Perspectives of chalcopyrite-based CIGSe thin-film solar cell: a review

Solar photovoltaic (PV) is empowering, reliable, and ecofriendly technology for harvesting energy which can be assessed from the fact that PV panels with total electricity generation capacity of 505 GW have been installed by the end of 2018. Thin-film solar cells based on copper indium gallium selenide (CIGSe) are promising photovoltaic absorber material owing to an alternative to crystalline silicon (c-Si)-based solar cells because of the huge potential for low-cost solar electricity production with minimal usage of raw materials. The efficiency record of 23.4% was achieved recently in CIGSe solar cells, which was comparable to c-Si solar cells (27.6%). The manufacturing cost of $0.34/W is expected for 15% efficient CIGSe module. The present review article discusses the perspectives of CISe/CIGSe-based thin-film solar cells with the focus on absorber material. Different vacuum and non-vacuum techniques for fabricating these materials are discussed along with the operation of solar cells and their manufacturability. The working mechanism of CIGSe solar cells with the characteristic features of the open-circuit voltage and current density as well as the factors influencing the efficiency in different fabrication techniques are reviewed. Moreover, some strategies toward the improvement of solar cells performance contemplating modified deposition are reviewed. Furthermore, how these strategies can be executed in order to make it cost effective methods is also discussed in detail. Prevailing constrictions for the commercial maturity are deliberated, and future perspectives for improvement at lab as well as industrial scalabilities are outlined

A Survey on Industrial Control System Testbeds and Datasets for Security Research

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-by-design concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community. In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic