In this paper, two distinct strategies were used to enhance problem-solving abilities. The first strategy involved developing a conjugate gradient algorithm in which several new parameters were derived and proposed. The second strategy included hybridizing the dwarf mongoose optimization (DMO) algorithm in two ways, the first using the community by taking advantage of the developed conjugate gradient algorithm that was extracted from the first strategy and obtaining the hybrid algorithm (CG-DMO) that gives better results than the results of the original algorithm. The second method is to combine the sand cat swarm optimization algorithm (SCSO) and the dwarf mongoose optimization algorithm (DMO), and a hybrid algorithm (SCSO-DMO) is obtained. The dwarf mongoose optimization (DMO) algorithm uses three mongoose social groups: the alpha group, the scout group, and the babysitter group to replicate their foraging behavior. The Alpha group underwent hybridization, using the attack method of sand cats, known for their keen hearing of low-frequency sounds and their adeptness at detecting prey by digging. This hybrid approach led to the development of an equation for identifying candidate food sites within the alpha group. The proposed algorithms (CG-DMO) and (SCSO-DMO) underwent extensive testing on standard test functions, resulting in superior results compared to the original algorithm.

This study uses a quantitative technique and questionnaires to assess geo-disaster literacy among Cameron Highlands schoolchildren, where flash floods and landslides are common. Simple random sampling with descriptive and inferential analysis (ANOVA and Spearman’s Rho correlation) was used to sample 327 Form 3 students. The findings demonstrate strong knowledge, attitude, and practice (M=4.34, SP=2.08). A one-way ANOVA study reveals a significant difference between knowledge and attitude (F=6.372, P=<0.001, p-value < 0.05). A one-way ANOVA analysis shows a significant relationship between geo-disaster knowledge and practice (F=7.901, P=0.001, p-value 0.05). Additionally, One-way ANOVA analysis reveals a significant difference in geo-disaster attitudes and practices (F=4.106, P=<0.001). Spearman’s Rho analysis indicates a weak positive correlation (r=.406, p<0.001) between knowledge and attitude. The association between knowledge and practice is moderate (r=.412, p0.001) and positive. A moderate positive connection exists between attitude and practice (r=.415, p0.001). In conclusion, students understand and practice geo-disasters. Students are exposed to geo-disaster literacy. The government must create a geo-disaster literacy program.

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In the present work we investigate the collapsing and expanding solutions of the Einstein's field equation of anisotropic fluid in spherically symmetric space-time and with charge within the framework of ${f(R, T)}$ theory, where $R$ denotes the Ricci scalar and $T$ denotes the trace of the energy$-$momentum tensor. We also evaluate the expansion scalar, whose negative values result in collapse and positive values yield expansion. We analyzed the impacts of charge in ${f(R, T)}$ theory on the density and pressure distribution of the collapsing and expanding fluid and noticed the involvement of anisotropic fluid in the process of collapsing and expanding with charge in $ {f(R, T)}$. Furthermore, the definition of mass function has been used to analyse the condition for the trapped surface, and it has been found that in this case there is only one horizon. In all scenarios, the effects of coupling parameters $\lambda$ and $q$ have been thoroughly examined. Additionally, we have created graphs representing pressures, anisotropy, and energy density in ${f(R, T)}$ theory and check the effect of charge on these quantities.

This study examines the delicate balance between cultural practices and environmental conservation in Bali through the use of turtles in Balinese Hindu rites. Due to cultural value, traditional usage persists notwithstanding Government Regulation Number 7 of 1999 and international accords like CITES. The Turtle Conservation and Education Centre (TCEC) in Serangan Village, known as Turtle Island, educates the community, preserves local traditions, and promotes sustainable tourism to conserve turtles. We assessed turtle conservation programmes and their effects on local traditions through interviews with key community members and village observations. 75% of respondents indicated that they would continue to use turtles in traditional ceremonies. While some argue that the law permits the offering of turtles in traditional ceremonies due to their holy status, others hold a different view. The findings indicate that this society values cultural heritage and biodiversity conservation, yet they frequently clash. The perspectives of the Serangan Islanders on TCEC show that it is possible to effectively conserve turtles while simultaneously fulfilling economic needs and preserving traditional events. This study emphasises the need for socio-culturally adaptive conservation techniques to protect endangered turtle species. It requires increased community engagement and education to link traditional practices with conservation demands to preserve culture and the environment.

This paper explores the field of FPGA implementation and emulation of memristor devices, providing insights into the advancements, challenges, and future directions. The paper discusses various techniques used for FPGA-based memristor emulation, emphasizing the importance of accurate memristor modeling and performance evaluation. It identifies challenges in the field, including improving accuracy, scalability, real-time adaptation, standardization, integration with design tools, and exploring novel applications. Additionally, the results of the study show that FPGAs are one of the viable solutions for emulating memristors. The study concludes that FPGA based memristor emulation holds a promise for studying memristor-based circuits and systems, with potential applications in neuromorphic computing, machine learning accelerators, and analog/mixed-signal circuit design.

This paper proposes enhancing the K-means clustering method by incorporating the Crow Search Algorithm (CSA) and Calinski-Harabasz (CH) index to address the issue of determining the optimal number of clusters and attribute selection. The proposed approach, called Crow Search Algorithm K-mean clustering (CSAK_means), aims to explore the search space more effectively to find the best solutions. The efficiency of the CSAK_means algorithm is evaluated using a comparative experimental study for five datasets from the UCI repositories: Wine, Bodega, Cmc, Zoo, and Abalone. The results confirm that the proposed method outperforms the default algorithms in terms of average feature selection performance and silhouette value.

