Effects of metformin on cancers in experimental and clinical studies: Focusing on autophagy and AMPK/mTOR signaling pathways.

Metformin (MET) is a preferred drug for the treatment of type 2 diabetes mellitus. Recent studies show that apart from its blood glucose-lowering effects, it also inhibits the development of various tumours, by inducing autophagy. Various studies have confirmed the inhibitory effects of MET on cancer cell lines' propagation, migration, and invasion. The objective of the study was to comprehensively review the potential of MET as an anticancer agent, particularly focusing on its ability to induce autophagy and inhibit the development and progression of various tumors. The study aimed to explore the inhibitory effects of MET on cancer cell proliferation, migration, and invasion, and its impact on key signaling pathways such as adenosine monophosphate-activated protein kinase (AMPK), mammalian target of rapamycin (mTOR), and PI3K. This review noted that MET exerts its anticancer effects by regulating key signalling pathways such as phosphoinositide 3-kinase (PI3K), LC3-I and LC3-II, Beclin-1, p53, and the autophagy-related gene (ATG), inhibiting the mTOR protein, downregulating the expression of p62/SQSTM1, and blockage of the cell cycle at the G0/G1. Moreover, MET can stimulate autophagy through pathways associated with the 5' AMPK, thereby inhibiting he development and progression of various human cancers, including hepatocellular carcinoma, prostate cancer, pancreatic cancer, osteosarcoma, myeloma, and non-small cell lung cancer. In summary, this detailed review provides a framework for further investigations that may appraise the autophagy-induced anticancer potential of MET and its repurposing for cancer treatment.

Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability.

The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.

Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management.

Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.

Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.

Application of artificial intelligence in drug design: A review.

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

Advances in phase change materials and nanomaterials for applications in thermal energy storage.

Phase-changing materials are nowadays getting global attention on account of their ability to store excess energy. Solar thermal energy can be stored in phase changing material (PCM) in the forms of latent and sensible heat. The stored energy can be suitably utilized for other applications such as space heating and cooling, water heating, and further industrial processing where low-temperature heat energy is required. The presented work attempts to evaluate past, present, and future trends in the development of energy storage materials and their encapsulation techniques for efficient utilization of the available energy. Hybrid PCM with nanoparticles has excellent potential to tailor thermo-physical properties and uplift the efficiency of energy storage systems. Synergistic use of PCM with nanomicromaterial can further improve the capacity of energy storage system along with the charging and discharging efficiencies of the system. Impacts of the size of particle, concentration ratio, and shape of particle have been studied to assess their effectiveness in enhancing storage efficiency of the systems. Waste heat recovered and stored in energy storage materials can undoubtedly improve the total energy availability of the source, thus enhancing the exergy efficiency with simultaneous reduction in the entropy generation rate. Core-shell nanoparticles can further improve the optical absorptance spectra towards an infrared region of thermal energy. Paraffin wax-based NEPCMs with graphene nanoplatelets achieve 2.14 W/(m·K) thermal conductivity, enabling faster and more efficient heat transmission and lowering charging and discharging times for thermal storage devices.

Perception of lean construction implementation barriers in the indian prefabrication sector.

Prefabricated construction, increasingly recognized as a sustainable method, enhances productivity while mitigating the drawbacks of traditional approaches. Lean construction, pivotal for sustainability, targets waste reduction and cost efficiency while delivering value to customers. In India's prefabrication sector, numerous barriers impede the implementation of lean principles, necessitating their identification and resolution to advance lean practices. This study aims to identify and analyze primary barriers to implementing lean principles within India's prefabrication industry, focusing on professionals' perceptions. Employing exploratory factor analysis, it examines these barriers' interconnections and causal relationships, providing actionable recommendations for enhanced lean construction effectiveness. Through a review of the literature, 26 significant barriers were identified and primary data was obtained with the help of a questionnaire. 25 barriers were discerned after pre-exploratory factor analysis, culminating in ten common components. Notably, the study highlights a primary barrier: understanding of lean construction. Drawing from expert insights, substantial recommendations are provided, intending to guide the prefabricated building sector in overcoming barriers to on-site lean construction. These findings and recommendations offer valuable direction for industry stakeholders.

Two-dimensional Graphene/MoS2 vertical heterostructure for detection of hemoglobin concentration in blood samples.

This study demonstrates the use of computational methods to simulate the molecular dynamics involved in hemoglobin concentration sensing, utilizing Material Studio and the TCAD Silvaco device simulator. A non-invasive and flexible Graphene/MoS2 heterostructure has been proposed for sensing hemoglobin concentration in blood samples. The findings reveal a notable shift in the wavelength-dependent refractive index and extinction coefficient, as well as significant changes in the absorption coefficient and reflectivity of the Graphene/MoS2 heterostructure in response to different hemoglobin concentrations, specifically within an approximate range of 0.3 µm to 1 µm. Moreover, the spectral response of the heterostructure demonstrates that at a particular wavelength of approximately 600 nm, a maximum response is obtained. This wavelength can be considered optimal for detecting various levels of hemoglobin using this heterostructure. The anticipated outcome is a comprehensive understanding of the fundamental principles, ultimately resulting in the development of an exceptionally sensitive platform for detecting hemoglobin concentration.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks.

