This study aimed to evaluate the proliferation and migration of fibroblast cells derived from chronic wounds and investigate the impact of low-level laser therapy (LLLT) on their growth and migration. A total of 60 dermal samples were obtained from three sites of chronic pressure ulcers and diabetic ulcers in 20 patients. Following collection in the operating room, the dermal samples were isolated using the procedure described by Freshney RI. Fibroblasts from the wound base (Site 1) exhibited a slow proliferation rate and a decrease in proliferative capacity. Fibroblasts from wound margins (Site 2) and adjacent healing skin (Site 3) could be successfully isolated and maintained for multiple generations without any morphological changes. Fibroblasts from chronic wounds at Sites 2 and 3 showed slower proliferation rates and reduced migration capabilities compared to normal dermal fibroblasts (control samples). The fibroblast cells were exposed to LLLT at different energy levels (2.5, 3, and 3.5 J/cm2). The laser-treated groups demonstrated a significantly higher increase in cell count compared to the control sample, with the highest increase observed at an energy level of 3 J/cm2. These findings suggest that LLLT at an energy level of 3 J/cm2 could effectively enhance fibroblast migration, indicating its potential as a therapeutic intervention for chronic wounds.

The advancement of technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of Finite Impulse Response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of the comprehensive analysis of the impact of different genetic operators show that the genetic algorithm-based filter outperforms conventional filter designs.

Over the past decade, Knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purpose, understanding how effectively lev-erage entity-based representation still needs to be resolved. This paper proposes a Purely Enti-ty-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method, (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method, for queries whose all terms are annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.000). Thus, using our approach with any other document retrieval method would be an added value, unless that method achieves the maximum nDCG@5 score for those highly annotated queries.

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

A novel prediction model for crack development of reinforced concrete (RC) piles with localized chloride corrosion in marine environment is proposed. A discrete method is used to solve the corrosion pit radius model and a crack extension model is developed to investigate the initiation and extension of cracks. The maximum corrosion degree of reinforced concrete pile is predicted according to the limit crack criterion, and finally a sensitivity analysis is carried out on the important parameters of crack extension. The results show that the radius of corrosion pit, the depth corrosion pit and cross-sectional area loss of reinforcement gradually increase as the corrosion level increases. The loss of local reinforcement section at crack initiation decreases with the increase of the ratio of concrete cover to initial diameter, and increases with the increase of pitting factor. The required pit depth for reinforcement cracking increases with the increase of the ratio of concrete cover thickness to diameter. The loss of cross-sectional area of reinforcement and the radius of corrosion pit increase with the increase of initial diameter of reinforcement. Increasing the pitting factor will reduce the pit depth and make the crack width develop faster before reaching the limit crack width. Increasing the concrete cover thickness can provide an improvement in the propagation of cracks. A comparative analysis shows that the localized corrosion pattern is more in conformity with marine engineering practice.

The Lambousa fishtank, an archaeological structure entirely carved in bedrock, can be easily recognized and measured in plan on Google Earth (GE). We surveyed in situ this excellent archaeological marker in 2016 through direct measurements using traditional field measurements, such as metric tapes and invar rods, and terrestrial photogrammetry, using Structure from Motion (SfM) methods. The bedrock on which the fishtank is founded is an Upper Pleistocene calcarenite also containing Persistrombus Latus. The age of the studied fishtank has not been previously published, but on the basis of the construction technique and the interpretation provided by [1] and references therein . We assume that it was built in the period between 2.1 and 1.8 ka BP, like similar fishtanks in the Mediterarranen area. Architectural structures consist of an evident crepido ora a stone base, and a tunnel which allows for sea water exchange during high tides. The aforementioned hole is well correlated with the crepido which lies around the fishtank. These architectural components allow to evaluate the palaeosea level with significant precision during the time when the fishtank was active. MIS 5.5 coastal deposits that outcrop in the study area are located at a maximum altitude of a few meters, while the inner margin of the MIS 5.5 terrace allows us to hypothesize a “quasi-tectonic stability” also in the long-term. We have also tested several GIA predictions of relative sea level at Lambousa for the past 3.5 kyr according to GIA models ICE-6G (VM5a), ICE-7G (VM7) and to the GIA model by Lambeck and Purcell (2015), obtained with the SELEN4 sea level equation solver.

The hinterland of the Taranto Gulf in Basilicata (southern Italy) provides a great opportunity for the study of coarse-grained coastal systems belonging to a staircase of Quaternary terraced ma-rine-deposits. Among gravelly successions, beach deposits abound in the stratigraphic record, offering exceptional outcrops useful to allow detailed information on their facies features. In this paper, we describe sedimentary facies, textural variations and depositional architecture of these deposits in order to: - demonstrate that the area is an excellent training ground for the study of gravelly beaches in microtidal settings; - discuss about the use of beach deposits as proxy of even small relative sea level variation.

Object-level Simultaneous Localization and Mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an Inertial Measurement Unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object’s surface and not the object’s shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.

In this work, a systematic study was conducted on the fabrication of multi-material components obtained employing Laser-Powder Bed Fusion (L-PBF) technology. The idea of making multi-material components is a winning capability of additive technologies because it allows the fabrication of Functionally Graded Materials (FGMs) with the customization of parts according to different required properties. The transition from one material to another was achieved gradually and continuously within the same layer using ad-hoc equipment designed for L-PBF systems with a powder spreading technique based on coaters or rollers. The influence of the relative position of the different materials within the powder chamber and the geometry of the developed equipment on the metallurgical and mechanical properties of the manufactured samples was evaluated. The performed tests involved the use of two materials, a nickel-based superalloy, and a stainless steel, having different chemical, physical and mechanical properties to obtain gradual properties variation in the manufactured samples. Based on the results of post-process characterization obtained through metallographic, chemical, and mechanical analysis, the relative positions of the materials and the geometry of the developed equipment have a limited effect on the sample’s manufactured properties. The characteristics of the FGM zone depend on the nature of the employed powders, and its extent coincides with that defined during the design of the divider.

Background: Depending on the phase of the menstrual cycle the athlete is in, some kinetic, physiological and fatigue variables will show differences. The aim of this study is to analyze whether there are changes in these variables over the course of the menstrual cycle. Methods: Eight regular female runners and triathletes performed a maximal treadmill test, as well as a submaximal test (6’ stages at 50%, 60% and 80% of maximal aerobic speed) in each of the phases of the menstrual cycle: bleeding phase (day 2.4 ± 0.7), follicular phase (day 10.4 ± 2.2) and luteal phase (day 21.8 ± 2.1). Running dynamics were measured (using RunScribe) at the end of each test, as were lactate concentration, heart rate and fatigue (evaluated on a scale of 0 to 5). Results: Higher shock (G) values were recorded in the bleeding phase and higher vertical spring stiffness (kN/m) in the follicular phase. The phase of the menstrual cycle had a significant effect on average and peak heart rate, which was significantly higher in the follicular phase. Conclusions: Higher vertical spring stiffness was observed in the follicular phase, as well as higher peak and average heart rate.

During two decades back, the growth of the fisheries sector in Indonesia was showing increasing trend, however, behind the rapid development of the sector, small-scale fisheries role is often neglected as one of the main actors to support the whole sector. They remain poor and doing fishing activities traditionally. Therefore, this study aims to describe the real situation of small-scale fishers, analyzing fishers’ ownership of the boat, analyzing their income level to reveal their poverty condition, to analyze the factors affecting their income. The regression analysis results indicate that fishing days, boat category, sea fish catch, fish selling price, variable cost, and secondary job have significant affect to fishers’ net income.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, then EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required, with respect to the non-ML framework.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

Solid spent nuclear fuel from nuclear power plants has 3.4% fission products (80-160amu), contributing to over 99.8% radioactivity. On the other hand, liquid high-level radioactive waste (HLRW) from spent fuel reprocessing has 98.9% bulk elements (0-60amu) with 0.1% radioactivity. A separation mechanism on the mass categories as groups presents unique opportunities in managing HLRW for the long term with a considerable cost reduction. This paper proposes a thermal plasma-based separation system incorporating atmospheric pressure plasma torches for HLRW mass separation into low-resolution mass groups. Several engineering issues, such as waste preparation, waste injection into the plasma and waste collecting after mass separation, need to be addressed. Using COMSOL Multiphysics simulation, the generic system can be studied using noble gas mass separation and further analyze the mass filter capabilities. This paper provides the history of plasma-based mass separation. Functional modelling of a thermal plasma mass separation system is proposed under atmospheric pressure. Finally, aspects of mass separation simulation using noble gas Argon and Helium inside the plasma mass separation system were studied in COMSOL Multiphysics.

The wind observations for multiple levels (40–200 m) have been conducted for a long time (2016–2020) on Jeju Island of South Korea. This study aims at understanding the vertical and temporal characteristics of lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal monsoon wind. The maximum wind speed appears in the lower layer during day time and is delayed in the upper layer during latter time in diurnal cycle. In summer season, the surface layer increases up to 160 m during day time via dominant solar radiation effect, which is higher than those for other seasons. However, the maximum wind speed in winter season appears irregularly among altitudes, and the surface layer is ~100 m, which is lower than that in summer season. It can be attributed to the increase in the mean wind speed in diurnal cycle caused by the strong northwestern wind for winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal monsoon winds. These results are expected to contribute to site selection criteria for wind farms and to the assessment concerning planetary boundary layer modeling.

