Ventilation in cities is crucial for the well being of their inhabitants. Therefore, local governments require air ventilation assessments (AVAs) prior to the construction of new buildings. In a standard AVA, however, only neutral stratification is considered, although diabatic and particularly unstable conditions may be observed more frequently in nature. The results presented here indicate significant changes in ventilation within most of the area of Kowloon City, Hong Kong, included in the study. A new definition for calculating ventilation was introduced, and used to compare the influence of buildings on ventilation under conditions of neutral and unstable stratification. The overall ventilation increased due to enhanced vertical mixing. In the vicinity of exposed buildings, however, ventilation was weaker for unstable stratification than for neutral stratification. The influence on ventilation by building parameters, such as the plan area index, was altered when unstable stratification was considered. Consequently, differences in stratification were shown to have marked effects on ventilation estimates, which should be taken into consideration in future AVAs.

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

Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal Large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality GPT (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4, requiring no human efforts. SQ-GPT approaches GPT-4’s performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjin, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

Higher traffic density in Russian cities and less uniform traffic due to congestions are reasons for increased emissions of pollutants contained in exhaust gases of vehicles. The paper presents the modeling results of pollutant dispersion in Magnitogorsk. The analysis of these results shows that now many blocks of flats and social infrastructure of the city are in a zone with an excess of maximum allowable concentration of pollutants. Having summarized the methodological framework formulated by Russian and foreign researchers, the authors systemized the existing methods used to decrease pollutants produced by vehicles. It is proposed to choose an optimal combination of such methods applying a developed mathematical model. By adopting an optimal combination of the methods, it is possible to ensure minimum air pollutant emissions from vehicles, allocating limited financial resources. The mathematical modeling of the solutions on changing traffic density and structure and implementing methods used to decrease pollutants on one of the Magnitogorsk crossroads with the heaviest traffic is used to draw a conclusion about efficiency of the designed model used as a tool for strategic planning of development and transformation of street networks.

The once-held wisdom of the supreme efficiency of one-way streets has been gradually sup-planted by the perceived sustainability of two-way streets in the design of livable cities that prioritizes the safety of pedestrians and thriving of local businesses. However, it is rarely dis-cussed on whether one-way street conversions have truly improved the long-term traffic effi-ciencies on urban street networks, as conflating socioeconomic factors such as vehicular popula-tion growth and induced travel demand may render empirical analysis inconclusive. In this study, microscopic traffic simulations implemented on SUMO platform was performed to ana-lyze the effect of street conversion in Downtown Brickfields, Kuala Lumpur. This approach can control and standardize travel demand in both one-way and two-way street networks, and would therefore give a fairer evaluation by precluding all socioeconomic factors. It was found that one-way streets do not necessarily improve the traffic efficiency of the network, as it is very dependent on the traffic scenario evolution over time. One-way streets perform better at the on-set of traffic congestion due to its higher capacity, but on average, the 4-fold longer travel times that made it harder to clear traffic by getting vehicles to their destinations compared to two-way streets. As time progresses, congestion in one-way streets may become twice as worse compared to two-way streets. This study may contribute to a more holistic assessment of traffic circulation plan designed for smart and livable cities

Street vended foods are ready-to-eat food and beverages prepared and/ or sold in the streets. This trade provides for 85-99% of total employment in most African countries and 50% or more is constituted by women. The preparation of street vended foods is normally under unsatisfactory conditions and these may lead to the contamination of food. This descriptive survey was conducted in Maseru around the taxi ranks amongst 141 participants (48 food handlers and 93 consumers) using a semi structured questionnaire, open ended questionnaire and observation checklist. Majority of the food handlers were females (n=35, 60%) and males constituted only (n=23, 40%). On average the vendor population that participated in this study was considered to have poor knowledge of food safety since they scored 49%±11. With regard to the consumers, 63% were males and 37% were females, and only 6% reported that they never buy street vended foods mainly due to the food safety issues and hygiene. Based on the results of this study, it is thus recommended that educational interventions be implemented. The observation study showed that they also operated under unhygienic conditions and 95% of food handlers had the incorrect knowledge that washing utensils with detergent leaves them free of contamination. Regarding the consumer perceptions, they highlighted that the trade has the potential to grow and be profitable on condition that hygiene is emphasized and infrastructure improved so as to provide safe quality food.

This paper presents a device for measuring and maintaining the constant water level in a tank. The device uses a capacity sensor to measure the water level. The sensor has a DC voltage output proportional to the water level in the tank. This voltage can be used in other automation too. The water level, in percentages, is displayed on a vu meter with 8 LEDs. The circuit for maintaining the constant level consists in a comparator with hysteresis. The level of the water is adjustable, using a reference voltage from a potentiometer (marked in percentages). The level of the water is compared with the prescribed level and the difference between them commands the start/stop of the water pump which supplies the tank through a relay. The device is powered by 220 V and is isolated from the environment.

Overcoming the issue of land value and cost in urban areas will not provide a miraculous solution to the problems there. Appropriate land use cost, especially for residential and commercial land, is just one of the issues to be settled in the debate. Therefore, this study aims to build a new urban land price determination model by investigating the urban syntactical analysis, street width, and their economic effects on land value. The study attempts to determine the impact of syntactic analysis of streets and street width on land prices; it also seeks to identify the factor most affected by the land cost. Ultimately, the study built a model for urban land price prediction. The case selected is evaluated and compared in three aspects of the analysis, including; the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The result showed a significant impact of the urban street network on the price of land; this result can be used to enhance future urban design regarding urban economy improvements.

Today’s urban spaces face precipitous challenges and complications, which include the COVID-19 pandemic, urbanization, population growth, and climate change. Proximity plays an essential role in influencing the integrity of public spaces and urban environments, while proxemics studies people’s experience, and the benefits, of urban space, and is based on a behavioral system of activities that includes ‘territoriality’. There is a need to study the behavioural patterns of people in given situations with proxemic dimensions in order to sustain both human beings and the quality of urban space. However, people behave spontaneously within environmental settings and reflect each other’s responses, meaning it is important to distinguish between different cultures, which tend to respond in particular ways to the meaning of proximity. This suggests there is no universal indicator for phenomena such as crowding. The aim of this paper is to highlight the interrelationship between urban form and urban life. This study focuses on two patterns of activity - street life and social life - by adopting a descriptive-analytical approach to documentary and desk research. The findings reveal a vital distinction between social and public patterns of proximity, while other proximities, although critical, are not observed within interactions between people.

Street view images can help us better understand the city environment and potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce parameters and computations of the model while ensuring the segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on its basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.

The pandemic of Covid-19 has contributed to the increase of the number of cases of anxiety, depression, and stress, especially in vulnerable workers. The objective of this study was to analyze factors associated with mental health of waste pickers and street sweepers in Brasília, Brazil, during the Covid-19 pandemic. It was a cross-sectional cohort study with a quantitative approach. SRQ-20 test was used to assess mental health. The study sample included 886 workers: 601 street sweepers and 285 waste pickers. Of the total, 71 workers (8.0%) had SRQ-20 ≥ 7, which is equivalent to adverse mental health. The characteristics associated with adverse mental health were women (PR: 3.36; 95%CI:1.86;6.08), worker between 18-39 years old (PR: 2.19; 95% CI 1.32;3.65), waste pickers (PR: 1.65; 95%CI:1.01;2.70), worker who reported two (PR: 3.24; 95%CI:1.72; 6.12), three or more (PR: 4.73; 95%CI:2.40;9.32) health problems. For the symptoms, 17.05% of the people interviewed said they felt sad and 18.25% nervous, tense, and worried. Female waste pickers, young adults, with many health problems were more susceptible to adverse mental health and it doesn't just affect them, but also their families. Public policies are needed to protect these professionals so that they can work safely and establish early interventions to maintain these workers' mental integrity.

