Search

Abstract This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.

Abstract Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system.

Abstract With an aging population and an increase in chronic sickness, healthcare systems are under increasing pressure. Internet of Things (IoT) technology has recently garnered a lot of interest for its ability to alleviate this burden. The IoT is a network of interconnected wireless digital devices that can gather, transmit, and store data automatically, without the need for human or computer intervention. In order to better anticipate health problems, diagnose, treat, and monitor patients in and out of the hospital, the IoT offers numerous advantages that could streamline and improve health care delivery. As a matter of urgency, decision-makers and heads of state around the world are enacting policies to provide healthcare services through the use of technology. Thanks to recent advancements in the IoT, healthcare has been able to evolve at a rapid pace. Research on Internet IoT uses in healthcare and newly developed emergency equipment are summarized in this article.

Abstract The judiciary is a foundation of democracy, upholding the rule of law and protecting rights. Efficient court administration is vital for public trust, justice, and timely proceedings. Currently, courts face challenges such as inconsistencies, adjournments, and absence of transparency, undermining the justice system. Traditional manual processes and paper-based documentation cause significant backlogs, slow resolutions, and limited public access. As case volumes and complexities rise, modernizing court administration through digital transformation is progressively critical. Currently, many countries are integrating technologies in the courts for its administration and other activities. In recent years, courts and judges have been subjected to pressure to improve performance, uplifting judicial effectiveness to a top priority. Subsequently, several countries have integrated simplification and digitization strategies in judicial initiatives to enhance court efficiency. Switzerland’s Justitia 4.0 and Brazil’s PJE are notable initiatives that focused to strengthen court administration through digitalization. These aspects motivated this study to examine the detailed integration of industry 4.0 technologies such as the Internet of things, cloud computing, blockchain, big data analytics, robotics, drones, Metaverse, digital twins, and artificial intelligence for court administration with digitalized infrastructure. According to the study, integrating these technologies in less complex cases helps minimize expenditures and save time, making to resolve cases conveniently, efficiently, and superiorly. The study also identified the challenges and issues associated with industry 4.0 technologies such as evidence gathering, evidence preservation, robot judges for pre-judgment analysis, and judgment delivery, which future studies need to be explored.

Plant diseases are increasingly becoming a serious threat to food security as well as sustainable agriculture sets. Traditional methods for detecting crop diseases, especially in Finger Millet, are cumbersome with chances of error. Therefore, automated solutions are necessary. This work proposes a comprehensive framework for detection and prediction of the disease in Finger Millet leaves using a combination of Graph Networks, Dyna Networks, Autoencoders, and Recurrent Neural Networks. Here, the proposed model brings about both spatial and temporal dynamics in the progression of the disease for earlier and more accurate detection. The core contributions revolve around basic use of Graph Networks in encoding spatial relationships within the leaf structures and application of Dyna Networks in incorporating time-series data for predicting disease progression. Autoencoders allow efficient feature extraction, which is compressing high-dimensional data to meaningful representations, and RNNs allow for plant health to be monitored in real time. Also, the proposed model surpasses the current models in terms of accuracy reaching 95.6 % precision, recall, and F1-score of over 94 %, within those diseases such as Powdery Mildew, Blast, and Leaf Spot. The computational efficiency was also demonstrated to be superior. As shown, training time was reduced to 12 h and prediction time to 0.035 s per image samples. The result would promote the model in actual time application of precision agriculture through more precise, efficient, and scalable solution of plant disease management.

AbstractThis study explores the potential benefits of integrating Industry 4.0 technologies into educational settings, focusing on Artificial Intelligence/Machine Learning, Augmented Reality, Big Data, Blockchain, Cloud Computing, Internet of Things, Metaverse, Robotics, and Virtual Reality. The aim is to develop learning environments that are more responsive to the diverse needs of students and better equipped to meet those requirements. In today’s educational system, diversity manifests in various forms, including differences in learning styles, talents, interests, and cultural backgrounds. To effectively accommodate students with diverse profiles, educators must adapt and adjust their learning and teaching approaches accordingly. This study proposes that the incorporation of Industry 4.0 technologies into educational settings may offer adaptable solutions to meet the varying needs of students. It highlights the potential advantages of these technologies in fostering inclusive learning environments for modern education. By utilizing the power of technology, educators can create personalized learning experiences that cater to the unique needs of each student, thereby promoting inclusivity and equity in education. In conclusion, this study underscores the importance of embracing a more inclusive model of education that harnesses the potential of technology to enhance learning outcomes and support the diverse needs of students in today’s educational landscape.

