Land suitability assessment is one of the key aspects of modern urban development. If there is any mistake in assessing land suitability, environmental hazards, and financial losses may occur. Land suitability assessment is a necessity in modern urban development, taking into account the rapid growth of the urban population and the increase in infrastructural development. Though there is a huge amount of geospatial information available, land suitability assessment is a complex and time-consuming process, considering the fragmented geospatial information gathered from various sources. To avoid the complexities in the land suitability assessment process, which involves gathering geospatial information from various sources, a digital solution is proposed for efficient collection of geospatial information from various sources using a single platform. The solution uses smart algorithms for assessing land suitability based on environmental conditions. This solution provides safer construction practices, which helps minimize environmental risks. Moreover, economic losses are avoided by ensuring efficient use of land. This solution improves efficiency, accuracy, and reliability in land suitability assessments.

Waste management has become a critical environmental and social challenge due to rapid urbanization and population growth. Improper segregation of waste reduces recycling efficiency and increases health risks for workers involved in manual sorting. This paper presents an Automated Waste Segregation System using a Robotic Arm, designed to segregate waste into metal and non-metal categories. The proposed system uses an inductive proximity sensor for metal detection, a microcontrollerbased control unit, and a servo motor driven robotic arm for physical segregation. The system minimizes human intervention, improves safety, and provides a compact and cost-effective solution suitable for small-scale applications. Experimental results show reliable detection and accurate placement of waste materials into respective bins.

ABSTRACT With rapid population growth and the shortage of land resources, ocean space utilization has become a critical area of research. While multi-use offshore platforms have been extensively studied, challenges remain in ensuring their operability and survivability under offshore conditions. This work aims to develop an innovative floating island concept designed to facilitate multi-purpose ocean space utilization in Europe. First, hydrodynamic numerical analysis is carried out for a previously proposed single hexagonal floating platform. The numerical results show good agreement with the reference data from literature. Then, a floating renewable energy platform is proposed to host a 10-megawatt (MW) wind turbine and photovoltaic (PV) panels by the Pareto multi-objective optimization method. Key geometric parameters, including the side length (20–50 m), height (5–15 m), and panel thickness (0.5–2 m), are considered as the main optimization variables. The optimization objectives include its energy performance and construction cost, and the criteria include freeboard, initial stability, and the natural periods of heave and pitch motions. The dynamic responses of the designed floating energy island are also evaluated. This concept will be further developed in the future by combining more considerations, such as the configurations of PV arrays and the share of wind and solar power.

Rapid population growth challenges governance systems, housing markets, infrastructure capacity, and social cohesion, yet it is often treated as a predictable and uniform process. This structured comparative review synthesizes four distinct rapid-growth literatures: energy boomtowns, amenity-migration destinations, gateway communities, and mega-event host towns, to examine how different growth drivers shape community resilience. Using systematic forward and backward citation tracking grounded in community theory, the review identifies recurring patterns across otherwise separate research traditions. The analysis shows that outcomes are shaped less by growth itself than by institutional and spatial conditions. Extractive boomtowns and mega-event hosts experience compressed cycles of disruption and recovery that test adaptive capacity, while amenity-migration destinations and gateway communities face sustained pressures related to housing affordability, land-use conflict, and social boundary formation. Across contexts, three interrelated dimensions of adaptive capacity consistently structure trajectories: multilevel governance coordination, housing and land-use elasticity, and the management of social equity and cohesion. The findings advance a conceptual resilience framework that interprets rapid population change as a socio-spatial shock filtered through institutional and spatial conditions, with implications for sustainable urban design, flexible infrastructure planning, and inclusive governance.

Keywords IoT Smart Agriculture Aquaponics Hydroponics Real-Time Monitoring Water Quality Sensors pH Sensor TDS Sensor Turbidity Sensor DHT11 Sensor Ultrasonic Sensor LDR Sensor Arduino NodeMCU Cloud Computing Automation Sustainable Farming ABSTRACT With the rapid growth in global population and the increasing pressure on agricultural resources, there is a strong need for efficient and sustainable farming solutions. Techniques such as aquaponics and hydroponics have gained attention as they enable cultivation without soil while using significantly less water. However, maintaining stable environmental and water conditions in these systems requires continuous monitoring and precise control. This paper presents an IoT-based smart monitoring system designed to enhance the performance of aquaponics and hydroponics setups through real-time sensing and automation. The system employs multiple sensors, including pH, temperature and humidity (DHT11), TDS, turbidity, light intensity (LDR), ultrasonic, and water temperature sensors, to track critical parameters affecting both plant growth and aquatic health. The collected data is processed by a microcontroller and transmitted to a cloud platform for visualization and remote access. An automated control strategy is implemented using predefined threshold values, enabling timely corrective actions and reducing human intervention. This approach improves system reliability, minimizes resource wastage, and ensures optimal growing conditions. Furthermore, the integration of IoT supports continuous monitoring and data-driven decision-making, leading to better productivity and sustainability.The proposed system offers a cost-effective, scalable, and environmentally friendly solution, making it suitable for modern agriculture, particularly in urban and resource-limited environments.

