In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

In Data Science we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, described are the origins and many evolutions of the Data Science theme. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues.

NaoMaiTong (NMT: Radix et Rhi-zoma Rhei, Radix Ginseng, Radix Puerariae, and Rhizoma Ligustici Chuanxiong as 9: 9: 6: 6) is a traditional Chinese medicine prescription for treating ischemia cerebral apoplexy. In this work, four cell injury models of ischemic stroke were establish, namely hypoxic injury, glutamate damage, injury of potassium chloride and hydrogen peroxide damage model. The protective effects of NMT and its single herbs-containing sera of different time points on PC12 cell damage were evaluated respectively, and the corresponding efficacy-time curves were drawn. Cell viability was measured by MTT (3-(4,5)-Dimethylthiahiazo(-z-y1)-3,5-di-phenytetrazoliumromide) assay. Furthermore, an statistical methods of support vector machine (SVM) were used to establish the correlation between concentration-time-efficacy for the first time. These results revealed that NMT-containing serum has obvious protective effect on the four injury models and can significantly improve cell viability. The PK-PD correlation between fifteen ingredients in the NMT compound with four model efficacy indexes indicated that rhein, puerarin, and 3'-methoxy puerarin might be the most important constituents controlling the pharmacological effects of NMT. The study suggested that these fifteen components are likely to be the material basis of NMT and recommended to increase the amounts of Pueraria in the NMT compound. That provided the scientific basis and demonstration for the research of efficacy material base of Traditional Chinese Medicine (TCM).

The article explores the subject of information and accounting solutions for accounts receivable management that include appropriate accounting solutions and analytical techniques. The study focuses on the statutory framework for accounting for accounts receivable under the Russian Accounting Standards (RAP), International Financial Reporting Standards (IFRS), and the generally accepted accounting principles (GAAP). The analytical techniques are described in the context of the authors' view on the essence of accounts receivable management that implies analysis, the establishment of a credit policy and of a discount policy. The article places emphasis on the use of available information technology for accounts payable management, such as blockchain-based smart contracts.

It is of merit to study the appropriate amount of fiber to add to free-range chickens feed to improve the microbial diversity and gut health in times of plant fiber deprivation. OptiCell is a useful source of fiber as a type of eubiotic lignocellulose, and its positive effects on the growth performance and laying performance of chickens has already been proven. However, few researchers have researched the effects of adding OptiCell on the gut microbiota of chickens. In this research we added three different levels of OptiCell (0%, 2% and 4%) to the feed of caged and free-range Bian chickens from September to November, aiming to observe the effects of adding OptiCell and different feeding modes on the gut microbial diversity and gut health of chickens, and aiming to determine an appropriate amount of OptiCell. The results showed that adding OptiCell could increase the thickness of the cecum mucus layer and the abundance of Akkermansia and Faecalibacterium in caged chickens, and 4% OptiCell was optimum. In addition, adding OptiCell increased the microbial diversity and the abundance of the butyrate-producing bacteria Faecalibacterium and Roseburia of fee-range chickens. The α-diversity and the length of the small intestine with 2% OptiCell in free-range chickens were better than with 2% OptiCell in caged chickens. In addition, compared with caged chickens, the free-range chickens had longer small intestine and lower GLP-1. Taken together, an appropriate amount of OptiCell benefitted the microbial diversity and health of chickens; it was necessary to add dietary fiber to the feed of free-range chickens when plant fibers was lacking, and 2% OptiCell was found to be optimum.

Liquid biopsies are an integral part of the diagnosis of cancer. Here, we have extended previous validation studies of a new targeted NGS panel to include the detection of copy number amplifications (CNAs), fusions, and exon skipping variants. Detection of these gene classes included specimens from clinical and healthy donors and cell lines (fusions: ROS1, EML4-ALK, NTRK1), (exon skipping: MET exon 14), and (CNAs: HER2, CDK6, EGFR, MYC and MET). The limit of detection (LOD) for fusion/skipping was 42 copies (QC threshold = 3 copies) and was verified using 3 additional fusion/skipping variants. LOD for CNAs was 1.40-fold-change (QC threshold = 1.15-fold change) and was verified with 3 additional CNAs. In precision studies, all fusion/skipping variants were detected in all runs and all days of testing (n=18/18; 100%); average CV for repeatability was 20.5% (range 8.7% - 34.8%), and for reproducibility was 20.8% (range 15.7% - 30.5%). For CNAs, 28/29 (96.6%) copy gains were detected. For CNAs, average CV of 1.85% (range 0% to 5.49%) for repeatability and 6.59% (range 1.65% to 9.22%) for reproducibility. The test panel meets the criteria for being highly sensitive and specific and extends its utility for the detection of clinically relevant variants in cancer.

The QBIT theory is an attempt toward solving the problem of consciousness based on empirical evidence provided by various scientific disciplines including quantum mechanics, biology, information theory, and thermodynamics. This theory formulates the problem of consciousness in the following four questions: (1) What is the nature of qualia? (2) How are qualia generated? (3) Why are qualia subjective? (4) Why does a quale have a particular quality or meaning?In sum, the QBIT theory proposes that (1) when a pack of quantum information is compressed beyond a certain threshold, a quale is generated; (2) a quale is a superdense pack of maximally entangled qubits in a pure state; (3) when information-theoretic certainty of a system about an external stimulus exceeds a particular level, the system becomes conscious of that stimulus; (4) subjectivity of consciousness is due to the fact that maximally entangled pure states are private and unshareable.

Free vibration analysis of the porous functionally graded circular plates has been presented on the basis of classical plate theory. The three defined coupled equations of motion of the porous functionally graded circular/annular plate were decoupled to one differential equation of free transverse vibrations of plate. The one universal general solution was obtained as linear combination of the multiparametric special functions for the functionally graded circular and annular plates with even and uneven porosity distributions. The multiparametric frequency equations of functionally graded porous circular plate with diverse boundary conditions were obtained in the exact closed-form. The influences of the even and uneven distributions of porosity, power-law index, diverse boundary conditions and the negligibled effect of the coupling in-plane and transverse displacements on the dimensionless frequencies of the circular plate were comprehensively studied for the first time. The formulated boundary value problem, the exact method of solution and the numerical results for the perfect and imperfect functionally graded circular plates have not yet been reported.

Regional product labeling can help develop regional business activities, especially with traditional regional products. Their general popularity is a significant source of income for the less developed regions. The Gemer-Malohont region belongs to economically underdeveloped areas with high unemployment rate. The subject of the survey was regional food products, which are made by small farmer. The analysis was carried out on a questionnaire survey in the period April-July 2017 in each of the region's districts. The results obtained by questionnaire survey have been statistically processed using the statistical method (two-step cluster analysis, radar chart, box-plots, regression analysis) using Microsoft Excel and IBM SPSS Statistics 23 software. Through cluster analysis and based on the preference of regional food, we divided consumers into two groups - knowledgeable regional food purchasers and priced oriented consumers. We have shown that the more developed regions (Rimavská Sobota, Rožňava) show a higher rate of purchase of regional foods. Less developed regions (Poltár, Revúca) are represented by lower-income consumers for whose the food price is the decisive criterion. Local residents of backward regions should pay attention to domestic food and local small farmers, who are the way to create local capital and local development.

With the outbreak of coronavirus disease 2019 (COVID-19), there is a need for efficient management of patients with mild or no symptoms, which account for the majority. The aim of this study is to introduce the structure and operation protocol of a living and treatment support centre (LTSC) operated by Seoul National University Hospital in South Korea. The existing accommodation facility was converted into a 'patient centre' where patients was isolated. A few Medical staff here performed medical tests and responded to emergencies. Another part of the LTSC was 'remote monitoring centre'. In this center, patients’ self-measured vital signs and symptoms were monitored twice a day, and the medical staff staying here provided video-consultation via a smartphone. During the 3 weeks from March 5 to March 26, 2020, 113 patients were admitted and treated. LTSC could be an efficient alternative to hospital admission in pandemic situation like COVID-19.

Swift and diligent resilience response is mandatory in sustainable geo-distributed ecosystems. The real-time geo-spatial resilience requires agility in millions of parallel and distributed data processing tasks on data acquired from regional condition monitoring(RCM) systems. These tasks include expiditous resolution of complex sustainability conflict sets, promptly anomalies characterization in chaos sets, and resilience response uniqueness. This work is an archetype of a paragon geo-resilience support system(GRSS) for regional sustainability using a novel melioration in DADO production machine. The proposed expert system capitalized the synergic strengths of RETE, TREAT, LEAPS and GATOR networks was designed and implemented as a synthetic DADO machine(SDM). The generic architecture of DADO machine was improved in this work by enrichment in rule set conditions and solution set for regional scale resilience rule set conditions. The condition left-hand side(LHS) X equal to the solution set 2X for right-hand side (RHS) was the goal achieved by working memory(WM) optimization and conflict resolution strategy(CRS) in alpha and beta networks rules. A round-trip time of 80.2 seconds for first event response set using 1492 segment size and sequence number 360,000 with maximum packets at a single geospatial structure was 21 packets/sec was a noticeable landmark in this work. LEAPS and Concurrent-read algorithm for GATOR cluster networks in the proposed synthetic DADO machine architecture was the overall implementation that enabled urban scale resilient system practically possible on physical SHM deployment.

