The proposal that the genetic code was formed on the basis of protein synthesis directed by tRNA dimers is reviewed and updated. The tRNAs paired through the anticodon loops are an indication on the process. Dimers are considered mimics of the ribosomes – structures that hold tRNAs together and facilitate the transferase reaction, and of the translation process – anticodons are at the same time codons for each other. The primitive protein synthesis system gets stabilized when the product peptides are able to bind the producers therewith establishing a self-stimulating production cycle. The chronology of amino acid encoding starts with Glycine and Serine, indicating the metabolic support of the Glycine-Serine C1-assimilation pathway, which is also consistent with evidence on origins of bioenergetics mechanisms. Since it is not possible to reach for substrates simpler than C1 and compounds in the identified pathway are apt for generating the other central metabolic routes, it is considered that protein synthesis is the beginning and center of a succession of sink-effective mechanisms that drive the formation and evolution of the metabolic flow system. Plasticity and diversification of proteins construct the cellular system following the orientation given by the flow and implementing it. Nucleic acid monomers participate in bioenergetics and the polymers are conservative memory systems for the synthesis of proteins. Protoplasmic fission is the final sink-effective mechanism, part of cell reproduction, guaranteeing that proteins don’t accumulate to saturation, which would trigger inhibition.

From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

Introduction: Thyroid nodules are extremely common and require complex management to prevent unnecessary surgical intervention and ensure that no malignant disease is overlooked. Several diagnostic tools and scoring systems are available to evaluate the Risk of Malignancy (ROM). The goal is to assess variables that can aid and support the clinical recommendations suggested by the updated Bethesda System for Reporting Thyroid Cytopathology (TBSRTC), such as the ultrasonographic features of thyroid nodules, particularly for indeterminate categories III (atypia of undetermined significance) and IV (follicular neoplasm). Methods: We retrospectively analysed the correlation of the demographic and ultrasonographic characteristics of thyroid nodules with the cytopathological and histopathological diagnoses of TBSRTC categories III (atypia of undetermined significance), IV (follicular neoplasm), V (suspicious for malignancy), and VI (malignant) in patients who underwent surgery in a single Portuguese centre over a 10-year period. Results: In total, 360 nodules were evaluated in 341 patients and 57% were histopathologically malignant or borderline. The majority were included in the TBSRTC indeterminate categories III and IV with an ROM of 44% and 43%, respectively. The ultrasonographic characteristics associated with a higher TBSRTC category and a greater ROM value were hypoechogenicity, presence of microcalcifications, irregular margins, and the presence of cervical adenopathy. When correlating with a malignant histology, only adenopathy and the presence of microcalcifications were observed to be statistically significant. Discussion: The indeterminate categories of the TBSRTC have been the most challenging ones to manage. The new TBSRTC (2023) guidelines as well as the ultrasonographic characteristics of a patient can be helpful in assessing the ROM and deciding on an appropriate course of treatment. Other resources, such as molecular tests, are also gaining a more important role in the clinical decision process and may become crucial in the future. Conclusion: The clinical management of thyroid nodules requires a careful analysis of clinical history and an evaluation of demographic details, the symptoms of the patient, comorbidities, personal and family history, ultrasonographic features, and the results of cytopathology, thyroid function, and molecular/genetic tests to provide the best care possible. Patients with thyroid nodules of an indeterminate character can obtain a definitive diagnosis through surgery. However, by employing a complete analysis of all relevant data, some patients may be suitable for active surveillance instead of a surgical intervention.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

In this paper, an extendable fuzzy robust speed controller suitable for induction motor drive system was proposed. Firstly, the two-degrees-of-freedom (2DOF) robust control technology with feedforward control and disturbance elimination method was adopted. Upon parameter variation and load disturbance, the motor drive system could utilize a robust controller to generate compensation signals and reduce the impact on controlling performance of the motor drive system. The magnitude of the compensation signal was adjusted via the weighting factor. However, should a fixed weighting factor be adopted, system instability might be generated easily when time delay and saturation of control force occur. Based on the above, the smart method of extendable fuzzy theory (EFT) was adopted in this paper to adjust adequate weighting factors, where the controlling performance of the induction motor drive system could be improved accordingly. The method divided the speed difference between motor speed command and actual speed of the induction motor, and the variation rate for such speed difference into 20 regional categories. The correlation degree between the speed difference of actual feedback, variation rate for speed difference and each regional category was then calculated, where more suitable weighting factor was selected. Therefore, the proposed extendable fuzzy robust 2DOF controller with variable weighting factor designed will allow better performance for response in speed tracking and load regulation. Lastly, the simulation software Matlab/Simulink was applied to simulate the utilization of the controlling method proposed for the induction motor drive system. For both the response in speed tracking and load regulation, the simulation results proved that the extendable fuzzy robust speed controller proposed provided better controlling performance than the conventional robust controller.

Obstructive sleep apnea (OSA) is a common sleep disorder that is associated with a wide variety of health conditions, including cardiovascular, cerebrovascular, metabolic, neoplastic, and neurocognitive manifestations. OSA is mainly characterized by repeated upper airway obstructions during sleep that cause episodes of chronic intermittent hypoxia (IH), resulting in tissue hypoxia-reoxygenation. Decreased arterial oxygen pressure (PaO2) and hemoglobin saturation (SatO2) stimulate reflex responses to overcome the obstruction. The prevalence of OSA is significant worldwide, particularly in women during pregnancy, due to the physiological changes associated with this stage of life, especially in its later stages. It is associated with an increased risk of hypertension, pre-eclampsia and diabetes, among others. OSA during pregnancy can interfere with normal fetal development and is associated with growth retardation, preterm birth, or low term weight. Carotid body stimulation and hypoxia-reoxygenation episodes contribute to cardiovascular disease and oxidative stress, that can harm both, mother and the fetus, even into the adulthood. Because gestational intermittent hypoxia (GIH) is the hallmark of OSA in pregnancy, this review examines the impact of GIH on maternal, fetal, and offspring respiratory outcomes. Most adverse outcomes can be prevented by continuous positive airway pressure (CPAP) therapy and lifestyle changes if gestational OSA is detected early and treated appropriately.

This research aims to explore factors that influence sustainable music education from a psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines stochastic nonlinear system of control theory and identifies relevant factors affecting sustainable music learning by establishing a data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music education from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring sustainable music education and students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music education from an emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on sustainable music learning. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of sustainable student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on sustainable music learning are obtained.

In the current complex and uncertain macroeconomic situation, improving the inherent resilience of the new energy vehicle industry chain is the key to effectively addressing various risks and challenges, and achieving dynamic balance between high-quality industrial development and industrial chain security. Based on the basic concept of resilience and the actual situation of new energy vehicle industry chain development, this study constructs a comprehensive evaluation index system for the resilience of the new energy vehicle industry chain, which includes four dimensional first-level indicators of coping resistance, adaptive resilience, autonomous control and leading competitiveness. In addition, it is measured by combining the data related to the new energy vehicle industry chain in Changzhou City. Our study shows that the situation on the new energy vehicle industry chain is complicated, and the comprehensive evaluation index system is more favorable for evaluating its resilience level.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

: Microplastics and nanoplastics (MNPs) are becoming an increasingly severe global problem due to their widespread distribution and complex impact on living organisms. Apart from their environmental impact, the effects of MNPs on living organisms have also continued to attract attention. The harmful impact of MNPs has been extensively documented in marine invertebrates and larger marine vertebrates like fish. However, the research on the toxicity of these particles on mammals is still limited, and their possible effects on humans are poorly understood. Considering that MNPs are commonly found in food or food packaging, humans are primarily exposed to them through ingestion. It would be valuable to investigate the potential harmful effects of these particles on gut health. This review focuses on recent research exploring the toxicological impacts of micro- and nanoplastics on the gut, as observed in human cell lines and mammalian models. Available data from various studies indicate that the accumulation of MNPs in mammalian models and human cells may result in adverse consequences, in terms of epithelial toxicity, immune toxicity, and disruption of gut microbiota. The paper also discusses the current research limitations and prospects in this field, aiming to provide a scientific basis and reference for further studies on the toxic mechanisms of micro- and nanoplastics.

