Liquid exfoliated molybdenum disulfide (MoS₂) nanosheets and polyaniline (PANI) nanoparticles are dispersed in polystyrene (PS) matrix to fabricate hybrid polymer composites with high dielectric and electromagnetic interference (EMI) shielding behavior. A phase-separated morphology is formed when PANI and MoS₂ are incorporated into polystyrene (PS) matrix. An increasing concentration of MoS₂ nanoparticles inside PS/PANI (5 wt %) polymer blend forms an interconnected network, resulting in high electrical conductivity and dielectric behavior, making them a suitable candidate for EMI shielding application. An increment in dielectric constant and loss, up to four and five orders of magnitude, respectively, is recorded at a maximum concentration of 1 wt % of MoS₂ in PS/PANI-5 polymer blend at 100 Hz. The enhanced dielectric characteristics for PS/PANI/MoS₂ composites are then theoretically evaluated for the estimation of EMI shielding effectiveness in the frequency range of 100 Hz to 5 MHz. The maximum dielectric constant and loss achieved for PS/PANI-5 wt %/MoS₂-1 wt % are responsible for estimated shielding effectiveness of around 92 dB at 100 Hz. The increase in dielectric behavior and shielding effectiveness is probably due to the increased number of charged dipoles accumulated at the insulator–conductor interface.

The incredible advancements in data transmission technology have opened up more potentials for data security than ever before. Numerous methods for data protection have been developed during the previous decades, including steganography and cryptography. The security and integrity of medical data have emerged as major barriers for healthcare service systems as the Internet of Things has evolved dramatically in the healthcare business. Communication between two devices securely is a difficult problem. Numerous cryptographic algorithms are already available, including data encryption standard (DES), Rivest–Shamir–Adleman (RSA), and advanced encryption standard (AES). In this paper, we present a hybrid security model for the protection of diagnostic text data contained in medical photographs. The proposed model is built by combining a proposed hybrid encryption system with either a 2D Discrete Wavelet Transform 1 Level (2D-DWT-1L) or a 2D Discrete Wavelet Transform 2 Level (2D-DWT-2L) steganography technique. The suggested model encrypts secret data and hides them using 2D-DWT-3L. As text covers, color and grayscale images are employed. The suggested system’s performance was tested using PSNR, SSIM, MSE, and Correlation. Associated to state-of-the-art approaches, the proposed model masked personal patient data with high capacity, imperceptibility and minimum deterioration in the received stego-image. We use MATLAB to build the proposed mechanism, and measures such as throughput and execution time are used to assess performance.

Access to uninterrupted power is not a luxury but a basic need. Rural communities living far from the national grid, particularly those in the southern region of Pakistan’s Khyber Pakhtunkhwa province, have limited access to a reliable power supply. In order to provide sustainable electricity, small-scale off-grid renewable energy systems are increasingly used for rural electrification. These systems are commonly known as stand-alone home systems or community micro-grids. This paper proposes an off-grid solar–biogas micro-grid for rural communities in the Lakki Marwat district of Khyber Pakhtunkhwa, Pakistan. The area is mainly dependent upon income from the agricultural and livestock sectors. HOMER was used to simulate the electric power system, while RET-Screen was used to analyze the economics of the system. The optimized system’s results demonstrate that the most economically and technically possible system, which produces 515 kWh and 338.50 m³ biogas daily, is made up of a 30-kW photovoltaic system coupled with a 37-kW biomass hybrid system, a 64-kWh battery storage capacity, and a 20-kW invertor. The system will meet the cooking and power needs of 900 individuals who reside in 100 homes. In addition to household users, the system will provide fixed-priced electricity to productive buildings, and free electricity to community buildings. The system will generate 1300 kg of organic fertilizer each day, which will be sold to local farmers for 50% less than what it would cost on the open market. The proposed approach is techno-economically viable based on the payback period and internal rate of return.

In this research, silica nano-particles are deposited over the borosilicate glass and silicon wafer substrates by indigenously developed Aerosol impact deposition assembly using SiH₄, oxygen, and helium as precursors. The coating process involves deionization of gases leading towards nucleation sites for silica in the presence of plasma, while tuning the pressure difference between reaction and deposition chamber controls the coating thickness, porosity, and refractive index. The deposited coating layer on the substrate enhanced the transmittance to 99.6% at 600 nm wavelength. The induced porous nature and the graded index of the coated layer as observed from the AFM and SEM resulted in superhydrophilic behavior with a water contact angle of near to 0°. The super-hydrophilicity of the coating contains self-cleaning properties, suggesting an improvement of the performance of solar PV modules as well.

Sentiment Analysis (SA) is a novel branch of Natural Language Processing (NLP) that measures emotions or attitudes behind a written text. First applications of SA in healthcare were the detection of disease-related emotional polarities in social media. Now it is possible to extract more complex attitudes (rank attitudes from 1 to 5, assign appraisal values, apply multiple text classifiers) or feelings through NLP techniques, with clear benefits in cardiology; as emotions were proved to be veritable risk factors for the development of cardiovascular diseases (CVD). Our narrative review aimed to summarize the current directions of SA in cardiology and raise the awareness of cardiologists about the potentiality of this novel domain. This paper introduces the readers to basic concepts surrounding medical SA and the need for SA in cardiovascular healthcare. Our synthesis of the current literature proved SA's clinical potential in CVD. However, many other clinical utilities, such as the assessment of emotional consequences of illness, patient-physician relationship, physician intuitions in CVD are not yet explored. These issues constitute future research directions, along with proposing detailed regulations, popularizing health social media among elders, developing insightful definitions of emotional polarity, and investing research into the development of powerful SA algorithms.

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.01 kWh/${\mathrm{m}}^2/\mathrm{d}$. The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.01 kWh/m²/d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.13 kWh/${\mathrm{m}}^2/\mathrm{d}$, while the reduction for yearly was 91.6%, 0.11 kWh/${\mathrm{m}}^2/\mathrm{d}$, 0.2 kWh/${\mathrm{m}}^2/\mathrm{d}$. The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.16 kWh/${\mathrm{m}}^2/\mathrm{d}$, while the yearly values were 0.47%, 0.18 kWh/${\mathrm{m}}^2/\mathrm{d}$, and 0.004 kWh/${\mathrm{m}}^2/\mathrm{d}.$