Huge Amount Research Articles

A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning

AbstractCounting people is an important part of people-centric applications, and the increase in the number of IoT devices has allowed the collection of huge amounts of data to facilitate people counting. The present study seeks to provide a novel, low-cost, automatic people-counting system for the use at bus stops, featuring a sniffing device that can capture Wi-Fi probe requests, and overcoming the problem of Media Access Control (MAC) randomization using deep learning. To make manual data collection considerably easier, a “People Counter” app was designed to collect ground truth data in order to train the model with higher accuracy. A user-friendly, operating system-independent dashboard was created to display the most relevant metrics. A two-step methodological approach was followed comprising device choice and data collection; data analysis and algorithm development. For the data analysis, three different approaches were tested, and among these a deep-learning approach using Convolutional Recurrent Neural Network (CRNN) with Long Short-term Memory (LSTM) architecture produced the best results. The optimal deep learning model predicted the number of people at the stop with a mean absolute error of ~ 1.2 persons, which can be considered a good preliminary result, considering that the experiment was done in a very complex open environment. People-counting systems at bus stops can support better bus scheduling, improve the boarding and alighting time of passengers, and aid the planning of integrated multi-modal transport system networks.

A Multisensor Device Intended as an IoT Element for Indoor Environment Monitoring.

This work presents a multisensor device which is intended as an element of IoT for indoor environment (IE) monitoring. It is a portable, small-size, lightweight, energy-efficient direct-reading instrument. The device has an innovative design and construction. It offers real-time measurements of a wide spectrum of physical and chemical quantities (light intensity, temperature, relative humidity, pressure, CO2 concentration, content of volatile organic compounds including formaldehyde, NO2, and particulate matter), data storage (microSD; server as an option), transmission (WiFi; GSM and Ethernet as options), and visualization (smartphone application; PC as an option). Commercial low-cost sensors were utilized, which have been arranged in the individual sensing modules. In the case of gas sensors, dynamic exposure was chosen to ensure a minimum response time. The MQTT protocol was applied for data transmission and communication with other devices, as well as with the user. The multisensor device can collect huge amounts of data about the indoor environment to provide the respective information to the IoT. The device can be configured to control actuators of various auxiliary devices and equipment including external systems used for ventilation, heating, and air conditioning. The prototype is fully operational. The exemplary results of IE monitoring were shown.

A sustainable temperature-swing process for CO2 capture and mineralization at below 100 °C using a recyclable chelating agent and bottom ash

Industrial waste utilization for carbon dioxide (CO2) mineralization offers a highly promising approach to significantly reduce CO2 emissions; however, it faces the challenge of consumption of huge amounts of chemicals without their recovery. This study proposes an improved CO2 mineralization process that employs a highly recyclable solution of environmentally friendly chelating agent, N,N-Dicarboxymethyl glutamic acid (GLDA) enriched with NaHCO3 and involves the temperature swing-facilitated dissolution and carbonation of Ca-bearing industrial wastes under ambient pressure and low temperature (<100 °C). Using bottom ash collected from plastic incineration as a representative solid waste, this study emphasizes the significance of temperature control during the Ca extraction and carbonation processes. Ambient temperatures prove favorable for efficient Ca extraction from incinerator bottom ash, while a relatively higher temperature (95 °C) is recommended for subsequent Ca carbonation; the carbonation process was then self-promoted due to the pH increase from carbonation. Subsequently, the solution was efficiently regenerated after use for CO2 capture. Across five CO2 mineralization cycles using the same solution, a comparable CaCO3 generation efficiency was demonstrated, suggesting the high recyclability of the solvent and sustainability of this approach. Although heavy metal accumulation was observed in the extraction solution during cycling, it did not negatively affect the Ca extraction and heavy metal-free CaCO3 was successfully produced. Overall, the proposed CO2 mineralization process utilizing the recyclable GLDA solution has significant potential as a green technology crucial for the global pursuit of CO2 mitigation.

Taylor jellyfish search algorithm enabled feature selection and deep learning for big data classification using mapreduce framework

ABSTRACT Nowadays, the increasing growth of technology contributes various organisations to storing a huge amount of records using web servers in the form of big data. The big database possesses a vast amount of relevant and irrelevant features, and the less significant features are difficult to remove which makes the process time-consuming and more complex. Hence, this paper proposes an optimisation algorithm, named the Taylor Jellyfish Search Optimisation (TaylorJSO) for selecting the relevant features. Here, an optimisation-based deep learning approach called Taylor Jellyfish Search Optimisation-Deep Maxout Network (TaylorJSO-DMN) is introduced to classify big data more effectively by selecting optimal features. In this model, the input database is preprocessed using quantile normalisation and uses the oversampling of imbalanced classes for data augmentation. In the MapReduce framework, the selection of optimal features is performed in the mapper phase, whereas in the reducer phase, the DMN is used for the classification of big data. The DMN is trained using the introduced TaylorJSO algorithm to improve the classification performance of big data classification. Moreover, the TaylorJSO-DMN model attained greater performance with a maximum of 92.5% accuracy, 91.6% sensitivity, and 92.9% specificity than other traditional approaches.

