Sustainable Green City Development Project Analysis using the Critical Path Method (CPM) and the Crashing Project Method on Time and Cost Optimization

Due to the increase in population all over the world, the term city development has emerged using the method of sustainable green cities, which are cities designed taking into account the environmental impact to reduce the required input from energy, water, and food production. This study discusses this topic with three main objectives; the first is to analyze the project work network of the Sustainable Green City Development Project, the second is to investigate the impact of speeding up the project on network planning, and the third is to determine the cost differences before and after acceleration. The Critical Path Method (CPM) is valuable for managing complex projects if the activity durations are known. However, in reality, the projected durations may vary in the course of a project due to multiple factors, such as equipment breakdowns, human errors, and material shortages. This study used the CPM and crashing methods to determine the optimal project turnaround time using critical path optimization techniques. Analysis using CPM showed that the initial project duration was 1205 weeks but can be reduced to 1197 weeks. However, with acceleration, there is a slight difference, corresponding to a 0.67% decrease in the duration and approximately 8 weeks. The study showed that the total cost of the project work is US $55M at the normal duration and became US$59M after acceleration, which is a slight increase

Sustainable Hybrid Design to Ensure Efficiency and Air Quality of Solar Air Conditioning

This research work aims to investigate and subsequently optimize the operating parameters that affect thermal comfort and indoor air quality in the school environment. The proposed design uses a coupling between solar ventilation and the absorption chiller-air conditioning. The heating tower of an adsorption chiller connected to an air conditioning system can be driven by the waste heat from a solar ventilation (exhausted hot air) system thank to this linkage. In order to simulate variables like the velocity magnitude distribution in the air-conditioned room, mathematical modeling is numerically executed. Air temperature evolution along the height of the conditioned room in the mid-length and the air velocity evolution along the length of the conditioned room in the mid-height are studied. According to the numerical simulation results, the inlet air temperature soars as the inlet air velocity rises. Inlet air velocities of 0.05m/s, 0.5m/s, and 1m/s are correlated with inlet air temperatures of 20.7°C, 21.2°C, and 21.3°C, respectively. We conclude that an inlet air velocity in the order of 1m/s (in relation to a maximized air change rate) is in agreement with the general ASHRAE standards for indoor air quality in the case of the school environment, coupled with the essential need to limit as much as possible the spread of viruses

Grupiranje zasnovano na skupljanju dokaza s vjerojatnosno-neizrazitim C-means pristupom za dijagnozu bolesti

Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy C-Means (PFCM) as base cluster generating algorithm into the ‘standard’ Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique’s effectiveness, its performance on both synthetic and real medical datasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.Tradicionalno, metode nadziranog strojnog učenja predstavljaju prvi izbor za zadatke koji uključuju klasifikaciju podataka. Ovo istraživanje prikazuje nekonvencionalnu hibridnu alternativnu (pEAC) tehniku koja kombinira vjerojatnosno-neizraziti C-Means (PFCM) kao osnovni algoritam grupiranja u standardno grupiranje korištenjem grupiranja zasnovanog na skupljanju dokaza (EAC). PFCM objedinjuje zasebna svojstva vjerojatnosnog C-Means (PCM) i neizrazitog C-Means (FCM) algoritama u sofisticirani algoritam grupiranja. Usprkos ogromnim mogućnostima koje nudi ova tehnika, u smislu strukture, ona je nalik cjelovitim hEAC i fEAC tehnikama grupiranja realiziranim integracijom K-Means i FCM algoritama grupiranja u EAC tehniku.Kako bi se validirala učinkovitost, njeno ponašanje je ispitano na sintetičkim i stvarnim medicinskim podacima te su provedene usporedbe s pojedinačnim široko rasprostranjenim metodama, drugim nenadziranim tehnikama grupiranja i nekim nadziranim metodama učenja. Rezultat prikazuje kako predložena pEAC tehnika nadmašuje pojedine metode grupiranja i druge tehnike nenadziranog učenja u smislu točnosti u dijagnozi hepatitisa, kadiovaskularnih bolesti, raka dojke i dijabetesa, korištenih u eksperimentu.Značajno, u usporedbi s odabranim nadziranim modelima klasifikacije, predložena pEAC tehnika pokazuje bolju točnost dijagnoze na dvama korištenim bazama podataka za rak dojke, što ukazuje na to da čak i bez označenih podataka predložena tehnika nudi efikasnu klasifikaciju medicinskih podataka