This study explores the feasibility of using an oscillating plate downstream of a cylindrical body to produce mechanical energy from a Von Kármán vortex street. The study aims to quantify the impact of the plate length, its separation from the cylinder, and a machine damping factor on the power coefficient and the blade’s displacement to identify the optimal configuration. This preliminary assessment assumes that the plate oscillation is small enough to avoid changes in the vortex dynamics. This assumption allows the construction of a surrogate model using CFD to evaluate the effect of plate length and separation from the cylinder on the fluctuating lift forces over the plate. Later, the surrogate model, combined with varying machine damping factors, facilitates the description of the device’s dynamics through the numerical integration of an angular momentum equation. The results showed that a plate with 0.52D length, 5.548D separation from the cylinder, and a damping factor of 0.013 achieved a power coefficient of 0.147 and a perpendicular displacement of 0.266D. These results demonstrate a substantial improvement in the performance of bladeless generators.

Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women's walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: 1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and more dispersed distribution of scores. 2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. 3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety.

New approaches to collect in-situ data are needed to complement the high spatial (10~m) and temporal (5-day) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowd-sourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: 1) what is the spatial availability of these images across the European Union (EU)? and 2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary - the largest open-access platform for such images - against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337031 points. For 37.78% of the LUCAS points a crowd-sourced image is available within a 2-km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowd-sourced image within 300-m from a LUCAS point, illustrating the huge potential of crowd-sourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowd-sourced imagery looking at parcels was assessed for 8 different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowd-sourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

On the afternoon of January 31, 2022, the northwest and central part of the city of Quito (Ecuador) was hit by a mud-flow that originated in one of the ravines of the Pichincha volcanic complex. According to the official reports, there were 28 fatalities, one missing person, 52 injured, and 53 families affected. The mudflow was triggered by unusual and heavy rainfall in the upper part of the drainage. However, the analysis of the deposits allowed us to establish the existence of anthropogenic elements as crucial components of a local disaster that affected daily life in the city for more than a week. Based on the preliminary data, we were able to determine that the mud and water flowed through different streets of the city, reaching a maximum distance of 3.2 km from the overflow point (collector, construction works to dam the drainage under the city). The analysis of the multimedia material published on social networks and in the press also made it possible to detect the change in the behavior of the flow during its evolution, which went from a hyper-concentrated flow to muddy water. A similar event occurred in 1975, and although it was a larger event (52000 m3, seven times larger than in 2022), it caused two deaths and little damage in the same area. The main causes of the disaster included urban development that did not meet the needs of the area and a lack of awareness or ignorance about the management and intervention of drainage in Quito City.

Currently, historical areas in China are facing issues of color authenticity and excessive commercialization. Colors, as an important element of historical areas, reflect their history and culture. However, there are some flaws in the existing research methods, so a more scientific and systematic method is needed to obtain accurate and objective color visualization results to promote the sustainable development of historical areas. In order to explore the methods and applications of visualizing the environmental color genes of historical areas, Shiquan Street in Suzhou, China was taken as the research object, ArcGIS 10.5 was used to obtain the street veiw photo samples, Matlab was used to summarize the architectural colors of the historical areas, and K-means clustering was used to generate the central color card of the historical areas, thus completing the color visualization of the historical areas, followed by further searching for the color patterns and central colors, so as to explore the strategies for sustainable development of historical areas. The various quantified color models of historical areas can explore the regional uniqueness and color locality of historical areas, which avoids problems such as homogenization of block colors, unclear styles and features, and excessive proportion of commercial colors caused by poor color guidance and control in the design related to historical areas, providing approaches to the research on the harmonious unity of environmental colors and features related to historical areas.

The populations of clearwing moth borers in Korea have recently exploited and severe damage on pin oaks (Quercus palustris Munchh.). We conducted the field monitoring and the molecular analyses to identify them in an integrated manner. Morphological examination and molecular analyses on COI-gene based intra-and inter-specific genetic divergences (GD) were revealed that the borers were recognized as two invasive species, Sphecodoptera sheni and Paranthrenella pinoakula sp. nov.. The maximum intraspecific GD was found to be 1.9%, whereas the minimum interspecific GD was confirmed as 8.1%, with a distinct barcoding gap. Both MJ network and NJ tree were also showed that 18 haplotypes (HAPs) were detected from the 52 COI sequences. The borers were revealed total 17 HAPs: i) H1-H7 were detected from all seven regions with S. sheni; ii) Wonju and Goyang populations of S. sheni were revealed as more than three HAPs; iii) H7 was closely connected with H8 of Chinese population of S. sheni; iv) H9-H10 were detected as the other samples from Wonju population with P. pinoakula sp. n., and closely located with congeneric species. Maximum likelihood tree also revealed that P. pinoacula sp. n. nested within the congeneric species by genetically separating from the S. sheni.

Food safety is a persistent global health challenge. Improper handling of foods increases the chances of food contamination, which may endanger the consumers’ health. The knowledge gap and improper hygienic practices of food handlers are the key factors for the transmission of foodborne diseases. This study aimed to investigate the level of food safety knowledge, attitudes, and practices among street food vendors in Dhaka City, Bangladesh. In this cross-sectional study, a randomly selected 350 street vendors from seven locations in Dhaka City were enrolled. Data were collected by trained interviewers using a questionnaire. Only 36.5% of the respondents had primary, and 48% had secondary education. The majority of the vendors who handled food in front of observers showed good handwashing practice. However, very few vendors showed positive attitudes for washing hands before handling raw or cooked food (3.7%), practicing good personal hygiene (1.2%), and other food safety attitudes (less than 1%). This study showed a significant correlation between the education level of the participants, work experience, and qualification of the head chef with knowledge about hand washing, wearing gloves, and food-borne illnesses. The study demonstrated that formal education played a significant role in vendors’ knowledge and practice of health safety measures of food handlers in preventing foodborne illness.

In an urban context, the street network can be considered the most substantial element, as its role is to create the whole urban setting. The street network is a spatial pattern that can be recognized as a linkage bonding other components of the urban context, such as buildings and open spaces. Basically, the street is not only a collection of lines that transfer people, vehicles and goods but also denote a broad range of activities and influences people’s responses. In this respect, some studies address the street as a network that entails two key elements, link and node; the link can be defined by the type of mechanism that it connects, while the node can be identified according to the analysis technique. Defining the street’s elements is a crucial step in ascertaining the expression of whatever is a link or/and a node. Thus, some scholars use the link to refer to a street, and node for the intersection, while others employ the opposite meaning. Two different viewpoints deal with spatial network analysis; the first is a technique based on topological depth, such as Space Syntax SS, and the second is Multiple Centrality Assessment (MCA) that is employed for metric analysis. This paper compares Space Syntax (SS) and Multiple Centrality Assessment (MCA) by measuring the integration and centrality of the street, and highlights the points of similarity and difference between the two techniques.

With the development of society and the economy, the unified planning of architectural style has become a difficult problem in the competition between urban expansion and the protection of tra-ditional buildings in villages and towns. At the same time, it also allows people to re-examine the appearance and quality of life of traditional village buildings. In this paper, the Conditional Gen-erative Adversarial Network (CGAN) is used to construct a method of building facade generation in villages and towns, so as to gradually realize the governance of the style of villages and towns. At the same time, it has also reduced the restoration of the facades of villages and towns and the graphic design of rural tourism products, showing its application value and potential in the field of planning and design. In the research, taking villages and towns in the Wuyishan area of China as an example, the method is used to carry out model training, image generation, and comparison of the derivation results of different assumed building contours and product contours. The research shows that: (1) CGAN can be used to derive and design the facades of conventional civil buildings in villages and towns. (2) In terms of product graphic design, especially the common tourist cultural products fans and water cups, show significant potential. (3) The construction of this method is not only applicable to villages and towns under the World Heritage City, but can be further promoted and used in the future for cities and villages that have a demand for architectural style consistency.

