Normal Events Research Articles

Clean Intermittent Catheterisation via a Mitrofanoff is superior to long term Suprapubic Catheters for Bladder Drainage.

AimSome children require long-term drainage of the bladder but do not tolerate clean intermittent catheterisation (CIC) urethrally. We aimed to compare long term suprapubic catheter (SPC) drainage vs Mitrofanoff conduit (allowing CIC) by comparing the survival of the drainage methods and rates of urinary tract infection (UTI). MethodRetrospective review of a single surgeon’s experience (2007-2023). Data collection included diagnosis, age at procedures, date of surgery, date of most recent follow-up and date and reason for unplanned surgery. For SPCs, initial insertion of SPC and then conversion to a Foley catheter under GA was taken as normal (event free survival) and only further operations were counted as complication events. For Mitrofanoffs, any subsequent operation was counted as a complication.Data were given as a number (%) or median (interquartile range) as appropriate. Data analysed by Fisher exact and Mann-Whitney U-test. Kaplan Meier (KM) survival of drainage routes was compared, P<0.05 taken as significant. ResultsThere were 45 patients (n = 86 SPC episodes) compared to 108 patients (n = 110 Mitrofanoff procedures). Data were available in 73 SPC episodes and 109 Mitrofanoff episodes, including 3 redo procedures (one from another centre). There was no difference in gender (SPC group, 67% male vs. Mitrofanoff group, 77%; N.S.). There was no difference in age at procedure [7.5 (2.9 - 11.5) years vs 8.3 (5.9 - 11.4) years respectively; N.S.)KM comparison showed that Mitrofanoff have a better event-free survival than SPC (91% vs 52% at 1 year; 80% vs. 13% at 5 years; P<0.0001). Paired data showed a significant (86%) reduction in rate of UTI with conversion from SPC to Mitrofanoff drainage in 15 patients; SPC: 0.13 (0 - 0.46) UTIs/month vs subsequent Mitrofanoff: 0.02(0 - 0.08) UTI/month, P=0.04). ConclusionSPCs had a much higher rate of unplanned surgery than Mitrofanoffs, creating a considerable unplanned burden of care for families and clinical staff. For long-term bladder drainage CIC via a Mitrofanoff conduit should be considered in preference to SPC where urethral CIC is not possible.

Connecting the dots: Exploring the relationship between menstrual hygiene and bacterial vaginosis in eastern India

ABSTRACT Introduction: Menstruation is a normal physiological event that takes place in a rhythmic fashion during the reproductive period of a woman. Menstrual hygiene management (MHM) practices differ and depend on the individual’s socioeconomic status, local traditions and beliefs, and access to water and sanitation resources. Insufficient and incorrect information regarding menstruation and lack of knowledge and awareness leads to poor personal hygienic practices, which further leads to many infections. Bacterial vaginosis, the most common RTI, is characterized by an alteration in the vaginal micro biome with a decline in hydrogen peroxide producing Lactobacillus colonization and an overgrowth of facultative anaerobic bacteria like Gardnerella spp., Mobiluncus spp., etc. Aims: The primary objective of the study was to find out the association of bacterial vaginosis with menstrual hygiene practices. The secondary objective was to determine the prevalence of bacterial vaginosis among patients presenting with vaginal discharge to the outpatient department of a tertiary care hospital. Methods: Women in the reproductive age group 18-45 years having regular menstrual cycles were included in the study. Participants were enquired for sociodemographic data, menstrual hygiene practices and symptoms of abnormal vaginal discharge. High vaginal swab was collected for the symptomatic patients and sent to investigate for presence of bacterial vaginosis. Results and observation: The prevalence of BV was found to be 13% among symptomatic patients presenting with abnormal vaginal discharge. Apart from abnormal discharge, itching and burning sensation was also seen to have a positive association with bacterial vaginosis (p &lt; 0.001). The women who used clothes as absorbent were more prone to develop BV than women using sanitary pad. The other parameters to have positive association with BV were decreased frequency of taking body bath. Unsatisfactory sanitation of latrine was found to be strongly associated with BV after adjusting for confounders. Conclusion: In conclusion, the result of this study provided a strong association between poor MHM practices with BV.

Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system that has multiple contributions including a random batch selection mechanism to reduce interbatch correlation and a normalcy suppression block (NSB) which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block (CLB) is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. An extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate the superior anomaly detection capability of our approach.

