The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

Annotating training data is a time consuming and labor intensive process in deep learning, especially for images with many objects present. In this paper, we propose a method to allow deep networks to be trained on data with reduced numbers of annotations (per image) in heatmap regression tasks (e.g. object detection and counting), by applying an asymmetric loss function. In a real scenario, this reduction of annotations can be imposed by the researchers (e.g. ask the annotators to label only 50% of what they see in each image), or can potentially counteract unintentionally missing labels from the annotators. To demonstrate the effectiveness of our method, we conduct experiments in two domains, crowd counting and wheat spikelet detection, using different deep network architecture. We drop various percentages of instance annotations per image in training. Results show that an asymmetric loss function is effective across different models and datasets, even in very extreme cases with limited annotations provided (e.g. 90% of the original annotations reduced). Whilst tuning of the key parameters are required, we find that setting conservative parameter values can help more realistic situations, where only small amounts of data have been missed by annotators.

The extrusion-based additive manufacturing is a popular fabrication method, which has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the feed of filament that is presented in the production G-code. The feed of filament is calculated according to the layer height, the extrusion width and the length of printing path. All the required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs that are placed next to each other at a fixed distance. In special curved paths, the distance of adjacent ones is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. Therefore, making a special computer code that can be used to create various functions of curved paths is investigated in this research, and also the feed of filament parameter is studied in detail. Next, by introducing a correction technique, the feed of filament is changed during the curved path to distribute the polymer material uniformly. Finally, composite samples (which have variable stiffness) consisting of curved fibers are produced with the proposed method, and the high quality of printed samples confirm the suggested code and technique.

We study a localization of gravity through the matching point between non-inertial frames and local inertial frames. This localization of gravity is done through defining relative gravitational red-shift. This lead to an emergence of a timeless state in a mathematically consistent way. In this timeless state of gravity, we find a geometric interpretation of the speed of light and mass. The experimental support of the timeless state is the quantum entanglement and internal symmetries that are independent of time. Therefore gravity would be responsible for measurements independent of time including quantum entanglement. Timeless state in that sense explain why do we have gravity/gauge correspondence. We conjecture that the universe emerged from a black hole with a global $SU(3)\times SU(2)\times U(1)$ symmetry on its event horizon that produces gauge fields Electromagnetism, weak and strong nuclear force through localization of this global symmetry. Through the localization in the gravity field, the timeless measurements such as spin will be correlated with the varying of timeless measurement which is relative gravitational red-shift. We present a gravitational or geometric interpretation of spin-0, spin-1, and spin-1/2 states. We present an interpretation of why do we measure matter rather than anti-matter. We found that the Higgs scalar field is represented by the gravitational red-shift at every point in the space around the black hole. We derive the numerical value of the cosmological constant that agrees with experimental observations.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior.

Influence of core flow inlet swirl angle on aerodynamic performances of an exhaust nozzle with scarfed lobed mixer was studied by the validated computational approach. The computational simulation was conducted by resolving the steady form of discretized three-dimensional Reynolds Averaged Navier-Stokes equations with the shear stress transport k-Ω turbulence model. Simulation results depict that swirling motions have ignorable influence on the flow field of the top part in the cross sections slightly downstream of the lobed trailing edge. Besides, for the flow field downstream of the L/D=0.1 cross section, the swirling motions are suggested to cause the clockwise stream-wise vortex to stretch into several smaller-scale vortexes. When the case with a bigger swirling angle is investigated, the induced smaller-scale vortexes are more strengthened by the swirling motions. Concerning the 15° swirling case, the loss caused by the destroyed vortex pattern and the benefit induced by the improved smaller-scale vortexes almost counteract with each other with respect to the thermal mixing efficiency. In the last studied cross section as compared with the baseline case, the case with a maximum swirling angle of 30° has increased 6.94% for the thermal mixing efficiency and decreased 0.42% for the total pressure recovery coefficient.

Dynamic mechanical analysis (DMA) is a versatile technique that complements the information given by the more traditional thermal analysis techniques such as differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and thermal mechanical analysis (TMA). Dynamic constants such as storage modulus, loss modulus, and loss factor are temperature dependent and provide information about interfacial bonding between reinforced fibre and polymer matrix of composite material. To study the above mentioned properties at the glass transition region, for unidirectional fibrous composites reinforced with continuous fibers a reliable model was applied. In particular, the composite material was considered as composed of three phases with the intermediate phase between matrix and fibres, the interphase, to have variable properties depending on those of main phases and the mode of preparation of the overall material. The glass transition temperature is defined as the point at which the specific volume versus temperature curve changes abruptly slope marking the region between rubbery polymer and glassy polymer nature. Hence, the behaviour of unidirectional fibrous composites was investigated at this region. Examination of the glass transition temperature, which constitutes an upper limit for the structurally important glassy region through the loss factor, was performed by its consideration as a combination of glass transition temperature of matrix and interphase.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

The recent provision of energy-harvesting capabilities to wireless sensor networks has entailed the redefinition of design objectives. Specifically, the traditional goal of maximizing network lifetime has been replaced by optimizing network performance, namely delay and throughput. The present paper contributes to this reformulation by considering the routing problem for the class of time-driven energy-harvesting WSN (EH-WSN) under regular or quasi-periodic energy sources. In particular, this paper shows that the minimum hop count (MHC) criterion maximizes the average duty cycle that can be sustained by nodes in this type of scenarios. This is a primary objective in EH-WSN, since large duty cycles lead to enhanced performance. Based on a previous result, a general expression is first obtained which gives mathematical form to the relationship between duty cycle and traffic load for any node in a time-driven EH-WSN fed by a regular energy source. This expression reveals that the duty cycle achievable by a node decreases as its traffic load increases. Then, it is shown that MHC minimizes the average traffic load over the network, and thus it maximizes the average duty cycle of nodes. This result is numerically validated via simulation by comparison with other well-known routing strategies. Accordingly, this paper suggests assigning top priority to the MHC criterion in the development of routing protocols for time-driven EH-WSN under regular energy sources.

Environmental studies, metabolic research, and state of the art neurobiology point towards the reduced amount of natural day and sunlight exposure of the developing child’s organism as the consequence of increasingly long hours spent indoors online as the single unifying source of a whole set of health risks identified worldwide, as is made clear in this review of the current literature. Over exposure to digital environments, from abuse to addiction, now concerns even the youngest (ages 0 to 2), and triggers, as argued on the basis of clear examples herein, a chain of interdependent negative and potentially long-term metabolic changes. This leads to a deregulation of the serotonin and dopamine neurotransmitter pathways in the developing brain, currently associated with online activity abuse and/or internet addiction, and akin to that found in severe substance abuse syndromes. A general functional working model is proposed under the light of evidence brought to the forefront in this review.

Traumatic brain injury (TBI) is a major source of worldwide morbidity and mortality. Patients suffering from TBI exhibit a higher susceptibility to bone loss and an increased rate of bone fractures; however, the underlying mechanisms remain poorly defined. Herein, we observed significantly lower bone quality and elevated levels of inflammation in bone and bone marrow niche after controlled cortical impact-induced TBI in in-vivo CD-1 mice. Further, we identified dysregulated NFB signaling, an established mediator of osteoclast differentiation and bone loss, within the bone marrow niche of TBI mice. Ex vivo studies revealed increased osteoclast differentiation in bone marrow-derived cells from TBI mice, as compared to sham injured mice. Finally, we found bone marrow derived extracellular vesicles (EVs) from TBI mice enhanced the colony forming ability and osteoclast differentiation efficacy of bone marrow cells and activated NFB signaling genes in bone marrow-derived cells. Taken together, we provide evidence that TBI-induced inflammatory stress on bone and the bone marrow niche may activate NFB leading to accelerated bone loss. Targeted inhibition of these signaling pathways may reverse TBI-induced bone loss and reduce fracture rates.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. Following are the main 5 DR stages: none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all 5 stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an Enhanced Super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement; augmentation techniques are then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

Background: We aimed to assess the association between DII and PD and the mediation effect of DII in the association of PD with systemic inflammation. Using the National Health and Nutrition Examination Survey (NHANES) 2009-2010, 2011-2012 and 2013-2014, participants that received periodontal exam and provided dietary recall data were included. The inflammatory potential of diet was calculated via DII. Periodontitis was defined according to the 2012 case definition. The clinical outcomes of interest were mean periodontal probing (PPD), mean clinical attachment loss (CAL) and thresholds of PPD and CAL. White blood cells (WBC), segmented neutrophils and C-reactive protein (CRP) were used as proxies for systemic inflammation. The periodontal measures were regressed across DII values using adjusted multivariate linear regression. Adjusted mediation analysis appraised the influence of DII in the association of periodontitis and systemic inflammation. 10,178 participants were included. DII was significantly correlated with mean PPD, mean CAL, thresholds of PPD and CAL, WBC, segmented neutrophils and DII (p<0.01). A linear regression logistic adjusted for multiple confounding variables confirmed the association between DII and mean PPD (B = 0.02, Standard Error [SE]: 0.02, p<0.001) and CAL (B = -0.02, SE: 0.01, p<0.001). The association of mean PPD and mean CAL with both white blood cells and segmented neutrophils were mediated by DII (from 2.1 to 3.5%, p<0.001). In the 2009-2010 subset, the association of mean CAL with serum CRP was mediated by DII (52.0%, p<0.01). In conclusion, inflammatory diet and PD may be associated. Also, the inflammatory diet significantly mediated the association of leukocyte counts and systemic inflammation with periodontitis.