Optimized Solid–Liquid Separation of Phenolics from Lavender Waste and Properties of the Dried Extracts

Lavender distillation produces huge quantities of solid waste 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. The 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%), whereas 43% was found in the solid waste. Drying of the solid waste before extraction affected the process efficiency. UAE led to a higher phenolic content and greater antioxidant properties compared to MAE. Drying (spray or freeze) the extracts did not significantly affect their phenolic content, whereas the use of maltodextrin as a drying agent improved the drying process yield, especially when using 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 results in a stable bioactive powder.

Development and Practical Applications of Computational Intelligence Technology

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.

A combined impact assessment of climate and land use/land cover change in an Eastern Himalayan watershed in northeast India.

The current study investigates the joint impact of projected land use/land cover change (LULCC) and climate change on the discharge of river Puthimari using Soil and Water Assessment Tool (SWAT). Puthimari, flowing through part of Bhutan and the northeastern region of India, earns its significance by contributing a fairly huge amount of discharge to the mainstream Brahmaputra causing frequent floods downstream, specifically in the monsoon season. The analysis was carried out from 2025 to 2099, by dividing this entire period into three sub-periods, 2025‒2049, 2050‒2074, and 2075‒2099, each of 25 years duration. To evaluate the impact of climate change, this study considered future climate data of five different CMIP5 (Coupled Model Intercomparison Project) climate models and their ensemble for RCP4.5 and RCP8.5 (Representative Concentration Pathways). The changes in LULC were incorporated by projecting the future LULC for 2035, 2065, and 2085 for each of the periods using the CA (Cellular Automata)-Markov model. SWAT performed well for both calibration and validation. The respective Nash-Sutcliffe efficiency (NSE) values for calibration and validation were found to be 0.74 and 0.77. Also, 0.75 and 0.79 coefficient of determination (R2) values were obtained for calibration and validation, respectively. The analyses reveal a 19.76% increase in rural settlement, and a 6.30%, 16.45%, and 8.76% decrease in forest, cropland, and waterbodies in the watershed by the end of this century. The average monsoon rainfall would increase by 14.16% and 38.92%, with a corresponding increase in discharge by 34.27% and 64.67%, under RCP 4.5 and RCP 8.5, respectively. This comprehensive study represents a pioneering effort to thoroughly analyze the future hydrological dynamics of the Puthimari River. This research serves as a vital resource for policymakers and government agencies, offering valuable insights to guide both structural and non-structural measures aimed at safeguarding the river from potential flood devastation. Additionally, it provides essential information to support the implementation of effective watershed management practices.

Fake news text detection based on convolutional neural network

The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.

PERFORMANCE OF NATURAL POZZOLAN-BASED GEOPOLYMER REINFORCED WITH BANANA FIBERS

ABSTRACT Türkiye has approximately 50.000 acres of banana plantations. Banana cultivation produces a huge amount of waste that has no commercial value. The main purpose of this study is to investigate the possibilities of using waste banana fiber in natural pozzolan-based geopolymer mortar to increase its ductile fracture behavior. The effects of fiber content and length on physical and mechanical properties were experimentally carried out. The optimum banana fiber content and length were found to be 1.5% and 20 mm, respectively. Above this limit, fibers made it difficult to obtain a workable matrix and generated fiber agglomeration. Although increasing the fiber content from 0.5% to 1.5% and length above 20 mm led to a decrease in the ultrasound pulse velocity, modulus of elasticity and compressive strength due to the higher porosity of the matrix, the increasing ratios of the flexural strength and toughness were consistent. Furthermore, banana fiber-reinforced geopolymer mortars have adequate porosity (22.87%), water absorption ratio (9.25%), swelling thickness (0.58%), saturation coefficient (78%), drying shrinkage (195x10–6), water vapor diffusion resistance index (5.73), flexural strength (6.88 MPa), compressive strength (8.75 MPa), and comply with the performance requirements of the related standards. By considering the adequate physical, mechanical and ductile fracture performance of the material, waste banana fiber can be utilized in the production of geopolymers.

Magnetic Shape Memory Nanocomposites Assembled with High Speed High Pressure Torsion.

