In recent decades, the world has witnessed a variety of emerging infectious diseases, some of which developed to pandemic world threatening outbreaks, the ongoing COVID-19 is known to be taking the ...lead in claiming lives around the globe and thus, urging people to trail its increasing figures. Therefore, this research aims to emphasize the role of urban planning in containing such outbreaks through running a series of analytical and statistical studies on European cities, worst inflicted region, to analyze the main urban features they share and that may be propagating the disease spread according to their population size, density, form, intracity connectivity and intercity connectivity. This study, as far as we know of, is the first practice to evaluate both the individual and combined impacts of these factors on recorded rates of infections. According to the context of this research, it is concluded that the diversity found in urban features are, to a large degree, related to cities being more vulnerable than others. Intracity connectivity through public transport is found to be the possible prime factor of this study, and is followed by population size, density, and intercity connectivity. Urban morphology seems to also contribute to such outbreak, with both radial and grid cities being associated to higher infections rates as to linear cities. Henceforth, setting priorities in post-pandemic urban planning schemes is essential for planning resilient cities that are capable to thrive and maintain functionality with lowest possible infections amid else possible diseases that are to follow in severity.
•Study factors share a combined contribution to the COVID-19 infection rates with a 60.2% model effectiveness.•Intracity connectivity is the most influential factor in the spread of COVID 19 and is statistically significant at α= 0.01.•This study supports the findings of previous studies in claiming the ineffectiveness of density on the spread of COVID-19.•Intercity connectivity, city size, and city Form revealed an insignificant impact on the transmission of COVID-19.•Improving public transportation efficiency before people start considering long-term switches to private vehicles.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Despite the increasing number of flood studies, the interrelationships between urban form indices (UFIs) and flood resilience (FR) have received little attention and hold miscellaneous perspectives. ...Consequentially, this study identifies how UFIs at various spatial scales affect FR by synthesizing article findings and proposing insights for future research. Scientometric analysis has been used to analyze the gathered peer-reviewed articles from nine research engines without time restrictions. One hundred and eighteen relevant articles were included and thoroughly investigated using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Our findings indicate that divergent and dialectical perspectives about the efficacy of UFIs are due to multiple disciplines, methodologies, and different case study contexts. The included studies were classified according to urban scale as macro (citywide), meso (districts), micro (block), and multi-scalar analysis by 80.5%, 6.8%, 10.2%, and 2.4%, respectively. Furthermore, the included studies were categorized based on analysis type into realistic case studies, literature reviews, modeling, and hybrid analysis, with 74.6%, 7.6%, 14.4%, and 3.4%, respectively. At the macroscale, city density and spatial distribution degree have the most significant effect on FR. At the same time, mixed uses, connectivity, coverage ratio, block arrangements, and street characteristics are on the meso and micro scales. Further studies on the trade-offs and commonality between UFIs, FR, and overall urban resilience are required to shape climate-adaptive, sustainable communities.
Obesity is one of leading risk factors for type 2 diabetes and other types of chronic diseases. Synchronized intestinal electrical stimulation (SIES) has been explored for treating obesity and ...diabetes. In SIES, electrical stimulation is delivered to the small intestine in synchronization with the intrinsic intestinal myoelectrical activity (its basic rhythm is called slow wave) and therefore, the accurate detection of intestinal slow waves is critically important for SIES. The aim of this study is to detect the peaks in intestinal slow waves in real-time based on the automatic multiscale peak detection (AMPD) method. In this paper, we introduce an efficient technique for real-time detection of peaks in intestinal slow waves. The presented method is based on peak estimation of a given quasi-periodic signal using the AMPD method. This method uses a multi-scale approach to identify the peaks of the intestinal slow waves with high detection accuracy and a minimal delay. Throughout the experiments, the multi-scale technique is used to estimate the quasi-periodic signals using different signal-to-noise ratio, λ (optimal scale), and the “lag” β (number of datapoints for right hand estimation) as important performance factors. The performance of the presented method is also calculated and utilized in the comparison process for 10 datasets of the intestinal slow waves from rats at
λ
= 150 ms and two values of
β
= 100 ms and 150 ms. The experimental results show that the presented method has good overall accuracy for online peak detection while maintaining low memory and computational complexity. Numerically, the overall accuracy is above 90%, and 98% for the rodent intestinal slow waves at a time-lag of 150 ms. The developed SIES system has been applied to successfully reduce postprandial blood glucose in a rodent model of hyperglycemia. In conclusion, the developed algorithm is adequate for on-line peak detection of the intestinal slow waves; the SIES method used the developed peak detection algorithm which is effective in reducing postprandial blood glucose in a rodent model of hyperglycemia.
Graphical Abstract
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The increasing air pollution across the globe has given rise to a global health crisis that is increasing at an alarming rate. Every year, millions of people lose their lives due to health risks ...caused by air pollutants. Hence, there is a pressing need for better solutions to accurately measure the amount of air pollution. This work is aimed at designing a highly compact, accurate, low-cost, self-resettable, and easy-to-use gravimetric-based particulate matter sensor solution for portable applications. Previous attempts have failed to realize true miniaturization, due to the size constraints of the
needed-a mechanism that segregates the particulate matters based on their sizes. Our complete particulate matter sensor solution consists of three components (i) a piezoelectric resonating membrane, (ii) a virtual impactor, and (iii) a thermophoretic mechanism to reset the sensor. This paper presents a novel design of the virtual impactor, based on a folded configuration. This helps realize the entire system in a volume of 20 mm × 20 mm × 10 mm. We report here the design, working principles, fabrication, and experimental results of the virtual impactor.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), ...Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.
