A series of new thiourea derivatives of 1,3-thiazole have been synthesized. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds were also tested for their in vitro tuberculostatic activity against the Mycobacterium tuberculosis H37Rv strain, as well as two ‘wild’ strains isolated from tuberculosis patients. Compounds 3 and 9 showed significant inhibition against Gram-positive cocci (standard strains and hospital strain). The range of MIC values is 2–32 µg/mL. Products 3 and 9 effectively inhibited the biofilm formation of both methicillin-resistant and standard strains of S. epidermidis. The halogen atom, especially at the 3rd position of the phenyl group, is significantly important for this antimicrobial activity. Moreover, all obtained compounds resulted in cytotoxicity and antiviral activity on a large set of DNA and RNA viruses, including Human Immunodeficiency Virus type 1 (HIV-1) and other several important human pathogens. Compound 4 showed activity against HIV-1 and Coxsackievirus type B5. Seven compounds resulted in cytotoxicity against MT-4 cells (CC50<10 µM).

In the evolving field of human–computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.

Purpose: The purpose of this study is comparison of the diffusion processes performed using the commercial available dopant paste made by Filmtronics and the original prepared liquid dopant solution. To decrease prices of industrially produced silicon-based solar cells, the new low-cost production processes are necessary. The main components of most popular silicon solar cells are with diffused emitter layer, passivation, anti-reflective layers and metal electrodes. This type of cells is prepared usually using phosphorus oxychloride diffusion source and metal pastes for screen printing. The diffusion process in diffusion furnace with quartz tube is slow, complicated and requires expensive equipment. The alternative for this technology is very fast in-line processing using the belt furnaces as an equipment. This approach requires different dopant sources. Design/methodology/approach: In this work, the diffusion processes were made for two different types of dopant sources. The first one was the commercial available dopant paste from Filmtronics and the second one was the original prepared liquid dopant solution. The investigation was focused on dopant sources fabrication and diffusion processes. The doping solution was made in two stages. In the first stage, a base solution (without dopants) was made: dropwise deionized (DI) water and ethyl alcohol were added to a solution consisting of tetraethoxysilane (TEOS) and 99.8 per cent ethyl alcohol. Next, to the base solution, orthophosphoric acid dissolved in ethyl alcohol was added. Findings: Diffused emitter layers with sheet resistance around 60 Ω/sq were produced on solar grade monocrystalline silicon wafers using two types of dopant sources. Originality/value: In this work, the diffusion processes were made for two different types of dopant sources. The first one was the commercial available dopant paste from Filmtronics and the second one was the original prepared liquid dopant solution.

Low-density parity-check (LDPC) codes are known to be one of the best error-correction coding (ECC) schemes in terms of correction performance. They have been utilized in many advanced data communication standards for which the codecs are typically implemented in custom integrated circuits (ICs). In this paper, we present a research work that shows that the LDPC coding scheme can also be applied in a system characterized by highly limited computational resources. We present a microcontroller-based application of an efficient LDPC encoding algorithm with efficient usage of memory resources for the code-parity-check matrix and the storage of the results of auxiliary computations. The developed implementation is intended for an IoT-type system, in which a low-complexity network node device encodes messages transmitted to a gateway. We present how the classic Richardson–Urbanke algorithm can be decomposed for the QC-LDPC subclass into cyclic shifts and GF(2) additions, directly corresponding to the CPU instructions. The experimental results show a significant gain in terms of memory usage and decoding timing of the proposed method in comparison with encoding with the direct parity check matrix representation. We also provide experimental comparisons with other known block codes (RS and BCH) showing that the memory requirements are not greater than for standard block codes, while the encoding time is reduced, which enables the energy consumption reduction. At the same time, the error-correction performance gain of LDPC codes is greater than for the mentioned standard block codes.

Thirty new derivatives of palmitic acid were efficiently synthesized. All obtained compounds can be divided into three groups of derivatives: Thiosemicarbazides (compounds 1–10), 1,2,4-triazoles (compounds 1a–10a) and 1,3,4-thiadiazoles (compounds 1b–10b) moieties. ¹H-NMR, ¹³C-NMR and MS methods were used to confirm the structure of derivatives. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds 4, 5, 6, 8 showed significant inhibition against C. albicans. The range of MIC values was 50–1.56 μg/mL. The halogen atom, especially at the 3rd position of the phenyl group was significantly important for antifungal activity. The biological activity against Candida albicans and selected molecular descriptors were used as a basis for QSAR models, that have been determined by means of multiple linear regression. The models have been validated by means of the Leave-One-Out Cross Validation. The obtained QSAR models were characterized by high determination coefficients and good prediction power.