The rapid growth of the elderly population has increased the demand for intelligent and reliable safety monitoring systems, particularly for elderly individuals living independently. Elderly people are more prone to risks such as falls, sudden health deterioration, and delayed emergency response. Conventional monitoring approaches often rely on manual supervision or wearable devices, which may be inconvenient or ineffective during critical situations. This paper proposes an AI-Enabled Elder Safety Monitoring System using a Smart Alert Interface that continuously monitors elderly activity using smartphone sensors and artificial intelligence techniques. The system analyses activity patterns to detect abnormal behaviour and automatically generates emergency alerts for caregivers or healthcare professionals. By integrating AI-based anomaly detection with mobile and cloud technologies, the proposed system improves response time, reliability, and accessibility. The system is designed to support independent living while ensuring timely assistance and enhanced elderly safety.

The rapid growth of the global population and the intensification of agricultural practices have significantly increased the vulnerability of crops to diseases and pest infestations, leading to substantial yield losses and economic instability in the agri-food sector. Traditional crop disease identification and pesticide application methods are largely manual, time-consuming, and prone to human error, often resulting in delayed intervention and excessive or inefficient pesticide usage. In recent years, deep learning has emerged as a powerful paradigm for automated crop disease detection due to its superior capability in learning complex visual patterns from largescale image data. This research presents a deep learning–based approach for enhancing crop disease detection and pesticide management by leveraging advanced convolutional neural networks and intelligent decision-support mechanisms. The proposed approach aims to achieve accurate and early-stage disease identification while facilitating targeted and optimized pesticide recommendations, thereby minimizing chemical overuse and environmental impact. By integrating image-based disease recognition with intelligent inference models, the system supports precision agriculture objectives, including improved crop health monitoring, sustainable pest control, and increased agricultural productivity. The study synthesizes recent advances in deep learning architectures, dataset augmentation strategies, and evaluation metrics relevant to real-world agricultural deployment. The findings underscore the potential of deep learning–driven systems to transform crop protection practices by enabling scalable, real-time, and cost-effective disease detection and pesticide optimization.

Agricultural systems currently occupy nearly 40–50% of the Earth’s terrestrial surface and are central to global food security, yet they face increasing pressure due to rapid population growth and environmental degradation. However, increasing population pressure and the need for higher food production have intensified the demand for efficient nutrient management strategies. Conventional fertilizers, although widely used, suffer from low nutrient use efficiency due to substantial losses through leaching, volatilization, runoff, and fixation, leading to environmental degradation and economic inefficiencies. These losses contribute to groundwater contamination, eutrophication, greenhouse gas emissions, and deterioration of soil health. In response to these challenges, smart fertilizers have emerged as an innovative approach to enhance nutrient use efficiency and promote sustainable agriculture. These include nano fertilizers and slow or controlled release fertilizers, which are designed to synchronize nutrient availability with plant demand. Nano fertilizers, owing to their small particle size, high surface area, and enhanced reactivity, facilitate improved nutrient absorption and targeted delivery while minimizing environmental losses. Similarly, slow and controlled release fertilizers regulate nutrient release through coating materials and matrix systems, ensuring a steady nutrient supply over time and reducing the frequency of fertilizer application. The adoption of these advanced fertilizer technologies offers multiple agronomic, environmental, and economic benefits, including improved crop productivity, reduced nutrient losses, enhanced soil fertility, and lower environmental risks. Furthermore, they play a crucial role in achieving sustainable intensification of agriculture by optimizing resource use and minimizing ecological footprints. Overall, smart fertilizers represent a promising solution for addressing the limitations of conventional fertilization practices and advancing sustainable agricultural systems. Continued research and technological development are essential to improve their efficiency, scalability, and field-level applicability under diverse agro-ecological conditions.

