The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings.Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Anthropogenic activities are the main cause of climate change globally. The World Health Organization (WHO) recognized that heating and precipitation are the reason for climate change due to anthropogenic actions over the past 30 years and claim 150,000 lives annually. Climate change is also affecting the atmospheres of polar regions. Numerous diseases are linked with climate change like cardiovascular mortality and respiratory illnesses. Due to climate change, lack of long-term and high-quality data sets, variations in drug resistance and immunity, the amplification or the resurgence of diseases, as well as many socio-economic factors, are uncertain.Potentially susceptible regions consist of the temperate latitudes, the regions near the Indian and Pacific oceans are affected due to the heavy rainfall where the heat of the cities could increase life-threatening climatic proceedings. The association between climate and health poses a risk to health under future guesses. Over the current decades, warming has contributed to increased mortality and morbidity in various provinces of the globe. The current study reviews the potential effects of climate change on temperature, human health, air quality, food & nutrition, livelihoods, and livestock & fisheries in detail.

Antarctica and its surrounding environment are considered untouched and thought that it is free from microplastics (MPs) pollution. However, recent studies and science projects have reported MPs in both water and sediment in the South Polar Regions. The reports state that the MP’s pollution occurs in this region due to fishing, tourism, and research activities by the nearby countries and nature's circulation is also part of it. The Antarctic Treaty System (ATS) has received attention on MP’s pollution and initiated research on it. MPs are tiny plastic particles with a size of less than 5 mm. It has two types, 1. Primary MPs have been manufactured directly for various applications like cosmetics and scrubbing etc 2. The secondary MPs are generated by photochemical degradation of large plastics.Although several studies have been done there is a quite gap in our understanding of the concentration, characteristics, and impact of plastics on the ecosystem of the Antarctic Region. The impact of MP’s pollution in this region may be very high. The presence of MP is a serious issue that is affecting not only the aquatic environment but also humans. It is an alarming situation that causes environmental damage. The main objective of this paper is to review MP's introduction, occurrence, sources, harmful effects, and detection methods. This review highlighted the various methodologies and analyses like density separation, microscope observation of MP’s properties Fourier-transform infrared spectroscopy (FTIR), and Raman spectrometer respectively, and urged for more research in the future and gave several recommendations to maintain the pristine region near Antarctica.

The world of query-response systems heavily depends on the cloud storage solutions, distributed data transfers and locality of users etc. When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. This leads to the question, what data to cache in which location. To answer this, we are developing an adaptive distributed data caching framework that can identify suitable data chunks to cache and move across a network of community cache locations. This paper details the first step of the process: the sub-query fragmentation technique to fragment data into portable objects. Evaluation suggests that sub-query fragments enable distributed learning methods to understand query patterns and association between sub-queries. The sub-query objects can be modelled easily as input dataset to implement machine learning models to assist cache maintenance.

Air pollution is a man-made environmental-related problem and has importance globally among all environmental issues. It is described as an atmospheric situation in which several components appear at high attention above their ambient level and generate a quantifiable effect on vegetation, animals, and people. Air pollution and its control are a universal problem and have become an issue of great trouble for us in current years. However, monitoring and measurement of the concentration of air pollutants are most difficult in assessment to monitoring other components in the environment. Air pollutants are transported to polar regions by different mechanisms such as long-range atmospheric transport or grasshopper effects and via anthropogenic activities. After reaching the polar regions, they settle down and remain as such for a long-time due to their persistent nature. They can distress the environment and wildlife of the polar regions. Generally, dangerous chemicals may cause a risk of well-being effects, whereas lethal pollutants focus on one physiological reaction. We reviewed the available information on air pollution (occurrence, sources, and harmful effects) in the polar regions.

Microplastics have been detected as widespread in an aquatic environment at the microscale, also known as plastic debris. They have continuously increased due to the increase in population, production of synthetic plastics and poor waste management. They are ubiquitous and slowly degrade in soil and water. They are emerging contaminants that have received attention from research communities and public audiences over the last few years. They have high stability and can absorb several other pollutants like heavy metals, pesticides, etc. After entering the environment, they can accumulate and persist for a long time. They can create a serious threat to freshwater ecosystems and human health. These particles can cause physical damage to freshwater organisms. Raman spectroscopy, Fourier-transform infrared spectroscopy, visual identification, density separation, microscopic method, chemical method, thermos-analytical method and hyperspectral imaging method are the commonly known approaches for identification and quantification of microplastics. The noticed concentration of microplastics depends on the analysis method, sampling location and technique. The authors reviewed the sources, health impact, transport and treatment of microplastics in freshwater environments in detail. This study will provide the baseline data for the researchers to do more research on microplastic pollution in the future.

Clustering is a data mining task used for the data extraction from the data bases or files. Clustering is used to find unknown groups present in the data sources like files or data bases. This paper focuses on clustering algorithms performance dependency on the parallel clustering platforms and the clustering algorithms along with their clustering criteria. The problems with the present Traditional clustering algorithms were throughput and data source size changes (scalability). So they can’t address the big data. So for handling the huge volumes of data, parallel clustering algorithms along with clustering criteria were used. For processing the big Data Parallel clustering algorithms are of two types based on computing platforms used. They were the horizontal scaling platforms and vertical scaling platforms.

Number of confirmed cases of COVID-19 caused by SARS-CoV-2 exceeded 5 million as of May 21, 2020. Global average of the case fatality rate of COVID-19 is about 7% so far. There exist variations in case fatality rates among countries. Particularly, Singapore and Qatar have exceptionally low case fatality rates with 0.1% while France’s rate is almost 20%. Since no magic bullet treatment for COVID-19 exists, we investigated SARS-CoV-2 strains specific to Singapore in this study to identify a clade with low pathogenicity. Variant analysis revealed that a clade with variants ORF1ab L3606F, A4489V, S2015R, T2016K, and N P13L is common in Singapore. Based on our analysis of variants and historical case statistics, the clade is dominant in a recent surge. Therefore, we suggest that low case fatality rate of Singapore possibly is attributed to the clade. Although contribution of each variant to the low pathogenicity is not clear, L3606F alone does not accomplish such low pathogenicity from the comparison with case fatality data from Japan, where L3606F is dominant. Further investigation is necessary to conclude to validate this finding.

Climate change is one of the widely acknowledged challenges faced by international communities in the 21^st century. It poses challenges to fundamental elements of our understanding of appropriate goals for socioeconomic policies, such as the connection of prosperity, growth, equity and sustainable development. Human activities-induced climatic changes have been widely perceived as threatening criteria for the long-term resilience of societies and communities throughout the world. The United Nations Intergovernmental Panel on Climate Change (UNIPCC) reported that greenhouse gas (GHG) emissions due to human activities, such as the burning of fossil fuels for energy generation, is changing the climate of Earth. Despite contributing less towards the causes, poor citizens of developing countries bear a lot of its impacts. In particular, women and girls face a major impact and finally, they migrate due to the social gender division and scarcity of food. Studies on gender and climate change-induced migration are scarce. This study provides an overview of the induced impact of climate change on the livelihood, health and migration of women and girls. The aim of this study would significantly advance our understanding of the current inter-linkages between migration, displacement patterns and climate change impacts.

E-waste is considered the fastest-growing global waste stream and has become an emerging environmental challenge. The widespread usage of digital devices has generated a large amount of e-waste. According to the Ministry of Commerce, India annually generates ~14.5 lakh metric tons of e-waste and recycles only ~5 % of e-waste. Annually, the global production of e-waste has been expected to increase by 4–5 %. Advancements in IT and communication divisions have improved the usage of electronic equipment. Heavy metals such as (Pb), mercury (Hg), arsenic (As), tin (Sn), and organic chemicals such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated biphenyls (PBBs), polybrominated dibenzo-p-dioxins (PBDDs), polybrominated diphenyl ethers (PBDEs) are subsequently released from e-wastes and contaminates soil and water sources. These toxic compounds are hazardous to human health and the environment. They are capable of entering various biological systems and causing alterations in respiratory functions of the lungs, DNA damage, impaired functioning of the thyroid, and increased risk of chronic diseases such as cancer and cardiovascular ailments. Such concerns raise the quintessential need to manage e-waste to prevent future hazards. This manuscript critically reviews the sources, health and environmental impact, disposal techniques, recycling, and regulation of e-waste.

Spontaneity and its connections to mental health and wellbeing are a central issue in both theory and practice of psychodrama, and the specialized literature sometimes even associates the presence of pathology to a lack of spontaneity. This paper describes spontaneity in Moreno's theory, its assessment, its association with other measures and concepts, and scientific advances in the field. Specifically, we present results obtained with the English and Portuguese-language versions concerning its assessment. The main purpose of this work was to study the spontaneity measure (SAI-R) in a 3 first-order factors version for a Portuguese sample of 719 subjects, divided into two independent samples. The participants were aged 18 to 69. The mean age of participants was 28.5 years (SD = 7.51), and ages ranged between 18 and 69 years. Data was collected through an online platform of a Portuguese market research company. The results revealed that the 3-factor model has acceptable validity when items 1 (“Creative”) and 7 (“Euphoric”), confirming the composite reliability, the convergent validity, but not the discriminant validity. Through multigroup analysis, the model showed to be stable. Notwithstanding the need for complementary studies, including clinical samples, the SAI-R is a short and valid instrument in clinical and non-clinical contexts when evaluating spontaneity.