Geographical Information System Based Spatial and Statistical Analysis of the Green Areas in the Cities of Abha and Bisha for Environmental Sustainability

This study compares the environmental sustainability of two cities in Saudi Arabia, Abha, and Bisha, through their green spaces, by analyzing green spaces in both cities. And the application of spatial statistics tools in the Arc Map program, to measure the spatial relationship between the green areas depending on the measurement of the location, shape, dimensions and areas, as this distribution is linked to statistical laws leading to the construction of a spatial model for the green areas in the two cities, and among these methods is the spatial average, The central phenomenon, the distribution trend, the standard circle, and finally the neighborhood analysis. The study seeks to recognize the parameters that contribute to environmental sustainability through green spaces. Understanding the effectiveness of green spaces in promoting environmental sustainability is crucial for policymakers to make informed decisions about urban planning and development. Sustainability in the environment is making responsible use of natural resources while also taking measures to safeguard the surrounding area to maintain high standards of environmental quality over the long term. The concept entails the preservation of equilibrium among economic, social, and environmental considerations to guarantee the satisfaction of current societal requirements while safeguarding the capacity of forthcoming generations to fulfill their own necessities. Environmental sustainability is crucial for the well-being of the planet and all living beings that inhabit it. Green spaces play a vital role in environmental sustainability. The provision of green spaces is associated with a multitude of advantages, including but not limited to the mitigation of air and noise pollution, temperature regulation, and enhancement of the overall visual appeal of urban areas. The study employed Geographic Information System (GIS) and spatial statistical analysis to investigate the spatial arrangement of environmental sustainability in the two urban areas. The study also relied on fieldwork, including a questionnaire, to gather data from the residents of the cities. The research study found that the standard distance measures the average distance between each green space and the mean center. In this case, the standard distance indicates how dispersed or clustered the green spaces are around the mean center. A smaller standard distance value suggests that the green spaces are more clustered around the mean center, while a larger value suggests a more dispersed distribution

Latex-Based Membrane for Oily Wastewater Filtration: Study on the Sulfur Concentration Effect

Nitrile butadiene rubber (NBR) latex/graphene oxide (GO) membranes were fabricated through a latex compounding and curing method which is a relatively new method to produce membranes for wastewater treatment. Hence, the steps in the production of the membrane through this new approach need to be evaluated to optimize the performance of the membrane. In this paper, the effect of sulfur loading in the range of 0.5 to 1.5 parts per hundred rubber (phr) on the morphology, crosslink density, tensile properties, permeation flux and oil rejection rate performance of NBR/GO membranes was studied. The sulfur loading was found to influence the surface morphology and integrity of the membrane which in turn affects the performance of the membrane in terms of strength, water flux and rejection rate of oil. Inaccurate sulfur loading produced a membrane with micro cracks, low surface area for filtration and could not withstand the filtration pressure. In this research work, the membrane with 1.0 phr sulfur provides the highest water flux value and oil rejection rate of 834.1 L/m2·hr and 92.23%, respectively. Surface morphology of 1.0 phr sulfur-loaded membrane revealed the formation of continuous membrane with high structural integrity and with wrinkles and folded structure. Furthermore, micro cracks and a less effective surface area for filtration were observed for membranes with 0.5 and 1.5 phr sulfur loading

A Comparative Study of Regression Model and the Adaptive Neuro-Fuzzy Conjecture Systems for Predicting Energy Consumption for Jaw Crusher