Despite the uptake of tobacco smoking declining in the UK, smoking is still the leading cause of preventable poor health and premature death. While improved approaches to smoking cessation are necessary, encouraging and assisting smokers to switch by using substantially less toxic non-tobacco nicotine products may be a possible option. To date few studies have investigated the rates of smoking cessation and smoking reduction associated with the free provision of electronic-cigarettes (e-cigarette) to smokers. In this study the Blu Pro e-cigarette was given to smokers for use in place of tobacco for 90 days. The rates of smoking abstinence and daily smoking patterns were assessed at baseline 30 days, 60 days and 90 days. The response rate was 87%. After 90 days, the complete abstinence rate was 36.5% from 0% at baseline. Frequency of daily smoking reduced from 88.7% to 17.5% (P<0.001) and median consumption of cigarettes/day from 15 to 5 (P<0.001). Median days per month participants smoked also dropped from 30 to 13 after 90-days (P<0.001). On the basis of these results there may be value in smoking cessation services and other services ensuring that smokers are provided with e-cigarettes at zero or minimal costs for at least a short period of time.

Orbit and clock products are used in real-time GNSS precise point positioning without knowing their quality. This study develops a new approach to detect orbit and clock errors through comparing geometry-free and geometry-based wide-lane ambiguities in PPP model. The reparameterization and estimation procedures of the geometry-free and geometry-based ambiguities are described in detail. The effects of orbit and clock errors on ambiguities are given in analytical expressions. The numerical similarity and differences of geometry-free and geometry-based wide-lane ambiguities are analyzed using different orbit and clock products. Furthermore, two types of typical errors in orbit and clock are simulated and their effects on wide-lane ambiguities are numerically produced and analyzed. The contribution discloses that the geometry-free and geometry-based wide-lane ambiguities are equivalent in terms of their formal errors. Although they are very close in terms of their estimates when the used orbit and clock for geometry-based ambiguities are precise enough, they are not the same, in particular, in the case that the used orbit and clock, as a combination, contain significant errors. It is discovered that the discrepancies of geometry-free and geometry-based wide-lane ambiguities are coincided with the actual time-variant errors in the used orbit and clock at the line-of-sight direction. This provides a quality index for real-time users to detect the errors in real-time orbit and clock products, which potentially improves the accuracy of positioning.

Traditionally, overnight fasting before elective surgery has been the routine in medical practice for risk reduction of pulmonary aspiration of gastric contents. Several original study and international societies recommend a 2‐h preoperative fast for clear fluids and a 6‐h fast for solids in most elective patients. We conducted a narrative review of the literature, searching electronic databases (Medline and CINAHL). We used PICO approach. The results of our review suggest that nutrition support in the perioperative period is very important to reduce length of hospital stay and reduced postoperative complication.

The mutual information between the state of a neural network and the state of the external world represents the amount of information stored in the neural network that is associated with the external world. In contrast, the surprise of the sensory input indicates the unpredictability of the current input. In other words, this is a measure of inference ability, and an upper bound of the surprise is known as the variational free energy. According to the free-energy principle (FEP), a neural network continuously minimizes the free energy to perceive the external world. For the survival of animals, inference ability is considered to be more important than simply memorized information. In this study, the free energy is shown to represent the gap between the amount of information stored in the neural network and that available for inference. This concept involves both the FEP and the infomax principle, and will be a useful measure for quantifying the amount of information available for inference.

There is evidence that behavioral risk factors such as western type diet, and life style can predispose to oxidative stress, deficiency in antioxidant status, hyperglycemia, dyslipidemia and increase in inflammation in tissues of various organs: beta cells of the pancreas, LDL receptors in the hepatocytes, endothelium, neurons, osteocytes and gut. Further studies indicate that diets rich in antioxidant flavonoids, omega-3 fatty acids and fiber in foods were inversely associated whereas western-type foods were positively associated with risk of mortality due to cardiovascular diseases (CVDs). One important cause for beneficial effects of diet may be certain foods and nutrients such as vegetables, fruits and whole grains that are rich in fiber and flavonoids, known to produce liters og molecular hydrogen in the gut. It seems that, high-fiber diets, prebiotic and probiotics can produce greater hydrogen, which acts as an antioxidant and may inhibit free radical generation. Recent studies indicate that molecular hydrogen can inhibit hydroxyl and nitrosyl radicals and can directly act as antioxidant in the cells and tissues, which can cause marked decline in oxidative stress and inflammation leading to significant decline in CVDs and metabolic diseases. Clinical and experimental studies indicate that hydrogen therapy such as hydrogen rich water can provide benefits in the management of CVDs and metabolic diseases. Larger studies are necessary to verify the role of hydrogen administration in CVDs.

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the laser and the number of measurement points that can be employed per second. In this paper, we evaluate the performance and characteristics of combinations of modulation and spreading technology that can be used for OCDMA, and study optimal combinations according to varying operating environments.

We have considered a SEIR-SEI Vector-host mathematical model which captures malaria transmission dynamics, described and built on 7-dimensional nonlinear ordinary differential equations. We compute the basic reproduction number of the model; examine the positivity and boundedness of the model compartments in a region using well established methods viz: Cauchy’s differential theorem, Birkhoff & Rota’s theorem which verifies and reveals the well-posedness, and carrying capacity of the model respectively, the existence of the Disease-Free (DFE) and Endemic (EDE) equilibrium points were determined and examined. Using the Gaussian elimination method and the Routh-hurwitz criterion, we convey stability analyses at DFE and EDE points which indicates that the DFE (malaria-free) and the EDE (epidemic outbreak) point occurs when the basic reproduction number is less than unity (one) and greater than unity (one) respectively. We obtain a solution to the model using the Variational iteration method (VIM) (an unprecedented method) to each population compartments and verify the efficacy, reliability and validity of the proposed method by comparing the respective solutions via tables and combined plots with the computer in-built Runge-kutta-Felhberg of fourth-fifths order (RKF-45). We illustrate the combined plot profiles of each compartment in the model, showing the dynamic behavior of these compartments; then we speculate that VIM is efficient and capable to conduct analysis on Malaria models and other epidemiological models.

\textsc{Background} Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. The search and wet lab testing of these combinations costs a lot in terms of time, investment and energy. In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but for a majority, it is still not known which higher ($\geq 2$) order combinations might be playing a greater role in the pathway. \textsc{Results} The pipeline provides a prioritised list of important $2^{nd}$ order combinations of a range of family of genes involved in the Wnt pathway. More specifically, it reveals the various unexplored FZD-WNT combinations that have been untested till now in the pathway. In relation to ETC-1922159 affected combinations, the down-regulation of LGR-RNF family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 combination. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. \textsc{Conclusion} A pipeline has been developed to prioritise an $n^{th}$ order combination of factors that affect a signaling pathway. It takes into account the sensitivity indices computed from variance based (SOBOL) and density-kernel based (HSIC) methods to estimate the influence of each factor or combination of factors. These are then fed as feature vectors into a powerful support vector ranking algorithm that produces a ranked list of the interactions/combinations.

Intracranial hemorrhage is a serious medical problem that requires rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in the treatment of the patient. Diagnosis requires an urgent procedure, and the detection of hemorrhage is a difficult and time-consuming process for human experts. In this paper, we propose methods based on EfficientDet’s deep-learning technology that can be applied to the diagnosis of hemorrhages and thus become a decision-support system. Our proposal is two-fold. On the one hand, the proposed technique classifies slices of computed tomography scans for the presence hemorrhage or its lack, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our methodology provides visual explanations of the classification chosen using the Grad-CAM methodology.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

We review recent progress in high-speed orbital angular momentum (OAM) multiplexed free-space optical communication systems. The outdoor atmospheric turbulence is emulated by an indoor turbulence emulator, which is based on split-step beam propagation method. Adaptive optics, channel coding, Huffman coding combined with LDPC coding, and spatial offset are used for turbulence mitigation; while OAM multiplexing and wavelength-division multiplexing (WDM) are applied to boost aggregate capacity.

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

Currently, old-style personal medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time-consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patients discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin is used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real-time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors which brings a breakthrough in wearable sensing automation.

Background: Gluten neuropathy (GN) is defined as an otherwise idiopathic peripheral neuropathy in the presence of serological evidence of gluten sensitivity (positive antigliadin and/or transglutaminase or endomysium antibodies). We aimed to compare the quality of life (QoL) of GN patients with control subjects and to investigate the effect of a gluten free diet (GFD) on the QoL. Methods: All consecutive patients with GN attending a specialist neuropathy clinic were invited to participate. Overall Neuropathy Limitations Scale (ONLS) was used to assess the severity of neuropathy. The SF-36 questionnaire was used to measure participants’ QoL. A strict GFD was defined as effectively been able to eliminate all circulating gluten sensitivity-related antibodies whilst on the diet. Results: Fifty-three patients with GN and 53 age and gender matched controls were recruited. Compared to controls, GN showed significantly worse scores in physical functioning, role limitations due to physical health, energy/fatigue and general health subdomains of SF-36. After having adjusted for age, gender and disease severity, being on a strict GFD correlated with better SF-36 scores on the pain domain of the SF-36 (beta 0.317, p=0.019) and the overall health change domain of the SF-36 (beta 0.306, p=0.017). Conclusion: In GN physical dysfunctioning is the major determinant of poor QoL compared to controls. Routine checking for elimination of gluten sensitivity-related antibodies that results from a strict GFD should be encouraged as such elimination ameliorates the overall pain and health scores, indicating better QoL.

