The rapid development of the photovoltaic (PV) industry will result in an increase in the amount of electrical and electronic waste from used PV panels. Only in Poland, the total capacity installed in photovoltaic sources in Poland in May 2020 exceeded 1,950 MW, and the installation weight was approximately 120,000. tone. The problem arises in the recycling or management of this waste. This work presents methods used to recycle waste into photovoltaic modules. The authors investigated the possibility of mechanical and chemical processing of crystalline silicon. A method of thermal treatment of the panel was also proposed. As a result of the research, it was found that the stage of separating materials (crystalline cream, EVO foil, aluminum frame) plays an important role in the recycling process, which is not easy. Chemical treatment allows the silicon to become plastic and the temperature melts the back layer of the panel.

Coffee processing generates a huge amount of waste which contains many natural products. Here, we report the discovery of a panel novel cell-penetrating and metal-ion binding microproteins designated coffeetide cC1a-c and cL1-6 from the husk of two popular coffee plants, Coffea canephora and Coffea liberica, respectively. Combining sequence determination and database search, we showed that the prototypic coffeetide cC1a is a 37-residue, eight-cysteine microprotein with a hevein-like cysteine motif but without a chitin-binding domain. NMR determination of cC1a revealed a compact structure that confers it resistance to heat and proteolytic degradation. Disulfide mapping together with chemical synthesis revealed that cC1a has a ginsentide-like, and not a hevein-like, disulfide connectivity. In addition, transcriptomic analysis showed that the 98-residue micrcoproten-like coffeetide precursor contains a three-domain arrangement like ginsentide precursors. Molecular modeling together with experimental validation revealed a Mg2+ and Fe3+ binding pocket at the N-terminus formed by three glutamic acids. Importantly, cC1a is amphipathic with a continuous stretch of 19 apolar amino acids, and which enables its cell penetration to target intracellular proteins, despite being highly negatively charged. Our findings suggest that coffee by-products could provide a source of ginsentide-like bioactive peptides that have potential to target intracellular proteins.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

Background: Food waste has been a major social problem in recent years. Annually, 1.3 billion tons of food is wasted worldwide. It is recommended that food waste should be reduced at every stage of production. By-products from food processing have high nutritional value; therefore their use in the design of new products is advisable. Methods: By-products from tomato processing (tomato waste-TW) and pepper (defatted pepper seeds-DPS, pepper placenta-PP) were used at a level of 10-30% to produce extruded pasta. The farinographic characteristics, chemical composition, cooking quality, and color of the pasta were studied. Results: The results showed a significant up to 27% increase in the protein content in the TW30 samples, compared to the control. The TDF content increased over five times in DPS30 and TW30 (27.99% and 25.44%). The amino acid composition of the pasta improved with the fortification, but failed to achieve the complete protein by FAO. The DPS30, PP20, PP30, and all TW samples can be considered high-protein products according to the EU definition (min. 20% energy from protein). Conclusion: Vegetable waste can be a valuable additive for improvement of the nutritional value of food. Their addition is part of the zero-waste trend, which has a positive impact on the environment.

In terms of obtaining fuels as well as chemicals with or without catalysts at different conditions, the plum seed stands out as an alternative biomass source. Under varying heating rates (10, 50, and 100ºC min-1) and pyrolysis temperatures (400, 450, 500, 550 and 600 °C), the plum seed was pyrolysed at constant sweep gas flow at a constant rate (100 cm3min-1) in a tubular fixed bed reactor. According to the results, an oil yield reaching to a maximum of %45 was procured at the heating rate of 100 ºCmin-1 and the pyrolysis temperature of 550ºC in non-catalytic procedure. The catalytic pyrolysis was carried out in optimum conditions with two selected commercial catalysts, namely ZSM-5 and PURMOL-CTX and clinoptilolite (natural zeolite, NZ) with catalyst ratio of 10 % of raw material. Along with the catalyst addition, the quantity and the quality of bio-oil increased including the calorific value, removal of oxygenated groups, and hydrocarbon distribution. An increase related to the desirable products like phenols, alkene, and alkane and a decrease in undesirable products like acids were observed in the presence of catalysts. When all the results are considered and evaluated, using zeolite materials in the pyrolysis as catalysts is a recommendable option to achieve enhanced chemicals and fuels.

The emergence of the circular economy, and the evolving paradigms in the treatment and management of wastewater, have opened up an opportunity for co-digestion of organic waste (i.e., food waste) with sewage sludges to enhance resource recovery at wastewater treatment plants (WWTPs). This paper reviewed the potential for anaerobic co-digestion of food waste and sewage sludges, as well as alternative sustainable food waste handling systems in South Africa. The promotion of the circular economy by the latest national solid waste management strategy and the ongoing efforts for resource recovery by the wastewater sector suggests that anaerobic co-digestion of food waste and sewage sludge is possible in South Africa. Furthermore, an integrated food waste disposer (FWD) system was identified as a sustainable alternative for food waste handling. To formulate a roadmap for future food waste and sewage sludge co-digestion implementation, a multi-disciplinary investigation is required to bridge the literature gap.

Economic expedience of waste heat recovery systems (WHRS), especially for low temperature difference applications, is often questionable due to high capital investments and long pay-back periods. By its simple design isobaric expansion (IE) machines could provide a viable pathway to utilize otherwise unprofitable waste heat streams for power generation and particularly for pumping liquids and compression of gases. Different engine configurations are presented and discussed. A new method of modelling and calculation of the IE process and efficiency is used on IE cycles with various pure and mixtures as a working fluid. Some interesting cases are presented. It is shown in this paper, that the simplest non-regenerative IE engines are efficient at low temperature differences between a heat source and heat sink. Efficiency of non-regenerative IE process with pure working fluid can be very high approaching Carnot efficiency at low pressure and heat source/heat sink temperature differences. Regeneration permits to increase efficiency of the IE-cycle to some extent. Application of mixed working fluids in combination with regeneration permits to significantly increase the range of high efficiencies to much larger temperature and pressure differences.

The extraction of grape processing waste (wine pomace) via microwave-hydrodiffusion and gravity (MHG) from three different cultivars grown in Sicily (Syrah, Perricone and Nero d’Avola) rapidly affords aqueous extracts highly concentrated in valued biophenols including flavonoids, anthocyanins and phenolic acids. The method does not employ organic solvent, acid or base and does not require grinding or freeze drying of the wine pomace nor separation of the grape skins from seeds and stem. All the extracts have a pronounced stability as shown by their red-violet color fully retained after storage for more than a year (15 months) in freezer under air. Concentrations of phenolics up to 2000 ppm were detected in the aged extracts of Sicily’s local cultivar Perricone, which also has the highest content of flavonoids. These findings provide a simple and economically viable extraction route to biophenol-rich red extracts that can be used as food colorants as well as to formulate nutraceutical, cosmetic and personal care products starting from an agricultural by-product available in >10 million tonne yearly amount.

The practice of collecting, treating and management of solid waste prior to disposal has become a necessity in developing and modern societies. Over the years, it is known that most wastes that are disposed have a second hand value. However, the construction cost for conventional Material Recovery Facility(s) (MRFs) has been a major barrier for implementation. These technologies require considerable technical expertise, which is often not available in developing nations to successfully operate the MRFs. Covenant University; a private mission institution through her waste to wealth scheme is focused on managing and processing used materials to reusable products. These include Pet bottles, Paper wastes, Food wastes from cafeteria, plastic food packs, nylon, tin cans and others. Specific areas chosen for the Survey include the residential areas for staff and students and the two cafeterias. The waste generated was characterized based on the waste stream so as to quantify the amount of recyclable waste generated and most occurring. The survey involved the use of structured questionnaires, on-site observations and measurements. The study reveals an average amount of recyclable waste generated per day in the institution as 13.46% pet bottles, 4.03% paper, 55.56% food waste, 12.64% plastic, 9.63% nylon and 4.68% tin cans. The study established that adequate waste characterization is a requirement for effective integrated solid waste management which would boost resource recovery, reuse and recycling.

The integral extraction via microwave-assisted hydrodiffusion and hydrodistillation of water-soluble bioproducts contained in the peel of Opuntia ficus-indica white and red cultivars harvested in Sicily, affords red and stable aqueous extracts mostly containing valued betanin, pectin and biophenols. Potentially useful as nutraceutical products, these aqueous extracts are a source of valued ingredients in high demand for a number of important food, cosmetic, beverage and nutraceutical applications.

This academic paper explores the synergistic potential of Agroforestry systems and Climate-Smart Agriculture (CSA) in enhancing climate change adaptation strategies. Focusing on Australia and Pacific Island Countries, the study delves into the pivotal role of these approaches in mitigating the impacts of changing climate variables on agricultural production. By analysing important climate variables, their potential alterations, and their subsequent effects on crop production, the paper advocates for a holistic and forward-thinking approach to sustainable agriculture in the face of climate change.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. The preceding methodology, AEC Industry 6.0, has substantially improved the efficacy and sustainability of construction by using new technology, digitalization, and environmentally conscious methods. Extensive discussions have occurred on the various benefits of AEC Industry 5.0. These merits include heightened stakeholder involvement, increased efficiency via automation and robots, decision-making processes centered around data, and the optimization of resources. Nevertheless, this study also examines the obstacles linked to the complete adoption of AEC Industry 6.0. These challenges require proficient professionals who can effectively utilize contemporary technologies, manage diverse stakeholder technological competencies, establish interoperable systems and standards, and reinforce cybersecurity measures. This study examines the viability of implementing concepts from Industry 6.0 to create intelligent, sustainable, and environmentally friendly structures. The objective is to showcase the potentially revolutionary impact that these principles may have on the construction industry. This study explores the possible applications of AEC Industry 6.0 within the Architecture, Engineering, and Construction (AEC) sector, explicitly highlighting the anticipated industry-wide consequences of the forthcoming paradigm shift. This paper comprehensively examines big data analytics, the Internet of Things (IoT), and collaborative robots as they pertain to the AEC Industry 6.0. This research investigates the interplay between organizational dynamics, human actors, and robotic systems to provide light on the opportunities and obstacles associated with integrating AEC Industry 6.0 concepts into the construction of buildings.