Microplastics (MPs) are small pieces of plastics. They are ubiquitous in the environment and can enter the freshwater environment from surface run-off and wastewater effluent (treated and untreated), industrial effluent, degraded plastic waste, and atmospheric deposition. They are not usually destroyed but convert into one phase to another. They are a source of air pollution, occurring in dust and airborne fibrous particles. Mostly MPs are non-biodegradable while some MPs are biodegradable, which can be decomposed in the presence of ultraviolet (UV) light or by the action of microorganisms. Popular methods: chemical, spectroscopic, and thermo-analytical are available for the determination of the chemical composition and size of plastic particles. This chapter discusses the uses, health hazards, sources, and transport of MPs particles.

Solid waste has become a tenacious issue worldwide. It has been increasing exponentially due to urbanization and the increase in the population. Since the twentieth-century technological revolution, there have been significant changes in the composition of solid waste. It poses significant challenges for waste management systems worldwide. Waste management is the process of handling waste right from its creation to its final disposal, including transport, collection, treatment, and monitoring. The present solid waste management (SWM) system is affected by unfavorable institutional, economic, technical, legislative, and operational constraints. Poor waste management is affecting ecosystems and human health, damaging our finite natural resources, impeding human economic progress, and harming people's quality of life. Now, researchers are concerned about the environment's degradation, a decline in quality of life, and risks related to waste management grow as the volume of solid waste. So, they are focusing on sustainable waste management practices which will be crucial for creating a cleaner and healthier environment for future generations. This chapter focuses on the concept of solid waste, its types, management, and its effect on the health of humans and the environment.

Information and telecommunications technology (ITT) have expanded into many aspects of modern life and have positively affected human life even in the most remote areas of developing countries. The rapid growth in ITT has led to an improvement in the capacity of computers but simultaneously to a decrease in the product’s lifetime as a result of which increasingly large quantities of e-waste are generated annually. It contains hazardous components that if it is not properly managed, can have adverse environmental and health effects. The management of e-waste poses significant challenges in developing countries. Due to the lack of adequate infrastructure, e-waste is burned or dumped in open areas. Informal and inefficient recycling practices are generally employed which further exacerbate pollution and health risks. So, the implementation of e-waste management, adequate recycling, and waste disposal facilities have become crucial concerns. This article reviews the concept of e-waste generation, challenges, disposal, and its impact on human health & environment in detail.

The health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) technique is used to retrieve the health data back and for reducing security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.

The field of human microbiome and gut microbial diversity research has witnessed a profound transformation, driven by advances in omics technologies. These advancements have unveiled essential connections between microbiome alterations and severe conditions, spurring the development of new frameworks through epidemiological studies. Traditionally, each individual was thought to harbor unique microbial communities acquired early in life, but recent research challenges this belief. The neonatal microbiome's onset, influenced by factors like delivery mode and maternal health, remains a subject of intense debate, hinting at potential intrauterine microbial processes. In-depth research reveals associations between microbiome profiles and specific health outcomes, ranging from obesity to neurodevelopmental disorders. Understanding these diverse microbiome profiles is essential for unraveling the intricate relationships between the microbiome and health outcomes.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

During the most recent years, a lot of research has been done in creating robots with more self-governance so that they can overcome the challenges that real world environments present. The robot's limited versatility in real world applications can be overcome by the development of Legged robots. Also, as they permit movement in unavailable territory to robots with wheels, Legged Robots are more advantageous. But the potency of the legged robots explicitly its energy usage among alternate points of view really fall behind robots that use wheels. So, the present status of development, there are as yet a few perspectives that need to be analysed, optimized and enhanced. This paper presents review of literature of various biologically inspired legged robots, various techniques adopted for their analysis and optimization and the analysis and optimization of the ones that are not biologically inspired

The retinal pigment epithelium (RPE) is an important monolayer of cells present in the outer retina forming a major part of the blood-retina-barrier (BRB). It performs many essential tasks for the maintenance of retinal integrity and function. With increasing knowledge of the retina, it is getting clearer that both common retinal disorders, like age related macular degeneration, and rare genetic disorders originate in the RPE. This calls for a better understanding of the functions of various proteins within the RPE. In this regard, mice enabling an RPE-specific gene deletion are a powerful tool to study the role of a particular protein within the RPE cells in their native environment, simultaneously negating any potential influences of systemic changes. Moreover, since the RPE cells interact closely with the adjacent photoreceptors, these mice also provide an excellent avenue to study the importance of a particular gene function within the RPE to the retina as a whole. In this review, we outline and compare the features of various Cre mice created for this purpose, allowing researchers a well-informed decision on the choice of Cre mouse to use in relation to their research needs.

Environment approachable products such as fuels and lubricants are among the best choices in several countries that contain renewable products as alternatives. To protect human life, alternative methods of saving the environment and production balance is needed to reduce the effects of the crisis and the contamination of the Environment. This research concentrated on the Manufacturing and testing of bio- grease from sugar cane filter mud vegetable oil as a lubricant for friction reduction and determining the properties of both bio grease and Mineral oil grease with environmental problems. SC filter mud oil as an alternative use for bio-grease preparation in addition to mineral oil grease. The Testing was accepted to determine the quality of the eco-friendly grease produced. For the preparation of this bio- grease, the SC Filter cake sample is taken from different sugar factory which found in Ethiopia. The sample has a moisture content of 78%. Using this sample first oil is extracted by Soxhlet apparatus, n-hexane solvent and temperature for extraction from 42 -68℃. The maximum temperature and time for extraction of oil were 68 and 7hrs respectively. The extracted oil's physical properties include color, acid value, specific graphite, saponification value, iodine value, density, and viscosity. Finally, the friction behavior in function of time for both grease at identical load, the friction coefficient vs time for SC filter Cake Grease_ Na_ MoS2 and industrial mineral one (I_ Greasily _ MoS2), grease was determined.

Reducing the use of stabilizers is one of the main challenges in food emulsions, especially for beverages. This work aimed to produce oleogel structured nanoemulsions (NEs) without additional surfactants. Lecithin-stearic acid (LSa) and lecithin-sorbitan tristearate (LSt) oleogels formed stable NEs under optimized sonication conditions. Microscopy and rheometry revealed that the presence of self-assembled fibrous networks (SAFiNs) in both dispersed and continuous phases provided steric stabilization to NEs. Lecithin acted as crystal habit modifier of SAFiNs and facilitated their phase partitioning. Notably, short fibers of LSt showed better emulsifying efficiency than the long fibers of LSa. Curcumin release studies under simulated gastrointestinal conditions demonstrated that SAFiNs affect the release capabilities of NEs. Polydispersity index, zeta potential and oil syneresis data showed that emulsions are stable for six months. Moreover, NEs showed thermal stability upon curcumin release at 25 and 50 °C. These results suggested that the developed oleogel-based NEs are suitable for delivery of bioactive agents for beverages and other food applications.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

Since a few decades, the world has been facing severe global challenges with context to water, food, agriculture science, energy resources, healthcare, medicines, diseases, etc. One of the causes of these challenges is excessive human activities. So, there has arisen a necessity to tackle these challenges using sustainable solutions so as to minimize the effect of human interference. Nanotechnology (NT) has gained pace as a new technique that provides cost-effective, efficient, and eco-friendly solutions to such challenges. The advantages of the usage of NT have increased in every field of science. As compared to traditional methods, NT possesses wider use and benefits as it is designed at the nano levels and also keeps the environment clean. It is the science of tiny particles that has an important role to play in international efforts in sustainability. The innovation of NT is revolutionary and therefore subsequently will cover a wider space in terms of scientific achievements. Technology saves time, money, and human resources. This paper discusses and highlights the usage of NT with respect to its application, knowledge, and sustainable development. It also gives a broad view of the approaches, properties, and applications of NT in various sectors including the treatment of water bodies, agricultural science, drug delivery systems, medicines, and energy saving.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat the COVID-19 disease. Several approaches are being used by scientists for investigation including 1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease and 2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, new B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries such as the Netherlands, Switzerland and France.

Plants have the ability to decrease the pollution level from the environment by absorbing inorganic/organic pollutants through their roots. After that, these pollutants are translocated or accumulated in less toxic forms in various parts of plants. This ability of plants is known as phytoremediation. It is an eco-friendly, and cost-effective approach for controlling environmental pollution. It can provide a sustainable way to improve the economics of developing countries. However, the biomass formed during this process can re-contaminate the environment through secondary pollutants. The contamination due to heavy metals (HMs) has become an environmental challenge globally. Metals such as cadmium (Cd), chromium (Cr), mercury (Hg), arsenic (As), nickel (Ni), lead (Pb), etc. can enter the water and soil largely through anthropogenic activities as well as other natural processes. After that, these metals pose a severe threat to living organisms. Due to their non-biodegradable nature, these metals can remain as such for a long time. These metals can initiate oxidative pressure in plants as a result affects agricultural production and yield. To mitigate this problem, phytoremediation has come forward as a safe, affordable, and ecologically sustainable solution than conventional physicochemical decontamination methods. Initially, several edible crops had been identified for the remediation. But at the present time, scientists are focusing on non-edible crops like aromatic/medicinal plants. There is no risk of food contamination by using these plants. This chapter describes the classification & uses of Tulsi and the phytoremediation mechanism of plants in detail.