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood-brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure-activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, q²_CV = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

This paper explores the suppression of ideas within academic scholarship by academics, either by self-suppression or because of the efforts of other academics. Legal, moral, and social issues distinguishing freedom of speech, freedom of inquiry, and academic freedom are reviewed. How these freedoms and protections can come into tension is then explored by an analysis of denunciation mobs who exercise their legal free speech rights to call for punishing scholars who express ideas they disapprove of and condemn. When successful, these efforts, which constitute legally protected speech, will suppress certain ideas. Real-world examples over the past five years of academics who have been sanctioned or terminated for scholarship targeted by a denunciation mob are then explored.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

The most common type of non-Hodgkin lymphoma in adults is diffuse large B-cell (DLBCL). There is a historical unmet need for more effective therapies in the 2nd and 3rd line setting. Emerging immunochemotherapies have shown activity in small studies of heavily pre-treated patients with prolonged remissions achieved in some patients. Anti-CD19 CAR (chimeric antigen receptor) T cells are potentially curative in the 3rd line and beyond setting and are under investigation in earlier lines of therapy.1 Antibody-drug conjugates (ADC’s) such as polatuzumab vedotin targeting the pan-B-cell marker CD79b has proven effectiveness in multiply-relapsed DLBCL patients. Tafasitamab (MOR208) is an anti-CD19 monoclonal antibody producing prolonged remissions when combined with Lenalidomide in patients who were not candidates for salvage chemotherapy or autologous stem cell transplant. Selinexor, an oral, small-molecule selective inhibitor of XPO1-mediated nuclear export (SINE), demonstrated prolonged activity against heavily-pretreated DLBCL without cumulative toxicity and is being investigated as part of an oral, chemotherapy-free regimen for relapsed aggressive lymphoma. This article reviews current strategies and novel therapies for relapsed/refractory DLBCL.

The aim of this study (first analytical approach) was to obtain data on the fatty acid composition of gluten-free foods (bakery products) for celiac people. The study included 35 different products (snacks, biscuits, bakery products, pasta, flours, etc.) from several manufacturers. After extraction and esterification, the fatty acid content was determined by GC-MS. The monounsaturated fatty acids (MUFAs) are the major constitutes (57%) of the fatty acids pool followed by saturated fatty acids (SFAs) (30%) and polyunsaturated fatty acid (13%). Only fifteen, of the thirty-five gluten free samples analyzed, provide adequate energy intake, while in eleven samples, saturated fatty acids take more energy than that recommended by EFSA. It has emerged that local producers generally use the finest raw materials (olive oil, etc.) compared to the industries which, as has been pointed out, in many cases use palm and palm kernel oils although gluten free commercial products are high added value foods, expensive and intended for a particularly sensitive public.

NASA mission systems proposals are often compared using an equivalent system mass (ESM) framework wherein all elements of technology to deliver an effect- its components, operations and logistics of delivery- are converted to effective masses since this has a known cost scale in space operations. To date, ESM methods and the tools for system comparison have not considered complexities wherein systems that serve a mission span multiple transit and operations stages, such as would be required to support a crewed mission to Mars, and thus do not account for the different mass equivalency factors operational during each period and the inter-dependencies of the costs across the mission. Further, ESM does not account well for the differential reliabilities of the underlying technologies. Less reliability should incur an equivalent mass cost for technologies that might otherwise provide a mass advantage. We introduce an extensions to ESM to address these limitations and show that it provides a direct method for analyzing, optimizing and comparing different mission systems. We demonstrate our extended ESM (xESM) calculation with crop production technologies -- an aspect of the developing offworld biomanufacturing suite -- since it represents a case with strong coupling among stages of the mission and a relatively high-risk profile.

Label-free confocal photothermal (CPT) microscopy was utilized for the first time to investigate malignancy in mouse skin cells. A laser diode (LD) with 405nm or 488nm was used as a pump and 638nm LD as a probe for the CPT microscope. The Grey Level Cooccurrence Matrix (GLCM) for texture analysis was applied to the CPT images. Nine parameters of GLCM were calculated for the intracellular super-resolved CPT images, and the parameters Entropy and Prominence were found to be most suited among the nine parameters to discriminate between healthy cells and MM cells in case pump wavelength of 488nm is used.

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Genetic disorders are quite a major topic of discussion and debate in the recent world of biological sciences. Turner’s syndrome is one such disorder caused by a chromosome aneuploidy and it has characteristic symptoms in the patient or the affected individual. The amniotic fluid is a complex biological material found in the amniotic sac of pregnant women and they can provide valuable knowledge and understanding of the pathogenesis of this particular chromosomal abnormality. In this study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was used to detect genes/organ systems which may be significant in the pathophysiology of Turner’s syndrome. The cell-free RNA from the amniotic fluid of five mid-trimester Turner’s syndrome fetuses and five euploid female fetuses matched for age of gestation were extracted, amplified and hybridized onto Affymetrix U33 Plus 2.0. array. The paired t-test was used to identify the significantly differentially regulated genes. Biological interpretation was conducted using ingenuity pathway analysis and BioGPS gene expression atlas. Of the genes, XIST was especially downregulated and SHOX was not expressed differentially. One of the most highly represented organ systems was the hematologic/immune system, differentiating the transcriptome of Turner’s syndrome from other chromosomal aneuploidies that are discussed in this area of science. The differences in the transcriptome of the Turner’s syndrome are due to genome-wide dysregulation. The hematologic/immune system differences are significant in early-onset autoimmune dysfunction. There are other genes which have been identified that are associated with the cardiovascular and the skeletal system, as these are often seen to be affected in the female patients with turner’s syndrome. Hopefully, such knowledge gained from this study will help us to understand the deeper mechanisms of this disorder and the possible treatments of this disease.

As reported by us and others previously (1, 2), the D614G mutation appeared in the spike glycoprotein (SPG) of the SARS-CoV-2 (the pathogen behind COVID-19) at the early stages of the pandemic and then G614 containing variant of SARS-CoV-2 became the predominant strain in most human populations across the world. However, one of the most recent reports from India (3) stated the incidence of G614 to be only 26% in the Indian population. This report is contradictory to the information available through the GenBank (4) SARS-CoV-2 sequence deposits made by various laboratories from India. The above stated report currently circulating in the Indian media is likely to create a public perception that the Indian strain is less contagious and such a notion could be harmful to people’s welfare. In view of this concern we have re-evaluated, updated and recalculated the incidence of the G614 variant in the Indian population by analyzing 395 Indian SARS-CoV-2 genomic sequences available in the GenBank as of June 26, 2020. In our analysis we have categorized the samples by the month in which the samples were collected. We have used an alignment-free software tool named Compare (5, 6), and the Basic Local Alignment Search Tool (BLAST) (7) in the present analysis. We finally inspected each of the 395 sequences physically for the presence of aspartic acid (D) or glycine (G) at the 614^th position of the spike glycoprotein. We analyzed an Australian cohort in parallel for comparison. We have found that the prevalence of G614 variant in the Indian samples for the month of June 2020 is 90.6%. The trends are similar with the Australian samples.

Conservation history of D614 residue is valuable in predicting the consequences of D614G mutation in the SARS-CoV-2 spike glycoprotein (SGP). We report here that the D614 belonged to an extraordinarily conserved, densely hydrophobic eleven amino acid peptide-motif vavlyqdvnct (11-aa), in the Sarbecovirus group and the variant carrying vavlyqdvnct had appeared in Chinese samples and became predominant in several geographical hotspots in late March, 2020. Interestingly a 2009 annotation of SARS-CoV contained the same mutation.

Creating self-learning algorithms, developing deep neural networks and improving other methods that "learn" for various areas of human activity is the main goal of the theory of machine learning. It helps to replace the human with a machine, aiming to increase the quality of production. The theory of artificial neural networks, which already have replaced the humans in problems of detection of moving objects, recognition of images or sounds, time series prediction, big data analysis and numerical methods remains the most dispersed branch of the theory of machine learning. Certainly, for each area of human activity it is necessary to select appropriate neural network architectures, methods of data processing and some novel tools from applied mathematics. But the universal problem for all these neural networks with specific data is the achieving the highest accuracy in short time. Such problem can be resolved by increasing sizes of architectures and improving data preprocessing, where the accuracy rises with the training time. But there is a possibility to increase the accuracy without time growing, applying optimization methods. In this survey we demonstrate existing optimization algorithms of all types, which can be used in neural networks. There are presented modifications of basic optimization algorithms, such as stochastic gradient descent, adaptive moment estimation, Newton and quasi-Newton optimization methods. But the most recent optimization algorithms are related to information geometry, for Fisher-Rao and Bregman metrics. This approach in optimization extended the theory of classic neural networks to quantum and complex-valued neural networks, due to geometric and probabilistic tools. There are provided applications of all introduced optimization algorithms, what delighted many kinds of neural networks, which can be improved by including any advanced approaches in minimization of the loss function. Afterwards, we demonstrated ways of developing optimization algorithms in further researches, engaging neural networks with progressive architectures. Classical gradient based optimizers can be replaced by fractional order, bilevel and, even, gradient free optimization methods. There is a possibility to add such analogues in graph, spiking, complex-valued, quantum and wavelet neural networks. Besides the usual problems of image recognition, time series prediction, object detection, there are many are other tasks for modern theory of machine learning, such as solving problem of quantum computations, partial differential and integro-differential equations, stochastic processes and Brownian motion, making decisions and computer algebra.