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

Considering citizens' perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps, and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.

There is a growing need to utilize community interventions to address modifiable behaviors that lead to poor health outcomes like obesity, diabetes, and heart disease. Poor health outcomes can be tied to community-level factors such as food deserts (identified areas with low access to fresh fruit, vegetables, and other healthful whole foods) and individual behaviors like sedentary lifestyles, consuming large portion sizes, and eating high-calorie fast food and processed foods. Through a social ecological approach with family, organization and community, the Faithful Families Cooking and Eating Smart (FFCES) intervention was created to address these concerns in a rural South Carolina community. FFCES used gatekeepers to identify 18 churches and 4 apartment complexes in low-income areas. 176 participants completed both pre- and post- survey measures. Student’s t-test measures found statistically significant change in participant perception of food security (0.39, p-value=0.005), self-efficacy with physical activity and healthy eating (0.26, p-value=000), and cooking confidence (0.17, p-value=.01). There was not significant change in cooking behaviors as assessed through the Cooking Behaviors Scale. FFCES shows that a social ecological approach can be effective at increasing and improving individual healthy behaviors and addressing community-level factors in low-income rural communities.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

Buttonweed (Cotula coronopifolia) is native to South Africa but invasive in wetlands in Europe, North America, and Australasia, where it excludes native plants. Despite being dry-fruited, field studies suggest migratory waterbirds can disperse its seeds via gut passage (endozoochory), aiding its expansion. To explore the potential for endozoochory in different regions and habitats, we collected seeds from six populations in Spain, Sweden, and the UK. Germination was tested under different salinity levels (0, 5, 10, 15 g/L) and simulated gut passage treatments: scarification, acidification, or both. No germination occurred at 15 g/L. Higher salinity reduced and delayed germination, but full gut passage treatment increased germinability and accelerated germination. Scarification or acid treatment alone resulted in intermediate germination patterns. There were significant salinity x population and gut passage x population interactions on germinability. The acceleration effect of gut passage on germination was stronger at 5-10 g/L than at 0 g/L. This study highlights how migratory birds can facilitate the spread of alien plants introduced by humans. Non-classical endozoochory by waterbirds is an understudied mechanism for the long-distance dispersal of dry-fruited alien plants. Further research on C. coronopifolia, including population genetics, is necessary to understand dispersal mechanisms and facilitate management strategies.

The main purpose of this work was to tests of form and size of fatigue wear of the top layer of oil gear pump wheel (3PW-BPF-24). The object of tests was operated during a determined number of hours at strictly controlled operational and environmental parameters. A microscope of focal differentiation by Alicona InfiniteFocus G4 (with Focus-Variation optical measuring technology) was used for an identification, measurement and description of noticed superficial defects, which resulted from the fatigue process of the top layer and an earlier thermochemical treatment. The work was finished with an analysis of obtained results (based on the analysis of 3D scans) and conclusions concerning about optimization of the thermal treatment. Taking into consideration the presented results (suggestions) in the technology of improving strength properties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fatigue endurance of the top layer, i.e. a minimization of observed superficial defects.

Internet of Things (IoT) devices are going to be the primary data source in smart cities, often generating and communicating critical, sensitive, and private data that face threats of confidentiality breaches due to the rise of quantum computers. Even though NIST has approved post-quantum cryptography (PQC) schemes that promise to protect from quantum computer threats, these schemes can not be implemented efficiently on IoT devices, which often have computational and memory constraints. Hence, due to the substantial key size requirements and computationally intensive nature of PQC schemes, their direct deployment on IoT devices within smart city architecture is impeded by inherent computational and storage limitations. Consequently, it is imperative to devise PQC-based, resource-efficient authentication protocols tailored to IoT devices operating within smart cities. In this paper, we propose an offloading scheme that uses PQ Edge Server (PQES) for the network that performs PQC-related computation and memory operations for IoT devices in the network. The suggested technique transfers all storage and processing responsibilities from IoT devices in a network to ensure their normal operation and performance without any compromise. PQES generates a distinct PQ key pair for every network device and oversees a separate stream for each device. Therefore, IoT devices in the network may function without cryptographic processes, improving their efficiency by removing the need for conventional cryptography. Our results show that the proposed scheme reduces RAM usage to 1.05 KB and CPU usage to 1.75%, with maximum CPU context switches of 92.8. The proposed scheme uses TCP, a transport layer protocol, which is applicable to lightweight end devices without application-level protocols like HTTP or MQTT. When benchmarked on a Raspberry Pi, the PQES can handle 1000 concurrent connections and use a maximum of 73.5% of RAM. In comparison, previous research used at least 5.1 MB of RAM for one concurrent connection and may use up to 552.6 MB of RAM for 1000 concurrent connections. The maximum CPU usage for our method was 1.75%, whereas the previous proposal had a utilization of 67.1%. The greatest CPU context switches for our system were 92.8, far lower than the 3449 observed for the usual approach. Hence, our proposed scheme reduces the computational, memory, and network load for IoT devices by manifolds and increases device performance while providing PQC security.

In the contemporary era, there has been a notable surge in the systematic utilization of therapeutic drugs that exhibit stimulus-responsive drug release patterns in specific target areas. However, challenges such as premature drug release and limited biocompatibility persist. To address these issues, the application of a lipid coating on mesoporous silica nanoparticles (MSNs) has emerged as a promising strategy. This lipid coating not only enhances the stability and biocompatibility of nanocarriers but also facilitates targeted drug delivery to diseased cells while minimizing drug release throughout the body. MSNs, renowned for their unique attributes including high porosity, morphology, and controllable pore size, have been widely recognized as suitable platforms for drug/gene delivery systems. Furthermore, graphene-based nanomaterials such as graphene oxide and reduced graphene oxide have garnered significant interest in the fields of biology and biomedicine. These materials possess exceptional characteristics such as a large surface area, distinct surface properties, high biocompatibility, and pH sensitivity, making them ideal candidates for incorporating drugs, genes, photosensitizers, and other cargo to design innovative drug delivery systems. This study aims to emphasize the ongoing efforts and advancements in enhancing the capacity and versatility of nanocomposites comprising graphene MSN composites for applications in drug delivery.

Life in the modern century is heavily reliant on an enormous amount of electricity consumption as technology has become the most integral part of daily life. In this context, smart grid systems play a pivotal role to maintain the uninterrupted power supply which needs to be monitored in a timely fashion to keep track of the electric consumers’ usage pattern. The smart meter is the one of smart applications of the smart grid that collects huge amounts of consumer load data on a daily basis which has become a focus for various researchers and analyzers to study load characterization. In this paper, an approach has been proposed to recognize the energy consumption patterns among diverse types of consumers ranging from residential to industrial levels. This approach is worth considering not only for load pattern recognition but also for involving customers in different events such as demand response or peak shaving. In such a way, this analytical mechanism certainly assists in reducing power wastage and saving costs. The proposed methodology is based on a two-fold clustering algorithm with the use of state-of-the-art technology, machine learning. The primary goal is to classify electric customers' data collected from smart meters. Then, analyzing the classified results with an aim to predict power consumption patterns for the customers in the future and making the right energy policy that will benefit both the grid operator and consumers as well.

Smart mechatronics system in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, as results, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of the smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules, and various networks applied in agriculture processing were examined. Finally, we were explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strong related keywords for different journals. The virtually limited use of sophisticated mechatronics in the agricultural industry, and at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics system in agricultural sectors would be taken into consideration in order to overcome these issues.

While immutability is Blockchain's* much celebrated covenant, change is the rule of life. The paradox has seriously limited real-world deployability of Smart Contracts (SC), faltering its mainstream adoption and sustainability. Once implemented, SC remains unstoppable even if its execution makes losses, as evident in the recently exploded $50+B DeFi industry. How do we reconcile the two and make DeFI/Blockchain profitable and sustainable? A DISC (Dynamic Immutable Smart Contract) hypothesis was proposed to resolve the paradox. Using an existing decentralized IoT device framework we test the DISC hypothesis, by designing/implementing a DISC protocol that delivers an algorithmically controlled dynamic off-chain data feed into a self-executing SC. The experiment successfully introduced limited dynamism into SC without compromising its immutability or undermining user control over their SC terms. If consistently reproduced in diverse settings, the DISC protocol could earn an important milestone in the evolution of Blockchain's decentralized economy of the future.