When a severe plastic deformation (SPD) process is performed at high temperatures, it becomes more versatile. Designed originally for the bulk nanoconstruction of hard-to-deform alloys, high-speed high-pressure torsion (HSHPT) is an SPD method used in this research for assembling multiple layers of shape memory nanocomposites. Three hard-to-deform magnetic alloys in the cast state were used. Soft magnetic shape memory alloys, NiFeGa and FePdMn, and a potentially hard magnetic alloy, CoZr, were assembled in various composites. Both grain refinement and strong layer bonding were achieved in ZrCo/FePdMn and ZrCo/NiFeGa composites in seconds. The very short SPD time is specific to HSHPT because of the intense friction that occurs under high pressures, which generates huge amounts of heat. After SPD, the temperature rises in bulk material like a pulse, being dissipated mostly through heat conduction. The SPD parameters were carefully controlled with an advanced automation system using a programmable logic controller. Nevertheless, the major drawbacks of high-pressure torsion were overcome, and large SPD discs were obtained. Various investigation techniques (optical microscopy, scanning electron microscopy, energy dispersive spectroscopy and atomic force microscopy) show well-defined interfaces as well as a fine and ultrafine structure.

Development of Dual Intake Port Technology in ORC-Based Power Unit Driven by Solar-Assisted Reservoir

The ORC-based micro-cogeneration systems exploiting a solar source to generate electricity and domestic hot water (DHW) simultaneously are a promising solution to reduce CO2 emissions in the residential sector. In recent years, a huge amount of attention was focused on the development of a technological solution allowing improved performance of solar ORC-based systems frequently working under off-design conditions due to the intermittence of the solar source availability and to the variability in domestic hot water demand. The optimization efforts are focused on the improvement of component technology and plant architecture. The expander is retained as the key component of such micro-cogeneration units. Generally, volumetric machines are adopted thanks to their better capability to deal with severe off-design conditions. Among the volumetric expanders, scroll machines are one of the best candidates thanks to their reliability and to their flexibility in managing two-phase working fluid. Their good efficiency adds further interest to place them among the best candidate machines to be considered. Nevertheless, similarly to other volumetric expanders, an additional research effort is needed toward efficiency improvement. The fixed built-in volume ratio, in fact, could produce an unsteady under- or over-expansion during vane filling and emptying, mainly when the operating conditions depart from the designed ones. To overcome this phenomenon, a dual intake port (DIP) technology was also introduced for the scroll expander. Such technology allows widening the angular extension of the intake phase, thus adapting the ratio between the intake and exhaust volume (so called built-in volume ratio) to the operating condition. Moreover, DIP technology allows increasing the permeability of the machine, ensuring a resulting higher mass flow rate for a given pressure difference at the expander side. On the other hand, for a given mass flow rate, the expander intake pressure diminishes with a positive benefit on scroll efficiency. DIP benefits were already proven experimentally and theoretically in previous works by the authors for Sliding Rotary Vane Expanders (SVRE). In the present paper, the impact of the DIP technology was assessed in a solar-assisted ORC-based micro-cogeneration system operating with scroll expanders and being characterized by reduced power (hundreds of W). It was found that the DIP Scroll allows elaboration of a 32% higher mass flow rate for a given pressure difference between intake and expander sides for the application at hand. This leads to an average power increase of 10% and to an improvement of up to 5% of the expander mechanical efficiency. Such results are particularly interesting for micro-cogeneration ORC-based units that are solar-assisted. Indeed, the high variability of hot source and DHW demand makes the operation of the DIP expander at a wide range of operating conditions. The experimental activity conducted confirms the suitability of the DIP expander to exploit as much as possible the thermal power available from a hot source even when at variable temperatures during operation.

Standards for Auctioneers in Vietnam and Alberta (Canada)

Auctioneering as a profession has been in existence for many years. The advantage of selling goods through a public auction compared to a normal willing buyer-willing seller process is that the consignor of the goods is likely to get a better price through a competitive bidding process. A professional consignee known as an auctioneer moderates the competitive bidding process during the sale of goods. However, because of the nature of the business and the frequent huge amounts of money involved in the transactions, there is a need to regulate the auctioneering. Every jurisdiction where auctioneering has specific rules that dictate regulate auctioneer standards. This article looks at the auctioneering standards that have been set and legislated in Alberta (Canada) and Vietnam. The paper has identified the minimum requirements that a prospective auctioneer has to meet in the jurisdictions for them to be given a licence to practice. Also, the paper has identified some regulations that guide the auctioneering process in the said jurisdictions. In the article, the researcher further identified key legal prohibitions that are part of the auctioneering standards.