Rethinking prediction-based video anomaly detection from local–global normality perspective

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Prediction-based VAD methods have gained a lot of attention in recent years due to their simplicity and effectiveness, which learn normality through a frame prediction tasks. However, we find that they often consider only local or global normality in the temporal dimension thus are difficult to generalize for various scenarios. Specifically, some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events while the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies. In this paper, we rethink these two types of methods and find that the former methods are difficult to adapt to simple scenarios due to powerful representation allows these methods to represent some anomalies and causes miss detection while the latter struggle with complex scenarios due to the limitations for diverse normal patterns and causes false alarm. To verify our discoveries, we design a two-branch model, Local–Global Normality Network (LGN-Net), to simultaneously learn local and global normality. We verified the effect of local and global normality on the VAD model by ablation study and find that the model can be more adaptive to different scenarios when accounting for both normality. The code is available online: https://github.com/Myzhao1999/LGN-Net.

Advances and challenges in friction pendulum bearings for seismic isolation systems

Friction pendulum bearings (FPBs) have emerged as critical structural isolation devices in the field of earthquake engineering. Extensive research and development efforts have led to the exploration and refinement of these bearings, resulting in the creation of various models with unique structural configurations and superior mechanical properties. This paper offers an in-depth review of the friction materials employed in FPBs, alongside an analysis of the mechanical performance and advancements in research regarding single-pendulum, double-pendulum, multiple-pendulum, and other types of friction bearings. Characterized by their low sensitivity and high stability, these bearings are adept at resisting seismic forces, thus providing dependable isolation protection for structures. This review also includes a succinct examination of the seismic performance and engineering applications of these isolation structures. With robust self-centering capabilities, excellent isolation, and energy dissipation mechanisms, FPBs are capable of quickly resuming normal operations post-seismic events. This reduces structural damage and maintenance expenses, significantly improving the seismic resilience of structures. Moreover, the paper outlines the current challenges in the research and development of FPBs and suggests future research directions, including optimizing friction materials, enhancing the design and performance of isolation structures, and improving the seismic performance and engineering application efficiency of FPBs. By identifying underexplored areas and synthesizing findings differently, this review provides a comprehensive and novel perspective that advances the field of earthquake engineering.

Delving into CLIP latent space for Video Anomaly Recognition

We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel method AnomalyCLIP, the first to combine Vision and Language Models (VLMs), such as CLIP, with multiple instance learning for joint video anomaly detection and classification. Our approach specifically involves manipulating the latent CLIP feature space to identify the normal event subspace, which in turn allows us to effectively learn text-driven directions for abnormal events. When anomalous frames are projected onto these directions, they exhibit a large feature magnitude if they belong to a particular class. We also leverage a computationally efficient Transformer architecture to model short- and long-term temporal dependencies between frames, ultimately producing the final anomaly score and class prediction probabilities. We compare AnomalyCLIP against state-of-the-art methods considering three major anomaly detection benchmarks, i.e. ShanghaiTech, UCF-Crime, and XD-Violence, and empirically show that it outperforms baselines in recognising video anomalies. Project website and code are available at https://lucazanella.github.io/AnomalyCLIP/.

Flood risk assessment of a small river with limited available data

Flood risk modeling of small watercourses is challenging when only limited input data are available. Therefore, this study assessed the flood characteristics of a small river (Tarna River: entire watershed-C, upper-VS, middle-TMS, and lower section-TOS) from 1990 to 2019. The assessment focused on modeling, model calibration, and validation using feature event-based time-series data in data-scarce environments. We showed that since the 2000s, the number of high-water levels above 250 cm, and the frequency of three flood types had increased. Flood simulation results showed the largest flooded area in the TMS section, followed by the VS, and then the TOS. The outcomes from the VS, TMS, and TOS sections did not exhibit superior performance compared to the C area. Models performed well for larger flood events, with Kling Gupta Efficiency corresponding well to NRMSE and Nash-Sutcliffe efficiency metrics. Accordingly, flood events characterized by the longest duration and high-water levels yielded outstanding results across all areas, followed by moderate flood events with good accuracy. Normal water level events exhibited significant deviations from the reference across all sections. In summary, despite the event-based modeling challenges in data-limited environments, such models can still mitigate potential flood events and improve decision-making processes.