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.

Prefabricated construction, increasingly recognized as a sustainable method, enhances productivity while mitigating the drawbacks of traditional approaches. Lean construction, pivotal for sustainability, targets waste reduction and cost efficiency while delivering value to customers. In India's prefabrication sector, numerous barriers impede the implementation of lean principles, necessitating their identification and resolution to advance lean practices. This study aims to identify and analyze primary barriers to implementing lean principles within India's prefabrication industry, focusing on professionals' perceptions. Employing exploratory factor analysis, it examines these barriers' interconnections and causal relationships, providing actionable recommendations for enhanced lean construction effectiveness. Through a review of the literature, 26 significant barriers were identified and primary data was obtained with the help of a questionnaire. 25 barriers were discerned after pre-exploratory factor analysis, culminating in ten common components. Notably, the study highlights a primary barrier: understanding of lean construction. Drawing from expert insights, substantial recommendations are provided, intending to guide the prefabricated building sector in overcoming barriers to on-site lean construction. These findings and recommendations offer valuable direction for industry stakeholders.

Abstract According to the United Nations, Sustainable Development Goals (SDG) goal 6 and goal 14 seeks to ensure the sustainable management of water present over the earth for all. Urban cities saw a lot of expansion in terms of population and the number of industries established there. Water quality analysis becomes a huge requirement of today’s scenario due to the impurities present in water which harms the livelihood of society. Some of the hazardous impurities like heavy metals destruct the surroundings. In this study, the removal of heavy metals from wastewater with an efficient technique i.e. Bioremediation is represented with the analysis and evaluation of results recorded in the laboratory testing. Two samples were taken from two different sites which are being analyzed with the implementation methodology represented in the present article. The inclusion of the ANOVA model for the optimization of the outcome generated is evaluated and received the P-value and F-crit value. Two algae Chlorella Minutissima & Chlorella Singularis were evaluated based on their impurity removal efficiency as well an analysis of the biological treatment method over other chemical methods has been examined. The results were analyzed and represented in the form of a table as well and the variation in the value of WQP is shown in the form of graphs. The observation shows that the variation in WQP after the integration of algae lies under the permissible limit. Alkalinity is estimated in the range of 20–40 mg/l, Hardness lies in the range of 0–60mg/l, and pH comes approximately in a range of 6.5–8. The results of the ANOVA model is also depicted in graphical form highlighting the P-value and F-crit value of different result generated. Finally, the summary of the proposed work is illustrated with the challenges faced and future recommendations have been provided. Based on the evaluation, the framework is generated for the efficient technique used for heavy metal removal i.e. Bioremediation which provides a great advancement in the efficacy of removal of heavy metals.

On Earth, there is an abundance of soil that has been utilized to build homes for millions of people. Manufacturing compacted stabilized adobe blocks requires adequate water added to the appropriate soil type that has been admixed with binders and fibers to attain maximum density. The mixture is then compressed using the appropriate adobe-forming machine. Currently, the major environmental and human health risks worldwide come from industrial and agricultural wastes because of disposal concerns. The production and use of cement and cement blocks bring numerous economic and environmental issues. Utilizing locally available resources and enhancing standard production and testing methods are two feasible options for sustainable growth. Researchers have seen the promise of earthen construction as an alternative building material, and it is becoming more popular in the context of sustainable development. Marble dust (MD) (Industrial waste) , sugarcane bagasse ash (SBA), and paddy straw fiber (PSF) (Agricultural wastes) were utilized in this research to manufacture the unfired admixed soil blocks. This study utilizes marble dust composed up to 25%–35%, paddy straw fiber constituted 0.8%–1.2%, and bagasse ash made up 7.5%–12.5% of the soil. The marble-dust-bagasse-ash-soil mix was strongly adherent to PSF, according to SEM investigation. In addition, as is apparent from the image, the number of pores is insignificant. These images support the preceding conclusions regarding this sample’s increased flexural and tensile strength. The primary constituents discovered on the surface of an unfired ad-mixed soil block strengthened with PSF of length 75 mm were silica (Si) and oxygen (O), according to the EDS examination. Aluminum (Al) and magnesium (Mg) were found in trace amounts. The endurance characteristics of the block were determined by conducting different tests on the eighty-one (81) design mixes of the produced unfired ad-mixed adobe blocks, followed by modeling, optimization and microstructural analysis. The results show that the recommended technique improves the durability characteristics of admixed soil blocks without burning better than burnt bricks.