Nanoscience is a rapidly growing science stream that has been one of the cost-effective and energy-efficient approaches with wide applications. Water pollution is a major issue that is increasing continuously due to anthropogenic activities and several organic and inorganic contaminants. To overcome this issue, nanoparticles (NPs) have been successfully used to treat water. These NPs possess many beneficial applications in many fields like agriculture, cosmetics, photoconductors, environmental biotechnology, glass and alloy production, solar cells, nanomedicine and drug deliveries, biosensors, food industries, etc. The properties comprising high surface area, greater chemical reactivity, and mechanical properties play a vital role in imparting such beneficiary properties. As a result, there has been an increasing demand for the synthesis of NPs. However, excess usage of NPs further creates toxic by-products. Therefore, scientists and researchers are now focussing on the biogenic synthesis of NPs. The biological routes of NP synthesis have been crucial because they are eco-friendly and environmentally safe. This chapter highlights the usage of different approaches and biogenic sources for the production of NPs. It also discusses the recent advancement in the use of biogenic NPs with respect to applications for wastewater treatment.

Air pollution has been a major environmental issue worldwide from the last few decades. It is a man-made issue that has a serious harmful impact on the environment and the health of humans. There are different sources of emission, but industrial processes and motor vehicles contribute to a chief part of air pollution. It has acute as well as chronic effects on the health of humans and affects different organs. It is thought the main environmental risk factor in the incidence and progression of several diseases such as lung cancer, asthma, Alzheimer's and Parkinson's diseases. Long-term exposure to air pollution has different toxicological impacts on humans such as respiratory and cardiovascular diseases, neuropsychiatric complications, eye irritation and skin diseases. As per the study by the World Health Organization (WHO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), volatile organic compounds (VOCs) and heavy metals are the major air contaminants. These pollutants are different in their properties, composition and time of disintegration. The aim of this study is to review the different sources, types, impacts, challenges and mitigation of air pollution in detail.

In this work, we report synthesis of Cu, Pt and Pd doped SnO₂ powders and their comparative CO gas sensing studies. Dopants were incorporated into SnO₂ nanostructures using chemical and impregnation methods by using urea and ammonia as precipitation agents. The synthesized samples were characterized using X-ray diffraction (XRD), Raman spectroscopy, Scanning electron microscopy (SEM) and High resolution transmission electron microscopy (HR-TEM). The presence of dopants within the SnO₂ nanostructures was evidenced from HR-TEM. Doped powders utilizing chemical methods with urea as precipitation agent presented higher sensitivities compared to the remaining, which is due to the formation of uniform and homogeneous particles resulted from the temperature assisted synthesis. The particle sizes of doped SnO₂ nanostructures were in the range of 40-100 nm. An enhanced sensitivity around 1783 was achieved with Cu doped SnO₂ when compared with two other dopants i.e., Pt (1200) and Pd: SnO₂(502). The high sensitivity of Cu: SnO₂ is due to formation of CuO and its excellent association and dissociation in the presence of CO with adsorbed atmospheric oxygen at sensor operation temperatures resulted in high conductance. Cu: SnO₂ may be an alternative and cost effective sensor for industrial applications.

Educational institutions worldwide have adopted e-learning due to the COVID-19 pandemic. While this shift to online learning (OL) has presented challenges for students and teachers, it has also sparked innovative educational approaches. This study investigates college students' perceptions regarding OL, specifically focusing on gender differences in the experience with OL A quantitative survey gathered information from distinct college students in Gulf Cooperation Council (GCC) nations who experienced OL during the pandemic. The survey included questions related to satisfaction with OL and specific aspects such as course content delivery, interaction with instructors, behavioral changes, and challenges with OL. Male and female student OL experience was compared statistically. The results conclude that male students are more satisfied than female students. Students struggled with internet connection, OL adaptation, focus, and workload during OL. Understanding these gender differences in students' satisfaction with OL is crucial for educators and institutions as they strive to optimize the effectiveness of e-learning strategies. By recognizing and addressing male and female students' unique needs and challenges, educational institutions can enhance the overall educational experience during times of crisis and beyond.

In this paper, we present the definition, some properties, and solve a problem on special primes. These properties help in providing us with a better understanding of the problem posed related to special primes on the open problem garden website. The problem involves finding all the primes q, given a prime p such that q≡1(mod p) and 2^((q−1)/p)≡1(mod q). We prove that a prime number q is a special prime of p if and only if the order of 2 in U(q) divides q−1p. Also, we prove that a prime number q is not a special prime for any prime number if 2 is a generator of the group U(q) and that there exist infinitely many special primes for any given prime number.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

The holy town of Joshimath, gateway to the India-China border and religious places like Badrinath, Hemkunt Sahab, Valley of flowers in the Uttarakhand state of India experienced cracks in many of the residential and commercial buildings in the month of November-December 2022 which created panic and relocation of people. The reason(s) for the subsidence is still not known but using the InSAR technology and data from Sentinel-1, SAR data for the region was processed using HyP3 and MintPy for January-December 2022 to understand when the phenomenon started and how much uplift/subsidence the area has underwent in the last one year, as no such study is available till today. The entire town can be classified into two zones with respect to the annual subsidence values. Severe subsidence has been observed in the North and East regions whereas most of the southern region experienced moderate to low subsidence. Since the town is built on the debris of an old landslide, the subsidence may be attributed to the change in the course of the underground water channels due to heavy and continuous construction happening in the region and also due to drop in the water table. The results shows that the subsidence in the region was ongoing since June (from January till May, almost negligible vertical movement was recorded) which accelerated after September (when an uplift was recorded which lasted till October), while the subsidence peaked in the month of December with recorded subsidence of as much as 10 cm in some areas located around the Joshimath town, where most of the residential and commercial buildings are located.

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

Abstract — Game development using technology was started in 1950 around the first video game created Tennis for Two by Higginbotham as a fun diversion, to show the power of technology. The advanced development of Artificial intelligence (AI), has turned out to be the heart of game development technology and AI has revolutionized gaming and brought so many features to life.in reality, it isn't easy to give a definition and meaning to it even though games are becoming a part of human survival. It is difficult to define the real meaning of a game but it is part of human survival as it usually refers to the cleverness of the way the human being involvement in the characters of the game. As a part of game development, many technologies were used to create a game for the players with creativity, complex design, variety of levels, and aspects for the player to use choices effectively and efficiently to interact with the character in the game and enhance the experience to the next levels. This paper initially gives the importance of game development and need for the technology usage to enhance the current state of games. Then analyzes the history and present state of Artificial intelligence in game development. Finally, it explains the highlights of potential changes in game development and the impact and influence of artificial intelligence and machine learning models in the future development of games.

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24^th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25^th May to 11^th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

Enzymes are biological molecules produced by living entities for carrying out biological processes. The application of enzymes for waste treatment has been gaining pace commercially to solve concerns related to agricultural residues, wastewater, replacement of synthetic processes with natural ones, etc. The application of enzymes for waste management has been an environmentally reliable and sustainable process. Treatment of waste with enzymes such as xylanase, proteases, hydrolases, cellulose, peroxidases, chitinases, laccases, etc. has been studied to be effective. These enzymes act upon the waste products and transform them into biodegradable forms that can be recycled, reused and converted to value-added products. They have wide applications and utility as it has been an effective approach, economically cheaper and sustainable techniques. Application of such enzymes for waste management would be beneficial for reducing the quantity of waste, diminishing the negative effects of waste and pollution on the environment, and would be beneficial in bio-converting the waste products into alternate sources of energy. The current chapter focuses on different types of enzymes, their applications for waste management, and their limitations. This chapter also emphasizes the usage of some prominent microorganisms, their secreted enzymes and their proposed mechanisms of action involved with the degradation of the waste products.

Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.

Oral mucositis (OM) is inflammation of the mouth caused by damage to the mucous membranes that line the mouth and throat. It is a side effect of cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy, chemotherapy, and/or immunotherapy with immune checkpoint inhibitors. The etiology and pathogenic mechanisms of OM is complex and multifaceted, involving cytotoxicity (cell death), inflammation, infection, change in microbiome, and immune-mediated cytotoxicity. We summarize the literature about attempts to use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) to elucidate the biological pathways associated with the development or the severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and local environment (microbiomics).

The individual and combined effects of extracellular products (ECP) from Arthrobotrys oligospora (Ao) and A. musiformis (Am) growth in liquid media against Haemonchus contortus L3 (HcL3) were assessed. The following methodological steps were established: isolation, morphological (MI) and molecular identification (Mol-I), assessment of nematocidal activity (NA) of fungal liq-uid culture filtrates (LCF) produced in two liquid media alone and in combination and finally, the myco-compound profile identification (MCP). The MI of both isolates suggested that the fungi corresponded to the species Ao and Am. This result was confirmed by PCR analysis followed by sequencing, alignment and obtaining coverage and similarity with respect to isolates reported in the NCBI database. Likewise, the highest Hc mortalities were 91.4% with individual LCF of Am and 86.2% with those of Ao at the highest concentration (100 mg/mL) in Czapek-Dox Broth. The combination of both LCF resulted in a similarly high larval mortality with no statistical differ-ences in relation to individual activity (P>0.05). The MCP showed the presence of alkaloids in both fungi. Coumarins, triterpenes and saponins were found only in Ao. Both fungi produced ECP with a high NA that could be identified and assessed in future studies as potential natural an-thelmintic compounds.

