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There is a growing demand for detailed building façade models (Level-of-Detail 3: LoD 3) in a variety of applications. Despite the increasing number of papers addressing this issue in the literature, occlusions are still a significant problem when processing building façade elements. Conversely, depending on the view angle of the images, the detected elements might not be projected to their accurate locations causing uncertainties in their 3D locations. In this paper, we address the aforementioned issues utilizing multi-view images. Using a building footprint layer, we first locate the points belonging to buildings. We then detect the 2D windows and doors in images by combining Faster R-CNN and Segment Anything (SAM) deep learning models. The 2D borders are projected into the 3D object space using a pinhole camera model and collinearity equations. Utilizing the multi-view capabilities of mobile scanning systems, this method effectively mitigates uncertainties associated with occlusion and exterior orientation parameters (EOP). This study provides a comprehensive evaluation of 3D spatial accuracy, achieving an average of 84% Intersection over Union (IoU) accuracy for 12 different single-sided façades over 750 multi-view images for 312 windows and doors of various sizes with rectangular and curved shapes.

According to the infrared patch image (IPI) model theory, the infrared image background has a low rank and the target is sparse. The low-rank model can be used to separate the background and identify the target. However, in a noisy environment, the recognition effect will be affected. The higher the noise, the harder it would be to detect a small target. The residual strong fault and background edges could reduce the detection rate and increase false alarms. The traditional IPI model is adaptable to the background with the lower noise. This paper combines weighted nuclear norm minimization (WNNM) optimization with sparse representation based on the local IPI model. The background details are described more prominently by improving the nuclear norm weighting factor. The target is much easier to detect under the specific bright clouds and ground buildings background with high noise. At the same time, post-processing with image local contrast analysis is performed to compare traditional spatial filtering and local infrared patch image model algorithms. Our method has a good suppression effect on complex noise backgrounds and achieves a higher signal to clutter ratio gain (SCRG). It could also improve the target detection rate and reduce false alarms.

Accurate three-dimensional mapping and digital twinning provides a powerful tool for effective maintenance of civil infrastructure and supports efficient future planning of new developments. Three-dimensional mapping can be efficiently performed with a Mobile Mapping System (MMS) that records geospatial data from platform-mounted sensors. However, it is expensive to continuously update datasets by re-capturing with MMS. This paper outlines a novel method allowing camera-only approaches for updating and change detection. It resolves key issues with inherent resolution differences between MMS laser scanner point-clouds and camera images. An intermediary is used to register two disparate datasets. This novel approach to synthetic camera images (SCIs) bridges the differences between MMS point-clouds and camera images and aid in coarse registration of camera images to an outdoor MMS point-cloud. SCI coarse registration precision is maximized by generating surfaces, interpolating intensity values, and reducing noise with a median filter. Landmark features coarsely register the camera image to the MMS point-cloud. The coarse registration is most precise when the whole scene is captured either from the same location as the SCI or further from the scene. Landmarks precisely detect scenes when changes are less than 20%, and foliage does not exceed 20% of the camera image.

Ultraviolet (UV)-reflectance is an essential signal of many plant species, which use wavelength-selective pigments in floral reproductive structures to determine the color of flowers and how they appear to their aerial pollinators, primarily bees. This paper presents a pollinator-inspired remote-sensing system incorporating UV reflectance into a flower detector for strawberry crops. We designed a compact, cost-effective UV-sensitive camera for aerial remote sensing over crop rows. Our camera and a deep-learning algorithm comprise our Nature-Inspired Detector (NID) system. We trained YOLOv5 and Faster R-CNN on our dataset of strawberry images incorporating the UV spectrum (300–400 nm). Our results showed that NID-based YOLO V5 outperformed NID-based Faster R-CNN in training time (0.3 vs. 4.5–5.5 hours) and mean Average Precision—mAP (0.951 vs. 0.934). We also present the field-test of our NID-based YOLOv5 system on a drone platform to validate its ability to detect strawberry flowers.

In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for the first time to retrieve sea surface salinity, and a comparison is made with the traditional artificial neural network (ANN) and random forest (RF) machine learning algorithm. The results show that: (1) Through linear fitting with the Argo salinity, the R2 of the three machine learning methods are 0.9299, 0.88 and 0.83, respectively. The corresponding RMSE were 0.2360, 0.3004, and 0.3156 psu, and MAE were 0.1816, 0.2486, and 0.2641 psu, respectively. (2) The spatial distribution of salinity of Argo and SMOS was compared with the inversion results of the model. It was found that the salinity of the sea area was lower at (83°–88°E, 24°–27°S) and (68°–72°E, 17°–20°S), and higher at 30°–35° south latitude, showing consistent with Argo. (3) The stability of the model was independently verified using the data from January to March 2020, and it was found that the R2 of the RF model shows the best stability, while the R2 of the ANN model shows the worst stability.

The objective of this paper is to identify the influencing factors of farmers’ willingness for early adoption of enhanced irrigation technologies in Tunisia. We estimate a multinomial logit model with data from 931 farmers in Central (Chebika) and Northern (Fernana) of Tunisia. Regression results reveal that early adoption is positively influenced by levels of extension service quality, trust in farmers’ associations and extension agents, farmer’s perception towards the innovation, credit access, and off-farm income. However, it is negatively affected by market access issues, risk aversion, and age. Risk, trust, and perception towards technology are important factors in driving early adoption decision. The findings imply that farmers training on water conservation technologies, financial support for innovation adoption, awareness of young farmers about the opportunities of agricultural innovation, incentives to farmers’ associations in order to improve their market access, and inclusive participatory approaches during technology generation and transfer are all accelerators of early adoption speed of innovations by farmers.