Food security in Sahelian countries is increasingly threatened by climate variability, rapid population growth, and dependence on monoculture staples. Indigenous Fruits and Vegetables (IFVs) are underutilized resources with potential to improve nutrition and resilience. This study examines IFV consumption patterns in Burkina Faso and Niger, focusing on nutritional benefits and socio-demographic determinants. Structured surveys were conducted with 458 respondents across both countries. Data were analyzed using descriptive statistics and the Heckman two-stage model to assess consumption and expenditure patterns, while correcting for sample selection bias. IFV consumption was associated with greater dietary diversity among households. Average expenditures were higher in Burkina Faso (3,904 CFA) compared to Niger (1,961 CFA). Gender, education, and religion significantly influenced consumption, while occupation and consumer characteristics affected expenditure levels. Nutritional knowledge was a major driver in Burkina Faso (76.6% of respondents) but less so in Niger (23.4%).Findings highlight IFVs as valuable contributors to nutrition and food security in the Sahel, though disparities in knowledge and socio-demographic factors limit utilization. Strengthening market infrastructure, promoting nutritional awareness, and adopting culturally sensitive, gender-inclusive policies are recommended to increase equitable access and maximize IFV potential.

This thesis deals with the case study on the properties of GFRP (Glass Fiber Reinforced Polymer) In this Thesis we use the GFRP to find out the behaviour of the final product madeby this material which is line transmission tower. Due to the rapid population growth and rapid urbanization, the natural resources are depleting day by day. So, the natural aggregates are very hard to obtain. So many people are opting to use the GFRP (Glass Fiber Reinforced Polymer) instead of the conventional iron or steel channels. The cost of conventional iron or steel channels as compared to GFRP is also very high due to the high demand of it in construction works. GFRP will reduce the quarrying and mining of iron and steel ores thereby reducing the use of natural resources excessively. The land surface can be prevented from any unwanted excavation and hence ecological disturbances will be reduced to conserve the conventional natural iron ore for other important construction works. The compression and the tensile tests are carried out in the thesis to find out the behaviour of the different types of the joints and channel sections with different types of connections. And the result is carried out and accordingly the conclusion is given. This study focuses on the structural behaviour of bolted joints in Glass Fiber Reinforced Polymer (GFRP) transmission line towers, specifically investigating the use of GFRP for these structures as an alternative to traditional steel.

African food systems are at a crisis point. Climate change, rapid population growth, hyperinflation, wars, conflicts, and pandemics are among the causes of this disarray. Deliberations on transforming the system and empowering it to meet its mandate are ongoing. This article contributes to these ongoing deliberations by exploring the diagnostic potential of financial technology (FinTech) to improve African food systems, focusing on Sub-Saharan Africa (SSA). This is achieved by identifying the challenges of SSA food systems and exploring how FinTech can be used to address these challenges. The review article draws from a content analysis of secondary literature on food systems and FinTech, focusing on the possibilities of intersection. The article shows that Fintech can help streamline financial processes in SSA food systems, facilitating digital payments and lending, making it easier for food system actors to access credit and insurance, thus improving their operations. FinTech solutions like blockchain can also improve supply chain transparency and traceability. FinTech enables AI-powered tools for monitoring crop and animal health and optimising operations, ultimately contributing to food systems' efficiency, sustainability, and resilience. Despite several challenges, the article concludes that FinTech wields much potential to transform SSA food systems by improving financial access, enhancing transparency, promoting sustainability, and optimising operations across the food chain.

The rapid growth of urban populations and waste generation has created significant challenges in waste management, with global municipal solid waste expected to exceed 3.4 billion tons annually by 2050 and inefficient collection systems increasing operational costs by nearly 20–30%, while smart waste management technologies are projected to grow at over 18% annually. Additionally, improper waste handling leads to environmental pollution, health hazards, and unhygienic urban conditions, emphasizing the need for intelligent monitoring systems. Traditional waste management systems rely on fixed collection schedules and manual inspection, which often result in overflowing bins, unnecessary fuel consumption, and increased labor costs. Furthermore, conventional systems lack real-time monitoring, waste classification, and route optimization capabilities, reducing their effectiveness in modern urban environments. To address these challenges, the proposed IoT Smart Trash Bin Management System utilizes the ESP32 microcontroller to develop an intelligent and automated waste monitoring solution. The system integrates ultrasonic sensors to measure dry and wet waste levels, along with a wet/dry sensor for accurate waste segregation. An IR sensor enables touch-free lid operation using a servo motor, enhancing hygiene. Real-time status is displayed on an LCD, while a buzzer alerts when the bin reaches capacity. IoT connectivity allows continuous data transmission to cloud platforms for remote monitoring, and a GPS module provides location tracking for optimized waste collection routing. This smart system improves efficiency, reduces environmental impact, minimizes operational costs, and supports the development of sustainable and intelligent urban waste management systems

