The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.

Multi-level transformerless inverters are widely used in grid-tied PV systems since they characterized by higher efficiency and lower cost. In this context, new topologies, modulation, and control schemes were presented to solve problems of a common-mode voltage and leakage current. This work proposes a transformerless five-level inverter with zero leakage current and ability to reduce the harmonic output content for a grid-tied single-phase PV system. The neutral of the grid links to a common on which the negative and the positive terminals of the DC-link are connected via parasitic capacitors that can eliminate the leakage current. The proposed topology, with its inherent circuit structure, leads to boost the overall efficiency. Simulation and experimental results show almost zero leakage current and a high-quality output when maintaining balanced capacitor voltages on the DC-link input. The experimental results show 1.07% THD and 96.3% maximum efficiency when injecting a power of 1.1 kW that verify the performance of the proposed inverter with PV sources.

Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.

Building-integrated solar photovoltaic (BIPV) systems have gained attention in current years as a way to recover the building’s thermal comfort and generate sustainable energy in building structures. BIPV systems can provide shade against sunshine while generating ancillary electrical power. Over the last decades, engineers have been trying to improve the efficiency of BIPV systems. BIPV systems with various installation types, including rooftop, balcony, curtain, sunshade, and wall façade types, are being constantly researched and intensively presented for improving power efficiency and reducing air-conditioning use. This work provides an overview of solar BIPV systems and focuses particularly on existing applications of the bifacial type of BIPV systems. The motivation and an overview of BIPV systems are first introduced, followed by the study methodology considered and the contributions. This work discusses PV technologies of bifacial PVs (monocrystalline and polycrystalline bifacial modules), BIPV installation [curtains, rooftop, flat rooftop, transparent faced, balcony windows (transparent), wall opaque facade, flat roof-faced, and skylight sunshade types], simulation and optimization software (simulation software and future trends), zero-energy BIPV technology, and optimization techniques of BIPV systems. Last, suggestion amendments to the current BIPV design that possibly contribute to growing the system’s effectiveness, reliability, and cost as future design theories for the whole system are presented.

Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.

In the literature, there are many different photovoltaic (PV) component sizing methodologies, including the PV/inverter power sizing ratio, recommendations, and third-party field tests. This study presents the state-of-the-art for gathering pertinent global data on the size ratio and provides a novel inverter sizing method. The size ratio has been noted in the literature as playing a significant role in both reducing power clipping and achieving system optimization. The majority of researchers observed that due to varying irradiance distributions and operating temperatures at particular sites, the sizing ratios were dependent on geographic latitude. This study will identify the issue that makes it challenging to acquire dependable and optimum performance for the use of grid-connected PV systems by summarizing the power sizing ratio, related derating factor, and sizing formulae approach. The present study recommends a Deep Learning technique that might, due to the dynamic behavior of the PV technologies, provide fully automatic computation for the DC/AC sizing ratio, and effectively lower the whole return on investment (ROI) over a variety of circumstances and climatic changes.