Although congenital sensorineural hearing loss (SHL) in the bilateral cochleae mainly results from genetic abnormalities, chronic SHL progressing in later life is often influenced by systemic immune disturbances, including autoimmunity, chronic inflammation, and immunosenescence. We have investigated the relationship between the inner ear and systemic immunity and reviewed the possibilities to prevent SHL, including autoimmune SHL and age-related SHL. We also demonstrated two lymphocyte populations, interleukin 1 receptor type II (IL-1R2)-positive T cells (T1R2) and naturally occurring regulatory T cells (nTregs) in CD4⁺ T cells, which increase with aging, suppress host immune function and promote organ degeneration. Alterations in systemic immunity by fewer microbial antigen challenges in the living environment, elimination of immune suppressive lymphocytes, or immune rejuvenation with a reconstituted thymus may contribute not only to renew the cochlear function in SHL, but also to extend the healthy life of functional organs in a vigorous and youthful body, one of humanity’s greatest dreams.

Many causal factors to marine traffic accidents (MTA) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to the accident mechanisms, the complex structural chains on causes to MTA systems were analyzed combining the Human Failure Analysis and Classification System (HFACS) with theoretical Structural Equation Modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of MTA, and the constituent elements of the causes of accident was conducted. Second, a hypothetical model of Human factors classification was proposed applying the practice of the structural model. Third, with the data resource from ship accident cases, this hypothetical model was discussed and simulated, and as a result the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behaviors. Application examples show that relationships in HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

Ecological changes evoke many felt losses and types of grief. These affect sustainability efforts in profound ways. Scholarship on the topic is growing, but the relationship between general grief research and ecological grief has received surprisingly little attention. This interdisciplinary article applies theories of grief, loss, and bereavement to ecological grief. Special attention is given to research about “non-death loss” and other broad frameworks about grief. Dynamics related to both local and global ecological grief are discussed. Kinds of potential losses arising out of ecological issues are clarified with the frameworks of tangible/intangible loss, ambiguous loss, nonfinite loss, and shattered assumptions. Various possible types of ecological grief are illuminated by discussing the frameworks of chronic sorrow and anticipatory grief/mourning. Earlier scholarship about disenfranchised ecological grief is augmented by further distinctions about various forms it may take. The difficulties in defining complicated or prolonged grief in ecological context are discussed, and four types of “complicated ecological grief” are explored. Based on the findings, three special forms of ecological loss and grief are named and discussed: transitional loss and grief, lifeworld loss, and shattered dreams. The implications of the results for ecological grief scholarship, counselling and coping are briefly discussed. The results can be used by psychological and healthcare professionals and researchers, but also by members of the public who wish to reflect on their eco-emotions.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

Amyloids are fibrillar protein aggregates that are associated with diseases such as Alzheimer’s disease, Parkinson’s disease, type II diabetes and Creutzfeldt–Jakob disease. The process of amyloid aggregation involves three pathological protein transformations; from natively-folded conformation to the cross-β conformation, from biophysically soluble to insoluble, and from biologically functional to non-functional. While amyloids share a similar cross-β conformation, the biophysical transformation can either take place spontaneously via a homogeneous nucleation mechanism (HON) or catalytically on an exogenous surface via a heterogeneous nucleation mechanism (HEN). Here, we postulate that the different nucleation pathways can serve as a mechanistic basis for an etiological classification of amyloidopathies, where hereditary forms generally follow the HON pathway, while sporadic forms follow surface-induced (including microbially-induced) HEN pathways. Furthermore, the conformational and biophysical amyloid transformation results in loss-of-function (LOF) of the original natively-folded and soluble protein. This LOF can, at least initially, be the mechanism of amyloid toxicity even before amyloid accumulation reaches toxic levels. By highlighting the important role of non-protein species in amyloid formation and LOF mechanisms of toxicity, we propose a generalized mechanistic framework that could help better understand the diverse etiology of amyloid diseases and offer new opportunities for therapeutic interventions including replacement therapies.

This paper constructs a model of climate-related damage for small island developing states (SIDS). We focus on the loss of private productive capital stocks through extreme climate events. In contrast to most economic analyses of climate impacts, which assume temperature-dependent damage functions, we draw on the engineering literature to allow for a greater or lesser degree of anticipation of climate change when designing capital stocks and balancing current adaptation expenditure against future loss & damage. We apply the model to tropical storm damage in the small island developing state of Barbados and show how anticipatory behavior changes the damage to infrastructure for the same degree of climate change. Thus, in the model, damage depends on behavior as well as climate variables.

The effective management of Fusarium wilt of bananas (FW) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: To estimate disease intensity and impact, and to investigate the spatial and temporal dynamic of FW. Fields planted with Silk (n = 10), Pome (n = 17) or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed and diseased plants were georeferenced. The incidence of FW and the impact of the disease on yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FW incidence ranged from 0.09 to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8,192.5 kg.ha-1.year-1, with a median of 935.2 kg.ha-1.year-1. The higher economic impact of the disease was observed on Silk cultivar with a median loss of US$ 910.5 ha-1.year-1. Overall, estimated losses increased on average by US$ 109.8 ha-1.year-1 at each 1% of incidence. Aggregation of FW was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FW progress and the Gompertz model best fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants were detected. FW is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub (e.g. airports) or to evacuate a disaster zone. Many methods have been used to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have increasingly become more popular in this kind of applications, given their robustness and scalability. In this paper, we have implemented an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be able to solve the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we present the integration and functionality of these methods applied in a pair of NAO robots.

Climate change causes weather extremes to rise in frequency and severity, which could have detrimental effects on human life, property and livelihood activity. There is significant uncertainty about the influences of anthropogenic climate change on the occurrence and severity of small-scale, sudden onset weather phenomena such as hailstorms and subsequent loss and damage. Yet, several studies indicate that there is an apparent stable connection between hailstorm activity and hailstorm damage. Severe hailstorm events are observed in Bangladesh in recent years, which are, in fact, rapid-onset disasters but low exposure in terms of giving government response and media consideration. Hence this study examines potential impacts and management strategies for loss and damage resulting from hailstorm events among smallholder farmers in Kurigram District's Phulbari Upazila of Bangladesh. Firstly, the direct and long term economic and non-economic loss and damage caused by the hailstorm on human well-beings and livelihoods were assessed. Then, the study evaluated the current adaptation, coping, management and response strategies at the institutional and community level in the context of such extreme events. Finally, a regulatory framework and implementation approaches had suggested achieving the country's resilience against disaster and climate change-induced loss and damage. Participatory Vulnerability Analysis, Key Informant Interviews and Sample Surveys accumulated the primary data for the study. In addition, secondary data were collected through analysis of literature, published and unpublished scientific articles and media reports, etc. This research outcome will help countries develop a guideline to address climate change and disaster-related loss and damage.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.

At present, the world is now passing a very far different time than normal situation due to the COVID-19 pandemic crisis. The global life-style and human civilization is currently progressing with down-stream that affecting almost every sectors necessary for human civilizations except the current environmental situation. To control the COVID-19 spreading, most of the countries are following lockdown process that reduces human mobility, thus reducing the CO2 emission to the environment. Though the COVID-19 pandemic is a blessing for the present environment, however, the post-COVID world will face a massive thrust of energy and only conventional energy resources may not be enough to mitigate the energy demands. Solar power generation technology mainly the photovoltaic (PV) systems and their advancement can be the leading possibilities to minimize the gap between the power demand and generation. It is now time to think how we can improve the PV power generation in future and the post-COVID world. In this encyclopaedia communication, we report on Nano-technological approach to improve the conversion efficiency of GaAs solar cells. We have designed and optimized several types of nano-structured assemblies that can be implemented to reduce the front surface incident light reflection losses thus can assist to improve the conversion efficiency of GaAs solar cells.

After developed the formulation of a "general relativity" in C⁴ [2], we proceed with the formulation of a Hamilton-Jacobi equation in C⁴. We argue that in this consideration, the usual problems of the ADM formalism, do not exist, due to the complex time as it exists in our consideration. Specically, we can derive a suitable dispersion relation in order to work with and nd a generalised super Hamiltonian

Family Phyllanthaceae is one of the largest segregates of the eudicot order Malpighiales and its species are herb, shrub, and tree, which are mostly distributed in tropical regions. Certain taxonomic discrepancies exist at genus and family level. Here, we report chloroplast genomes of three Phyllanthaceae species—Phyllanthus emblica, Flueggea virosa, and Leptopus cordifolius— and compare them with six others previously reported Phyllanthaceae chloroplast genomes. The species of Phyllanthaceae displayed quadripartite structure, comprising inverted repeat regions (IRa and IRb) that separate large single copy (LSC) and small single copy (SSC) regions. The length of complete chloroplast genome ranged from 154,707 bp to 161,093 bp; LSC from 83,627 bp to 89,932 bp; IRs from 23,921 bp to 27,128 bp; and SSC from 17,424 bp to 19,441 bp. Chloroplast genomes contained 111 to 112 unique genes, including 77 to 78 protein-coding, 30 transfer RNA (tRNA), and 4 ribosomal RNA (rRNA) that showed similarities in arrangement. The number of protein-coding genes varied due to deletion/pseudogenization of rps16 genes in Baccaurea ramiflora and Leptopus cordifolius. High variability was seen in number of oligonucleotide repeats while analysis of guanine-cytosine (GC) content, codon usage, amino acid frequency, simple sequence repeats analysis, synonymous and non-synonymous substitutions, and transition and transversion substitutions showed similarities in all Phyllanthaceae species. We detected a higher number of transition substitutions in the coding sequences than non-coding sequences. Moreover, the high number of transition substitutions was determined among the distantly related species in comparison to closely related species. Phylogenetic analysis shows the polyphyletic nature of the genus Phyllanthus which requires further verification. We also determined suitable polymorphic coding genes, including rpl22, ycf1, matK, ndhF, and rps15 which may be helpful for the reconstruction of the high-resolution phylogenetic tree of the family Phyllanthaceae using a large number of species in the future. Overall, the current study provides insight into chloroplast genome evolution in Phyllanthaceae.