A modified free-wake vortex ring model is proposed to compute the dynamics of a floating horizontal-axis wind turbine. The model is divided into two parts. The near wake model uses a blade bound vortex model and trailed vortex model, which is developed based on vortex filament method. By contrast, the far wake model is based on the vortex ring method. The proposed model is a good compromise between accuracy and computational cost, for example when compared with more complex vortex methods. The present model is used to assess the influence of floating platform motions on the performance of a horizontal-axis wind turbine rotor. The results are validated on the 5MW NREL rotor and compared with other aerodynamic models for the same rotor subjected to different platform motions. It was found that the results from the proposed method are more reliable than the results from BEM theory especially at small angles of attack in the region of low wind speeds, on the one hand, and high wind speeds with blade pitch motions, on the other hand. And also the proposed method is less time consuming and has similar accuracy when comparing with more advanced vortex based methods.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Alkyl moieties—open chain or cyclic—are common in drug molecules and can be internal or terminal. The open-chain alkyls can be linear or branched; the cyclic alkyls may be substituted. Usually, the branches in open-chain alkyls and substituents in cycloalkyls influence both the pharmacokinetics and pharmacodynamics of a drug molecule. The hydrophobicity of alkyl moieties in drug molecules is modified by metabolic hydroxy functionalization via free-radical intermediates to give primary, secondary, or tertiary alcohols depending on the class of the substrate carbon. The hydroxymethyl groups resulting from the functionalization of methyl groups are mostly oxidized further to carboxyl groups to give carboxy metabolites. As observed from the surveyed cases in this review, hydroxy functionalization leads to loss, attenuation, or retention of pharmacologic activity with respect to the parent drug. On the other hand, carboxy functionalization leads to a loss of activity with the exception of only a few cases in which activity is retained. The exceptions are those groups in which the carboxy functionalization occurs at a position distant from a well-defined primary pharmacophore. Some parent-drug equiactive hydroxy metabolites have been developed into metabolite ester prodrugs, while some of the parent-drug carboxy metabolites have been developed into metabolite drugs. In this review, we present and discuss the above state of affairs for a variety of drug classes, using selected drug members to show the effect on pharmacologic activity as well as dependence of the metabolic change on drug molecular structure. Some subtexts found to manifest from the hydroxy and carboxy functionalization of alkyl moieties in drug molecules are presented and discussed. The review provides basis for informed predictions of (i) structural features required for metabolic hydroxy and carboxy functionalization of alkyl moieties in existing or planned small drug molecules; and (ii) whether the resulting metabolites will possess pharmacologic activity or be devoid of it.

The present article aims at investigating the role of social support in the emotional well-being of young offenders. We hypothesized that perceived social support would be positively related to the emotional well-being of juvenile offenders. The methods were worked out to study perceived and received social support, psychological well-being and emotional state of the juvenile offenders. The entire sample consisted of 56 males aged from 15 to 18 years old (M = 16.5; SD = 0.8). 32 subjects (57%) were in pretrial detention and 24 participants (43%) were in a correctional camp located in the central region of Russia. The study detected that the level of psychological well-being of the respondents from the camp was correlated neither with perceived social support nor with the frequency of seeking assistance from the different sources of support. For the respondents in the pretrial detention, the level of psychological well-being was directly connected to the degree of the perceived support from the friends. The obtained differences might be associated with the influence of social environment in the pretrial detention and in the camps.

In Europe we can see a change in the social structure in the last period. Average life expectancy has increased dramatically over the last 50 years. Because of the improved life situation and advanced level of health care, older people are slower. With advancing age, the likelihood of experiencing a variety of constraints such as visual impairment, reduced hearing or physical ability increases. In such a life stage tenants are often forced to leave their long-term living space because these homes can not serve "new" individual needs and the resulting personal protection goal. This transition from the privacy of their home to the new environment often appears to be a painful change. They will take their familiar and well-known surroundings, because their homes can not be adapted to serve new needs. It must be the policy’s role to create a new inclusive social space and the requirement for architects and designers to create new goals for the design of an adaptable environment. This is a comprehensive approach to the design of the outer and inner space that could serve people even if there is an unexpected situation and changes in movement and physiological limitations of older people. The contribution shows the results of the survey conducted in Germany and Slovakia. In the survey respondents expressed their opinion on what they considered important in creating an adaptive environment. Results are processed graphically with explanation. The results were mainly for designers and developers of the indoor environment. Based on the results of the questionnaire survey, studies of possible modifications in the interior of the flats were then prepared. The contribution yielded these results in three age groups of respondents; i. people aged 35, 50 and over 50.

Complex systems can be described as the network of interactions between their components. In directed networks, information, energy and matter flow in dominant directions. Undirected networks, on the other hand, cannot easily capture these asymmetries. The local flow of information depends on the node spin, which is a function of the number of incoming and outgoing links attached to it. And yet, how the heterogeneous association between in-degree and out-degree constraints global dynamics remains poorly understood. Here, we develop a new theoretical model to study the relationship between node directionality and global flow dynamics. Our computational analysis of random and scale-free directed networks indicates that structural diversity constrains the dimensionality of reachable dynamical space. These results suggest how local directionality might be an universal driver of global dynamics in many systems, from social networks, to technological systems to the connectome.

In this review, we explore a broad-based view of technologies for supporting human activities on the Moon. Primarily, we assess the state of life support systems technology beginning with physicochemical processes, waste processing, bioregenerative methods, food production systems and the robotics and advanced biological technologies that support the latter. We observe that the Moon possesses in-situ resources but that these resources are of limited value in CELSS – indeed, CELSS technology is most mature in recycling water and oxygen, the two resources that are abundant on the Moon. This places a premium on developing CELSS that recycles other elements that are rarified on the Moon including C and N in particular but also other elements such as P, S and K which might be challenging to extract from local resources. Although we focus on closed loop ecological life support systems, we also consider related technologies that involve the application of biological organisms to bioregenerative medical technologies and bioregenerative approaches to industrial activity on the Moon as potential future developments.

While current studies have focused on adoption and the relevant content of the app to become a decision support system, very few studies have focused on the farmer's intention and initial decision to adopt. Based on a survey of 394 smallholder farmers this study investigated Mexican farmers’ willingness to adopt an agricultural advice app. A Structural Equation Modelling approach, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. To understand farmers’ adoption decision, extended constructs were studied (e.g. mastery-approach goals) along with farmers’ age and participation in an innovation hub. Results showed that the intention to adopt the app is predicted by how farmers believe that technical infrastructure exists and by the expectation of the farmers using the app to acquire new knowledge. The multi-group analysis revealed that performance expectancy is a relevant predictor of the intention to adopt, whereas the mastery-approach goal is relevant only for younger and farmers not connected to the innovation hub. The results may well be a baseline to research further suitable non-financial incentives for different farmers’ groups, then encourage initial adoption and enhance uptake. The findings are useful for practitioners and app developers designing digital decision support tools.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

Background: Few studies examined the contributions of childhood adversities, intimate partner violence and social support to antenatal depression (AD). This study aims to 1) evaluate association of these psychosocial factors with AD symptoms in early pregnancy; and 2) examine the mediating effect of social support on the relationship between psychosocial stressors and AD symptoms.Methods: Participants were 120 pregnant women aged from 18 to 49 in less than 16 gestational weeks and attending at Antenatal Care Center at Khon Kaen hospital, Thailand. AD symptoms were assessed by the Edinburgh Postnatal Depression Scale (EPDS). Childhood adversities, intimate partner violence and social support were measured using the Adverse Childhood Experiences Questionnaire (ACE questionnaire), Abuse Assessment Screen (AAS), and Multidimensional Scale of Perceived Social Support (MSPSS). Results: We found that the EPDS score was significantly and positively associated with adverse childhood experiences (ACEs) and negatively with social support. Partial Least Square analysis showed that 49.1% of the variance in the depressive subdomain of the EPDS score was predicted by ACEs, namely psychological and physical abuse and neglect, emotional or physical abuse by the partner, unplanned pregnancy, and no satisfaction with their relationship. The effects of adverse childhood experience due to neglect on the EDPS score was mediated by social support by friends. Limitations: ACEs were assessed retrospectively and, therefore, may be susceptible to recall bias.Conclusion: Prenatal depression scores are to a large extent predicted by psychological distress as indicated by early lifetime trauma, abuse by partner, relation satisfaction, and implications of unintended pregnancy.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

Institutional support and quality education are linked in a significant way. During Covid-19, institutional support is critical to closing the huge academic gap that has emerged as physical academic practices have been moved to a virtual education system using technology. This research aims to assess institutional support for online education in Bangladesh during the Covid-19 pandemic. This analysis is focused on the three main elements of the Adapted Model of Institutional Support (AMIS) of Bond et al, 2007, and the Institutional Support Model (ISM) of Valverde and Rodriguez, 2002, namely Financial Support (FS), Technical Support (TS), and Mentoring Support (MS). According to the findings, a few universities in Bangladesh have provided average support for continuing online education, while others have just started taking online classes. Several problems have been discovered, such as the lack of dedicated software for conducting online academic activities, lack of training and grooming, lack of mentoring, poor internet access, lack of smartphones, high internet package rates, and so on. This study concludes with some policy recommendations for a smooth online education system in Bangladesh.

Despite their obvious differences, biological systems at different scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are usually obscured by the parcelled terminology employed by various scientific sub-disciplines. To explore these commonalities, this papers a comparative study of diverse applications of the maximum entropy principle, ranging from amino acids up to societies. By presenting these studies under a common language, this paper establishes a unified view over seemingly highly heterogeneous biological scenarios.