The creation of crop-type maps from satellite data has proven challenging, often impeded by a lack of accurate in-situ data. This paper aims to demonstrate a method for crop-type (ie. Maize, Wheat and Other) recognition based on Convolutional Neural Networks using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery. Classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for other. Given that wheat and maize are the two most common food crops globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved crop-type monitoring globally. Challenges remain in addressing the noisy aspect of street-level imagery (ie. buildings, hedgerows, automobiles, etc.), where a variety of different objects tend to restrict the view and confound the algorithms

Attention deficit hyperactivity disorder (ADHD) results in various functioning impairments in children’s lives and families. Parents of children with ADHD report high levels of parenting stress, low levels of parental self-efficacy and use of more authoritarian and/or permissive parenting practices than parents of typically developing children. Intervention programs need to address both children’s and parents’ needs and multimodal intervention programs could cover this demand. The aim of this study was to examine the efficacy of “Child ViReal Support Program” - a multi-level evidence-based comprehensive program - on parenting stress, parental self-efficacy, parenting practices, and the core symptoms of children’s ADHD. Families with a child diagnosed with ADHD (n = 16) were randomly allocated to two groups (PC and CP; P = parent training, C = child training) and a cross-over design was utilized. Participating parents completed, in four different times during the study, the Parenting Stress Index-Short Form, the Parenting Sense of Competence Scale, the Parenting Styles and Dimensions Questionnaire-Short Version and the parent form of the ADHD Rating Scale-IV. Parents from both groups, after their participation in the parent training, demonstrated reduced parenting stress, enhanced parental self-efficacy and increased the employ of democratic parenting practices. More than that, they reported decreased levels of inattention and impulsivity/hyperactivity for their children. Evidence-based multi-level intervention programs could produce positive effects in parents and children by incorporating effective methods and tools in accordance with the needs and the demands of the family context.

Silicon-based photodetectors, as low-cost and environmentally friendly optical sensors, are attractive. Also, their compatibility with complementary metal-oxide-semiconductor (CMOS) technology is advantageous for the development of silicon photonics systems. However, extending optical responsivity of silicon-based photodetectors to the mid-infrared (mid-IR) wavelength range remains challenging. In developing mid-IR infrared Schottky detectors, nanoscale metals are critical. Nonetheless, one key factor is the Fermi level pinning effect at the metal/silicon interface and the presence of metal-induced gap states (MIGS). Here, we demonstrate the utilization of the passivated surface layer on semiconductor materials as an insulating material in metal-insulator-semiconductor (MIS) contacts to mitigate the Fermi level pinning effect. The removal of Fermi level pinning effectively reduces the Schottky barrier height by 12.5% to 16%. The demonstrated devices exhibit a high responsivity of up to 234 μA/W at a wavelength of 2 μm, 48.2 μA/W at 3 μm, and 1.75 μA/W at 6 μm. The corresponding detectivities at 2 and 3 μm are 1.17×10^8 cm Hz^1/2 W^-1 and 2.41×10^7 cm Hz^1/2 W^-1, respectively. The expanded sensing wavelength range contributes to the application development of future silicon photonics integration platforms.

In this paper an overview of waste materials in underground coal mines is presented, whereas waste materials can be of anthropogenic or geogenic origin. Complex interactions with mine water and waste materials take place, especially during mine water level rebound. Bearing in mind that under the increasing societal pressure most, if not all underground coal mines are going to be closed rather sooner than later, it is important to understand these interactions and to predict the composition and dynamics of mine water lever rebound in underground coal mines to prevent adverse effects to the environment, especially receiving waters and drinking water resources. The second part of this article focusses on the experiences gained in the Ruhr area, a closed underground coal mining region in Western Germany, where mine water rebound has been ongoing for decades. In this regard, the modelling program DMT-Boxmodel has been developed and applied to predict the hydrodynamics and hydrogeochemical development of the water rebound. As an example, simulation results of water rebound as well as solute and PCB transport in central province of Ruhr mining area are compared to the monitoring data, and predictions over 3 decades are given. The experiences gained in the mine water management in Ruhr area can be applied in mining areas worldwide.

Existing research lacks studies that explore the impact of government fiscal support on the high-quality development of regional economy and society from the perspective of livelihood-based infrastructure construction. The investigation in this paper explores the economic and social effects of government fiscal support behavior from the perspective of livelihood infrastructure construction. On the basis of the TOPSIS-Entropy Method, this paper constructs an evaluation indicator that closely conforms to the denotation and requirements of high-quality development and then carries out empirical regressions in combination with government expenditure on people's livelihood to verify the theoretical hypothesis, whose transmission mechanism and moderating effect are further empirically tested. The results reveal that increasing government expenditure on people's livelihoods can effectively improve regional high-quality development, but the marginal improvement degree varies with different periods and locations. The mechanism test proves that the regional consumption level plays a significant part in the mediating effect, accounting for 24.27% of the total effect. Urbanization is also the critical impetus for regional high-quality development and positively moderates the promotion effect of people's livelihood fiscal expenditure. Our study presents a theoretical basis and policy inspiration for improving residents' quality of life and welfare level and improving people's livelihood in the midst of high-quality economic development.

The objective of the current investigation is to identify the first or first draining node or sentinel lymph node (SLN) from the primary tumor mass in a regional lymphocenter. Four different indirect lymphography (IL) methods were employed in 96 canine patients with different types of cancer between 2018 and 2021. The IL technique involved intradermal, submucosal, and peritumoral injections of 2ml contrast agent in the four-quadrant principle which were divided into equal aliquots. Lymphatic mapping with lipiodol (iodized oil) was 100% in squamous cell carcinoma of the head and neck, anal sac apocrine gland adenocarcinoma, mast cell tumor, squamous cell carcinoma of the skin, mammary carcinoma, and with methylene blue dye, 100% detection was achieved in testicular tumor and mammary carcinoma. Instead of a very short washout time of 2 minutes, Iohexol showed an excellent detection in indirect CT lymphography for histiocytic sarcoma and in indirect radiographic lymphography for lymphosarcoma. The significance of contrast and blue dyes in detecting the lymphatic spread of canine cancers is clearly emphasized in the current investigation. The nature of cancerous tissue was again analyzed through image and machine learning approach in this work. Supervised machine learning technique is applied in this work for automatic classification of cancerous and non-cancerous regions. Various statistical and texture-based features are extracted from X-Ray images and support vector machine with linear, polynomial, multilayer perceptron (MLP), and RBF kernel functions are applied for classification. Highest 95.53%, 94.64%, 93.05% sensitivity, specificity, accuracy, respectively, is achieved using RBF kernel function.

Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards a more and urgently needed sustainable food system. Lately, novel eco-labels that depict a product’s environmental life-cycle assessment on a multi-level scale are being tested across Europe’s retailers. This study elicits consumers preferences and willingness to pay (WTP) for a multi-level eco-label. A Discrete Choice Experiment was conducted in Austria. Individual partworth utilities were estimated by means of Hierarchical Bayes. Results show higher WTP for a positive evaluated multi-level label, revealing consumers’ perceived benefits of colorful multi-level labels over binary black-and-white designs. Even a negative evaluated multi-level label showed higher WTP compared to no label, pointing towards limited effectiveness of eco-labels. Respondents’ preferences for eco-labels were independent from their subjective eco-label knowledge, health consciousness, and environmental concern. The attribute “protein source” was most important, and preferences for animal-based protein source (beef) was strongly correlated with consumers’ meat attachment, implying that a shift towards more sustainable protein sources is challenging and sustainability labels have only a small impact on the meat product choice of average consumers.

Multimodal medical image fusion is a fundamental but challenging problem in the fields of brain science research and brain disease diagnosis, and it is challenging for sparse representation (SR)-based fusion to characterize activity level with single measurement and no loss of effective information. In this paper, the Kronecker-criterion-based SR framework is applied for medical image fusion with a patch-based activity level integrating salient features of multiple domains. Inspired by the formation process of vision system, the spatial saliency is characterized by textural contrast (TC), which is composed of luminance and orientation contrasts to promote more highlighted texture information to participate in the fusion process. As substitution of the conventional l1-norm-based sparse saliency, a metric of sum of sparse salient features (SSSF) is used for promoting more significant coefficients to participate in the composition of activity level measure. The designed activity level measure is verified to be more conducive to maintain the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verify the effectiveness of the proposed fusion method on both visual quality and objective assessment. Furthermore, the research work of this paper is helpful for further detection and segmentation of medical images.

Background : Our objectives were to investigate the incidence of elevated methemoglobin level among COVID-19 patients at intensive care unit. The correlation of methaemoglobinemia with mortality and some haematological parameters was also tested. Methods: The diagosis of coronavirus -19 infection was confirmed by RT-PCR. The quantitative method for determination of methemoglobin was it’s cyanide derivative by spectrophotometry. The reference range was less than 2 percent. Results: There were altogether 46 patients (11 male, 35 female) included. Their median age value was 70 y. ( 29 – 89). The methemoglobin median value was 4,3%. 15 of 46 patients died. The methemoglobin median value of departed patients was 8 % , and 2,5% was among survivors. ( P= 0,001) 19 patients were blood transfused. Their methemoglobin median was 11 % , otherwise the non transfused patients presented 2,7 % methemoglobin median. ( p= 0,001). We performed two binary logistic regression calculation, in order to judge the elevated methemoglobin level as an independent risk factor for the mortality. The predictor of methaemoglobin was 0,062 the constant -1,266 the odds ratio 1,06. The other binary logistic regression tested the fact of transfusion for the mortality. Predictor of transfusion 1,1474, constant -1,2527 odds ratio 3,15 There was no significant correlation between methemoglobin and CRP level. Discussion: The methemoglobin is not able to transport oxygen. It’s association with mortality is discussed by some papers.There are several hypothesis in the literature to explain it’s occurrence in COVID-19. The role of coronavirus proteins as oxydative agents was mentioned. The importance of enzyme defect is emphasized. The impact of reactive oxygen free radicals in inflammation is also probable. Some papers mentioned applying local anesthetics and azythromycin as a risk. The role of transfusion is obscure, because the methemoglobin can be elevated in blood conserves during storage. Conclusion: Testing the methemoglobin seems to be important, but the pathomechanism needs further research.. The traceability and standardization of different measurement methods at intensive care units is the key for defining it’s pathogenetic role.