The incorporation of Industry 4.0 has integrated various innovations into fire safety management, thus changing the mode of identifying, assessing, and controlling fire risks. This review aims at how emerging technologies like IoT, AI, cloud technology, and BIM are making changes to fire safety in structural structures. With IoT-enabled sensors, data, and analytics coupled with predictive algorithms for real-time scenarios, fire safety systems have become dynamic systems where early detection, quick response, and risk management can be achieved. In addition, cloud web-based solutions improve the storage of information while providing the predictive aspect for certainty of safety measures. This paper also largely focuses on such activities through the likes of ISO/IEC 30141 and IEEE 802.15.4, thus making a critical role in maintaining effective connectivity between IoT devices, which is necessary for the effective performance of fire safety systems. Furthermore, the implementation issues, including the high costs, the difficulty in scaling up the projects, and the cybersecurity concerns, are considered and compared to the possible solutions, which include upgrading in stages and the possibility of subsidies from the government. The review also points out areas for further study, such as the creation of small cell networks with lower latency, the use of AI to carry out the maintenance of IoTs, and the enhancement of protection mechanisms of systems that are based on the IoTs. In general, this paper highlights the vast possibilities offered by Industry 4.0 technologies to support organizational fire safety management or decrease fire fatalities and improve built environment fire safety.

Abstract Human activities have degraded lakes in Uttarakhand, endangering their vital role in urban sustainability, which includes providing essential services like water supply, flood mitigation, agriculture support, and biodiversity conservation in the Himalayan region. This study focused on Nainital district lakes, utilizing remote sensing and GIS techniques to assess their condition. Time series Landsat 8 satellite imageries acquire by USGS earth explorer from 2017 and 2022 were captured, pre-processed, and subjected to spectral-based classification algorithms in ArcGIS software to calculate Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) indices to assess changes in vegetation, water bodies, and build-up area in and around the lakes respectively. The results indicate a decrease in built-up areas for Nainital district lakes from 2017 to 2022: Naini Lake (1.42%), Bhimtal Lake (1.83%), Naukuchiatal Lake (1.45%), Sattal Lake (2.18%), Khurpatal Lake (2.25%), and Sariyatal Lake (1.3%). Additionally, Bhimtal, Naukuchiatal, and Khurpatal lakes exhibited reductions in shrub and grass vegetation by approximately 12%, 16%, and 0% over the five-year period. Notably, Sattal and Khurapatal lakes demonstrated significant decreases in built-up areas, likely attributed to restoration efforts or landslides. Findings emphasize the need for conservation, sustainable land-use practices, and effective management to protect lake ecosystems.

Abstract The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

This study demonstrates the use of computational methods to simulate the molecular dynamics involved in hemoglobin concentration sensing, utilizing Material Studio and the TCAD Silvaco device simulator. A non-invasive and flexible Graphene/MoS2 heterostructure has been proposed for sensing hemoglobin concentration in blood samples. The findings reveal a notable shift in the wavelength-dependent refractive index and extinction coefficient, as well as significant changes in the absorption coefficient and reflectivity of the Graphene/MoS2 heterostructure in response to different hemoglobin concentrations, specifically within an approximate range of 0.3 μm to 1 μm. Moreover, the spectral response of the heterostructure demonstrates that at a particular wavelength of approximately 600 nm, a maximum response is obtained. This wavelength can be considered optimal for detecting various levels of hemoglobin using this heterostructure. The anticipated outcome is a comprehensive understanding of the fundamental principles, ultimately resulting in the development of an exceptionally sensitive platform for detecting hemoglobin concentration.