Crushing is a vital process for different industrial applications where a significant portion of power is consumed to properly blast rocks into a predefined size of fragmented rock. An accurate prediction of the energy needed to control this process rarely exists in the literature, hence there have been limited efforts to optimize the power consumption at the crushing stage by a jaw crusher; which is the most widely used type of crusher. The existence of accurate power prediction as well as optimizing the steps for primary crushing will offer vital tools in selecting a suitable crusher for a specific application. In this work, the specific power consumption of a jaw crusher is predicted with the help of the adaptive neuro-fuzzy interference system (ANFIS). The investigation included, aside from the power required for rock comminution, an optimization of the crushing process to reduce this estimated power. Results revealed the success of the model to accurately predict comminution power with an accuracy of more than 96% in comparison with the corresponding real data. The obtained results introduce good knowledge that may be used in future academic and industrial research

A Linear Probabilistic Resilience Model for Securing Critical Infrastructure in Industry 5.0

Critical infrastructures are designed for securing interconnecting networks from different influencing factors such as adversaries, unauthorized platoons, cyber threats, etc. These infrastructure hosts include human, physical elements, and cyber paradigms. The vital part is cyber resilience against weak and volatile authentication and security administrations. For strengthening cyber security, this article introduces the Artificial Intelligence-induced Constructive Resilience Model (AI-CRM). The proposed model accounts for the security requirements of the adversary impacting infrastructure elements based on probability. This probability is computed using previous adversary impacts on infrastructure failures and session drops in handling operational services. The computation for linearity or stagnancy is validated using a recurrent learning paradigm over different service transitions. The resilience is improved by augmenting security measures that are identified as an output of linear impacts over the services. Based on the linear incremental probability the resilience between two successive service transitions is computed. Identifying the non-linear or stagnant probability is the converging solution of recurrent learning. The recurrent learning optimizes the stagnancy and linear impact (probability) by repeatedly computing the failures and drops due to adversary injection. This improves resilience through security augmentations and modifications. This model is analyzed using adversary detection ratio, session drops, infrastructure failures, time lag, and service dissemination ratio

Statistical and spatial analysis of air pollution in the cities of Abha and Bisha in the Kingdom of Saudi Arabia

The Kingdom of Saudi Arabia has experienced a notable impact as a result of population growth. Consequently, there is a heightened concern regarding urban air pollution and its effects on both individuals and society. This issue is of great importance in the development and expansion of cities within the region. The presence of air pollutants has been linked to various negative health outcomes, including the development of diseases, allergic reactions, and even fatalities in humans. Additionally, air pollution can have detrimental effects on other living organisms, such as animals and food crops, and can also cause harm to the natural environment, including contributing to climate change, ozone depletion, and land degradation in urban areas. The present study employs statistical analysis to regulate spatial theory to examine air pollution in the urban areas of Bisha and Abha in the Kingdom of Saudi Arabia. The results showed that carbon emissions are lower in Abha than Bisha. The spatial theory shows that there are no carbon emissions from the factories in Abha and Bisha as the factories are located outside of the urban reach of both cities. The research findings indicate significant differences at a significance level of 0.05 in terms of the mean responses of the study participants from Abha and Bisha cities. The results favored Abha, with a study concerning carbon emissions was 0.005. In addition to the aforementioned results, various conclusions and recommendations were derived from the present investigation

Modeling, Simulation, and Optimization of a Solar-Based System of Desalination Using Humidification and Dehumidification

Solar desalination systems are characterized by low freshwater production compared with the usual techniques of mineral and salt removal from water. The usual methods include, but are not limited to, multi-stage flash distillation, multiple-effect distillation, vapor-compression desalination, and reverse osmosis. Solar desalination requires various modifications to make it more productive than the usual methods. The method is suitable for energy and environmental protection, making it the most effective system. The adjustments involve using the humidification and dehumidification principle (HD). The three configurations of the HD solar desalination system in this project are designed to accommodate variations in climate conditions and seasonal changes. Mathematical models are designed to test the workability of the system in an ideal environment. The models are based on universal fluid equations that regulate the functioning of each component of the system. After the model is designed, a regulation algorithm is designed based on the model. The simulation results show that the gain in freshwater production using a regulation algorithm is in the order of 33%