Smoking have been linked to male infertility by affecting the sperm epigenome and genome. In this study, we aimed to determine possible changes in the transcript levels of the PTPRN2, PGAM5, and TYRO3 genes in heavy smokers compared with non-smokers; and to investigate their association with fundamental sperm parameters. One hundred eighteen sperm samples (63 heavy-smokers (G1) and 55 non-smokers (G2) were included in this study. Semen analysis was performed according to WHO guidelines. After total RNA extraction, RT-PCR was used to quantify the transcript levels of studied genes. In G1 a significant decrease in standard semen parameters in comparison to non-smokers has been shown (p<0.05). Moreover, PGAM5 and PTPRN2 were differentially expressed (P≤0.03 and P≤0.01, respectively), and downregulated in spermatozoa of G1 compared to G2. In contrast, no difference was observed for TYRO3 (p≤0.3). In G1, mRNA expression level of studied genes correlated negatively with motility, sperm count, normal form, vitality, and sperm membrane integrity (p<0.05). Therefore, smoking may affect gene expression and male fertility by altering DNA methylation patterns in genes associated with fertility and sperm quality, including PGAM5, PTPRN2, and TYRO3.

This study evaluates the agricultural carbon emission efficiency of Shandong Province from 2011 to 2020 using the DEA-SBM model. The nexus between endogenous and exogenous variables is estimated using the Tobit model. The findings reveal an overall increasing trend in agricultural carbon emissions, with significant variations in efficiency values among different cities, leading to a severe polarization. Spatial evaluation shows a high distribution trend in the central region and low in the eastern and western regions of Shandong Province. The empirical tests conducted for Shandong Province and its three regions indicate that urbanization plays a major role in sup-porting the growth of agricultural carbon emission efficiency, while the education level of the la-bor force has a suppressive impact. Economic development and crop cultivation structure, how-ever, have no significant influence. The impact of these variables varies across the eastern, central, and western regions. The proposed countermeasures include improving planting structure and reducing brain drain in the eastern region, strengthening agricultural and rural inputs and in-creasing the added value of agricultural products in the central region, and intensifying the link-age between urbanization and industrial layout in the western region to reduce agricultural car-bon emissions efficiency in Shandong Province.

Coastal areas situated at lower elevations are becoming more vulnerable to flooding as a result of the accelerating global sea level rise. As the sea level rises, so does the groundwater. Barriers designed to shield against marine flooding do not provide protection against flooding caused by rising groundwater. Despite the increasing threat of groundwater flooding, there is limited knowledge about the relationship between sea level rise and groundwater fluctuations. This hinders the ability to adequately consider sea level rise induced groundwater flooding in adaptation initiatives. This study aims to investigate how local groundwater in Juelsminde, Denmark, responds to changes in sea level and to evaluate the predictability of these changes using a machine learning model. The influence of the sea on the shallow groundwater level has been investigated using six groundwater loggers located between 45 and 210 m from the coast. An initial manual analysis of the data revealed a systematic delay in the rise of water levels from the coast to inland areas, with a delay of approximately 15–17 hours per 50 m of distance. Subsequently, a support vector regression model was used to predict groundwater levels 24 hours into the future. This study shows how the groundwater level in Juelsminde is affected by sea level fluctuations. Results suggest a need for increased emphasis on this topic.

Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

This study explores several global and country cluster sustainable development (herein SD) models. It jointly examines constructs from the “Neoliberal-turn” ((e.g., Institutional Enhancers (e.g., business freedom, property rights, government integrity, and judicial effectiveness), Financial Enhancers (e.g., government spending and monetary, trade, investment, and financial freedoms), Foreign Direct Investment, Global Competitiveness-institutions, and Global Competitiveness-innovation-sophistication)), and the “Social-turn” (five SD pillars (e.g., Planet, People, Peace, Prosperity, and Resources), which synthesize the 17 United Nations SD goals); and Happiness and Life Satisfaction (herein H&LS). The study examines a large and diverse set of relationships encompassing 11 years of data of 108 social, economic, and environmental indicators from 129 countries at the global, country cluster, and country levels. We analyze data using structural equation modeling. Remarkably, Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the "neoliberal-turn" constructs and H&LS. Results show model configurations and scale effects. Furthermore, findings reveal both synergies between neoliberal and the five SD pillars as well as both negative and non-significant relationships among them. The diversity of results calls for further model integration and specificity. Diverse findings entail caution and difficulties in generalizing knowledge. Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) and or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels.

Memristor has attracted a lot of interest due to its high processing speed, low power consumption and high integration ability, which is critical for electronic systems and memory-centric computing. However, the memristor programming circuit and strategy are still inflexible and complex, since the signal generator/collector and stimulate pulse must be carefully matched and designed based on memristor intrinsic characteristics without reconfigurable. Here, a simple and effective circuit only consists a parallel reference-resistor-and-NMOS is designed to program memristor with a more than 99% memristance precision. And the amplitude and width of stimulate pulse are fixed to ±4V and 5ms, respectively. In order to cope with the device variation, such as ±10% tolerance of transition voltage, an optimized programming strategy was proposed and demonstrated great robustness. Additionally, a set of reference resistors and NMOSs have been added to facilitate multi-level memristance operation without requiring any changes to the circuit structure. This program circuit was also employed to program memristor crossbar remains 99% precision. In the end, a memristor-based convolutional neural network which controlled by our optimized programming circuit was used for image recognition, and 89.36% accuracy can be achieved even under 15.8% memristance tolerance. This novel circuit demonstrates a simple and flexible strategy in memristor programming, providing a new way to control memristor crossbar for practical application.

Purpose To analyse diagnostic and therapeutic impact of molecular imaging TNM (miTNM) stage obtained with 18F-DCFPyL versus 18F-Fluorocholine in head to head comparison in biochemical recurrence (BCR) prostate cancer (PCa). Material and methods Patients with BCR PCa after radical treatment with previous 18F-Fluorocholine-PET/CT (negative or oligometastatic disease) were derived to 18F-DCFPyL-PET/CT. Patients were classified attending to: grade group, EAU classification, PSA, PSA doubling time (PSAdt) and PSA velocity (PSAvel). The overall detection rate (DR) and miTNM stage according to PROMISE criteria were assessed for both radiotracers and also correlation (Kappa). The influence of PSA and kinetics on both PET/CT (DR and miTNM) and predictive value of unfavourable kinetics on miTNM were determined. Cut-off PSA, PSAdt and PSAvel values able to predict PET/CT results were determined. Change in miTNM and treatment derived of 18F-DCFPyL information with respect to 18F-Fluorocholine was also evaluated. Results We studied 138 patients. 18F-DCFPyL showed a higher DR than 18F-Fluorocholine (64.5% vs 33.3%). 18F-DCFPyL and 18F-Fluorocholine detected T in 33.3% versus 19.6%, N in 27.5% versus 13.8%, M in 30.4% versus 8.7%. Both tracers DR showed significant associations with PSA and PSAvel. Significant association was only found between miTNM and PSA on 18F-Fluorocholine-PET/CT (p=0.033). For 18F-Fluorocholine and 18F-DCFPyL PET/CT a PSAdt cut-off of 4.09 and 5.59 months, respectively, were able to predict M stage. 18F-DCFPyL changed therapeutic management in 40/138 patients. Conclusion 18F-DCFPyL provides a higher DR and superior miTNM staging than 18F-Fluorocholine in restaging BCR, especially with high PSA and unfavourable PSA kinetics, showing a fair agreement to 18F-Fluorocholine.

In this paper, based on the pseudo-totem pole (PTP) circuit, a family of five-level PTP dual boost converters (PDBC) is proposed. Dual boost converter has some unique advantages such as no bridge arm shoot-through risk and no switch body diode reverse recovery problem, so that it has a good application prospect. Compared with conventional three-level PTP converters, PDBCs has higher power density, higher efficiency, and low voltage and current stress. The topological derivation process, working principle, modulation and strategy of the topology are analyzed firstly. Further, the number of power devices, the switches voltage and current stress of the proposed topology is analyzed. Finally, a representative five-level PDBC experimental prototype is designed with AC input 220V/50Hz, DC output 400V/1kW, and peak efficiency of 98.27%. The experimental results show that the five-level PDBC proposed in this paper has higher efficiency and verify the correctness of the topology.

Structured extraction of emergency event information can effectively enhance the ability to re-spond to emergency events. This article focuses on the extraction of Chinese document-level emergency events, which mainly faces two key issues in this field: first, related datasets; Secondly, existing DEE (document-level event extraction) studies mostly use sequence annotation to extract candidate entities in the subtask of candidate entity extraction without considering the problem of role overlapping between candidate entities. On the one hand, this article constructs a Chinese document-level emergency extraction dataset, CDEEE, which first annotates the issues of argu-ment scattering, multiple events, and role overlapping. On the other hand, this article proposes a model RODEE for the problem of role overlapping in DEE tasks. This model first uses two in-dependent modules to represent the head and tail positions of candidate entities, then uses a multiplication attention mechanism to interact with the two to obtain a scoring matrix. Finally, role-overlapping candidate entities are predicted to assist in completing DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results showed that RODEE can effectively solve the problem of role overlapping in candidate entities and improve the performance of the DEE model.

Background: Low-level (LLLT) and high-intensity laser therapy (HILT) can be beneficial additions to knee osteoarthritis (KOA) rehabilitation exercises; however, it is still being determined which modality is more effective. Aim: To compare the effects of LLLT and HILT as adjuncts to rehabilitation exercise (LL+EX and HL+EX) on clinical outcomes in KOA. Methods: Thirty-four adults with mild to moderate KOA were randomly allocated to either LL+EX (n = 17) or HL+EX (n = 17) groups. All participants underwent their designated laser treatment combined with rehabilitation exercises weekly for twelve consecutive weeks. The Knee Injury and Osteoarthritis Outcome Score (KOOS), Numerical Pain Rating Scale (NPRS), active knee flexion, and Timed Up-and-Go test (TUG) were assessed at baseline and immediately post-intervention. Results: Post-intervention, both groups significantly improved their KOOS, NPRS, active knee flexion, and TUG scores compared to baseline (p < 0.01). The mean difference of change in KOOS, NPRS, and active knee flexion scores for the HL+EX group surpassed the minimal clinically important difference threshold. In contrast, the LL+EX group only demonstrated clinical significance in NPRS scores. Conclusions: Incorporating HILT as an adjunct to usual KOA rehabilitation led to significantly higher improvements in pain, physical function, and knee-related disability compared to LLLT.