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

Studies have shown that in many practical applications, data interpolation by splines leads to better approximation and higher computational efficiency as compared to data interpolation by a single polynomial. Data interpolation by splines can be significantly improved if knots are allowed to be free rather than at a priori fixed locations such as data points. In practical applications, the smallest possible curvature is often desired. Therefore, optimal splines are determined by minimizing a derivative of continuously differentiable functions comprising the spline of the required order. The problem of obtaining an optimal spline is tantamount to minimizing derivatives of a nonlinear differentiable function over a Banach space on a compact set. While the problem of data interpolation by quadratic splines has been accomplished analytically, interpolation by splines of higher orders or in higher dimensions is challenging. In this paper, to overcome difficulties associated with the complexity of the interpolation problem, the interval over which data points are defined, is discretized and continuous derivatives are substituted by their discrete counterparts. It is shown that as the mesh of the discretization approaches zero, a resulting near-optimal spline approaches an optimal spline. Splines with the desired accuracy can be obtained by choosing an appropriate mesh of the discretization. By using cubic splines as an example, numerical results demonstrate that the linear programming (LP) formulation, resulting from the discretization of the interpolation problem, can be solved by linear solvers with high computational efficiency and resulting splines provide a good approximation to the optimal splines.

Synergy, emerges from synchronized reciprocal positive feedback loops between a network of diverse actors. For this process to proceed, compatible information from different sources synchronically coordinates the actions of the actors resulting in a nonlinear increase in the useful work or potential energy the system can manage. In contrast noise is produced when incompatible information is mixed. This synergy produced from the coordination of different agents achieves non-linear gains in free energy and in information (negentropy) that are greater than the sum of the parts. The final product of new synergies is an increase in individual autonomy of an organism that achieves increased emancipation from the environment with increases in productivity, efficiency, capacity for flexibility, self-regulation and self-control of behavior through a synchronized division of ever more specialized labor. Examples that provide quantitative data for this phenomenon are presented. Results show that increases in free energy density require decreases in entropy density. This is proposed as a law of thermodynamics.

This study examines the potential of combining decision support approaches to identify optimal bundles of ecosystem services. A forested landscape, Zona de Intervenção Florestal of Paiva and Entre-Douro and Sousa (Portugal), is used to test and demonstrate this potential. The landscape extends over 14,000 ha, representing 1,976 stands. The property is fragmented into 376 holdings. The overall analysis was performed in three steps. First, we selected six alternative solutions (A to F) in a Pareto frontier generated by a multiple criteria method within a decision support system (SADfLOR) for subsequent analysis. Next, an aspatial strategic multi-criteria decision analysis (MCDA) analysis was performed with the Criterium DecisionPlus (CDP) component of another decision support system (EMDS) to assess the aggregate performance of solutions A to F for the entire forested landscape with respect to their utility for delivery of ecosystem services. For the CDP analysis, SADfLOR data inputs were grouped into two sets of primary criteria: Wood Harvested and Other Ecosystem Services. Finally, a spatial logic-based assessment of solutions A to F for individual stands of the study area was performed with the NetWeaver component of EMDS. The NetWeaver model was structurally and computationally equivalent to the CDP model, but the key NetWeaver metric is a measure of the strength of evidence that solutions for specific land stands were optimal for the unit. Solutions D and B performed best in the aspatial strategic MCDA analysis, and a composite of the maps generated by NetWeaver demonstrated the spatial basis for the performance of solutions D and B in individual land stands. We conclude with a discussion of how the combination of decision support approaches encapsulated in the two systems could be further automated.

Barriers to inventing electronic devices involve challenges of iterating electronic designs due to long lead times for professional circuit board milling or high-costs of commercial milling machines. To overcome these barriers this study provides open source (OS) designs for a low-cost circuit milling machine. First, design modifications for mechanical and electrical sub-systems of the OS D3D Robotics prototyping system are provided. Next, Copper Carve, an OS custom graphical user interface, is developed to enable circuit board milling by implementing backlash and substrate distortion compensation. The performance of the OS D3D circuit mill is then quantified and validated for: positional accuracy, cut quality, feature accuracy and distortion compensation. Finally, the return on investment is calculated for inventors using it. The results show by properly compensating for motion inaccuracies with Copper Carve, the machine achieves a motion resolution of 10 microns, which is more than adequate for most circuit designs. The mill is at least five times less expensive than all commercial alternatives and the material costs of the D3D mill are repaid from fabricating 20-43 boards. The results show that the OS circuit mill is of high-enough quality to enable rapid invention and distributed manufacturing of complex products containing custom electronics.

Photoelectrochemical water splitting is a promising field for sustainable energy production using hydrogen. Development of efficient catalysts is essential for resourceful hydrogen production. The most efficient catalysts reported to date have been extremely precious rare-earth metals. One of the biggest hurdles in this research area is the difficulty of developing highly efficient catalysts comparable to the noble metal catalysts. Here, we report that non-noble metal dichalcogenide (Co₃Se₄) catalysts made using a facile one-pot electrodeposition method, showed highly efficient photoelectrochemical activity on a Si photocathode. To enhance light collection and enlarge its surface area even further, we implemented surface nano-structuring on the Si surface. The nano-structured Si photoelectrode has an effective area greater than that of planar silicon and a wider absorption spectrum. Consequently, this approach exhibits reduced overvoltage as well as increased photo-catalytic activity. Such results show the importance of controlling the optimized interface between the surface structure of the photoelectrode and the electrodeposited co-catalyst on it to improve catalytic activity. This should enable other electrochemical reactions in a variety of energy conversion systems.

Coffee is not only a delicious beverage but also an important dietary source of natural antioxidants. We live in an oxidative world where it is impossible to avoid pollution, stress, food additives, radiation, and other sources of oxidants that eventually lead to severe health disorders. Fortunately, there are chemicals in our diet that counteract the hazards posed by the reactive species that trigger oxidative stress. They are usually referred to as antioxidants; some of them can be versatile compounds that exert such a role in various ways. This review summarizes, from a chemical point of view, the antioxidant effects of relevant molecules found in coffee. Their ways of action and trends in activity are analyzed, considering the data gathered so far from both theory and experiments. The influence of the media and pH in aqueous solution, and structure-activity relationships are discussed. The protective role of the explored compounds is examined. A particular section is devoted to derivatives of some coffee components, and another one to their bioactivity. Hopefully, the information provided here will promote further investigations into the amazing chemistry contained in our morning cup.

Meixner's historical remark in 1969 "... it can be shown that the concept of entropy in the absence of equilibrium is in fact not only questionable but that it cannot even be defined...." is investigated from today's insight. Several statements --such as the three laws of phenomenological thermodynamics, the embedding theorem and the adiabatical uniqueness-- are used to get rid of non-equilibrium entropy as a primitive concept. In this framework, Clausius inequality of open systems can be derived by use of the defining inequalities which establish the non-equilibrium quantities contact temperature and non-equilibrium molar entropy which allow to describe the interaction between the Schottky system and its controlling equilibrium environment.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Research rarely explores LGBTQ+ youth bullying in the context of culture-specific outcomes (e.g., LGBTQ+ identity development) and what can mitigate the impact of peer stressors. This study used a concurrent mixed methods design to explore how experiences of peer victimization predicted LGBTQ+ youth’s identity development (i.e., stigma sensitivity, concealment motivation, and difficult process) and whether social support and outness served as protective, moderating factors. The mixed-methods approach provides a culture-specific context via qualitative inquiry to inform whether the quantitative findings align with how youth qualitatively discuss their experience of bullying, negative outcomes, and social support. Our sample consisted of 349 LGBTQ+ youth 14-17 years old who completed a survey (quantitative sample), and a subset of 39 LGBTQ+ youth who completed a semi-structured interview (qualitative sample). Our quantitative findings indicated that greater overall peer victimization was positively related to LGBIS-revised subscales of stigma sensitivity, concealment motivation, and difficult process, where both outness and social support moderated such relations. Qualitatively, victimized youth also reported stigma sensitivity and concealment motivation, while also endorsing how being out and having a support system played a role in their experience of being victimized. These qualitative findings align with our quantitative findings that classmate support mitigated the effects of peer victimization on difficulty of coming out. Implications for practitioners and researchers are provided.

Keywords: free radicals; oxidative stress; hepatocellular carcinoma; anti-oxidants; kaempferol

Fatty acids (FAs) represent an important class of metabolites, impacting on membrane building blocks and cellular regulatory networks. In nature, prokaryotes are characterized with the most impressing FA structural diversity and the highest relative contents of free fatty acids (FFAs). Thereby, nitrogen-fixing bacteria (order Rhizobiales), often found in symbiosis with legumes, attract a special interest. Indeed, FAs impact on the structure of rhizobial nodulation factors, required for successful infection of plant root. Although the FA patterns can be addressed by GC- and LC-MS, these methods are time-consuming and suffer from compromised sensitivity, low stability of derivatives and artifacts. In contrast, MALDI-TOF-MS represents an excellent platform for high-efficient metabolite fingerprinting, also applicable to FFAs. Therefore, here we propose a simple and straightforward protocol for high-throughput relative quantification of FFAs in rhizobia by the combination of Langmuir technology and MALDI-TOF-MS, which is featured with high sensitivity, accuracy and precision of quantification. Here we propose a step by step procedure comprising rhizobia culturing, pre-cleaning, extraction, sample preparation, mass spectrometric analysis, data processing and post-processing. To demonstrate the analytical potential of the protocol we illustrate it by a case study – comparison the FFA metabolomes of two rhizobia species - Rhizobium leguminosarum and Sinorhizobium meliloti.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

Bone marrow-derived mesenchymal stem cells (BMSCs) have been considered a promising therapeutic approach to cardiovascular disease. This study intends to compare the effect of BMSCs through a standard active cardiac support device (ASD) and intravenous injection on global myocardial injury induced by isoproterenol. BMSCs were cultured in vitro, and the transplanted cells were labeled with a fluorescent dye CM-Dil. Isoproterenol (ISO) was injected into the rats; two weeks later, the labeled cells were transplanted into ISO-induced heart-injury rats through the tail vein or ASD device for five days. The rats were sacrificed on the first day, the third day, and the fifth day after transplantation to observe the distribution of cells in the myocardium by fluorescence microscopy. The hemodynamic indexes of the left ventricle were measured before sacrificing. H&E staining and Masson’s trichrome staining were used to evaluate the cardiac histopathology. In the ASD groups, after three days of transplantation, there were many BMSCs on the epicardial surface, and after five days of transplantation, BMSCs were widely distributed in the ventricular muscle. But in the intravenous injection group, there were no labeled-BMSCs distributed. In the ASD+BMSCs-three days treated group and ASD+BMSCs -five days-treated group, left ventricular systolic pressure (LVSP), the maximum rate of left ventricular pressure rise (+dP/dt), the maximum rate of left ventricular pressure decline (-dP/dt) increased compared with model group and intravenous injection group (P<0.05). By giving BMSCs through ASD device, cells can rapidly and widely distribute in the myocardium and significantly improve heart function.