Insulin resistance/hyperinsulinemia as an independent risk factor for the development and worsening of HFpEF. Serafino Fazio MD¹, Valentina Mercurio MD, PhD2, Valeria Fazio MD3, Antonio Ruvolo MD⁴, Flora Affuso MD, PhD⁵. Authors’ Affiliation: ¹ Department of Internal Medicine (retired Professor). Federico II University, Via Sergio Pansini 5. 80135 Naples, Italy. 2 Department of Translational Medical Sciences. Federico II University. Via Sergio Pasini 5. 80135 Naples, Italy. ³ UOC Medicina Interna, Azienda Ospedaliera di Caserta, 81100 Caserta, Italy, ⁴ UOC Cardiologia AORN dei colli PO CTO, Viale Colli Aminei 21, 80100 Naples, Italy. ⁵ Independent researcher, Viale Raffaello 74, 80129 Naples, Italy. Abstract Heart failure (HF) has become a subject of continuous interest since it has been declared new pandemic in 1997 because of the exponential increase in hospitalizations for HF in the latest years. HF is the final state to which all heart diseases of different etiologies lead if not adequately treated, therefore it is highly prevalent worldwide, with a progressive increase with age, reaching a prevalence of 10% in subjects over the age of 65 years. It should also be underlined that lately, while the prevalence of HF with left ventricular reduced ejection fraction (HFrEF) is slowly decreasing, the incidence of HF with left ventricular preserved ejection fraction (HFpEF) is progressively increasing. In deed, among patients consecutively hospitalized for HF between 1987 and 2001 in a study from the Mayo Clinic Hospitals in Olmsted County, Minnesota, prevalence of HFpEF increased from 38% to 54%. HFpEF is typically characterized by concentric remodeling of the left ventricle (LV) with impaired diastolic function and increased filling pressures. Over the years, also the prevalence of insulin resistance/hyperinsulinemia in the general adult population has progressively increased, primarily due to lifestyle changes, particularly in developed and developing countries, with a range that globally ranges between 15.5% and 46.5%. Notably, insulin resistance (IR)/hyperinsulinemia (Hyperins) is present in up to 60% of patients with HF and is significantly higher in subjects with HfpEF. In the scientific literature it has been well highlighted that the increased circulating levels of insulin are associated with conditions of insulin resistance are responsible for progressive cardiovascular alterations over the years. In the setting of HFpEF, insulin stimulates the concentric remodeling of the left ventricle with associated diastolic dysfunction, which, coincidentally. Unfortunately, IR/Hyperins, despite being a well-known cardiovascular risk factor, is still not screened in the general population and is not treated as promptly as it should be, and this could at least in part concur in the progressive increase in prevalence of HFpEF in the general adult population. The aim of this manuscript was to review scientific literature that support a pathophysiologic connection between IR/Hyperins and HFpEF to stimulate the scientific community towards the identification of hyperinsulinemia associated with insulin resistance as an independent cardiovascular risk factor in development and worsening of HF, believing that its adequate screening in the general population and appropriate treatment could reduce the prevalence of HFpEF and improve its progression.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

This article examines the diagnosis of the Agricultural Innovation System (SIA) in the department of Tolima, Colombia, as part of a project led by the Colombian Ministry of Agriculture and Rural Development and the Technological University of Pereira (TUP), which also evaluated the National Agricultural Innovation System (NAIS) in regions such as Chocó, Bolívar, and Meta. A methodology was used that involved a survey with 58 questions on a Likert scale to 300 participants, evaluated with congruence and relevance indexes, identifying key variables in the operation of the Agricultural Innovation System (AIS). A survey was also applied to actors of the National Innovation System for Higher Education in Agriculture, A survey was also applied to map the participating actors in the regional agricultural innovation systems of Tolima to identify gaps in extension, innovation, development, sustainability, ICT, education and training. Among the conclusions, it stands out that the associativity subsystem presents the lowest lag and is close to a score of two on the scale of gaps, evaluation carried out among the five subsystems including extension, ICT, environmental aspects, and pub-lic policy. The department of Tolima shows low performance in innovation and ICT, while the extension is less lagging. In Chaparral there is a general lag, with innovation being the worst evaluated subsystem. Rovira obtains high scores in most subsystems, although innovation and ICT have lower scores. At the national and regional levels, innovation is low, and articulation among NARS actors is insufficient, which highlights the need to promote good practices, strategies, and projects.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) makes it difficult to accommodate renewable energy with a high proportion. It is thus necessary to excavate its potential flexibility to improve its operational performance and consume renewable energy. This paper unlocks the adjustment potential of flexible buildings and builds a flexible thermal load regulation model according to the dynamic thermal characteristics and thermal comfort elastic interval of the buildings and a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates multiple flexible load regulations and the variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanism in different types of flexible loads. Its results show a saturation effect between the flexible thermal and electrical loads in increasing renewable energy consumption and a synergy effect in decreasing the total cost. This synergy can reduce the total cost by 0.73%. Besides, the total cost can be reduced by 15.13%, and the renewable energy curtailment rate can decrease by 12.08% if the flexible electric and thermal loads are integrated into the operation optimization of HRIES.

Trace amines are a separate, independent group of biogenic amines, close in structure to classical monoamine neurotransmitters such as dopamine, serotonin and norepinephrine. G protein-coupled Trace Amine-Associated Receptors (TAAR) are mostly investigated for their involvement in the pathogenesis of neuropsychiatric disorders, but expression of TAAR family receptors is observed also in various populations of cells of the immune system. The review is focused on the basic information on the interaction of trace amines and their receptors with cells of the general immune system of humans and other mammals, as well as on data on TAARs role in the function of individual populations of myeloid and lymphoid cells. With further research on the regulatory role of the trace amine system in the pathogenesis of the immune response and determination of the biological mechanisms for their action it would be possible to develop new methods for treating diseases related to immune system dysfunctions.

The review scrutinizes the currently performed research on the new methods for enhancing bituminous binder performance through radiation and radical grafting of polymer modifers of bitumen. It investigates innovative methods, including using waste polymers as modifiers, and applying radiation for polymer grafting, to overcome challenges like high costs, low aging resistance and storage stability issues, of which separation of phases polymer/bitumen is the most significant obstacle. These advanced modification techniques promise sustainability through the decrease of the carbon footprint of transportation systems by improving the properties and durability of binders. Additionally, the review discusses the parameters and mechanistic aspects from a scientific perspective, shedding light on the underlying processes that contribute to the improved performance of modified bituminous binders.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

An existing simplified simulation model of global climate, population, economy, and governance is extended, through statistical and other data analysis, to include several measures of human well-being and population displacement. The revised model is used to explore (in testing to the year 2060) vicious cycles and causal cascades that, some have warned, could lead to acceleration of climate change in the coming decades. Model scenario testing addresses two uncertainties, namely the strength of the effect of climate change on governance erosion, and the strength of the “heat trap” effect from physical tipping points such as ice loss and permafrost thaw. This testing indicates that a strong heat trap effect could very well accelerate climate change, but that even a strong governance erosion effect is unlikely to do so. Governance erosion would hurt human well-being but is probably not much of a climate threat per se. These results are tentative but point to the possibility of formally modeling causal cascades beyond what the larger climate models have done to date.

Background Corpus callosum abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosum abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications on the postnatal outcome. Methods In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. Results The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%) and hypoplasia with dysplasia (4.2%). Amongst them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. Conclusion The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a diagnosis suspected on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in the cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future.

In the paper the experimental methodology for the single-crystal circular plate deformation measurement and subsequent procedure for mechanical properties quantitation are developed. The procedure is based on a new numerical-analytical solution of nonlinear boundary-value problem for finite deformations of a circular anisotropic plate. Using the developed method, a study of the deformation of single-crystal circular plates formed on the basis of a silicon-on-insulator structure was carried out. The values of residual stresses are determined and it is shown that the presence of these stresses increases the flexural rigidity of the plate in several times.

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, the total annualized cost and maximizing product yield. The results indicate that a higher TVR discharge pressure, or MVR compression ratios, along with higher feed temperatures enhance evaporation with but increase operational costs. The most economic option includes three evaporator effects with TVR for achieving 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates, an annual cost reduction ranging from 10.75 to 44% is achieved. Also, a forecasted 20% (or more) reduction of biomass-based steam cost can lead to lower annual expenditure comparing with the nominal NG-based steam Case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9 - 45% reduction in the total cost depending on the Case under consideration.

Nowadays, the increasingly serious environmental pollution and energy problems urgently require internal combustion engine-based transportation vehicles to upgrade or replace, so the new energy transportation vehicles based on hybrid power and fuel cells have gradually stepped on the stage and continue to innovate. The shipping industry is one of the main sources of global greenhouse gas emissions. The development of clean energy ships represented by fuel cells has attracted wide attention. Fuel cell has the advantages of clean, pollution-free and low noise, but it has some disadvantages such as insufficient dynamic response performance and fast performance decay. Limited by the characteristics of a single energy source, a variety of energy sources and bidirectional DC converters are usually mixed together to form a hybrid ship to improve the flexibility, stability and economy of the ship, and enhance its adaptability to complex sea conditions through the reconfiguration of power system energy. Taking fuel cell ferry "FCS Alsterwasser" as the research object, this study proposed an improved equivalent minimum hydrogen consumption energy management strategy based on fuzzy logic control. First, the power system of the modified mother ship was simulated, and a hybrid power system including fuel cell, lithium iron phosphate battery and supercapacitor was proposed. Then, the dynamic system simulation model and double closed loop PI control model are established in MATLAB/Simulink, and the reliability of the model is verified by the simulation analysis of charge and discharge characteristics. Then, the feasibility of the proposed method is demonstrated by designing simulation experiments based on typical working conditions of the mother ship. The simulation results show that under the premise of meeting the load requirements, the control strategy designed in this paper has a better optimization effect than the S-type penalty function in terms of lithium battery power, lithium battery SOC, bus voltage stability and equivalent hydrogen consumption, which improves the stability and economy of the power system, and has certain engineering practical value.

Ultralight power generation equipment has high requirements for the power density, continuous operation, and transient stability of the whole machine, and there is a direct conflict between high power density design and substantial stability control in the power unit design. In order to meet the power density requirements of ultra-light power stations, three main problems need to be solved, namely engine speed oscillation and flameout in the process of load power mutation, matching of the generator and engine torque, and stabilizing the voltage waveform of the AC output end. In this paper, we proposed an exquisite control strategy for ultralight power generation systems in engines, generators, and inverters. The effectiveness and practicability of the proposed strategy are verified by both simulation and experiment. The results show that the proposed control strategy can effectively solve the instability problem of the ultralights generator set and improve the stability of the system, where the response recovery can be achieved within 0.9 seconds under the condition of total load increase or decrease, and the mismatching degree of the generator following the engine is reduced by 90%. The strategy could also guarantee long-term stable operation with high-quality electrical energy output.