Mango flowering phenostages were recorded under global warming conditions to assess the relationship between environmental cues and flowering time shifts and their consequences on reproductive success. Phenological transition to floral destiny was studied in relation to standard metrological week and weather parameters. Shifting of phenostage was recorded in Bud swelling, which is important for the shoot transitioning from vegetative to flowering meristem. Variations were also recorded for panicle elongation and early anthesis. The critical temperature for Bud burst stage was estimated for Dashehari, Langra, Amrapali and Chausa while Chausa had the propensity for late flowering correlated with higher temperature. The shifting of phenological calendar in response to weather parameters was obvious and needed a bigger range of yearly data for arriving at the climatic drives. In general, the results revealed that flower intensity was positively correlated with temperature, sunshine hours and evaporation while humidity showed a negative correlation. Hermaphrodite Flower % is an important attribute affecting fruit set and yield and was found most variable in the two years in the case of the three alternate bearing cultivars. In conclusion, it can be inferred that weather parameters prevailing in subtropics affect flowering intensity and consequently its fate i.e.; fruit yield.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

The metabolic syndrome, first introduced by Hermann Haller in 1975, was sometimes also known as insulin resistance syndrome, syndrome X, and plurimetabolic syndrome. In 1989 it was rechristened by Kaplan as "Deadly Quartet" based on a consolidation of central obesity, impaired glucose tolerance, dyslipidemia, and systemic hypertension. Metabolic syndrome is positively associated with a pro-inflammatory and pro-thrombotic state, attributed to increased pro-thrombotic and inflammatory markers activity. Moreover, Metabolic syndrome is frequently associated with increased atherosclerotic cardiovascular disease, impaired glucose tolerance, hyperuricemia, obstructive sleep apnea, and chronic kidney disease. Despite concerted endeavors worldwide, the complexity of the pathophysiology of metabolic syndrome is still not clearly understood. Currently, therapeutic possibilities are confined to individual therapy of hyperglycemia, hypertension, hypertriglyceridemia, hyperuricemia, regular physical exercise, and a restricted diet.

Diffuse cavernous hemangioma of the rectum (DCHR) is a rare benign lesion of vascular malformation. According to previous studies, the most reported major symptoms of DCHR are recurrent painless rectal bleeding or iron deficiency anemia[1][2][3][4][5][6]. We present a case of an 8Y11M boy who was diagnosed with DCHR by MRI. The patient underwent Laparoscopic-assisted transanal distal sigmoidectomy and sigmoido-anal anastomosis（Laparoscopic Swenson Procedure）, and recovered well. After the operation, the child had short-term fecal incontinence and loose stools, which returned to normal after half a year. Regardless of age and symptoms, early and correct diagnosis and treatment of DCHR and postoperative restoration of normal anal function are necessary. Laparoscopic Swenson procedure may be a good option for the treatment of DCHR.

We report an all-optical radio-frequency (RF) spectrum analyzer with a bandwidth greater than 5 terahertz (THz), based on a 50-cm long spiral waveguide in a CMOS-compatible high-index doped silica platform. By carefully mapping out the dispersion profile of the waveguides for different thicknesses, we identify the optimal design to achieve near zero dispersion in the C-band. To demonstrate the capability of the RF spectrum analyzer, we measure the optical output of a femtosecond fiber laser with an ultrafast optical RF spectrum in the terahertz regime.

Antimicrobial resistance (AMR) is a growing public health issue worldwide that affects the world economy. The concern associated with this issue increased during the COVID-19 pandemic. The misuse of antibiotics during the COVID-19 pandemic will have catastrophic implications for the control of AMR. In 2019, the coronavirus illness COVID-19 first appeared in China. COVID-19 infection is a surface-to-surface communicable disease. It is considered the most vital global health disaster of the century. It has rapidly spread around the world and is the greatest challenge for humankind. This chapter discusses the origin, symptoms, transmission, treatment, and recommendations of the COVID-19 disease.

Background: Trichomoniasis is the most common non-viral STI globally and yet is not a reportable disease. Trichomonas Vaginalis is an important source of reproductive morbidity and may increase risk of acquisition and transmission of HIV. WHO and CDC recommend various regimens of Nitro-Imidazoles for treatment. The common Nitro-Imidazoles used for Trichomoniasis are Metronidazole and Tinidazole, which vary in their cost, efficacy and side effect profile and it is relevant to study these factors, for better management of the patients. Objectives: This study aims to compare and study the efficacy, compliance of various treatment regimens, their outcomes and side-effects for Trichomoniasis, among STI clinic attendees in Trinidad. Methods: A clinical trial study was designed and after obtaining the informed consent a routine clinical examination was conducted and the swabs for Trichomoniasis tests were collected for diagnosis from the 692 participants. Out of 692 participants, Eighty two (82) patients with established diagnosis of Trichomonas infection were quasi-randomly treated using different regimens. Compliance to treatment, side effects and outcome were evaluated. Results: The prevalence of the Trichomoniasis in population attending our STI clinic is 11.9% and prevalence of HIV is 9%. Of the total 82 participants for the treatment, 80% were females; nearly 90% of the patients belonged to age group 15-45 years and over 60% were below 30 yrs. Among those diagnosed for Trichomonas vaginalis, 13.3% had associated HIV infection. The compliance with respect to single dose treatment was significantly better than the long duration oral regimen and has significant relation with side effects of the treatment. The outcome is generally better and comparable and shows no significant difference between different treatment regimens used in the study. Conclusions: Metronidazole and Tinidazole are commonly used drugs in various regimens. compliance is better with those treated with Tinidazole and Metronidazole stat, than with other groups. Outcome is comparable between these regimens, especially when combined with other important factors like abstinence and treatment of the partners. The treatment regimens mainly differ in the compliance and side effects profile, which suggests that to improve the compliance the drugs with less side effects, short course regimen would be a preferred choice.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Background: Passive drinking is prevalent in adolescents worldwide, but its prevalence and harm are understudied. Methods: Secondary students (n=5840, grades 7-12) from 23 selected schools in Hong Kong participated in the survey from 2015-16. Students reported the harm of passive drinking, perceived health status, Patient Health Questionnaire-2, Perceived Stress Scale-4, perceived happiness, family health, happiness, and harmony in the questionnaire. The associations were analyzed using multivariable logistic regression (odds ratio, OR) and linear regression (unstandardized coefficient, b), adjusted for confounders. Results: 29.1% (95% CI 27.8 to 30.5%) of students experienced passive drinking in the past 30-day. Past 30-day parental passive drinking was associated with a higher level of depressive symptoms (AOR 1.63, 95% CI 1.26 to 2.10), stress (adjusted b 0.76, 0.42 to 1.10), and lower level of perceived happiness (adjusted b -0.52, -0.72 to -0.33). Past 30-day parental passive drinking was associated with a lower level of family health (adjusted b -1.39, 95% CI -1.66 to -1.11), family happiness (adjusted b -1.36, -1.64 to -1.08), and family harmony (adjusted b -1.40, -1.70 to -1.10). Conclusion: Passive drinking was associated with poorer mental health, family wellbeing, and lower level of happiness among Hong Kong Chinese adolescents.

The COVID-19 pandemic is adversely impacting food and nutrition security and requires urgent attention from policymakers. Sustainable intensification of agriculture is one strategy that attempts to increase food production without adversely impacting the environment, by shifting from water-intensive crops to other climate-resistant and nutritious crops. This paper focuses on the Indian state of Andhra Pradesh by studying the impact of shifting 20% of the area under paddy and cotton cultivation to other crops like millets and pulses. Using FAO’s CROPWAT model, along with monsoon forecasts and detailed agricultural data, we simulate the crop water requirements across the study area. We simulate a business-as-usual base case and compare it to multiple crop diversification strategies using various parameters – food, calories, protein production, as well as groundwater and energy consumption. Results from this study indicate that reduced paddy cultivation decreases groundwater and energy consumption by around 9-10%., and a calorie deficit between 4-8% - making up this calorie deficit requires a 20-30% improvement in the yields of millets and pulses. We also propose policy interventions to incentivize the cultivation of nutritious and climate-resistant crops as a sustainable strategy towards strengthening food and nutrition security while lowering the environmental footprint of food production.

Suppressing the charge recombination at the interface of photoanode/electrolyte is the crucial way to enhance the photovoltaic performance of quantum dot sensitized solar cells (QDSSCs). In this scenario, ZnS/SiO2 blocking layer was deposited on TiO2/CuInS2 QDs to inhibit the charge recombination at photoanode/electrolyte interface. As a result, the TiO2/CuInS2/ZnS/SiO2 based QDSSCs delivers a power conversion efficiency (η) value of 4.63%, which is significantly higher than the 2.15% and 3.23% observed for QDSSCs with a TiO2/CuInS2 device and TiO2/CuInS2/ZnS, respectively. Electrochemical impedance spectroscopy and open circuit voltage decay analyses indicate that ZnS/SiO2 passivation layer on TiO2/CuInS2 suppress the charge recombination at the photoanode/electrolyte interface and prolongs the electron lifetime.