A smart city is a futuristic approach that evolves with new technologies. It can start as a simple community and evolve into future cities. In most developing countries, where the population growth is faster, and where the organization and the urbanization are slower, there is a serious need for a framework for progressive change towards sustainable cities. This article discusses the level of intelligence of smart city components and proposes indicators as well as a methodology for measuring intelligence based on the objectives to be achieved as well as a certain number of technical requirements and some cognitive processes implemented.

In this study, a preload monitoring method using impedance signatures obtained from a piezoelectric-based smart interface is presented for bolted girder connections. Firstly, the background theory of the piezoelectric-based smart interface and its implementation into health monitoring of bolted connections are outlined. A simplified electro-mechanical (EM) impedance model of a smart interface-embedded bolted connection system is formulated to interpret mechanistic understanding of EM impedance signatures under the effect of bolt preload. Secondly, finite element modeling of a bolted connection is carried out to show the numerical feasibility of the presented method and to predetermine the sensitive frequency band of impedance signatures. Finally, impedance measurements are conducted on a lab-scaled bolted girder connection to verify the predetermined sensitive frequency range and to assess the bolt preload changes in the test structure.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

Background: to analyze the impact of COVID-19 pandemic on characteristics and management of uveal melanoma (UM) in National Referral Center in Poland. Materials and Methods: the retrospective analysis of 1336 patients who were newly diagnosed with UM at Department of Ophthalmology and Ophthalmic Oncology, Jagiellonian University Collegium Medicum Krakow, Poland between January 1, 2018 and December 31, 2021. The demographic and clinical data were compiled including localization, size and treatment methods of tumors. Results: In total, 728 patients with UM were included before COVID-19 pandemic in years 2018-2019 and 608 were included during COVID-19 pandemic in years 2020-2021. Fixed-base dynamics indicators for the incidence of uveal melanoma (base year 2018) in the national referral center in Poland were 80.22% and 86.81% in year 2020 and 2021, respectively. UMs were statistically significantly larger and more frequently localized anterior to the equator of the eye globe in year 2021 than in year 2018 (Chi^2 Pearson test p= 0.0001 and p= 0.0077, respectively). The rate of patients treated with enucleation increased from 15.94% in year 2018 to 26.90% in year 2021 (Chi^2 Pearson test p=0.0005). Conclusions: statistically significant differences were found in the management of uveal melanoma in National Referral Center in Poland during COVID-19 pandemic with tumors being larger, more frequently localized anterior to the equator of the eye globe and more often enucleated.

The aim of this study was to evaluate soil properties and Eragrostis tef (teff) growth on Technosols produced from coarse and fine coal wastes from Moatize Mine, Mozambique. The experiment was performed in triplicate in 30 L containers filled with different substrate conditions, composed of fine coal waste, coarse coal waste, agricultural soil, and sewage sludge as an organic matter source. The soil analyses included bulk density, available water capacity, permeability, and fertility. Plant growth was monitored for biomass production and plant tissue composition. All the substrates presented a good available water capacity and a proper drainage condition. Regarding the soil fertility, there were shortages of potassium and boron in the substrates composed exclusively of coal wastes, which was reflected in the composition of the plant tissue. Even so, plant growth was statistically equivalent to the control in all conditions, except for the substrate produced exclusively with fine coal waste and sewage sludge, which presented a better performance. Technosols are an alternative for reducing the final mine waste volume, and Eragrostis tef used as a means for land use after the mining process, with social gains, and as a tool in an ecological process for restoring coal mining sites.

Scope, time, and cost permanently effects each other and most of Information Technology projects fails due to these three factors. Scope shifting mostly occur due to time and cost. At project start, lack of understanding of project and product scope is focal involvement that leads to unsuccessful projects. Complete software scope definition determines quality of project. Defining the customer requirement and the definite scope of project has key role for implementation of project management. The complications originates when systems are developed from impractical expectations and misunderstanding requirements. These problems are cause of many changes, occurs in system development and leads to poor scope management. Scope creep is one of the momentous prompting parameter on the success of project. The failure in manage scope creep leads for 80 percent of software projects failure. However, using agile approach the impact of scope creep on projects become insignificant. A correctly distinct scope tends us to develop a quality product, within identified plans and decided cost to the stake-holders.

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real-time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities.

The poor solvent crystallization of carbapenem CS-023 has been performed at 25 °C. The following results have been obtained: 1) A solvate crystal Form A (5/2 Ethanol·1/2 H2O) is recovered from 80 v/v% ethanol solution, 2) Form A transforms to Form H (4H2O) by the solid-phase transition through the solvate-free polymorph, Form A-2, and Form A also transformed to Form C (1Ethanol·3H2O) by the solvent-mediated transformation. In the present study, we found that Form C also transforms to Form H by the solid-phase transition through the solvate-free polymorph, Form C-2. The three polymorphs, Form A, H, and C, were composed of the helical-chain structures, respectively. However, there was an incomprehensible affair in the solid phase transition among the three polymorphs. Namely, Form A comprised a left-handed helix.On the other hand, Form C and Form H’s helix chains were in a left- and right-handed helix complex. The solid-phase transition of Form A into Form H suggested the switch of helicity in the solid. We attempted to explain the helicity change in the solid phase transition. As a result, it was suggested that the over-absorption of water of Form A-2 at high humidity plays a vital role in the helicity change.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempted to shed light on the fact that a response may be different depending on the health system capacity and each individuals’ social status. For that, we analyzed the correlation between different societal variables (i.e. education, income levels, population density, etc.) along with healthcare capacity related variables (i.e. hospital occupancy rates, percentage of essential workers, etc.) with regards to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a strong relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090, and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km², predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica's adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.

Traditional thermal insulation panels consume large amounts of energy during production and emits pollutants into the environment. To mitigate this impact, the development of bio-based materials is an attractive alternative. In this context, the characteristics of the Eucalyptus fiber bark (EGFB) make it a candidate for insulation applications. However, more knowledge about the manufacturing process and in-service performance is needed. The present study characterized the properties that determinate the in-service behaviour of the EGFB insulation panel. The assessment involved two different manufacturing process. The results indicated that the hot plates and the saturated steam injection manufacturing system can produce panels with similar theoretical and bulk density. The thermal conductivity fluctuated between 0.064 and 0.077 W/m.K, which indicated good insulation. Furthermore, the values obtained for thermal diffusivity and water vapor transmission properties are comparable with other commercially available panels. To guarantee a good in-service performance, the panels need to be treated with flame retardant and antifungal additive. The good performance of the panel is relevant because bio-based Eucalyptus bark panels generate less CO2 eq and require less energy consumption compared to traditional alternatives, contributing to the sustainability of the forestry and the construction industry.

Areca palm sheath leaves were an incredibly noble food packaging system. In ancient times, naturally available wood, leaves, stems, and barks were used to store food materials. But all these were turned down upon the rise of civilization. Today, many modern food packaging systems are used; paper and paperboard packaging materials are used mainly as a replacement for plastic materials. But somehow, paper-based materials have major drawbacks when temperature-dependent products are stored; leaching out components poses a higher threat to health. To alleviate it, bio-based components were used in the manufacturing of paper materials, but the migration of those components was still risky. This study focuses on how the areca palm sheath leaves can be effectively used to prevent this migration of components.

By the end of 2023, biowaste must be completely separated or recycled at source based on the EU legislation. Separate biowaste collection and valorisation towards biofuels could play an essential role in the biobased circular economy. In this context, the principal goal of this paper was to demonstrate in pilot scale the technological solution of bioethanol production via the utilisation of urban source-separated biowaste within the city context of Athens, Greece. More specifically, the main aim was the demonstration of a pilot system in continuous operation (over 10 operating cycles) with real feedstock – wet separately collected biowaste. From the 11 pilot trials performed with wet feedstock, the mean starch and cellulose degradation of the pilot trials amounted up to 80.69±16.27% and 79.41±10.37% respectively, while the bioethanol yield was 74.05±6.82%. The latter was comparable to that of more intensive pretreatment methods. Homogenization and shredding that was applied in this study, stand as promising and cost-effective pretreatment methods for bioethanol production from wet feedstock. Further research is needed to optimize conditions and evaluate scalability. Nevertheless, pilot scale testing is a crucial step in the development and deployment of this technology since it serves as a bridge between laboratory research and full-scale implementation, offering a practical and controlled environment to validate and optimize the technology while minimizing risks and uncertainties.

In this work, oligoesters with terminal hydroxyl groups were obtained by directed glycolytic degradation of polyethylene terephthalate. The possibility of obtaining bifunctional reactive oligomers with an average molecular weight of 865 g mol-1 by directed glycolytic destruction via a dissolution-degradation strategy in dimethyl sulfoxide at a low concentration of ethylene glycol (32.3 mass parts per 100 mass parts of polyethylene terephthalate) was shown. This process allows us to partially solve the urgent problem of recycling post-consumer polyethylene terephthalate.

Growing population leads to industrialisation and urbanization which in turn generate huge amount of waste that represents a big problem for many developed and developing countries. Emerging solution for this problem can be use of wastes as a sustainable source of energy in the form of heat, electricity, fertilizer and biofuel like bioethanol. Type of technology employed is mainly based on the composition of waste whether it is rich in organic matter like MSW or not. WTE technologies reduce the volume of waste as well as decrease the dependence on fossil fuels for energy generation.This study focuses on overview of various available waste to energy conversion technologies like pyrolysis, gasification, incineration, biochemical treatments like landfill gas, aerobic and anaerobic digestion of wastes.