The rapid growth of urban populations and smart city initiatives has increased the demand for efficient urban utility management systems, with over 55% of the global population living in cities and municipal inefficiencies causing nearly 20–30% resource wastage annually, while IoT-based smart city solutions are projected to grow at over 19% annually. Additionally, issues such as water overflow, inefficient streetlight operation, and manual pump control contribute to energy loss, safety hazards, and increased operational costs. Traditional urban utility management systems rely heavily on manual monitoring and fixed schedules, which often lead to delayed responses, water wastage, overflow incidents, and inefficient energy consumption. Furthermore, conventional systems lack realtime data visibility, remote accessibility, and automated control mechanisms, reducing their effectiveness in dynamic urban environments. To address these challenges, the proposed IoT Urban Utilities Management System utilizes the ESP32 microcontroller to develop an intelligent and automated municipal management solution. The system integrates ultrasonic sensors to monitor manhole water levels and prevent overflow, while a GSM module sends real-time alerts to authorities. An RTC module enables scheduled control of water pumps and streetlights, ensuring efficient energy usage. The ESP32 processes sensor data and supports IoT connectivity for remote monitoring and data visualization, while an LCD and buzzer provide local alerts and system status updates. This smart system enhances operational efficiency, reduces resource wastage, improves public safety, and supports the development of sustainable and intelligent urban infrastructure management.

Rapid urbanization and population growth have increased the need for multistorey buildings in cities. Designing such structures requires proper analysis of loads, stability, and structural behaviour to ensure safety. Nowadays, structural analysis software like ETABS and STAAD.Pro are widely used to simplify and improve the accuracy of building design. This project focuses on the comparative analysis of a G+6 reinforced concrete building located in Vijayawada using both software tools. The same building model, with identical geometry and material properties, is used in both software to maintain consistency.

Samarinda's rapid population growth is not matched by the readiness of sanitation infrastructure, causing residents of RT 03, Sindang Sari Village, to consume murky water and openly burn waste, which poses a risk to respiratory health. The focus of this research problem is to optimize household waste management and water quality through the implementation of the Community-Based Total Sanitation (STBM) program. Key stages of the research include situational analysis using the USG method, providing education on waste sorting, training in making eco-enzymes from organic waste, and installing simple water filter technology for sixty selected respondents. The quantitative research findings show a significant increase in residents' understanding from a pre-test score range of 60–100 to 90–100 in the post-test stage. Although the Wilcoxon test results yielded a statistically significant value of 0.102, which does not indicate a significant difference due to the already high initial score, descriptively there has been a real change in behavior in the form of a reduction in the practice of burning waste and an increase in the ability to purify water independently. The main conclusion of this study confirms that the participatory intervention strategy through STBM is very effective in building community independence in maintaining environmental hygiene. This initiative has successfully transformed old habits into sustainable clean living patterns to improve the quality of life and public health through a beautiful and safe residential environment for the future of all residents in the area as a whole and integrated.

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management.

The Mara River Basin, a critical transboundary system within the Lake Victoria and Nile basins, sustains the globally renowned Mara–Serengeti ecosystem and diverse indigenous livelihoods. Land cover change, land use dynamics, and climate variability are recognized globally as the major drivers of hydrological instability and ecosystem stress. Africa is particularly vulnerable to climate change impacts due to widespread poverty and reliance on rain-fed agriculture. In East Africa, recurrent droughts and floods have been widely reported, with climate variability exerting strong pressure on water resources. The Mau Forest Complex in Kenya is facing deforestation despite being a vital water tower regulating flows into the Mara River thus, further destabilizes the Mara River hydrological regimes. Rapid population growth and land use conversion have driven forest excision and encroachment into fragile ecosystems, directly reducing river flow volumes. This study employed the use of Soil and Water Assessment Tool (SWAT) hydrological model, Multiple Regression and Correlation analysis, ANOVA, and scatter plot visualization to examine relationships between water yields and predictor variables of land use, land cover, and climate using long-term datasets (1983–2014) and projections to 2030. Results revealed a perfect model fit (R² = 1.000), with rainfall explaining 93% of river flow variability (r = 0.939, p = 0.010). Temperature (r = 0.061, p = 0.461) and forest cover (r = -0.670, p = 0.108) showed weaker, non-significant effects, though forest cover exhibited an inverse relationship with flows due to infiltration and evapotranspiration dynamics. ANOVA confirmed significant differences in water yields across land cover scenarios, while scatter plots provided visual validation of rainfall’s dominant influence. In summary, rainfall is the primary driver of flow variability, while land use and cover changes exacerbate extremes. Sustaining the Mara River’s ecological and hydrological functions requires urgent conservation measures, integrated watershed management, and climate adaptation strategies informed by both global science and local realities.