Paleoclimatic and paleoceanographic studies routinely rely on the usage of foraminiferal calcite through faunal, morphometric and physico-chemical proxies. The application of such proxies presupposes the extraction and cleaning of these biomineralized components from ocean sediments in the most efficient way, a process which is often labor intensive and time consuming. In this respect, in this study we performed a systematic experiment for planktonic foraminiferal specimen cleaning using different chemical treatments and evaluated the resulting data of a Late Quaternary gravity core sample from the Aegean Sea. All cleaning procedures adopted here were made on the basis of their minimum potential bias upon foraminiferal proxies, such as the faunal assemblages, degree of fragmentation, stable isotope composition (δ18O and δ13C) and/or Mg/Ca ratios that are frequently used as proxies for surface-ocean climate parameters (e.g., sea surface temperature, sea surface salinity). Six different protocols were tested, involving washing, sieving, and chemical treatment of the samples with hydrogen peroxide and/or sodium hexametaphosphate (Calgon ®). Single species foraminifera shell weighing was combined with high-resolution Scanning Electron Microscopy (SEM) and synchrotron X-ray Microtomography (SμCT) of the material processed by each of the cleaning protocols, in order to assess the decontamination degree of specimen’s ultrastructure and interior. It appeared that a good compromise between time and cleaning efficiency is the simultaneous treatment of samples with a mixed hydrogen peroxide and Calgon solution, while the most effective way for an almost complete decontaminate of the calcareous components from undesirable sedimentary material is a two-step treatment - initially with hydrogen peroxide and subsequently with Calgon solutions.

Background: Diabetic patients are more likely to experience morbidity and mortality as a result of microvascular complications such as retinopathy, neuropathy, nephropathy, and stroke. There are many synthetic anti-diabetic agents available which are expensive and have undesirable pathological effects. Thus, it is essential to look for cost-effective, natural, and safe antidiabetic agents. The aim of this study was to screen phytoconstituent and evaluate the in-vitro and in-silico α-amylase inhibitory activity of ethanolic extract of Adhatoda vasica leaves. Method: The extraction of Adhatoda vasica leaves was performed with ethanol through Soxhlet extraction process. Different concentrations (0.1 mg/ml to 1 mg/ml) of ethanolic extract, Acarbose, and Sitagliptin, were prepared and all concentrations were evaluated for α-amylase inhibitory activity through the spectrophotometric method. Molecular docking (AutodockVina 1.2.0) and toxicity profiling (SToPToX web server) studies were performed. Results: The plant extract showed highest inhibition of α-amylase (56.763±0.0035) at a concentration of 1 mg/ml. This inhibitory activity was supported by the in-silico study. Vasicoline (C5) and quercetin (C9), active constitute of plant Adhatoda vasica, showed the best binding energy of 8.3 and 8.0 Kcal/mol respectively with α-amylase enzyme (PDBID: 4W93). Toxicity study revealed the safety profile of plant extract. Conclusion: It was concluded that Adhatoda vasica leaves possess some bioactive compounds which are responsible for controlling blood glucose levels and its identification, purification, and isolation may lead to the development of newer therapeutic agent with lesser side effects.

Cancer is a disease caused by the abnormal growth of cells in different parts of body is one of the top causes of death globally. Microarray gene expression data plays a critical role in the identification and classification of cancer tissues. Due to recent advancements in Machine Learning (ML) techniques, researchers are analyzing gene expression data using a variety of such techniques to model the progression rate & treatment of cancer patients with great effect. But high dimensionality alongside the presence of highly correlated columns in gene expression datasets leads to computational difficulties. This paper aims to propose the use of ML classification techniques- Linear Discriminant Analysis (LDA) & Random Forest (RF) for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. Six distinct categorization performance measures were used to evaluate these approaches; RF method provided us with higher accuracy than LDA method. The method and results of this article should be helpful to researchers who are dealing with many genes in microarray data.

The study examines malnutrition's triple burden, including anaemia, overweight, and stunting, among children aged 6-59 months. Using data from the National Family Health Survey-5 (2019-21), the study identifies risk factors and assesses the contribution at different levels to existing malnutrition burden. A random intercept multilevel logistic regression model and spatial analysis are employed to identify child, maternal, and household level risk factors for stunting, overweight, and anaemia. The study finds that 34% of children were stunted, 4% overweight, and 66% anaemic. Stunting and anaemia prevalence were higher in central and eastern regions, while overweight was more prevalent in the north-eastern and northern regions. At macro-level, the coexistence of stunting, overweight, and anaemia circumstantiates the triple burden of childhood malnutrition with substantial spatial variation (Moran’s I: stunting-0.53, overweight-0.41 and anaemia-0.53). Multilevel analysis reveals that child, maternal, and household variables play a substantial role in determining malnutrition burden in India. The nutritional health is significantly influenced by a wide range of determinants, necessitating multilevel treatments targeting households to address this diverse group of coexisting factors. Given the intra-country spatial heterogeneity, the treatment also needs to be tailor-made for various disaggregated levels.

Amyotrophic Lateral Sclerosis (ALS) stands as a debilitating neurodegenerative condition characterized by the progressive degeneration of motor neurons. Despite extensive research in various model animals, the cellular signal mechanisms of ALS remain elusive, impeding the development of efficacious treatments. Among these models, a well-characterized and diminutive organism, Caenorhabditis elegans (C. elegans), has arisen as a potent tool for investigating the molecular and cellular dimensions of ALS pathogenesis. This review summarizes the contributions of C. elegans models to our comprehension of ALS, emphasizing pivotal findings pertaining to genetics, protein aggregation, cellular pathways, and potential therapeutic strategies. We analyze both the merits and constraints of C. elegans system in the realm of ALS research, and point towards future investigations that could bridge the chasm between C. elegans foundational discoveries and clinical applications.

Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

In the present era, there is no longer a necessity to rely on outdated and laborious methods for attendance management in the field of education. Managing attendance for large groups of students in a classroom can be a challenging and time-consuming task when it comes to recording data. Fortunately, modern technologies offer us the opportunity to streamline attendance management and make it more efficient. One effective approach is Real-Time Face Recognition, which simplifies the process of tracking attendance for numerous students daily. We employ the Haar cascade classifier to identify positive and negative facial attributes, along with the LBPH algorithm for accurate face recognition. These techniques are implemented using Python programming language and the OpenCV library while incorporating a user-friendly Tkinter GUI (Graphical User Interface) for seamless usability. By embracing these advancements, we can revolutionize how attendance is managed in educational environments.

This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets, with a specific application focus on wood surface defect detection. The proposed framework integrates sparse modeling techniques, particularly Lasso and proximal gradient methods, into a comprehensive pipeline for efficient and interpretable feature selection. Leveraging pre-trained models such as VGG19 and incorporating anomaly detection methods like Isolation Forest and Local Outlier Factor, our methodology addresses the challenge of extracting meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score, alongside visualizations, are employed to assess the performance of the sparse modeling techniques. Through this work, we aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.

Background: A new coronavirus (SARS-CoV-2) that emerged from Wuhan, Hubei Province, China, has spread throughout the world and is declared a pandemic by the World Health Organization (WHO). A lot remains to be understood of SARS-CoV-2 and the disease (COVID-19). SARS-CoV-2 has until recently been identified as responsible for both asymptomatic and serious life-threatening infections. The unavailability of specific therapeutic agents is a major hurdle in the treatment and management of COVID-19 patients. The present review attempts to evaluate the immunobiochemical aspects of the pathogenesis, diagnosis, and management of SARS-CoV-2 infection. Main Body: This review is a comprehensive evaluation of the data collected through various sources, including Google Scholar, PubMed, and Scopus. The articles were searched and selected using key words such as “Coronavirus disease (COVID-19)”, “Diagnosis of COVID-19”, Pathogenesis of Covid-19”, “management of COVID-19”, “Immunology of COVID-19”, and “Complications of COVID-19”. The study noted that the novel Coronavirus infection could result in an exaggerated immune response, causing a cytokine storm and damaging several organs of the body. The infected patients develop several complications, including immunological, hematological, and biochemical alterations. Consequently, COVID-19 patients may develop cardiovascular, liver, renal, and neurological complications, among others. Conclusion: An increased understanding of the immunobiochemical aspects of the disease may contribute to better management of SARS-CoV-2-infected persons, as evidenced from the available literature. A holistic approach to the management of COVID-19 patients taking into consideration the effect of COVID-19 infection on various organs of the body assumes increased significance in patient management.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that disproportionally accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. In this review, we highlight the complexity of the transforming growth factor-beta family (TGF-β) pathway and discuss how the dysregulation of the TGF-β pathway promotes oncogenic attributes in TNBC which negatively affects patient prognosis. Moreover, we discuss recent findings highlighting TGF-β inhibition as a potent method to target mesenchymal (CD44⁺/CD24^-) and epithelial (ALDH^high) cancer stem cell (CSC) populations. CSCs are associated with tumorigenesis, metastasis, relapse, resistance, and diminished patient prognosis; however, due to differential signal pathway enrichment and plasticity, these populations remain difficult to target and persist as a major barrier barring successful therapy. This review highlights the importance of TGF-β as a driver of chemoresistance, radioresistance and reduced patient prognosis in breast cancer and highlights novel treatment strategies which modulate TGF-β, impede cancer progression and reduce the rate of resistance generation via targeting the CSC populations in TNBC and thus reducing tumorigenicity. Potential TGF-β inhibitors targeting based on clinical trials are summarized for further investigation which may lead to the development of novel therapies to improve TNBC patient prognosis.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that disproportionally accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. While there is immense focus on the development of novel therapies for TNBC treatment, a persistent and critical issue is the rate of heart failure and cardiomyopathy which is a leading cause of mortality and morbidity amongst cancer survivors. In this review, we highlight mechanisms of cardiotoxicity post-chemotherapeutic exposure, assess how this is assessed clinically and highlight the transforming growth factor-beta family (TGF-β) pathway and discuss its role as a mediator of cardiomyopathy. We highlight recent findings demonstrating TGF-β inhibition as a potent method to prevent cardiac re-modeling, fibrosis and cardiomyopathy. We describe how dysregulation of the TGF-β pathway is associated with negative patient outcomes across 32 types of cancer including TNBC. We then highlight how TGF-β modulation may be a potent method to target mesenchymal (CD44+/CD24-) and epithelial (ALDHhigh) cancer stem cell (CSC) populations in TNBC models. CSCs are associated with tumorigenesis, metastasis, relapse, resistance, and diminished patient prognosis; however, due to plasticity and differential regulation these populations remain difficult to target and persist as a major barrier barring successful therapy. TGF-β inhibition represents an intersection of two fields: cardiology and oncology. Through inhibiting cardiomyopathy, cardiac damage and heart failure may be prevented and through CSC targeting, patient prognosis may be improved. Together, both approaches, if successfully implemented would target the two greatest causes of cancer-related morbidity in patients and potentially lead to a breakthrough therapy.