The purpose of this paper is to analyze the technical efficiency of dairy cattle farms in Tizi Ouzou region as one of the main dairy basins in Algeria. To do so, a two-step analysis was applied. First, Data Envelopment Analysis (DEA) was used to quantify the technical efficiency of dairy cattle farms. In the second step, a Tobit model to examine factors affecting farms’ technical efficiency is used. The analysis used cross-sectional data collected from 146 dairy cattle farmers. The study found that the average technical efficiency under VRS assumption is relatively high, suggesting that farms can reduce their inputs by an average of 17% while maintaining the same level of output. The study also highlights the crucial role that agricultural advisory system and traditional insemination play in enhancing technical efficiency. In this region, where cattle breeding is traditionally practiced on a small scale, increasing herd size can result in reduced performance. The study also recommended that agricultural policies should be adapted to local specificities and that a more supportive strategy should be adopted for small-scale family dairy farms instead of promoting the large farm model.

As climate change intensifies, Inuvialuit in Canada's Western Arctic are facing a rapidly changing environment and associated impacts on human health, safety, and food security. Learning to cope with these changes requires context-based and current information that can inform subsistence activities and environmental management, and no one is better positioned to acquire this information than Inuvialuit themselves. This paper presents findings from in-depth interviews conducted in 2012 with six knowledge holders and seasonal residents of Kendall Island (Ukiivik in Uummarmiutun), a traditional whaling camp situated along the Beaufort Sea coast bordering the Okeevik Tarium Niryutait Marine Protected Area. A transdisciplinary and Inuvialuit-led effort, this research documents observations of change at this culturally important site and explores how residents are adapting to changing conditions. Interview transcripts were analyzed using iterative rounds of qualitative coding in NVivo software. Findings reveal pervasive social and environmental change on Kendall Island and in adjacent harvesting areas and highlight how changing conditions are affecting residents’ lives. This study identifies benchmarks upon which to compare and evaluate subsequent changes at this site and documents Inuvialuit knowledge and perspectives that can inform local-scale environmental monitoring, management, and climate change adaptation planning.

This paper explores cultural narratives and oral histories related to two examples of traditional northern transport – long seal hunting journeys in the Baltic region, and nomadic reindeer travel in northeastern Siberia. Ample material has been collected and reviewed regarding means of non-motorised transport in the polar regions. However, scientific literature concerning the experiences and perspectives of the Indigenous and traditional community members who took part in these journeys remains scarce. This article explores the recorded narratives of two people involved in the journeys described. It assesses how these long distance travels form significance, meaning, observations and belonging to landscapes for these people. This analysis reveals that, rather than inhabiting northern ice and tundra scapes that are often described as barren and devoid of life, seal hunters and reindeer herders live in story-scapes rich in meaning. The sacred places, surrender to ice flow dynamics and ways of living ‘inside’ northern habitats that emerge from these testimonies offer a skeleton key for re-reading the misunderstood homelands of arctic and tundra communities.

The roles of Indigenous youth in environmental research remain largely unexplored with little practical guidance for achieving meaningful engagement in environmental research. This paper aims to characterize the varying types of Inuit youth engagement in environmental research conducted in Inuit Nunangat. Findings were derived from a community-engaged participatory research approach in Mittimatalik (Pond Inlet, Nunavut). Our typology of Inuit youth engagement in environmental research suggests three types of engagements: “participate”, “conduct”, and “control”. Results highlight that Inuit youth who are interested in undertaking their own environmental research projects expect to enhance their knowledge of natural and life sciences more than those who may seek short-term supportive research roles. Strategies employed by researchers seeking to enhance youth research capacity may also vary based on youth wants and expectations. Our findings suggest that there is no one-size-fits all solution. None of the engagement types identified were necessarily and inherently considered better than the others by project contributors, unlike what has been proposed in other, hierarchical, typologies. Our proposed typology contributes to a better understanding of the varying roles that Inuit youth can play in environmental research, as well as inform potential frameworks for enhancing Inuit youth engagement and leadership in research.

This Indigenous-led project aims to better understand historical and contemporary ways in which Alaska Native Peoples steward salmon and the values connected to salmon stewardship. Indigenous Peoples have stewarded Alaska lands and waters for thousands of years yet have been largely excluded from western science and management systems. In this project we utilize a participatory approach that allows for the equitable valuing of ideas and knowledges to document the breadth and depth of Yup’ik and Athabascan knowledge and governance systems in southwestern Alaska. We reshape research methodologies by centering Indigenous frameworks and research methodologies including circle dialogues and multi-generational interviews led by Indigenous scholars and students in their home communities and regions. In this paper, we share the Yup’ik and Athabascan values, knowledge, management, and governance mechanisms that can improve the long-term sustainability and equity of Alaska salmon systems. This research elevates the voices of Alaska Native salmon stewards and experts from the Kuskokwim Bay and the Kuskokwim River. We elaborate on five key themes that emerged from this research including: traditional Indigenous ways of life, Indigenous stewardship, self-determination, food and livelihood sovereignty, and ecosystem changes, and identify a more equitable and sustainable path forward for salmon and people in Alaska.

Proximity marketing is becoming an important strategy that enables linking retailers and consumers to enhance the customer experience. This marketing action aims to consider the customer location to provide a significant information at the right moment and space. Beacon technology is a real advancement in this domain of study. Beacons are a small device-wireless communication that allow detecting the presence of a person in the store through the low-energy Bluetooth signals and deliver impressive offers to visitors’ mobile phone from their area or product proximity inside the store. The objective is to offer an in time individual experience and interact efficiently with visitors. This paper presents a quantitative empirical study that aims to analyze the results of embodying the beacon technology in the large Moroccan commercial malls. For this, an online questionnaire was prepared to assemble consumers’ feedback regarding the use of this proximity marketing action. The results of this empirical study will help notifying marketers in their strategies by coming up with quantitative data collection that analyzes the customer perception of this technology, consequently, Moroccan malls may consider this relevant proximity marketing strategy in the future decisions to improve the customer experience for a long-term loyalty.

This paper focuses on the development of Ground Effect Craft (GEC). The ultimate objective of this paper is to comprehensively outline all publicly disclosed and well-known GECs. The scope of this paper encompasses a review of the design challenges and architectures associated with GECs that have been prevalent for more than 60 years. It delves into research, development, and enhancements of existing GECs throughout their history. In conclusion, considering current technological advances, GECs present substantial opportunities and potential to evolve into a viable mode of transportation, effectively bridging the gap between aircraft and ships. The incentive to overcome the economic challenges associated with fabricating and operating GECs is worthwhile, encouraging exploration and development toward a commercially viable GEC.