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm (n) observations are generated for the parameters, and in the inner level (m) observations of the simulation model are generated with the value of parameters fixed at the value generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent, showing how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments using this forecast model.

We present an attempt to estimate the long-term changes of Relative Sea Level (RSL) and the different factors contributing to such trends on a local and regional scale using a statistical linear model. The time series analysed correspond to 17 tide-gauges grouped in three different areas: the northern and western Atlantic coasts of the Iberian Peninsula, the Canary Islands, and the southern and eastern coasts of the Iberian Peninsula and Balearic Islands. The analysis was performed for two periods: 1948-2019, using tide-gauge data, and 1993-2019, using both tide-gauge and altimetry data for comparison. The trends for the period 1948-2019 ranged between 1.09 0.14 (Canary Islands) and 2.05 0.21 mm/yr, for the northern and western Atlantic Iberian Peninsula. Altimetry data during the period 1993-2019 yielded quite homogeneous results for all the locations and regions, ranging between 2.7 0.4 and 3.0 0.3 mm/yr. On the contrary, the results obtained from tide-gauge data for this recent period showed a large dispersion, very likely due to local effects or even levelling or instrumental errors. Nevertheless, when the results were averaged for each area, the observed trends were comparable to the altimetry results, with values of 2.3 0.8, 2.7 0.5 and 2.8 0.8 mm/yr for the three regions of study. A stepwise forward linear regression has been used to relate the observed RSL variability to the atmospheric forcing and the thermosteric and halosteric components of the sea level. Surprisingly, the thermosteric and halosteric contributions were not significantly correlated to the observed RSL in many cases, and consequently the steric, the total addition of mass, the mass of salt, and the freshwater contributions to the observed sea level trends could not be reliably estimated. This result seems to be the consequence of the scarcity of temperature and salinity data. This hypothesis is confirmed by the exception of L’Estartit tide-gauge. This location is close to a well sampled region. In this case, the atmospheric variables and the thermosteric and halosteric terms explained the 80 % of the observed RSL variance and the contributions of these terms could be estimated. The freshwater contribution for this location was between 1.3 and 1.4 mm/yr, consistent with recent estimations of the contributions of glaciers and Greenland and Antarctica Ice Sheets. These results highlight the importance of monitoring programs and routine sampling for the determination of the different factors contributing to the sea level variability.

This paper deals with the use of low earth orbit (LEO) satellite links in long-term monitoring of water, ice and snow levels across wide geographic areas, where no other communication links can be applied. Unlike geostationary satellites, LEO satellites do not maintain the same position relative to the ground station, and transmissions need to be scheduled for satellite overfly periods. For our application, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our approach is based on online learning, and is applicable to any LEO satellite transmissions.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

Malaria infection is a multifactorial disease partly modulated by host immuno-genetic factors. Recent evidence has demonstrated the importance of Interleukin-17 family proinflammatory cytokines and their genetic variants in host immunity. However, limited knowledge exists about their role in parasitic infections such as malaria. We aimed to investigate IL-17A serum levels in patients with severe and uncomplicated malaria, whether IL-17A gene polymorphisms are involved in severe malaria susceptibility and the polymorphism’s influence on the IL-17A serum levels. 125 malaria patients and 48 free malaria controls were enrolled in this research. Malaria patients were classified into severe malaria (SM) and uncomplicated malaria (UM). IL-17A serum levels were measured with ELISA. PCR and DNA sequencing were used to assess host genetic polymorphisms in IL-17A. We performed a multivariate regression to estimate the impact of human IL-17A variants on IL-17A serum level and malaria outcome. Elevated serum IL-17A levels accompanied by increased parasitemia were found in SM patients compared to UM and controls (P<0.0001). Also, the IL-17A levels were lower in SM patients who were deceased than in those who survived. In addition, the minor allele frequencies (MAF) of two IL-17A polymorphisms (rs3819024 and rs3748067) were more prevalent in SM patients than UM patients indicating an essential role in SM. Interestingly, the heterozygous rs8193038 AG genotype was significantly associated with higher levels of IL-17A than the homozygous wild type (GG). According to our results, it can be concluded that IL-17A may play a role in protection against fatal malaria outcomes.

The combined heat and power system is based on the principle of "cascade utilization of thermal energy" for system integration and optimization. The rational utilization of energy, as well as the rational arrangement of energy flow and the process optimization combination of the overall system are taken into account in order to achieve multifunctional goals in the field of thermal engineering. The present paper presents a detailed assessment of combined heat and power systems from above standpoints. This article is divided into two parts “CHP based on thermo-dynamic cycles” and “CHP based on non-thermodynamic cycles”. Each part is then classified based on power subsystem energy consumption efficiency (high or low), including Rankine cycle, ORC, Stirling cycle, gas turbine, reciprocating engine, PVT and fuel cell. The present paper also helps identify the research gaps in this area and provides direction on future studies on combined heat and power systems.

Abstract: This work is presenting a water level control and monitoring system in a tank, by using the development boards Arduino Uno, NodeMCU ESP8266 and the ultrasonic sensor HC-SR04. The water tank is supplied through a pump controlled by the development board Arduino Uno by means of a relay. The author has issued a control algorithm for controlling the water level and a program in the development environment Arduino IDE that has been implemented and validated on the experimental stand. For monitoring the development board NodeMCU ESP8266 has been used that takes the information from the ultrasonic sensor HC-SR04 and sends it through Wi-Fi to the platform ThingSpeak. Based on the experimental results it has been found out that the water level control system in the tank provides the performances imposed through the designing stage, and the pump running has been automatically stopped when the limit, settled through the program for the water maximum level in the tank, has been reached. The measured value of the water level is shown on the LCD 1602 display. In the water circuit of the home consumers or irrigation systems supplied from the tank a pump controlled by means of a mobile phone has been installed. In order to implement this control system, the author has issued a program for another development board Arduino Uno, whilst for the mobile phone he used the application Arduino Bluetooth Control. The advantage of this water level control and monitoring system in the tank is given by the very low costs in comparison to the cost of an industrial type control system.

When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions, and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in 1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or 2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms, and species.

Women have been leading organizations and communities for centuries, but their journeys in leadership have sometimes been challenging. This paper aims to reflect on women's positive and negative experiences in leadership roles and understand the factors contributing to their successes and challenges.To explore these issues, we will draw on various academic literature, including studies on gender and leadership and case studies of women in leadership positions. We will also examine the cultural and social factors that affect women's experiences in leadership, including bias and discrimination, societal expectations, and work-life balance challenges.One key theme from this analysis is the importance of role models and support networks in helping women succeed in leadership roles. Women with access to mentorship, sponsorship, and peer networks are more likely to thrive in their roles and overcome the challenges they may face. However, lacking female role models and mentors can make it difficult for women to advance in their careers and contribute to feelings of isolation and imposter syndrome.Another important theme is the need for organizations and society to address systemic biases and discrimination that prevent women from reaching their full potential. This can include initiatives such as unconscious bias training, flexible work arrangements, and policies that support gender diversity and inclusion.In conclusion, this paper highlights women's positive and negative experiences in leadership roles and emphasizes the need for ongoing efforts to support and empower women in leadership. By addressing the challenges women face and building a culture of inclusion and support, organizations and society can help unlock the full potential of women leaders and create a more equitable and just world.

This research paper aimed to explore the impact of childhood and adolescent experiences on the leadership development of Emirati women in the United Arab Emirates (UAE). Using a qualitative research methodology, the study utilized in-depth narrative semi-structured interviews, document analysis, and diary writing to understand the complex issue of leadership development. Findings indicate that childhood and adolescent experiences, including family upbringing, education, culture, and societal expectations, have significant impacts on the leadership development of Emirati women. Specifically, family upbringing and cultural values shape Emirati women's leadership aspirations and behaviors, while education and societal expectations provide opportunities and barriers for women's leadership development. The implications of these findings for policymakers and practitioners seeking to promote women's leadership development in the UAE are discussed.

Continuous water resources monitoring is needed for sustainable urban water supply. Remote sensing techniques have proven useful for monitoring some water qualitative parameters with optical characteristics. The study area was the Marateca reservoir in central inland Portugal. The aims were the following: (1) to explore the water quality parameters at the monitoring points of the Marateca reservoir that may explain the event; (2) to validate optical water quality parameters with the monitoring points data; and (3) to model the reservoir water characteristics regarding its deepness, trophic state, and turbidity. The parameters total phosphorus, total nitrogen, and chlorophyll-a were used to compute a trophic level index. The Sentinel-2 imagery was used to compute spectral indices and bands image ratio; to obtain spectral signatures for the monitoring points, and to model water characteristics. The water parameters were above the recommended values at the reservoir entry point from the Ocreza River. The reservoir trophic level was Hypereutrophic and Eutrophic. The spectral signatures confirmed a Hypereutrophic pattern in the entry point. The Marateca reservoir’s water characteristics modeling forecasted problematic zones by contamination. The methodological approach developed can be easily applied to other reservoirs and is a key support tool for decision-makers.

Accurate measurement of low-level thoron gas and high-accuracy calibration of thoron measurement devices are essential for assessing and preventing thoron radiological risks. This study aimed to develop a thoron gas measurement technique using an airflow-through scintillation cell for both low-level measurement and high-accuracy calibration. To achieve this, a compartment model was developed to estimate the influence of progeny deposition and accumulation on the wall of the scintillation cell to prevent overestimation of thoron. A self-developed scintillation cell was utilized to implement and validate this technique. The lower detection limit and measurement uncertainty were then evaluated to assess the feasibility of the technique for low-level measurement and high-accuracy calibration. The results showed that the compartment model effectively addressed the influence of the progeny deposition. The measurement technique achieved a lower detection limit below 100 Bq m-3 even with the coexistence of 100 Bq m-3 of radon and attained a measurement uncertainty (k = 2) below 10% when the concentration of thoron exceeded 1,000 Bq m-3. In summary, this study developed a reliable and practical thoron gas measurement technique using an airflow-through scintillation cell with consideration for progeny deposition, and is expected to contribute to the assessment and prevention of thoron radiological risk.