A series of novel superabsorbent composites of poly(acrylic acid)/semicoke were prepared by polymerization of acrylic acid using ammonium persulphate as initiator, N,N’-methylenebisacrylamide as crosslinker and semicoke which was the by-product of coal carbonizing as the inorganic components. FTIR and SEM analysis indicated that the superabsorbent composites had been successfully polymerized and the semicoke participated into the construction of 3D polymeric network. Meanwhile, the effects of initiator, crosslinker, semicoke, neutralization degree as well as the pH value were investigated, and the results showed that superabsorbent composites containing 10% of semicoke possessed the maximum water absorbency of 584 g/g in distilled water and 75 g/g in 0.9 % NaCl solution. The superabsorbent composites kept the high water absorbency within a wide pH range of 4-11, and still exhibited better re-swelling capability even after seven times. The superabsorbent composite with the excellent performance is a potential water-retaining agent used in the agriculture.

The aim of the study was to examine the biogas and methane yield of maize silage, a wastes in agricultural point of view and compare it to the biogas productivity of commonly used mixture of maize silage and mixed fodder with water. The experimental study was carried out into 2 parts. Firstly, the batch experiment was conducted in Mesophilic conditions (35°C), at five different hydraulic retention times (HRT): 6 DAT, 11 DAT 20 DAT 29 DAT and 41 DAT. The results revealed that maize silage was generated the highest biogas yield of 537 mL /241 mL at the HRT of 6days /41 days. Mixed fodder produced the highest biogas yield of 421 mL at the HRT of 6th days and the lowest one was 252 mL at the HRT of 20th days. Finally, the methane, carbon dioxide and hydrogen sulphide of biogas produced from maize silage and mixed fodder were analyzed using a gas chromatography. The results shown that biogas from maize silage precedes the gas less than the mixed fodder. In the field level experiment proved that from mixed fodder produced gas which would be enough for 7 member’s family used 3 months for the purpose of their cooking.

Mushroom production in Poland is an important and dynamically developing element of diverse agriculture. Mushroom spent compost (MSC) is major waste generated during production, i.e., MSC: mushrooms is ~5:1. To date, the main use of MSC is soil application as organic fertilizer. To date, several methods of MSC treatment have been researched and developed including production of compost, bioethanol, biogas, enzyme lactase, xylo-saccharides, and hydrogen. Torrefaction may be considered a novel approach for biomass valorization. Thus, we are pioneering the potential use of MSC valorization via torrefaction. We explored valorizing the waste biomass of MSC via thermal treatment – torrefaction (‘roasting’) to produce biochar with improved fuel properties. Here for the first time, we examined and summarized the MSC torrefaction thermogravimetric analyses, fuel properties data of raw biomass of MSC and biochars generated from MSC via torrefaction. The effects of torrefaction temperature (200~300 °C), process time (20~60 min), on fuel properties of the resulting biochars were summarized. The dataset contains results of thermogravimetric analysis (TGA) as well as proximate analyses of MSC and generated biochars. The presented data are useful in determining MSC torrefaction reaction kinetics, activation energy and to further techno-economical modeling of the feasibility of MSC valorization via torrefaction. MSC torrefaction could be exploited as part of valorization resulting from a synergy between an intensive mushroom production with the efficient production of high-quality renewable fuel.

Spent nuclear fuel and high-level radioactive waste can be disposed in deep horizontal drillholes in sedimentary, metamorphic, or igneous rocks. Horizontal drillhole disposal has safety, operational, and economic benefits: The repository is deep in the brine-saturated zone far below aquifers in a reducing environment of formations that can be shown to have been isolated from the surface for millions of years; its depth provides safety against inadvertent intrusion, earth¬quakes, and near-surface perturbations; it can be placed close to the reactors and interim storage facilities, minimizing transportation; disposal costs per ton of waste can be kept substantially lower than for mined repositories by its smaller size, reduced infrastructure needs, and staged imple¬mentation; and, if desired, the waste could be retrieved using “fishing” technology. In the proposed disposal concept, corrosion-resistant canisters containing unmodified fuel assemblies from commercial reactors would be placed end-to-end in up to 50 cm diameter horizontal drill¬holes, a configuration that reduces mechanical stresses and keeps the temperatures below the boiling point of the brine. Other high-level wastes, such as capsules containing 137Cs and 90Sr, can be disposed in small-diameter drillholes. We provide an overview of this novel disposal concept and its technology, discuss some of its safety aspects, and compare it to mined repositories and the deep vertical borehole disposal concept.

Early and accurate mapping of winter canola planting areas provides critical support for sustainable cropland management. Although some methods have been proposed to map the winter canola at the flowering or later stage, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. This study proposed an early-stage winter canola area mapping method by using the combination of optical and synthetic aperture radar (SAR) data. We analyzed the spectral features, backscatter coefficients, and textural features of winter canola based on Sentinel-2 and Sentinel-1 images. Random forest (RF) and Support Vector Machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then apply the best model to corresponding satellite data in 2018-2022. Results showed that: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV, DVI and GOSAVI, which also showed greater differences than the other features; (2) Based on Sentinel-1 and Sentinel-2 data, winter canola could be earliest mapped around 130 days prior to ripening (i.e., early overwinter stage) with the F-score over 0.85 and the OA over 81%; (3) Adding Sentinel-1 could improve the OA by about 2%-4% and the F-score by about 1% -2% in winter canola mapping. (4) The F-score of winter canola mapping based on the classifier transfer approach in 2018-2022 varied between 0.75 and 0.97, and the OA ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could provide valuable and timely information for stakeholders and decision makers.

This systematic literature review meticulously explores the complexities of technological innovation and entrepreneurship, with a particular focus on the strategies and management practices involved in new product development from an interdisciplinary standpoint. By conducting an extensive analysis of 323 key articles from 11 top-tier journals published between 2020 and 2023, the review delineates the current research trajectories within these fields, underscoring significant scholarly interest and development. The study particularly emphasizes the emergence of artificial intelligence-generated content technology since 2022 and critically assesses its influence on innovation processes and product development practices among both nascent entrepreneurs and well-established corporations. This emergent technology's role in shaping the competitive landscape and its potential to drive significant shifts in market dynamics are thoroughly examined.

Trematode infections cause long-term suffering and debilitation, posing a significant threat to global animal health and production and leading to considerable economic losses. Studies on the epidemiology and control of these infections in Tanzania are limited, and most have been conducted in abattoir settings. The study aims to fill the knowledge gap by determining the prevalence, distribution, and risk factors for trematode infections in domesticated ruminants in two ecological zones of Tanzania. A cross-sectional study was conducted in Lake Victoria and the southern highlands of Tanzania. Rectal fecal samples were collected and examined for Fasciola spp., paramphistomes, and schistosome infections using the sedimentation technique. A total of 1367 domesticated ruminants were sampled and examined for trematode infections. Prevalence of trematode infections was found to be 65.7%. The individual frequency of F. gigantica, paramphistomes, and S. bovis (based on egg morphology only,) was 35.1%, 60.2% and 3.1%, respectively. Adult cattle were more likely to be infected with paramphistomes (AOR: 1.98; 95% CI: 1.40-2.78) and S. bovis (AOR: 8.5; 95% CI: 1.12–64.19) than weaners. It was found that trematode infections in domesticated ruminants are prevalent across Tanzania, therefore effective and community-acceptable prevention and control strategies are highly needed.

New digital interventions have shown potential in managing asthma and improving patients' quality of life compared with conventional interventions. Our objective was to conduct an exhaustive survey of the application of digital health technologies in evaluating, treating, and self-managing psychological and psychopathological elements linked to asthma. We analyzed a compendium of research papers and systematic reviews pertaining to asthma, encompassing themes such as outdoor air pollution, early life wheezing disorders, atopic dermatitis, digital strategies for asthma self-management, psychiatric conditions and asthma, familial impacts on pediatric asthma, and the utilization of mobile health apps for managing asthma. We scrutinized eight chosen studies to evaluate the capacity of digital health technologies to enhance the man-agement and treatment outcomes of psychological factors related to asthma. The studies under review indicate that eHealth interventions, mixed reality instruments, mHealth technolo-gy-augmented nurse-led interventions, and smartphone apps incorporating Bluetooth-enabled sensors for asthma inhalers can markedly enhance self-management of symptoms, quality of life, and mental health outcomes, particularly in children and adolescents with asthma. Nonetheless, additional research is required to ascertain their efficacy and practicability across diverse populations and environments. Customized interventions that cater to the unique needs and preferences of asthma patients and associated psychological elements are vital to ensure the persistent and equitable adoption of these technologies. We underscore the significance of addressing psychological elements in the management and treatment of asthma and advocate for ongoing research and development in this domain.