Feasibility study of a method for tuning wave energy converters

A Wave Energy Converter (WEC) cannot operate efficiently if left alone floating to the mercy of the random sea surface. For a WEC to be efficient, its natural frequency should be close to the peak frequency of the local dominant waves. This paper introduces a method for tuning any natural frequency of a WEC to the peak frequency of a wave spectrum and converting dynamic responses into mechanical power. An innovative lightweight and dual-drag force subsea structure with a huge amount of entrapped seawater can be attached to any vessel using cables. This structure increases the roll mass moment of inertia of a medium size barge, and the mass of a small heaving floater, reducing their natural frequencies. The tension in the cables is converted into mechanical power. The study focuses on the Galápagos Islands, where swells with a typical 13 s peak period and significant wave height between 1 to 3 m dominate the wave climate. For the dynamic systems based on roll and heave responses, annual averages of mechanical power are 178 and 73 kW with efficiencies of 49 and 67% respectively. The method developed in this paper can improve the efficiency of existing and novel WECs.

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

Unlabeled learning algorithms and operations: overview and future trends in defense sector

In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.

OpenEOcubes: an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes

In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.

Improving the Economic Efficiency of Heat Pump Integration into Distillation Columns of Process Plants Applying Different Pressures of Evaporators and Condensers

The electrification of process industries is one of the main challenges when building a low-carbon society since they consume huge amounts of fossil fuels, generating different emissions. Heat pumps are some of the key players in the industrial sector of the carbon-neutral market. This study proposes an approach to improve the economic feasibility of heat pumps within process plants. Initial energy targeting with grand composite curves was used and supplemented with the detailed design of an evaporator and a compressor for different condensation and evaporation pressures. The trade-off between the capital cost of the heat pump and the electricity cost was investigated, and optimal configurations were selected. This case study investigates the gas fractioning unit of a polymer plant, where three heat pumps are integrated into distillation columns. The results demonstrate that the heat recovery is 174 MW and requires an additional 37.9 MW of electricity to reduce the hot utility by 212 MW. The selection of the evaporation and condensation pressures of heat pumps allows 21.5 M EUR/y to be saved for 7 years of plant operation. The emission-saving potential is estimated at 1.89 ktCO2/y.

Essential Oils from Azorean Cryptomeria japonica Female Cones at Different Developmental Stages: Variations in the Yields and Chemical Compositions

Cryptomeria japonica (Thunb. ex L.f.) D. Don (Cupressaceae) is widely cultivated in the Azores archipelago (Portugal) for landscaping and timber production, generating a huge amount of biomass residues. Among these, female cones (FC) emerge as a source of particularly valuable essential oils (EOs), namely, as promising broad-spectrum antimicrobial agents. However, phytochemical research on C. japonica FC EOs remains scarce. This study evaluated the EO yields and chemical compositions of immature and mature FC (IFC and MFC) from the same batch of Azorean C. japonica. IFC and MFC EOs, obtained via hydrodistillation, presented different yields (0.72% vs. 1.12% w/w, dry weight), and their composition, determined via gas chromatography/mass spectroscopy, revealed that the monoterpenes class was dominant (51.84% vs. 69.13%), followed by the sesquiterpenes (36.44% vs. 26.28%) and diterpenes (10.96% vs. 3.03%) classes. However, the correspondent oxygen-containing terpenes classes were 1.5–2.0 times higher in IFC. Thus, the maturation process revealed an increase in EO yield and α-pinene content (19.5% vs. 41.3%) but a decrease in other important bioactive terpenes/terpenoids (terpinen-4-ol, bornyl acetate, elemol, γ-eudesmol, phyllocladene, and nezukol) content. In conclusion, IFC and MFC EOs, due to their unique composition, may have differential commercial applications and, consequently, contribute to increasing the commercial potential of C. japonica’s EO industry.

Improving Tenebrio molitor Growth and Nutritional Value through Vegetable Waste Supplementation.

Huge amounts of vegetable wastes are generated by the food industry. Their bioconversion into valuable products (e.g., insect flours or biofertilizer) through insect farming is a promising solution to reduce their negative environmental and economic impacts. This study evaluates the growth of Tenebrio molitor larvae and their nutritional profile after supplementing their diets with vegetable wastes. Over a 6-week period, 45-day larvae were fed a diet comprising wheat bran supplemented (1:1) with cucumber or tomato wastes from both conventional and ecological crops. The control diet consisted of wheat bran and an equivalent amount of water to compensate for the waste moisture. Larval weight was measured weekly, and length measures were taken fortnightly. Nutritional composition and fatty acid profile were analyzed at the end of the study in 90-day larvae. Regardless of using vegetable waste from conventional or ecological harvesting, the weight of 6-week supplemented larvae almost doubled that of larvae fed with just wheat bran, and their length was 15% higher. Supplementation also increased larval polyunsaturated fatty acid percentage by 22-37%, with linoleic acid being the most abundant. Likewise, larval protein content reached 50% after supplementation. This study demonstrates that both cucumber and tomato wastes from conventional or ecological crops are excellent supplements for T. molitor's diet, improving their nutritional value and reducing the time necessary for larvae growth.