“Digitalization of forest”, as this phrase itself suggests the sustainable implementation of cutting-edge technologies into forests for improving the current trends that are being used for forest environment monitoring, data acquisition, and analysis in the field of research and development. Technologies that can be used effectively for achieving these objectives include the Internet of Things, Wireless Sensor Networks, Internet of Trees, Deep Learning, etc. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in forests to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as forest fire incidents, illegal logging of trees, poaching, etc. have been discussed briefly. In addition to that, generalized architectures have been proposed which can be used directly in the future for advancements in research and development related to data collection and processing applications like flora analysis, forest fire predictions, etc. without the need for any changes in forest deployment systems. Various methods for enhancement of tribal livelihood, high yield marketing of minor forest produces, and wildlife monitoring is also discussed relating to the areas concentric on the forest environment. Effective connectivity, sustained deployment of real-time sensing systems, and energy harvesting are some of the vital recommendations included and addressed in this study for aiding the proper implementation of digital networks in the forest ecosystem.

Abstract It has been speculated that by the year 2050, technology will have progressed to the point of complete autonomy. This paper scrolls through patent pathways and intellectual developments throughout the industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The patent trails and the recent IPR inputs are expected to assist readers in fast‐tracking up to speed on the bleeding edge of the current research pools while having an eagle's eye perspective on the previous developments so far. The research pools of Industry 4.0 are classified and explored. A lack of Human‐machine workforce synergy in Industry 4.0 and the nascent “customized manufacturing” concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Lastly, Industry 5.0's niche research areas are checked and a suitable pathway to achieve the goals set for the sixth revolution is proposed.

In the context of an early warning system for landslides, monitoring of prone areas is a long-lasting process, little human intervention, and a resource less environment. Data changes in the monitoring area may be noticed in many days, months, or years depending on the weather characteristics. Therefore, a frequent and large amount of data of monitored area is not required to send on a cloud server. Moreover, Long-range communication provided comprehensive spectrum communication protocol and low power consumption with fewer data rates. Over the advantage of LoRa technology, we designed a customized sensor node and gateway node to monitor the changes periodically with low energy power consumption. We evaluated the distinct metrics of spreading factor, sensitivity, time-on-air, energy consumption, link budget, and battery life of sensor and gateway nodes. Finally, this study concludes with challenges faced in real-time in which the sensor data received via a customized sensor node and gateway on the cloud server.

Landslides are a frequent natural hazard during the rainy season, causing infrastructural and economic damage globally. Several studies on landslide monitoring techniques have been conducted in order to reduce various types of losses. There is an extensive review in this article of the ground monitoring method that uses a variety of sensors and some of the primary advances that help to enhance architecture and fulfill user needs. Study of cost-effective ground monitoring technique analysis in different landslide warning systems and some known cases in research article based on coverage area and energy harvesting methods also discussed. Moreover, WSN architecture identified and creates classes according to their benefits and drawbacks as well as performance is evaluated such as efficiency, reliability, quality of service, and network lifetime.

An isolation improvement for a closely spaced quad port multiple-input multiple-output (MIMO) antenna for WLAN applications is presented. The proposed antenna consists of four L-shaped monopole microstrip patch antennas with a rectangular slot at the center and truncated edges at the corner of each patch element. Interelement isolation is improved by more than 12 dB by considering the patch size, ground size, and distance between the patch elements. The designed antenna uses λ/4 distance between the patch elements. The slot in each patch improves the bandwidth, and truncated edges at the corner of the patch improve the impedance matching of the antenna. The proposed MIMO antenna is designed with the overall dimensions of 45.5 mm × 45.5 mm × 1.96 mm. The MIMO parameters for the proposed technique, which include envelope correlation coefficient (E.C.C.) are less than 0.5, and the diversity gain (D.G.) is 10 dB, suggesting this antenna’s suitability for MIMO operations. The average peak realized gain (A.P.R.G.) and radiation efficiency (RE) is 2.8 dBi and greater than 80% for the prescribed band of interest. The antenna element is fabricated and tested using microwave analyzer N9917A. The measured result seems to be in good contact with the simulation. The proposed MIMO antenna is well-suitable for WLAN applications.

Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.