The treatment of critically ill patients remains an evolving and controversial issue. Mechanical circulatory support through a percutaneous approach for the management of cardiogenic shock has taken place in recent years. The combined use of IABP and Impella 2.5 device may have a role to play for this group of patients. A simulation approach may help with a quantitative as-sessment of the achievable degree of assistance. In this paper, we analyse the interaction be-tween Impella 2.5 pump applied as LVAD and IABP using the numerical simulator of the cardi-ovascular system developed in our laboratory. Starting with pathological conditions reproduced using literature data, the simulations were performed setting different rotational speeds for the LVAD and driving the IABP in full mode (1:1) or partial mode (1:2, 1:3 and 1:4). The effects in-duced by drug administration during the assistance were simulated too. The haemodynamic pa-rameters under investigation were: mean aortic pressure, systolic and diastolic aortic pressure, mean pulmonary arterial pressure, mean left and right atrial pressure, cardiac output, cardiac index, left and right ventricular end-systolic volume, left ventricular end-diastolic volume and mean coronary blood flow. The energetic variables considered in the study were: left and right ventricular external work and left and right atrial pressure-volume area. The outcome of our simulations shows that the combined use of IABP and Impella 2.5 achieves adequate support in the acute phase of cardiogenic shock compared to each standalone device. This would allow fur-ther stabilisation and transfer to a transplant centre should escalation of treatment be required.

Multiple Sclerosis (MS) is a neurodegenerative disease that mainly affects young adults. The high prevalence of the disease and its impact on patients' quality of life led to priority being given to the etiopathogenesis of the disease to identify possible modifiable factors and consequent effective intervention strategies. Recent hypotheses suggest that the etiopathogenesis of MS is multifactorial and includes factors related to the immune system, neuroinflammation and neuro-degeneration. In this scenario, sleep has a close - although indirect - relationship with MS, through its relationship with each of those factors. In particular, the role of cytokines in immune system impairment and neuroinflammation, mitochondrial dysfunction and oxidative stress in neurodegeneration all are associated with sleep in different forms. Furthermore, melatonin in relation with vitamin D has a potential therapeutic effect in MS sleep-related problems. Given the growing interest of research in the mechanisms underlying MS and therapies able to alleviate MS-related symptoms at all stages of the disease, a more in-depth study of the role of sleep disturbances and the factors intrinsically related to sleep and MS could be useful both to investigate the etiopathogenetic factors of MS and to develop potential non-invasive intervention strategies for MS treatment.

We propose a silviculture with a purely biological logic that takes soil into account. The article is divided into three parts: 1) presentation of the issue in the context of biodiversity and forest pro-tection; 2) impact of the soil functioning on forestry; 3) possible implementation of soil functioning in forestry practice, to support and protect a non-or minimally anthropized evolution of the forest. The first chapter illustrates the importance of the forest for the future of the planet and our human species, referring to recently published works on the topic of climate warming and biodiversity. The second illustrates the aspects that link soil dynamics to the silvogenetic cycle, focusing on the humipedon (organic and organo-mineral surface horizons, approximately the first 30 cm of forest soil), and more significant carbon storage in old forests since up to 2/3 is found in the soil. In the third chapter we consider the possibility of lengthening the forestry cycle, to allow the return of nutrients to the soil. Theoretical graphs and distribution models of the number of trees as a func-tion of diameter and age are reported, with examples of logging in uneven Alpine forests, with reference to Swiss fir-beech forests published on the Pro-Silva website.

In this paper presents analytical findings regarding the influence of gravity disturbance on INS. A data from the high-precision gravity model, EGM2008, we introduce a novel real-time gravity disturbance compensation approach for INS. This method estimates gravity disturbances along paths for platforms on both land and water, utilizing the Multi-Layer Perceptron (MLP) technique. To estimate these gravity disturbances, we developed four distinct MLP models and compared their training results using Half-Mean Square Error (half-MSE) and Root Mean Square Error (RMSE). This comparative analysis allowed us to identify the MLP model that exhibited the best performance. In order to validate the proposed gravity compensation method, field test was conducted using a test vehicle. The results of this field test unequivocally demonstrated the effectiveness of our approach in enhancing positioning accuracy. Over the course of a 2-hour field experiment, it was evident that employing the proposed gravity compensation method improved positioning accuracy by a notable 24%.

In this paper, we concentrate on the study of the properties of residual Tsallis entropy for order statistics. Order statistics have an important role in reliability structural engineering for example for modelling lifetimes of series and parallel systems. The residual Tsallis entropy of ith order statistic from a continuous distribution function and its deviation from the residual Tsallis entropy of ith order statistics from a uniform distribution is investigated. In a mathematical framework, a method to express the residual Tsallis entropy of the ith order statistic from a continuous distribution in terms of the residual Tsallis entropy of the ith order statistic from a uniform distribution is provided. This approach may provide insight into the behavior and properties of the residual Tsallis entropy for order statistics. Further, we study the monotonicity properties of the residual Tsallis entropy of order statistics under dierent conditions. By studying these properties, deeper understanding of the relationship between the position of order statistics and the resulting residual Tsallis entropy is gained.

The investigation of the spatiotemporal propagation characteristics of the "meteorological-agricultural-hydrological" drought system, under diverse climatic conditions, is crucial for the development of a robust drought warning system and the effective implementation of proactive drought prevention and resilience strategies. To achieve this, the current study utilizes the Soil and Water Assessment Tool (SWAT) model to simulate key components of the watershed water cycle, such as evaporation, surface water, soil water, and groundwater. Specifically, the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Nonlinear Joint Hydrological Drought Index (NJHDI) are employed to characterize meteorological drought, agricultural drought, and hydrological drought, respectively. By analyzing the correlation between these types of drought, the propagation characteristics of the "meteorological-agricultural-hydrological" drought system are elucidated using the rigorous strongest correlation coefficient method. The Yellow River Basin (YRB) is chosen as the case study for this research. Results showed that (1) The propagation time from meteorological to agricultural drought exhibited distinct seasonal characteristics, with durations of 5-6 months in spring, 2-3 months in summer, 3-5 months in autumn, and 6-8 months in winter. Compared to 1961-1990, the propagation time increased in spring and summer but decreased in autumn and winter during 1991-2010 for most YRB regions. (2) The agricultural to hydrological drought propagation showed no clear seasonal differences but increased over time. Specifically, zone C (arid/semi-arid with moderate temperatures) had shorter propagation time of 1-5 months, while zones B (transitional plateau to mid-latitude) and E (semi-arid/semi-humid temperate continental climate) experienced longer propagation time of 7-12 months. (3) Despite the extended timescales, agricultural-hydrological drought correlation was weaker than meteorological-agricultural linkage. This is because meteorological deficits directly reduce soil moisture, rapidly inducing agricultural drought. However, groundwater sustaining baseflow during agricultural drought delays streamflow deficits, prolonging the agricultural-hydrological propagation time.

The smart management of water resources is an increasingly important topic in today's society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. In such a way, a continuous assessment of water quality and state of health of the pipeline infrastructure can be achieved, allowing an early detection of leaks and water contamination. Acoustic wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable to replace traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media to be employed for data communication. This paper aims at reviewing those works dealing with acoustic-based communication networks in real large-scale urban water supply networks. An overview on the characteristics, trends and design challenges of existing works is provided as a guideline for future research.

We here investigate the dynamical behavior of a fractional-order predator-prey system with anti-predator behavior and Holling IV type functional response. First we study the non-negativity, existence, uniqueness, and boundedness of solutions to the system from a mathematical analysis perspective. Then, we analyze the stability of its equilibrium points and the possibility of bifurcations using stability analysis methods and bifurcation theory, and prove the existence of a supercritical Hopf bifurcation in the system. After providing numerical simulations to illustrate the conclusions theoretically derived, by summarizing those various analytical results obtained, we finally present three interesting conclusions that can contribute to better understanding and preservation of ecological systems.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

As a consequence of applying advanced maintenance practices, the theoretical probability of failures is relatively low. However, observation of low market intelligence and maintenance management has been reported. The experimental investigation is supported by findings from a survey targeting asset-intensive companies applying hydraulic power systems. Next, the study incorporates multidimensional data analysis using CA-AHC (Correspondance Analysis with Agglomerative Hierarchical Clustering) approach. The non-parametric machine learning models are used from generated feature subspace to extract features affecting maintenance performance indicators. The results support empirical evidence that equipment age increases the probability of failures. However, the novel findings show that number of maintenance personnel, equipment size measured by nominal working energy consumption, and activities dedicated to maintaining fluid cleanliness impact regression results of companies utilising hydraulic applications.