Mangrove forests, which are essential for the maintenance of terrestrial and marine biodiversity on tropical coasts and constitute the main blue-carbon ecosystems for the mitigation of global warming, are among the world’s most threatened ecosystems. Mangrove conservation can greatly benefit from paleoecological and evolutionary studies, as past analogs documenting the responses of these ecosystems to environmental drivers such as climate change, sea level shifts and anthropogenic pressure. A database (CARMA) encompassing nearly all studies on mangroves from the Caribbean region, one of the main mangrove biodiversity hotspots, and their response to past environmental shifts has recently been assembled and analyzed. The dataset contains over 140 sites and ranges from the Late Cretaceous to the present. The Caribbean was the cradle of Neotropical mangroves, where they emerged in the Middle Eocene (~50 million years ago; Ma). A major evolutionary turnover occurred in the Eocene/Oligocene transition (34 Ma) that set the bases for the shaping of modern-like mangroves. However, the diversification of these communities leading to their extant composition did not occur until the Pliocene (~5 Ma). The Pleistocene (the last 2.6 Ma) glacial-interglacial cycles caused spatial and compositional reorganization with no further evolution. Human pressure on Caribbean mangroves increased in the Middle Holocene (~6000 years ago), when pre-Columbian societies began to clear these forests for cultivation. In recent decades, deforestation has significantly reduced Caribbean mangrove cover and it has been estimated that, if urgent and effective conservation actions are not undertaken, these 50 million-year-old ecosystems might disappear in a few centuries. A number of specific conservation and restoration applications based on the results of paleoecological and evolutionary studies are suggested.

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.

This paper presents the results of a study aimed at understanding how technology maturity level influences the incidence of university technology transfer to the private sector. The study examined the topic from the perspective of private sector organizations. It used data from a random sample of patent applications filed with the United States Patent and Trademark Office and a theoretically guided sampling of multiple cases of private sector organizations that contemplated obtaining and assimilating technologies created at universities in the United States. The patent application data were analyzed using nonparametric statistical techniques and the case data were analyzed using qualitative comparative analysis (QCA). The findings of the study suggest that the typical maturity level of technologies created at U.S. universities is a TRL-5 or lower on as scale adapted from the NASA technology readiness level (TRL) scale. A technology maturity level of TRL-6 or higher is likely an insufficient but necessary part of at least one unnecessary but sufficient configuration of conditions that tends to result in the occurrence of university technology transfer. However, under certain circumstances, a technology maturity level of at least TRL-6 could be a sufficient but unnecessary condition for the occurrence of university technology transfer. These findings have several important implications. First, they provide support for the notion that university technology transfer is subject to causal complexity. Moreover, it may be possible to increase the incidence of university technology transfer in the United States by implementing public policy and practices that explicitly take technology maturity level into consideration.

Introduction: Since its emergence COVID-19 has become a global health threat. In Uganda, a number of COVID-19 Standard Operating Procedures (SOPs) have been put in place by the Ministry of Health Uganda, to mitigate the outbreak of the deadly COVID-19 pandemic among the population. Despite the putting in place COVID-19 SOPs, the deadly COVID-19 pandemic is still ravaging the population of Iganga District. This study investigated the level of compliance to guidelines for prevention of COVID-19 in public places in Iganga District so as to guide decision makers in making recommendations towards the entire public in order to prevent community acquisition and spread of COVID-19. Methodology: To deliver answers to the research objectives, the study adopted a cross-sectional survey design to guide the study process. Quantitative method of data collection using observational checklist were adopted for this study. The same data abstraction form helped to collect data from 3 public places which included: 3 markets, 5 public transport stations and 8 financial banks. Compliance was measured were the YES scored 1 and NO scored 0 and the ten indicators measured included: temperature screening, wearing of face masks, hand washing or sanitizing, physical distancing of at least 2 meters, information display on COVID-19, regular cleaning of public service places, cleaning of communal places, adequate ventilation, adequate waste management facilities and cleanliness of the work area. Overall a 4 point Likert scale used was: 0-2 poor, 3-5 fair, 6-8 good, 9-10 excellent were each indicator was equally important. Data was entered, cleaned and analyzed using Microsoft Excel 2019 spread sheet packages to generate tables and bar graphs. Results: Out of 5 public transportation stations studied, 80% were observed to be poorly compliant to covid-19 SOPs. On the other hand, out of 8 Financial Banks included in this study, 75% were observed to have excellent compliance levels to COVID-19 SOPs. Meanwhile, out of 3 Market places studied, 66.7% were observed to be poorly compliant to covid-19 SOPs. Conclusion; The results of the study revealed that; public places in Iganga district generally have low level of compliance to COVID-19 SOPs in terms of temperature screening, wearing of face masks, hand washing or sanitizing, social distancing of at least 2 meters and information display on COVID-19 Dos and Don’ts. Our findings suggest that there is a need to mobilize the population in Iganga district to comply with COVID-19 SOPs in terms of temperature screening, wearing of face masks, hand washing or sanitizing, social distancing of at least 2 meters and information display on COVID-19 Dos and Don’ts.

Aim: This study aimed to evaluate the sociodemographic, behavioral, and biological profile and its relationship with the emergence of chronic non-communicable diseases in riverside populations in the Xingu region, Pará, Brazil. Methods: Characteristics related to health indicators and which risk factors are considered most important were also analyzed. This is a cross-sectional, exploratory, and descriptive study. The sample consisted of riverside people over 18 years of both sexes. The sample size (n.86) was calculated with a confidence level of 95% and a sample error of 5%. To divide the groups, the K-means clustering algorithm was adopted through an unsupervised method and the values were expressed as median. For continuous and categorical data, the Mann-Whitney and Chi-square tests were used, respectively, and the significance level was set at p<5%. To classify the degree of importance of each variable, the multi-layer perceptron algorithm was applied. Results: Based on this information, the sample was divided into two groups: the group with low or no education, with bad habits and worse health conditions, and the group with opposite characteristics. The risk factors considered for cardiovascular diseases and diabetes in the groups were low education (p < 0.001), sedentary lifestyle (p < 0.01), smoking, alcoholism, body mass index (p < 0.05) and the waist-hip ratio, with values above the expected, in both groups. Conclusions: The factors considered important to have a good health condition or not were the educational and social conditions of these communities and a part of the riverside population was considered healthier than the other.

In this paper, the 1st level general fractional derivatives of arbitrary order are defined and investigated for the first time. We start with a generalization of the Sonin condition for the kernels of the general fractional integrals and derivatives and then specify a set of the kernels that satisfy this condition and posses an integrable singularity of power law type at the origin. The 1st level general fractional derivatives of arbitrary order are integro-differential operators of convolution type with the kernels from this set. They contain both the general fractional derivatives of arbitrary order of the Riemann-Liouville type and the regularized general fractional derivatives of arbitrary order considered in the literature so far. For the 1st level general fractional derivatives of arbitrary order, some important properties including the 1st and the 2nd fundamental theorems of Fractional Calculus are formulated and proved.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. The estimate is global in the sense that no a priori knowledge is required on the parameters to be determined. Different neural network algorithms are tested on a series of data sets. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.

Emotional intelligence and educational level are commonly associated with success in life and well-being. While multiple studies have explored these concepts independently, few studies have examined the impact of educational level on emotional intelligence and associated variables, such as well-being. A total of N = 202 participants were recruited online. Participants completed measures of emotional intelligence, well-being (i.e., happiness, self-esteem, personal growth). Overall, participants who previously obtained a bachelor’s degree displayed higher levels of emotional intelligence, happiness, self-esteem, and personal growth compared to those without an undergraduate degree. However, no differences were observed on any variable between individuals currently enrolled in a university and non-students. The results suggest that, while completing a university degree increases emotional intelligence and well-being, the stress of being a student may negatively impact the benefits obtained from attending university. Limitations and possible directions for future research are discussed.

At present, diversified and highly concurrent businesses in the Internet industry often require heterogeneous databases formed by multiple databases to meet the needs. This report introduces database kernel SG-ColBase we developed. After achieving read and write concurrency control, data rollback, atomic log writing, and downtime data redo to ensure complete transaction support. The parallelism of database kernel execution is extended through field level locks and snapshot reads. Use the Bloom filter, resource cache pool, memory pool, skip list, non blocking log cache, and asynchronous data writing mechanism to improve the overall execution efficiency of the system. In terms of data storage, column storage, logical key and LSM-tree are introduced. While improving the data compression ratio and reducing data gaps, all disk data operations are written in incremental order. With the characteristics of asynchronous batch operation, the data writing speed is greatly improved. Thanks to the continuous feature of vertical data brought by column storage, the disk scanning brought by vertical traversal is reduced, which is a qualitative leap in efficiency compared with traditional relational databases in the big data analysis scenario. SG-ColBase can reduce the use of heterogeneous databases in business and improve R&D efficiency.

The study examined the learner autonomy in EFL learning at the undergraduate level in the context of Bangladesh. It investigated the effectiveness of learner autonomy on EFL learning. Besides, it explored the factors affecting autonomous learning in English language class. This study adopted a mixed- method approach. Data were collected from 60 students who had just completed their second semester at a private university in Dhaka city. The respondent students attended the compulsory English foundation course including their two semesters. Data were also collected from 12 ELT teachers teaching at the tertiary education level. Five EFL classes were observed following a sample of classroom observation schedule. Both open-ended and closed-ended questionnaires were used by the researchers for collecting the data from the students and teachers. Major findings of the study showed that lack of confidence, teachers’ dependency, lack of motivation, short-term goals in learning, huge confusions and confinement in memorization, tendency of using mother tongue and unawareness to self-evaluation of students affected learner autonomy in EFL learning of the undergraduate students. However, the study revealed that motivated students hardly faced any difficulties in achieving maximum outcomes with autonomous learning in the EFL class. Finally, the researchers gave a number of recommendations to bring out effective EFL learning with learner autonomy.

Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is ​​calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.