Facilitators and barriers of midwife-led model of care at public health institutions of dire Dawa city, Eastern Ethiopia, 2022: a qualitative study

BackgroundThe midwife-led model of care is woman-centered and based on the premise that pregnancy and childbirth are normal life events, and the midwife plays a fundamental role in coordinating care for women and linking with other health care professionals as required. Worldwide, this model of care has made a great contribution to the reduction of maternal and child mortality. For example, the global under-5 mortality rate fell from 42 deaths per 1,000 live births in 2015 to 39 in 2018. The neonatal mortality rate fell from 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018. Even if this model of care has a pivotal role in the reduction of maternal and newborn mortality, in recent years it has faced many challenges.ObjectiveTo explore facilitators and barriers to a midwife-led model of care at a public health institution in Dire Dawa, Eastern Ethiopia, in 2021.Methodology: A qualitative approach was conducted at Dire Dawa public health institution from March 1–April 30, 2022. Data was collected using a semi-structured, in-depth interview tool guide, focused group discussions, and key informant interviews. A convenience sampling method was implemented to select study participants, and the data were analyzed thematically using computer-assisted qualitative data analysis software Atlas.ti7. The thematic analysis with an inductive approach goes through six steps: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up.ResultTwo major themes were driven from facilitators of the midwife-led model of care (professional pride and good team spirit), and seven major themes were driven from barriers to the midwife-led model of care (lack of professional development, shortage of resources, unfair risk or hazard payment, limited organizational power of midwives, feeling of demoralization absence of recognition from superiors, lack of work-related security).ConclusionThe midwifery-led model of care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally. A multidisciplinary and collaborative effort is needed to solve those challenges.

Molecular Developmental Biology of Fibrodysplasia Ossificans Progressiva: Measuring the Giant by Its Toe.

When a genetic disease is characterized by the abnormal activation of normal molecular pathways and cellular events, it is illuminating to critically examine the places and times of these activities both in health and disease. Therefore, because heterotopic ossification (HO) in fibrodysplasia ossificans progressiva (FOP) is by far the disease's most prominent symptom, attention is also directed toward the pathways and processes of bone formation during skeletal development. FOP is recognizable by effects of the causative mutation on skeletal development even before HO manifests, specifically in the malformation of the great toes. This signature skeletal phenotype is the most highly penetrant, but is only one among several skeletal abnormalities associated with FOP. Patients may present clinically with joint malformation and ankylosis, particularly in the cervical spine and costovertebral joints, as well as characteristic facial features and a litany of less common, non-skeletal symptoms, all stemming from missense mutations in the ACVR1 gene. In the same way that studying the genetic cause of HO advanced our understanding of HO initiation and progression, insight into the roles of ACVR1 signaling during tissue development, particularly in the musculoskeletal system, can be gained from examining altered skeletal development in individuals with FOP. This review will detail what is known about the molecular mechanisms of developmental phenotypes in FOP and the early role of ACVR1 in skeletal patterning and growth, as well as highlight how better understanding these processes may serve to advance patient care, assessments of patient outcomes, and the fields of bone and joint biology.

Smartphone-sensor-based human activities classification for forensics: a machine learning approach

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones’ sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones’ data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies.

Semi-Arid to Arid Scenario Shift: Is the Cabrobó Desertification Nucleus Becoming Arid?

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis.

A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.

The MEMENTO code for modeling of macroscopic melt motion in fusion devices

The MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks) code is a new numerical implementation of the physics model originally developed for the MEMOS-U code with the objective to self-consistently describe the generation of melt and its subsequent large scale dynamics in fusion devices and to assess the damage of metallic reactor armor under powerful normal and off-normal plasma events. The model has been validated in multiple dedicated EUROfusion experiments. MEMENTO solves the heat and phase transfer problem coupled with the incompressible Navier–Stokes equations in the shallow water approximation for the thin liquid film over the solid metal and with the current propagation equations on a domain that features a time-evolving deforming metal-plasma interface. The code utilizes non-uniform and adaptive meshing along with sub-cycling in time facilitated by the AMReX open-source framework as well as AMReX’s built-in parallelization capabilities.

Driving Forces of Impunity in South Asia: A Human Rights Framework

This paper is the outcome of a research on impunity in South Asia. Undertaken by Forum Asia in 2021, the research collected cases from Afghanistan, Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka. The cases related to impunity were collected by the country researchers deputed in these countries. The lead author and co-author of this paper were the main contributors in compiling the country reports, processing the data, editing them and publishing them as country reports and South Asia regional report. Out of the huge compilation, this paper mainly sketches the debates over the constructs on impunity prevailing in these countries. The paper highlights how these countries are combating impunity with respect to the drivers and to what extent this effort is compatible in the realm of global human rights framework. Impunity is found deeply rooted in South Asia both in the cognitive, productive, economic, and political lives of the victims as well as on the psychosocial mindset of society. Individual victims of gross violations of human rights have been denied access to justice at large. This develops post-traumatic stress disorder (PTSD) along with loss of memory, nightmares, excessive fear, loss of attention, loss of self-esteem, self-isolation, distrust, withdrawal from normal life events as well as a refusal to participate in civil, cultural and social interactions. The paper concludes that most South Asian states follow democratic norms and values as they hold periodic elections and form governments. These states, however, severely lack concrete plans and tools to combat impunity. The paper suggests the vibrant role of the legislation penal codes in these countries to make accountable to the stakeholders involved in impunity matters both as perpetrators and those acting against the perpetrators.