In post-COVID-19 syndrome, clinical presentation of the nerve fiber dysfunction plays an important role. The possibility of autoantigen cross-mimicry of coronavirus infection and the peripheral nervous system need to be investigated. The bioinformatic analysis was applied to search for possible common protein sequences located in the immunological epitopes. Among the autoantigens of the human nervous system, fibroblast growth factor receptor protein 3, myelin protein P0, myelin protein P2, sodium channel protein type 9, alpha protein subunit, plexin-D1 protein and ubiquitin-carboxyl-terminal hydrolase protein of the L1 isoenzyme were selected. The “Alignmentaj” program was created. The UniProt database, Protein Data Bank and AlphaFold databases were used. The analysis of protein sequence similarities of spike glycoproteins in human coronaviruses revealed common pentapeptides of the MERS-CoV-2 virus with the fibroblast growth factor receptor 3 and myelin protein P2. Among seasonal coronaviruses, common peptide sequences were identified in HCoV-HKU-1 virus with sodium channel protein type 9 subunit alpha and Plexin-D1, HCoV-OС43 with Plexin-D1, as well as HCoV-NL63 with Plexin-D1 and Ubiquitin carboxyl-terminal hydrolase isozyme L1. The data obtained make it possible to identify new potential targets for the development of autoimmune reactions that occur against the background of the activity of highly pathogenic and seasonal coronaviruses.

Abstract：Ballast pick-up velocity is a crucial parameter in the design of a slurry system in excavating devices, it influences transportation stability, safety, and economy of the system. In this study, a vertical pipe hydraulic experiment with different ballast sizes under water condition was conducted to investigate the ballast pick-up velocity in the shield slurry system. Thereafter, the coupling method of computational fluid dynamics and the discrete element method (CFD-DEM) was employed to establish a numerical model of the ballast pick-up velocity in the shield slurry system, and its applicability was verified by comparing the experiment results. Additionally, the ballast pick-up process under actual working conditions was studied, and the influence of ballast size and concentration on pick-up velocity was investigated. Overall, the primary findings of the study are as follows: 1) The numerical method can be applied to predict both the single ballast and ballast group pick-up velocity. 2) The pick-up process of the ballast group can be divided into four stages. 3) The conveying velocity of the ballast should be higher than the pick-up velocity of the single ballast to facilitate smooth discharge. 4) For a certain increase in ballast concentration, the pick-up velocity tends to be stable.

The economical load dispatch problem is a critical concern in thermal power systems to ensure cost-effective operation. This paper conducts a comparative analysis between the Social Spider Algorithm (SSA) and Particle Swarm Optimization (PSO) for economical load dispatch in a 6-unit thermal power system with the integration of 11,000 plug-in electric vehicles (PEVs). Both SSA and PSO are population-based optimization techniques designed to minimize the overall fuel cost while satisfying the load demand by determining the optimal power output of each unit. SSA emulates the social behavior of spiders, while PSO simulates the collective intelligence of a swarm of particles. Performance evaluation of the algorithms considers power system data, such as unit fuel costs, minimum and maximum loads, and total load demand. The objective is to minimize fuel cost while meeting the load demand. Comparative analysis of SSA and PSO includes convergence speed, solution quality, and computational efficiency. Experimental results indicate that SSA outperforms PSO in achieving a more optimal and economical load dispatch solution for the 6-unit thermal power system. SSA demonstrates faster convergence and provides superior-quality solutions compared to PSO. This paper contributes to the field by highlighting SSA's effectiveness in achieving cost-efficient operation of thermal power systems. The findings suggest that SSA holds promise as an optimization technique for similar power system optimization problems, enhancing overall operational efficiency and reducing costs for thermal power plants.

Barry Turner’s 1978 Man-made Disasters and Charles Perrow’s 1984 Normal Accidents were seminal books but a detailed comparison has yet to be undertaken. Doing so is important to establish content and priority of key ideas underpinning contemporary safety science. Turner’s research found socio-technical and systemic patterns that meant that major organisational disasters could be foreseen and were preventable. Perrow’s macro-structuralist industry focus was on technologically deterministic but unpredictable and unpreventable ‘system’ accidents, particularly rare catastrophes. Andrew Hopkins and Nick Pidgeon respectively suggested that some prominent writers who wrote after Turner may not have been aware of, or didn’t properly acknowledge, Turner’s work. Normal Accidents didn’t cite Turner in 1984 or 1999. Using a methodology involving systematic reading and historical, biographical and thematic theory analysis, a detailed review of Turner’s and Perrow’s backgrounds and publications sheds new light on Turner’s priority and accomplishment, highlighting substantial similarities as well as clear differences. In a previously unpublished letter, Perrow confirmed reading Man-made Disasters while writing Normal Accidents. Turner became better known after a 1997 second edition but under-acknowledgment issues continued. Ethical citation and potential reasons for under-acknowledgment are discussed. It is concluded that Turner’s foundational importance for safety science should be better recognised.

Mincle, a C-type lectin, is expressed predominantly in macrophages, where it plays a role in the macrophage response to various microorganisms such as mycobacteria. Docking is a computational procedure in which various software packages generate different positions at which ligands bind to their receptors. Discovery Studio docking was used in Molegro Virtual Studios. ChemDraw and ChemDraw3D were used for ligand preparation and construction because the protein preparation performed using docking software was a very lengthy process. Mincle plays a novel and non-redundant role in the induction of inflammatory signaling in response to mycobacteria. One disadvantage of Molegro studio docking is that it does not show the metallic atoms with which it interacts but rather shows hydrogen bonding and amino acid interactions. Hypothetically, trehalose dimycolate (TDM) and trehalose dibenzenate (TDB) detected by macrophages ultimately lead to CARD9 signaling, thereby producing pro-inflammatory cytokines and chemokines that have the potential to be used therapeutically.

The ageing of the population is growing significantly and will challenge healthcare systems. Chronic diseases in the older population require a change in service delivery, and new technologies can be a key element in ensuring the viability and sustainability of these systems. However, the generation gap and the physical and cognitive decline commonly associated with the older generation are barriers to the transition to these models of care. Despite this, there has been a trend towards digital healthcare, which has many potential benefits for the older population. Numerous studies have assessed the acceptability of new technologies for older people in health care. These studies highlight the importance of perceived usefulness, compatibility, ease of use and personalisation of the technology. Personalisation is necessary to ensure that the system is useful for users, and different characteristics such as country of origin, gender, age or comfort with the technology should be taken into account. A person-centred approach in the development of new health technology systems is essential to ensure that applications can be better tailored to the needs of different ageing populations. Many organisations have dedicated time and resources to ensure a person-centred approach in the development of new health technology systems, and putting the individual first is the best way forward in digital health. This article presents the work carried out in this regard in the framework of the European TeNDER project together with an analysis of the results obtained in terms of satisfaction, usefulness and usability by end users.

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.

Mobile Mapping System (MMS) plays a crucial role in generating high-precision 3D maps for various applications. However, the traditional MMS that uses tilted LiDAR (light detection and ranging) has limitations in capturing complete information of the environment. To overcome these limitations, we propose a panoramic vision-aided Cartographer simultaneous localization and mapping (SLAM) system for MMS, named "PVL-Cartographer". The proposed system integrates multiple sensors to achieve accurate and robust localization and mapping. It contains two sub-systems, early fusion and middle fusion. In the early fusion, range-maps are created from LiDAR points in a panoramic image space, facilitating the incorporation of visual features. The SLAM system works with both visual features with and without augmented ranges. In the middle fusion, a pose graph combines camera and LiDAR nodes, with IMU (Inertial Measurement Unit) data providing constraints between each node. Extensive experiments in challenging outdoor scenarios demonstrate the effectiveness of the proposed SLAM system in producing accurate results, even in conditions with limited features. Overall, our proposed PVL Cartographer system offers a robust and accurate solution for MMS localization and mapping.

Different hypotheses of carcinogenesis have been proposed based on local genetic factors and physiologic mechanisms. It is assumed that changes of the metric invariants of a biologic system (BS) determine general mechanisms of cancer development. Numerous data demonstrate an existence of three invariant feedback patterns of BS: negative feedback (NFB), positive feedback (PFB) and reciprocal links (RL). These base patterns represent basis elements of a Lie algebra sl(2,R) and imaginary part of coquaternion. Considering coquaternion as a model of a functional core of a BS, conditions of the system can be identified with the points of three families of hypersurfaces in R42: hyperboloids of one sheet, hyperboloids of two sheets and double-cones. Corresponding quadratic form relates negative and positive entropy contributions of base elements to the energy level of the system, so that anabolic states of the system will correspond to the points of a hyperboloid of one sheet, while catabolic conditions to the points of a hyperboloid of two sheets. Equilibrium states will lie in a double cone. Hypothetically anabolic and catabolic states dominate intermittently oscillating around the equilibrium. Deterioration of base elements increases positive entropy and causes domination of catabolic states which is the main metabolic determinant of cancer. Corresponding trajectory of a malfunctioning system will have a tendency to remain inside the double cone.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

Different hypotheses of carcinogenesis have been proposed based on local genetic factors and physiologic mechanisms. It is assumed that changes of the metric invariants of a biologic system (BS) determine general mechanisms of cancer development. Numerous data demonstrate an existence of three invariant feedback patterns of BS: negative feedback (NFB), positive feedback (PFB) and reciprocal links (RL). These base patterns represent basis elements of a Lie algebra and imaginary part of coquaternion. Considering coquaternion as a model of a functional core of a BS, conditions of the system can be identified with the points of three families of hypersurfaces in R⁴₂: hyperboloids of one sheet, hyperboloids of two sheets and double-cones. Corresponding quadratic form relates negative and positive entropy contributions of base elements to the energy level of the system, so that anabolic states of the system will correspond to the points of a hyperboloid of one sheet, while catabolic conditions to the points of a hyperboloid of two sheets. Equilibrium states will lie in a double cone. Hypothetically anabolic and catabolic states dominate intermittently oscillating around the equilibrium. Deterioration of base elements increases positive entropy and causes domination of catabolic states which is the main metabolic determinant of cancer. Corresponding trajectory of a malfunctioning system will have a tendency to remain inside the double cone.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

We are in amidst of COVID-19 pandemic. Since Dec 2019, severe acute respiratory corona virus (SAR-CoV-2) has infected more than half a billion people killing nearly 7 million people worldwide. Now the BA.5 variant of SARS-CoV-2 is causing mayhem and driving the global surge. Epidemiologist are aware of the fact that this virus is capable of escaping immunity and likely to infect the same person multiple times despite adequate vaccination status. Elderly people of age more than 60 years and those with underlying health conditions are considered as high-risk who are likely to suffer complications and death. While it is tempting to frame complications and mortality from COVID-19 as a simple matter of too much of a virulent virus in too weak of a host, much more is at play here. Framing the pathophysiology of COVID-19 in the context of the Chrousos and Gold model of the central stress response system can shed insight into its complex pathogenesis. Understanding the mechanisms by which pharmacologic modulation of the central stress response system via administration of clonidine and/or dexamethasone may offer an explanation as to why a viral pathogen can be well tolerated and cleared by one host while inflaming and killing another.