Anemia is a significant public health problem among children, especially school-age children because their body quickly produces red blood cells to provide sufficient blood volume with plasma expansion to maintain blood concentration. This research aimed to study the anemia situation, knowledge about anemia, food consumption behaviors, and the association between knowledge, food consumption behaviors, and hematocrit level among school-age children in primary school, in Thasala district, Nakhon Si Thammarat Province. This is a descriptive study among 408 students, Grades 4 to 6, aged 9-12 years. Research instruments included the demographic data of the children and their knowledge about anemia, food consumption behaviors, and hematocrit assessment. Data were analyzed using descriptive, Spearman’s rank correlation coefficient, and logistic regression statistics. The results revealed that 23.2% of the samples had anemia, whereas 22.22% had mild anemia and 0.98% had moderate anemia. The children knew about anemia, where the mean score was at a moderate level (Mean= 6.63; SD= 2.51) out of 10. The mean score on food consumption behaviors was in the moderate level (Mean= 17.49; SD= 3.68) out of 24. There were significantly positive correlations between the knowledge about anemia and the hematocrit level at a moderate level (r= 0.45, p< 0.001). Correlation analysis revealed moderate food consumption behaviors with the hematocrit level (r= 0.40, p< 0.001). When confirm with Logistic regression found that knowledge about anemia (OR = 9.15, 95% CI: 4.57-18.34), and food consumption behaviors (OR = 19.09, 95% CI: 9.71-37.53) were significantly associated with the hematocrit level. Conclusions: This study showed that knowledge and appropriate food consumption behaviors are associated with hematocrit levels. Enhancing knowledge about anemia and food consumption behaviors may be reduced the prevalence of anemia in school-age children. The health care providers in primary care should be encouraged children to have health education and eat sufficient food.

The article presents an induction generator connected to the power grid using the AC / DC / AC converter and LCL coupling filter. In the converter, both from the generator side and from the power grid side, three-level inverters were used. The algorithm realizing Pulse Width Modulation (PWM) in inverters has been simplified to the maximum. Control of the induction generator was based on the Direct Field Oriented Control (DFOC) method. At the same time, voltage control has been used for this solution. The MPPT algorithm has been extended to include the variable pitch range of the wind turbine blades. The active voltage balancing circuit has been used in the inverter DC voltage circuit. In the control system of the grid converter with an LCL filter, the number of measurements was limited to the measurement of power grid currents and voltages. Synchronization of control from the power grid side is ensured by the use of a PLL loop with the system of preliminary suppression of undesired harmonics (CDSC).

Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall the exploitation of records of this attenua-tion for precipitation monitoring purposes is an innovative and inexpensive solution. Performance data from mobile operators' networks is crucial for the implementation of this technology. And, moreover for near real-time quantification. To meet this requirement, a real-time system for col-lecting and storing CML power levels from the cellular phone operator "Telecel Faso" in Burkina Faso was implemented. This new acquisition system, which uses the Simple Network Manage-ment Protocol (SNMP) can simultaneously record the transmitted and received power levels from all the CML to which it has access, with a time resolution of one minute. Installed at “Laboratoire des Matériaux et Environnement de l’Université Joseph KI-ZERBO (Burkina Faso)”, this acquisi-tion system is dynamic and has gradually grown from eight to more than 1000 radio links Tele-cel Faso CML links between 2019 and 2021. The system covers the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou and Kaya) as well as the axes connecting Ouagadougou to these cities.

Sediment thickness increases can cause floodplains and the water level increases. This has the potential to generate a flood. Using electromagnetic waves, Time Domain Electromagnetic (TDEM) detected resistivity or conductivity contrast of lithology in the subsurface. It is measured in the time domain. TDEM method has been developing for decades. Here we tried to develop a 1-D forward modelling program for central loop configuration in the water environment using the Adaptive Born Forward Mapping (ABFM) method. We simulated this program in several water environment conditions (such as freshwater, brackish water and saline water) to know its response. Preventing natural hazards, especially flood hazards which are caused by the floodplain increases is our motivation in this research. Our simulation shows that Central-Loop Configuration Time-Domain Electromagnetic Method is able for imaging the sediment thickness clearly. The response of this method is extremely sensitive in saline water to depth changing than in other water environments.

Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

The article is devoted to solving the problem of searching for universal explainable features that can remain explainable for a wide class of objects and phenomena and become an integral part of Explainable AI (XAI). The study is implemented on the example of an applied problem of early diagnostics of plant stress, using Thermal IR (TIR) and HSI, presented by 8 vegetation indices/channels. Each such index was presented by 5 statistical values. A Single-Layer-Perceptron classifier was used as the main instrument. TIR turned out to be the best of the indices in terms of efficiency in the field and sufficient to detect all 7 key days with 100% accuracy. Our study shows also that there are a number of indices, inluding NDVI, and usual color channels Red, Green, Blue, which are close to TIR possibilities in early plant stress detection with 100% accurasy or near, and can be used for wide class of plants and in different conditions their treatment. The stability of the stress classification in our study was maintained when the training set was reduced up to 10% of the dataset volume. The entropy-like feature of (max-min) for any indices/channels have determined as a leadersheep universal high-level explainable feature for the plant stress detection, which used in interaction with some of other statistical features.

The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a known phenomenon, called “catastrophic forgetting.” In a nutshell, the performance of the model drops on the previous data by learning from the new instances. This paper explores this issue in the table detection problem. While there are multiple datasets and sophisticated methods for table detection, the utilization of continual learning techniques in this domain was not studied. We employed an effective technique called experience replay and performed extensive experiments on several datasets to investigate the effects of catastrophic forgetting. Results show that our proposed approach mitigates the performance drop by 15 percent. To the best of our knowledge, this is the first time that continual learning techniques are adopted for table detection, and we hope this stands as a baseline for future research.

Steam Generator Level Control System (SGLCS) is one of the key instrumentations and control subsystems to ensure the safe operation of nuclear power plants. It is highly recommended to perform reliability analysis for SGLCS for making better maintenance strategy. SGLCS is a digital control system with complex redundant configuration and failure\self-diagnosis\repair action, traditional fault tree is not applicable due to its static property, Markov model is not a good choice too because of heavy burden of creating state transition graph for even medium-size system. Boolean Logic Driven Markov Process (BDMP) can describe complex failure-repair state transition via ‘trigger link’ and three kinds of leaf nodes, as well as keep compact and easy-read struct like fault tree. So BDMP is adopted to analysis reliability of SGLCS. Graphical BDMP modeling environment KB3 and Monte Carlo simulator YAMS is used for BDMP modeling and quantitative analysis respectively, and unavailability of SGLCS is obtained. The contribution of three parts of SGLCS to the unavailability of the system is also evaluated, and it is found that the water supply flow control part contributes the most to the failure of SGLCS.

Until now, the literature on Chinese International Online Shopping (CIOS) (B2C export from China) mainly concentrated on the potential income that it constitutes for Chinese international trade. However, regarding International Online Consumers' (IOCs) purchase behaviors, research does not provide insight into the impact of Countries' Level of Economic Development (CLED) on the IOCs' preferences and choices about Chinese brands. Based on 9971 purchases about Chinese mobile phone brands, countries' macroeconomic data, and a multinomial logistics model (MLM), we examined IOCs' preferences and choices about Chinese brands. The result shows that the CLED influences IOCs' preferences and choices. Consequently, accounting of CLED in consumers’ preferences and choices introduces a new dimension in understanding IOCs' behaviors and attitudes towards Chinese mobile phone brands. This work contributes to Chinese brands' globalization research from the perspective of CLED. Such a model can be used to guide e-retailers and brand managers.

State transition in the multiple-levels system has the great potential applications in the quantum technology. In this article we employ a deterministic approach in complex space to analyze the dynamics of the 1s-2p electron transition in the hydrogen atom. The electron’s spin motion is embodied in the framework of quantum Hamilton mechanics that allows us to examine the transition dynamics more precisely. The transition is driven by an oscillating electric field in the z-direction. The electron’s transition process can be visualized by monitoring its motion in the complex space. The quantum potential and the total energy proposed in this paper provide new indices to observe the dynamic changes of electrons in the transition process.

Sexual vs asexual reproduction—unisexual vs bisexual populations—diploid vs polyploid biotypes—genetic vs environmental sex determination: all these natural phenomena are associated with the genus of teleost fish, Carassius. Two Carassius entities with completely different biological characteristics exist: one globally widespread and invasive Carassius gibelio, and the other C. carassius with decreasing trend of natural occurrence. Comprehensive biological and cytogenetic knowledge of both entities, including the physical interactions between them, can help to balance advantages of highly invasive and disadvantages of threatened species. For example, benefits of a wide-ranged colonization can lead toward extinction of native species or be compensated by parasitic enemies and lead to equilibrium. This review emphasizes the comprehensive biology and cytogenetic knowledge and importance of the Carassius genus as one of the most useful experimental vertebrate models for evolutionary biology and genetics. Secondly, the review points out that effective molecular cytogenetics should be used for identification of various species, ploidy levels, and hybrids. The proposed investigation of these hallmark characteristics in Carassius may be applied in conservation efforts to sustain threatened populations in their native ranges. Also, the review focuses on consequences of co-occurrence of native and non-native species and outlines future perspectives of Carassius research.

Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps 1) teroVIR, a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and 2) teroWAT, a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from 55% to 99% in spatial coverage.A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of 0.638 m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and 5% improved respectively in error and correlation. teroWAT shows unprecedented excellent results in the Arctic, using a L1 products based algorithm instead of L2 one, reducing the error of almost 4 m on average. To further compare teroWAT with existing methods, a new scoring option, teroSCO, is presented, measuring the quality of the validation of time series transversally and objectively across different strategies. Finally, teroVIR and teroWAT are implemented as platform-agnostic modules and used by flood forecasting and river discharge methods as relevant examples. A review of various applications for miscellaneous end-users is given, tackling the educational challenge raised by the community.

The main functions of the automated systems rely on the advanced sensors for detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect the potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in the extreme weather conditions such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, a triple radar arrangement (long-range, medium-range, and short-range radars) based on sensor fusion technique is proposed to detect objects with different size in level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including truck, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.

