Dynamic neuro-fuzzy systems for rainfall-runoff modelling

Urbanization has significant impact on the hydrological processes that have caused an increase in magnitude and frequency of floods; therefore, a reliable rainfall-runoff model will be helpful to estimate discharge for any watershed management plans. Beside physically-based models, the data driven approaches have been also used frequently to model the rainfall-runoff processes. Neuro-fuzzy systems (NFS) as one of the main category of data-driven models are common in hydrological time series modeling. Among the different algorithms, Adaptive network-based fuzzy inference system (ANFIS) is well-practiced in hydrological modeling. ANFIS is an offline model and needs to be retrained periodically to be updated. Therefore, an NFS model that can employ different learning process to overcome such problem is needed. This study developed dynamic evolving neuro fuzzy inference system (DENFIS) model for event based and continuous rainfallrunoff modeling and the results were compared with the existing models to check model capabilities. DENFIS evolves through incremental learning in which the rulebase is evolved after accommodating each individual new input data and benefitted from local learning implemented through the clustering method, Evolving Clustering Method (ECM). In this study, extreme events were extracted from the historical hourly data of selected tropical catchments of Malaysia. The DENFIS model performances were compared with ANFIS, the hydrologic modeling system (HECHMS) and autoregressive model with exogenous inputs (ARX) for event based rainfall-runoff modeling. DENFIS model was also evaluated against ANFIS for continuous rainfall-runoff modeling on a daily and hourly basis, multi-step ahead runoff forecasting and simulation of the river stage. The average coefficients of efficiency (CE) obtained from DENFIS model for the events in testing phase were 0.81, 0.79 and 0.65 for Lui, Semenyih and Klang catchments respectively which were comparable with ANFIS and HEC-HMS and were better than ARX. The CEs obtained from DENFIS model for hourly continuous were 0.93, 0.92 and 0.62 and for daily continuous were 0.73, 0.67 and 0.54 for Lui, Semenyih and Klang catchments respectively which were comparable to the ones obtained from ANFIS. The performances of DENFIS and ANFIS were also comparable for multistep ahead prediction and river stage simulation. This study concluded that less training time and flexibility of the rule-base in DENFIS is an advantage compared to an offline model such as ANFIS despite the fact that the results of the two models are generally comparable. However, the learning algorithm in DENFIS was found to be potentially useful to develop adaptable runoff forecasting tools

Dynamic neuro-fuzzy systems for rainfall-runoff modeling

Urbanization has significant impact on the hydrological processes that have caused an increase in magnitude and frequency of floods; therefore, a reliable rainfall-runoff model will be helpful to estimate discharge for any watershed management plans. Beside physically-based models, the data driven approaches have been also used frequently to model the rainfall-runoff processes. Neuro-fuzzy systems (NFS) as one of the main category of data-driven models are common in hydrological time series modeling. Among the different algorithms, Adaptive network-based fuzzy inference system (ANFIS) is well-practiced in hydrological modeling. ANFIS is an offline model and needs to be retrained periodically to be updated. Therefore, an NFS model that can employ different learning process to overcome such problem is needed. This study developed dynamic evolving neuro fuzzy inference system (DENFIS) model for event based and continuous rainfallrunoff modeling and the results were compared with the existing models to check model capabilities. DENFIS evolves through incremental learning in which the rulebase is evolved after accommodating each individual new input data and benefitted from local learning implemented through the clustering method, Evolving Clustering Method (ECM). In this study, extreme events were extracted from the historical hourly data of selected tropical catchments of Malaysia. The DENFIS model performances were compared with ANFIS, the hydrologic modeling system (HECHMS) and autoregressive model with exogenous inputs (ARX) for event based rainfall-runoff modeling. DENFIS model was also evaluated against ANFIS for continuous rainfall-runoff modeling on a daily and hourly basis, multi-step ahead runoff forecasting and simulation of the river stage. The average coefficients of efficiency (CE) obtained from DENFIS model for the events in testing phase were 0.81, 0.79 and 0.65 for Lui, Semenyih and Klang catchments respectively which were comparable with ANFIS and HEC-HMS and were better than ARX. The CEs obtained from DENFIS model for hourly continuous were 0.93, 0.92 and 0.62 and for daily continuous were 0.73, 0.67 and 0.54 for Lui, Semenyih and Klang catchments respectively which were comparable to the ones obtained from ANFIS. The performances of DENFIS and ANFIS were also comparable for multistep ahead prediction and river stage simulation. This study concluded that less training time and flexibility of the rule-base in DENFIS is an advantage compared to an offline model such as ANFIS despite the fact that the results of the two models are generally comparable. However, the learning algorithm in DENFIS was found to be potentially useful to develop adaptable runoff forecasting tools