Abstract
This study addresses the environmental risks associated with the accumulation of keratin waste from poultry, which is resistant to conventional protein degradation methods. To tackle this ...issue, microbial keratinases have emerged as promising tools for transforming resilient keratin materials into valuable products. We focus on the
Metalloprotease
(
MetPr
) gene isolated from novel
Pichia kudriavzevii
YK46, sequenced, and deposited in the NCBI GenBank database with the accession number OQ511281. The
MetPr
gene encodes a protein consisting of 557 amino acids and demonstrates a keratinase activity of 164.04 U/ml. The 3D structure of the protein was validated using Ramachandran's plot, revealing that 93% and 97.26% of the 557 residues were situated within the most favoured region for the
MetPr
proteins of template
Pichia kudriavzevii
strain 129 and
Pichia kudriavzevii
YK46, respectively. Computational analyses were employed to determine the binding affinities between the deduced protein and beta keratin. Molecular docking studies elucidated the optimal binding affinities between the
metalloprotease
(
MetPr
) and beta-keratin, yielding values of − 260.75 kcal/mol and − 257.02 kcal/mol for the template strains
Pichia kudriavzevii
strain 129 and
Pichia kudriavzevii
YK46, respectively. Subsequent molecular cloning and expression of the
MetPr
gene in
E. coli DH5α
led to a significantly higher keratinase activity of 281 ± 12.34 U/ml. These findings provide valuable insights into the potential of the
MetPr
gene and its encoded protein for keratin waste biotransformation, with implications for addressing environmental concerns related to keratinous waste accumulation.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Most coastal cities have been experiencing unprecedented urbanization-induced flood risk, climatic events, and haphazard anthropogenic activities, jeopardizing residents' lives and building ...environments. Despite mounting flood-related studies, analyzing the correlation between the spatiotemporal dynamics of Built-up Expansion patterns (BE) and flood risk remains unknown and holds divergent perspectives. In this context, the coastal city of Alexandria, Egypt, characterized by multiple urban patterns and experiencing heavy rainfall annually, was selected as a testbed. Our method defined the spatiotemporal rates of BE from 1995 to 2023, quantified flood risk spatially, and finally investigated the correlation between BE and flood risk through spatial and statistical analysis. Our results show the built-up area occupied 30.32 % of the total city area till 2023, and the infilling pattern dominated the BE growth by 45.21 % of the total built-up area, followed by leapfrogging and edge expansion by 33.25 % and 21.55 %, respectively. The unplanned-infilling pattern is predominantly highly correlated with the flood-vulnerable peaks (correlation coefficient (rk) = 0.975, p-value < 0.05) and lowers dramatically towards planned-infilling regions with flood protections. Meanwhile, a spatial mismatch exists between high-risk peaks and leapfrogging and edge expansion (rk = 0.118 and 0.662, respectively, with a p-value < 0.01), indicating that controlling the built-up amount is inadequate for mitigating flood risk. Porosity-based urban configuration and spatial distribution of built-up patches in harmony with nature-based solutions are recommended for shaping flood-resilient and effective urban planning.
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•Spatial congruency and discrepancy between the spatiotemporal dynamics of built-up patterns and flood risk are revealed.•The built-up growth amount does not adequately reflect the peaks of flood risk, especially with climate change intensifying rainfall.•Porosity-based urban configuration and spatial distribution of built-up patches should be considered for fostering flood-resilient communities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Currently, Heart failure disease is considered a multifaceted clinical disease affecting millions of people worldwide. Hospitals and cardiac centers rely heavily on ECG as a regular tool for ...evaluating and diagnosing Heart failure disease as an initial stage. The process of Heart failure disease identification from the ECG signal aims to reduce the time of the diagnostic process for patients with heart failure and to improve the outcomes of the detection process applied to these patients. The information acquired from the ECG signal simplifies the laboratory evaluation and other traditional diagnosis methods to evaluate and diagnose Heart failure disease. Unluckily, the problem of segmentation of the ECG signal is complicated because of similarities in time interval and amplitude between several ECG signal as well as the presence of noise in ECG signals. Most cardiologists use the ECG signal to identify Heart failure disease and their decision depends on the identification process, to determine whether surgery or medical treatment is required. This paper offers a new identification technique to overcome the current problems, such as overlapping in heart rate duration from the time interval from one PQRST wave to the next. In this study, the aim is to develop a new automatic method using improved support vector machine to diagnosis HFD from ECG signals. This is particularly relevant to ECG signals for the diagnosis of HFD as the first step to treatment and care of patients in general and specifically those with early heart disease to increase their overall survival. This paper outlines a hybrid approach of dual SVM and nonparametric algorithm to spot HFD in ECG signals leading to increase reliability and accuracy of identification and diagnosis of heart failure classes in the early stages using the proposed algorithm. The nonparametric algorithm is used to train SVM and its dual to get two models of SVM. The dual problem gives a different view that is better and sometimes simpler than the original problem. This feature is used to detect Heart failure disease in ECG signals by comparing the outputs of SVM model and those of dual SVM model. Experiments show that the hybrid approach produces good results, is more efficient and increases accuracy of Heart failure disease detection with an acceptable accuracy of 94.97% when compared with other algorithms to which the paper refers to. This is especially noted in patients with multiple diseases who were not initially identified as heart failure.
Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and ...used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signal-to-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection.
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