A novel method proposed in the production of Calophyllum inophyllum biodiesel has been investigated experimentally. In this study, we report the results of biodiesel processing with electromagnetic induction technology. The method used is to compare the results of Calophyllum inophyllum biodiesel processing between conventional, microwave and electromagnetic induction. The degumming, transesterification, and esterification process of the 3 methods are measured by stopwatch to obtain time comparison data. Characteristics of viscosity, density, and Fatty Acid Metil Ester (FAME) were obtained from testing of a Gas Chromatography-mass Spectrometry (GCMS) at the Polytechnic Chemistry Laboratory of the State of Malang. The results show that the biodiesel produced by this method satisfies the biodiesel standards and their characteristics are better than the biodiesel produced by conventional and microwave methods. The electromagnetic induction method also offers a fast and easy route to produce biodiesel with the advantage of increasing the reaction rate and improving the separation process compared to other methods. This advanced technology has the potential to significantly increase biodiesel production with considerable potential to reduce production time and costs.

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this work, we combined atomistic simulations and conformational dynamics analysis with the ensemble-based mutational profiling of binding interactions for a diverse panel of SARS-CoV-2 spike complexes with nanobodies. Using this computational toolkit, we identified dynamic signatures and binding affinity fingerprints for the SARS-CoV-2 spike protein complexes with nanobodies Nb6 and Nb20, VHH E, a pair combination VHH E+U, a biparatopic nanobody VHH VE, and a combination of CC12.3 antibody and VHH V/W nanobodies. Through ensemble-based deep mutational profiling of stability and binding affinities, we identify critical hotspots and characterize molecular mechanisms of SARS-CoV-2 spike protein binding with single ultra-potent nanobodies, nanobody cocktails and biparatopic nanobodies. By quantifying dynamic and energetic determinants of the SARS-CoV-2 S binding with nanobodies, we also examine the effects of circulating variants and escaping mutations. We found that mutational escape mechanisms may be controlled through structurally and energetically adaptable binding hotspots located in the host receptor-accessible binding epitope that are dynamically coupled to the stability centers in the distant epitope targeted by VHH U/V/W nanobodies. The results of this study suggested a mechanism in which through cooperative dynamic changes, nanobody combinations and biparatopic nanobody can modulate the global protein response and induce the increased resilience to common escape mutants.

A new optical steganography scheme is proposed that transmits a stealth optical code-division multiple-access (OCDMA) signal through a public binary phase-shift keying (BPSK) channel. Polarization beam splitters and arrayed waveguide gratings are used to implement a spectral-polarization coding (SPC) system with an incoherent optical source. We employ a Walsh–Hadamard code as the signature code of the user who wants to transmit stealth information using the system. A free space optical link applied to this system maintains the polarization states of light during propagation. The secret data is extracted using correlation detection and balanced subtraction in the OCDMA decoder of the intended receiver, and the other signal from the public channel is reduced by the OCDMA decoder. At the demodulator of the public channel, BPSK demodulation eliminates the stealth signal so that the public channel is not affected by the stealth signal. The two signals cannot interfere with each other. The results of this study show that our proposed optical steganography system is highly secure. The stealth signal can be favorably hidden in the public channel when the average source power of the stealth signal, public noise, and public signal are −5, −3, and 0 dBm, respectively.

Celiac disease (CD) is a genetically conditioned autoimmune process that appears in susceptible people. It can affect people of any age, and slightly predominates in females. It has a fairly homogenous global distribution, with an average prevalence of 1-2%, the frequency having increased in recent decades. The only effective treatment is a strict and permanent gluten-free diet (GFD), although the level of compliance with it is poor, at about 50% of cases. To monitor the effectiveness of the GFD, several procedures involving various approaches are employed: a) periodic interviews by nutritionists; b) clinical follow-up; c) serological controls of specific antibodies; d) endoscopies with collection of duodenal biopsies; e) structured questionnaires; f) determination of gluten peptides derived from gluten in feces and/or urine. All of these procedures are useful when applied, alone or in combination, depending on the cases. Some patients will only need to consult to their doctors, while others will require a multidisciplinary approach to assess their compliance with the GFD. In children, normalization of duodenal mucosa was achieved in 95% of cases within 2 years, while it is more delayed in adults, whose mucosa take longer to heal completely.

The confinement experienced due to the COVID-19 pandemic has prompted a rethink of the teaching-learning process to which teachers have responded without planning and instead using their resources. This study aims to analyze the relationships between work-family interactions, technostress, and perceived organizational support in teachers during the confinement period in Spain that began in March 2020. An online survey was administered to 640 pre-school, primary, and secondary school teachers. Positive reciprocal work-family interactions and their relationship with organizational support were found, with differences according to gender, with women showing a more negative perception of the impact on the family. There were no marked levels of technostress in the overall sample, although higher levels of perceived ineffectiveness and skepticism were found in teachers aged 46 years or older. Teachers in private and subsidized schools showed a higher level of perceived support than those in public schools. There is a need to continue this work to verify the values of these dimensions in other contexts and to apply institutional measures and public policies to improve these indicators in this group.

A label-free four-step procedure to identify amino acids (AAs) is described. In Step 1 molecules of AA, a tRNA, the related tRNA synthetase (AARS), and ATP are confined in a cavity to enable charging of tRNA. In Step 2 the tRNA, charged or uncharged, is separated from the other reactants (ATP, AARS, and possibly AMP and free AAs). In Step 3 the separated tRNA is subjected to non-enzymatic deacylation to dissociate the AA if tRNA is charged. In Step 4 the products are transferred to an electrolytic cell with a nanopore, where a current blockade occurs if and only if there is a dissociated AA. If a blockade is observed AA is immediately known, identification is unambiguous because of tRNA superspecificity. The exact blockade size need not be known (for any AA) in Step 4 so there is no need to distinguish among different AAs. This is unlike other nanopore-based methods, which are crucially dependent on precise blockade level measurements. The procedure is done in parallel with N (20 ≤ N ≤ 61) copies of AA, N reservoir-cavities, and N pairs of e-cells each with a different tRNA, related AARS, and ATP. At least one of the tRNAs gets charged. Assuming no charging errors, if N₁ tRNAs get charged in Step 1 and at least one of N₂ (≤ N₁) charged tRNAs is deacylated in Step 3, then with N₁N₂ tRNA molecules entering Step 1 AA can be identified with probability 1.

United States is the largest producer and the second largest exporter of broiler meat in the world. In the U.S, broiler production is largely converting to antibiotic-free programs which has caused an increase in morbidity and mortality within broiler farms. Escherichia coli and Clostridium perfringens are two important pathogenic bacteria readily found in the broiler environment and result in annual billion-dollar losses from colibacillosis, gangrenous dermatitis, and necrotic enteritis. Broiler industry is in search of non-antibiotic alternatives including novel vaccines, prebiotics, probiotics, and housing management strategies to mitigate production losses due to these diseases. This review provides an overview of the broiler industry and antibiotic free production, current challenges, and emerging research on antibiotic alternatives to reduce pathogenic microbial presence and improve bird health.

The current study aims to study the optimal fermentation conditions for producing microbial bioactive compounds. The microwave parameters consist on 2450 MHz, and 500-watt for 20, 30, and 40 seconds. The solubility of solvents was tested for the extraction of antioxidant compounds from fermented rice (Koji) by A. flavus, Ethyl acetate was the best solvent used for extraction purposes. Antioxidant properties were differentiated by blocking the oxidation of the linoleic acid with an inhibition rate of 73.13% at a concentration of 200 mg/mL, in addition to increasing its effectiveness for free radical extraction and reduction strength by increasing concentrations gradually. The bond ability to irons was lower compared to the EDTA-2Na, in addition to the obtained total content corresponding to phenolic compounds in the ethyl acetate extract of fermented rice (Koji) by A. flavus was 232.11 mg, on the basis of galic acid/mg. The stability of the antioxidant compounds of the ethyl acetate extract of fermented rice (Koji) by A. flavus was also studied; showing stability under neutral conditions, as well as at high temperatures (185 °C during two hours). However, no stability was obtained under acidic and alkaline conditions.