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

Background: Since circa 1980, a model of neocortical interactions, Statistical Mechanics of Neocortical Interactions (SMNI) has been successful in calculating many experimental phenomena, including fits to electroencephalographic (EEG) data in attention tasks, using an importance-sampling code Adaptive Simulated Annealing (ASA). The SMNI model is developed in the context of classical path-integrals, which affords intuitive insights as well as direct numerical benefits, e.g., using the effective Action as a a cost/objective function for parameter fits to data. Objective: Previous authors have fit affective EEG data to neural-network models. This project seeks to use models based on physics and biology to fit this same data. Previous work showed improvements in fits to EEG for attention states; this project extends these methods to affective states. Method: Path integrals are used in both classical and quantum contexts. Classical path integrals are used to define a cost/objective function to fit data, and quantum path integrals are used to derive a closed-form analytic expression for Ca-ion waves in the presence of a magnetic vector potential which is generated by highly synchronous neuronal firings which give rise to EEG. ASA is used to fit EEG data. Results: The mathematical-physics and computer parts of the study are successful, in that cost/objective functions used to fit EEG data using these models are consistent with previous work published by other authors. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues. The results here are consistent, not better, than previous work using neural-network models, albeit only one parameter was used here, instead of multiple filters and kernels used previously on such data. Conclusion: Although these quantum effects are highly speculative, explicit calculations have shown them to be consistent with experimental data, at least to date. The current supercomputer project extends this model to affective/emotion data. Results from several authors using neural-network approaches at individual electrode sites show some predictive capabilities; the results given here are consistent with these other results. However, since the SMNI model includes these quantum effects, this is another reason to continue examining these issues.

Understanding the distribution in drought and floods plays an important role in disaster risk management. The present study aims to explore the trends in the standardized precipitation index and extreme precipitation days in China, as well as to estimate the economic losses they cause. We found that in the Northeast China, northern of North China and northeast of Northwest China were severely affected by drought disasters (average damaged areas were 6.44 million hectares) and the most severe drought trend was located in West China. However, in the north of East China and Central China, the northeastern of the Southwest China was severely affected by flood disasters (average damaged areas were 3.97 million hectares) and the extreme precipitation trend is increasing in the northeastern of the Southwest China. In the Yangtze River basin, there were increasing trends in terms of drought and extreme precipitation, especially in the northeastern of the Southwest China, where accompanied by severe disaster losses. By combining the trends in drought and extreme precipitation days with the distribution of damaged areas, we found that the increasing trend in droughts shifted gradually from north to south, especially in the Southwest China, and the increasing trend in extreme precipitation gradually shifted from south to north.

We analyzed signal transduction of three constitutively activating mutants (M410T, L469R, and D590Y) and two inactivating mutants (D417N and Y558F) of the eel luteinizing hormone receptor (eel LHR), known to occur in human LHR. The objective of this study was to assess the functional effects of these mutations in signal transduction and cell surface loss of receptor. Mutant receptors were transiently expressed in Chinese hamster ovary (CHO-K1) cells. Eel LH-stimulated accumulation of cyclic adenosine monophosphate (cAMP) was measured by homogeneous time-resolved fluorescence (HTRF) assays. The loss of receptors from the cells surface was measured using an enzyme-linked immunosorbent assay (ELISA) in human embryonic kidney (HEK) 293 cells. The cAMP response in cells expressing the wild type eel LHR was increased in a dose-dependent manner using eel LH ligand stimulation. Compared with the wild type, cells expressing the activating mutants (M410T, L469R, and D590Y), exhibited a 4.0-, 19.1-, and 7.8-fold increase in basal cAMP response without agonist stimulation, respectively. Their maximal responses to agonist stimulation were approximately 65%, 52%, and 98%, respectively, of those of the wild type. The inactivating mutants (D417N and Y558F) did not completely impair signal transduction, and their maximal responses were only 33% and25 % of those of wild type. These data clearly showed that the eel LHR-L469R and D590Y, activating mutants enhanced the rate of the loss of cell surface receptors following treatment with eel LH. Thus, the loss of cell surface receptors in cells expressing mutant eel LHRs was consistent with the eel LH agonist-induced production of cAMP. Our results suggested that the activation of the eel LHR requires appropriate loss of LHR-ligand complexes from the cell surface.

In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

Sometimes when diabetic retinopathy (DR) is found and treated quickly, vision loss can indeed be spared. This study deploys a deep learning (DL) model that can discover all 5 stages of DR more accurately than other methods. The proposed methodology shows two cases scenarios: case 1 with image enhancement using CLAHE and ESRGAN, and case 2 without image enhancement. Augmentation techniques are then employed to produce a balanced dataset with the identical criteria for both scenarios. The generated model using DenseNet-121 on the APTOS dataset outperformed other approaches for locating the 5 stages of DR, with an accuracy of 98.7 percent for case 1 and 81.2 percent for case 2. Using CLAHE and ESRGAN was shown to improve a model's performance and ability to learn.

Controlled drainage (CD) is an important agricultural measure for maintaining soil moisture and nutrients, controlling groundwater level, and increasing crop yield. In arid regions, CD can be used to improve the water supply in agriculture and reduce environmental pollution. In this study, we investigated the effect of CD, including a drainage depth of 40 cm (CWT1) and 70 cm (CWT2) during the plant growth period, free drainage (FD), and open ditch drainage (OD) on the migration of water, nutrients, and salts in the soil; the dynamics of groundwater level; the loss of soil nitrogen; and the growth of oilseed sunflower plants. Compared with FD, CD increased the water and nutrient content in the soil, reduced nitrogen loss, and enhanced the ability of the soil to continuously supply nitrogen to the oilseed sunflower plants, which benefited plant growth at later growth stages and reduced environmental pollution. During the period between irrigation at the budding stage and harvest stage, the average soil water content in the 0–20 cm soil layer in CWT1 increased by 3.67%, 4.78%, and 0.55%, respectively, compared with that in CWT2, FD, and OD. The soil mineral content in CWT1 was 25.17%, 35.05%, and 17.78% higher than that in CWT2, FD, and OD, respectively, indicating that higher soil salinity occurred at the later stage of plant growth in CWT1, which actually had little effect on the plants due to their enhanced salt tolerance and increased need for water and nutrients at that stage. In addition, CD delayed the decline in groundwater level, which allowed the plants to use groundwater at later growth stages, and as a result the yield and water use efficiency were improved. CWT1 significantly increased oilseed sunflower yield by 4.52–11.14% and increased water use efficiency by 1.16–10.8%. Moreover, CWT1 also increased the survival rate of the oilseed sunflower plants by 2.62–2.92%, and the plants demonstrated good growth. Therefore, under CD conditions, plants used soil water and nitrogen more efficiently and, as a result, their productivity was increased, and the water quality was improved.

Turbine generator operates with complex cooling system due to the challenge in controlling the peak temperature of the stator bar caused by ohm loss, which is unavoidable. Therefore, it is important to characterise and quantifies the thermal performance of the cooling system. The focus of the present research is to investigate the heat transfer and pressure loss characteristics of typical cooling system, so-called stator ventilation duct. A real scale model was built at its operating conditions for the present study. The direction of cooling air is varied to consider its operation condition, so that there are (1) outward flow and (2) inward flow cases. In addition, the effect of (3) cross flow (inward with cross flow case) is also studied. The transient heat transfer method using thermochromic liquid crystals is implemented to measure full surface heat transfer distribution. A series of Computational Fluid Dynamics analysis is also conducted to support the observation from the experiment. For the inward flow case, the results suggest that the average Nusselt number of the 2nd duct is about 30% higher than the 3rd duct. The trend is similar with the effect of cross flow. The CFD results are in good agreement with the experimental data.

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

Partial shading on solar photovoltaic (PV) arrays is a prevalent problem in photovoltaic systems that impair the performance of PV modules and is responsible for reduced power output as compared to that in standard irradiance conditions thereby resulting in the appearance of multiple maximas on panel output power characteristics. These maxims contribute to mismatch power losses among PV modules. The mismatch losses depend on shading characteristics together with different interconnected configuration schemes of PV modules. The research presents a comparative analysis of partial shading effects on a 4 x4 PV array system connected in series(S), parallel (P), serries-parallel (SP),total-cross-tied (TCT),central-cross-tied(CCT),bridge-linked(BL),bridge-linked total cross-tied (BLTCT) ,honey-comb(HC), honey-comb total-cross-tied (HCTCT) and ladder (LD) configurations using MATLAB/Simulink. The PV module SPR-X20-250-BLK was used for modeling and simulation analysis. Each module is comprised of 72 number of PV cells and a combination of 16 PV modules was employed for the contextual analysis. Accurate mathematical modeling for the HCTCT configuration under partial shading conditions (PSCs) is provided for the first time and is verified from the simulation. The different configuration schemes were investigated under short-narrow,short-wide,long-narrow,long-wide, diagonal, entire row distribution, and entire column distribution partial shading condition patterns with mathematical implementation and simulation of passing clouds. The performance of array configurations is compared in terms of maximum power generated ), mismatch power loss (∆), relative power loss ) and the fill factor (FF). It was inferred that on average, TCT configuration yielded maximum power generation under all shading patterns among all PV modules interconnection configurations with minimum mismatch power losses followed by hybrid and conventional PV array configurations respectively.