In the rapidly evolving FinTech sector, risk management is paramount for dealers and brokers. This paper underscores the significance and progression of risk management within this realm. Initially, it reviews the traditional risk management technologies and their inherent limitations. The discourse then shifts to contemporary solutions, specifically emphasizing the role of artificial intelligence, machine learning algorithms, and big data analytics in forecasting and mitigating potential risks. The paper also presents case studies of companies effectively utilizing these advanced techniques, highlighting their benefits in real-world scenarios. Additionally, it addresses forthcoming challenges, including regulatory compliance, data privacy issues, and the imperative for strengthened cybersecurity measures. The conclusion acknowledges that the adoption of these innovative technologies not only boosts operational efficiency but also markedly diminishes financial risks, thereby contributing to a more stable and reliable financial market

A mathematical procedure enabling the transformation of an arbitrary tessellation of a surface into a bi-colored complete graph is introduced. Polygons constituting the tessellation are represented by vertices of the graphs. Vertices of the graphs are connected by two kinds of links/edges, namely, by a green link, when polygons have the same number of sides, and by a red link, when the polygons have a different number of sides. This procedure gives rise to a semi-transitive, complete, bi-colored Ramsey graph. The Ramsey number was established as R_trans (3,3)=5. Shannon entropies of the tessellation and graphs are introduced. Ramsey graphs emerging from random Voronoi and Poisson Line tessellations were investigated. The limits ζ=lim┬(N→∞)⁡〖N_g/N_r 〗, where N is the total number of green and red seeds, N_g and N_r, were found ζ=0.272±0.001 (Voronoi) and ζ= 0.47±0.02 (Poisson Line). The Shannon Entropy for the random Voronoi tessellation was calculated as S= 1.690±0.001 and for the Poisson line tessellation as S =1.265±0.015.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically Heat Pumps (HPs) and Electric Vehicles (EVs). Additionally, it addresses Photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to get economic benefits for private households and at the same time facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with Phase Change Material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of active distribution grid with 8 houses using Power Factory software and DigSILENT Simulation Language (DSL). This scalable size of the active distribution network facilitates to carefully study symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today’s workplaces, particularly in manufacturing setups due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker’s performance in a human-robot collaborative environment. In this study, factory workers’ mental workload has been assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals have been collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot’s movement speed, and cobot’s payload capacity on the mental stress of a human worker have been observed, for a task designed in the context of a smart factory. Task complexity and cobot’s speed have proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually, they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) have been utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression has performed better for most of the targets and the best correlation (rsq-adj = 0.654146) has been achieved for predicting missed beeps, behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm has been used to evaluate the accuracy of correlation between traditional measures and physiological variables, with highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Abstract: The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

Concern about the planet's future has motivated governments around the world to adopt policies that promote more responsible energy consumption. Reducing emissions of the principal cause of climate change man-made greenhouse gases requires greater system efficiency and the utilization of renewable energy sources. The generation of electricity is the single largest source of hazardous pollutants, followed by the transportation sector. This is true due to the extreme reliance on petroleum and its derivatives that these industries have. Electric mobility is receiving more attention from businesses, governments, and academic institutions as a means to reduce the negative impacts of current energy consumption. Without measures to address common technical issues, electric vehicles would strain electrical infrastructure. In order to determine the best strategy for switching operations and feeder reconfiguration, this research presents a helpful hybrid genetic algorithm particle swarm optimization (HGAPSO) method. Meet transmission limits while decreasing real power losses and enhancing the system's bus voltage profile. The effects of HGAPSO on power losses and voltage distributions in the network are analyzed. Then, a report is produced contrasting GA and PSO. This cross-breeding of GA and PSO led to HGAPSO. This important problem can be solved more effectively with the use of new or improved algorithms. To find the optimal reconfiguration and properly rearrange transmission network interconnection, it employs a plethora of heuristic optimization algorithms. The objective is to limit bus voltage variations while maintaining the system's radial topology and minimizing power usage. The reliability and performance of the procedure were evaluated using MATLAB's IEEE 33-bus communication network. The outcomes demonstrate the effectiveness of the proposed technique, which cuts down on power waste during standalone runs and speeds up the processing time.

A hybrid smart process and material design system for nanoimprinting is proposed, which combines with a learning system based on experimental and numerical simulation results. Instead of carrying out extensive learning experiments for various condition, the simulation learning results are partially complimented where the results can be theoretically predicted by numerical simulation. In other word, the lacking data in experimental learning are complimented by simulation-based learning results. Therefore, the prediction of nanoimprint results under various conditions without experimental learning could be realized even for unknown materials. In this study, material and process design for a low-temperature nanoimprint process using glycerol-containing polyvinyl alcohol are demonstrated. Experimental results under limited conditions are learned to investigate optimum Glycerol concentrations and process temperatures. On the other hand, simulation-based learning is used to predict the dependence on press pressure and shape parameters. The prediction results for unknown Glycerol concentrations agreed well with the follow-up experiments.

The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH4, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

The new IoT/IoE (internet of things/everythings) paradigm and architecture permits to rethink about the way the Smart City infrastructures are designed and managed, on the other hand a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizen, also thanks the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits to setup heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall scenario of Big Data, Machine Learning and Data Analytics. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting off-grid, security, safety, cloud and mixed solutions.

We introduce a device for automatic detection and reporting of crawling insects in urban environments. It is a monitoring device for urban pests that complies with the context of smart homes, smart cities and is compatible with the emerging discipline of the Internet of Things (IoT). We believe it can find its place to every room of a hotel, hospital, military camp and residence. This box-shaped device attracts targeted insect pests, senses the entering insect and takes automatically a picture of the internal space of the box. The picture is communicated through the Wi-Fi commonly found in such establishments to an authorized person/stakeholder receiving the picture to take proper action. The e-trap includes strong attractants (pheromone and/or food) to increase capture efficiency. The insect is trapped on the sticky floor of the device. The device carries the necessary optoelectronic sensors to guard all entrances of the trap. As the insect enters it interrupts the infrared light source. This triggers a detection event; a picture is taken, and a time-stamp is set before reporting the event through the Wi-Fi. The device can be integrated seamlessly in urban environments and operates unobtrusively to human activities. We report results on various insect pests and depending on the insect species, can reach a detection accuracy ranging from 96-99%.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

This study aims to examine Turkish companies from a sustainability and green innovation point of view. Through this purpose, this research’s objective is to find out relationship between sustainability drivers and green innovation and also to search for green organizational culture’s mediation effect in this relationship. Survey was carried in companies operating in Turkey which were listed among İstanbul Chamber of Industry (ICI) Top 500 companies for last 3 years successively and have ISO14001 Environmental Management Certificate. According to the survey results, it was manifested that factors directing companies to sustainability having a positive relationship with green organizational culture and green innovation. It is found that there is a partial mediation effect of green organizational culture between motivating factors for sustainability and green innovation.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on the smart cane. The intelligent guide system relies on 2D LiDAR, RGB-D camera, IMU, GPS, Jetson nano B01, STM32 and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on Cartographer algorithm, thus achieving Simultaneous localization and mapping (SLAM). At the same time, through the improved yolov5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved yolov5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the wheels and omnidirectional wheels at the bottom of the smart cane, and give the smart cane a self-leading blind guide function like a "guide dog", which can effectively guide the visually impaired to avoid obstacles and reach the predetermined destination, and can quickly and effectively identify the obstacles on the way out. The laser SLAM speed of this system is 25~31FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor envionments. The improved yolov5 helps to identify 86 types of objects, the recognition rate for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25-26FPS.

Climate-smart agriculture (CSA) is a new and emerging concept in climate discourse to address the issues of climate change impacts in agriculture. Few researchers and organizations have concentrated on CSA policies and practices in Nepal. This study aims to review and analyze the policies and practices in relation to CSA and define local level CSA indicators in the Central and Western Nepal. This study adopted the scoping review of climate policies and practices in Nepal and also utilized the household survey information particularly focusing on climate adaptation relating to agriculture. Through the scoping review, it is recognized that the number of climate policies in Nepal has specifically prioritized agriculture and food security as an important thematic area. Among those policies, National Adaptation Programme of Action (NAPA), National Framework for Local Adaptation Plan of Action (LAPA) and National Adaptation Plan (NAP) have specific sections on CSA practices, however, these policies/plans lack specific indicators. Different categories of CSA practices are common in Nepal, but many of these practices are popular as agricultural and environmental practices prior to the CSA concept and approach. The output indicators specific to CSA practices help the communities and policymakers to measure and monitor the progress of its implementation. Additional researches are required in this field to better understand the CSA concept, approaches, and mechanisms in the Nepalese context.

There is limited real-world data from India examining the treatment characteristics, safety, and efficacy of ceftazidime-avibactam against Gram-negative organisms especially multidrug-resistant pathogens including Carbapenem-Resistant Enterobacterales and Carbapenem-Resistant Pseudomonas. In this retrospective study, the real-world treatment patterns, effectiveness, and safety of ceftazidime-avibactam in treating Gram- negative infections were assessed. Data was extracted from electronic health records of adult patients admitted to the hospital with documented Gram-negative infection who had received treatment for at least 48 hours with ceftazidime-avibactam as a part of routine clinical management. Among the 189 patients, on day 3, clinical symptoms improvement was recorded in 79.6% of patients. Clinical success was achieved in 79.5% and 76.3% of assessed patients on day 7 and day 14/End-of-treatment, respectively. Microbiological success was reported in 76% of patients on day 7 and in 60.3% of patients on day 14 or EOT. The mean treatment duration of ceftazidime-avibactam therapy was 6.92 (± 4.1) days. No new safety concerns were identified. In conclusion, this study provides real-world evidence on treatment patterns and clinical outcomes associated with ceftazidime-avibactam in India, complementing the previously reported literature. The results suggest ceftazidime-avibactam is an effective and tolerable option for the management of Gram-negative infections in critically ill patients.

