling.

Nussbaum-type Neural Network-based Control of Neuromuscular Electrical Stimulation with Input Saturation and Muscle Fatigue

Neuromuscular electrical stimulation is a promising technique to actuate the human musculoskeletal system in the presence of neurological impairments. The closed-loop control of NMES systems is non-trivial due to their inherent uncertain nonlinearity. In this paper, we propose a Nussbaum-type neural network-based controller for the lower leg limb NMES systems. The control accounts for model uncertainties and external disturbances in the system and, for the first time, provides a solution with rigorous stability analysis to the adaptive NMES tracking problem with input saturation and muscle fatigue. The proposed controller guarantees a uniformly ultimately bounded tracking for the knee joint angular position. To evaluate the control performance, a simulation study is taken, where the adaptation mechanism of the Nussbaum-type gain and the controller's robustness to muscle fatigue and input saturation are discussed.