Drone and the Internet of Things are different technologies, actively utilised and implemented across various fields. Connectivity is the difference between the IoT drone and the regular drone. IoT drones can receive and transmit data and command signals in real-time while being controlled through the IoT network. By implementing both drone and IoT, the flying IoT (IoT-Drone) is built as a new form of IoT device. This work improves how to use the drone effectively in many aspects; data transferring, data collecting, real-time monitoring, and control. With the emergence of the Internet of Things, Artificial Intelligence, and advanced communication, the IoT drone technology has been utilized in many fields, especially in agriculture and inspection, and discussed in smart cities, logistic delivery, and the military. This paper reviews the IoT drone implementation in those fields and reviews the researchers’ discussion and studies on IoT drone optimization.

Assembly of multiple satellites enables to replace the functionality of one large satellite by multiple smaller satellites for many applications and missions such as formation flying, multi robot planetary exploration, and satellites warms. In this paper, a novel decentralized guidance and control (G&C) algorithms is developed for multi-satellite assembly in proximity operations based on Non linear Model Predictive Control (NMPC).The two-body relative motion model is utilized in designing the G&C systems. Decentralization avoids the single point of failure (i.e., the leader satellite), which enhances the robustness of the system. The NMPC is utilized because of itsability to handle the output and the input constraints. Collision avoidance is ensured by defining a quadratic constraint equation. Moreover, the optimal thrust vector is computed while considering the control input saturation. The mission is to assemblemultiple satellites into a cubic configuration, where each satellite approaches a cube vertex. Totes the algorithms’ effectiveness and to increase the level of confidence prior flight, the proposed closed-loop G&C system is demonstrated by using MATLAB andtested for a relative motion model with J2 perturbation. The simulation results showsmooth conversion of all satellites to the target while satisfying the input and outputconstraints.

This paper presents an innovative device-to-device (D2D) network adapted to optimize the rail experience for both passengers and carriers. The meaning of the proposed system is a smart wearable device specially designed for wearers. This device serves two purposes, enabling fair dynamic pricing based on baggage weight and length of trip, and facilitating health monitoring by continuously checking vital signs such as heart rate and pulse. In case of an emergency, the device is equipped to send timely notifications to both the passenger mobile application and the railway service. For passengers, the D2D network offers a variety of proximity services that enhance their rail experience by providing real-time location information to various facilities such as taxis, hotels, restaurants and internet hubs. An integrated application has been developed to further enhance the passenger experience. This app helps with porter booking, fare estimation, taxi booking and hotel search, contributing to a smooth travel experience. The portable portable prototype is built on state-of-the-art technology that includes modules such as ESP32 and pulse sensors for connectivity, monitoring and pricing. Rigorous tests have demonstrated the feasibility of a D2D network in implementing key functions such as concierge health monitoring, location-aware passenger services, and fair pricing mechanisms. While the results show promising progress, there are recognized areas that need further refinement. These include improving the accuracy of remote monitoring, optimizing health monitoring algorithms and strengthening network security measures. This paper presents a robust D2D network architecture that benefits both carriers and passengers at train stations. Addressing certain pain points and using technological innovations. The proposed system shows its potential to significantly improve the overall railway experience.

This manuscript presents the experimental study on low-cost sensor sensitivity of inspection quadcopter’s anti-collision system. This inspection quadcopter is designed to be operated in aerodrome or hanger for the inspection of wide body aircraft for lightning strikes. One of most important features that the inspection quadcopter must equipped with is anti-collision system. The anti-collision system will ensure the inspection quadcopter maintain a safe distance from the aircraft and other structures during the inspection process. As one of the project’s objectives is to design and develop an anti-collision system for the inspection quadcopter, it is aimed at developing using low-cost sensors and components. Experiments were done to choose the best low-cost sensor to be used as the rangefinder in the inspection quadcopter’s anti-collision system. The sensitivity of the low-cost sensors were tested by comparing the measured distance and the actual distance of obstacles. The percentage error reflects the sensitivity of the sensor under certain circumstances. The second objective of this research is integrating the best low-cost sensors the anti-collision system. The anti-collision system was programmed using Arduino IDE software. Lastly, the third object of this research, which is to experiment the performance of anti-collision system was achieved by performing the ground test on the anti-collision system of the inspection quadcopter. The performance of anti-collision was shown on the PWM correction signal generated by the anti-collision system. In conclusion, this paper shows the engineering approach on an industrial problem on developing an anti-collision system using low-cost sensors.

Current aircraft was designed with winglet at the tip. Winglets reduce the drag and improve fuel efficiency and range. The winglets have been in use for nearly 90 years, but it have been remained fixed. This paper presents the effects of winglet Cant angle on the aerodynamic characteristics of the wing. Winglet Cant Angle (WCA) is the angle between the vertical exis and the winglet. Computational Fluid Dynamics (CFD) has been used to simulate the Cant angles at constant angle of attack of 0⁰. In this paper the Cant angle was varied from 105⁰, 88⁰, 75⁰, 60⁰, 50⁰, 45⁰, 35⁰ and the simulation works were performed at three different Flight levels of Sea level, 10000 and 15000 meters. The analysis analysis at 100 m/s velocity shows that Cant angle of 45⁰ is the best performing Winglet while Cant 60⁰ is the lowest performance in overall lift to drag Ratio. Similar results were obtained when Turbulent Kinematic Viscosity were measured at 20 m behind the wing. Flow visualization also showed the similar results Cant angle 450 is the best performance compared to other angles. For the best performing winglet, the lift to drag Ratio was lowest for perpendicular crosswinds. This study shows the effect of only winglet cant angle, however other factors involved to impact the performance of winglet are, Winglet height, winglet sweep angle, Winglet Cant angle, Winglet Toe angle, and crosswinds. To implement the best overall drag coefficient winglet, the optimization must be done mid-air. While calculating the parameters, the best most optimized winglet can be chosen for cruising flight level.