The rapid population growth in urban areas has increased the demand for Laundry services. PT. Laundry Kotak Indonesia, Pekanbaru Branch, plays a key role in managing and developing its workforce to ensure efficient operations and quality customer service. However, issues such as tardiness, safety negligence, lack of skills, and high employee turnover have negatively affected employee performance and overall productivity. Despite these challenges, no formal performance measurement system for employees has been implemented. This study aims to evaluate employee performance using the Human Resource Scorecard (HRSC) and Analytical Hierarchy Process (AHP) methods. The objective is to improve employee performance to support the company’s vision and competitiveness. The findings identify 14 strategic performance indicators, with the Financial perspective receiving the highest weight (0.437), providing a basis for more focused HR strategies, policy development, training resource allocation, and continuous performance evaluation. Keywords:, AHP, HRSC, Employee Performance Measurement, Performance

To safeguard global food security against rapid population growth and a warming world, the effective genetic improvement of cereals is imperative. Flower opening time (FOT) critically affects the seed setting rate. In this study, we identified a gene, EARLY-MORNING FLOWERING 3 (EMF3), in which single-nucleotide substitutions strongly modulate FOT in rice in a semi-dominant manner, resulting in wide variation in FOT from earlier to later FOT than the wild-type. EMF3 knock-out mutants showed significantly reduced FOT synchrony and disrupted anther dehiscence, leading to fertilisation failure. EMF3 encodes a plasma membrane-localised polypeptide of 723 amino acids with an armadillo repeat fold and four transmembrane segments. Furthermore, EMF3 is specifically expressed in the anthers starting from nighttime on the day of flowering, with substantial impacts on the transcriptomes of both anther and lodicule, which suggested an exclusive role of EMF3 in flowering events. Modifying EMF3 alleles of O. sativa enabled the adjustment of FOT among Oryza species and subspecies, potentially facilitating cross-fertilisation by overcoming one of the major challenges of inter-specific hybridisation to exploit heterosis. Introducing the EMF3 alleles with the earlier FOT into popular rice cultivars resulted in flowering at an earlier time of day when the temperature was cooler, efficiently increasing seed setting rate under heat stress. This discovery unveils the novel mechanism of anther control of flower opening time through the EMF3 gene, while also enabling the use of EMF3 alleles in breeding strategies for efficient fertilisation for increasing hybrid rice seed production and mitigating future heat-stress damage at flowering.

The expansion of the textile and pharmaceutical industries, driven by rapid population growth and evolving lifestyles, has led to an increased release of synthetic chemicals into the environmental matrices, thereby posing risks to aquatic ecosystems and human health. A novel composite adsorbent, fabricated by integrating thermally activated red mud (ARM) with MIL-100 (Fe) (referred to as MARM) via co-precipitation, was utilized for the adsorptive removal of methylene blue (MLB), Congo red (CNR), and levofloxacin (LVX) from an aqueous solution. The assessment of textural properties showed MARM-II (containing 40% MIL-100 Fe) with a high specific surface area (SSA) of 651.741 m2 g−1. The enhanced SSA and distinctive surface charge profile of MARM-II promoted the effective adsorption of MLB, CNR, and LVX onto the MARM-II surface. At optimal conditions (contact time: 150 min, initial MLB, CNR, and LVX concentration: 10 mg L−1 each, MARM-II dose: 0.4 g L−1, solution pH: 7, and temperature: 27 ± 3 °C), MLB, CNR, and LVX removals were recorded to be 93.077 ± 0.593%, 89.739 ± 1.119%, and 96.102 ± 0.997%, respectively. The pseudo-second-order kinetic model confirmed chemisorption was the governing mechanism, while the Sips isotherm best explained the adsorptive nature of MLB, CNR, and LVX on the surface of MARM-II composite. The maximum adsorption capacity for MLB, CNR, and LVX was found to be 123.021 ± 11.926, 143.934 ± 24.248, and 97.657 ± 5.686 mg g−1, respectively. Additionally, the thermodynamic investigation indicates that the adsorption process was characterized as exothermic and spontaneous. The mechanistic insights showcased that the adsorption process was mainly driven by electrostatic interaction, hydrogen bonding, π–π interaction, and chemisorption.