Background: One of the many requirements for the packaging of pharmaceutical preparations is to provide active pharmaceutical ingredients (API) with the most effective protection against harmful external factors, including radiation. Insufficient isolation of the drug from the environment can significantly reduce its pharmaceutical properties and lead to ineffective pharmacotherapy. Therefore, a quick and effective assessment of the photoprotective properties of pharmaceutical packaging is of great importance in the drug manufacturing process. The aim of the study was to evaluate the total directional hemispherical reflectance (THR) for outer packaging (cardboard boxes) and those in direct contact with the drug (blister package) for tablets containing cefuroxime. Radiation leads to photoisomerization reactions and photolysis of the β-lactam ring, which is a key structural element for the antimicrobial activity of cephalosporin antibiotics.

Methods: Three types of measurement areas were analyzed within the packaging of four unexpired pharmaceuticals containing cefuroxime, i.e. white and colored areas within the cardboard outer package as well as a non-transparent blister made of aluminum and PVC. The THR was measured using SOC-410 Directional Hemispherical Reflectometer (USA) within a wide wavelength range from 335 nm to 2500 nm. Each of the selected areas was measured three times. To compare the results between the areas, Statistica 13 software was used.

Results: For the blister of each tested pharmaceutical product, the reflectance values changed the least between different wavelength ranges. Mean THR values varied significantly between blisters, white areas of the outer packaging, and colored areas of the outer packaging in each analyzed pharmaceutical (p<0.001). Blisters of all tested products showed the best photoprotection within the wavelength range from 335 nm to 380 nm, i.e. within UV radiation as well as within the infrared ranges of 1000-1700 nm, and 1700-2500 nm compared to white and colored areas of outer packaging (p<0.001 each). In turn, the white outer package had the best photoprotection of the tablets within the radiation ranges of 400-540 nm, 480-600 nm, 590-720 nm and700-1100 nm (p<0.001 each), which covered visible light and near-infrared.

Conclusions: Aluminum blisters and white cardboard packaging of pharmaceutical preparations protect the solid dosage forms against radiation to the greatest extent.

The study was funded within the project PCN-1-058/K/2/O.

According to data from the BUET Accident Research Institute (ARI), in 2022, motorbikes accounted for 62% of all vehicles on the road, with 26 accidents occurring for every 10,000 motorcycles making up the majority of all traffic accidents in Bangladesh. This is due to their accessibility, affordability, and ride-sharing use. Hence, it is essential to investigate the risk factors that contribute to motorcycle accidents, how they affect risk assessment, and how to develop the necessary policy implications.
Data on perceived risk were gathered for this study from 1,559 participants in offline and online questionnaire surveys. Demographic data together with rantings on the perceived risk of 37 precursors to motorcycling accidents in the setting of Dhaka were gathered. Then ten combined attributes were identified from all precursors. With a 73% prediction accuracy, the Random Forest algorithm has been utilized to predict perceived risk. Moreover, the contribution of different precursors on safety status has been demonstrated by structural equation modeling. Lastly, different contour maps for features’ correlation, heat map, deployment of result using flask in public server for user interface (which allows model accessible to a wider audience & receive predictions), and policy implications have been analyzed in this study. In conclusion, any developing country's urban context will benefit greatly from the provided prediction tools for accident analysis and prevention.

Machine Learning Algorithms are integrated into Computer-Aided Design (CAD) methodologies to support medical practitioners in diagnosing patient disorders. This research seeks to enhance the accuracy of classifying malaria-infected erythrocytes (RBCs) through the fusion of Machine Learning Algorithms, resulting in a hybrid classifier. The primary phases involve data preprocessing, segmentation, feature extraction, and RBC classification. This paper introduces a novel hybrid Machine Learning Algorithm, employing two combinations of supervised algorithms. The initial combination encompasses Stochastic Gradient Descent (SGD), Logistic Regression, and Decision Tree, while the second employs Stochastic Gradient Descent (SGD), Xgboost, and Random Forest. The proposed approach, implemented using Python programming, presents an innovative hybrid Machine Learning Algorithm. Through a comparative analysis between individual algorithms and the proposed hybrid algorithm, the paper demonstrates heightened accuracy in classifying malaria data, thus aiding medical practitioners in diagnosis. Among these algorithms, SGD, Logistic Regression, and Decision Tree yield individual accuracy rates of 90.63%, 92.23%, and 93.43% respectively, while the hybrid algorithm achieves 95.64% accuracy on the same dataset. The second hybrid algorithm, combining SGD, Xgboost, and Random Forest, outperforms the initial hybrid version. Individually, these algorithms achieve accuracy rates of 90.63%, 95.86%, and 96.11%. When the proposed hybrid algorithm is applied to the same dataset, accuracy is further enhanced to 96.22%.