This study examined the association between pressure injuries and complexity of abdominal temperature measured in residents of a nursing facility. The temperature served as a proxy measure for skin thermoregulation. Refined multiscale sample entropy and bubble entropy were used to measure the complexity of the temperature time series measured over two days at 1-minute intervals. Robust summary measures were derived for the multiscale entropies and used in predictive models for pressure injuries that were built with adaptive lasso regression and neural networks. Both types of entropies were lower in the group of participants with pressure injuries (n=11) relative to the group of non-injured participants (n=15). This was generally true at the longer temporal scales, with the effect peaking at scale τ=23 minutes. Predictive models for pressure injury on the basis of refined multiscale sample entropy and bubble entropy yielded 92% accuracy, outperforming predictions based on any single measure of entropy. Combining entropy measures with a widely used risk assessment score led to the best prediction accuracy. Complexity of abdominal temperature series could therefore serve as an indicator of risk of pressure injury.

Lithium batteries and an increasing focus on CO₂ reduction have become an integral part of daily life and business for many people. Boron and boron compounds have been widely studied together in the history and development of lithium batteries. With a broad examination of battery components and systems but a boron-centric approach to raw materials, this review seeks to summarize past and recent studies on the following: which boron compounds are studied in lithium battery, in which parts of lithium batteries, what improvements are offered for battery performance, and what improvement mechanisms can be explained. The uniqueness of boron and its extensive application beyond batteries contextualizes the interesting similarity with studies on batteries. The paper predominantly focuses on lithium-ion batteries (LIBs) but also mentions other lithium batteries. At the end, the article aims to predict prospective trends for future studies that may lead to the successful and extensive use of boron compounds on a commercial scale.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

Four discrete models using the exact spectral derivative discretization finite difference (ESDDFD) method are proposed for a chaotic five-dimensional, conformable fractional derivative financial system incorporating ethics and market confidence. Since the system considered was recently studied using the conformable Euler finite difference (CEFD) method and found to be hyperchaotic, and the CEFD method was recently shown to be valid only at fractional index , the source of the hyperchaos is in question. Through numerical experiments, illustration is presented that the hyperchaos previously detected is in part an artifact of the CEFD method as it is absent from the ESDDFD models.

This work presents a control scheme to control the grid-connected single-phase photovoltaic (PV) system. The considered system has four 250W solar panels, a non-inverting buck-boost DC-DC converter, and DC-AC inverter with LCL filter. The control system aims to track and operate at the maximum power point (MPP) of PV panels, regulate the voltage of DC link, and supply the grid with a unity power factor. To well achieve these goals, the proposed control system consists of three parts, that are MPP tracking controller module with a fuzzy-based modified incremental conductance (INC) algorithm, a DC-link voltage regulator with a hybrid fuzzy proportional-integral (PI) controller, and a Current Controller module using the linear quadratic regulator (LQR) for grid-connected power. Based on fuzzy control and LQR, this work introduces a full control solution for grid-connected single-phase PV systems. The key novelty of this research is to analyze and prove that the newly proposed method is more successful in numerous aspects by comparing and evaluating the previous and present control methods. The designed control system settles quickly, which is critical for output stability. In addition, as compared to backstepping approach used in our past study, the LQR technique is more resistant to sudden changes and disturbances. Furthermore, backstepping method produces the larger overshoot, which has a detrimental impact on efficiency. Simulation findings under various weather conditions were compared to theoretical ones to indicate that the system can deal with variations in weather parameters.

Intradermal injection of botulinum neurotoxin is a frequently performed procedure in aesthetic dermatology to improve facial skin tone, texture, fine wrinkles, and enlarged pores. In practice, botulinum neurotoxin type A is also used to reduce skin oiliness of the face. There is increasing evidence that acetylcholine plays specific roles in sebum production, suggesting that botulinum neurotoxin type A may reduce sebum production by interfering with cholinergic transmission between sebaceous glands and autonomic nerve terminals. Botulinum neurotoxins can also inhibit several pathogenetic components of acne development, suggesting that botulinum neurotoxins can be used as a safe and effective treatment modality for acne and other skin disorders related to the overactivity of sebaceous glands. This review aims to explore the current evidence behind the treatment of oily skin and acne with botulinum neurotoxin type A.

Alzheimer’s and Parkinson's diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in medi-ating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

Land and climate are strongly connected through multiple interface processes and climate change may lead to significant changes in land use. In this study, high-resolution observational gridded datasets are used to assess modifications in the Köppen-Geiger and Worldwide Bioclimatic (WBCS) Classification Systems, from 1950‒1979 to 1990‒2019 in Portugal. A compound Bioclimatic-Shift Exposure Index (BSEI) is also defined to identify the most exposed regions to recent climatic changes. The temporal evolution of land cover with vineyards and olive groves between 1990 and 2018, as well as correlations with areas with bioclimatic shifts, are analyzed. Results show an increase (decrease) of CSa Warm Mediterranean climate with hot summer (CSb, warm summer) of 18.1% (‒17.8%). The WBCS Temperate areas also reveal a decrease of ‒5.11%. Arid and semi-arid ombrotypes areas increased, conversely to humid to sub-humid ombrotypes. Thermotypic horizons depict a shift towards warmer classes. BSEI highlights the most significant shifts in northwestern Portugal. Vineyards have been displaced towards regions that are either the coolest/humid, in the northwest, or the warmest/driest, in the south. For oliviculture, the general trend for a relative shift towards cool/humid areas suggests an attempt of the sector to adapt, despite the cover area growth in the south. As vineyards and olive groves in southern Portugal are commonly irrigated, options for the intensification of these crops in this region may threaten the already scarce water resources and challenge the future sustainability of these sectors.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

To enhance the mechanical properties (i.e. strength and elongation) of the face-centered cubic (fcc) α-phase in the Au-Cu-Al system, this study focused on the introduction of the martensite phase (doubled B19 (DB19) crystal structure of Au2CuAl) via the manipulation of alloy compositions. Fundamental evaluations, such as microstructure observations, phase identifications, thermal analysis, tensile behavior examinations, and reflectance analysis have been conducted. The presence of fcc annealing twins was both observed in the optical microscope (OM) and the scanning electron microscope (SEM) images. Both the strength and elongation of the alloys were greatly promoted while the DB19 martensite phase was introduced into the alloys. Amongst all the prepared specimens, the 47Au41Cu12Al and the 44Au44Cu12Al alloys performed the optimized mechanical properties. The enhancement of strength and ductility in these 2 alloys was achieved while the stress plateau was observed during the tensile deformation. A plot of the ultimate tensile strength (UTS) against fracture strain was constructed to illustrate the effects of the introduction of the DB19 martensite phase on the mechanical properties of the alloys. Regardless of the manipulation of the alloy compositions and the introduction of the DB19 martensite phase, the reflectance stayed almost identical to pure Au.

System-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a High Level Architecture (HLA) simulation of 3 networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

Cancer is the second biggest cause of death in children. With the development of chemotherapy, there is a substantial increase in overall survival rate over recent 30 years. However, the overall mortality rate in children with cancer still remains 25%, and many survivors experience a decline in overall quality of life and long-term adverse effects caused by treatments. Although cancer cells share common characteristics, pediatric cancers are different from adult cancers in their prevalence, mutation load, and drug response. Therefore, there is an urgent unmet need to develop therapeutic approaches specifically designed for children with cancer. Nanotechnology can potentially overcome the deficiencies of conventional methods of administering chemotherapy and ultimately improve clinical outcomes. The nanoparticle-based drug delivery systems can decrease the toxicity of therapy, provide a sustained or controlled drug release, improve pharmacokinetic properties of loading contents, and achieve a targeted drug delivery with achievable modifications. Further, therapeutic approaches based on combining nanoformulated drugs with novel immunotherapeutic agents are emerging. In this review, we discuss the recently developed nanotechnology-based strategies for treating blood and solid pediatric cancers.