An atomic-scale approach is employed to simulate the formation of precipitates with different lattice misfits in the early stages of aging of supersaturated aluminum alloys. Simulation results reveal that the increase of lattice misfit could significantly promote the nucleation rate of precipitates, which results in a larger number and smaller size of the precipitates. The morphologies of the precipitates also vary with the degree of lattice misfit. Moreover, the higher the lattice misfit, the earlier the nucleation of the second phase occurs, which can substantially inhibit the movement of dislocations. The research on the lattice misfit of precipitation can provide theoretical guidance for the design of high-strength aluminum alloy.

Cell migration is essential for physiological, pathological and biomedical processes such as, in embryogenesis, wound healing, immune response, cancer metastasis, tumour invasion and inflammation. In light of this, quantifying mechanical properties during the process of cell migration is of great interest in experimental sciences, yet few theoretical approaches in this direction have been studied. In this work, we propose a theoretical and computational approach based on the optimal control of geometric partial differential equations to estimate cell membrane forces associated with cell polarisation during migration. Specifically, cell membrane forces are inferred or estimated by fitting a mathematical model to a sequence of images, allowing us to capture dynamics of the cell migration. Our approach offers a robust and potentially accurate framework to compute geometric mechanical membrane forces associated with cell polarisation during migration and also yields geometric information of independent interest, we illustrate one such example that involves quantifying cell proliferation levels which are associated with cell division, cell fusion or cell death.

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of -1 mm coal dust into the fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. In order to understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of -1 mm coal dust and 0.3-0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed was examined, as well as the local mixing properties of the Wide-grained Dense Medium. It was discovered that when low vibrational energy was introduced at the same gas velocity, particle separation was enhanced in comparison to the usual fluidized bed. Little bubbles were found to ensnare fine coal dust and migrate upward, increasing density separation. The introduction of high vibrational energy leads to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of vibration on the bed are diminished when the gas velocity is gradually increased under specified vibration circumstances. This causes big bubbles to develop more frequently, which increases particle mixing. Among them, the 1-0.5 mm fine-grained coal exhibits the best mixing state; it is less impacted by variations in air velocity and vibration levels, whereas the -0.5 mm coal dust is more affected by each of these variables. Furthermore, a wide range of air velocity regulation without a significant segregation state change was discovered to be achievable by the particle system under vibration circumstances of f=25Hz A=1mm and f=25Hz A=2mm.

Lidocaine hydrochloride (HCl) 2% with 1:100,000 epinephrine (LW/E) is widely used to prevent pain during dental procedures and has been associated with injection sting, jittering effects, slow onset, and a bitter aftertaste. Since LW/E’s introduction in 1948, no significant modifications have been proposed. This study aims to design and characterize an improved dental lidocaine HCl injectable formulation without epinephrine (LW/O/E) via buffers, sweeteners, and amino acids. LW/O/E injections were prepared with pH and osmolality values of 6.5 – 7.0 and 590-610 mOsm/kg. Using the electronic tongue (ETongue), the LW/O/E injectable formulations were characterized for viscosity, injectability, and taste analysis. The results were compared with the LW/E control. In vivo efficacy and anesthetic duration of the samples were measured through radiant heat tail-flick latency (RHTFL) and hot plate (HP) tests and local toxicity was assessed after a single intra-oral injection in Sprague Dawley rats (SDR). The viscosity and injectability values of the LW/O/E samples were found to be comparable to the LW/E injection. ETongue taste analysis showed an improvement in bitterness reduction of the LW/O/E samples compared to the LW/E formulation. Toxicity studies of samples in SDR showed minor and transient signs of erythema/eschar and edema. Anesthetic duration via RHTFL and HP paw withdrawal latency time in SDR were found to be comparable for the LW/O/E Sample 3A and the LW/E injection (p<0.05). In conclusion, the buffered, higher osmolality and reduced bitterness developed LW/O/E formulation (Sample 3A) could be considered a promising alternative to the LW/E formulation for dental use.

Steel products have experienced long-standing problems such as unstable product quality and low product homogeneity. In the continuous casting process, realizing constant temperature pouring is an effective way to improve product homogeneity. Plasma heating can compensate for the temperature drop during casting with a tundish and maintain a stable degree of superheating of the molten steel in the tundish. Plasma heating has a certain impact on the cleanliness of the molten steel and on the tundish covering flux in the tundish while compensating for the temperature drop. This paper uses SEM-EDS, XRD and FactSage to analyze the cleanliness of the molten steel and the characteristics of the tundish covering flux before and after heating. The results show that the number density of inclusions in the tundish is significantly lower after heating, improving the floating removal rate of small-sized inclusions; after heating, the surface morphology of the tundish covering flux sample appears transparent and glassy, with uniform morphology; XRD results show that the tundish covering flux after plasma heating exhibits no crystal precipitation and is amorphous and that there is a certain regularity before and after heating; there are no obvious changes in the composition of the tundish covering flux in the liquid phase area.

Hydrogenated water has emerged as a novel approach in the field of health and wellness. It is be-lieved to have potent antioxidant properties that can neutralize harmful free radicals in the hu-man body. It has also been shown to be beneficial in mitigating oxidative stress-induced damage through its anti-inflammatory and anti-apoptotic pathways. We aim to conduct a systematic re-view to evaluate the potential benefits of hydrogenated water. The review protocol was uploaded on PROSPERO. After the initial search criteria, the articles were reviewed by two blinded inves-tigators, and a total of 25 articles were included in the systematic review. The potential benefits of hydrogenated water on various aspects of health, including exercise capacity, physical endurance, liver function, cardiovascular disease, mental health, COVID-19, oxidative stress, and anti-aging research, are a subject of growing interest and ongoing research. Although preliminary results in clinical trials and studies are encouraging, further research with larger sample sizes and rigor-ous methodologies is needed to substantiate these findings. Current research needs to fully ex-plain the mechanisms behind the potential benefits of hydrogenated water. Continued scientific exploration will provide valuable insights into the potential of hydrogenated water as an adjunc-tive therapeutic approach in the future.

OBJECTIVE A quality improvement study to assess catheter-associated urinary tract infection (CAUTI) rate post-implementation of a bladder catheter with integrated active drain line urine clearance and automated intra-abdominal pressure monitoring in a burn intensive care unit (ICU). DESIGN Eight-year retrospective before and after study (2015–2022). SETTING A single American Burn Association verified Burn Center with fourteen inpatient beds. PATIENTS Patients meeting criteria for admission to a Burn Center METHODS Retrospective cohort study following the implementation of a novel urine output monitoring system with integrated drain line and urine clearance. Data from a 48-month (from January 2015-December 2018) historical control (period 1) were compared to data from a 28-month (from January 2020 to April 2022) post-implementation period (period 2). Pre- and post-implementation CAUTI event incidences were compared. Charts were reviewed to characterize the patients. RESULTS A total of 42 CAUTIs in 2243 patients were identified using the National Health and Safety Network (NHSN) definition during the analyzed period. There were 40 CAUTI events in period 1 and two CAUTIs in period 2. The incidence of CAUTI events pre-implementation was 0.030 (mean of 10 CAUTI events per year) compared to 0.002 (mean of 1 CAUTI event per year) post-implementation of an automatic drain line clearing UO monitoring system showing a significant reduction in CAUTI events (P<0.01, risk ratio novel vs. gravity bladder catheter 0.071, 95% confidence interval: 0.017-0.294). CONCLUSIONS CAUTIs were reduced in the period following the implementation of a novel urinary catheter system with an integrated active drain line and urine clearance in burn patients.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

Skin cancer is a prevalent and heterogenous disease with several subtypes, such as melanoma, basal cell carcinoma, and squamous cell carcinoma. Among them, melanoma is the most aggressive subtype, with a higher propensity to spread compared to most solid tumors. The application of OMICS approaches has revolutionized the field of melanoma research by providing comprehensive insights into the molecular alterations and biological processes underlying melanoma development and progression. This review aims to offer an overview of melanoma biology, covering its transition from primary to malignant melanoma, as well as the key genes and pathways involved in the initiation and progression of this disease. Utilizing online databases, we extensively explored the general expression profile of genes, identified the most frequently altered genes, and gene mutations, and examined genetic alterations responsible for drug resistance. Additionally, we studied the mechanisms responsible for immune checkpoint inhibitors resistance in melanoma.