Lack of due attention to the orientation of streets and establishment of urban blocks without regard for climatic characteristics and conditions of the environment have an adverse effect on thermal comfort in open urban spaces. Construction of new settlements without taking into account climatic requirements undermines thermal comfort for pedestrians and other users, especially in cold regions. Considering the coldness of the region under study and the significance of the orientation of streets in absorbing radiation and providing heat to outdoor urban spaces, this study investigates the effect of the orientation of streets on microclimatic comfort in one of the residential towns of Hamadan City in Iran. For this purpose, microclimate simulation was performed using ENVI-met software. A residential block with four different orientations (the most common orientations of its surrounding buildings) were simulated in the coldest day of winter and the hottest day of summer. The results suggest that streets have different thermal behavior in different orientations. Orientation affects mean radiant temperature (Tmrt), the duration of exposure to direct sunlight, wind speed, and physiological equivalent temperature (PET), which are all important factors in thermal comfort. Based on these findings, north-south streets in Hamedan receive more radiant temperature during winter compared to other simulated orientations and provide more desirable thermal comfort. The average PET value on a winter day at a point on the north-south passage was 4.5-8 °C warmer than other orientations. In summer, streets with intercardinal orientations (i.e., northeast-southwest and northwest-southeast) provided the lowest PET (about 2 °C cooler than other orientations) and better thermal comfort

Level index was introduced in 2017 for rooted trees which is a component of Gini index. In the origin, Gini index is a tool for economical investigations but Balaji and Mahmoud defined the graph theoretical applications of this index for statistical analysis of graphs. Level index is an important component of Gini index. In this paper we define a new graph polynomial which is called level polynomial and calculate the level polynomial of some classes of trees. We obtain some interesting relations between the level polynomials and some integer sequences.

In recent years, given the enormous competition for new products and services, many companies have begun to behave more creatively in an aim to be the best in the market. “Guerrilla marketing” refers to an advertising technique that uses unconventional and cost-effective approaches, employing a variety of strategies that reduce costs. Since the emergence of this concept in the second half of the last century, substantial research has looked into its application and usefulness. This study presents a systematic survey of the field of marketing and advertising: by analyzing the main scientific publications on guerrilla marketing through content analysis in the Web of Science, Scopus, and EBSCO databases. One hundred and sixty-four articles were analyzed in depth and divided into two separate lists: one with publications corresponding to Hutter and Hoffmann’s first classification, and another corresponding to the new variables that emerged from the in-depth study that was carried out. As a consequence, a new taxonomy is proposed for the field, based on the identification of some novel variables characterizing the different existing approaches.

The study purpose was to train and validate a deep-learning approach to detect micro-scale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep-learning models for eight micro-scale features guided by the Microscale Audit of Pedestrian Streetscapes-Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train--correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N=512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between micro-scale features and GIS-measured- and participant reported-macro-scale walkability. Classifier precision, recall, and overall accuracy were all >84%. Total micro-scale was associated with overall macro-scale walkability (r=0.300,p<.001). Positive associations were found between model-detected and self-reported sidewalks (r=0.41,p<.001) and sidewalk buffers (r=0.26,p<.001). Computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

This paper describes the use of a new obtrusive light module of the Illumina v2 model to estimate the light that may enter bedroom windows. We used as input to the model, 1- the sources’ flux and spectrum derived from the color images taken by astronauts from the international space station, 2- an association between source spectrum and angular emission, and 3- a per zone inventory of obstacles properties and lamp height. The model calculate the spectral irradiance incident to buildings’ windows taking into account for the orientation of the street. By using the color information from an ISS image, we can classify pixels as a function of their spectra. With the same image, it is also possible to determine the upward photopic radiance for each pixel. Both serve as inputs to the model to calculate the spectral irradiance on any window. By having the spectral irradiance, it is possible to determine the Melatonin Suppression Index and the photopic irradiance on the window. Such information can later be used to perform epidemiological studies. The new methodology is applied to the case of Montréal in Canada for a set of houses’ locations. The computations are made for 2013 (pre-LED era).

Purpose: to assess any differences on marginal bone loss between bone-level or tissue-level dental implants through a review of literature until September 2019. Materials and methods: MEDLINE, Embase and other database were searched by two independent authors. The search was limited to articles in English. Results: The search provided 1028 records and, after removing the duplicates through titles and abstracts screening, 45 full-text articles were assessed for eligibility. For qualitative analysis 20 articles were included, 17 articles of them for quantitative analysis. A total of 1161 patients (mean age 54,4 years) and 2933 implants were observed, 1427 (Tissue-level) and 1506 (Bone-level). The survival rate and the success rate were more than 90%, except for 2 studies with a success rate of 88% and 86.2%. No studies reported any differences between groups in term of success and survival rates. Three studies showed that BL-implants had statistically less marginal bone loss (P< 0.05). Only one study reported statistically less marginal bone loss in TL-implants (P< 0.05). In the most part of the studies, differences between implant types in marginal bone loss were not statistically significant. Conclusion: Despite to the peri-implant tissue around transmucosal implants has been reported to be inflammation-free because of the absence of bacterial infiltration in the micro-gap between the fixture and abutment, no clinical and radiological differences were highlighted between groups from the included studies after a variable period of follow-up ranged between 1 to 5 years.

The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

Many reported sea level records are often not a single measurement. They are then a composition of different records from several tide gauges. Sometimes, they are from the same tide gauge, but the tide gauge stability has been affected by earthquakes. This is the case of Guam as discussed in the present manuscript. The claimed sea level acceleration of Guam is only the result of two earthquakes that have compromised the stability of the tide gauge.

Data collected at the Cascais tide gauge, located on the west coast of Portugal Mainland, have been analyzed and sea level rise rates have been updated. Based on a bootstrapping linear regression model and on polynomial adjustments, time series are used to calculate different empirical projections for the 21^st century sea level rise, by estimating the initial velocity and its corresponding acceleration. The results are consistent to an accelerated sea level rise, showing evidence of a faster rise than previous century estimates. Based on different numerical methods of second order polynomial fitting, it is possible to build a set of projection models of relative sea level rise. Appling the same methods to regional sea level anomaly from satellite altimetry, additional projections are also built with good consistency. Both data sets, tide gauge and satellite altimetry data, enabled the development of an ensemble of projection models. The relative sea level rise projections are crucial for national coastal planning and management since extreme sea level scenarios can potentially cause erosion and flooding. Based on absolute vertical velocities obtained by integrating global sea level models, neo-tectonic studies and permanent Global Positioning System (GPS) station time series, it is possible to transform relative into absolute sea level rise scenarios, and vice-versa, allowing the generation of absolute sea level rise projection curves and its comparison with already established global projections. The sea level rise observed at the Cascais tide gauge has always shown a significant correlation with global sea level rise observations, evidencing relatively low rates of composed vertical land velocity from tectonic and post-glacial isostatic adjustment, and residual synoptic regional dynamic effects rather than a trend. An ensemble of sea level projection models for the 21st century is proposed with its corresponding probability density function, both for relative and absolute sea level rise for the west coast of Portugal Mainland.