The concept of transformative resilience emerges from complex recent literature and represents a way to interpret the potential opportunities to change in vulnerable territories, where a socio-economic change is required. This article extends the perspective of transformative resilience to assessing of the landscape multi-functionality of inland areas, exploring the potentials to identify a network of synergies among the different municipalities able to trigger a process of territorial resilience. A Spatial Decision Support System (SDSS) for multi-functionality landscape assessment aims to support the local actors to understand local resources and multi-functional values of the Partenio Regional Park (PRP) and surrounding municipalities, in the South of Italy, stimulating their cooperation to the management of environmental and cultural sites and the co-design of new strategies of enhancement.

Korea Atomic Energy Research Institute (KAERI) established a multi-dimensional hydrogen analysis system to evaluate a hydrogen release, distribution, and combustion in the containment of a nuclear power plant using MAAP, GASFLOW, and COM3D. KAERI developed the COM3D analysis methodology on the basis of the COM3D validation results against the experiments of ENACCEF and THAI. The proposed analysis methodology accurately predicts the peak overpressure with an error range of approximately ±10% using the Kawanabe turbulent flame speed model. KAERI performed a hydrogen flame acceleration analysis using the multi-dimensional hydrogen analysis system for a severe accident initiated by a station blackout (SBO) under the assumption of 100% metal-water reaction in the reactor pressure vessel for evaluating an overpressure buildup in the Advanced Power Reactor 1400 MWe (APR1400). The COM3D calculation results using the established analysis methodology showed that the calculated peak pressure in the containment was much lower than the fracture pressure of the APR1400 containment. This calculation result may have resulted from a large air volume of the containment, a reduced hydrogen concentration owing to passive auto-catalytic recombiners installed in the containment, and a lot of stem presence during the hydrogen flame acceleration in the containment. Therefore, we can know that the current design of the APR1400 containment maintains its integrity when the flame acceleration occurs during the severe accident initiated by the SBO accident.

Cascading Tank Village Systems (CTVSs) of Sri Lanka historically provided a resilient community-based social-ecological water management system in the rural dryzone of Sri Lanka [1]. The CTVS has been described as the ‘lifeblood’ of communities in the rural dry zone [2] and as a globally important Agricultural Heritage System by the FAO [3]. After being abandoned for many centuries, their restoration is now being pursued by different national and international actors as a key to climate change mitigation and sustainable livelihoods for communities [4]. Rural livelihoods in the dry zone are at risk due to multiple factors, poor access and management of water, economic and health pressures, as well as resource limitations and degradation [5]. Despite recent efforts to restore CTVS systems, no social-ecological approach (SES) nor sustainable livelihoods framework (SLF) focused approach to ensuring resilient and sustainable livelihood outcomes has been taken [6]. As part of an on-going PhD project, this paper describes the background, current challenges and potential for an SES focused resilience thinking approach to CTVS combined with a focus on sustainable livelihoods for future sustainable livelihood opportunities and outcomes. The study finds current restoration efforts are at a crossroads between restoring the past (system adaptability) or transforming for the future. It introduces relevant SES and resilience thinking concepts and analyses the CTVS from this perspective. A particular contribution of this study is to point to the significant overlaps and complementarities in social-ecological (SES) resilience thinking and SLF approaches to analysis and proposals for resilient rural development. Employing resilience thinking principles it recommends strategies to create positive livelihood outcomes for communities and households. Keywords: cascading tank village system; sustainable livelihoods; resilience thinking; Sri Lanka; rural dry zone; community rural development

The Smartphone industry has expanded significantly over the last few years. According to the available data, each year, a marked increase in the number of devices in use is observed. Most consumers opt for Smartphones due to the extensive number of software applications that can be downloaded on their devices, thus increasing their functionality. However, this growing trend of application installation brings an issue of user protection, as most applications seek permission to access data on a user’s device. The risks this poses to sensitive data is real to both corporate and individual users. While Android has grown in popularity, this trend has not been followed by the efforts to increase the security of its users. This is a well-known set of problems, and prior solutions have approached it from the ground up; that is, they have focused on implementing reasonable security policies within the Android’s open-source kernel. While these solutions have achieved the goals of improving Android with such security policies, they are severely hampered by the way in which they have implemented them. In this work, a framework referred to as CenterYou is proposed to overcome these issues. It applies a pseudo data technique and a cloud-based decision-making system to scan and protect Smartphone devices from unnecessarily requested permissions by installed applications and identifies potential privacy leakages. The current paper demonstrated all aspects of the CenterYou application technical design. The work presented here provides a significant contribution to the field, as the technique based on pseudo data is used in the actual permissions administration of Android applications. Moreover, this system is user and cloud-driven, rather than being governed by over-privileged applications.

The work presents an approach to instrument the load sensing bearings for automotive applications for estimation of the loads acting on the wheels. The system comprises fiber-optic sensors based on addressed fiber Bragg structures (AFBS) with two symmetrical phase shifts. A mathematical model for load-deformation relation is presented, and the AFBS interrogation principle is described. The simulation includes (i) modeling of vehicle dynamics in a split-mu braking test, during which the longitudinal wheel loads are obtained, (ii) the subsequent estimation of bearing outer ring deformation using a beam model with simply supported boundary conditions, (iii) the conversion of strain into central wavelength shift of AFBS, and (iv) modeling of the beating signal at the photodetector. The simulation results show that the estimation error of the longitudinal wheel force from the strain data acquired from a single measurement point was 5.44% with root-mean-square error of 113.64 N. A prototype load sensing bearing was instrumented with a single AFBS sensor and mounted in a front right wheel hub of an experimental vehicle. The experimental setup demonstrated comparable results with the simulation during the braking test. The proposed system with load-sensing bearings is aimed at estimation of the loads acting on the wheels, which serve as input parameters for active safety systems, such as automatic braking, adaptive cruise control, or fully automated driving, in order to enhance their effectiveness and safety of the vehicle.

Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has been confirmed in over 10,000,000 individuals worldwide and has resulted in more than 500,000 deaths in a few months since it first surfaced. With such a rapid spread it is no surprise that there has been a massive effort around the world to collectively elucidate the mechanism by which the virus is transmitted. Despite this, there is still no definitive consensus regarding droplet versus airborne transmission of SARS-CoV-2. Public health officials around the world have introduced guidelines within the scope of droplet transmission. However, increasing evidence and comparative analysis with similar coronaviruses, such as severe acute respiratory syndrome (SARS-CoV-1) and middle eastern respiratory syndrome (MERS), suggest that airborne transmission of SARS-CoV-2 cannot be effectively ruled out. As the data supporting COVID-19 airborne transmission grows, there needs to be an increased effort in terms of technical and policy measures to mitigate the spread of viral aerosols. These measures can be in the form of broader social distancing and facial covering guidelines, exploration of thermal inactivation in clinical settings, low-dose UV-C light implementation, and greater attention to ventilation and airflow control systems. This review summarizes the current evidence available about airborne transmission of SARS-CoV-2, available literature about airborne transmission of similar viruses, and finally the methods that are already available or can be easily adapted to deal with a virus capable of airborne transmission.

A growing number of studies suggest that SARS-CoV-2 could interfere with homeostatic mechanisms in the lung but the implications of this possible interference have not been fully explored in the literature. In this work, we examine the consequences that can be drawn from this hypothesis according to currently available knowledge. We suggest that one such consequence is the potential disruption of normal ventilation and perfusion of lung regions that may be distant from the infection sites. Loss of ventilation might result in local alveolar hypoxia and contribute to hypoxemia, which in turn could trigger homeostatic responses that enhance blood oxygenation by redistributing pulmonary blood circulation. Sudden changes in perfusion might then lead to the development of hydrostatic edema and eventually to vascular remodeling and inflammation. Therefore, the immune response might not be the only source of the substantial inflammation observed in lung tissues of patients with severe COVID-19, as is often assumed in the literature. The balance between the homeostatic and the immune reaction in each patient could account for the observed heterogeneity of the clinical manifestations of COVID-19.

The tumor organoid (tumoroid) model in three-dimensional (3D) culture systems has been developed to reflect more closely the in vivo tumors than 2D-cultured tumor cells. Notably, extracellular vesicles (EVs) are efficiently collectible from the culture supernatant of gel-free tumoroids. Matrix metalloproteinase (MMP) 3 is a multi-functional factor playing crucial roles in tumor progression. However, roles of MMP3 within tumor growth and EVs have not unveiled. Here, we investigated the protumorigenic roles of MMP3 on integrities of tumoroids and EVs. We generated MMP3-knockout (KO) cells using the CRISPR/Cas9 system from rapidly metastatic LuM1 tumor cells. Moreover, we established fluorescent cell lines with palmitoylation signal-fused fluorescent proteins (tdTomato and enhanced GFP). Then we confirmed the exchange of EVs between cellular populations and tumoroids. LuM1-tumoroids released large EVs (300-1000 nm) and small EVs (50-200 nm) while the knockout of MMP3 resulted in the additional release of broken EVs from tumoroids. The loss of MMP3 leads to a significant reduction in tumoroid size and to the development of the necrotic area within tumoroids. MMP3 and CD9 (a category-1 EV marker tetraspanin protein) were significantly down-regulated in MMP3-KO cells and their EV fraction. These weakened phenotypes by MMP3 KO were markedly rescued by the addition of MMP3-rich EVs or conditioned medium (CM) collected from LuM1-tumoroids, which caused a dramatic rise in the expression of MMP3, CD9, and Ki-67 (a marker of proliferating cells) in the MMP3-null/CD9-low tumoroids. Notably, MMP3 enriched in tumoroids-derived EVs and CM deeply penetrated into recipient MMP3-KO tumoroids, resulting in a remarkable enlargement of solid tumoroids, while MMP3-null EVs did not. These data demonstrate that EVs can mediate molecular transfer of MMP3 resulting in increasing the proliferation and CD9+ tumorigenesis, indicating crucial roles of MMP3 in tumor progression.

In this paper, initially a mathematical model is formulated for transient frequency of power system considering time delays which occur while transmitting the control signals in open communication infrastructure. Time delay negligence in a power system leads to improper measurement of frequency variation in power system. The study of impact of time delays on the stability of power system is performed by estimating the decay rate of frequency wave form using Kalman Filter (KF). In power system, there is a possibility of multiple time delays. This paper also focusses on developing Interacting Multiple Model (IMM) Algorithm with multiple model space using Kalman Filter (KF) as state estimator tool. The multiple time delays in power system is considered as multiple model space. The result shows that KF provides better estimate of correct model for a particular input-set. The qualitative properties of Riccati difference equation (RDE) in terms of state error covariance of IMMKF are also analyzed and presented.

An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

A multi-server parallel system dispatches the incoming job, which contains kn tasks into n servers. A job is considered to be computed if all the tasks associated with the job are processed. One job’s tasks can be encoded into at least kn “replicas” such that the job is considered to be served if any kn replicas finishing computation. In this paper, we analyze the random scheduling policy of a multi-server computing system under discrete time model in terms of Quality of Exponent (QoE), which is defined as the probability exponent that a typical job can be computed within a given number of time slots. We let kn/n be a constant. Assuming that any task of any job can be randomly dispatched by a “scheduler” to any server, and computing each task takes exactly one time slot. We divide the calculation of probability exponent into two parts, exponent of numerator and exponent of denominator. For the denominator, we give the almost exact exponent using Lagrange multiplier method, while for the numerator, an upper bound of the numerator’s exponent is provided. In addition, we also express the exponent in terms of information theoretical quantities and reconsider both of exponents in the context of large deviation theory.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

Objective: The private healthcare providers (PHCP) are believed to improve access to healthcare services in Public-Private Mix (PPM) projects, as they are considered first point of contact for healthcare. The purpose of this study was to know the satisfaction level of TB patients. Design: A questionnaire-based, cross-sectional study was carried out during November and December 2017 for 572 under-treatment patients registered with PHCPs in the PPM project. Lot quality assurance sampling technique was used to randomly select 19 districts from sample frame of 75 districts. In each selected district, data collector retrieved TB register of 8 months (Jan – Aug 2017) and systematically selected patients by fixed periodic interval. SPSS (version 24.0) was used to analyze the data. Results: Study included 53% (n=301) of males and 47% (n=271) of females, with mean age of 38 years (SD, ±18). Almost half of the participants were illiterate (51%, n=289) and 64% (n=365) were non-earning members of the family. In practice, most of the participants visit private providers (71%, n=407), including private hospital/clinic (44%) and traditional practitioners (27%; n=153). 55% of participants visited the current doctor because of clinic’s proximity to residence. 82% of the participants (n=469) were satisfied with the TB care services and 85% (n=488) said that they would recommend this clinic to others. Conclusion: PHCPs are preferred providers for individuals, which is consistent with findings of other studies. Though they are satisfied with TB care and services, interventions should be introduced to reduce the financial burden on the patient. Partnering PHCP is a way forward to ensure universal health coverage, health system strengthening and better health outcomes of the population.

The underlying molecular basis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is not well understood. Characterized by chronic, unexplained fatigue, a disabling payback following exertion (“post-exertional malaise”) and variably presenting, multi-system symptoms, ME/CFS is a complex disease which demands concerted biomedical investigation from disparate fields of expertise. ME/CFS research and patient treatment have been challenged by the lack of diagnostic biomarkers and finding these is a prominent direction of current work. Despite these challenges, modern research demonstrates a tangible biomedical basis for the disorder across many body systems. This evidence is largely comprised of disturbances to immunological and inflammatory pathways, autonomic and neurologic systems, abnormalities in muscle and mitochondrial function, shifts in metabolism, and gut physiology or gut microbiome disturbances. It is possible that these threads are together entangled as parts of an underlying molecular pathology reflecting a far-reaching homeostatic shift affecting each of these systems. Due to the variability of non-overlapping symptom presentation or precipitating events such as infection or other bodily stresses, the initiation of body-wide pathological cascades with similar outcomes stemming from different causes may be implicated in the condition. Patient stratification to account for this heterogeneity is therefore one important consideration during exploration of potential diagnostic developments.

The success of source protection in ensuring safe drinking water is centered around being able to understand the hazards present in the catchment then plan and implement control measures to manage water quality risk to levels which can be controlled through downstream barriers. The programs in place to manage source protection are complex sociotechnical systems involving policy, standards, regulators, technology, human factors and so on. This study uses System Theoretic Process Analysis (STPA) to analyze the operational hazards of a typical drinking water source protection (DWSP) program and identify control measures to ensure safe operations. To validate the results a questionnaire was developed and distributed to specialists in DWSP in Taiwan, Australia and Greece. Using Principle Components Analysis (PCA) of the questionnaire responses, the study identified four critical success factors (CSFs) for DWSP. The four factors identified are ‘Policy and Government Agency Support of Source Protection’, ‘Catchment Risk Monitoring and Information’, ‘Support of Operational Field Activities’ and ‘Response to Water Quality Threats’. The results of this study provide insight into the approach of grouping of source protection measures to identify a series of targeted CSF for operational source protection programs. Using CSF can aide catchment management agencies in ensuring that the risk level in the catchment is managed effectively and that threats to public health from drinking water are managed appropriately.

Background: Mitigating climate change requires fundamentally redesigned energy systems in which renewable energy sources ultimately replace fossil fuels such as natural gas. In this context, the question how and to which extent the gas sector can contribute to an increasingly climate-neutral future EU energy system is heavily debated among scholars, energy industry experts and policy makers. Methods: We take a two-step approach: We begin with a review of studies from energy industry and academia to discuss potential gas sector contributions from a holistic energy system design point of view; this is followed by a comprehensive discussion of technical potentials, micro-economic conditions and societal implications of renewable gas. We then enrich our findings with the results of an empirical focus group process. Results: The gas sector can not only contribute to balancing volatile renewable energy production but also enable the supply of renewable energy to end-users in gaseous form; based on existing infrastructure. This could reduce costs for society, increase public acceptance and ultimately speed up the energy system transformation. There is the theoretical technical potential to substitute major parts of natural gas with renewable gas of biogenic and synthetic nature. This, however, crucially requires a supportive policy framework like the one established for renewable electricity. Conclusion: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.

Farmers’ conventional tillage (CT) and residue removal practices in rice-maize systems in South Asia’s Eastern Gangetic Plain (EGP) are input-intensive, costly and soil degradative. We conducted a rice-maize-mungbean (R-M-MB) system experiment with six tillage and three residue management treatments in Bangladesh representing the EGP. Maize yields were significantly (p≤0.05) higher under permanent (PB) or fresh (FB) beds and strip tillage (ST) than CT but no differences in mungbean yields. Rice yields under PB, FB and CT were similar, but significantly higher than under zero or minimum tillage and ST. Yields of all crops increased significantly (p≤0.05) with residue retention compared to no retention. Total system productivity was highest under PB followed by FB and ST. Compared with CT, gross margins in PB, FB and ST increased by 18, 13 and 11%, and soil organic matter (SOM) and total N contents across tillage treatments increased by 11-16% and 12-24%, respectively. After three years, SOM and total N and available P and S contents increased significantly (p≤0.05) by residue retention. Results demonstrate the potential of PB, FB and ST with residue retention, for improving the productivity, profitability and soil health under R-M-MB systems in Bangladesh and similar soils in the EGP.

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

The hybrid personal cooling system (HPCS) consisted of ventilation fans and phase change materials (PCMs) covered with insulation pads is a promising wearable cooling system to mitigate heat strain and heat-related illnesses of occupational workers with heavy labor in hot environments. Effects of clothing characteristics (e.g., thermal resistance of insulation pads, latent heat and melting temperature of PCMs) on the thermal performance of the HPCS have been investigated in detail in our previous study. Apart from the aforementioned factors, environmental conditions, i.e., environmental temperature and relative humidity, also significantly affect the thermal performance of the HPCS. In this paper, a numerical parametric study was performed to investigate the effects of the environmental temperature and relative humidity (RH) on the thermal management of the HPCS. Five levels of air temperature under environmental RH=50% were chosen (i.e., 32, 34, 36, 38 and 40 ºC) to study the impact of environmental temperature on the HPCS’s cooling performance. In addition, four levels of environmental RH at ambient temperatures of 36 and 40 ºC were selected (i.e., 30, 50, 70 and 90%) to examine the effect of RH on cooling performance of the HPCS. Results show that high environmental temperatures could accelerate the PCM melting process and thereby weaken the cooling performance of HPCS. In the moderately hot environment (36 °C), the HPCS presented good cooling performance with the maximum core temperature at around 37.5 °C during excise when the ambient RH≤70%, whereas good cooling performance could be only seen under RH≤50% in the extremely hot environment (40 °C). Thus, it may be concluded that the maximum environmental RH for the HPCS exhibiting good cooling performance decreases with the increase in the environmental temperature.