This paper investigates different approaches for achieving isolation in a MIMO antenna design. It provides an in-depth comparison of these techniques, analyzing their advantages and disadvantages. The challenges of obtaining sufficient isolation in modern MIMO antenna design are discussed, and various isolation methods developed for the MIMO design are examined. The study introduces a compact 28 GHz 4-port MIMO antenna design, which is placed on a Rogers RT/Duroid 5880 substrate. The design includes a rectangular patch with semicircles at the ends and dual slots etched from it. A partial ground plane is integrated into the antenna to achieve an operating frequency range from 22 to 29 GHz, centered at 24 GHz. To reduce mutual coupling between elements, four elements are arranged orthogonally and four stubs are added at a specific frequency band to enhance isolation. The ground plane also incorporates a defected ground structure (DGS) to improve gain. To optimize the antenna’s bandwidth, a ground cut technique is used, resulting in a 0.7 GHz bandwidth enhancement at the cost of some isolation. The antenna operates in the range of 22.5– 29.1 GHz, with a peak gain of 6.39 dBi. Each technique is compared based on parameters such as S-parameters (return loss or reflection coefficient), voltage standing wave ratio (VSWR), isolation level, and peak gain. Simulated results are shown for each of the techniques to compare their performance by using Ansys HFSS simulations which confirm that the designed antenna meets the target band requirements and could be used in 5 G communications.

The United Nations Development Programme (UNDP) 2030 agenda illustrates the requirement of expanding infrastructure and advancing technology for delivering modern and sustainable energy services for all in developing countries. Moreover, UNDP also set a goal of increasing the renewable energy share in the global energy. Renewable energy resources are eco-friendly and widely available resources from nature for generating energy. Geothermal energy, wind energy, solar energy, tidal energy, and biomass energy are renewable energy sources. Solar energy is one of the renewable energy generation approaches that harvests energy widely from sun radiation. Photovoltaic (PV) and concentrating solar power (CSP) are the primary technologies to capture solar energy. This study presents the significance of utilizing solar energy for electricity generation globally using PV and CSP technologies. Furthermore, the distinct energy capturing and storing mechanisms of PV and CSP technologies are presented in detail. This article presents the significance and implementation of thermal energy storage for storing energy obtained through CSP technology. Finally, the study presents a considerable gap between PV and CSP in terms of development with future trends.

According to sustainable development goals, the construction industry is one of the vital industries that can build resilient and sustainable infrastructure for human settlements. As the traditional approaches in the construction industry are causing distinct challenges including environmental pollution and excess energy usage, however, the integration of emerging technologies will assist us to reduce the impact and also enhance the activities in the construction industry. Motivated by the facts, this study aims to address the significance of automation in the construction industry with distinct emerging technologies like the Internet of things (IoT), automation, radio frequency identification (RFID), building information modeling (BIM), augmented reality (AR), and virtual reality (VR). The large amount of data generated from the IoT, RFID, BIM, and AR/VR provided an opportunity for big data and artificial intelligence (AI) to extract meaningful insights related to the events in the construction industry. Furthermore, edge and fog computing technology encourages us to implement AI at the edge network for analytics at the end of the edge device. Based on the above analysis, the article discussed recommendations that could assist in further enhancement and implementation of automation in the construction industry. Cloud-assisted AR/VR, integration of AI with IoT infrastructure, 4D printing, adopting blockchain in the construction industry, and smart robotics are the recommendation addressed in this article.

The United Nations emphasized a significant agenda on reducing hunger and protein malnutrition as well as micronutrient (vitamins and minerals) malnutrition, which is estimated to affect the health of up to two billion people. The UN also recognized this need through Sustainable Development Goals (SDG 2 and SDG 12) to end hunger and foster sustainable agriculture by enhancing the production and consumption of fruits and vegetables. Previous studies only stressed the various issues in horticulture with regard to industries, but they did not emphasize the centrality of Industry 4.0 technologies for confronting the diverse issues in horticulture, from production to marketing in the context of sustainability. The current study addresses the significance and application of Industry 4.0 technologies such as the Internet of Things, cloud computing, artificial intelligence, blockchain, and big data for horticulture in enhancing traditional practices for disease detection, irrigation management, fertilizer management, maturity identification, marketing, and supply chain, soil fertility, and weather patterns at pre-harvest, harvest, and post-harvest. On the basis of analysis, the article identifies challenges and suggests a few vital recommendations for future work. In horticulture settings, robotics, drones with vision technology and AI for the detection of pests, weeds, plant diseases, and malnutrition, and edge-computing portable devices that can be developed with IoT and AI for predicting and estimating crop diseases are vital recommendations suggested in the study.