Deep neural networks have been extensively researched in the field of document image classification to improve classification performance and have shown excellent results. However, there is little research in this area that addresses the question of how well these models would perform in a real-world environment, where the data the models are confronted with often exhibits various types of noise or distortion. In this work, we present two separate benchmark datasets, namely RVL-CDIP-D and Tobacco3482-D, to evaluate the robustness of existing state-of-the-art document image classifiers to different types of data distortions that are commonly encountered in the real world. The proposed benchmarks are generated by inserting 21 different types of data distortions with varying severity levels into the well-known document datasets RVL-CDIP and Tobacco3482, respectively, which are then used to quantitatively evaluate the impact of the different distortion types on the performance of latest document image classifiers. In doing so, we show that while the higher accuracy models also exhibit relatively higher robustness, they still severely underperform on some specific distortions, with their classification accuracies dropping from ~90% to as low as ~40% in some cases. We also show that some of these high accuracy models perform even worse than the baseline AlexNet model in the presence of distortions, with the relative decline in their accuracy sometimes reaching as high as 300-450% that of AlexNet. The proposed robustness benchmarks are made available to the community and may aid future research in this area.

The recent global calamity of sea level rise and flooding is a major disaster. Some settlements on Java Island, Indonesia, have been reported to be sinking. Bedono Village is one village on the Central Java Coast that is vulnerable to sea level rise. Aside from the possibility of sea level rise, the calamity in this village is linked to prior mangrove degradation and fish pond expansion. This tragedy has displaced 78.63% of the population over a decade, and the remaining population is still surviving in the remaining lands. This study aims to investigate the effects of sea-level rise-related permanent inundation on the lives of the remaining residents in Bedono Village, with a focus on their adaptation and resilience methods. The results disclosed that remaining populations have taken active initiation by adopting multiple strategies that include structural prevention measures and nature based solution through mangrove reforestations. As a result based on mangrove assessment using Geographic Information Systems, mangrove cover increased from 1 ha in 2010 to 9.5 ha in 2012. This achievement has provided an alternative income through mangrove ecotourism. Women and multi stakeholder participation have also contributed to the implementation of adaptation strategy. Despite those significant achievements, the adaptation and resilience was challenged by lack of education and awareness towards sea level rise issues and their confounding disasters. The nature-based solution and local wisdom in Bedono Village is an example that is in line with GPDRR UNDRR 2022, the Sendai Framework, and The Bali Agenda for Resilience that ensures a whole-of-society approach to sea-level rise by ensuring that no one is left behind, creating an inclusive environment, and promoting gender.

The ISO 2859 and ISO 3951 series provide acceptance sampling procedures for lot inspection, allowing both sample size and acceptance rule to be determined, starting from a specific value either for the consumer or producer risk. However, insufficient resources often make it difficult to implement “ISO sampling plans.” In cases where the sample size is determined by external constraints, the focus shifts from determining sample size to determining consumer and producer risks. Moreover, if the sample size is very low (e.g. one single item), prior information should be included in the statistical analysis. For this reason, it makes sense to work within a Bayesian theoretical framework, such as that described in JCGM 106. Accordingly, the approach from JCGM 106 is adopted and broadened so as to allow application to lot inspection. The discussion is based on a “real-life” example of lot inspection on the basis of a single item. Starting from simple assumptions, expressions for both the prior and posterior distributions are worked out, and it is shown how the concepts from JCGM 106 can be reinterpreted in the context of lot inspection. Conceptual differences regarding the definition of consumer and producer risks in JCGM 106 and in the ISO acceptance sampling standards are elucidated and a numerical example is provided.

AbstractLocal estimates of coastal flood risk are required for coastal planning and development, including the location and design of sea-defences and coastal buildings, such as harbours and associated infrastructure. This paper discusses the use of three parameters associated with estimating such risks; the flood return period, the instantaneous flood probability and the flood design risk, and it describes the mathematical background for their derivation. The discussion is extended to include the effects of sea level rise and how it can be incorporated into the calculations. Flood height can vary quite rapidly with distance along the coast, being affected by coastal topology, which may magnify or diminish the tidal and surge effects. Similarly land heave influences the local effects of sea level rise and can be influenced by water extraction, tectonic movements and melting ice. Tide gauge measurements provide a local historical record from which the various parameters can be retrieved. This paper discusses the algorithms used to derive these measures from tide-gauge records. The figures have been derived for four tide gauges located on the UK east coast.

In this review, the potential of an emerging field of interdisciplinary climate research, that is the Coastal Boulder Deposits (CBDs) as natural archives for intense storms, is explored with particular reference to the Mediterranean region. First, the identification of the pertinent scientific articles was performed by using Web of Science (WoS) engine. Thus, the selected studies have been analysed to feature CBDs produced and/or activated during the last half century. Then, the meteorological events responsible to the literature reported cases were analysed in some details using the web archives of the Globo-Bolam-Moloch model cascade. The study of synoptical and local characteristics of the storms involved in the documented cases of boulder production/activation proved useful to assess the suitability of selected sites as geomorphological storm proxies. It is argued that a close and fruitful collaboration involving several scientific disciplines is required to develop this climate research field.

Japan Aerospace Exploration Agency (JAXA) is going to launch Advanced Land Observing Satellite 3 (ALOS-3) after 2022. ALOS-3 satellite is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of land cover and vegetation information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of land cover and vegetation types at Genus-Physiognomy-Ecosystem (GPE) level. We acquired and simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the simulated ALOS-3 images were utilized for the classification and mapping of land cover and vegetation types in three sites (Hakkoda, Zao, and Shiranuka) in northern Japan. This research dealt with 17 land cover and vegetation types in Hakkoda site, 25 land cover and vegetation types in Zao site, and 12 land cover and vegetation types in Shiranuka site. Ground truth data were newly collected in three sites, and we employed eXtreme Gradient Boosting (XGBoost) classifier with the implementation of 10-fold cross-validation method for assessing the potential of ALOS-3S images. The classification accuracies obtained in Hakkoda, Zao, and Shiranuka sites in terms of f1-score were 0.810, 0.729, and 0.805 respectively. The fine scale (3.2m) land cover and vegetation maps produced in the study sites showed clear and detailed view of the distribution of plant communities. Regardless of the limited number of the temporal images, ALOS-3S images showed high potential (at least 0.729 F1-score) for the land cover and vegetation classification in all three sites. The availability of more cloud free temporal scenes is expected for improved classification and mapping in the future.

This article presents results of the polarization laser studies of the optical and microphysical characteristics of the high-level clouds (HLC). The high-altitude matrix polarization lidar (HAMPL; Tomsk, Russia) is described. HAMPL measures vertical profiles of all elements of the backscattering phase matrix (BSPM) of the HLC. Based on the joint analysis of lidar and radiosonde observations it is shown that the spatial structure of the HLC containing oriented ice crystals is inhomogeneous in the horizontal wind direction. It includes local areas with oriented particles; the sizes of such areas are estimated together with the most probable meteorological conditions of their formation. The shortcomings of the radiosonde observations performed closest to the location of the HAMPL are described. The applicability of the ERA5 reanalysis data of the European Centre for Medium-Range Weather Forecasts for use as an alternative source of information on the vertical profiles of meteorological quantities for the interpretation of HLC lidar sensing data in Western Siberia was checked.

The urgency of the COVID19 pandemic caused a surge in related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used.

The Multi-Level Model (=MLM) was suggested by A. Levichev as a description of quarks and gluons. The review recalls MLM-terminology while MLM-findings (for quarks and leptons) are compared to the theoretical and experimental data as accepted by the Standard Model (=SM). MLM is based on Segal’s compact space-time U(2) and on the sequence of embeddings: U(2) into U(3), U(2) into U(4), etc. These groups were called the levels (of matter): U(2) - the 0th (that is, our mundane world), U(3) - the 1st, U(4) - the 2nd, etc. Such a convention matches the SM-quarks' generations list. Each SM-quark is viewed either as a sunken proton, or as a captured proton. The MLM-proton is elementary and indestructible (hence no need for confinement). For MLM-quarks, in terms of the notion of a ruling group, flavor and color are defined mathematically. The number of colors (and of flavors) is level-dependent. For levels U(3) through U(6) the correspondence with the SM-quarks is established. Three new quarks and two new leptons are predicted. The SM-puzzle of quark and lepton generations is solved. Using the Han-Nambu scheme, the notion of the quark’s electric charge is expressed in terms of color charges. The original part of the review suggests studying the proton’s properties (like mass, shape and inner pressure) on the ba-sis of its wave function.

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

Automation technologies are present in almost every domain of human activity and they are now more and more present in our everyday life. The reason for this massive deployment of automated systems would reside in all the benefits they offer to the users. In experimental settings, multiple studies have demonstrated the positive effects the introduction of automation can have on human decision-making and performance. However, studies have also demonstrated that the introduction of automation can have important negative effects as well. Considering that automation is now introduced in sensitive domains like military defense or medicine, more than ever we need a complete understanding of the effects caused by these systems on human performance and decision-making, and particularly in tasks and contexts with social or moral dimension. In this paper we will firstly review the main effects produced on a human agent’s behaviors by the introduction of automation. Then, we will review the conditions identified as underlying factors of these effects, and see how they are currently integrated in models of human – automation interaction. We will conclude this review by highlighting new directions for future investigations on human – automation interaction.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

Glaucoma is one of the most common causes of irreversible vision loss worldwide. It is an insidious disease with a multifactorial pathogenesis. Despite progress in treatment methods, prevention and lifestyle modifications may be useful in slowing the progression of this disease. This systematic review aimed to evaluate the influence of diet, oxidative stress, and disturbances in blood serum levels of nutrients on the incidence and severity of glaucoma based on scientific reports on the role of nutrition in the pathogenesis and course of glaucoma. This paper presents an analysis of the above issues; however, further research is required to develop this topic. Future clinical trials are needed to assess the influence of nutrition and to develop nutritional management strategies for patients with glaucoma.