Frequency of Helicobacter Pylori Infections and Its Associated Risk Factors in Patients Attending Tertiary Care Hospital of Bhakkar, Pakistan

Background: Helicobacter Pylori  is a gram-negative bacteria that is the main cause of chronic gastritis and plays a significant role in peptic ulcers, gastric carcinoma, and gastric lymphoma. The prevalence of H. pylori cases is 75-90% worldwide. The objective of the study was to determine the frequency of H. pylori and its associated risk factors in the Bhakkar district.Methods: A total of 102 participants with problems in the gastrointestinal tract were taken from June 2021 to May 2022. Stool antigen was performed to confirm H. pylori infection. A complete blood count (CBC) was also performed on the blood sample. Results: The current study showed that a total of 102 samples were collected in this study. It was concluded that out of 102 participants, 63 (61.8%) were H. pylori positive and 39 (38.2%) were H. pylori-negative participants. Females were more infected with H. pylori 38 (60.3%) as compared to males 25 (39.7). The frequency of factors such as smoking (52% vs 51%), weekly consumption of junk food (52.4% vs 43.6%), fizzy drinks (33.3% vs 23.1%) and drinking of unfiltered water (54% vs 53%) was more in H. pylori-infected group compared with the uninfected group but difference was not statistically significant with odds ratio less than 1.Conclusion: The current study concluded that female genders, ethnicities, and history of stomach infection are risk factors for H. pylori. Exposure to Smoking, unfiltered drinking water, fizzy drinks, and Junk food is more in the affected group than in the unaffected group.

Self-Esteem among Orphans and Non- orphans: A Comparative Study

The study's goal was to look at the connection between orphans and non-orphans' autonomy, social, physiological, and consciousness, and learners’ self. This study is designed as a quantitative research, cross sectional used for data collection through simple random sampling technique. The sample consisted of 199 orphans and non-orphans. Boys and girls ranging in age from 7 to 18 years old were selected from various schools and neighborhoods in Bahawalpur and Ahmadpur East. The results show that there are no changes in self between boys and girls. There are no differences in self-esteem among urban and rural pupils. There is no change in self-esteem based on orphan and non-orphans. In terms of selfesteem, there is no substantial variation in education grade. This study will help to focus on other reasons of high or low self-esteem except orphans

Cost Performance in Construction Industry of Pakistan

Construction industry is notorious and infamous as far as cost base lines and project budgets are concerned. More than 90 percent of projects delay gets over budgeted or completely abandoned due to either paucity of funds or mismanagement at different levels. Despite a major contributor in the Gross Domestic Product of a country, its full potential has never been exploited. Perhaps this retrogressive atmosphere has been cultivated by callous, careless and unprofessional attitudes of all stakeholders of construction industry. The primary stakeholders which affect the projects positively or negatively in cost dimension are; the government, the contractors, the consultants and the clients or owners. The authors conducted interviews as well as surveys with construction professionals, contractors, architects, design engineers, suppliers and sub contractors in order identify the most occurring causes of cost overruns in construction projects. In addition to this contemporary literature was studied and reviewed with a purpose to assess the current and ongoing issues in the construction industry. A questionnaire was distributed among respondents on cost performance of various completed and under construction projects, with a view to highlight the concrete reasons which push the projects out of approved budgets. The major conclusions from this research paper which have been drawn are; corruption and bribery, political interests, poor site management, delay in site mobilization, rigid attitude by consultants, extra work without approvals, frequent changes during execution, gold platting, safety and health and limited access to job sites. In order to avoid, eliminate or mitigate effects of these causes viable recommendations have been recommended. Keywords: Cost Performance, Construction Industry, Pakista

Focused abdominal CT scan for acute appendicitis in children: can it help in need

OBJECTIVE: To evaluate the focused abdominal CT scan [FACT] in clinically equivocal cases of acute appendicitis in paediatric population. METHODS: A cross-sectional study was conducted at the Radiology Department of Aga Khan Hospital, from August 2007 to November 2008. A total of 84 patients (42 males & 42 females) with clinically equivocal signs and symptoms of acute appendicitis referred to radiology department for CT evaluation were studied. CT findings were compared with histopathology and clinical follow-up. RESULTS: The sensitivity of focused CT for acute appendicitis was 91%; specificity was 69% and accuracy of 76% while PPV and NPV were 58%, 94% respectively. CONCLUSION: Focused unenhanced CT is a quick, accurate and non invasive modality for the assessment of clinically equivocal cases of acute appendicitis for ruling out patients and reducing negative appendectomies