The legal ecosystem is continuously confronted with new challenges and disruptions as a result of the technological invasion initiated by cutting-edge technologies, such as Artificial Intelligence (AI) and Robotics, which have taken over the world. The amalgamation of AI-enabled mechanisms and robotics into human life has elevated significant issues. This digital juggernaut cannot stay constant by the legal landscape, and some degree of assimilation is permitted to pave the way for the efficient administration of justice. The current study is significant since there is a substantial absence of legal research into the implications of AI and robotics on legal rights, which undoubtedly impacts the legal ecosystem. In this study, we have examined the significance, progress, and challenges of integrating Robotics and AI into the legal ecosystem, as they pave way for resilient legal infrastructure. Issues such as privacy, ethical grievances, data protection, confidentiality, and integrity issues are evaluated in this study. The study reviewed existing research into AI and robotics intervention in the legal ecosystem to propose a framework for addressing the increased concerns about the implications of technological apparatus in the legal ecosystem. Finally, the study concludes with recommendations that can be adopted for future work.

An investigation of auto-reconfiguration scheme for interchanging the electrical connections of photovoltaic (PV) array of 3 × 3 size, from series-parallel (SP) to total cross-tied (TCT) configuration under partial shading conditions (PSCs) is presented. The progressive increment shading patterns- 1, 2 and 3 are considered for the extensive comparative analysis for experimental and MATAB/Simulink system. The experimental system comprises three sections: under section I, four relays are used in 3 × 3 PV modules matrix, under section II, an Arduino based relay circuit operation control scheme is designed to modify the electrical connections of PV module and the wireless based User End Operating System (UEOS) is discussed in section III. Furthermore, the mathematical modeling of PV array is validated under the environment of MATLAB simulation to procured. The performance analysis of the system is successfully carried out in terms of power and voltage at global maximum power point (GMPP), power losses and fill factor (FF). Keywords: Photovoltaic array, Series-parallel, Total cross-tied, Power loss, Fill factor

The United Nations’ sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.

According to United Nations (UN) 2030 agenda, the pollution detection system needs to be improved for the establishment of fresh air to obtain healthy life of living things. There are many reasons for the pollution and one of the reasons for pollution is from the emissions of the vehicles. Currently digital technologies such as the Internet of Things and Long-Range are showing significant impact on establishment of smart infrastructure for achieving the sustainability. Based on this motivation, this study implemented a sensor node and gateway-based Internet of Things architecture to monitor the air quality index value from any location through Long-Range communication, and Internet connectivity. To realize the proposed system, a customization of hardware is carried out and implemented the customized hardware i.e., sensor node and gateway in real-time. The sensor node is powered with node mapping to minimize the data redundancy. In this study, the evaluation metrics such as bit rate, receiver sensitivity, and time on air are evaluated by spreading factor (SF), code rate (CR), bandwidth, number of packets, payload size, preamble, and noise figure. The real-time sensor values are logged on the cloud server through sensor node and gateway. The sensor values recorded in the cloud server is compared with optimal values and concluded that the PM10, PM2.5 are high in the air and remaining values of NO2, O3, CO are optimal in the air. Along with this an architecture is proposed for interfacing the hardware with blockchain network through cloud server and API for node authentication.

The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib.

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.

Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals.

In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.

The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-criminals. They exploit the vulnerabilities of the open-source IoT framework due to the absentia of robust and standard security protocols, hence discouraging existing and potential stakeholders. The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic to prevent malicious actors from accessing the IoT network and its peripherals. The gradient boosting machine (GBM) ensemble approach is used to train the binary classifier using pre-processed recorded data packets to detect the anomaly and prevent the IoT networks from zero-day attacks. The positive class performance metrics of the model resulted in an accuracy of 98.27%, a precision of 96.40%, and a recall of 95.70%. The simulation results prove the effectiveness of the proposed model against cyber threats, thus making it suitable for critical applications for the IoT.

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.

Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.