Abstract The article discusses about the current situation of Novel Corona Virus also called as the COVID-19 that hinder for all human’s life including the education. Rapidly escalating COVID-19, has caused havoc in quality education and every educational institution are closed. As the UNESCO report it showed that 1.6 billion children being affected due to the close of institution across 191 countries. With the alternative method every education institution started blended learning virtual classes in order to continue learning environment in students. The articles investigate COVID-19 impact on student’s quality education in Nepal and social work implication. The findings of the study shows that the COVID-19 has seriously effects on the students learning environment. It showed the huge gap between getting the good education in Nepal. However, Nepal has also made some policies to provide equal quality education to all the children through the ICT and also encourage social work to actively participate on providing education to all the majority of group children in Nepal. Whereas social work applied the micro, messo, and macro level of implication in practice to provide the education for children in remote area of Nepal.

This work presents an approach for detecting mathematical formulas in scanned document images. The proposed approach is end-to-end trainable. Since many OCR engines cannot reliably work with the formulas, it is essential to isolate them to obtain the clean text for information extraction from the document. Our proposed pipeline comprises a hybrid task cascade network with deformable convolutions and a Resnext101 backbone. Both of these modifications help in better detection. We evaluate the proposed approaches on the ICDAR-2017 POD and Marmot datasets and achieve an overall accuracy of 96% for the ICDAR-2017 POD dataset. We achieve an overall reduction of error of 13%. Furthermore, the results on Marmot datasets are improved for the isolated and embedded formulas. We achieved an accuracy of 98.78% for the isolated formula and 90.21% overall accuracy for embedded formulas. Consequently, it results in an error reduction rate of 43% for isolated and 17.9% for embedded formulas.

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. The image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network(HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses the dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While the earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3428 from scratch. Thereby, we outperform state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.

The orthodontic treatment brings numerous benefits and, in most cases, the benefits outweigh the possible disadvantages. Root resorption (RR) is a common adverse phenomenon associated with orthodontic treatment. This study evaluates the role of low-level laser emission / Photobiomodu-lation (LE/P) in quantitative measurements of root resorption (QRR). The application of LE/P was done after each orthodontic activation with 4 types of treatment intervention (TI) on the RR after fixed orthodontic treatment (FOT) of the upper arch with ectopic eye tooth/teeth [EET]. 32 Orthodontic patients scheduled for FOT were selected and assigned to the 4 groups. These were LE/P+Self ligating bracket (SLB), LE/P+Conventional bracket (CB), Non-Photobiomodulation (non-LE/P)+SLB, and non-LE/P+CB. Standard management stages of FOT were followed in the maxilla. Each patient received a single application of LE/P labially/buccally and palatally, a total of 5 different points during each activation or appointment. The main outcome measure was QRR in maxillary anteriors before and after FOT assessed via cone-beam computed tomography (CBCT) using 3D OnDemand software. Insignificant QRR was found between before and after FOT in SLB, CLB, and LE/P, non-LE/P groups (p > 0.05). QRR in the SLB vs CB and LE/P vs non-LE/P group was significantly different in 11, 13, and 23 (p < 0.05). QRR in the LE/P+SLB group (p < 0.05) was significantly different in 11, 13, and 23 than that in the other groups. The most severe QRR was found on the 13 (0.88 ± 0.28mm and 0.87±0.27mm) and 23 (1.19 ± 0.14 mm and 1.16±0.13mm) in the CB and non-LE/P group (p < 0.001). LE/P+SLB showed highly significant superior outcome (p < 0.001) in relation to non-LE/P+CB, the QRR of 23 were 0.813± 0.114mm and 1.156± 0.166mm respectively. Significantly higher amount of QRR found in EET patients after FOT treated with the CB, non-LE/P, and non-LE/P+CB system and warrants further investigation to explore potential specific causes.

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Consistent calibration and monitoring is a basic prerequisite for providing reliable time series of global and regional sea level variations from altimetry. The precision of sea level measurements and regional biases for six altimeter missions (Jason-1/2/3, Envisat, Saral, Sentinel-3A) is assessed at eleven GNSS-controlled tide gauge stations in the German Bight (SE North Sea) for the period 2002 to 2019. The gauges are partly located at the open water, partly at the coast close to mudflats. The altimetry is extracted at virtual stations with distances from 2 to 24 km from the gauges. The processing is optimized for the region and adjusted for the comparison with instantaneous tide gauges readings. An empirical correction is developed to account for mean height gradients and slight differences of the tidal dynamics between gauge and altimetry which improves the agreement between the two data sets by 15-75%. The precision of the altimeters is depending on location and mission and is shown to be at least 1.8 to 3.7 cm based on an assumed precision of 2 cm for the gauges. The accuracy of the regional mission biases is strongly dependent on the mean sea surface heights near the stations. The most consistent biases are obtained based on the CLS2011 model with mission dependent accuracies from 1.3 to 3.4 cm. Hence, the GNSS-controlled tide gauges operated operationally by WSV might complement the calibration and monitoring activities at dedicated CalVal stations.

Fixed offshore wind turbines continue to be developed for high latitude areas where not only wind and wave loads need to be considered, but also moving sea ice. Current rules and regulations for the design of fixed offshore structures in ice-covered waters do not adequately consider effects of ice loading and its stochastic nature on fatigue life of the structure. Ice crushing on such structures results in ice-induced vibrations, which can be represented by loading the structure using a variable-amplitude loading (VAL) sequence. Typical offshore load spectra are developed for wave and wind loading. Thus, a combined VAL spectrum is developed for wind, wave, and ice action. To this goal, numerical models are used to simulate the dynamic ice-, wind-, and wave-structure interaction. The stress time-history at an exemplarily selected critical point in an offshore wind energy monopile support structure is extracted from the model and translated into a VAL sequence, which can then be used as a loading sequence for the fatigue assessment or fatigue testing of welded joints of offshore wind turbine support structures. This study presents the approach to determine combined load spectra and standardized time series for wind, wave, and ice action.

Mild Cognitive Impairment (MCI) is a clinical syndrome characterized by a moderate decline in one or more cognitive functions with a preserved autonomy in daily life activities [1]. MCI exhibits cognitive, behavioral, psychological symptoms [2]. The executive functions (EFs) are a set of key functions for everyday life and physical and mental health; and allow adapting the behavior to external changes [3-5]. Higher-level executive functions develop from basic EFs (inhibition, working memory, attentional control, and cognitive flexibility). They are planning, reasoning, problem- solving, and fluid intelligence (Gf) [3]. This systematic review investigates the relationship between higher-level executive functions and healthy and pathological aging, assuming the role of executive functions deficits as a predictor of cognitive decline. The systematic review was conducted according to the PRISMA Statement [6-7]. A total of 73 studies were identified. The results indicate that 65.8% of the studies confirm significant EFs alterations in MCI (100% problem solving, 71.4% fluid intelligence, 56.8% planning, 50% reasoning). These results seem to highlight a strong prevalence of higher-level executive functions deficits in MCI elderly than in healthy elderly.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

Cervical fusion has been a standard procedure for treating the abnormalities associated with the cervical spine. However, the reliability of anterior cervical discectomy and fusion (ACDF) has become arguable due to its adverse effects on the biomechanics of adjacent segments. One of the drawbacks associated with ACDF is adjacent segment degeneration (ASD) which has served as the base for the development of dynamic stabilization systems (DSS) and total disc replacement (TDR) devices for cervical spine. However, the hybrid surgical technique has also gained popularity recently but their effect on the biomechanics of cervical spine is not well researched. Thus, the objective of this FE study was to draw the comparison among single, bi-level and hybrid surgery with DCI implant with traditional fusion. Reduction in range of motion (ROM) for all the implanted models was observed for all the motions except extension, compared to intact model. The maximum increase in ROM of 42% was observed at C5-C6 level in Hybrid-DCI model. The maximum increase in adjacent segment’s ROM of 8.7% was observed in multilevel fusion model. The maximum von Mises stress in the implant was highest for the multilevel DCI model. Our study also showed that the shape of DCI implant permits flexion/extension relatively more compared to lateral bending and axial rotation.

Background: Supportive supervision has lately been gaining traction in various national health systems as an effective way of boosting the performance of community health workers in a constructive and sustainable way. However, not much is known about the basis/mandate of supportive supervision and its approach in maternal and child health programs in India. The current analysis contributes to a clearer understanding of the paradigms within which supportive supervision is envisioned to operate within India and identifies potential strengths and areas requiring attention. Method: Document analysis of implementation documents such as guidelines/ operational manuals/operationalization modules/ training modules of nationally implemented maternal and child health programs, with data extraction according to a pre-determined domain-based template. Results: Many of the documents reviewed do not mention supportive supervision at all. In the few documents where supportive supervision is mentioned, the paradigms within which it is supposed to operate (who will do it, when will it be done, how to do it, training and logistic support, reporting formats, etc.) have not been clearly identified in most programs. Conclusion: Even though supportive supervision is being increasingly identified as an effective way of performative improvement in national health programs in India, more effort needs to be put into identifying and enforcing the tenets of supportive supervision in practice, in order to bring about the desired change.

The Lewis basicity of selected organic bases, modeled by the enthalpies of adduct formation between gaseous BF3 and the bases in dichloromethane (DCM) solution, is critically examined. Although experimental enthalpies for a large number of molecules have been reported in the literature, it may be desirable to estimate missing or uncertain data for important Lewis bases. We have decided to use high-level ab initio procedures, combined with a polarized continuum solvation model, in which the solvated species are the clusters formed by specific hydrogen bonding of DCM with the Lewis base and the Lewis base/BF3 adduct. This mode of interaction with DCM corresponds to a specific solvation model (SSM). The results actually show that the enthalpy of BF3 adduct formation in DCM solution is clearly influenced by specific interactions, DCM acting as hydrogen-bonding donor (HBD) molecule in two ways: base/DCM and adduct/DCM, confirming that specific solvation is an important contribution to experimentally determined Lewis basicity scales. This analysis allows us to conclude that there are reasons to suspect some gas-phase values to be in error by more than the stated experimental uncertainty. Some experimental values in DCM solution that were uncertain because of identified reasons can be complemented by the computed values.