India’s contributions to the development of alternative energy sources are substantial. India has almost tripled the capacity of its renewable energy installations over the last five to six years, with coastal wind and solar photovoltaic (PV) installations accounting for the vast bulk of this growth. Since land scarcity is a big challenge nowadays, it is an excellent idea to construct solar plants on rivers, lakes, and dams, which are otherwise idle water bodies. The progressive depletion of fossil resources and the ever-increasing need for electricity both pose serious obstacles to progress. The electricity generated by floating solar photovoltaic systems is significant. India has completed a number of large-scale projects, including the 100MW Ramagundam, Telangana project and the NTPC Simhadri Solar Plant. This investigation highlights the potential benefits and challenges of these plants. Feasibility analysis of 5MW FSPV at Somasila Dam, Andhra Pradesh is also done in this paper. Additional to this, the article gave the economic and technological analysis of floating solar photovoltaics, in addition to discussing several case studies.

Due to the spread of corona virus, healthcare systems are facing significant strain, with an increase in demand for medical supplies and healthcare professionals. Many solutions were proposed in that sense, especially with the emergence of the Internet of Things (IoT) as a powerful communication and information technology. This paper proposed one of the approaches targeting the environmental health as a whole. This is especially to decrease the pressure on health systems during crisis times. It is mainly about the creation of a real-time corona virus monitoring system. It is in the form of a wristband that measures the blood oxygen levels and the body’s temperature. These measurements are essential in determining a patient’s health status and the need for immediate ICU intervention. Additionally, the wristband has a tracker that can be used to monitor the patient’s adherence to social distancing guidelines. Once the patient’s parameters are above a certain threshold, the doctors and health professionals are notified instantly. This device is very optimal in terms of energy consumption. It is considered as a promising tool in handling the spread of the virus by providing patients and healthcare providers with instant access to healthcare services, regardless of their location.

Cities can identify trends and needs by analyzing data from IOT devices and sensors. Wireless sensor networks (WSNs) are generally used in numerous field of interest to detect phenomena, process the detected data and take appropriate actions. Due to the limited sources of stationary sensor nodes, WSNs must work with mobile robots such as unmanned aerial vehicles (UAVs) and Unmanned Ground Vehicles(UGVs) in their development. Consequently, it is necessary to maintain the energy efficiency of UAVs since they are approved by data centers so they must be deployed and charged frequently. Researchers worked on this area and came across various schemes for efficient data collection with minimal energy usage. This paper gives a structured and extensive review of different schemes proposed by different authors around the globe. This paper also evaluates the datasets utilized by different researchers for data collection method. Finally, the challenges involved in data collection method and power consumption are highlighted in conclusion section. We have also listed the future work in conclusion section.

In recent days, Indoor Air Quality (IAQ) has become a matter of interest for researchers from both scientific and industrial community. During pandemic situation public settle more time at indoor in place of outdoor environment. With rapid growth in science and technology people have progressive necessity for active facility parameters with respect to indoor temperature, humidity and wind movement etc. Asthma, lung cancer, hypertension, cardiovascular issues, and respiratory illnesses are just a few of the chronic health issues that have been linked to frequent exposure to hazardous material in enclosed spaces. However, real-time monitoring technologies are being adopted in smart city projects to identify and remedy such unfavourable conditions, ultimately improving the general quality of life. This paper surveyed the current proposed IAQ monitoring systems in terms of Internet of Things (IoT). In addition, the research work highlights composition of diverse monitoring systems, research summary, challenges together implying future directions for the researchers. The current level of knowledge on the close relationship between IoT and IAQ is presented in this research.

Developing a team of business risk assessors in pursuit of establishing an aero-manufacturing company is necessary in producing desired outcomes. Quantitative assessment had proven to be reliable and accurate, however this comes with a cost. Therefore, this paper attempts to discuss the elements of risks and threats involved in establishing an aero-manufacturing business post COVID-19 pandemic, in a qualitative manner. It acts as a preliminary report, and a qualitative review of existing literatures related to the airlines and aerospace industry. The risks and threats data and justifications are then categorized and compiled according to Michael Porter’s Five Competitive Forces Model (FCFM) in an attempt to visually map the threats and risks involved with the business, either directly or indirectly. Based on the discussion, the overall impact is structured into a comprehensive table which will give interested parties an overall view of the assessment.

Efficient traffic flow prediction is paramount in modern urban transportation management, contributing significantly to energy efficiency and overall sustainability. Traditional traffic prediction models often struggle in complex urban traffic networks, especially at multi-intersection junctions. In response to this challenge, this research paper presents a pioneering approach that not only enhances traffic flow prediction accuracy but also indirectly supports energy efficiency. This study leverages deep learning techniques, specifically the Gated Recurrent Unit (GRU), to analyze traffic patterns simultaneously at multiple intersections within a city. By treating the entire traffic network as a distributed system, the model provides real-time predictions, allowing for better traffic management and reduced fuel consumption. Moreover, the incorporation of data fusion techniques, which integrate data from various sources, including traffic sensors and historical traffic information, bolsters the accuracy and robustness of predictions. By predicting traffic flows with precision, this research aids in optimizing traffic signal timing, reducing congestion, and ultimately promoting more efficient transportation systems, which, in turn, reduces fuel wastage and emissions. This study, therefore, advances intelligent transportation systems and offers a promising pathway toward improved energy efficiency in urban mobility.

The paper obtained during the study was mainly obtained from wheat straw cellulose and Jerusalem artichoke cellulose, which contained chitosan of various concentrations. An increase in the mechanical properties of the obtained papers was observed in samples using the substance chitosan. A 1.0% chitosan was added to the paper, mainly based on various plant celluloses. It should be noted that the obtained paper products were used without bleaching due to the fact that it was recommended to use them for wrapping paper. The youngest results were observed in samples prepared from a composition of wheat straw and artichoke pulp. From the fiber morphology, it was observed that the sized paper fibers became stronger, denser and smoother. Keywords: chytazone, Natural polymers, fibers, cellulose. Biocomposites of chitosan (CS)/cellulose nanocrystals (CN) were prepared by using solution casting method. Influences of solution preparation method and CN content on the properties of composites were investigated. Mechanical stirring/ultrasonication or micro fluidization were used to disperse nanocrystals in the chitosan matrix. The prepared nanocomposites were characterized by FTIR, XRD, SEM, DSC, TGA, TMA and contact angle measurements.