The amount of outdoor particles that indoor environments receive depends on the particle infiltration factors (Fin), peculiar of each environment, and on the outdoor aerosol concentrations and size distributions. The respiratory doses received, while residing indoor, will change accordingly. This study aims to ascertain to what extent such doses are affected by the vertical distance from the traffic sources. Particle number size distributions have been simultaneously measured at street level and at about 20 m height in a street canyon in downtown Rome. The same Fin have been adopted to estimate indoor aerosol concentrations, due to the infiltration of outdoor particles and then the relevant daily respiratory doses. Aerosol concentrations at ground floor were more than double than at 20 m height and richer in ultrafine particles. Thus, although aerosol infiltration efficiency was on average higher at 20 m height than at ground floor, particles more abundantly infiltrated at ground level. On a daily basis, this involved a 2.5-fold higher dose at ground level than at 20 m height. At both levels, such doses were greater than those estimated over the period of activity of some indoor aerosol sources, therefore they represent an important contribution to the total daily dose.

(1) Background: Evidence-based policymaking requires data about the local population's socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has $R^2=0.76$ and a correlation coefficient of $0.874$ with the true unemployment rate, while it achieves a mean absolute percentage error of $0.089$ and mean absolute error of $1.87$ on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

Polycyclic aromatic hydrocarbons (PAHs) in street dust pose a serious problem threatening both environment and human health. Street dust were collected from five different land use patterns (traffic areas TRA, urban area URA, residential areas REA, mixed residential commercial areas MCRA and suburban areas SUA) in a Saudi coastal city, Jeddah, and one in rural area (RUA) in Hada Al Sham. This study aimed to investigate the status, profile, sources of PAHs and estimate their human health risk. The results revealed an average concentration of total PAHs of 3320 ng/g in street dust of Jeddah and 223 ng/g in RUA dust. PAHs with high molecular weight represented 83.38% of total PAHs in street dust of Jeddah, while the carcinogenic PAH compounds accounted 57.84%. The highest average concentration of total PAHs in street dust of Jeddah was found in TRA (4980 ng/g) and the lowest in REA (1660 ng/g). PAHs ratios indicated that the principal source of PAHs in street dust of Jeddah is pyrogenic, mainly traffic emission. Benzo(a)anthracene/ chrysene (BaA/CHR) ratio suggests that PAHs in street dusts of Jeddah come mainly from emission of local sources, while PAHs in RUA might be transported from the surrounding urban areas. The estimated Incremental Lifetime Cancer Risk (ILCR) associated with exposure to PAHs in street dusts indicated that both dermal contact and ingestion pathways are major contributed to cancer risk for both children and adults. Based on BaP_equivalence concentrations of total PAHs, ILCR_Ingestion, ILCR_dermal and cancer risk values for children and adults exposed to PAHs in street dust of different areas in Jeddah were found between 10⁻⁶ and 10⁻⁴, indicating potential risk. The sequence of cancer risk was TRA > URA > MCRA > SUA > REA. Only exposure to BaP and DBA compounds had potential risk for both children and adults.

Time-modulated sparse circular array (TMSCA) with elements uniformly excited is designed and synthesized in this paper. By utilizing rotationally symmetric structure, the array geometry and the time modulation of the TMSCA is presented, and the radiation patterns in frequency domain are formulated. In order to achieve the desired radiation pattern at the operating frequency and suppress the sideband radiation, the synthesis problem is established and solved by the proposed harmony search-convex programming (HS-CP) algorithm in two dependent optimizations. Two designs are presented, and the numerical results indicate that the TMSCA with rotationally symmetric structure has the advantages of designing time-modulated sparse arrays with superior performance.

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.

Autonomous vehicles (AVs) may not only reduce parking demand, but also free up on-street parking spaces. From the perspective of bus priority, converting on-street parking lots into dedicated bus lanes can both ensure bus priority and accommodate non-bus traffic, helping to reduce congestion and pollution. In order to study the impact of conversion of on-street parking lots to bus lanes on traffic flow, a microscopic simulation framework based on VISSIM is proposed in this paper. Data was collected using the floating car method and video recording method to quantify the factors affecting traffic flow due to the conversion of on-street parking. The VISSIM simulation model was then calibrated. According to the proportion of the on-street parking lots into a bus lane, the on-street parking lots conversion simulation model of different scenarios was established, and the influence of different on-street parking lots conversion ratio on traffic efficiency and pollution emission was quantitatively analyzed. Finally, the optimization method based on bus lane network connectivity is discussed. The results indicate that: When the on-street parking lots conversion ratio is approximately 100%, bus speed increases by 21.03%, bus delays decrease by 14.27%, and bus parking instances decrease by 11.31%. Total emissions are negatively correlated with vehicle speed. As the conversion rate of on-street parking increases, the emissions of road segments and the entire road network gradually decrease. At a conversion ratio of 100%, the average total emissions for road segments have the highest reduction rate, decreasing by 4.12%. At a conversion ratio of 50%, the average emission total for the road network shows the largest reduction, falling by 2.88%.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

This paper presents Tiltification, a multi modal spirit level application for smartphones. The non-profit app was produced by students in the master project “Sonification Apps” in winter term 2020/21 at the University of Bremen. In the app, psychoacoustic sonification is used to give feedback on the device’s rotation angles in two plane dimensions, allowing users to level furniture or take perfectly horizontal photos. Tiltification supplements the market of spirit level apps with the unique feature of auditory information processing. This provides for additional benefit in comparison to a physical spirit level and for more accessibility for visu- ally and cognitively impaired people. We argue that the distribution of sonification apps through mainstream channels is a contribution to establish sonification in the market and make it better known to users outside the scientific domain. We hope that the auditory display community will support us by using and recommending the app and by providing valuable feedback on the app functionality and design, and on our communication, advertisement and distribution strategy.

Accurate forest reference and emission level (FRL, FREL) with related policies and regulations are the key determinants in establishing sustainable forest ecosystem management programmes (e.g. REDD+). This fundamental is for promoting and sustaining climate smart agricultural practices in a changing climate. With the aim to deliver better knowledge to the scientific community and policy makers on regulations and existing tools for more rigorous scientific communication when it comes to FRL and FREL accountability and policies. Thus, this review investigates forest in the changing climate and policies and underlines the performance of land use transition and intensity analysis towards deforestation with some key examples and achievements (e.g. Togo). Simply put, (i) forest as break of greenhouse gas (GHGs) and ecosystem regulator, (ii) policies and REDD+ actions, (iii) potential drivers and (iv) transition and intensity analysis approach for their accountability are discussed. In sum, impressive studies, policies and regulations are under initiations and implementations regarding the role, place and evaluation of forest losses and its ecosystem functions and services. However, there are still some gaps when it comes to: the choice of the evaluation methods in the real context of a specific ecosystem as well as the firm implementations of formulating policies in developing countries. This paper concludes with some policy measures for forest sustainability, carbon enhancement and accountability.

The purpose of the study is to show the effect of the intake of vitamin C obtained from a diet rich in fruit and vegetables or vitamin C supplements on the level of vitamin C in the plasma. The level of vitamin C in plasma were determined before the study (T1), and after five months (T2). Moreover, a medical record was carried out and a questionnaire on the habits such as general eating and specific consumption of food rich in vitamin C. Both of them were completed before and after five months. In addition, related anthropometric analyses were carried out. The results of the first determination of vitamin C level in plasma were between 1.0 and 16.0 mg/L and the results of the second determination of vitamin C in plasma were between 4.9 and 14.1 mg/L. Furthermore, it was identified that 50% of the questioned people had a low fruit intake. Raw vegetables were consumed by fewer people, the intake being higher during the main meals. The correlation of plasma vitamin C with the eating habits of the subjects to the study points out an interdependence between the consumption of fruit and vegetables and the level of vitamin C in the plasma.

