The medical industry has made significant strides thanks to the use of many autonomous systems to identify various ailments in this day, surrounded by numerous technology. A crucial medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears. It may offer helpful details about the patient’s health, such as the identification of Acute Lymphatic Leukemia or other serious illnesses.
In this study, a framework for recognizing acute lymphoblastic leukemia from a white blood cell's microscopic picture is proposed. Microscopic images must first undergo a thorough pre-processing step in order to be classified. In this study, a collection of textural, geometrical, and statistical features are retrieved from the segmented region following the segmentation of WBCs from blood smear pictures and morphological procedures.
In order to compare these algorithms in terms of various performance measures, four distinct machine learning techniques—namely, Random Forest (RF), Support Vector Machine (SVM), Naive Bayes classifier (NB), and K nearest neighbor (KNN) are also deployed. After careful comparison, it can be seen that the SVM is effective at classifying and identifying the acute lymphoblastic cell that causes leukemia malignancy.
A single classifier is nearly useless given the variety of blood smear pictures. As a result, we thought about using an EMC-SVM to classify leukocytes. The suggested method properly separates WBCs from blood smear images, according to experimental findings, and correctly classifies each segmented cell into its relevant category, which includes neutrophil, eosinophil, basophil, lymphocyte, and monocyte.

This work uses three-dimensional green and biodegradable adsorbent from cellulose nanocrystals and a machine learning technique to simulate and optimize the removal of zinc (II) from synthetic acid mine drainage. The adsorption process was modelled and optimized using three machine learning algorithms: Response Surface Methodology (RSM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN). The predictive modelling of the RSM, ANN and ANFIS models demonstrated good correlation with R² of 0.987, 0.992 and 0.997 respectively. According to the findings, the created models successfully predicted the adsorption behaviour, with the AMFIS model performing best with the lowest error rate. Low values of calculated error functions of MPSD (ANFIS=0.0108; RSM=0.0199 and ANN=0.0122), RMSE (ANFIS=0.0015; RSM=0.0218 and ANN=0.01328), ARE (ANFIS=0.0011; RSM=0.0118 and ANN=0.0153, and HYBRID (ANFIS=0.0005; RSM=0.0331 and ANN=0.0098) indicated good harmony between experimental values and models’ predictions. The result showed that the order of the models’ effectiveness for Zinc (II) removal is: ANFIS > ANN > RSM. RSM was used to optimize the process, and the ideal conditions for maximal Zinc (II) removal efficiency were established. Initial pH of 6, contact time of 300 min, initial concentration of 250 mg/L, and sorbent dose of 15 mg and adsorption capacity of 350.23 mg/g was the optimal condition. The isotherm investigation demonstrated that the Freundlich isotherm with R² of 0.995 best represented the equilibrium modelling, however the kinetic analysis revealed that the pseudo second order (R² = 0.998) and Elovich (R² = 0.995) models better accounted for the kinetics of the experimental data. The study's findings might help develop cost-effective and efficient systems for treating polluted water supplies.

The prevalence of chronic diseases and the growing geriatric population make consumers across the globe are becoming health-conscious. They are now shifting from chemically derived products to preventive healthcare items like nutraceuticals that contain safer, natural, and healthier ingredients. Also, there is an increasing number of health and fitness clubs drive dominance of sports and energy drinks in functional foods and beverages which lead companies to launch new products and business. In this work, we studied the potential of supercritical fluid CO₂ extraction technology without co-solvent to produce a healthy product from almond wastes because their content of minerals and phenolics. The analysis of phenolics in the resulting extracted product was performed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and showed vanillin, catechin and the acids dihydroxybenzoic, vanillic and syringic as main phenolic compounds (PC). In addition, the analysis of minerals carried out by Inductively Coupled Plasma Optical Emission spectroscopy (ICP-OES) showed a wide range of macroelements like Magnesium (Mg) and Potassium (K) in quantities up to 1.7g/kg (Mg) and 6 g/kg (K), so that represent a value matrix to be integrated into functional drinks targeting sporty people while promoting the circular economy and the food upcycling.

Hydroxymethylfurfural (HMF) is one of the compounds formed due to the heat treatment and storage of honey. The maximum level of HMF in honey has been set at 40 ppm under codex standards. In this study, the effects of heating temperature (55, 65 and 75°C), heating time (10, 20 and 30 min) as well as storage temperature (25 and 40°C) were assayed during the three months of storage based on response surface methodology (RSM). The effect of the above-mentioned variables on physicochemical properties (Lab color factors, pH and moisture) and the samples' HMF content (based on spectrophotometric technique) was studied. The prediction model of each treatment was calculated. The outcomes during the 45 and 90 days of storage were analyzed. Results showed that temperature, time of heat treatment and storage duration did not affect pH, moisture content and color; while storage temperature had a significant effect on L^* and a^*. HMF content was affected by all the variables, so its rate increased significantly with increasing thermal process and storage time. Among the studied samples, HMF content exceeded the standard limit in the sample heated at 75°C for 20 min and kept at 40°C for 90 days. The optimal level of HMF resulted by heating at 55°C for 10 min and under the storage temperature of 25°C for 45 days.