Corrugated board is an environment-friendly, commonly used packing material. Its basic structure consists of two liners and a flute between them. Mechanical properties and strength of corrugated board depend on constituent papers but also on its geometry. Which , however, can be distorted due to various factors related to its manufacture process or use. The greatest distortion occurs in the corrugated layer, which, due to crushing, significantly deteriorates functional properties of cardboard. In this work, two algorithms for automatic classification of corrugated board types based on images of deformed corrugated boards using artificial intelligence methods are presented. A prototype of corrugated board sample image acquisition device was designed and manufactured. It allowed to collect an extensive database of images with corrugated board cross-sections of various types. Based on this database, two approaches for processing and classifying them were developed. The first method is based on identification of geometric parameters of the corrugated board cross-section using a genetic algorithm. After this stage, a simple feedforward neural network was applied to classify the corrugated board type correctly. In the second approach, the use of a convolutional neural network for corrugated board cross-section classification was proposed. The results obtained using both methods were compared, and the influence of various imperfections in the corrugated board cross-section was examined.

Mooring systems are incorporated from mooring (Thin) lines that are constituted of fiber ropes, steel wires, and chains. Mooring systems are used for station keeping of floating units during the drilling process of oil and gas from offshore deep water and unloading of productions to the shuttle storage tanker. However, it is crucial to monitor the mooring system for early-stage failure detection in mooring lines during the offshore mooring operation to avoid any unexpected losses including human injuries, and catastrophic failure. This paper addresses the challenges of mooring line detection and proposes a deep learning-based approach for the detection of mooring lines from marine images using the bounding box. A convolutional neural network, Inception V3 is used for the detection and classification of thin line objects from marine images and it is a pre-trained model with 1000 classes. Besides, a framework has been designed that shows the step-by-step procedure for the detection of mooring line objects from images. Furthermore, various testing samples have been evaluated for assessing the performance of the pre-trained proposed model. According to the results, it has been observed that the proposed model obtained 87.63% highest accuracy in classifying the mooring line objects from images and failed to accurately detect mooring lines. Furthermore, in a few highlighted cases, the performance of the model was decreased in terms of accuracy due to misclassification and wrong detection of mooring line objects. Despite this, the proposed study furnishes a potential solution for the detection of failure in mooring lines from marine images.

Perovskite solar cells (PSCs) have drawn much attention in recent years due to their high efficiency at a lower cost. However, lack of stability restricts the wide use of PSC in modern technology. Reported literature shows, 2D perovskite material provides better tunability and a reduced number of defects in the crystal structure, leading to increased stability compared to 3D-perovskite. Although, 2D-PSCs still face challenges such as lower power conversion efficiencies compared to 3D perovskites and greater susceptibility to moisture and temperature changes. Therefore, ongoing research efforts are focused on improving the performance and stability of 2D perovskite solar cells. Furthermore, mixed-dimensional (2D/3D) perovskite solar cells are also expected to provide substantial stability and higher efficiency. This research is focused on optimizing the mixed dimensional (2D/3D) perovskite solar cells BA₂MA₂Pb₃I₁₀ and BA₂MA₂Pb₄I₁₃ as 2D and with MAGeI₃ as 3D perovskite, which results in a 27.15% and 27.58% increase in efficiency through the simulations handled by SCAPS-1D software. A detailed study of the structure and distinctive features of 2D and mixed dimensional 2D-3D perovskites has also been studied. Furthermore, the band alignment of 2D/3D perovskite solar cells has been studied by analyzing the structural and chemical composition. The effect of defect densities on 2D-3D mixed perovskites and on PSCs has also been presented.

In order to plan and manage low-carbon investments in wide real estate assets, strategic approaches should be considered to act on building stocks as a whole, with the aim of overcoming the single-building perspective and identify the energy retrofit level leading to the maximum possible benefit.