We report a photonic-based radio frequency (RF) arbitrary waveform generator (AWG) using a soliton crystal micro-comb source with a free spectral range (FSR) of 48.9 GHz. The comb source provides over 80 wavelengths, or channels, that we use to successfully achieve arbitrary waveform shapes including square waveforms with a tunable duty ratio ranging from 10% to 90%, sawtooth waveforms with a tunable slope ratio of 0.2 to 1, and a symmetric concave quadratic chirp waveform with an instantaneous frequency of sub GHz. We achieve good agreement between theory and experiment, validating the effectiveness of this approach towards realizing high-performance, broad bandwidth, nearly user-defined RF waveform generation.

We report a broadband radio frequency (RF) channelizer with up to 92 channels using a coherent microcomb source. A soliton crystal microcomb, generated by a 49 GHz micro-ring resonator (MRR), is used as a multi-wavelength source. Due to its ultra-low comb spacing, up to 92 wavelengths are available in the C band, yielding a broad operation bandwidth. Another high-Q MRR is employed as a passive optical periodic filter to slice the RF spectrum with a high resolution of 121.4 MHz. We experimentally achieve an instantaneous RF operation bandwidth of 8.08 GHz and verify RF channelization up to 17.55 GHz via thermal tuning. Our approach is a significant step towards the monolithically integrated photonic RF receivers with reduced complexity, size, and unprecedented performance, which is important for wide RF applications ranging from broadband analog signal processing to digital-compatible signal detection.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Current generation witnesses a huge interest in optical waveguide due to their salient feature such as low cost, immune to electromagnetic interference, easy to multiplex, compact size, etc. These features of the optical fibers makes it an useful tool for various sensing applications including medicine, automotive, biotechnology, food quality control, aerospace, physical and chemical monitoring etc. Among all the reported application, the device has been widely exploited to measure the physical and chemical variation in surrounding environment. Optical fiber based temperature sensor plays a crucial role in this decade to detect high fever and tackle COVID like pandemic. Recognizing the major developments in the field of the optical fibers, this article aims to provide recent progress in temperature sensor utilizing several sensing configuration including standard fiber, photonic crystal fiber, and Bragg grating fibers. Additionally, the article also highlights the advantages, limitations, and future possibilities in this area.

Graphene quantum dots (GQDs) have garnered significant attention in recent years due to their unique properties and promising applications in various fields such as electronics, optoelectronics, bioimaging, and sensing. The synthesis of GQDs is a critical step in harnessing their potential, and numerous methods have been developed to produce GQDs with specific properties. This review provides an overview of the different techniques employed for synthesizing GQDs, including top-down and bottom-up approaches. Furthermore, it discusses the advantages, limitations, and potential applications of each method, offering insights into the future direction of GQD synthesis research.

The objective of the present study is to isolate phages targeting multidrug resistant (MDR), extended spectrum beta lactamase (ESBL) positive clinical isolate of E. coli ( U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of monophage and phage cocktail in vitro and in vivo in a zebrafish model. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G), Cuoom River (designated as CR) and Hospital waste water (designated as M phage). The obtained phages were triple purified and enriched. U1G phages had a greater burst size of 124 PFU/cell and a latent time of 25 min.M phage had a burst size of 150 PFU/cell with a shortlatent time of 20min. Similarly CR phage has a short latent 20 min and a burst size of 115 PFU/cell. Based on capsid architecture, U1G phage resembles Podoviridae, CR phage is structurally similar to Myoviridae and M phage has morphology that resembles Siphoviridae. Genome sequencing and analysis revealed that the size of the U1G phage genome is 73275 bp Whereas that of CR phage and M phage are 45236 bp and 45294 bp, respectively. All three genomes were marked by the absence of genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen , LamB or OmpC for U1G FhuA, OmpC for CR phage and FhuA, LamB, TonB or OmpF for the M phage as the host receptor targeted by the receptor binding protein (RBP) of the phages. In vitro time kill assay showed that treatment with monophages alone and along with colistin resulted in regrowth whereas phage combinations significantly reduced the regrowth and by 24h the phage cocktail along with colistin produced a significant 3 log declinein cell counts relative to the untreated control. In vivo intramuscular infection study in zebrafish showed that phages were non toxic and a cocktail of dual (U1G +M) phage along with colsitin resulted in a significant 3.5 log decline in cell counts whereas triple phage combination along with colistin resulted in 3 log decline in cell counts probably due to host receptor competition. Our study highlights the potential of phage cocktail therapy in mitigating MDR clinical isolate of E. coli in vitro and in vivo.

Following our previous success in identifying new steroid-based anticancer agents, we herein disclosed the structural requirements for retaining high potency against cancer cells and associated modes of action. The structurally novel steroidal dimer by001 inhibited growth of different esophageal cancer cells and colony formation at low micromolar levels, elevated cellular ROS levels and caused mitochondrial dysfunction. Mechanistic studies showed that by001 induced cell death through the mitochondria and death receptor-mediated apoptotic pathways and autophagy induction, as well as inhibited migration.

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

Micro-combs [1-4] - optical frequency combs generated by integrated micro-cavity resonators – offer the full potential of their bulk counterparts [5,6], but in an integrated footprint. The discovery of temporal soliton states (DKS – dissipative Kerr solitons) [4,7-11] as a means of mode-locking micro-combs has enabled breakthroughs in many fields including spectroscopy [12,13], microwave photonics [14], frequency synthesis [15], optical ranging [16,17], quantum sources [18,19], metrology [20,21] and more. One of their most promising applications has been optical fibre communications where they have enabled massively parallel ultrahigh capacity multiplexed data transmission [22,23]. Here, by using a new and powerful class of micro-comb called “soliton crystals” [11], we achieve unprecedented data transmission over standard optical fibre using a single integrated chip source. We demonstrate a line rate of 44.2 Terabits per second (Tb/s) using the telecommunications C-band at 1550nm with a spectral efficiency – a critically important performance metric - of 10.4 bits/s/Hz. Soliton crystals exhibit robust and stable generation and operation as well as a high intrinsic efficiency that, together with a low soliton micro-comb spacing of 48.9 GHz enable the use of a very high coherent data modulation format of 64 QAM (quadrature amplitude modulated). We demonstrate error free transmission over 75 km of standard optical fibre in the laboratory as well as in a field trial over an installed metropolitan optical fibre network. These experiments were greatly aided by the ability of the soliton crystals to operate without stabilization or feedback control. This work demonstrates the capability of optical soliton crystal micro-combs to perform in demanding and practical optical communications networks.

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

Single-cell RNA sequencing data facilitates investigation of cell heterogeneity and subpopulations as well as differentially abundant states however modern single-cell RNA sequencing datasets are growing in size and complexity requiring advances in the bioinformatic methods that analyze them. Many methods exist for each step of analysis including read alignment, normalization, quality control, batch effect correction, imputation and dimensionality reduction. With so many options to choose from at each step of the analysis, benchmarking and a synthesis of the literature on the methods available is necessary to inform biological researchers on the most optimal workflow for their data. Here, recent key methods of analysis are highlighted with a focus on methods that facilitate identification of cell subpopulations and differentially abundant cell states. With a constantly expanding toolset for each step in single-cell RNA sequencing dataset analysis, biological researchers should stay informed to utilize the most applicable methods for their own analyses.

Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

In this review we look at the developments in the field of artificial intelligence (A.I) and their current impact as well as future implications in the field of cardiology, we as physicians and cardiologists have tried to put our perspective on the rapidly evolving field, In light of the fact that many of the healthcare workers and professionals across the world still find the concept of A.I quite complicated, elusive and somewhat paradoxical, we have tried to simplify the concept of artificial intelligence and make it easy for everyone to understand from all walks of life. The Databases utilized for our review are PubMed, PubMed Central, Google Scholar. The Keywords used for our Data Search are “Artificial Intelligence”, “A.I Cardiology”, “A.I in Cardiovascular Medicine”, “Machine Learning in Cardiology”. We screened all relevant since inception till March 25th and included 44 relevant articles after careful consideration into our paper. We have discussed the implementation of and scope of artificial intelligence across a broad spectrum of applications including cardiovascular imaging and diagnostics like electrocardiograms, echocardiograms, cardiac CT/MRIs to novel cardiac monitoring devices, CRISPR gene editing in cardiology and implementation of artificial intelligence in the field of Cardiovascular Bioprinting. This is the initial review in a series of reviews regarding A.I in Cardiology.