Prestress loss evaluation in prestressed strand is essential for prestressed structures. However, the sensors installed outside the duct can only measure the total prestress loss. The sensors attached on strand inside the duct also have several problems, such as inadequate durability in an aggressive environment, vulnerable damage at tensioning and so on. This paper proposes a new installation method for long-gauge fiber Bragg grating (LFBG) sensor to prevent accidental damage. Then the itemized prestress losses were determined in each stage of the pre-tensioning and post-tensioning according to the LFBG measurements. We verified the applicability of the LFBG sensors for prestress monitoring and the accuracy of the proposed prestress loss calculation method during pre-tensioning and post-tensioning. In the pre-tensioning case, the calculated prestress losses had less deviation from the true losses than those obtained from foil-strain gauges, and the durability of the LFBG sensors was better than foil-strain gauges, whereas in post-tensioning case, the calculated prestress losses were close to those derived from theoretical predictions. Finally, we monitored prestress variation in the strand for 90 days. The itemized prestress losses at each stages of post-tensioning were obtained by the proposed calculation method to show the prospect of the LFBG sensors in practical evaluation.

Difficult-to-cut materials are being increasingly used in many industries because of their superior properties, including high corrosion resistance, heat resistance and specific strength. However, these same properties make the materials difficult to machine using conventional machining techniques. Laser-assisted milling (LAM) is one of the effective methods for machining difficult-to-cut materials. In laser-assisted milling machining occur after the workpiece is locally preheated using a laser heat source. Laser assisted machining has been studied by many researchers on flat workpiece or micro end-milling. However, there is no research on the curved shape using laser assisted milling. This study investigated the use of laser assisted milling to machine a three-dimensional curved shape workpiece based on NURBS. A machining experiment was performed on Inconel 718 using different tool paths (ramping, contouring) under various machining conditions. Finite elements analysis was conducted to determine the depth of cut. Cutting force, specific cutting energy and surface roughness characteristics were measured, analyzed and compared for conventional and LAM machining. LAM significantly improved these machining characteristics, compared to conventional machining. There results can be applied to the laser-assisted machining of various three-dimensional shapes.

The use of whole-body vibration (WBV) for therapeutic purposes is far from being standardized and only very recently an empirical foundation for reporting guidelines for human WBV studies has been published. Controversies about safety and therapeutic dosage stll exist. The present study aimed to investigate the metabolic and mechanical effects of low-intensity WBV in according to the ISO 2631 norm on subjects with obesity. 41 obese subjects (BMI≥ 35 kg/mˆ2) were recruited to participate in a 3-week multidisciplinary inpatient rehabilitation program including fitness training and WBV training. During WBV the posture was monitored with an optoelectronic system with 6 infrared cameras (Vicon, Vicon Motion System, Oxford, UK). The primary endpoints were: variation in body composition, factors of the metabolic syndrome, functional activity (sit-to-stand and 6-min walking test), muscle strength, and quality of life. Secondary endpoints were: modification of irisin, testosterone, growth hormone, IGF1 levels. We observed significant changes in salivary irisin levels, Group 2 (p<0.01) as compared to the control group, while muscle strength, function, and other metabolic and hormonal factors did not change after a 3-week low-intensity WBV training respect control group. Future studies are needed to deeper investigate the potential metabolic effect of low-intensity WBV in managing weight.

This article reveals a best possible design for hybrid dispersion compensating fiber (HyDCF) with high birefringence established on modified broadband compensating structure through S, C and L telecommunication bands. The simulation outcome exhibits relatively higher birefringence of 3.76×10-2 at wavelength of 1550 nm. The suggested fiber also has dispersion compensation characteristics in an inclusive series of wavelengths which covers 1400-1625 nm. The reported design can achieve dispersion quantity of – 606 ps/ (nm.km) at 1550 nm effective wavelength. The reported fiber design matches the relative dispersion slope (RDS) 0.003694 nm-1 similar to single mode fiber at 1550 nm operating wavelength. This fiber demonstrates negatively flattened effective dispersion of – 2.703 ± 0.734 ps/ (nm.km) within 180 nm flat band ranging from 1460-1640 nm wavelength. It is also convenient to optical high bit rate communication systems. The low confinement loss is found 3.756×10-10 dB/m at the operating wavelength. This design also achieves highly nonlinear coefficient of 50.34 W-1km-1. In some cases, it can also be used in sensing applications.

With the development of energy internet, integrated energy system can effectively reduce carbon emissions and improve the utilization of renewable energy. In this paper, a low-carbon optimal scheduling model of integrated energy system considering heat loss of heat network pipeline is proposed. Based on the study of concentrating solar power (CSP) plant and heat storage tank (HS), an optimal scheduling model is established, which takes system operation cost, environmental pollution and penalty cost of abandoning wind and solar energy as objectives. Through the analysis of example results, it is proved that the model proposed in this paper can achieve the goal of reliable, low-carbon and economic operation of the system. At the same time, it shows that CSP unit can reduce the operation cost of system and increase energy coupling and utilization.

Since the discovery of the Coronavirus disease 2019 (COVID-19) outbreak, a vast majority of studies have been carried out that confirmed the worst outcome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in people with preexisting health conditions, including diabetes, obesity, hypertension, cancer, and cardiovascular diseases. Likewise, diabetes itself is one of the leading causes of global public health concerns that impose a heavy global burden on public health as well as socio-economic development. Both diabetes and SARS-CoV-2 infection have their independent ability to induce the pathogenesis and severity of multi-system organ dysfunction, while the co-existence of these two culprits can accelerate the pathophysiology and magnify the severity of the diseases. However, the exact pathophysiology of multi-system organ failure in diabetic patients after SARS-CoV-2 infection is still obscure. This review summarized the organ-specific possible molecular mechanisms of SARS-CoV-2 and diabetes-induced pathophysiology of several diseases of multiple organs, including the lungs, heart, kidney, brain, eyes, gastrointestinal system, and bones, and subsequent manifestation of multi-system organ failure.

The black hole information paradox is one of the most puzzling paradoxes in physics. Black holes trap everything that falls into them, while their mass may leak away through purely thermal Hawking radiation. When a black hole vanishes, all the information locked inside, if any, is just lost, thus challenging the principles of quantum mechanics. However, some information does have a way to escape from inside the black hole, that is, through gravitational waves. Here, a concise extension of this notion is introduced. When a black hole swallows something, whether it is a smaller black hole or an atom, the system emits gravitational waves carrying the information about the “food”. Although most of the signals are too weak to be detected, the information encoded within them will persist in the universe. This speculation provides an explanation for a large part, if not all, of the supposed “information loss” in black holes, and thus reconciles the predictions of general relativity and quantum mechanics.

In order to alleviate the problems regarding conventional damping modeling techniques in nonlinear structural simulations, using concentrated plasticity formulation including rotational end springs with no damping plus middle elastic parts with modified assigned damping has recently been proposed. However, a proper selection for springs’ stiffness is a source of contention. In this paper, the effect of choosing different ratios for springs’ rotational stiffness to the elastic part of the elements on the seismic performance of RC moment frames was investigated. Incremental Dynamic Analysis (IDA) was performed and the derived responses were used for seismic performance assessment methodology. The results demonstrate that using flexible springs may lead to a greatly conservative estimation of collapse capacity, annual losses, and repair costs compared to a rigid one. Due to lack of experimental data, it is not possible to certainly assert the most appropriate ratio. However, such variation in the seismic performance of a building model with different stiffness modification necessitates more investigation on this modeling strategy.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

Background: Meniere disease (MD) is an inner ear disorder associated with comorbidities such as autoimmune diseases or migraine. This study describes clinical and cytokine profile in MD according to the age of onset of the condition. Methods: A cross-sectional study including 83 MD patients: 44 with early onset MD (EOMD, <35 years old), and 39 with late onset MD (LOMD, > 50 years old), 64 patients with migraine and 55 controls was carried out. Clinical variables and cytokines levels of CCL3, CCL4, CCL18, CCL22, CXCL1 and IL-1β were compared among the different groups. Results: CCL18 levels were higher in patients with migraine or MD than in controls. Elevated levels of IL-1β were observed in 11.4% EOMD and in 10.3% LOMD patients and these levels were not dependent on the age of individuals. EOMD had a longer duration of the disease (p=0.004) and a higher prevalence of migraine than LOMD (p=0.045). Conclusions: Patients with EOMD have a higher prevalence of migraine than LOMD, but migraine is not associated with any cytokine profile in patients with MD. The levels of CCL18, CCL3 and CXCL4 were different between patients with MD or migraine and controls.

Leptin is the archetypal adipokine that promotes a negative whole-body energy balance largely through its action on brain leptin receptors. As such, the sustained weight loss and food intake suppression induced by Roux-en-Y gastric bypass (RYGB) surgery have been attributed to enhancement of leptin receptor signalling. We formally revisited this idea in Zucker Fatty fa/fa rats, an established genetic model of leptin receptor deficiency, and carefully compared their body weight, food intake and oral glucose tolerance after RYGB with that of sham-operated fa/fa (obese) and sham-operated fa/+ (lean) rats. We found that RYGB rats sustainably lost body weight, which converged with that of lean rats and was 25.5 % lower than that of obese rats by the end of the 4 week study period. Correspondingly, daily food intake of RYGB rats was similar to that of lean rats from the second postoperative week, while it was always at least 33.9 % lower than that of obese rats. Further, oral glucose tolerance of RYGB rats was normalized at the forth postoperative week. These findings assert that leptin is not an essential mediator of the sustained weight loss and food intake suppression as well as the improved glycemic control induced by RYGB, and instead point to additional circulating and/or neural factors.

Abstract: The trend of fasting until noon (omission or delayed breakfast) is increasingly prevalent in modern society. This eating pattern triggers discordance between endogenous circadian clock rhythms and the feeding/fasting cycle and is associated with an increased incidence of obesity and T2D. Although the underlying mechanism of this association is not well understood, growing evidence suggests that fasting until noon, also known as an “extended postabsorptive state,” has the potential to cause a deleterious effect on clock gene expression and disrupted regulation of body weight, postprandial and overall glycemia, skeletal muscle protein synthesis, appetite and may also lead to lower energy expenditure. This manuscript overviews the clock gene-controlled glucose metabolism during the active and resting phases. The consequences of postponing until noon the transition from postabsorptive to fed state on glucose metabolism, weight control, and energy expenditure. Finally, we will discuss the metabolic advantage of shifting more energy, carbohydrates (CH), and proteins to the early hours of the day.

Climate change, biodiversity loss, and other social and environmental problems pose grave risks. Progress so far has been incremental and insufficient, and as a result scientists, global policy experts, and the general public increasingly conclude that bold change is required across all sectors of society. At least two kinds of bold change are conceivable: reform of existing societal systems (e.g., financial, economic, legal, and governance systems), including their institutions, policies, rules, and priorities; and transformation, understood as the de novo development of and migration to new, improved systems. This paper is the second in a series of three that together propose a novel science-driven research and development program aimed at societal transformation. Moreover, the series advances a conceptual framework and formal mechanics by which societal transformation might be approached. Two of the underlying hypotheses are that new societal systems can be developed in a science-driven process to be fit for purpose, and system fitness can be compared across designs. Societies are viewed as superorganisms, and systems are viewed as a societal cognitive architecture. The first paper in the series provides definitions, aims, hypotheses, and a worldview. This paper discusses motivations, the role of science in societal transformation, a theory of change, and fitness metrics. The proposed R&amp;D program and theory of change are sound, viable, and affordable. The local-global-viral strategy invites the global science community to play a unique co-leadership role with local communities in the development, testing, and monitoring of new societal systems. Systems are implemented via a novel civic club model, where participation is voluntary. Clubs grow and replicate based on merit and aided by club networks, whose systems are also viewed as societal cognitive architectures. Benefits of the program and strategy are discussed.

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

Aero-fuel centrifugal pumps are important power plants in aero-engines. Unlike most of the existing centrifugal pumps, a combination impeller is integrated with the pump to improve its performance. First, the critical geometrical parameters of combination impeller and volute are given. Then, the effects of combination impeller on flow characteristics inside the impeller and volute are clarified by comparing simulation results with that of the conventional impeller, where the effectiveness of selected numerical method is validated by an acceptable agreement between simulation and experiment. Finally, the experiment is performed to test the external performance of studied pump. A significant feature of this study is that the flow characteristics are significantly ameliorated by reducing the flow losses emerged in impeller inlet, impeller outlet and volute tongue. Correspondingly, the head and efficiency of combination impeller are higher with comparison to conventional impeller. Consequently, it is a promising approach in ameliorating flow field and improving external performance by applying a combination impeller to an aero-fuel centrifugal pump.

TThe passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Question-naire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, (p=0.001), and the subscales of dieting (p=0.01) and bulimia. (p=0.001). Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. (p=0.01). The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both profes-sional and non-professional bodybuilding athletes.

In the new period, the state has made every effort to promote the new urbanization of public sports service. Entropy method, coupled coordination model, cold hot spot analysis is used to explore the dynamic relationship between public sports services and the development of new urbanization in China from 2008 to 2017. The results show that: (1) The development of the internal subsystem of public sports service is not balanced, the sports financial investment system grows rapidly, but the development of professional sports talent team is slow. (2) The imbalance of regional development cannot be ignored, and the public sports service level shows a decreasing gradient from east to west. (3) The regional development features are different. The eastern region is dominated by the coordinated development of sports public service and urbanization, while the central region is mainly in the type of public sports service lag, while the western region has multiple complex characteristics of low-level coordination and public sports service lag. Therefore, it is proposed that the construction of modern public sports service system should be investigated in the context of the development of new urbanization. It should be promoted from the following aspects: promoting the extension of public sports service to rural areas, engendering the "citizen's right" of sports health to the migrant population, conforming to the trend of mass sports intelligence, developing green sports and revitalizing idle sports assets in cities.

The KARS gene encodes the aminoacyl-tRNA synthetase (aaRS) which activates and joins the lysin with its corresponding transfer RNA (tRNA), through the ATP-dependent aminoacylation of the amino acid. The KARS gene mutations have been linked to diverse neurologic phenotypes such as: neurosensorial hearing loss, leukodistrophy, microcephaly, developmental delay or regression, peripheral neuropathy, cardiomyopathy, impairment of the mitochondrial respiratory chain, hyperlactatemia, among others. This article presents the case of a Colombian pediatric patient with two pathological missense variants in a compound heterozygous state in the KARS gene.

Complete pathologic response (pCR) predicts the long-term outcome of neoadjuvant treated (NAC) breast cancer (BC) but is reached in <10% of hormone-receptor-positive patients. Biomarkers able to guide adjustment or interruption of an ineffective therapy are desired. Here, we evaluated whether shifts in the serum concentration of thymidine kinase 1 (sTK1) during NAC could be utilized as a biomarker. In the PROMIX trial, women with localized HER2- BC received neoadjuvant epirubicin/docetaxel in six cycles. sTK1 was measured with an ELISA in 54 patients at cycles 1-4 and in a total of 131 patients before and 48h after cycle 1. The prognostic significance of the results was evaluated by log-rank tests of Kaplan–Meier estimates. Treatment resulted in a 2-fold increase of sTK1 before and 3-fold increase 48h after the cycles, except for the first cycle, where half of patients reacted with a decrease (post/pre sTK1- ratio <1.12) and the other half reacted with an increase (ratio >1.12). OS rates in ER+ patients with ratios of >1.12 and <1.12 were 97.7% and 78% (p=0.005), respectively, and DFS rates were 90.7% and 68% (p=0.006), respectively. Thus, response of sTK1 at the first cycle of chemotherapy could be used both as an early biomarker for guidance of chemotherapy and for the study of inherent tumor chemo-sensitivity, which could predict long-term outcome prior to therapy.

The purpose of this article is to simulate data transmission and calculate traffic parameters in SAGIN air segment for which Ad Hoc network of flying drones is considered as a model. Traffic modeling is based on the manet-routing-compare example from the ns3 simulator library, which has been supplemented with the code for calculation packet losses, throughput/goodput, and message transmission delays. The program allowed considering drones movement at both low and high speeds from 3.6 km/h to 72 km/h. The dependences of traffic losses on data transmission power, transaction sizes and data transmission rate are obtained and analyzed. The distribution of the average effective arrival rate λ and the throughput/goodput for drones has been studied. Comparing traffic characteristics in models with different numbers of drones allows judging how the required quality of service can be achieved by choosing the right transmission parameters.

The contribution aims to bring forth a novel synthesis route in developing transparent UV-LED curable coatings accounting various exposure options. A selection of perfluoropolyether (PFPE)-urethane methacrylate and acrylate resins, free-radical photo-initiator Omnirad 2100, and two distinct silane based crosslinking agents were blended under a weight ratio of 75:20:5 (without crosslinker) and 70:15:5:10, respectively. The coatings were cured under a UV-LED 4 x 3 matrix light emitting source, in a chamber under controlled atmosphere, by means of an in-house developed conveyor belt type platform, at different conveyor belt speed (5, 50, 150, 250, and 500 mm/s). The morphologies of fabricated coatings were characterized by FTIR revealing high conversion rates (e.g., from 98 to 100%) for increased exposure time as resulted from the 5 or 50 mm/s values, on all combinations. Dynamic-mechanical and optical properties of UV-LED cured transparent coatings were also investigated. A negative shift of a glass transition temperature values with a decrease in exposure time, in all combinations, from about 60 °C to 30 °C, along with storage moduli lowering in the glassy plateau further favor on higher exposure times for curing. The refractive indices of polymers were from 1.38 to 1.40, whereas the thermo-optic coefficients are showing minor changes around value of 2.55∙10-4 K-1.

Keywords: Chemical Intolerance, Drug Intolerance, Food Intolerance, QEESI, BREESI, Multiple Chemical Sensitivity, Toxicant-induced Loss of Tolerance, Prevalence

Vehicle driveability is one of the important vehicle attributes in range-extender electric vehicles due to the electric motor torque characteristics at low-speed events. The process of validating and rectifying vehicle driveability attributes is typically utilised by a physical vehicle prototype that can be expensive and required several design iterations. In this paper, a model-based energy method to assess vehicle driveability is presented based on a high-fidelity 49 degree-of-freedom powertrain and vehicle systems. Multibody dynamics components were built according to their true centre of gravity relative to the vehicle datum for providing an accurate system interaction. The work covered a frequency at less than 20 Hz. The results that consisted of the component frequency domination are structured and examined to identify the low-frequency sensitivity based on different operating parameters such as a road surface coefficient. An energy path technique was also implemented on the dominant component by decoupling its compliances to study the effect on the vehicle driveability and low-frequency response. The outcomes of the research provided a good understanding of the interaction across the sub-systems levels. The powertrain rubber mounts were the dominant components that controlled the low-frequency contents (< 15.33 Hz) and can change the vehicle driveability quality.

Application of the Ramsey graph theory to the analysis of physical systems is reported. Physical interactions may be very generally classified as attractive and repulsive. This classification creates the premises for the application of the Ramsey theory to the analysis of physical systems built of electrical charges, electric and magnetic dipoles. The notions of mathematical logic, such as transitivity and intransitivity relations, become crucial for understanding of the behavior of physical systems. The Ramsey theory explains why nature prefers cubic lattices over hexagonal ones for systems built of electric or magnetic dipoles. The Ramsey approach may be applied to the analysis of mechanical systems when actual and virtual paths between the states in the configurational space are considered. Irreversible mechanical and thermodynamic processes are seen within the reported approach as directed graphs. Chains of irreversible processes appear as transitive tournaments. These tournaments are acyclic; the transitive tournaments necessarily contain the Hamiltonian path. The set of states in the phase space of the physical system, between which irreversible processes are possible, is considered. The Hamiltonian path of the tournament emerging from the graph uniting these states is a relativistic invariant. Applications of the Ramsey theory to the general relativity become possible when the discrete changes in the metric tensor are assumed. Reconsideration of the concept of “simultaneity” within the Ramsey approach is reported.

The article is focused on detailed framework process for hierarchized construction of periodic freight train paths (PFTPs) – allocation of pre-arranged railway capacity to freight rail operators. The framework process considers fluctuations in demand for capacity from freight rail opera-tors, so the quality of a freight train path is related with its construction priority. Introduced framework process aims to offer freight rail operators attractive train paths, with low number of scheduled stops, and this way enhance competitiveness and decrease energy consumption of freight railway as a factor for sustainable development. The proposed generic process is in-tended for all time horizons of capacity allocation. Correctness of the framework process is tested on the example of mainline Prague – Dresden, in the context of prospective (denser) mod-el passenger timetable.

We generate numerically on a lattice an ensemble of stationary metrics, with spherical symmetry, which have Einstein action S_E « ħ. This is obtained through a Metropolis algorithm with weight exp(-β²S_E²) and β » ħ^-1. The squared action in the exponential allows to circumvene the problem of the non-positivity of S_E. The discretized metrics obtained exhibit a spontaneous polarization in regions of positive and negative scalar curvature. We compare this ensemble with a class of continuous metrics previously found, which satisfy the condition S_E=0 exactly, or in certain cases even the stronger condition R(x)=0 for any x. All these gravitational field configurations are of considerable interest in quantum gravity, because they represent possible vacuum fluctuations and are markedly different from Wheeler's ''spacetime foam''.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article's main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

We explore the importance of curvature in carbonaceaous species transformation and stability, using laser-induced vaporisation to evaporate and ionise a source of curved polyaromatic hydrocarbons: carbon nanobelts. Collision impacts between species cause mass loss and the resultant ions are catalogued via mass-spectrometry. To interpret the mass spectra, we perform a series of “in-silico” simulated systematic hydrogen-loss studies using density functional theory (DFT) modelling, sequentially removing hydrogen atoms using thermodynamic stability as a selection for subsequent dehydrogenation. In an initial sequence of H2 removal, the rings are maintained through stable carbyne chain and pentagon-chain formation, giving rise to circular strained dehydrobenzoannulene species. The chains subsequently break, releasing CH and C2. While theoretical closed-cage routes are identified, they are not observed experimentally. The results can serve as a useful guide to high-energy impact conditions observed in some astrochemical environments.

In quantum gravity perturbation theory in Newton's constant $G$ is known to be badly divergent, and as a result not very useful. Nevertheless, some of the most interesting phenomena in physics are often associated with non-analytic behavior in the coupling constant and the existence of nontrivial quantum condensates. It is therefore possible that pathologies encountered in the case of gravity are more likely the result of inadequate analytical treatment, and not necessarily a reflection of some intrinsic insurmountable problem. The nonperturbative treatment of quantum gravity via the Regge-Wheeler lattice path integral formulation reveals the existence of a new phase involving a nontrivial gravitational vacuum condensate, and a new set of scaling exponents characterizing both the running of $G$ and the long-distance behavior of invariant correlation functions. The appearance of such a gravitational condensate is viewed as analogous to the (equally nonperturbative) gluon and chiral condensates known to describe the physical vacuum of QCD. The resulting quantum theory of gravity is highly constrained, and its physical predictions are found to depend only on one adjustable parameter, a genuinely nonperturbative scale $\xi$ in many ways analogous to the scaling violation parameter $\Lambda_{\bar MS} $ of QCD. Recent results point to significant deviations from classical gravity on distance scales approaching the effective infrared cutoff set by the observed cosmological constant. Such subtle quantum effects are expected to be initially small on current cosmological scales, but could become detectable in future high precision satellite experiments.

To address the problems of poor recognition, low detection accuracy, a large number of model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study proposes a lightweight tennis ball detection algorithm YOLOv5s-Z based on Robomater EP. The main work is as follows: Firstly, constructing lightweight G-Backbone and G-Neck network layers to reduce the number of parameters and computation of the network structure. Secondly, the convolutional coordinate attention is incorporated in G-Backbnone to embed the location information into the channel attention, which makes the network obtain the location information in a larger area through multiple convolutions and enhances the expression ability of the mobile network learning features. In addition, the Concat module in the original feature fusion is modified into a weighted bi-directional feature pyramid W-BiFPN with settable learning weights to improve the feature fusion capability and achieve efficient weighted feature fusion and bi-directional cross-scale connectivity. The EIOU loss is introduced to split the influence factor of aspect ratio and calculate the length and width of the target frame and anchor frame respectively, combined with Focal-EIOU Loss to solve the problem of imbalance between difficult and easy samples. The activation function Meta-ACON is introduced to achieve an adaptive selection of whether to activate the neurons and improve the detection accuracy. Finally, the experimental results show that compared with the original algorithm, the YOLOv5s-Z algorithm reduces the number of parameters and computation by 42$\%$ and 44$\%$, the model size by 39$\%$, and 2$\%$ improvement in average accuracy mean value, which verifies the effectiveness of the improved algorithm and the light weight of the model to meet the deployment requirements of embedded devices, and adapts Robomaster EP for accurate detection and real-time recognition of tennis balls.

Although it is known that the gut microbiota (GM) can be modulated by diet, the efficacy of specific dietary interventions in determining its composition and diversity in obese patients remains to be ascertained. The present work aims to evaluate the impact of a moderately hypocaloric Mediterranean diet on the GM of obese and overweight patients (OB). The GM of 23 OB patients (F/M= 20/3) was compared before (T0) and after 3 months (T3) of the nutritional intervention (NI). Fecal samples were analyzed by Illumina MiSeq sequencing of the 16S rRNA gene. At baseline, the GM characterization confirmed the typical obesity-associated dysbiosis. After 3 months of NI, patients presented a statistically significant reduction of the body weight and fat mass, along with changes in the relative abundance of many microbial patterns. In fact, we observed an increased abundance in several Bacteroidetes taxa (i.e. Sphingobacteriaceae, Sphingobacterium, Bacteroides spp., Prevotella stercorea) and depletion of many Firmicutes taxa (i.e. Lachnospiraceae members, Ruminococcaceae and Ruminococcus, Veillonellaceae, Catenibacterium, Megamonas). In addition, the phylum Proteobacteria showed an increased abundance, while the genus Sutterella, within the same phylum, decreased after the intervention. Metabolic pathways, predicted by bioinformatic analyses, showed a decrease in membrane transport and cell motility after NI. The present study extends our knowledge of the GM profiles in OB, highlighting the potential benefit of a moderate caloric restriction in counteracting the gut dysbiosis.

Background: To compare the loss of preload in absence of loading and after a fixed number of ideal masticatory cycles in two different connection systems using all-on-four prosthetic model. Methods: Two equal models of an edentulous mandible rehabilitated with all-on-four technique with two types of abutment system (MUA and OT-Bridge) supporting a hybrid prosthesis, were used. Initial torque values of the prosthetic fixing screw, after ten minutes from initial screw tightening and after 400000 masticatory cycles were registered using a mechanical torque gauge. Differences between initial and final torque values were reported for each anchoring system and the two systems were finally compared. Results: No statistically significant differences regarding the loss of preload between MUA and OT-Bridge system were found after 400000 masticatory cycles; however, in MUA system it was found between anterior and posterior implant screws. A significant difference in preload loss was found only for MUA system comparing the initial screw torque to that measured after 10 minutes from the tightening in absence of cyclic loadings. Conclusions: MUA and OT-Bridge are reliable prosthetic anchoring systems able to tolerate repeated masticatory cycles also on distal cantilever in all-on-four rehabilitation model without any significant loss of preload in screw tightening

Seed viability is routinely measured on seeds that fail to germinate at the end of an experiment. Together with the number of germinants, this is used to estimate viability of the seeds at start of the experiment (i.e., initial viability) and provides the comparative basis on which germination success is determined. Perusal of the literature shows that sometimes (perhaps often, as the problem has yet to be recognized or reported) prolonged duration in the treatment, especially the control where little germination occurs, can lead to loss of viability. This results in underestimation of initial viability if that treatment is used. I caution against the routine use of end-of-trial germination and viability of ungerminated seeds as an estimate of initial viability in determining germination success of various treatments. I explore ways to deal with the problem but the preference is for estimates of initial viability to be undertaken on a separate sample of seeds concurrently with the experiment as this avoids the risk of seed death during the trial.

Keywords: Obesity; hypothalamus; appetite; glucose homeostasis; weight-loss drugs; AGRP; POMC; NTS; incretins.

Nowadays, many Muslim-majority countries have implemented a dual banking system, namely the sharia and conventional systems. The development of Islamic banks is to fulfill the Muslims' need for the existence of halal transactions in financial institutions. However, in some countries, it turns out that conventional banks still dominate the country's economy. Because of that, it is necessary to see whether there are differences in financial risk and Earnings management between Islamic and conventional banks. The samples are conventional and Islamic banks in Southeast Asia, analyzed by the purposive sampling method from 2010-2019. The analytical tool used is the statistical difference test and economometrics analysis using generalized least square (GLS) regression with panel data (time series and cross-sectional data). These models are intended to forecasting the macroeconomics effects in applying dual banking system in one country or region. The results using non parametrics means difference test showed that the first hypothesis is accepted It means that Earnings management in conventional banks is greater than in Islamic banks. The Random Model Effect (REM) for second and third hypotheses testing on Conventional banks shows the Bankruptcy Risk and NPL do not affect the dependent variable Earnings Management (LLP). While fixed effect model testing on Islamic banks, the second and third hypothesis testing is rejected. Therefor Islamic Banks the value of Bankruptcy Risk (z-score) and the value of Non-Performing Loans (NPL) do not affect Earnings management. It also means that hypothesis 2 and 3 are rejected both in conventional as well as Islamic Banking. Sensitivity analysis for conventional as well as Islamic banking altogether using fixed effect model shows that the second and third hypotheses show that the independent variables (Bankruptcy Risk and NPL) do not affect the dependent variable Earnings Management (LLP). These results can be concluded that Islamic bank are enganged in less earnings management. Therefor in the the long run there are still more research that should conduct in comparing dual banking system in one region.

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach.

The continuing development of industrialization and increasing population density has led to the emergence of noise as an increasingly common problem, requiring various types of sound absorption and insulation methods to address it. Meanwhile, the recycling of resources to ensure a sustainable future for the planet and mankind is also required. Therefore, this study investigates the potential of corrugated cardboard as a resource for noise reduction. The sound absorption and insulation performance of non-perforated corrugated cardboard (NPCC) were measured, and modified corrugated boards were fabricated by drilling holes either through the surface of the corrugated board alone or through the corrugated board in its entirety. The sound absorption/insulation performance both of perforated corrugated cardboard (PCC), and perforated corrugated cardboard with multi frequency resonators (PCCM) were measured using the transfer function method and the transmission matrix method. To determine the effectiveness of NPCC, PCC, and PCCM in noise reduction, the sound pressure level was analyzed by applying it to a home blender. The results showed PCCM’s sound absorption and insulation performance to be excellent. On the basis of these findings, we propose the use of PMMC as an eco-friendly noise reduction material.

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation together with the TG heat smoke release scheme leads to more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

Meniere Disease (MD) is a multifactorial disorder of the inner ear characterized by vertigo attacks associated with sensorineural hearing loss and tinnitus with a significant heritability. Although MD has been associated with several genes, no epigenetic studies have been performed in MD. Here we performed whole genome bisulfite sequencing in 14 MD patients and 6 healthy controls, with the aim of identifying a MD methylation signature and potential disease mechanisms. We observed a high number of differentially methylated CpGs (DMC) when comparing MD patients to controls (N= 9,545), several of them in hearing loss genes such as PCDH15, ADGRV1 and CDH23. Bioinformatic analyses of DMCs and cis-regulatory regions predicted phenotypes related to abnormal excitatory postsynaptic currents, abnormal NMDA-mediated receptor currents and abnormal glutamate-mediated receptor currents when comparing MD to controls. Moreover, we identified various DMCs in genes previously associated with cochleovestibular phenotypes in mice. We have also found 12 undermethylated regions (UMR) that were exclusive to MD, including 2 UMR in an inter CpG island in the PHB gene. We suggest that the DNA methylation signature allows to distinguish between MD patients and controls. The enrichment analysis confirms previous findings of a chronic inflammatory process underlying MD.

The results of applying drilling engineering tools in planned deviated and horizontal wells of oil and gas condensate fields have been examined. The basis for the implementation of a comprehensive approach was the information obtained from drilling horizontal wells in the West and East Siberia, Volga-Ural region, and Iraqi Kurdistan. A synthetic model is considered as an example of the data, incorporating geomechanical and geological properties. As a result of the conducted audit of the geomechanical model and drilling process, the methodologies used were further developed to obtain optimal drilling fluid densities, aiming to prevent cavity formation on well walls, hydraulic fractures, and other related issues. The implementation of the Python programming language was introduced in the stages of geomechanical model preparation, resulting in a time acceleration of up to 20%. During the pre-drilling stage, technological operations were synchronized with the geomechanical model, increasing the involvement of process participants through the synchronization of knowledge areas. The interrelation between drilling technology, drilling parameters, properties of flushing fluids, and rock stability analysis was established, ensuring a 5% increase in construction speed (through NPT reduction and modification of existing tools and technological operations) and accident-free drilling of complex deviated and horizontal wells, provided that the recommended drilling parameters are followed.

We investigated layered titanium nitride (TiN) and aluminum nitride (AlN) for color glasses in Building Integrated Photovoltaic (BIPV) systems. AlN and TiN are among suitable and cost-effective optical materials to be used as thin multilayer films, owing to the significant difference in their refractive index. To fabricate the structure, we used radio frequency magnetron deposition method to achieve the target thickness uniformly. A simple, fast, and cheap fabrication method is achieved by depositing the multilayer films in a single sputtering chamber. It is demonstrated that a multilayer stack that allows light to move from a low refractive index layer to a high refractive index layer or vice-versa can effectively create various distinct color reflections for different film thicknesses and multilayer structures. It is investigated from simulation based on wave optics that, TiN/AlN multilayer offers better color design freedom and cheaper fabrication process as compared to AlN/TiN multilayer films. Blue, green, and yellow color glasses with optical transmittance of more than 80% was achieved by ITO coated glass/TiN/AlN multilayer films. This technology exhibits good potential in commercial BIPV system applications.

This manuscript proposes a novel tangent-graph-based method that provides all distinctive topology paths, as a set of trajectories that can not be transformed into each other by gradual bending and stretching without colliding with obstacles. As the global optimal trajectory (limited by dynamic constraints) may not correspond to the shortest path (almost without dynamic constraints), it is important to provide all distinctive topology paths rather than just the globally shortest one. One of the application is provide initial paths for trajectory planning methods, such as time elastic band (TEB) and dynamic window approach (DWA). Considering that a mobile platform is always working in a dynamic changing environment and that update the whole graph is time consuming, a dynamic rectify method is proposed to update tangents according to local map provided by sensor reading. Finally, results under real grid maps are presented.

Rapeseed (Brassica napus) is one of the most important oil crops in the world; however, drought significantly curtails its growth and productivity. Identifying drought-tolerant germplasm is an efficient and low-cost strategy for addressing water shortages. Using water loss ratio (WLR) as an index of drought tolerance, we screened a panel of 265 B. napus lines. We identified eight low-WLR and six high-WLR accessions, which were regarded as drought-tolerant and drought-sensitive, respectively. Further validated these selected accessions at the seedling stage under drought-stress conditions. The drought-tolerant accessions had significantly greater fresh and dry weights under drought stress than the drought sensitive accessions. Using RT-qPCR, we showed that a set of previously reported drought-adaptive marker genes were expressed at higher levels in the drought-tolerant lines than in the drought-sensitive lines. These results indicated that the drought-tolerant genotypes could be identified from natural populations using WLR. Then, we performed a genome-wide association study to identify loci harboring single nucleotide polymorphisms (SNPs). A total of 139 SNPs were significantly associated with the WLR, of which chromosome A10 harbored the largest number. Furthermore, four putative candidate genes were selected by combining the SNP–WLR association results and transcriptional expression data with the changes in drought tolerance. Thus, we have identified two drought-tolerant B. napus cultivars and uncovered genome-wide variation differentiating B. napus lines related to WLR, in addition to providing insights for further research into WLR-related drought mechanisms.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

Genetic sensorineural hearing loss and Meniere disease have been associated with rare variations in the coding and non-coding region of the human genome. Most of these variants are classified as likely pathogenic or variants of unknown significance and require functional validation in cellular or animal models. Given the difficulties to obtain human samples and the raising concerns about animal experimentation, human induced pluripotent stem cells emerge as cellular models to investigate the interaction of genetic and environmental factors in the pathogenesis of inner ear disorders. The generation of human sensory epithelia and neuron-like cells carrying the variants of interest may facilitate a better understanding of their role during differentiation. These cellular models will allow us to explore new strategies for restoring hearing and vestibular sensory epithelia as well as neurons. This review summarizes the use of human induced pluripotent stem cells in sensorineural hearing loss and Meniere disease and proposes some strategies for its application in clinical practice.

A well known result for the interference of two single-mode fields is that the degree of coherence and the degree of indistinguishablity are same when we consider the detection of a single photon. In this article we present the relation between degree of coherence, path indistinguishability and the fringe visibility considering interference of multiple number of single-mode fields while being interested in the detection of a single photon only . We will also mention how Born’s rule of interference for multiple sources is reflected in these results.

Quantum noise limits the sensitivity of laser interferometric gravitational-wave detectors. Given the state-of-the-art optics, the optical losses define the lower bound of best possible quantum-limited detector sensitivity. In this work, we come up with the configuration which allows to saturate this lower bound by converting the signal recycling cavity to be a broadband signal amplifier using an active optomechanical filter. We will show the difference and advantage of such a broadband signal recycling scheme compared with the previous white-light-cavity scheme using the optomechanical filter in [Phys.Rev.Lett.115.211104 (2015)]. The drawback is that the new scheme is more susceptible to the thermal noise of the mechanical oscillator. To suppress the radiation pressure noise which rises along with the signal amplification, squeezing with input/output filter cavities and heavier test mass are used in this work.

Background: Vitamin D was studied in regards to its possible impact on body mass reduction and metabolic changes in adults and children with obesity yet there were no studies assessing the impact of vitamin D supplementation during a weight management programme in children and adolescence. The aim of our study was to assess the influence of 26 weeks of vitamin D supplementation in overweight and obese children undergoing an integrated 12-months’ long weight loss programme on body mass reduction, body composition and bone mineral density. Methods: A double-blind randomized placebo-controlled trial. Vitamin D deficient patients ( <30 ng/ml level of vitamin D) aged 6-14, participating in multidisciplinary weight management programme were randomly allocated to receiving vitamin D (1200 IU) or placebo for the first 26 weeks of the intervention. Results: Out of the 152 qualified patients, 109 (72%) completed a full cycle of four visits scheduled in the programme. There were no difference in the level of BMI change. Although the reduction was greater in the vitamin D vs. placebo group (-4.28 ± 8.43 vs. -2.53 ±6.10) the difference was not statistically significant (p=0.319). Similarly the reduction in fat mass – assessed both using bioimpedance and DEXa was achieved, yet the differences between the groups were not statistically significant. Conclusions: Our study ads substantial results to support the thesis on no effect of vitamin D supplementation on body weight reduction in children and adolescents with vitamin D insufficiency undergoing a weight management programme. Trial registration no: NCT 02828228; trial registration date: 8 June 2016 registered in: ClinicalTrials.gov.

When training deep neural networks tasks, the most popular choices are cross-entropy loss. On the other hand, in general speaking, a decent loss function can take on shapes that are considerably more flexible and ought to be adapted for different activities and datasets. In most of the classification tasks, generally if the true class is not correctly recognized by the network (top1), that class is placed among the five classes with the highest probability (top5). This shows that the network does not necessarily recognize the correct class with a low probability, but a class similar to it (such as 3 vs. 8 in mnist) assigns a higher probability and this causes a mistake in that task. Accordingly, we proposed a loss function deals with the error of class that the neural network incorrectly recognized as correct, in addition to the correct class error. We call our proposed loss as False Positive Loss (FPL), with the intention of viewing and designing loss functions not only through the utilization of true class but also through the utilization of the value of false positive classes. One of the core properties of our proposed loss is full adaptability, which makes False Positive Loss be fully capable of getting reformulated by using other widely used loss functions formulas based on the task or the need of the users. Extensive experimental results demonstrate that our suggested loss function outperforms other well-known losses on a variety of tasks and datasets. As can be observed, the performance of our False Positive Loss is superior to that of the cross-entropy loss when it comes to tasks involving 2D picture classification. We have compared our loss with cross entropy as the most common classification loss function on some models (such as ResNet-18, ResNet-50 and Efficientnet-V2) through classification known as a basic computer vision task. with both random or pre-trained initial weights. As a result, in some cases the models with our loss outperform the same tasks with cross entropy from the viewpoint of some metric (i.e. accuracy and FP). For example, the resnet-50 on cifar-10 dataset with random initialization indicated a top1 accuracy of 94.93 with cross entropy and 95.25 with our loss, while for top5 accuracy the results are 99.86 and 99.87, respectively.

Olfactory dysfunction affects often has severe consequences on patients’ quality of life. The most common complaint in these patients is their reduced enjoyment of food in both patients with olfactory loss and parosmia. How the different types of olfactory dysfunction differ in relation to food and cooking habits, sensory awareness, and food-related quality of life has not yet received much attention. By applying questionnaires on cooking, food, olfactory function, weight changes, sensory awareness, and food-related quality of life, we investigate how various aspects of eating differ between participants with olfactory loss (n=271), parosmia(n=251), and normosmic controls(n=166). Cooking habits in olfactory dysfunction revealed pronounced differences as compared with normosmic controls. Cooking with olfactory dysfunction was associated with e.g., a lack of comfort and inspiration for cooking and an inability to make new foods successfully. Significant differences in cooking were also found between olfactory loss and parosmia. Food items were less familiar in participants with olfactory loss and parosmia, while the ratings of liking food items differed between olfactory loss and parosmia, indicating the importance of adapting ingredients in meals separately for olfactory loss and parosmia. Parosmia was associated with a higher incidence of weight loss, but we found no difference in the food-related quality of life between participants with olfactory loss and parosmia. While olfactory loss and parosmia have wide-ranging consequences on patients’ cooking and food habits, adapting meals to include ‘safer food items’ and integrating multisensory stimulation may be a possible avenue for improving the enjoyment of food.

Foveal vision loss has been shown to reduce efficient visual search guidance due to contextual cueing by incidentally learned contexts. However, previous studies used artificial (T among L-shape) search paradigms that prevent the memorization of a target in a semantically meaningful scene. Here, we investigated contextual cueing in real-life scenes that allow explicit memory of target locations in semantically rich scenes. In contrast to the contextual cueing deficits in artificial scenes, contextual cueing in patients with age-related macular degeneration (AMD) did not differ from age-matched normal-sighted controls. We discuss this in the context of visuospatial working memory demands for which both eye-movement control in the presence of central vision loss and for memory-guided search may compete. Memory-guided search in semantically rich scenes may depend less on visuospatial working memory than search in abstract displays, potentially explaining intact contextual cueing in the former but not the latter. In a practical sense, our findings may indicate that Patients with AMD are less deficient than expected after previous lab experiments. This shows the usefulness of realistic stimuli in experimental clinical research.

We investigate new graph characteristics namely total (vertex, edge) face irregularity strength of gen- eralized plane grid graphs Gmn under k-labeling Phi of type (Alpha, Beta, Gamma). The minimum integer k for which a vertex-edge labelled graph has distinct face weights is called the total face irregularity strength of the graph and is denoted by tfs(Gmn). In this article, the graphs G = (V;E; F) under consideration are simple, finite, undirected and planar. We will estimate the exact tight lower bounds for the total face irregularity strength of some families of generalized plane grid graphs.

Congestion pricing has received lots of attention in the scientific discussion. Congestion pricing means that the operator increases prices at the time of congestion and the traffic demand is expected to decrease. In a certain sense, shadow prices are an optimal way of congestion pricing: users are charged shadow prices, i.e., the expectations of future losses because of blocked connections. The shadow prices can be calculated exactly from Howard’s equation, but this method is difficult. The paper presents simple approximations to the solution of Howard’s equation and a way to derive more exact approximations. If users do not react by lowering their demand, they will receive higher bills to pay. Many users do not react to increased prices but would want to know how the congestion pricing mechanism affects the bills. The distribution of the price of a connection follows from knowing the shadow prices and the probability of a congestion state. There is another interesting distribution. The network produces profit to the operator, or equivalently, blocked connections produce a cost to the operator. The average cost rate can be calculated from Howard’s equation, but the costs have some distribution. The distribution gives the risk that the actual costs exceed the average costs, and the operator should include this risk to the prices. The main result of this paper shows how to calculate the distribution of the costs in the future for congestion pricing by shadow prices and for congestion pricing with a more simple pricing scheme that produces the same average costs.

In the rescue operations the full time of action plays important role. It is a sum of planning, travel, and manipulation (in the action place) phases times. The time minimization of first two phases by autonomous vehicle for remote action is considered in the paper. For known a priori map the path planning consists of local optimal decision collected next in the general algorithm of the optimal path. Such approach significantly reduces time of path planning. The robot features and known sparse obstacles reduce the allowable robot speeds. The time of travel is calculated from allowable velocity profile. So, it can be used to estimate the travel performance. Genetic algorithm and random search-based methods for path finding with travel time optimization are exploited and compared in the paper. All the proposed time optimisation solutions of rescue operation are checked during computer simulations and results of simulation are presented.

Virus infection is one of the most common etiologies of hearing loss. Hearing loss associated with viral infection can be unilateral or bilateral, mild or severe, sudden or progressive, and permanent or recoverable. Many viruses cause hearing loss in adults and children; however, the pathogenesis of hearing loss caused by viral infection is not fully understood. This review describes cytomegalovirus, the most common virus causing hearing loss, and other reported hearing loss-related viruses, along with their pathogenic characteristics and research progress on their pathology, hearing phenotypes, possible associated mechanisms, treatment and prevention measures, aiming to provide diagnostic and treatment assistance to clinical workers.

To solve the problem that sampling-based Rapidly-exploring Random Tree (RRT) method is difficult to guarantee optimality. This paper proposed the Post Triangular Processing of Midpoint Interpolation method minimized the planning time and shorter path length of the sampling-based algorithm. The proposed Post Triangular Processing of Midpoint Interpolation method makes a closer to the optimal path and somewhat solves the sharp path problem through the interpolation process. The experiments were conducted to verify the performance of the proposed method. Applying the method proposed in this paper to the RRT algorithm increases the efficiency of optimization compared to the planning time.

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.

Humanity faces serious social and environmental problems, including climate change and biodiversity loss. Risks are increasing and conditions deteriorating. Increasingly, scientists, global policy experts, and the general public conclude that incremental approaches are insufficient and transformative change is needed across all sectors of society. However, the meaning of transformation is still unsettled in the literature, as is the proper role of science in fostering it. This paper is the first in a three-part series that adds to the discussion by proposing a novel science-driven research-and-development program aimed at societal transformation. More than a proposal, it offers a perspective and conceptual framework from which societal transformation might be approached and understood. While acknowledging the necessity of reform to existing societal systems (e.g., governance, economic, and financial systems), the focus of the series is on transformation understood as systems change or systems migration—the de novo development of and migration to new societal systems. The series provides definitions, aims, reasoning, worldview, and a theory of change, and discusses fitness metrics and design principles for new systems. This first paper proposes a worldview built using ideas from evolutionary biology, complex systems science, cognitive sciences, and information theory that is intended to serve as the foundation for the R&amp;D program.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

In case of the maximally rotating Black Holes (BH) through Kerr-Neumann frames, or as described in Boyer-Lindquist coordinates metrics, the rotation axis of the BHs inputs a frame dragging effect i.e., relativistically a Lens-Thirring Precession that accelerates the photon trajectories oscillates with a shaped induced rotations through the ring singularity, between alternate universes, as a means of an induced geodesics that takes a sharp turning points back and forth provided, in the prograde photon sphere, due to magnetorotational instability, the path tracing of a photons circulates as a smooth fiber bundles over the event horizon curves, that when gets interpolate between mixed trajectories behaves as a geodesics and thus forms a smooth Jacobi-fields through Jacobi-lines by mutual intersection of geodesics over the photon sphere which when somehow gets leaked inside the event horizon, then gets sucked in with not sufficient escape velocity for retardation and trapped in the compact singularity, oscillating back and forth through alternate universes.