However, decarbonization programs of urban compartments require highly detailed information about the energy-use and energy-efficiency-potentials of a huge amount of buildings, which ends up being a problem of mass-appraisal and screening evaluation.

The subject of this study is, therefore, the development of a decision support system for the planning and management of energy retrofit operations in large building portfolios. Energy improvement is here treated as an optimisation problem in which conflicting objectives and constraints are balanced. In order to develop the decision-making model, several techniques from various disciplines including statistics, economics, energy simulation, computer programming, optimisation and risk analysis were combined.

First, a set of four neural networks is developed to assess the energy consumption of buildings due to heating, cooling, hot water and electricity, based on deep learning and artificial intelligence procedures. Next, different energy-retrofit options are suggested, and different possible alternative intervention scenarios are determined, where a scenario represents any combination of the retrofit options on each building in the building stock. Three performance indices are then estimated to assess the benefits produced by each possible retrofit scenario in energy, economic and cultural terms. The energy savings are estimated using the neural networks, the monetary benefits are calculated on the basis of a Life Cycle Costing approach, while the cultural aspects are evaluated in terms of material and architectural compatibility of the retrofit measures with the building; in particular, it is with an Analytic Hierarchy Process, developed by interviewing a panel of ten experts in the field of energy retrofit, that the architectural compatibility of interventions is quantified through the estimation of a 'compatibility score'. It is then with a multi-attribute optimisation strategy that an evolutionary algorithm tests all possible retrofit scenarios until the optimal configuration is identified, i.e. the one that simultaneously maximises the three performance indices, respecting the domino of feasibility. Finally, a Monte Carlo simulation verifies the risk associated with the chosen retrofit configuration.

Multi-agent systems (MAS) have gained a lot of interest recently, due to their ability to solve problems that are difficult or even impossible for an individual agent. However, an important procedure that needs attention in designing multi-agent systems, and consequently applications that utilize MAS, is achieving a fair agreement between the involved agents. Researchers try to prevent agreement manipulation by utilizing decentralized control and strategic voting. Moreover, emphasis is given to local decision-making and perception of events occurring locally. This manuscript presents a novel secure decision-support algorithm in a multi-agent system that aims to ensure the system’s robustness and credibility. The proposed consensus-based model can be applied to production planning and control, supply chain management, and product design and development. The algorithm considers an open system i.e., the number of agents present can be variable in each procedure. While a group of agents can make different decisions during a task, the algorithm chooses one of these decisions in a way that is logical, safe, efficient, fast and is not influenced by factors that might affect production.

The article touches upon the development of dispersed reinforced concrete components and the improvement of their physical and mechanical properties, which can be used in road and defense structures, bridges, and takeoff and landing zones, i.e. To study the properties of dispersed reinforced concrete with basalt fibers, according to the standard requirements, water permeability and strength determination tests were carried out by changing the type and amount of additives used and the length and quantitative content (concentration) of basalt fibers. Multifunctional micro-reinforced fine-grained concrete compositions have been developed based on basalt fiber, where the limit of compression strength varies from 65.6 to 78.35MPa, flexural strength from 6.4 to 9.1MPa, and water permeability from 3.7 to 1.8%. Among the compositions of micro-reinforced concrete with basalt fiber, the best strength result was recorded in the case of 2% basalt fiber and 10% micro silica, with compression and flexural strengths of 78.35 and 9.1MPa, respectively. The best water absorption result of 1.8% was obtained only with basalt fiber concrete when the fiber content was increased to 3.2%. As a result, water absorption was reduced by 62% compared to the initial concrete. The increases in flexural and compression strengths were 42.19% and 13.8%, respectively.

Abstract

As an alternative to traditional photovoltaic semiconductors, perovskite materials like ABX₃ have recently caught the interest of researchers. These materials' unique physical traits and specific gap value, which have a significant impact on their overall effectiveness and performance, are what essentially led to this shift in attention. Using the ab initio method calculations. The structural and electrical characteristics of CaSiO₃, a tetragonal compound, are investigated in this work using first-principles calculations based on the full potential-linearized augmented plane wave technique (FP-LAPW) within the density functional theory (DFT). Our study thoroughly examines electrical properties, such as band structure and density of states (DOS), in order to predict CaSiO₃ viability as a potential photovoltaic material. CaSiO₃ is a promising candidate for future exploration because preliminary results indicate that it exhibits semiconductor properties.