An informative dual ForkNet for video anomaly detection

An autoencoder for video anomaly detection task is a type of algorithm with the primary purpose of learning an “informative” representation of the normal data that can be used for identifying the abnormal data by learning to reconstruct a set of input observations. Based on the encoding–decoding structure, we explore a novel dual ForkNet architecture that can dissociate and process the spatio-temporal representation. It is well-known in the information theory community that most autoencoders coding processes are inevitably accompanied by a certain loss of information. In this dual ForkNet, we focus on mitigating the information loss problem and propose a novel architectural recalibration approach, which we term the “Informetrics Recalibration” (IR). It can adaptively recalibrate latent feature representation by explicitly modeling the similarity between the corresponding feature maps of encoder and decoder, and retain more useful semantic information to generate greater differentiation between normal and abnormal events. Additionally, because the structure of the autoencoder itself determines the difficulty to obtain deep semantic information, we introduce a Secondary Encoder (SE) in each ForkNet, so as to recalibrate target features responses of latent feature representation. Our model is easy to be trained and robust to be applied, because it basically consists of some ResNet blocks without using complicated modules. Extensive experiments on the five publicly available benchmarks show that our model outperforms the existing state-of-the-art architectures, demonstrating our framework’s effectiveness.

The role of the pressure in the endotracheal tube cuff in the development of iatrogenic tracheal injuries

Background. The endotracheal tube insertion, which is a veritable patient care procedure, carries risks and possible adverse effects. Such complications vary in severity and range from mild symptoms such as sore throat or cough, which are often considered normal post-intubation events, to severe damage, including damage to the vocal cords, trachea, malacia, and fistula formation. A critical factor influencing the manifestation of these complications is the pressure exerted by the endotracheal tube cuff on the trachea. The purpose was to study the influence of high pressure of the intubation tube cuff on the incidence and severity of complications in an in vivo model. Materials and methods. A total of 12 rabbits were studied and divided into three groups according to target cuff pressure: 30, 50, and 100 cm H2O. Sevoflurane was used to maintain anesthesia, monitoring of vital functions included heart rate, pulse oximetry, capnography, and electrocardiography. Intubation was performed after induction with ketamine and administration of relaxants, and cuff pressure was measured with a mechanical manometer. The duration of pressure exposure was 20 minutes for each group, then the degree of tracheal damage was assessed by autopsy. Results. Increasing pressure generally leads to an increased risk of injury. In the third group (pressure of 100 cm H2O), all subjects had damage to the trachea, in 75 % of cases, it was a complete tracheal rupture. However, the dependence on pressure is not unambiguous for all types of injuries: the frequency of pneumothorax does not correlate directionally with the incidence of iatrogenic injuries and does not increase proportionally to pressure. So, for example, subcutaneous emphysema developed only in a third of cases of tracheal rupture. This emphasizes possible difficulties in timely diagnosis of this type of complications. Conclusions. The work highlights the risks associated with endotracheal intubation and emphasizes the need to maintain recommended practices and individual approach to each case. The most severe damage was observed at a pressure of 100 cm H2O, while at a pressure of 30 cm H2O, a smaller proportion of rabbits showed signs of damage.

Increasing risk of glacial lake outburst flood in Sikkim, Eastern Himalaya under climate warming

The increasing risk of Glacial Lake Outburst Floods (GLOFs) in the Eastern Himalaya is exacerbated by climate change-driven glacial ice mass loss, slowdown, and increasing infrastructure projects in the high-altitude regions. To quantify the current risk of potential future disasters we update the inventory of glacial lakes in Sikkim Himalaya, identify the most potentially dangerous glacial lakes (PDGL) and model their peak discharge in different scenarios. The updated glacial lake inventory includes 232 glacial lakes (of >0.01 km2) covering a cumulative area of 22.23 ± 0.10 km2. Our GLOF susceptibility mapping of all moraine-dammed glacial lakes using an Analytic Hierarchy Process (AHP) reveals one lake as very high risk, eight as high risk, 22 as medium risk, 56 as low risk, and 18 as very low risk. Further, we apply dam break flood simulations for the seven most dangerous lakes. Results reveal highest peak discharges of 9504 m3 s−1 and 8421 m3 s−1 in extreme case scenarios from the Khanchung and South Lhonak lakes, respectively. The lowest peak discharge of 622 m3 s−1 is estimated in a normal outburst event for Yongdi lake, with every scenario at least 447 m3 s−1 discharge is reaching to Chungthang town. We find that more than 10,000 people face direct threat of GLOF with potential large-scale infrastructure damage (∼1900 settlement, 5 bridges and 2 hydropower plants). The updated glacial lake dataset, GLOF susceptibility mapping, and modeling results demonstrate the urgent need to install an early warning system and control breaching of highly dangerous lakes.