This review article, “Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management,” explores the transformative impact of digital technologies in agriculture. It focuses on the integration of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) in enhancing sustainable farming practices. IoT technologies enable real-time monitoring of agricultural conditions using sensors and drones, while Cloud Computing provides robust data processing capabilities. AI leverages this data for predictive analytics in crop yields, disease detection, and automation of farming processes, optimizing resource use and adapting to climate change. The paper also examines the synergy of renewable energy sources with AI to power smart farming systems, proposing innovative solutions for energy-efficient, AI-driven agriculture. It highlights AI’s extensive role in improving crop management, from seed selection to soil analysis and crop health monitoring, thereby increasing yield quality and fostering sustainability. Furthermore, the article discusses the significance of space technologies in smart agriculture, showcasing advancements like drones and big data analytics. These innovations are crucial in developing economies, addressing global challenges like food security and contributing to economic growth. This review underlines the necessity of AI and IoT in revolutionizing agricultural methodologies, ensuring efficiency and sustainability in the face of a rapidly growing global population.

Since the dawn of civilization, grain storage has been necessary to ensure food security. Grain storage has a more extended history than one might think and is connected to farming history. India is the next-largest producer of wheat, followed by China. In terms of food security, storage is primarily intended to keep grains for a longer time. It guards against degradation, exposure to the outside environment, and potential microbe and insect attacks. Silo, CAP storage, and Godown storage were used to test the wheat’s temperature and moisture content. To obtain the reading, a temperature sensor and a RH sensor were installed within the silo. Grain storage in underground pits was employed in the past and is still used in some regions today. The holes were excavated and measured at 1 m in diameter and 1 m in depth, with a mixture of 3:3:1 cow dung, straw, and sorghum, amounting to around two quintals each. A total of 10–20 wheat samples kept in the silo were examined for fungus and fungal development. The samples were placed inside a glass bottle filled with sterile water and closely watched to ensure that no fungal source entered the container used for collection. Each colony was isolated and purified separately once it emerged to obtain an isolated single fungus. The pure fungus was positioned and examined using imaging and organized on figurative plates under a microscope. The germination percentage in various storage arrangements was tested for seed viability using wet paper towels. For the following three months of storage, the temperature within the silo increased rapidly from 28°C to 35°C. The grain retained its quality during the first three months, and there was little to no infestation. The lower ambient temperature and measured relative humidity of 95–97% are to blame for this. Before drying, the moisture content in the silo increased from 11 to 17%. During the storage period from April 2006 to November 2006, the moisture content in Godown bag storage increased at the same pace as in the silo. Wheat moisture content rises in direct proportion to its storage time. This is thought to be the result of an increase in insect population brought on by the respiration of insects and wheat, which helped raise moisture content. All of the isolated fungi in the silo were discovered to produce a significant number of spores and conidia on their surfaces. Aspergillus is a harmful toxin-producing fungus well-known for damaging various seeds, grains, and nuts. It is also famous for its destructive properties.

Sustainability has been the concern of all nations during the last years. In fact, it is about planning actual needs while keeping the ability to provide the future generations’ needs. It is quite challenging since it combines social, economic and environmental factors in order to create a stable society. This paper tackles an important topic, which is the Sustainable Development Goals, how the use of big data can help in their achievement and assessment. The focus of this work will be on two important SDGs which are SDG 6 and 7. We will handle SDG 6 which is about Clean Water and Sanitation, and SDG7 that concerns Affordable and Clean Energy. We will review the main and recent technologies of big data that are used in both fields. This is because big data technologies have shown excellent results in the evaluation and construction of prediction models to achieve sustainability.

Implementation of augmented reality (AR) in the aerospace industry could initiate a new path in terms of technological advancement. AR is widely known as a new immersive world made up of real and virtual features that can generate a humanly intuitive interaction with computer-based systems through gadgets. In this study, the repair manual for Boeing 737 has been improvised by using the software that can create an AR program system, which is Unity and Vuforia SDK. The AR repair manual system is developed in two methods, marker-based and markerless, to see which one has better performance and reliability in the industry. The application is believed to be a great help in realising a more convenient working environment and in situations where it requires less human contact, such as in the Covid-19 situation. The performance of the developed AR repair manuals is evaluated and compared with the existing repair manual. The AR repair manual is believed to have better performance in terms of time, efficiency and working environment than the paper-based manual. The study on the development of the AR repair manual and its implementation in the aerospace industry will be discussed in this paper.

The gut microbiota (GM) is a complex microbial community that exerts a considerable influence on human health, and its accurate characterisation has become essential to understanding its roles and implications in various medical conditions. Recent technological advances have paved the way for analytical methods that can uncover this complex microbial community. For these reasons, this review describes the methods used by research teams to discover and understanding the impact of GM in our environment. This paper explores the first phase of collecting and preserving samples, highlighting the impact of storage methods on sample stability and the reliability microbiological analyses. Then, it examines various methods for analysing GM. This diversity of approaches adapted to the specific objectives of each study, whether to characterise diversity using metagenomics, quantify specific micro-organisms using real-time PCR, or use techniques based on microbial targets. These methodologies promise new perspectives for understanding the physiological impact of the microbiota. Finally, concrete examples from Moroccan studies illustrate the application of these techniques to the characterisation of GM. By providing practical advice, this article guides researchers in the judicious choice of methods adapted to their research objectives and budgetary constraints, thereby contributing to the advancement of knowledge about the GM.

This article investigates the significant influence of smart radios on the energy efficiency of Wireless Sensor Networks (WSNs). It explores the incorporation of intelligent radios as a crucial element to enhance WSNs’ sustainability, providing a novel approach to tackle energy-related challenges. Smart radios are essential for prolonging the lifespan of sensor devices and reducing the environmental effect of WSNs by autonomously adjusting communication protocols and optimizing energy consumption. This paper also highlights the differences between 5G and 6G technologies within the WSNs framework. While 5G brought improvements in data speed and connectivity, 6G represents a significant progress by prioritizing energy efficiency as a fundamental goal. This shift represents a fundamental change, with a primary focus on achieving extremely low energy consumption and ensuring the sustainable operation of WSNs. The paper examines crucial technological factors enabling 6G to outperform its predecessor, establishing it as a revolutionary force in the field of wireless communication for sensor networks. This research illuminates the crucial function performed by smart radios in connecting the desire for energy efficiency with the changing environment of wireless sensor networks.