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.

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.

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..

The detection of SARS-Cov-2 in the sewage and water resources has increased the awareness among the people about the possibility survival of SARS-Cov-2 in the environment and the potential to transmit into the human through food chain or water resources. Moreover, the surface contaminated by the virus need to be disinfected frequently by using an effective disinfectant, the current chapter discussed the efficiency of the most traditional treatment process of the sewage and wastewater, and their role in the elimination of the virus as well as the sterility assurance level concept. Moreover, the chemical disinfectant used currently and their temporary efficiency has been reviewed.

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

Malaria is a serious infection affecting millions of people in Africa. Our study investigated the personal preferences and applications of antimalarial medicines in Ghana. Based on over 1000 questionnaires distributed in Ghana from January to May 2019, we noticed that although Western medications to fight this disease are widely available, most patients in Ghana prefer treatment with locally produced herbal remedies. This preference appears to be due to a combination of traditional venues for obtaining medicines “on the street” rather than in licensed pharmacies, trust in local and “green” products, extensive advertisement of such local products, and an inherent distrust of imported and synthetic or unnatural medicines. Going local and natural is a trend also observed in other countries across the globe, and adds to the acceptance or rejection of drugs regardless of their activity or toxicity. In fact, adverse side effects associated with herbal remedies, such as general weakness and swollen, sore mouth, do not seem to deter the respondents of this study in Ghana. We propose a combination of (a) increasing public awareness of the benefits of modern medicine and (b) an improvement and control of the quality of herbal remedies to raise the standard of malaria treatment in countries such as Ghana.

Several Lung diseases can result in lung damage structural changes, with lung transplantation as a only therapeutic option when the disease reaches an advanced progression, unfortunately, the success of lung transplantation is limited. In this context, lung bioengineering is considered a potential therapeutic alternative and the current approach is based on the use of the natural extracellular matrix (ECM) of the decellularized lung. The use of undifferentiated cells to seed an ECM can be a more practical strategy for lung bioengineering but the sterilization of the organ for this recellularization process is still a challenge. In this sense, Low-Level Laser Therapy (LLLT) can emerge as a potential treatment alternative. Therefore, the objective of this study was to verify if LLLT, can optimize lung recelularization with mesenchymal stem cells by cytokine modulation. For this, we decellularized lungs from C57BL/6 mice perfusing SDS 1% via pulmonary artery, sterilized with LLL (660nm, 100mW, 30s). Whole lung scaffolds were recellularized: (1) Recellularized Lung (2) Recellularized Lung + LLLT. Mesenchymal stem cells derived from human tooth pulp (DPSc) were seeded into lungs and incubated for 24 hours at 37ºC, 5% CO₂, in a bioreactor-like conditions, maintaining CPAP at 20cmH2O, 90% O2, and LLL at wavelength of 808nm, 100mW, 30s. Culture medium were analyzed at 24 hours. H&amp;E, immunostaining, SEM and ELISA assay were performed. We produced viable biological scaffolds absent of DNA content, preserving glycosaminoglycans and collagen I, III and IV, fibronectin, laminin and elastin and lung structure (SEM). The levels of IL-6 and IL-8 were stable during the 24 hours of culture. IFN-γ levels showed significant difference in the DPSc and DPSc+LLLT group in the 24-hour evaluation. The levels of IL-10 decreased after LLLT. These findings highlight LLLT's role in cytokine regulation and contamination prevention during lung recellularization with mesenchymal stem cells. Overall, these results demonstrated that LLLT's modulated cytokine levels and could prevent contamination during the lung recellularization process with mesenchymal stem cells, offering promise for enhanced therapeutic strategies in lung bioengineering

Background: In past years recreational diving has attracted more and more interest and among such divers an increasing number of diabetic divers. However, there is limited research on the accuracy of CGM systems under hyperbaric conditions and consistently diving guidelines make little reference to the use of CGM. Subject and Method: This exploratory study collected blood glucose samples of one diabetic diver over four dives and assessed the deviation of intersitial glucose values from two CGM systems based on 33 samples. A notable step of this method is to collect the data from actual dives under water. Results: The results indicate that a) under hyperbaric conditions of up to 2bar additional pressure and b) after decompression both CGM systems work comparably accurate as indictated by the manufacturer under normal conditions. This has been checked in terms of MARD and Clarke Error Grid Distirbution. Conculsion: For diabetic divers the results suggest they may take CGM monitors with them on dives to react to potential hypoglycemic events before they occur, i.e. when it is still safe to supplement glucose. For dive guidelines the results imply that these could reference CGM for dive planning and monitoring during the dive to increase divers‘ safety. For research this exploratory confirms the current hypothesis that neither increased pressure nor saltwater have an effect on blood glucose and the functioning of CGM systems. Further research may include additional sampling (number and depth) and more elaborate blood glucose measurement. The experimental setup applied is an efficient way to test further CGM systems.

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.

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&lt;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.

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.

In recent years, farmers and policymakers have faced ample challenges and have struggled to support the sustainability of the agricultural sector. Sustainable agriculture encompasses multiple concepts, and its performance produces extensive debate about data requirements, appropriate indicators, evaluation methods, and tools. Under the European Union (EU) financed project FLINT (Farm Level Indicators for New Topics in policy evaluation), detailed data have been collected at the farm level to provide broader coverage of sustainability indicators on a wide range of relevant topics to facilitate the assessment of sustainability performance. The approach has been applied in a pilot network of representative farms at the EU level, considering the heterogeneity of the EU farming sector to provide data infrastructure with up to date information for sustainability indicators. This study aims to assess sustainability performance at the farm level in Greece. Representative and dominant agricultural systems, such as permanent crops, olive trees, arable crops, and livestock (sheep) farms, comprise the Greek sample. It uses the analytical hierarchy process (AHP) methodology and attempts to gain insights into the sustainability performance of agricultural systems. The outcome of the sustainability assessment reveals knowledge and develops support for strategic farm choices in order to support both farmers and policymakers towards more sustainable development plans. The results indicate that three typical Mediterranean farming systems, like permanent crops, olive trees, and extensive livestock systems (sheep farms), are more sustainable in contrast to intensive and arable crop farms.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

Background: The prevalence of atopic dermatitis in domestic animals is one of the problems of modern veterinary. Treating with standard techniques using chemotherapeutic agents not always leads to a positive result of therapy; moreover, many drugs produce adverse side effects. Methods: Low level laser therapy, in particular, intravenous laser blood illumination (ILBI) has a pronounced and long-lasting impact on the immune system of animals. The combined technique including ILBI-635 (635 nm, 2 mW, 5 min) and LUVBI^® (365 nm, 2 mW, 3 min) every other day provides a positive change in clinical status of cats with allergic dermatitis after the 3rd-4th treatment session. Results: The increased level of erythrocytes and hemoglobin was identified in the course of treatment, and it indirectly indicates increased blood transport activity, which improves trophic provision and microcirculation. A double reduction of leukocytes and a significant decrease of neutrophil cells indicate the immunomodulatory effect of LILI (low-intensity laser illumination). The increase in the percentage of lymphocytes and the decrease of eosinophils and monocytes against the background of basophil concentrations deviations within physiological concentration result in the reduction of inflammatory mediators expression that induce itching. The reduction of total IgE concentration 32 times against control on the 7th day of treatment correlates with the decrease in the quantitative content of peripheral blood eosinophils, indicating the decrease in severity of an allergic process. Conclusion: LLLT is recommended against the background of standard drug therapy to achieve quick clinical outcome together with a long-lasting prolonged effect.