Pedestrians are one of the essential parts of the transportation system. In order to encourage walking and reduce the use of personal vehicles, pedestrian’s facilities need to be provided in the campus since they are facing many problems. Pedestrian’s level of service (LOS) is the common approach to estimate the quality of pedestrian facilities. The highway capacity manual (HCM) defines six Pedestrians (LOS) namely LOS A, B, C, D, E and F, where A shows high Levels of comfort and capacity, while F represents a poor level of comfort and capacity. Reconnaissance and Field survey measurement was done to collect and study the pedestrians and sidewalks characteristic by using video camera and Measuring tape. Walking speed also was studied to find the speed at which pedestrians appear in term of (15th, 50th, 85th, 98th) percentile speed. The study showed that LOS for study areas ranges between (B) to (E) also it was observed that pedestrians used the roadway in moving which indicate the inefficiency of sidewalk capacity. As the accident in front of university gates increased during crossing, the study suggested (4) alternatives facilities for pedestrians crossing which are: stairways, escalator, ramps and underpasses.

Face detection and recognition (FRD) technology is a very useful tool that involves taking pictures of people's faces and assessing their biological characteristics to compare and match facial data recorded in databases. Owing to its numerous advantages, including noncontact functionality, time and attendance tracking, medical applications and enhanced security and surveillance, this technology is finding increased application in a variety of contexts. Considering that the face images captured by these devices are influenced by many factors, such as light, posture, and backdrop environment, the recognition rate of current face recognition models remains inadequate. This paper presents a model that combines the You Only Live Once (YOLO) v3 algorithm for face detection with VGG16 networks for efficient face recognition. The model is specifically made to handle scenarios in which people share facial traits and to recognize people in various settings with accuracy. This paper uses two different public datasets to train and test the proposed model, WIDER FACE dataset for YOLO v3 and the Labelled Faces in the Wild (LFW) dataset for the VGG 16 networks, the improved network model performed better in identification and is more robust. Furthermore, the YOLO v3 network scored a little lesser accuracy of 95.9% in face detection, while the VGG 16 network achieved an amazing 96.2% accuracy in face recognition.

Gated communities have emerged as significant features of contemporary urban landscapes, prompting extensive scholarly inquiry into their socio-economic, cultural, and environmental dimensions. This paper presents a comprehensive bibliometric analysis of gated community research to elucidate its interdisciplinary nature, global perspectives, institutional affiliations, and emerging areas of interest. Methodologically, a systematic search within the Scopus database yielded 471 relevant articles published between 1996 and May 2024. Analysis revealed a notable upward trend in publication volume, predominantly comprising peer-reviewed journal articles (73.7%), followed by book chapters (15.7%) and conference papers (4%). Interdisciplinary collaboration was evident, with Social Sciences (47.1%) leading disciplinary contributions, followed by Environmental Science (13.4%) and Engineering (9.9%). Top platforms for dissemination included Housing Studies, Cities, and Urban Studies. Global perspectives showcased contributions primarily from the United States, the United Kingdom, China, and Canada. Institutional analysis highlighted leading contributors such as The City University of New York and University College London. Top researchers included Blandy, Roitman, and Landman, among others. Emerging thematic clusters were visualized, indicating evolving research trajectories and areas of interest, from foundational concepts to niche explorations. This bibliometric analysis provides a roadmap for future research endeavors, emphasizing the need for interdisciplinary collaboration to address the multifaceted challenges and opportunities presented by gated communities in contemporary urban environments.

Climate change’s impact can negatively influence human life, such as increasing temperatures and sea levels. This research aims to analyze the impact of climate change on air temperature during the rainy and dry seasons in East Java, specifically the Wlingi Reservoir area. Daily temperature observation data from 1990-2023 will be analyzed spatially using Inverse Distance Weighting (IDW) interpolation and statistically through analysis of long-term variability, rate of change in annual and seasonal scales and climate anomalies that occur. The results show a positive pattern of increase from 1990-2023, both on an annual and seasonal scale. The average temperature change rate at East Java climate stations increased to more than 1℃/34 years, with the most significant climate anomaly occurring in 2016. The humidity caused by the rain causes the temperature to be warmer than in the dry season. Thinner air pressure in mountainous areas causes cooler temperatures than coastal areas. During the 34 years of observation, the earth’s surface has warmed over the years and may continue to rise.

The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.

The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent model’s performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.

The towing limits for self-propelled rail track maintenance equipment (SP-TME) are influenced by a multitude of factors, including the type and weight of the equipment, speed, braking capabilities, track and weather conditions, traction, engine power, driveline performance, coupler/towing link integrity, and safety regulations. This study investigates these variables to determine their impact on the towing limits of SP-TME. Unlike traditional rail vehicles, SP-TME possesses unique operational constraints and specifications, necessitating careful consideration of its independent mobility. An extensive analysis was conducted on the towing usage and overuse of SP-TME during travel mode, examining various scenarios that incorporate different combinations of trailing load, rail track grade, rail curvature, and weather conditions. These scenarios, ranging from normal to worst-case, aim to simulate demanding operational environments. The parameters evaluated include structural strength, traction, engine and driveline performance, wheel rolling and skidding, braking capabilities, trailing load, speed, and track and weather conditions. Results indicate that under normal and moderate conditions, the equipment can tow significantly higher loads than the defined base load. However, in special situations, such as negotiating tighter curves and steeper grades in adverse weather conditions, wheel skidding and locking emerge as limiting factors. Findings related to service and parking brake performance during steep grade descents, particularly when the trailer lacks independent braking capabilities, are also presented. Recommendations and cautions are provided to ensure safe and efficient operation of SP-TME under various conditions.