The utilization of neural model techniques for predicting learner performance has exhibited success across various technical domains, including natural language processing. In recent times, researchers have progressively directed their attention towards employing these methods to contribute to socioeconomic sustainability, particularly in the context of forecasting student academic performance. Additionally, educational data frequently encompass numerous categorical variables, and the efficacy of prediction models becomes intricately tied to sustainable encoding techniques applied to manage and interpret this data. This approach aligns with the broader goal of fostering sustainable development in education, emphasizing responsible and equitable practices in leveraging advanced technologies for enhanced learning outcomes. Building on this insight, this paper presents a literature review that delves into the use of machine learning techniques for predicting learner outcomes in online training courses. The objective is to offer a summary of the most recent models designed for forecasting student performance, categorical coding methodologies, and the datasets employed. The research conducts experiments to assess the suggested models both against each other and in comparison to certain prediction techniques utilizing alternative machine learning algorithms concurrently. The findings suggest that employing the encoding technique for transforming categorical data enhances the effectiveness of deep learning architectures. Notably, when integrated with long short-term memory networks, this strategy yields exceptional results for the examined issue.

It is widely recognized that postharvest losses in agriculture have a significant impact on the sustainability and security of the world’s food supply. This essay aims to investigate the reasons behind these losses, their effects, and possible solutions. Several factors, such as insufficient infrastructure, inadequate storage facilities, improper handling procedures, and limited market access, are analyzed as potential causes of postharvest losses. The discussion primarily focuses on the negative consequences of these losses on farmers’ livelihoods, food supply, and resource utilization, as well as the economic, environmental, and social implications. The paper suggests several strategies to minimize postharvest losses, including strengthening value chains, promoting appropriate technology for processing and storage, enhancing farmer education and training, and upgrading transportation and storage infrastructure. Additionally, the significance of market processes and policy interventions in reducing losses is highlighted. By reducing postharvest agricultural losses, it is expected that food security, resource efficiency, and sustainable development will be improved.

— In this paper, we’ll keep an eye on the air conditioner’s gas leaking. Frequently, a minor gas leak can cause a gas concentration to grow up explosively over time. A small amount of explosive gas combined with fire can cause significant damage to our homes and loved ones’ lives if this gas leak is not discovered in time. By the usage of sensor helps to keeps an eye on gas leaks in the air conditioner. Gas presence in a space can be determined with a gas sensor. The microcontroller for this paper, an Arduino UNO, is utilized to process the sensor input. When a gas leak is discovered, the system alerts the user by sending an SMS through the GSM module and playing a buzzer sound.

This review article titled “Revolutionizing Agriculture: The Impact of AI and IoT” delves into the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It encapsulates the burgeoning wave of digitalization and its implications for data-driven farming practices. The paper comprehensively reviews the advancements in digital agriculture management, highlighting the integration of AI, IoT devices, machine learning, and sensor technology in enhancing farming efficiency and productivity. Emphasis is placed on the use of robotics, drones for crop monitoring, and smart irrigation systems, which leverage real-time data for optimized agricultural operations. The role of high-speed 5G networks in enabling these smart farming solutions is also discussed, particularly their impact on data transmission and connectivity in rural settings. Challenges and opportunities in implementing Smart Decision Support Systems (SDSS) in developing countries are explored, focusing on real-time soil analysis and decision-making aids for farmers. The review further discusses a proposed cloud-based system for remote farm monitoring, employing AI-based models for efficient data processing and improving farming practices. This article aims to provide a nuanced understanding of the current and future potential of AI and IoT in reshaping agricultural practices, addressing global food security challenges, and supporting sustainable development.

This review article, “Harvesting the Future: AI and IoT in Agriculture,” presents a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It synthesizes a range of studies to showcase how AI and Machine Learning (ML) algorithms are revolutionizing crop management, precision agriculture, and supply chain efficiency. Utilizing data from various sources like sensors, drones, and satellites, these technologies enable optimized resource use, enhanced crop yields, and better livestock health monitoring. The review highlights the role of IoT in agriculture, particularly its benefits in easy installation, reduced maintenance, and energy harvesting for device sustainability. It explores the integration of IoT with big data and knowledge-based systems, addressing key challenges in farm data management. Additionally, the paper delves into the growing adoption of smart agriculture techniques, driven by cost-effective IoT sensors and AI advancements. These technologies facilitate efficient resource management, predictive analytics, and autonomous farming, thereby modernizing traditional agricultural practices. The review also discusses the broader social implications and future trends in the adoption of smart agriculture systems, describing their significance in enhancing agricultural productivity, sustainability, and profitability.

The paper combines and reviews the experimental studies on performance of various potential nanofillers elements incorporated in the epoxy polymer resin that are used as protective coating for metal substrate. The epoxy composite formed by dispersing the nano sized secondary elements namely silica oxide, aluminum oxide, titanium oxide, cerium oxide, graphite oxide and nano clay in the epoxy matrix are studied in the perspective of corrosion resistance, adhesion strength and dispersion properties. The paper encompasses the study on epoxy nanocomposite with single element reinforcement particles and also the hybrid of two different elements that are used as reinforcement fillers. The discussion witness’s various corrosive protection assessing techniques such as electrochemical studies, pull of adhesion test, salt spray test and their results are studied and analyzed in the prospect of overviewing the performance of nanofillers in epoxy matrix.