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.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the rate of emissions to context mitigation actions and track progress. Here, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emissions rates in a case study of the City of Calgary, Alberta, Canada. While satellites are touted as a global methane monitoring system, we found limited observational coverage over Calgary that precluded the reliability of measurement-based models to track the annual emissions. In this work, we integrated valid observations over three years (2020-22) and used mass balance modeling to derive a long-term averaged emission estimate. The column-averaged dry-air mole fraction (XCH4) enhancement over Calgary was small, 4.7 ppb, but within the city boundary, we identified local hot spots in the vicinity of known sources (wastewater treatment facility and landfills). The city-scale emissions estimate from mass balance was 215.4 ± 132.8 t/d CH4. This estimate is approximately four- and six-times larger than estimates from Canada's gridded national inventory report of anthropogenic CH4 emissions and the Emissions Database for Global Atmospheric Research (EDGAR v8.0), respectively. Valid observations are more common in warmer months and occur during a narrow daily overpass timeslot over Calgary – these effects may bias the emissions estimate. Overall, the findings from this case study highlight the challenge of deriving routine (sub-annual to annual) satellite-based estimates of city-scale methane emissions in similar geographic settings and suggest that more granular emissions inventories are needed to improve methane reporting.

Infrastructure and urban network operators, city users and industrialists are faced with complex issues to ensure the sustainability of the service, maintain, operate and develop their urban systems, while integrating environmental, economic and societal impacts and being resilient to unexpected geopolitical and climatic upheavals. Digital Twins are now recognized as the cornerstone of digital transformation. In the field of smart cities, they can enable all stakeholders to collaborate across disciplinary silos and foster digital transformation in urban and territorial projects to ensure sustainability, resilience and increased inventiveness. However, the application of the Digital Twin technology to Smart Cities lacks of standards. This paper proposes a high-level architecture for Digital Twins as an enabler of various levels of interoperation between key stakeholders of a Smart City. Such a technology-agnostic architecture, when implemented for a given Smart City, produces a middleware for large scale interoperability between actors involved in multiple supply chains of this territory.

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.

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.

The national study analyzes sea level rise (SLR) impacts based on 36 different SLR and storm surge scenarios across 5.7 million geographic locations and 3 time periods. Taking an approach based on engineering design guidelines and current cost estimates, the study details projected cost impacts for states, counties, and cities. These impacts are presented from multiple perspectives including total cost, cost per-capita, and cost per-square mile. The purpose of the study is to identify specific locations where infrastructure is vulnerable to rising sea levels. The study finds that Sea Level Rise (SLR) and minimal storm surge is a $400 billion threat to the United States by 2040 that includes a need for at least 50,000 miles of protective barriers. The research is limited in its scope to protecting coastal infrastructure with sea walls. Additional methods exist and may be appropriate in individual situations. The study is original in that it is a national effort to identify infrastructure that is vulnerable as well as the cost associated with protecting this infrastructure.

Pain is an unpleasant emotional and sensory experience. For many years orthodontists have been looking for an effective method of reducing this feeling of discomfort. In recent years, low-level laser therapy (LLLT) has taken hold in the orthodontic field. Among the countless advantages it can modulate the painful feeling. The aim of this research is to identify the use of photobiomodulation in subjects undergoing fixed orthodontic treatment, to reduce the pain and discomfort that it causes. The research was conducted from the Web of Science, Pubmed and Scopus databases. Only 14 of all articles met the inclusion and exclusion criteria and were therefore used to conduct the research. The different studies compared, in most cases, patients whose mouth was divided into a part treated with laser therapy and a placebo part. The results show a statistically significant difference in perceived pain between the irradiated arch and the non-irradiated arch. Three authors didn’t find statistically significant results in favour of low-laser therapy, but it is important to remember that they used different parameters. To obtain generally valid studies, with consistent and reproducible results, it is necessary to standardize the different parameters used that are independent by operator performing the procedure.

The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, statistics and data (e.g., local monitoring of Sustainable Development Goals - SDGs) are hindered by the unavailability of spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, which has been captured in existing literature with a strong focus on household-level deprivation while giving limited attention to area-level deprivation. Within this scoping review, we build on existing literature on household- as well as area-level deprivation frameworks to arrive at a combined understanding of how urban deprivation is defined with a focus on LMIC cities. The scoping review was enriched with local stakeholder workshops in LMIC cities to arrive at our framework of Domains of Deprivations, splitting deprivation into three different scales and nine domains. (1) Socio-Economic Status and (2) Housing Domains (Household scale); (3) Social Hazards & Assets, (4) Physical Hazards & Assets, (5) Unplanned Urbanization and (6) Contamination (Within Area scale); and (7) Infrastructure, (8) Facilities & Services and (9) city Governance (Area Connect scale). The Domains of Deprivation framework provides a clear guidance for collecting data on various aspects of deprivation, while providing the flexibility to decide at city level which indicators are most relevant to explain individual domains. The framework provides a conceptual and operational base for the Integrated Deprived Area Mapping System (IDEAMAPS) Project for the creation of a data ecosystem, which facilitates the production of routine, accurate maps of deprived “slum” areas at scale across cities in LMICs. The Domains of Deprivation Framework is designed to support diverse health, poverty, and development initiatives globally to characterize and address deprivation in LMIC cities.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Failure to adapt to climate change is currently considered one of the major threats affecting humanity. Hence, much effort is being put into discussing adaptation approaches. While many adaptation options have been identified, the academic literature does not present a simple process that local councils and community members can use to rank adaptation options. In this context, community members participating on planning processes are presented with many adaptation options, but with no objective approach for selection, which adds challenge to the planning process. With the objective of addressing this issue, this work proposes a simple equation that allows calculating the applicability level of adaptation options. Results can then be plotted into graphs that allow correlating adaptation options and applicability level, which can be easily understood by community members. To develop such equation, this work built on existing sophisticated models from where the indicators used on the equation were identified, as well as the relationship between them. A scale was proposed to help on identifying adaptation options that should be implemented on the short, medium and long term, and options that should only be implemented if the circumstance change.

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Academic staffs’ organizational commitment has been a critical issue to determine work performance for successes of University, as well as, to keep its Academic staffs motivation granted for achieving better work performance. This subject has therefore, been investigated so as to draw attention for enhancement of effective work performance and success. The main objective of this study was to assess academic staffs’ level of organizational commitment in Haramaya University. The study also investigated whether significant differences exist in academic staffs’ level of organizational commitment in reference to their gender and level of education. Researchers used cross-sectional research design. Primary and secondary data sources were used to study the problem. A commitment scale questionnaire was used to collect data from 275 participants who were selected from 877 target population of the study using stratified sampling technique; furthermore, focus group discussion and document review were also used to triangulate the data. The quantitative data were analyzed using both descriptive and inferential statistics; the qualitative data were also analyzed using narration methods of analysis. The study revealed that academic staffs of the university have moderate level organizational commitment. This implies relatively no more expected effective work performance for success of the institution; In addition, there were relative implications of turnover, turnover intention, absenteeism, and demotivation among staffs. The study further revealed that although there is no significance difference in employees’ level of commitment with reference to gender, their level of organizational commitment was significantly different in reference to level of education.