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a Fuzzy Inference System (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules and one output. The FIS is responsible to monitor the restoration results, grade their validity and choose the one that yields to a better grade. These grades are produced autonomously by analyzing results of Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 um wire probe are compared with the restorations. Main aims are therefore a) Provide unsupervised deblurring for device independent EB measurement; b) Improve the reliability of the process; c) Apply deblurring without knowing the probe size. These, further facilitates the deployment and manufacturing of EB probes and probe independent and accurate EB characterization. It also makes restoration of previously collected EB measurements easier, where the probe sizes are not known or recorded.

In this paper, we observe that if X is a set and (Bin(X), □) is the semigroup of binary systems on X, then its center ZBin(X) consists of the locally-zero-semigroups and that these can be modeled as (simple) graphs and conversely. Using this device we show that we may obtain many results of interest concerning groupoids by reinterpreting graph theoretical properties and at the same time results on graphs G may be obtained by considering them as elements of centers of the semigroups of binary systems (Bin(X), □) where X = V(G), the vertex set of G.

Agriculture is the backbone of Kenya’s economy, supporting up to 80% of the rural livelihoods. Kenya’s export horticulture is currently the leading Agriculture subsector in Kenya has evolved from small-holder farming to agro-industrial large-scale export farming dominated by multinational companies. It is regarded as an agro-industrial food system based on the economies of scale producing for mass markets outside of the production area. Much of the food consumed from this food system has undergone multiple transformations and been subject to a host of formal and informal insitutions (rules, regulations, standards, norms and values). An Anthropological study of export horticulture in Northwest Mount Kenya was carried out utilizing qualitative data collection methods in Northwest Mount Kenya region. Data was coded and analysed thematically based on grounded theory approach. The study described the institutional settings of export horticulture from an emic perspective as changing and defining the operations of the food system access and management of common pool resources, namely water and land. With the agro-industrial food system competing for these scarce resources in a semi-arid zone, there is potential for conflict and also reduced production and overall benefits to the different actors in the study area.

This study presents application of demand response strategy in a standalone wind-solar-battery hybrid energy system (HES). Inputs for the designed HES are wind speed, solar radiation, temperature and load demand which is variable with time. In this study, hourly values of meteorological data and hourly load demand are considered in one year. An improved gravitational search algorithm (IGSA) is used to optimize the configuration of the standalone wind-solar-battery hybrid power system. The optimal objectives of the system are cost of the system in life cycle, the loss of power supply probability（LPSP）and the energy excess percentage（EXC）.The effect of demand response on economic benefit and energy storage allocation of the standalone wind-solar-battery system is studied. The obtained optimal configuration of the proposed HES can provide minimal energy cost with excellent performance and reduced waste and unmet load.

Pilots can be one of the factors in many air traffic accidents. When one or both pilots are impaired (e.g. fatigue, drunk), disabled, capable but wrong-headed, don’t have sufficient training, distracted, miscommunicate with the air traffic controller, or follow wrong instructions from the air traffic controller, the risk of accident will increase dramatically. In some of these cases, the risk can be mitigated by using big data and machine learning. The system will collect and analyze large amount of data about the state of the aircraft, e.g., the flight path, the immediate environment around the aircraft, the weather and terrain information, and the pilots’ input to control the aircraft. Additional sensors such as eye tracking devices and biological monitor can also be added to determine the condition of the pilots. If the pilots’ input do not match proper reaction to the situation or the pilots are impaired, the learning machine will first provide an advisory to the pilots. If both pilots are impaired or incapable, a warning will be sent to the flight attendants and air traffic controllers so that they can take appropriate actions. The learning machine will be trained by both accident database and an automatic training system.

Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was developed to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90 m, 5.13 m, 6.38 m, 6.05 m, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In the present paper, we analyze the spatiotemporal variations in terrestrial water storage (TWS) of the ACTR, including three of its largest pan-Arctic river basins (Lena, Mackenzie, Yukon), using monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, river runoff, and inundation extent area (IEA) data, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the winter of the following year, while in the Mackenzie River basin, the TWS levels in the autumn and winter has no significant impact on the following year. As global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline.

Serious damage caused by floor heave in the coal given chamber of a vertical coal bunker is one of the challenges faced in underground coal mines. Engineering practice shows that it is more difficult to maintain the coal given chamber (CGC) than a roadway. More importantly, repairing the CGC during mining practice will pose major safety risks and reduce production. Based on the case of the serious collapse that occurred in the bearing structure of the CGC at the lower part of the 214# coal bunker in Xiashijie mine, China, this work analysed (i) the main factors influencing floor heave and (ii) the failure mechanism of the load-bearing structure in the CGC using FLAC^2D numerical models and expansion experiment. The analysis results indicate that: the floor heave, caused mainly by mine water, is the basic reason leading to the instability and repeated failure of the CGC in the 214# coal bunker. Then a new coal bunker, without building the CGC, is proposed and put into practice to replace the 214# coal bunker. The FLAC^3D software program is adopted to establish the numerical model of the wall-mounted coal bunker (WMCB), and the stability of the rock surrounding the WMCB is simulated and analysed. The results show that: (1) the rock surrounding the sandstone segment is basically stable. (2) The surrounding rock in the coal seam segment, which moves into the inside of the bunker, is the main zone of deformation for the entire rock mass surrounding the bunker. Then the surrounding rock is controlled effectively by means of high-strength bolt–cable combined supporting technology. According to the geological conditions of the WMCB, the self-bearing system, which includes (i) H-steel beams, (ii) H-steel brackets, and (iii) self-locking anchor cables, is established and serves as a substitute for the CGC to transfer the whole weight of the bunker to stable surrounding rock. The stability of the new coal bunker has been verified by field testing, and the coal mine has gained economic benefit to a value of 158.026174 million RMB over three years. The new WMCB thus made production more effective and can provide helpful references for construction of vertical bunkers under similar geological conditions.

In this paper, firstly, some questionable formulas and conceptual oversights of previous reduced sigma set unscented transformation (UT) methods are revised through theoretical analysis. Then the revised UT methods based Kalman filters are used in a GPS/INS tightly-coupled system. The Kalman filter flows are the kind of square-root, since the square-root unscented Kalman filters (SRUKFs) can guarantee the stability of the system. By using the reduced sigma set SRUKFs (which contain simplex sigma set square-root unscented Kalman filter (S-SRUKF), spherical simplex sigma set square-root unscented Kalman filter (SS-SRUKF) and minimum sigma set square-root unscented Kalman filter (M-SRUKF)), the computation cost is greatly saved compared with the standard SRUKF, while the accuracy of the GPS/INS tightly-coupled system still maintained. The structure of the GPS/INS tightly-coupled system is in the form of error state, and the time updates of the state and the state covariance of SRUKFs are directly estimated without using UT, thus the computational time is also greatly saved. The pseudo-satellite is introduced to aid the system when the observation information is deficient, for example, when the GPS signal is deficient in the maneuver environment. By using the pseudo-satellite, the optimal performance of the system is guaranteed. Experiment of unmanned aerial vehicle (UAV) showed that the pseudo-satellite aided mechanism worked well.

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

Chromium (VI) is carcinogenic through intermediates formed in the cellular milieu by reduction with small reductants like glutathione (GSH), ascorbate (As), cysteine (Cys) and NADPH. Although the reduction of chromate by thiols have been investigated, the participation of Cr(IV) intermediates has been inferred only indirectly due to its refractive behavior towards EPR spectroscopy and the lack of true chromium (IV) complexes for comparative studies. Biological data from numerous reports indicate that Cr(IV) is the species most likely responsible for the carcinogenicity of Cr(VI). Our kinetic studies suggested that in acidic solutions the reduction of chromate with GSH affords mostly a chromium(IV) intermediate. As a step towards the full characterization of the paramagnetic species involved in the reduction of chromate by thiols at neutral pH, we embarked in the investigation of this reaction using the Superconducting QUantum Interference Device (SQUID) Magnetic Property Measuring System®. Our results indicate a strong influence of the temperature. At 2K, the saturation magnetization method was applied to the frozen reaction when it reached the peak of formation of intermediates. Contrary to our expectations, the contributions were calculated to be 30 % of Cr(IV) and 69 % of Cr(V) at this pH. When the Curie-Weiss method was utilized, the effective magnetic moment was dependent on the portion of the data utilized and generally higher proportion of Cr(IV) was found when the fitting is performed with data from higher temperature.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

A tailored model for the assessment of environmental benefits achievable by “light-weighting” in the automotive field is presented. The model is based on the Fuel Reduction Value (FRV) coefficient, which expresses the Fuel Consumption (FC) saving involved by a 100 kg mass reduction. The work is composed of two main sections: simulation and environmental modelling. Simulation modelling performs an in-depth calculation of weight-induced FC whose outcome is the FRV evaluated for a wide range of Diesel Turbocharged (DT) vehicle case studies. Environmental modelling converts fuel saving to impact reduction basing on the FRVs obtained by simulations. Results show that for the considered case studies, FRV is within the range 0.115–0.143 and 0.142–0.388 L/100 km × 100 kg, respectively, for mass reduction only and powertrain adaptation (secondary effects). The implementation of FRVs within the environmental modelling represents the added value of the research and makes the model a valuable tool for application to real case studies of automotive lightweight LCA.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.