&lt;span&gt;Evaluating the effect of Brasinolide on the salinity tolerance of spring onion (&lt;em&gt;Allium fistulosum L.)&lt;/em&gt; under saline irrigation conditions&lt;/span&gt;

Spring onions are an essential spice in Vietnamese food preparation for its favor and aroma. Tra Vinh, one of the coastal provinces, is greatly affected by climate change and saline intrusion. Hence, this study was conducted to access the effects of Brasinoline (Br) on the growth of Spring onion under normal conditions and salt stress events. The experiment embraced four Br concentrations (0 ppm, 0.05 ppm, 0.1 ppm, and 0.15 ppm) under both fresh water and 3‰ saltwater irrigation conditions, distributed according to the 2-factor Randomized Completely Block Design (RCBD). Preliminary results, with concentrations of Br 0.1 ppm and 0.15 ppm, show a high increase in leaf length, number of leaves/brush, number of shoots, stem diaphragm and plant green weight. Under 3‰ salt water irrigation conditions, Br concentration of 0.15 ppm showed supporting effectively the growth of Spring onions equivalent to that in fresh water.

The compound impact of rainfall, river flow and sea level on a watercourse through a coastal city: Methodology in making

Due to climate change, future weather conditions will become more extreme. During recent years, several severe damages have been caused by heavy rainfalls in combination with riverine events. Even though the effects of compound events are known to be influential for flood hazard, the method for investigating these types of events is a novel area of expertise. In this study, a methodology was developed to investigate a watercourse, acting as a part of a stormwater drainage system in an urban coastal area, in a hydrodynamic model to find areas prone to flooding. The method was applied for Ståstorpsån in Trelleborg, Sweden. The model was a unified model for seasonal variability and compound events with scenarios developed based on series of data representing normal values of the boundary conditions rainfall, river flow and sea level. The result was analysed graphically and statistically as a flood hazard. The data used was based on data collected during the past 10 years for rain and sea level and 16 years of simulated river flow. The constructed rain events from gauge data all had a return time of less than 10 years. Therefore, the chosen events are considered to represent normal levels. For Trelleborg, the results from the hydrodynamic model indicate that compound events will increase the flood hazard with anincreasing time horizon. The visual analysis converges with earlier flood events, and hotspots are generally seen around bridges and culverts. For the studied area, there is a large seasonal variation in the flood hazard and with climate change, all seasons will cause more severe flood hazards. The effects experienced during a summer event, which is the most severe event today, are to be expected for all seasons in 2100. The effect seen during summer eventsis a combination of all three drivers. However, rain intensity is likely to be more influential for normal events. When a certain threshold value for sea level is reached, sea level becomes the most influential driver, overtaking the other drivers in importance.

Histological Observations of the Developing Human Fetal Humerus at Different Trimesters of Pregnancy

Background: Although femur length is the preferred method for fetal age assessment, the humerus is sometimes the right choice, especially in the second half of pregnancy. Fetal bone development significantly impacts adult bone quality and senescent bone disorders, including osteoporosis. Purpose of the study: The present study aimed to ascertain the histological changes in the human fetal humerus across the three trimesters. After the institutional ethical committee clearance and parent consent, the study was carried out on stillborn or medically terminated human fetuses from the 10th to the 32nd week of intrauterine life. Results: First trimester: The primary bone collar appeared with the primary ossification center, marked by more vascular invasion and a pool of mesenchymal cells. Trabeculations begin from the bony collar and insignificant periosteum. Second trimester: The changes showed longitudinal growth of periosteal bone towards the proximal and distal ends of the growing bone. Growth plate with distinguished zones and gradual fusion of epiphysis with growth plate were observed. Third trimester: Trabeculation number and thickness increase with calcification. Towards the third trimester, the marrow cavity with increased and prominent trabeculations is consistent. Conclusion: Understanding normal microstructural and cellular events chronologically is an ideal platform for future studies to develop cell-based or cell-targeted therapies for adult bone disorders, traumatic bone injuries, or bone engineering. KEYWORDS: Histology, Developing Bone, Humerus, Fetuses, Embryology.