The country’s economic development and people’s lives require reliable transportation and logistics services. Therefore, road maintenance policies and techniques must be more comprehensive to ensure transportation infrastructure operations’ efficiency. The inefficiency of road maintenance, among others, is due to the failure to identify and predict the level of damage that can cause fundamental data anomalies. Therefore, the current scientific development of road maintenance must be able to take advantage of the convenience of technology. Widespread technology implementation on the Internet of Think (IoT), big data, and data mining (DM) can be a solution to developing a better pavement management system. However, implementing these technologies is challenging due to the weak conception and availability of currently available resources. This paper offers a pavement system management implementation concept that can be developed in Indonesia to improve the existing system. The study results show that IoT, big data, and DM are believed to support a more intelligent and comprehensive pavement management system. The concept offered consists of three main things: identification of damage and 3D pavement modeling, data analysis, decision support systems, and collaboration of intelligent solutions in implementing maintenance in the field. This paper is equipped with an illustration of the concept of road maintenance based on IoT, big data, and DM, which can be implemented intelligently and simply in Indonesia.

This paper discusses the use of well pattern compaction technology by drilling small-diameter wells in the conditions of the N field and identifying its effect on the oil recovery factor. An analysis of the effectiveness of the results of grid compaction by drilling using smalldiameter wells in the conditions of the N field is carried out, and an increase in the efficiency of field development by compacting the existing development grid by drilling small-diameter wells is also considered. Research methods include systems analysis methods; methods of mathematical modeling; mathematical programming; mathematical statistics; cluster analysis; numerical methods; computational experiments; database design theories.

This paper aims to select the best semi-empirical model for thinly sliced potato drying under active mode indirect solar dryer with variations of the exhaust air velocities. The exhaust air velocities to remove the vaporized moisture inside the drying chamber were set at 0.2m/s, 0.4m/s, 0.6m/s, and 0.8m/s. The solar intensity, temperature and relative humidity were measured. The Sigmaplot software was used to select the best thin layer drying model for sliced potatoes drying under indirect solar dryer assisted with a solar accumulator and exhaust fan. From the result, drying at 0.2m/s shows a significant drying performance with reduced mass percentages at 69%. The lowest the exhaust air velocity, the better reduction of the mass percentages and the higher the evaporation rate. Consequently, a Rational four (4) parameter was selected as the best of all the drying models, according to r2, RSME, and x2. This study gives a useful understanding of the significant effect of the variations of the exhaust air velocities on the drying performance.

Nusantara, the planned new capital city of Indonesia, aspires to be a green, smart, and sustainable city. This paper explores the political, economic, and social dimensions of Nusantara's development, focusing on the strategic decision to relocate the capital from Jakarta. The research analyzes the challenges and trade-offs involved in balancing environmental conservation with the need for urban development. It evaluates the policies and initiatives implemented to achieve the vision of a smart and sustainable city, considering the integration of technology, digital infrastructure, and data management. Drawing on comparative analysis, the paper examines historical parallels and precedents in capital relocation projects worldwide to derive lessons for Nusantara's development. The findings highlight the importance of environmental governance, public participation, and stakeholder engagement in achieving a sustainable and inclusive urban environment. The paper concludes with recommendations for policymakers and identifies avenues for future research in the field of sustainable urban development and capital relocation.

The pollutants from the environment can also degrade the surface of the glass, which result in loss of transmittance of the glass surface. This necessitates the formation of a dedicated crew for the correct maintenance of glass furniture, notably in the Middle East. In this paper, we describe sol-gel spin coating of titania thin films on glass substrates, which may function as a hydrophilic coating by spreading water molecules across the substrate and removing pollutants and other dust particles, hence eliminating the need for maintenance and wipers. The fabricated coating was tested with X-ray diffractogram (XRD) and evidenced anatase nature of titania and amorphous nature of silica at Bragg angle 25° in the coating. Fourier transform infrared spectroscopy (FTIR) investigations endorsed Ti-O-Ti and Si-O-Si functional linkages at wavenumbers 551, and 957 cm-1 demonstrating the presence of titania and silica in the as-developed coating. Cross sectional field emission scanning electron microscopy (FESEM) investigation evidenced the formation of composite titania-silica film on the glass substrate with a thickness about 36 nm. The coating transmittance was studied by ultraviolet-visible and near-infrared (UV-Vis-NIR) spectroscopy and found 95% transmittance in the visible region. Moreover, wetting study utilizing the sessile drop technique yielded a 74° contact angle when the water droplets were interacted.

This paper’s primary goal is to investigate and analyze how a particular PWM technique enhances the performance of PMSM drives. The objective of the analysis is to reduce torque ripples, which are fluctuations or variations in the motor’s torque output. Reducing these torque ripples involves combining the Hysteresis Control and Space Vector Pulse Width Modulation (SVPWM) techniques. Hysteresis Control is a method that compares the actual value of a parameter (in this case, torque) with a reference value and takes appropriate control actions based on the comparison. SVPWM, on the other hand, is a sophisticated technique used for generating pulse width modulation signals that can control the motor precisely and efficiently. The research findings suggest that combining the Hysteresis Control technique with SVPWM in the PMSM motor drive makes the system more effective and efficient. This combination likely reduces torque ripples, leading to a smoother motor operation. Using both techniques together probably enhances the precision and stability of the motor control system.

Currently, China is in the period of rapid urbanization and gradual improvement of the rail transportation network. With the increasing population in towns, the construction of many cities has become blind and disorderly to meet people’s housing needs, resulting in traffic congestion, uneven distribution of resources, and other “big city diseases” more serious. From the urban construction experience of developed countries, the mode that urban rail transportation and urban development complement each other and develop together, that is, the regional-wide TOD construction, is an effective way to promote sustainable urban development. This paper focuses on how to select the stations of the rail transit for priority development of TOD projects (TOD development advance sites) within the whole area of the town. When selecting stations, the investment potential and regional driving ability of each station are taken into consideration to make a comprehensive evaluation of each rail transit station, so that the TOD development advance sites can drive the gradual development of its surrounding areas and finally form an urban renewal pattern of interoperability and integrated development of the whole area. With this method, we can effectively solve the current situation of blind and disorderly construction of TOD and various problems in urban development in China, and help to build efficient, green, and livable cities in various places.