Safety management assessment systems for national level units’ in South Korea focus on responding capacity to cope with impending accident occurrence and danger occurrence. Since the four stage systems for prevention-preparation-response-recovery, which are core elements of national disaster management, assess the capacities by item such as those of individuals, disaster management departments, institutions, and management networks, there is no assessment function for the organic operation states of the entire systems. Therefore, for efficient disaster management, systematic evaluation indices that will enable active pre-checks in departments in organizations should be developed in place of the existing simply checking methods. In this study, an assessment model that will enable active disaster management centered on practice was developed using resilience engineering techniques. This model consists of disaster management items from the viewpoint of proactive responses instead of prevention. A total of 56 items that constitute four capacities; which are prediction (13 items), monitoring (14 items), proactive response (15 items), and safety learning (14 items) capacities were adopted in this model through Delphi analysis. Institutional capacities for infectious disease disaster management were evaluated based on this model and the resultant scores were prediction 4.41, monitoring 4.63, proactive response 4.69, safety learning 4.56 out of the full score of 5.0 points with an overall average of 4.51. This is an excellent capacity management score comparable to the score 4.57 of diagnosis of similar capacities by the WHO\_JEE (The Joint External Evaluation) in 2017. In fact, in 2015, when infectious disease capacity management was poor, in case of MERS (Middle East Respiratory Syndrome) infectious disease spread in South Korea, 36 patients died and 6,729 patients were isolated. However, through capacity reinforcement, in the case of MERS occurrence in South Korea in September 2018, a management capacity that prevented spread was shown as one confirmed case was completely cured in 10 days and 21 contacts were isolated and tested negative. Therefore, this capacity management assessment model is judged to be usable in enhancing disaster response and management capacities.

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a systematic literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID’s epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management.; Conclusion: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Ad-dressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.

Civil construction is one of the oldest known activities in humanity, reports indicate that builders of the Roman Empire already sought the reuse of materials. Currently, considering the depletion of the supply of natural resources, the recycling of solid waste from construction and demolition (CDW), not only provides new products, but also offers ecological and economic alternative. In this context, this research seeks new variables for the disposal of waste from construction and demolition having in the composition of artificial finishing stones an odd possibility to be analyzed. In this research was developed and analyzed a new composite from the construction and demolition waste using as a binder an orthophthalic polyester resin. The residue was sieved and separated by granulometry by the simplex centroid method, the best compacted mixture was determined statically by ANOVA and the Tukey test. The residue was characterized by X-ray fluorescence and the resin by infrared spectroscopy by Fourier transform. Artificial rock plates were produced with 85% residue and 15% resin, by mass, by vibro system, compression and vacuum, later were cut for mechanical, physical and chemical tests. Analysis was performed by scanning electron microscopy of fractured compositions as well as grain sizes. The artificial stone with the best results presented density of 2,256 g/cm3, water absorption of 0.69%, apparent porosity of 1.55%; bending strength of 34.74 MPa and compression of 111.96 MPa; good results in the alterability and thermal tests. In this satisfactory scenario, the use of this waste in the composition of artificial rocks is promising because it directly meets the concept of sustainable development; replacing the concept of end-of-life of the linear economy, by new circular flows of reuse, restoration and renovation, in an integrated process the circular economy; as well as the quality of the final product with properties similar to the artificial rocks already marketed.

The global textile sector, pivotal to economic development, significantly contributes to environmental degradation, prompting a shift towards sustainable raw material sources. Embracing circular economy principles, this industry is exploring the valorization of agricultural by-products. This study introduces an innovative approach to repurposing mango waste into valuable textile fibers, a byproduct of the fruit processing industry, predominantly generated during juice and jam production. The focus is on the lignocellulosic constituents of mango waste, specifically the peel, seed, and fibrous material, traditionally considered as refuse. Previous research has predominantly utilized mango peel in pectin extraction for food applications, whereas the potential of seed and fibrous content remains underexplored. This investigation endeavors to transform these fibrous by-products into high-value materials suitable for textile manufacturing. The process involved washing, drying, and comprehensive physicochemical characterization of mango fibers, emphasizing lignin quantification, revealing a composition of 12.2% lignin. An experimental design was employed to elucidate the impact of variables such as liquid-to-solid ratio, reaction duration, and sodium hydroxide concentration on lignin dissolution through alkaline hydrolysis. Optimal conditions—10% NaOH concentration, 30 mL/g liquid-to-solid ratio over six hours—achieved an 83% reduction in lignin content. The resulting fibers exhibited properties akin to conventional natural fibers, including a favorable aspect ratio indicative of their suitability for textile applications. A subsequent softening process using propylene glycol enhanced the fibers' spinability, producing a durable, textured fabric ideal for eco-conscious fashion applications, including bags, footwear, and accessories. This research demonstrates the feasibility of converting mango waste into a textile-worthy material and underscores the importance of waste revalorization in promoting sustainability within the circular economy framework.

Lavander distillation leads to huge quantities of solid waste produced yearly. This waste is usually discarded resulting in serious environmental issues. However, it still contains residual essential oil and other bioactive compounds. This research reports on the development and comparison of optimized solid-liquid separation methods, i.e. microwave (MAE) and ultrasound assisted extraction (UAE) of phenolic compounds from lavender distillation waste. Optimal pretreatment conditions, such as waste moisture content and particle size were also determined. The extracts were spray or freeze-dried and the resulting powders were characterized for their physicochemical properties. The majority of the original phenolic compounds in lavender were found in the leachate fraction after distillation (61%), while 43% in the solid waste. Drying of the solid waste before extraction affected the process efficiency. UAE led to higher phenolic content and antioxidant properties compared to MAE. Drying (spray or freeze) the extracts did not significantly affect their phenolic content, while the use of maltodextrin as a drying agent improved the drying process yield especially in the freeze-drying method. It is concluded that valorization of lavender distillation wastes can be achieved via an integrated process consisting of a green extraction method and a consequent drying process that result in a stable bioactive powder.

In response to the growing need for efficient energy conversion technologies, this pa-per introduces an innovative Tubular Thermoelectric Generator (TTEG), specifically de-signed for high-efficiency conversion of waste heat into electrical energy. Unlike conven-tional flat plate thermoelectric generators (FTEGs), the TTEG incorporates full-ring ther-moelectric elements within a tubular layout, offering a novel approach to thermal energy recovery from fluid-based systems. This design significantly enhances the heat transfer capabilities, thereby improving the efficiency of energy conversion. Comprehensive simulations and analyses form the core of this study, where the per-formance of the TTEG is rigorously compared with traditional FTEGs. These comparisons highlight the advantages of the tubular design in terms of energy conversion efficiency and practical applicability in various scenarios. The findings reveal that the TTEG not only outperforms its flat plate counterparts but also presents a scalable and adaptable so-lution for waste heat recovery in a wide range of industrial and automotive applications. This research contributes to the field of energy science and technology by presenting a detailed experimental setup, complete with reproducible procedures and results. It opens new pathways for the development of more sustainable and efficient energy conversion systems, aligning with the goal of harnessing renewable and sustainable energy sources. The potential applications of this technology in enhancing energy efficiency and reducing environmental impact are vast, making it a significant step forward in the quest for clean and sustainable energy solutions.

Recent years have experienced an increase in construction and demolition waste (C&DW), which is the most significant solid waste stream. C&DW is contributing to exceeding all planetary boundaries and presents a range of other issues. In order to better understand the existing research on C&DW, a global bibliographic analysis was undertaken through seven groups of keyword searches and the results visualised using VOCviewer. The results show that C&DW receives only a modest research attention compared to other areas of waste, and this is despite an exponential increase in C&DW research since 2016. The analyses also show that concrete is the most researched material in terms of C&DW, and that reuse, recycling, and circular economy are so far attracting only proportionally modest research attention. This signals a need for further acceleration of the C&DW research, and specifically for more research on reuse, recycling, and circular economy for materials other than concrete.

Printed Circuit Boards (PCBs) are essential to most electronic devices. Digitalization and upgradation of gadget generates lots of PCB-containing electronic waste. Conserving resources and protecting the environment requires recycling such e-waste. The present paper is focused on the recovery of metals from PCBs by physical pretreatment and hydrometallurgical processes. Initially, the waste PCBs were pre-treated to separate metallic and non-metallic fractions. The metallic concentrate was leached using nitric acid (strong oxidative agent) for dissolution of metals. The system was fully jacketed with scrubber and condenser so that no emission of toxic gases to the environment. The process parameters viz. effect of acid concentration, pulp density, temperature, time, etc. were optimized and the obtained data were scientifically validated. Kinetics leaching fitted well with the Shrinking core model, XB = kc.t for Cu, (1-(1-XB)1/2) for Ni and 1-3(1-XB)2/3 + 2(1-XB) for Pb. The optimized conditions derived from these studies will be useful for recovery of metals from waste PCBs, and tin left in the residue could be further treated for the purpose of safe disposal or the utilization in an environmentally friendly manner. The developed process provides environmental benefits and has potential to be commercialized after scale-up studies.