Rainfall runoff modelling in a large tropical catchment by ANFIS

Modeling the rainfall-runoff process is a significant task in hydrological modelling as it can be helpful in decreasing the damages of flooding and also managing the water resources. This could be very important for a tropical country such as Malaysia with approximately 2500 mm annual rainfall. To date, several models are developed to capture the rainfall-runoff relationship including physically-based models and system theoretic ones. Despite many uncertainties and complexities involved in physical models the system theoretic modeling techniques lately found applicants in a variety of hydrological problems including rainfall–runoff modeling. Among different types of system theoretic models Artificial Neural Networks (ANN) and Neuro-Fuzzy Systems (NFS) have been commonly used in hydrological modelling. Although ANNs have shown reasonably good performance in rainfall-runoff modelling they are suffering from several issues including long training time, non-transparent internal process, and requiring trial and error procedure to find an optimum structure. However, NFS which combine human-inspired reasoning style of fuzzy systems with learning and connectionist structure of neural networks have the significant advantage of reduced training time in comparison with ANNs. Moreover, NFS is not completely a black-box model as it can give an insight about its internal process in terms of IF-THEN rules. The well-known Adaptive Network-based Fuzzy Inference System (ANFIS) has been successfully employed in many engineering modelling applications including hydrological modelling. In ANFIS, the global parameter tuning has been considered by means of minimization of the global error of the model. Therefore, ANFIS has been found to be an appropriate tool in non-linear mapping problems between input and output such as rainfall-runoff modelling. The present study is an application of ANFIS in rainfall-runoff modeling in a large catchment (with area of 350 Km2) of Bekok River in the state of Johor, Malaysia. Approximately 85% of its area consists of agriculture fields, roads, utility reserves and the remaining 15% is in domestic use. Thirty years daily rainfall and runoff data was used in this study. The data was split into the training and testing datasets i.e. 80% for training and 20% for testing. The catchment has two rainfall stations; therefore, an input selection process based on correlation analysis was done to find the most appropriate rainfall and discharge antecedents for developing the model. Using 2 triangular membership functions and number of epoch of 30 were found to be appropriate for developing the ANFIS model

Methyl 4-{[(4-methyl­phen­yl)sulfon­yl]amino}­benzoate

In the mol­ecule of the title compound, C15H15NO4S, the dihedral angle between the two rings is 88.05 (7)°. The methyl ester group is nearly coplanar with the adjacent ring [dihedral angle = 2.81 (10)°], whereas it is oriented at 86.90 (9)° with respect to the plane of the ring attached to the –SO2– group. Weak intra­molecular C—H⋯O hydrogen bonding completes S(5) and S(6) ring motifs. The mol­ecules form one-dimensional polymeric C(8) chains along the [010] direction due to N—H⋯O hydrogen bonding and these chains are linked by C—H⋯O hydrogen bonds, forming a three-dimensional network

Impact of PUBG Game Addiction on Social Isolation and Narcissistic Tendencies among Gamers

The current research aimed to explore the relationship of PUBG game addiction with narcissistic tendencies and social isolation in gamers. For this correlation survey based research the data was conveniently collected from PUBG gamers (N= 101) age ranging from 13-30 years through online response method. The instruments included Online Game Addiction Scale (Kim, Namkoong, Ku, & Kim, 2008) Narcissistic Personality Inventory (Raskin & Hall, 1981) and Measures of Social Isolation (Zavaleta, Samuel, & Mills, 2017) for testing the hypothesis. According to the yielded results, an excellent reliability of these measures was established. The results also indicated that online game addiction, social isolation and narcissistic tendencies among PUBG game players are negatively correlated (<.05). It was concluded that online games do carry positive aspects of enhancing social skills and interactions among the players, while helping them exhibit behaviors and emotions that are not coherent with narcissistic tendencies. This paper also carries implications for families, friends, teachers and therapists of online gamers, who may use the findings to understand some of the positive aspects of playing online games

4-Methyl-N-{4-[(5-methyl-1,2-oxazol-3-yl)sulfamo­yl]phen­yl}benzene­sulfonamide

In the title compound, C17H17N3O5S2, the dihedral angle between the two benzene rings is 81.27 (8)° and the heterocyclic ring is oriented at 9.1 (2) and 76.01 (9)° with respect to these rings. Mol­ecules are connected via N—H⋯N and N—H⋯O hydrogen bonds, generating an R 2 2(8) motif, into chains running along the [001] direction. There is also an intra­molecular C—H⋯O hydrogen bond completing an S(6) ring motif. The polymeric chains are inter­linked through inter­molecular C—H⋯O hydrogen bonds