The paper presents the results of a study on 36 corn genotypes of Russian and foreign breeding origin under the conditions of the south of the Russian Far East. The research goal was to select corn varieties (hybrids) with the highest resistance to fungal pathogens and Ostrinia furnacalis Guenee for further use as breeding material. Diseases and pests cause significant damage to corn under the conditions of Primorsky kray. The research identified the following specimens that had the highest tolerance (the frequency of occurrence did not exceed 45%; the degree of disease progression was below 10%) to northern corn leaf blight (Helminthosporium turcicum Pass) and Fusarium ear rot (Fusarium moniliforme Scheldon): Krasnodarsky 291, LONG KEN 134, LONG KEN 1108, LONG KEN 1110, and KENDGY 717. Ostrinia furnacalis Guenee caused little damage (the degree of damage was 0.2-0.5) to Mashuk 175, Mashuk 250, Mashuk 300, N’yuton, KENDGY 717, DEMEYA 4, KENDGY 515, KENDGY 510, and LONG KEN 1110. High yield (7.2-8.8 t/ha) characterized Mashuk 250, N’yuton, Stella, KENDGY 717, KENDGY 510, DEMEYA 4, KENDGY 515, LONG KEN 134, and LONG KEN 1110.

One SSY type equipment clamp serviced in a 500 kV station was found to be cracked. In this paper, the crcaked cause and mechanism is investigated by means of macroscopic morphology observation, fracture surface morphology observation, X-ray digital detection, metallographic observation. The results indicated that the main reason for the fracture of the equipment clamp this time is that the terminal board is a cast part, which contains a large number of casting defects such as porosity and shrinkage, resulting in a decrease in its effective cross-sectional area and load-bearing capacity. In addition, insufficient modification treatment of the terminal board led to insufficient toughness due to needle or rod-shaped eutectic silicon in the organization. Finally, under the combined action of dynamic load and self weight caused by the swinging of the wire in the wind, the terminal board was brittle fractured along the bolt hole with obvious stress concentration effect.

The paper presents an assessment of the development prospects and transport accessibility of organic production zones in the regions of Siberia based on data from state information systems of the Russian Federation and open information systems of commercial digital aggregators. The assessment was carried out with the use of technologies and analytics of the ArcGIS 10.6 program. The Novosibirsk, Omsk, Barnaul, and Tomsk agglomerations have concentrated clusters of investment projects for the processing of agricultural products. The eastern part of the macrozone is characterized by more focal investment activity, the largest of which is the eastern agglomeration of Krasnoyarsk. Investment projects in Kuzbass and the Irkutsk region are dispersed along the main transport routes. The republics of Altai and Tyva and the northern municipalities of the Tomsk and Irkutsk regions remain on the periphery of active transport contacts. Inside the highway part of the region there are areas of low efficiency in the use of transport potential, which makes it possible to increase the investment attractiveness of individual agricultural locations based on a combination of interspecies interaction.

The purpose of this paper is to the results of experiments to study the effect of vermifertilizers on the agrochemical properties of soils and the quality of pepper in Yakutia. Experiments on testing vermifertilizers were carried out on peppers of the “Nezhnost” variety. Experiment scheme: 1 variant - was only with soil; variant 2 in addition to this vermicompost - 100 g; variant 3 vermicompost - 300 g; variant 4 vermicompost - 500 g; variant 5 - the biological product “Baikal EM 1” - 100 ml per well with 100 g semirotted manure. Vitamin C and nitrate content was studied in fruits. During the experiments, it was found that the optimal doses of vermicomposts made it possible to increase the content in soil mixture of nitrogen by 0.11%, P by 16.37%, K by 8.02% and organic matter by 0.34%. At the same time, vitamin C increased in the fruits of pepper and the content of nitrates decreased. The best result was obtained by the variant where 500 g of vermicompost was used, the second-best result was for the biological products “Baikal EM-1”.

In this contemporary era of rapid progress, the global demand for energy has reached unprecedented levels, placing considerable strain on existing energy supplies. To address this challenge, synthetic or substituted natural gas (SNG) has emerged as a groundbreaking energy source attained through the methanation reaction of hydrogen and carbon monoxide (CO). This paper unveils a successful synthesis method for fibrous silica zirconia (FSZr) exploiting the microemulsion procedure, subsequently applied in the CO methanation process. The catalyst underwent comprehensive characterization using advanced techniques, including Fourier Transform Infrared (FTIR) spectroscopy, Field-Emission Scanning Electron Microscopy (FESEM), x-ray diffraction (XRD), and N2 adsorption-desorption. The experimental results clearly demonstrate the exceptional catalytic performance of FSZr when compared to commercially available ZrO2. At a temperature of 500 ºC, FSZr achieved a CO conversion and CH4 yield of 20.76% and 11.52%, respectively. The remarkable achievements are credited to FSZr’s distinct fibrous structure, expansive surface area, and exceptional basic characteristics. The heightened surface area facilitates better access to reactive sites, while the strong basic properties enable easier adsorption of the reactant. These combined factors significantly enhanced the effectiveness of the CO methanation procedure. These findings underscore the significance of fibrous morphology in zirconia catalysts for CO methanation, presenting a promising avenue for further research and insights into meeting the global energy demands efficiently.

The paper presents a comprehensive study on the ash handling system upgrade from a hydraulic conveying system to a pneumatic system in a 550 MW coal-fired power plant located in Taiwan. The ash pond used to dispose of ash from the economizer and air preheater is almost full due to having been in service for more than forty years. An alternative location to dispose of the ash is sought. The design process of finding the new location and the equipment to be used is explored. Some options for the new economizer and air preheater ash disposal site, including building a new silo or using an existing silo, are taken into consideration in terms of design, cost, and timing of execution. The project’s execution procedure is reviewed, taking into account aspects of system integration, equipment installation, and commissioning. The primary objective of this paper is to detail the design, implementation, and outcomes of the ash handling system upgrade. This upgrade is accomplished in an effort to enhance operational efficiency, reduce maintenance requirements, and improve environmental compliance. The study explores the motivations behind adopting pneumatic conveying technology, focusing on its advantages over hydraulic systems, such as reduced maintenance needs and improved dust control. The case study provides insight into the lesson learned and recommendations for similar ash conveying system upgrades in other coal-fired power plants.