Natural Fibre Composites (NFC) are eco-friendly, sustainable alternatives to synthetic polymers. However, some intrinsic natural fillers´ properties hinder their widespread implementation as re-inforcement in polymeric matrices and require further investigation. In the scope of this study, the thermal, rheologic, mechanical (tension and flexural modes), morphological properties, water ab-sorption and dimensional stability of the NF polypropylene (PP) based injection moulded compo-sites with the rice husk (rh) and olive pits (op) of 20 wt% and 30% wt%, were investigated. The results suggest that the higher content of the rice husk and olive pits led to a similar reduction of the Melt Flow Index (MFI), independent of the additive type compared to virgin polypropylene (PPv). The melting and crystallization temperatures of the PPrh and PPop composites did not change with statistical significance. The composites are stiffer than the PP matrix by up to 49% and possess higher mechanical strength in what concerns the tension mode at the expense of de-creased ductility. PPrh and PPop have superior flexural modulus in the bending mode, while the flexural strength improvement was accomplished for the PP30%rh. The dimensional stability of the composites was improved as the linear shrinkage in the flow direction was decreased by 49% for PPrh and 30% for PPop, positively correlating with an increase in the filler content and stiff-ness. PPop was less susceptible to water sorption than PPrh due to fibres' composition and larger surface-to-area volume ratios.

Natural fibers have attracted increasing interest as an alternative to produce environmentally friendly and sustainable materials. Particularly, hemp fibers have revealed to be widely used in various industrial applications due to their extremely unique properties. However, hemp can generate a large amount of agro-waste, and results in an attractive source of biopolymers for the development of low-cost materials in alternative to the raw materials and conventional petroleum-based plastics. In addition, deep eutectic solvents (DESs), a new type of truly green solvents, have been tested to remove gums, lignin, and other non-cellulosic components from hemp fibers. Reusing these components dissolved into the DESs to fabricate new materials directly by electrospinning is a very attractive but still unexplored endeavor. Thus, this innovative research to venture new upcycling pathways is focused on the fabrication of composite nanofibers by electrospinning of a gel-based blend of Poly(vinyl alcohol) (PVA) and hemp agro-waste (HW) dissolved into choline chloride (ChCl) : Glycerol (1:2) and ChCl : Urea (1:2) DES mixtures. The results obtained revealed that the produced nanofibers displayed uniform appearance with diameters ranging from 257.72 ± 65.63 nm to 380.78 ± 133.98 nm. In addition, the mechanical properties of the electro-spun composite nanofibers produced from the gel-based blends of HW dissolved in DESs and PVA (HW-DESs_PVA) were found to be superior, resulting in an enhanced tensile strength and Young’s modulus. Furthermore, the incorporation of HW into the nanofibers was able to provide bioactive antioxidant and antibacterial properties. Overall, this study demonstrated a promising, more sustainable, and eco-friendly way to produce electrospun composite nanofibers using HW in a circular economy perspective.

An enormous interest in the development of efficient protocols for cellulose extraction has been demonstrated in the last years, however, they were usually based on non-sustainable chemical and thermal approaches. In this work we describe a new and more sustainable method for cellulose extraction from eucalyptus and pine tree wood waste products exclusively performed using microwave-assisted radiation. The methodology includes 3 main steps: i) alkaline treatment; ii) bleaching I, using H2O2; and iii) bleaching II, an acidic treatment. Samples obtained in each step were characterized by Fourier-transform Infrared (FTIR) spectroscopy, powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results were compared with those of starting materials, with the commercially available microcrystalline cellulose and with an industrial paper pulp. Results confirmed that for both type of wood wastes, cellulose was retained during the extractions procedures and that the removal of hemicellulose was achieved at the last step as seen by the FTIR spectra and TGA curves. The developed protocol is innovative since it constitutes an easy and quick approach to extract cellulose, from eucalyptus and pine tree wood waste using mild chemical and thermal conditions, microwave irradiation, and environmentally friendly purification steps. This approach offers the possibility of a future scale-up study to potentially apply the develop protocol to the extraction of cellulose in an industrial scale.

The paper presents a thorough examination of the burgeoning field of utilizing manufacturing waste for sustainable energy generation, aligning with the global imperative for resource efficiency and clean energy solutions. With the manufacturing sector being a significant contributor to waste generation, this study explores innovative approaches to transform waste materials into valuable resources, thereby mitigating environmental impacts and contributing to a circular economy. This work critically reviews current trends, emphasizing technological advancements, policy interventions, and market dynamics shaping the manufacturing waste utilization landscape. Noteworthy developments, such as advanced sorting technologies and the integration of digital solutions, are discussed in detail, showcasing their role in enhancing the efficiency of waste management processes. The study outlines future trends in the field, anticipating a shift towards closed-loop systems guided by circular economy principles. This comprehensive review aims to contribute to the discourse on sustainable waste management and energy generation by providing a holistic perspective on the field's current state and offering insights into the future trajectories that will propel manufacturing waste toward a more sustainable and circular future. The synthesis of technological innovation, collaborative efforts, and evolving regulatory frameworks presents a compelling case for the continued exploration of manufacturing waste as a valuable resource in the pursuit of a more sustainable and energy-efficient world.

To develop a lower-cost, excellent performance, and environmentally friendly phe-nol-formaldehyde (PF) resin, soybean meal was used to modify PF resin, and soybean meal-phenol-formaldehyde (SMPF) resins were prepared. Their physicochemical properties and curing performance were investigated, showing that SMPF resins have higher curing tempera-tures than PF resin. The Fourier transform infrared spectroscopy (FTIR) results indicated that the cross-linking reaction occurred between the amino groups of soybean protein and the hy-droxymethyl phenol. Moreover, with the addition of soybean meal, the viscosity of SMPF in-creased while the gel time decreased. It is worth mentioning that SMPF-2 resin has favorable viscosity, short gel time, low curing temperature (135.78 °C), and high water resistance and bonding strength (1.01 MPa). Finally, all the plywoods bonded with SMPF resins have good water resistance and bonding strength, which could meet the Standard (GB/T 17657-2013, type I) for plywood. The optimized SMPF resins showed the potential application to replace part of PF resin in the wood industry.

Calcium dihydrogen phosphate monohydrate [Ca(H2PO4)2·H2O] (a fertilizer) was successfully synthesized by the recrystallization process by using a prepared triple superphosphate (TSP) that derived from oyster shell waste as starting material. This bio-green, eco-friendly process to produce an important fertilizer can promote a sustainable society. The shell-waste-derived TSP was dissolved in distilled water and kept at 30, 50, and 80 °C. Non-soluble powder and TSP solution were obtained. The TSP solution fraction were then dried and the recrystallized products (RCP30, RCP50, and RCP80) were obtained and confirmed as Ca(H2PO4)2·H2O. Whereas the non-soluble products (NSP30, NSP50, and NSP80) were observed as calcium hydrogen phosphate dihydrate (CaHPO4·2H2O). The recrystallized yields of RCP30, RCP50, and RCP80 were found to be 51.0%, 49.6%, and 46.3%, whereas the soluble percentages were 98.72%, 99.16%, and 96.63%, respectively. RCP30 shows different morphological plate sizes, while RCP50 and RCP80 present the coagulate crystal plates. X-ray diffractograms confirm the formation of both the NSP and RCP. The infrared adsorption spectra confirmed the vibrational characteristics of HPO42‒, H2PO4‒ and H2O existed in CaHPO4·2H2O and Ca(H2PO4)2·H2O. Three thermal dehydration steps of Ca(H2PO4)2·H2O (physisorbed water, polycondensation, and re-polycondensation) were observed. Ca(H2PO4)2 and CaH2P2O7 are the thermodecomposed products from the first and second steps, whereas the final product is CaP2O6.

A brief technical and economic insight into producing the water-soluble yellow colorant limocitrol 3-O-6”-[3-hydroxyl-3-methylglutaryl)])-β-D-glucopyranoside from waste lemon peel via simple solid-liquid extraction in aqueous ethanol or via hydrodynamic cavitation of waste lemon peel in water, shows that the biocolorant can be obtained at affordable cost. Coupled to the simplicity and sustainability of the extraction processes suggested, the high chemical and physical stability of this polymethoxylated flavanol and the health benefits of citrus flavonoids, support industrialization of this new bioeconomy production.

A large amount of concrete waste is generated around the world and its recycling is an urgent issue. In this research, a new approach to recycle concrete waste with wooden waste was studied. Concrete and wooden wastes were crushed, mixed, and heat compacted to produce plates with different water contents and mix proportions at various temperatures, pressures, and durations of compaction. The bending strength of the plates was measured after compaction. The result indicated that with an increase in the percentage of wooden waste in the mixture, pressure, or temperature improved the bending strength. The increase in water content reduced the bending strength. Most of the products exhibited higher bending strength than that of ordinary concrete.

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and 3 industrial catalysts. Principal component analysis (PCA) has been applied over the dataset, showing that the main components are associated with feed composition (27.41% variance); operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

The problem of effective immobilization of liquid radioactive waste (LRW) is key to the successful development of nuclear energy. The possibility of using magnesium potassium phosphate (MKP) compound for LRW immobilization on the example of nitric acid solutions containing actinides and rare earth elements (REE), including high level waste (HLW) surrogate solution is considered in the research work. Under the study of phase composition and structure of the MKP compounds obtained by the XRD and SEM methods, it was established that the compounds are composed of crystalline phases - analogues of natural phosphate minerals (struvite, metaankoleite). The hydrolytic stability of the compounds was determined according to the semi-dynamic test GOST R 52126-2003. Low leaching rates of radionuclides from the compound are established, including a differential leaching rate of ²³⁹Pu and ²⁴¹Am - 3.5 × 10^-7 and 5.3 × 10^-7 g/(cm²∙day). As a result of the research work it was concluded that the MKP compound is promising for LRW immobilization and can become an alternative material combining the advantages of easy implementation of the technology like cementation and the high physical and chemical stability corresponding to a glass-like compound.

This study aims to analyse the technical viability and economic feasibility of rainwater harvesting systems for single and multifamily residential buildings in the city of Florianópolis, Brazil. Simulations were conducted for representative buildings in the city under different water usage scenarios and system designs, in a total of 36 simulation scenarios. An economic analysis was performed for four scenarios over a twenty-year period. Both initial and operational costs were considered and net present value, internal rate of return and payback used as feasibility indicators. Almost all of the cases simulated were technically viable for the group of houses, and up to 78.9% for flats. As for the economic analysis, for houses, between 60.1% and 74.8% of the cases were economically feasible, achieving a discounted payback period ranging from 6.2 to 8.6 years. For flats, between 57.8% and 64.2% of the cases were economically feasible, achieving a discounted payback period ranging from 4.8 to 5.6 years. As the water tariff in the city underwent changes recently, the former and current tariff formats were compared. The current tariff format provides more economic benefits for saving potable water, and leads to a higher net present value and a shorter discounted payback period.

Building small populations mortality tables has great practical importance in actuarial applications. In recent years, several works in literature explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations: many models dealing with this problem usually use a two-population approach, modelling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small population mortality tables, a general structure for a two-step two-populations model is proposed, its main element of novelty residing in a machine-learning based approach to mortality spread estimation. In order to obtain this, Contrast Trees and related Estimation Contrast Boosting techniques have been applied. A quite general machine learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model.

Thyroidectomy has been post-operatively managed by hormone replacement therapy in order to satisfy the reference ranges of thyroid stimulating hormone (TSH) and thyroxine (T4) levels. While medication and standardized reference ranges have proven to be effective, many patients continue to report unintentional weight gain despite adequate amounts of treatment and levels of TSH and T4. Physicians, over the years, have become complacent to these “normal” ranges, and have ignored the metabolic consequences that are affecting thyroidectomy patients. This paper aims to redefine the approach to post-thyroidectomy clinical care by: challenging the current standardized hormonal range values, exploring the gaps in thyroid hormone conversion, investigating the metabolic pathways of T3, considering the influence of inflammatory markers, and proposing the future for patient-centered management.

Aortic valve stenosis and malignancy frequently coexist and share the same risk factors as atherosclerotic disease. Data reporting the prognosis of patients with severe aortic stenosis and cancer are limited. Tailoring the correct and most optimal care for cancer patients with severe aortic stenosis is complex. Cancer patients may be further disadvantaged by aortic stenosis if it interferes with their treatment by increasing the risk associated with oncologic surgery and compounding the risks associated with cardiotoxicity and heart failure (HF). Nowadays, several therapeutic options are available for aortic valve stenosis (surgical valve replacement , transcatheter valve implantation, balloon valvuloplasty, or medical therapy), but in presence of malignancy, the use of one of these versus the others should be decided on a case-by-case approach, depending on cancer stage and associated treatment, expected outcome, comorbidities and after an accurate physical examination and Doppler-echocardiography which are the key tools for diagnosing and evaluating aortic stenosis. The current review considers the available data on the association between aortic stenosis and cancer, and therapeutic options.

Purpose- This study seeks to investigate the collaboration between Digitalization and traditional manufacturing industry and investigate, how digitalized management brings new chances and challenges of management. Design/methodology/approach – The author designed a content analytical study researching the role of digitalization in traditional manufacturing industries. The article presents a selective but systematical review of recent digitalization trend and digital twin to be applied for innovative management in Industry 4.0 era. Findings– Digitalization is highly correlated with traditional manufactory in Industry 4.0 era. The study brings a new outlook on the next wave of Digitalization and review of associated challenges and opportunities for management and innovation in traditional manufacturing industries. Research limitations/implications – The research focuses on two branches of traditional industries only: machine tools building and metalworking industries. Future research needs to examine other traditional industries. The research is limited to 2 high related traditional manufacturing industries, machine tools and metalworking industry. Practical implications – The study and application of digitalization should be highly recommended and launched in traditional manufacturing industries. Universities, institutes and vocational schools should update, optimize their courses systems according to prediction of future industry. Originality/value – The paper enhances understanding about the collaboration between digitalization and traditional manufacturing industry. This paper analyzes an important issue the digitalization serves traditional industries. It is unique in its broad analysis of the related terms – Digitalization, Machines 4.0, Industry 4.0 and innovative management in Industry 4.0 era.

Species of Acacia have been extensively entrenched in the tropical, and semi-arid regions as well as in the southern hemisphere. Acacia species have been bounteous faces the pest and pathogenic pressure. Integrated Pest Management (IPM) is an immensely important aspect of producing a healthy Acacia plantation without harmful impacts on the environment. Here an attempt was made to determine the possibilities of integrated pest management for Acacia. Overall, this information will be helpful to increase awareness about the integrated pest management of the members of the genus Acacia.

Sustainability of current energy policies and known mid-term policies are analised in their multiple facets. First an overview is given about the trend of global energy demand and energy production, analysing the share of energy sources and the geographic distribution of demand, on the basis of statistics and projections published by major agencies. The issue of sustainability of the energy cycle is finally addressed, with specific reference to systems with high share of renewable energy and storage capability, highlighting some promising energy sources and storage approaches.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Cement-free concrete has superior physical performance, such as strength and durability, compared to OPC concrete, however, it has the disadvantage of large shrinkage. Large shrinkage can cause cracks due to shrinkage stress in the long term. In this study, a shrinkage reducing agent (SRA) was used to reduce the shrinkage of cement-free mortar; its content was increased from 0.0 to 1.5 %. For an SRA content of 1.0 %, a calcium sulfoaluminate (CSA) expansive additive (EA) (2.5, 5.0, and 7.5%) was added. To calculate the shrinkage stress of cement-free mortar using the SRA and EA, the compressive strength, elastic modulus, and total and autogenous shrinkage were measured. The unit shrinkage stress of cement-free mortar was obtained by multiplying the elastic modulus by the length change and accumulated to obtain the shrinkage stress acting on the mortar according to the age. The shrinkage stress of cement-free mortar showed different tendencies as the age increased. At early ages, the shrinkage rate of the mortar occupied a large proportion of the shrinkage stress. In the long term, the shrinkage stress was significantly affected by the elastic modulus. As a result, SRA was found to be effective in reducing the shrinkage stress by decreasing both the elastic modulus and shrinkage. However, EA increased the shrinkage stress over the long term due to an increase in elastic modulus even though it compensated for early-ages shrinkage.

The circular economy is one of the main strategies for mitigating the environmental impacts of civil construction, due to the generation of construction and demolition waste (CDW). In this transition, evaluating alternatives for using buildings as material banks is a way to make the process of reusing construction components more efficient. Thus, the article aimed to evaluate the state of the art of publications on the relationship between the circular economy in civil construction and the conceptual model of buildings as material banks, to mitigate the environmental impacts of CDW. The authors chose the methodological design of Systematic Literature Review, using the Scopus and Web of Science databases for research, with the following search strings: ("construction" or "civil construction" or "built environment" or "construction industry" ) and ("circular economy" or "circular construction") and ("material banks" or "BAMB" or "buildings as material banks" or "building stocks" or "building materials") and ("construction waste" or "demolition waste" or "CDW" or "construction and demolition waste" or "environmental impacts"). After a screening in which only articles published in journals were selected, from 2013 to 2023, inclusion and exclusion criteria were applied, to evaluate only those that had a direct relationship with CDW management through circular economy strategies and buildings such as banks of material. As a result, 93 articles remained, which were analyzed using quantitative and qualitative approach. The quantitative results showed an increase in publications since 2020, which are concentrated in European countries and China. The predominance of applied studies was also noted, through case studies that evaluate the management of materials and waste in the urban environment. The qualitative analysis, carried out using a SWOT Matrix, highlighted the strengths of the buildings, such as material banks, the potential reduction of resource extraction and urban mining, promoting the circulation of construction products. However, the recycling of waste, such as aggregates, still stands out as the main end-of-life strategy adopted, even without occupying the top of the waste hierarchy.

Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods.

AbstractBaraton University dairy farm is an environment that attracts a microbiologist to inquire the composition of bacteria that exist there in. The knowledge of bacteria has in time and again amazed the life scientist community that have invested to acquire more information in this microbiology world.The study engages fundamental tests such as gram stain, endospore stain, and assays for specific microbial activities & enzymes, susceptibility on disinfectant and antibiotic, utilization of specific substrate and culture characteristics. The two organisms (gram negative and positive) tested positive for sucrose & lactose fermentation, Indole & Methly red, catalase & Oxidase, were both facultative and motile. On contrary, gram positive bacteria had spores and had a gamma haemolysis on Blood Agar, while gram negative bacteria haemolysed beta haemolysis. To draw a conclusion on the identity of the two organisms is that, the gram positive is a Bacillus ........ while gram negative is Escherichia coli.