Steam generator (SG) is the key equipment in the energy transfer process of nuclear power plant, and its level control is particularly important for the safe and stable operation of nuclear power plant. In the commissioning process of nuclear power plant, it is often necessary to adjust the control parameters of the steam generator level control system (SGLCS) to achieve performance optimization. Traditional solutions include model-based optimization (MBO) and model-free optimization (MFO), in which MBO depends on the accurate relationship model between control parameters and control performance. However, the level process of steam generator is time-varying and highly nonlinear, which makes it difficult to establish the model accurately. In addition, MFO is implemented without considering any prior information, and its optimization efficiency is also restricted to a certain extent. In order to make full use of prior data information and integrate the respective advantages of MBO and MFO, this paper proposes a multi-source hybrid data-driven optimization method based on the prior model accuracy (PMA-MDO) on the basis of the data-driven idea and stochastic approximation algorithm. Firstly, the method uses the prior data information to construct the initial optimization model. Then, the current iteration point is tested into the actual working condition and prior model to evaluate the accuracy of the local area of the current model. When the accuracy of the model meets the requirements, the model gradient estimation is used; otherwise, the online gradient estimation is used. Afterwards, a new iteration point is obtained by using step size calculation. Finally, the iteration termination criterion based on historical running data is taken as the judgment principle. If the new iteration point meets the iteration termination criterion, the optimal value will be output; otherwise, the current iteration data will be fused with the prior model for model reconstruction, and the iterative optimization process will be repeated until the system iteration process is optimal. In this paper, the PID parameter optimization tuning of three-impulse steam generator level control system is taken as an example. The simulation results show that this method has better optimization performance than the traditional SPSA, and can significantly improve the efficiency of steam generator level control performance optimization.

Due to the linear nature of the textile industry, vast amounts of non-renewable resources are utilized to manufacture low-cost clothing that is only worn a few times, leading to significant issues such as fiber waste and excessive water consumption. This study endeavors to demonstrate the sustainable production of an outdoor jacket, from fiber to finished product. A literature review is conducted, with a particular emphasis on utilizing waste-derived fibers and innovative processing techniques. The practical section of the report outlines the creation of a transformable jacket design with a zero-waste pattern. The goal is to inspire innovative thinking on sustainability in garment production and showcase the possibility of producing a stylish, long-lasting, and water-resistant jacket without further resource consumption. The primary aim of this research was to investigate the potential for upcycling waste materials to create a fashionable outdoor jacket. The focus is on using innovative, non-toxic materials sourced from waste and recycled products from the textile and other industries that are available in sufficient quantities. Moreover, the paper presents water-conserving, innovative processes and environmentally-friendly textile finishing materials. Despite the numerous challenges facing the fashion industry, the focus was placed on repurposing waste materials generated by the textile industry or other sectors. As demonstrated in this study, the production of such a sustainable jacket is nearly attainable. Although innovative processing techniques that minimize water and energy consumption and eliminate wastewater discharge are not yet widely available, a range of sustainable solutions is possible. Not only can small, sustainable brands make a difference, but even fast fashion companies can begin to effect change now.

The paper comprises a theoretical and practical section. The former outlines the materials and processes used to manufacture the outdoor jacket, while the latter concentrates on the actual implementation. One of the researchers created the design and zero-waste pattern for the jacket, which was then sewn in parts to demonstrate the efficacy of the transformable design. The goal was to create a stylish and practical outdoor jacket that could be worn in various weather conditions and seasons, thanks to its adaptable design. The final product was inspired by current trends, with the aim of introducing the jacket to the market. The intention was to create a garment for everyday use that would be sustainable, durable, and versatile, thanks to the transformable aspect of the design.

A worldwide energy crisis and increased greenhouse gas emissions are driving the search for renewable energy sources. The use of microalgae biofuels is expected to replace fossil fuels as a major source of renewable energy for sustainable development. A wide range of micro- and macroalgae have been explored as potential biofuel feedstocks. Most algal biomass also contains polysaccharides (sugars), pigments, minerals, antioxidants, and lipids (triglycerides), which are the raw materials for the production of bioethanol and biodiesel. Oil crops and lignocellulose-based biofuels suffer from major drawbacks, but microalgae biofuel has none. Algae-based biofuels have a number of advantages: a) Algae grow fast, b) High biofuel yields can be achieved with algae, c) Algae do not compete with agriculture, d) The microalgal biomass can be used as a fuel, feed, and food source, e) Algae can purify wastewater, f) Algal biomass can be used as an energy source, g) Algae can be used to produce many useful products, h) The algae industry is a job-creation engine including being technically and economically viable, cost-competitive, consuming minimal water, and emitting the least amount of CO_2, . The algae are considered a clean renewable energy source, as they do not pollute the environment.

Fibre reinforced hybrid composites is made by reinforcing a matrix with two or more types of fibres. For layered composite materials, it is shown from previous research that the flexural strength can be improved by hybridising carbon and glass fibres. The strain-to-failure is improved by including higher strain-to-failure glass fibre plies. The existence of hybrid effect can be potentially useful for achieving a balanced cost and weight optimal composite material.

The flexural properties of hybrid composites are affected by many parameters including fibre volume fraction, orientation of fibre, and degree of hybridisation or hybrid ratio. Finding the optimal configuration given the required flexural strength and/or flexural stiffness is not a trivial task. Traditional optimisation methods are usually based on non-dominated sorting GA-II (NSGA-II), and are very time-consuming so infeasible for practical applications.

In this paper, an optimisation method based on Design of Experiments (DoE) is presented. A factorial design is constructed and the response surfaces for the flexural strength and stiffness are obtained. The optimal design can be conveniently derived using these response surfaces.

Heavy use of mineral fertilizers unbalances biological processes in the soil. It is therefore necessary to develop more effective fertilizers or fertilizer complexes that ensure agricultural productivity and soil conservation. The hypothesis of this study is that complex mineral fertilizers (N₅P_20.5K₃₆) coated with a bacterial inoculant (Paenibacillus azotofixans, Bacillus megaterium, Bacillus mucilaginosus, Bacillus mycoides provided by JSC Nando, Lithuania) have a positive effect on the agrochemical composition of the soil and on the yield of spring barley. Experimental studies were carried out between 2020 and 2022 on sandy loam soil in four different treatments: no N₅P_20.5K₃₆ (control), 300 kg ha^-1 N₅P_20.5K₃₆ (Tr-1), 150 kg ha^-1 N₅P_20.5K₃₆ coated with a bacterial inoculant (Tr-2) and 300 kg ha^-1 N₅P_20.5K₃₆ coated with a bacterial inoculant (Tr-3). A positive effect of the bacterial inoculant on barley grain yield was found: in 2020, Tr-3 yielded 23% higher than the control and 8% higher than Tr-1. In 2021, yields were 67% and 7% higher, and in 2022, yields were 1.3 times and 17% higher than the control and Tr-1, respectively. The bacterial inoculant was found to slightly increase the potassium and phosphorus content in the soil. Comparing the spring results of 2020 and 2022, Tr-3 soil contained 14 mg kg^-1 more potassium and 13 mg kg^-1 more phosphorus than Tr-1 and comparing the autumn results of 2020 and 2022, 2 mg kg^-1 and 11 mg kg^-1, respectively. This means that the bacterial complex used helps to release phosphorus and potassium in the soil. The bacterial inoculant-enriched fertilizer can increase the yield of barley grain without exhausting the soil.

During the last ten years perovskite solar cell (PSC) technology gained the world scientific interest because of some peculiar features as the high absorption coefficient and the carrier transportation that permit to reach efficiencies about 26%. A typical PSC can be obtained by a n-i-p junction where the perovskite is the intrinsic semiconductor sandwiched between a p-type (HTM, Hole Transporting Material) and n-type (ETM, Electron Transporting Material) semiconductor. The HTM is the main source of instability within the solar cell stack. In the standard structure, on top of the HTM a metal counter-electrode is thermally evaporated. Gold is the most used counter-electrode for high efficiency cells, but it is corroded by halogen ions. For these reasons, the HTM and the metal top-electrode can be replaced by a cheap low temperature firing carbon black/graphite layer. Carbon-based perovskite solar cells (C-PSCs) are a cell concept introduced to address the issues of instability, manufacturing complexity and high costs. Low temperature carbon-based electrodes have been widely applied in perovskite solar cells because of their chemical inertness and compatibility with up-scalable techniques, signifying their solid potential for mass-production. If the low-cost perspective is achieved through the carbon electrode, few works about module upscaling processes are present in literature. In this work, we engineered the process to fabricate low temperature carbon-based cells and modules.