The synergic features and enhancing strategies for various photopolymerization systems are reviewed by kinetic schemes and the associated measurements. The important topics include: (i) photo crosslinking of corneas for the treatmnet of corneal deseases using UVA-light (365 nm) light and riboflavin as the photosensitizer; (ii) synergic effects by a dual-function enhancer in a 3-initiator system; (iii) synergic effects by a 3-initiator C/B/A system, having an electron-transfer and oxygen-mediated energy-transfer pathways; (iv) copper-complex (G1) photoredox catalyst in G1/Iod/NVK systems for free radiical (FRP) and cationic photopolymeriation (CP); (v) radical-mediated thiol-ene (TE) photopolymerizations; (vi) superbase photogenerator based-catalyzed thiol−acrylate Michael (TM) addition reaction; and the combined system of TE and TM usinghual wavelength; (vii) dual-wavelength (UV and blue) controlled photopolymerization confinement (PC); (viii) dual-wavelength (UV and red) selectively controlled 3D printing; and (ix) 3-wavelength selectively controlled in 3D printing and additive manufacturing (AM). With minimum mathematics, we present ( for the first time), the synergic features and enhancing strategies for various systems of multi-commponents, initiators, monomers, and under one- two- and there-wavelength light. Therefore, this Review provides not only the bridging between modeling and measurements, but also guidances for further experimantal stuides and new applications in 3D printings and additive manufacturing (AM), based on the innovative concepts (kinetics/schemes).

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Gut microbiota and its metabolites like short chain fatty acids (SCFAs) are linked with pathology of Alzheimer’s disease (AD)- a debilitating public health problem in older adults. However, strategies to beneficially modulate gut microbiota and its sensing signaling pathways remain largely unknown. Here, we screened, validated and established the agonists of free fatty acid receptor 2 (FFAR2) signaling, which senses beneficial signals from SCFAs produced by microbiota in the gut. We demonstrated that inhibition of FFAR2 signaling increases amyloid-beta (Aβ) stimulated neuronal toxicity. Thus, we screened FFAR2 agonists, using in-silico library of more than 144,000 natural compounds, and 15 compounds were selected based on binding with FFAR2 agonist sites. Further, cell culture toxicity and FFAR2 stimulatory experiments demonstrated that Fenchol (a natural compound commonly present in basil) was potent FFAR2 agonist in neuronal cells. Interestingly, we also demonstrated that Fenchol protects Aβ-stimulated neurodegeneration in FFAR2 dependent manner. In addition, Fenchol reduced AD like phenotypes such as Aβ-accumulation and, learning and memory behaviors in Caenorhabditis (C.) elegans. Fenchol increased Aβ-clearance by increasing proteasome/lysosome activity and reduced senescence in neuronal cells. These results demonstrated that the inhibition of FFAR2 signaling promotes Aβ-induced neurodegeneration, while activating it by Fenchol as a natural agonist reverse it by promoting Aβ-clearance and reducing cellular senescence; thus stimulation of FFAR2 signaling can be a therapeutic approach to prevent/ treat AD.

The design of torsional springs for Series Elastic Actuators (SEAs) is challenging, especially when it comes to balancing good stiffness characteristics and efficient torque robustness. This study focuses on the design of a lightweight, low-cost, and compact torsional spring for use in rotary series elastic actuator (ES-RSEA) of lumbar support exoskeleton. The exoskeleton is used as an assistive device to prevent lower back injuries. The torsion spring was designed following design for manufacturability (DFM) principles, focusing on minimal space and weight. The design process consisted of determining the potential topology and optimizing the selected topology parameters through finite element method (FEM) to reduce equivalent stress. The prototype was made using a waterjet cutting process with low-cost material (AISI-4140-alloy) and tested using a custom-made test rig. The results showed that the torsion spring had a linear torque-displacement relationship with 99% linearity, and the deviation between FEM simulation and experimental measurements was less than 2%. The torsion spring has a maximum torque capacity of 45.7 Nm and a stiffness of 440 Nm/rad. The proposed torsion spring is a promising option for lumbar support exoskeletons and similar applications requiring high stiffness, low weight-to-torque ratio, and cost-effectiveness.

Based on the institutional theory and resource-based theory and the "institution-strategy-performance" research paradigm, this research explores the mechanism of institutional support on the innovation performance of new ventures, focusing on the mediating role of entrepreneurs and the moderating role of innovative resource acquisition. An empirical analysis based on 278 survey samples shows that: ① (formal/informal) institutional support positively affects the innovation performance of new ventures; ② entrepreneurial orientation plays an intermediary role between institutional support and innovation performance of new ventures; ③ innovation resource acquisition not only positively regulates the relationship between entrepreneurial orientation and innovation performance of new ventures, but also enhances the mediation of entrepreneurial orientation between institutional support and innovation performance. The conclusion shows that institutional support plays an important role in the innovation practice of new ventures, and can provide guidance for the innovation management practices of new ventures.

High oxidative stress in cells due to inflammation process or excessive cell proliferation would produce oxidants or free radicals with biomarkers, one of which is malondialdehyde (MDA). Passion fruit seed’s contain high antioxidant and are expected to decrease the level of cholesterol and MDA. The objective is to identify the effect of passion fruit seed’s ethanol extract in Wistar rats that have been fed with atherogenic feed. The method was preclinical trial (post-test control group design) in rats, by administering passion fruit seed’s ethanol extract for 14 days. This study used 26 male rats aged two months, divided into 5 groups. The result showed significant difference in MDA level which was found in group that was given passion fruit seed extract 10mg/kg BW with positive control group that was given standard feed. Passion fruit seed’s extract showed significant difference in level of triglyceride, which was found in negative control group that was given atherogenic feed with group that was given passion fruit seed’s extract 5mg/kg BW (mean±standard deviation: 1.09±0.30 mg/dL vs 0.77±0.25mg/dL; p=0.048). This study showed that passion fruit seed’s ethanol extract had significant lowering effect in level of MDA, total cholesterol, and triglyceride for 14 days.

Free volume is an extremely important intrinsic defect in polymers. Structurally, the free volume is the randomly distributed holes in the polymer molecular chain segments. From the perspective of molecular movement, it is also the space needed for the movement of molecular segments. In proton exchange membrane fuel cells, the free volume is also the space needed for the directional conduction of protons. Irradiation grafting sulfonated polyvinylidene fluoride (PVDF) is one of the methods to produce proton exchange membrane with good proton channel rate. Using the LYS-1 program, based on the linear absorption coefficient and backscatter coefficient models, the percentage of annihilated positrons in PVDF in the two-layer model and the three-layer model was studied, and comparing the two fitting results it is determined that the model closest to the positron annihilation percentage required by the PALS experiment is the two-layer model, which allows the percentage of positrons that are annihilated in PVDF films with a thickness of 100 μm to reach 46% using the high energy positron source ⁴⁴Ti without using the positron moderator. Using positron annihilation lifetime spectrum (PALS) analysis methods, based on Eldrup's classical model of free volume, the influence of absorbed doses of α particles on the free volume and crystallinity of PVDF was investigated, respectively. Based on the two test results the model of cross-linking and degradation of polyvinylidene fluoride irradiated by α particles was determined. Afterwards, X-ray diffraction (XRD) methods was used to study the change of PVDF crystal area before and after irradiation. The XRD results are consistent with the results measured by PALS.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

The introduction of social commerce ushered in a new era in business-consumer interaction. As a result, more power has passed from the vendor to the buyer, primarily fueling e-commerce acceptance. As a result, understanding consumer behaviour in the context of social commerce adoption has become essential for businesses looking to persuade customers by using the power of social ties and support.Furthermore, such social ties will facilitate trust as the most promising benefit while reducing perceived risk, which has always been a critical problem with online commerce. This study proposes a paradigm for understanding the impact of social commerce on the stages of the consumer decision-making process: need recognition, information search, alternative evaluation, purchase decision, and post-purchase behaviour, with a focus on social support. In this respect, relevant literature in the subject of social commerce either (1) lacks an adequate explanatory model, (2) has a solid theoretical base, or (3) contains practically complex theories with insufficient empirical data. The research model applies the Social Commerce Constructs (SCC): recommendations and referrals, forums and communities, and ratings and reviews to study the respective influence on the consumer decision-making process phases. This paper aims to understand the influence of social commerce on an integrative model that incorporates all customer choice phases while expecting new knowledge. Furthermore, it is advised that this conceptual model be empirically verified to evaluate the practical consequences.