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Silicon photonics interfaced with microelectronics for integrated photonic quantum technologies: a new era in advanced quantum computers and quantum communications?

Silicon photonics is rapidly evolving as an advanced chip framework for implementing quantum technologies. With the help of silicon photonics, general-purpose programmable networks with hundreds of discrete components have been developed. These networks can compute quantum states generated on-chip as well as more extraordinary functions like quantum transmission and random number generation. In particular, the interfacing of silicon photonics with complementary metal oxide semiconductor (CMOS) microelectronics enables us to build miniaturized quantum devices for next-generation sensing, communication, and generating randomness for assembling quantum computers. In this review, we assess the significance of silicon photonics and its interfacing with microelectronics for achieving the technology milestones in the next generation of quantum computers and quantum communication. To this end, especially, we have provided an overview of the mechanism of a homodyne detector and the latest state-of-the-art of measuring squeezed light along with its integration on a photonic chip. Finally, we present an outlook on future studies that are considered beneficial for the wide implementation of silicon photonics for distinct data-driven applications with maximum throughput.

KlugOculus: A Vision-Based Intelligent Architecture for Security System.

Vision-based system has gained significant attention in detecting the abnormal activities of intruders and alerting security with the amalgamation of adaptive video analytics techniques. The implementation of this kind of system works on face recognition, where the dedicated hardware with better computation power is limited in the previous studies. In this study, vision-based intelligent architecture and systems are proposed to detect intruders through facial recognition and sensors with customized hardware. As a part of the training, each subject was trained with 6 different pictures for a total of 120 images. Facial recognition implemented with machine learning (ML) inspired support vector machine (SVM) along with a histogram of oriented gradients (HOG). During the real-time implementation, the SVM model loaded in Raspberry Pi 3 has attained 99.9% accuracy for 20 different subjects. The proposed system can provide an accuracy of 99.9% even with tilted images of the subject, so it can be adopted by the different security personnel to boost the security system for the identification of intruders.

The Use of Marble Dust, Bagasse Ash, and Paddy Straw to Improve the Water Absorption and Linear Shrinkage of Unfired Soil Block for Structure Applications.

Unfired admixed soil blocks are made up of soil plus stabilizers such as binders, fibers, or a combination of both. Soil is abundant on Earth, and it has been used to provide shelter to millions of people. The manufacturing and usage of cement and cement blocks raise several environmental and economic challenges. Due to disposal issues, agricultural and industrial waste is currently the biggest hazard to the environment and humanity in the world. Consequently, environmental degradation brought on by agricultural waste harms the ecology. As a result, researchers are attempting to develop an alternative to cement blocks, and various tests on unfired admixed soil blocks have been done. This investigation uses agricultural waste (i.e., paddy straw fiber and sugarcane bagasse ash) and industrial waste (i.e., marble dust) in manufacturing unfired admixed soil blocks. Under this investigation, the applicability of unfired soil blocks admixed with marble dust, paddy straw fiber, and bagasse ash was studied. The marble dust level ranged from 25% to 35%, bagasse ash content ranged from 7.5% to 12.5%, and the content of paddy straw fiber ranged from 0.8% to 1.2% by soil dry weight. Various tests were conducted on the 81 mix designs of the prepared unfired admixed soil blocks to find out the physical properties of the block followed by modeling and optimization. The findings demonstrate that the suggested method is a superior alternative to burned bricks for improving the physical properties of admixed soil blocks without firing.

Advancements in solar technologies for sustainable development of agricultural sector in India: a comprehensive review on challenges and opportunities.

Agriculture is the main occupation of the majority of people in India. The majority of the population in India is dependent (directly or indirectly) on agriculture as an occupation. The agriculture sector requires more freshwater and power for better yield in the current scenario. Nevertheless, the ever-increasing rate of energy consumption, limited fossil fuels, and rising pollution have made the expansion of renewable resources essential. Due to the suitable solar potential available in India, the deployment of solar energy has been more as compared to other renewable resources. The current study aims to discuss the various technologies, initiatives and policies of solar energy usage in agriculture. This work delivers an assessment of the advancement of solar energy vis-à-vis agricultural applications through the greenhouse concept and photovoltaic approach in India. Various agricultural applications of solar energy, such as solar water desalination system, solar water pumping system, solar crop dryer system for food safety, etc. are discussed as a means to promote solar-based technology. It also highlights the scenario of solar energy in India with important accomplishments, developmental approaches, and future potential. In-depth studies of various policies and government initiatives including those in research and development are also discussed. The current survey on solar technologies will be an aid to agribusiness frameworks to comprehend the statuses, obstructions, and extent of advancement. Finally, some future recommendations for further developments in this approach are discussed. This work sheds light on varied areas of solar energy-assisted agricultural systems as a potentially sustainable and eco-friendly pathway.