Multigranulation is a new approach to the Rough Set Theory, where several separability relationships are used to obtain different granulations of the universe. The Multigranulation starts from the existence of different contexts or subsets of features to characterize the objects of the universe. In this paper, a method for the generation of contexts from the construction of similarity relations is proposed.The proposed solution was evaluated in an international database using the KNN classifier. It was also applied in the solution of a real problem in Civil Engineering specifically in Traffic Engineering, the contexts generated from the proposal used to determine the features of higher incidence in the service level of the road. The results achieved both in the international database and in the proposed application demonstrate the applicability of the proposed method.

Protrusive defects on the color filter of thin-film transistor (TFT) liquid crystal displays (LCDs) frequently damage the valuable photomask. An fast method using side-view illuminations associated with digital charge-couple devices (CCDs) to detect the protrusive defect in the four substrates, which are the black matrix (BM), red, green, and blue. Between the photomask and substrate, the depth of field (DOF) is normally 300 μm for the proximity-type aligner; we select the four substrates to evaluate the detectability in the task. The experiment is capable of detecting measurements of 300 μm and even lower than 100 μm can be assessed successfully. The maximum error of the measurement is within 6% among the four samples. Furthermore, the uncertainty analysis of three standard deviations is conducted. Thus, the method is cost-effective to prevent damage for valuable photomasks in the flat panel display industry.

In the field of agricultural food production, the transition between organic and inorganic farming methods has been an issue of much debate. The debate, on one hand, stresses the urgency for the transition in order to preserve environment and health; and, on the other hand, emphasizes the pressure of maintaining food production for a large growing population. Thus, the dilemma is how to find an agricultural system that would balance between obtaining food security and ensuring a safe sustainably environment-friendly food production system. This article focuses on the debate, in the context of Bangladesh, and questions whether it is the proper time, and stage in the development process, to attempt the transition from inorganic conventional food production methods to organic food production methods. This article contemplates why the organic rice market is not expanding in Bangladesh, and attempts to explain the slow growth of the market through the two main factors of income constraint and lack of awareness among people about the environmental and health detriments of inorganic farming methods. The study is exploratory in nature, and finds that it is not mainly the lack of awareness but the income constraint that can be principally attributed to the slow expansion of the organic rice market in Bangladesh. Through exploring consumers’ awareness about organic farming methods and their demand for organic products, this study shows how income as the major constraint, besides price, affects consumers demand for organic and inorganic rice in Bangladesh. Income being identified as the major barrier reveals the potential of the organic rice market to grow in the future, as Bangladesh continues its journey towards becoming a middle-income country.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city's overall functionality. In this study, we apply machine learning to elucidate these biases within urban spatial perception research, utilizing a three-step methodology: objective mapping, subjective perception analysis, and perception deviation assessment. Our findings reveal that machine learning can expose hidden patterns within this research field, bearing substantial implications for urban planning and design. Of particular note, the study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Moreover, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. These findings underscore the importance of integrating both objective mapping and subjective perspectives in urban architectural functionality classification.

Keywords: green spaces, NDVI, level of greenness, urban environment, Aktobe

The development of U.S. Army and NATO data link systems is introduced first, and then the development trend of future intelligent data link is summarized into integration, generalization, multifunctionality and high security. A unit-level combat system architecture based on the global combat cloud, which is capable of realizing the flexible scheduling of global combat resources and maximizing the overall combat effectiveness, is proposed. Intelligent data link is an important part of this solution, providing strong information support for future urban unit-level warfare.

Abstract: Rural digital governance is an inevitable requirement to improve the efficiency of rural governance, and is also an important means to realize the modernization of rural governance. In the context of the digital rural development strategy, the index measurement system of rural digital governance level is built around five key governance areas of "digital economy, digital ecology, digital culture, digital people's livelihood, and digital government affairs". The entropy weight Topsis model is used to measure and evaluate the level of rural digital governance in 31 provinces in China in 2021. The results show that there is a large gap in the level of digital governance in China's counties and villages, and the level of each region presents a decreasing spatial distribution from "east - middle - west". In terms of digital economy, the eastern region has a high score and good development, while the central and western regions have poor development. In terms of digital ecology, only the eastern region is higher than the national average; In terms of digital culture, only the central region is higher than the national average; In terms of digital livelihood and digital government, the central and eastern regions are slightly higher than the national average; The top three provinces in overall scores are Zhejiang, Guangdong and Jiangsu.

Flood disasters have become an annual occurrence along the East Coast of North Sumatra (ECNS) and Medan City. Numerous studies have examined the causal factors flood disasters in this area, such as changes in land use, drainage issues, and population growth, but the intensity of flood disasters continues to rise. In this research, we focused on evaluating the unexplored factors of land subsidence and rising sea levels in this region. Our investigation uses multi-sensor satellite data and field surveys to conduct an assessment of the rate of land subsidence and sea level rise and assess their implications on flood disasters. This study highlights the potential of multi-sensor satellite data as a primary resource for assessing flood disasters in coastal areas and densely populated large cities. Furthermore, we propose a solution to reduce the negative impact of flood disasters by by engaging local communities and environmental enthusiasts in mangrove reforestation along the coastline.

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&lt;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&lt;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.

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.

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.

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.

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.

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.

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.

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.

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.

Yam (Dioscorea spp.) is a monocotyledonous herbaceous vine plant grown in the tropics and subtropics. It is a multi-species plant with varied intra- and interspecific ploidy levels. Of the 600 species, 11 are cultivated staple supporting the livelihood of over 300 million people. The paucity of information on ploidy and the genomic constitution is a significant challenge to the crop’s genetic improvement through crossbreeding. The objective of this study was to investigate the ploidy levels of 236 accessions across six cultivated and two wild species using chromosome counting, flow cytometry and genotyping-based ploidy determination methods. Results obtained from chromosome counting and genotyping-based ploidy determination were in agreement. In majority of the accessions, chromosome counting and flow cytometry were congruent, allowing future rapid screening of ploidy levels using flow cytometry. Among cultivated accessions, 168 (71%) were diploid, 50 (21%) were triploid, and 12 (5%) were tetraploid. Two wild species included in the study were diploids. Resolution of ploidy level in yams offers opportunities for implementing successful breeding programmes through intra- and inter-specific hybridization.

Investigation of sea-level positions during the highly-dynamic Marine Isotope Stage 3 (MIS 3: 29-61 kyrs BP) proves difficult because: i) in stable and subsiding areas, coeval coastal sediments are currently submerged at depths of few to several tens of meters below present sea level; ii) in uplifting areas, the preservation of geomorphic features and sedimentary records is limited due to the erosion occurred during the Last Glacial Maximum (LGM) with sea level at depth of -130 m, followed by marine transgression that determined the development of ravinement surfaces. This study discusses previous research in the Mediterranean and elsewhere, and describes new fossiliferous marine deposits laying on metamorphic bedrock of Cannitello (Calabria, Italy). Radiocarbon ages of marine shells (about 43 kyrs cal BP) indicate that these deposits, presently between 28 and 30 meters above sea level, formed during MIS 3.1. Elevation correction of the Cannitello outcrops (considered in an intermediate-to-far-field position with respect to the ice sheet) with the local vertical tectonic rate and Glacial Isostatic Adjustment (GIA) rate allows to propose a revision of the eustatic depth for this highstand. Our results are consistent with recently proposed estimates based on a novel ice sheet modelling technique.

In a safety-critical system typically not all provided services have the same criticality, which we call mixed-criticality systems. Criticality arithmetic, also called SIL arithmetic, is an approach to lower the development effort of a service by providing redundancy with tasks that are developed for a lower criticality level. In this paper we present ATMP-CA, which is a derivation of the multi-core scheduler ATMP. ATMP-CA is able to take into account the knowledge about the use of criticality arithmetic. ATMP-CA has a modified core allocation and procedure for utility optimisation, considering the context of the replicated tasks. We conducted experiments that show that ATMP-CA is able to provide the services using criticality arithmetic, while the reference schedulers were not.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

This article presents a new method of segmenting images with gray levels. The method is based on determining several thresholds for separation of gray levels. The determination of these thresholds is done using the certainty of the neutrosophic information. The concept of this method can be stated simply: to choose the local maximums for the neutrosophic certainty.

The humpback puffer, Tetraodon palembangensis, also known as Pao palembangensis, is a species of poisonous freshwater pufferfish mainly distributed in Southeast Asia (Thailand, Laos, Malaysia and Indonesia). Despite interesting biological features, such as its very inactive nature, tetrodotoxin production and body expansion mechanisms, molecular research on the humpback puffer is still rare because of the lack of a high-quality reference genome. Here, we reported a first chromosome-level genome assembly of an adult humpback puffer, of which the genome size is 362 Mb with ~1.78 Mb contig N50 and ~15.8 Mb scaffold N50s. Based on the genome, ~61.5Mb (18.11%) repeat sequences were also identified, and totally 19,925 genes were annotated, 99.20% of which could be predicted with function using protein-coding function databases. Finally, a phylogenetic tree was constructed with single-copy gene families from ten teleost fishes. The humpback puffer genome will be a valuable genomic resource to illustrate possible mechanisms of tetrodotoxin synthesis and tolerance, providing clues for future detailed studies of biological toxins.

Recent climatic changes have resulted in an increased frequency and prolonged periods of drought and strained water resources affecting plant production. We explored the possibility of reducing irrigation in a container nursery and studied the growth response of seedlings of economically important forest trees: broadleaf deciduous angiosperms Fagus sylvatica, Quercus petraea and evergreen conifers Abies alba and Pinus sylvestris. We also studied markers of water stress including modifications of biomass allocation, leaf anatomy, proline accumulation and expression of selected genes. Growth of the broadleaved deciduous species was more sensitive to the reduced water supply than that of conifers. Remarkably, growth of the shade tolerant Abies was not affected. Adjustment of biomass allocations was strongest in P. sylvestris, with a remarkable increase in allocation to roots. In response to water deficit both deciduous species accumulated proline in leaves and produced leaves with shorter palisade cells, reduced vascular tissues and smaller conduit diameters, but not conifers. Relative transcript abundance of a gene encoding a Zn-finger protein in Q. petraea and a gene encoding a pore calcium channel protein 1 in A. alba increased as water deficit increased. These findings suggest that in container nursery, the genetic selection can be initiated by water deficit. Our study shows major differences between functional groups in response to irrigation, with seedlings of evergreen conifers having higher tolerance than the deciduous species. This suggests that major water savings could be achieved by adjusting irrigation regime to functional group or species requirements.

Multiple studies have shown that hospital settings are poorly cleaned during terminal cleaning. The adequacy of these cleaning methods has been undermined by presence of multi drug resistant bacteria on hospital surfaces. This case is even more serious in developing countries leading to health care- associated infections that pose a great threat to patients, visitors and health care providers in hospital settings.This study used various microbiological techniques to test for antibiotic susceptibility profiles of bacteria present at Thika Level 5 Hospital surfaces, Kenya. A simple random cross sectional study was performed, with a total of 85 samples being collected from five different sites. The sites included male and female wards, health care personnel offices, latrine, and kitchen surfaces. Samples were collected using sterile swabs, dipped in normal saline, and transported to the laboratory within 2Hours for processing.Of the 85 plates cultured, 47 plates showed bacterial growth (55%) on selective media with a significant P value of 0.0357. Seven different species of bacteria were identified biochemically from all sites, Escherichia coli was the most abundant species (28%), and the least was Salmonella typhii (5%). Multiple drug resistance was common in the different bacteria identified. All isolates were resistant to chloramphenical and susceptible to gentamycin. The most resistant microorganism was Staphylococcus aureus (50%), and the least resistant microorganism was Klebsiella pneumoniae (12.5%). The antimicrobial resistant bacterial species identified in this study have been documented to cause serious health care associated infections. These results present a significant public health concern because there is a possibility of patients, staff and visitors contacting nosocomial infections when they come into contact with surfaces at Thika Level 5 Hospital surfaces, Kenya.

Background: Acute appendicitis (AA) is the most common cause of emergency surgery. Perforation is more common than adults. Early diagnosis and new markers are needed. The aim of this study was to investigate the effects of plasma Fetuin-A (FA) levels in patients with the acute abdomen (AB). Material and Method: This prospective study included 107 patients younger than 16 years of age who were admitted to the emergency department for abdominal pain between January 2018 and December 2018. The patients who presented to abdominal pain were divided into two groups as AA and other causes (OC) of AB. T Patients with acute appendicitis; intraperitoneal, retrocolic / retrocecal and appendicitis were divided into three groups. Also, the AA group was divided into two groups as perforated appendicitis and non-perforated appendicitis. Serum FA levels of the patients were evaluated in the emergency department. Results: In the AA group, C-reactive protein (CRP) and white blood cell (WBC) levels were higher, and FA levels were significantly lower than in the AB group. Intraperitoneal localization was 95.2% and perforation was frequent. When significant values in the univariate regression analysis for acute abdomen and perforation were compared in the multivariate regression analysis, CRP, WBC, and FA levels were found to be prognostic. Also, decreased FA levels were associated with AA while too much decreased FA levels were associated with the risk of perforation. Conclusion: While trying to diagnose AA in children, the FA level, CRP and WBC may be predictive values to identify risk factors.

There has been rampant fold-ups, merger and acquisitions occurring in the Ghanaian banking industry. Then, the questions arise: Is the Ghanaian Financial System in Crisis? This study was conducted to find answers to these problems unsolved with prior literature. A sample of seventy customers of the Royal Bank, 8 employees of the Royal Bank and 2 managers of the Royal Bank were selected for a case-survey. The study also monitored the Trend of the Ghanaian Financial System through the reading and monitoring of daily news on the Financial System and reports of banks. The data from the field and the secondary data from news and reports were analysed symmetrically. The study drew on Minsky’s Financial Crisis Theory to explain the phenomenon in the Ghanaian economy and to draw predictions of what would happen in other developing economies. The study found out that: (1) The Ghanaian financial system is fragile and it holds true for most developing economies; (2) The financial system suffers greatly when the confidence level of customers falls significantly; (3) Management information systems raises the confidence level of customers (borrowers and lenders) such that there is a greater fall and impact in times of instability in the economy; (4) The higher the level of MIS adoption in an unstable economy, the more fragile the Financial System becomes and (5) A higher adoption of Management Information Systems in a Fragile Financial System indirectly contributes to Financial Crisis of the Financial System.

Study nature of epidemiological risk factors and patterns of distribution of mortality from Alzheimer's (D_A) in 160 countries. An exponential Malthus model was used to construct adequate approximations of statistical data. Proportionality of average lifetime after 55 years (Δt) to education index (Ψ) was established. Ranking of countries by increasing value of ΨΔt showed that D_A depends exponentially on ΨΔt and all countries can be divided into three groups in accordance with level of their economic development. In series of countries of low, medium and highly developed, their average values of D_A increase exponentially. Growth of D_A in developed countries in post-industrial epoch was explained by emergence of a new risk factor Alzheimer's of a mental nature. A mismatch of complexity or, on contrary, primitiveness of profession with level of mental development of worker, provokes development of chronic stress in him, fraught with pathologies of cognitive function metabolism in elderly. The geography of Alzheimer's disease was explained by the dependence of the human mentality on latitudinal climate change and on the chiral factor of a solar nature acting at night on a sleeping person.