Increased screen time during the COVID-19 pandemic and the accelerated incorporation of technology into daily practices have directly contributed to the increased prevalence of eye disease. The human eye is not built for continual technological use and must adjust to exposure to digital screens. Overexposure to technology can result in eye strain and an increased risk of eye diseases such as myopia with complications later in life, including retinal tears, cataracts, and macular degeneration. As vision and ocular diseases become more prevalent, the study of eye diseases, including their causes and treatments, is especially relevant. There is a need to prevent ocular diseases before they become a burden to individuals, their families, and their communities by better understanding the pathophysiology of ocular diseases. Several ocular diseases are thought to be caused by imbalances in reactive oxygen species (ROS) and autophagy. ROS refers to a class of highly reactive oxygen-containing molecules that can undergo damaging reactions with other molecules in the cell. Autophagy is a cellular process of self-eating whereby damaged, harmful, or dead material in cells is broken down to maintain cellular homeostasis. This paper discusses the role of ROS and autophagy in the pathogenesis of computer vision syndrome (CVS), digital eye strain (DES), myopia, cataracts, glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve crush injury (OCN), optic nerve gliomas, and retinoblastoma. Lifestyle changes such as limiting screen time, ensuring adequate exposure to evening sunlight, and using blue light protection measures or protective eyewear are important in the prevention of eye disease. Nutrition also contributes significantly to eye health. A balanced diet rich in vitamins and antioxidants may help to prevent ROS and autophagy imbalance-induced eye disease. Medical and surgical treatments become necessary when preventative measures fail. This paper also addresses how government measures to decrease the onset of eye disease, including targeted programs to increase access to fresh vegetables and fruits in food deserts, are a critical macro-level avenue to reduce eye disease prevalence in the US, which according to the National Eye Institute, costs $139 billion yearly.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

Gastric emptying rate (GER) may impact diabetes and obesity in humans and could provide a method to reduce canine weight gain. Starch, the most common source of carbohydrates (CHOs) in pet food, is classified as rapidly or slowly digestible, or resistant to digestion. This study investigated starch source effects in commercial extruded dog foods on the GER of 11 healthy adult Siberian Huskies (5.63±0.72 years; mean±SEM). Test diets were classified as traditional, grain-free, whole-grain, and vegan. Dogs received each diet once, a glucose control twice, and acetaminophen (Ac) as a marker for GER in a randomized, partially-replicated, 6x6 Latin square design. Pre- and post-prandial blood samples were collected at 16 timepoints from -15 to 480 minutes. Serum Ac concentrations were assessed via standard spectrophotometric assays and fitted with a mathematical model to estimate parameters of GER. Data was analyzed using a repeated measures ANOVA, followed by a Tukey-Kramer post-hoc test when significant (p<0.05). More total emptying (p= 0.0430) occurred at faster rate (p=0.0668) in dogs fed the grain-free diet, which contained the lowest total starch (34.03 ± 0.23%) and highest resistant starch (0.52 ± 0.007%). This research may benefit future diet formulations to reduce the prevalence of canine weight gain.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

The COVID-19 pandemic affects not only physical health but also deteriorates mental health resulting in sleep problems, depression, and traumatic stress. Our research investigates the level of posttraumatic stress, perceived social support, opinions on positive and negative consequences of the pandemic, sense of security, and sense of meaning among nurses in the face of new and not fully understood global epidemiological phenomena. For this purpose, computer-assisted web interviewing methods were conducted between May 1 and May 15, 2020. Participating nurses completed the following research tools: The Impact Event Scale-Revised (IES-R), The Multidimensional Scale of Perceived Social Support (MSPSS), The Changes in Outlook Questionnaire (CIOQ), The Safety Experience Questionnaire (SEQ) and The Meaning in Life Questionnaire (MLQ). Three hundred and twenty-five nurses working throughout Poland joined the study of an average age of 39.18 ± 11.16 years. The average overall IES-R score in the study group was 1.78 ± 0.65. Among the dimensions of traumatic stress, the highest score was obtained in the dimension Avoidance 1.86 ± 0.73, then in the dimensions Hyperarousal 1.8 ± 0.78 and Intrusion 1.74 ± 0.83. Amongst studied nurses, the highest support rates were provided by significant others (22.58 ± 5.22), then from friends (21.91 ± 5) and family (21.45 ± 4.4). Among the surveyed nurses, higher average scores were noted in the subscale measuring positive psychological changes (18.56 ± 4.04). In contrast, a higher average level of assessment was noted on the Reflection on the safety dimension (4.21 ± 0.49). The mean MLQ score was 5.33 ± 0.87. A slightly higher result was observed in the subscale Presence (5.35 ± 1.14). The results of the research implemented during the period of severe psychological pressure associated with the COVID-19 pandemic provided information on symptoms of traumatic stress in the examined group of nurses. Their sense of security has been lowered and accompanied by an intensified reflection on issues concerning security. However, the sense of meaning (currently felt) in life remains higher than the tendency to searching for it. The surveyed nurses perceive individual support from the so-called significant others (apart from family and friends). They see positive changes resulting from painful experiences related to the COVID-19 pandemic, which can be characterized by adaptation in the form of posttraumatic growth.

Background User fee exemption for maternal and child health care service policy was introduced with a focus on providing free caesarean sections (CS) in Nigeria from 2011 to 2015. This policy had a positive impact on access to facility-based delivery, but its effect on socioeconomic and geographical inequality remains unclear. This study’s main objective is to examine access to birth by CS in the context of free maternal health care. Specifically, the study examines socio-demographic and geographical inequality in access to birth by CS among women who gave birth between 2011 and 2015 under the free maternal health care policy using a population-based survey data obtained from two of the six main regions of the country. Methods Data were obtained from 1227 women who gave birth during the period the policy was in operation selected using cluster random sampling between May and August 2016. Adjusted and unadjusted binary logistic regression models were used to examine whether there is socio-demographic and geographical inequality in access to birth by caesarean section. Results The overall caesarean section rate of 6.1% was found but varies by income (14.1% in monthly income of over $150 versus 4.9% in income of $150 and below), education (11.8% in women with higher education versus 3.9% among women with secondary education and less) and place of residence (7.8% in urban areas versus 3.6% in rural areas). Women who earn a monthly income of $150 or less were 48% less likely to have a birth by CS compared to those who earn more. Compared to women who were educated to tertiary level, women who had secondary education or less were 54% less likely to have birth by caesarean section. Conclusion This study shows that inequality in access to CS persists despite the implementation of free maternal health care services. Given the poor access to facilities with capabilities to offer CS in most rural areas, free maternal healthcare policy is not enough to make birth by CS universally accessible to all pregnant women in Nigeria.

The study of cell-free DNA (cfDNA) and other peripheral blood components (known as “liquid biopsies”) is promising and has been investigated especially in solid tumors. Nevertheless, it is increasingly showing greater utility in the diagnosis, prognosis, and response to treatment of hematological malignancies; in the future, it could prevent invasive techniques, such as bone marrow (BM) biopsy. Most of the studies about this topic have been focused on B cell lymphoid malignancies; some of them have shown that cfDNA can be used as a novel way for diagnosis and minimal residual monitoring in B cell lymphomas, using techniques such as next-generation sequencing (NGS). In myelodysplastic syndromes, multiple myeloma, or chronic lymphocytic leukemia, liquid biopsies may allow for an interesting genomic representation of the tumor clones affecting different lesions (spatial heterogeneity). In acute leukemias, it can be helpful in the monitoring of early treatment response and the prediction of treatment failure. In chronic lymphocytic leukemia, the evaluation of cfDNA permits the definition of clonal evolution and drug resistance in real-time. However, there are limitations such as the difficulty in obtaining sufficient circulating tumor DNA for achieving a high sensitivity to assess minimal residual disease or the lack of standardization of the method and clinical studies to confirm its prognostic impact. This review focuses on clinical applications of cfDNA on minimal residual disease in hematological malignancies.

The presence of mutated genes strongly correlates with the incidence of cancer. Decades of research, however, has not yielded any specific causative gene or set of genes for the vast majority of cancers. The Cancer Genome Atlas program was supposed to provide clarity but it only gave much more data without any accompanying insight into how the disease begins and progresses. It may be time to notice that epidemiological studies consistently show that the environment, not genes, has the principal role in causing cancer. Since carcinogenic chemicals in our food, drink, air and water are the primary culprits, we need to look at the biochemistry of cancer, with a focus on enzymes which invariably facilitate transformations in a cell. In particular, attention should be paid to the rate-limiting enzyme in DNA synthesis, ribonucleotide reductase (RnR) whose activity is tightly linked to tumor growth. Besides circumstantial evidence that cancer is induced at this enzyme’s vulnerable free-radical-containing active-site by various carcinogens, its role in initiating retinoblastoma and HPV-related cervical cancers is well documented. Blocking the activity of malignant RnR is a certain way to arrest cancer.

Here, we report the draft genome sequence of the type strain of Ideonella azotifigens DSMZ21438^T (formally 1a22^T = JCM15503^T). Ideonella azotifigens DSMZ21438T a novel betaproteobacterial non-symbiotic nitrogen-fixing grass rhizosphere dwelling microbe. The 891,561 paired-end shotgun reads were quality filtered and decontaminated with the ATLAS pipeline, then assembled with Unicycler. The genome size is 6,257,981 bp, an N50 size of 7,849 bp, with a G+C content of 66.71%, and with 5,882 predicted protein-coding genes. I. azotifigens DSMZ21438^T represents the first member of the genus isolated from rhizosphere soil, providing a framework for further study into non-alphaproteobacterial nitrogen fixation and synthetic biology applications.

Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and E-participation on public participation in Kisumu, Kenya. A 3D City model, created with CityEngine2016, was exported into a web-based geo-portal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. For e-participation, 300 questionnaires given out to planning practitioners. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varies within different professional groups while the questionnaires showed strong preference for e-participation methods, especially SMSs/USSDs and emails. The study concludes that the use of 3D visualization and E-participation has the potential for improving the quality and quantity of public participation and recommends further research on the subject.

Complementary and alternative treatment approaches are becoming more common among children with chronic conditions. The pravelance of CAM use among US adults was estimated to be around 42% in 2015, and around 44% to 50% among adults with neurologic disorders. Studies report children with chronic illnesses such as cancer, asthma, attention-deficit/hyperactivity disorder (ADHD), genetic disorders, and other neurodevelopmental disorders are treated with complementary and alternative treatments at higher rates. Dietary therapies are gaining increasing popularity in the mainstream population, due to the heavy media involvement. Although, majority of “fad” diets do not have enough supporting evidence, some dietary therapies have been utilized for decades and have numerous published studies. The objective of this review is to describe the dietary interventions used in children with the specific chronic conditions, to evaluate their efficacy based on published data, and to encourage pharmacist involvement in the management and care of such patients.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.