Background: Neuroendocrine tumors (NET) are a rare group of epithelial neoplasm present in gastrointestinal tract (GI) (67.5%), bronchopulmonary tree (25.3-30%), and in 15% of cases, the primary sites cannot be identified. Although endoscopic screening, improvement in pathological techniques, and early detection have shown improvement in NET survival rates, the prognosis of advanced, metastatic, and poorly differentiated NET is very poor. In this study, we aimed to evaluate the effect of Gastrointestinal and pancreatic (GEPs) NETs grade on overall survival. Method: We searched observational studies describing the overall survival or prognostic factors of primary GEP NETs from May 2011 -May 2021 following PRISMA guidelines. Studies describing the effect of primary grade 3 GEP NETs on overall survival were included. Meta-analysis was performed, and pooled hazard ratio and their 95% confidence interval (95% CI) were obtained. The forest plots were created using random-effects models and sensitivity analysis was performed to account for the heterogeneity. Results: Seven studies with 7692 confirmed patients were included. In our meta-analysis grade 3 GEP NET were associated with higher odds of poor survival (pooled HR: 2.73; 95% CI: 1.36–5.47; p = 0.005), with 92% heterogeneity between studies (p < 0.0001). To account for heterogeneity, sensitivity analysis was performed by removing two outlying studies (Fathi et al. and Foubert et al.) on funnel plots. The results after sensitivity analysis did not change and still showed significant association of grade 3 with poor survival (pooled HR: 4.53; 95% CI: 3.54–5.78; p < 0.00001), with no heterogeneity between studies (p = 0.72; I2 = 0%). Conclusion: Our meta-analysis found that grade 3 GEP NETs are associated with poor survival and additional future studies are needed to identify other risk factors associated with poor survival in GEP NETs to improve mortality.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

Background: Kawasaki disease (KD) is a form of vasculitis that primarily affects children under the age of 5 years old. Patients may be missed diagnosis when initial clinical symptoms do not fulfill the traditional criteria. We aimed to analyze factors that clinicians could use to differentiate febrile children suspected of KD. Method: We retrospectively enrolled a total of 83 febrile children who were initially suspected of KD, but they did not meet the American Heart Association (AHA) criteria for a diagnosis. However, some of these patients were diagnosed with KD during their second visit. We analyzed patients' characteristics, clinical symptoms, and laboratory data. Results: In total, 50 patients were enrolled in the study. Of those, ten patients were diagnosed with KD on their second visit (group 1), while the other 40 patients still did not fit a KD diagnosis (group 2). A patient with a neutrophil to lymphocyte ratio greater than 1.33 combined with a C-reactive protein more than 33 mg/L was more likely to have KD. Conclusion: Among patients suspected of KD that did not initially meet the criteria, clinicians should pay special attention to elevated neutrophil-to-lymphocyte ratios and CRP levels and closely follow up such patients.

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1,2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

Importance: Neuropsychiatric disorders like schizophrenia and bipolar disorder lack objective diagnostic markers, hindering accurate diagnosis and treatment. A novel approach using patient-derived cerebral organoids and digital analysis of electrophysiological recordings could provide much-needed objective biomarkers.Objective: To develop a digital analysis pipeline that can identify distinct electrophysiological signatures of schizophrenia and bipolar disorder using multi-electrode array recordings from patient-derived cerebral organoids.Design: This was an experimental study using previously published data. The study type can be classified as a case-control study, comparing cerebral organoids and 2D neuronal cultures derived from patients with schizophrenia, bipolar disorder, and healthy controls. The study utilized multi-electrode array recordings for analysis. Setting: The study was conducted in a laboratory setting using previously recorded data. Main Outcomes and Measures: The primary outcome was the accuracy of a support vector machine classifier in distinguishing between healthy control, schizophrenia, and bipolar disorder samples based on electrophysiological features extracted from the recordings. Key cellular-digital biomarkers were identified using minimum redundancy maximum relevance feature selection.Results:Our Support Vector Machine classifier achieved 95.8% accuracy in distinguishing SCZ from control samples in 2D neuronal cultures under both baseline and post-electrical stimulation (PES) conditions. For cerebral organoids, classification accuracy was 83.3% under baseline conditions, improving to 91.6% under PES when distinguishing between control, SCZ, and BPD samples. Key features for classification included channel-specific measures such as median, covariance, autocorrelation, and kurtosis of neural activity. Confusion matrices visualize classification performance, with most misclassifications occurring in the BPD class under baseline conditions. These results demonstrate that our digital analysis pipeline can effectively distinguish between healthy control and patient-derived samples using electrophysiological features from multi-electrode array recordings, particularly under PES conditions.Conclusions and Relevance: This digital analysis pipeline demonstrates the potential to identify objective electrophysiological signatures of schizophrenia and bipolar disorder using patient-derived cerebral organoids. This approach could lead to improved diagnostic accuracy and personalized treatment strategies for neuropsychiatric disorders.

In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.

A decade ago, non-radiative wireless power transmission reemerged a promising alternative to deliver electrical power to devices where a physical wiring proved to be unfeasible. However, conventional coupling-based approaches are neither scalable nor efficient when multiple devices are involved, as they are restricted by factors like coupling and external environments. Zenneck waves are excited at interfaces, like surface plasmons and have the potential to deliver electrical power to devices placed on a conducting surface. Here, we demonstrate, efficient and long range delivery of electrical power by exciting non-radiative waves over metal surfaces to multiple loads.Our modeling and simulation using Maxwells equation with proper boundary conditions shows Zenneck type behavior for the excited waves and are in excellent agreement with experimental results. In conclusion, we physically realize a radically different power transfer system, based on a wave, whose existence has been fiercely debated for over a century.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

Introduction: The presence of multiple comorbidities increases the risk of all-cause mortality, but the effects of the comorbidity sequence before the baseline date on mortality remained unexplored. This study investigated the relationship between coronary heart disease (CHD), atrial fibrillation (AF) and heart failure (HF) sequence on all-cause mortality risk in type 2 diabetes mellitus. Methods: This study included patients with type 2 diabetes mellitus prescribed antidiabetic/cardiovascular medications in public hospitals of Hong Kong between January 1st, 2009 and December 31st, 2009 with follow-up until death or December 31st, 2019. Cox regression was used to identify comorbidity sequences predicting all-cause mortality in patients with different medication subgroups. Results: A total of 249291 patients (age: 66.0±12.4 years, 47.4% male) were included. At baseline, 7564, 10900 and 25589 patients had AF, HF and CHD, respectively. Over follow-up (3524±1218 days), 85870 patients died (mortality rate: 35.7 per 1000 person-years). Sulphonylurea users with CHD developed later, but insulin users with CHD developing earlier, in the disease course had lower mortality risks. Amongst insulin users with two of the three comorbidities, CHD with preceding AF (hazard ratio [HR]: 3.06, 95% CI: [2.60-3.61], p<0.001) or HF (HR: 3.84 [3.47- 4.24], p<0.001) had a higher mortality. In users of lipid-lowering agents with all three comorbidities, those with preceding AF had higher risk of mortality (AF-CHD-HF: HR: 3.22, [2.24-4.61], p<0.001; AF-HF-CHD: HR: 3.71, [2.66-5.16], p<0.001). Conclusion: The sequence of comorbidity development affects the risk of all-cause mortality to varying degrees in diabetic patients on different antidiabetic/cardiovascular medications.

Delayed cancer detection is one of the common causes of poor prognosis in case of many cancers including the cancers of the oral cavity. Despite improvement and development of new and efficient gene therapy treatments, very little has been done to algorithmically assess the impedance of these carcinomas. In this work, we attempt to annotate viable attributes in oral cancer gene datasets for identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated to other forms of oral cancer detection.

Thienopyrimidines are a versatile group of compounds that contain a biologically active pharmacophore and reported to have anticancer efficacy in vitro. Here, we report for the first time, that thieno[3,2-d]pyrimidine - based compounds, designated the RP series, have efficacy in prostate cancer cells. The lead compound, RP-010, was efficacious in PC3 and DU-145 prostate cancer (PC) cells (IC50< 1µM). The cytotoxicity of RP-010 was significantly lower in normal cells. RP-010 (0.5, 1, 2, and 4 µM) arrested prostate cancer cells in the G2 phase of the cell cycle, induced mitotic catastrophe and apoptotic signaling in both PC cell lines. Mechanistic studies suggested that RP-010 (1 and 2 µM) inhibits the wingless-type MMTV (Wnt)/β-catenin signaling pathway, mainly by inducing β-catenin fragmentation, while down regulating important proteins in the pathway, i.e. LRP-6, DVL3, and c-Myc. Interestingly, RP-010 (1 and 2 µM) induced the nuclear translocation of the negative feedback proteins, Naked 1 and Naked 2, in the signaling pathway. In addition, RP-010 (0.5, 1, 2, and 4 µM) significantly decreased the migration and invasiveness of PC cells in vitro. Finally, RP-010 did not produce significant toxic effects in zebrafish at concentrations up to 6 µM. In conclusion, RP-10 is a promising anticancer compound in metastatic prostate cancer and did not produce overt toxicity in an in vivo zebrafish model. Future mechanistic and efficacy studies are needed in-vivo to optimize the lead compound RP-010 for clinical use.

A fluorescent probe, N'-((3-methyl-5-oxo-1-phenyl-4, 5-dihydro-1H-pyrazol-4-yl) methylene)-2-oxo-2H-chromene-3-carbohydrazide (MPMC), was synthesized and characterized. Characterizations of the synthetic MPMC were conducted by proton nuclear magnetic resonance (1HNMR) spectroscopy and carbon-13 nuclear magnetic resonance spectroscopy (13C NMR). The fluorescence emission behaviors of probe MPMC towards diverse metal ions were detected and the probe exhibited high sensitivity and selectivity towards Cu2+ over other metal ions via quenching of its fluorescence. Furthermore, the existence of other metal actions made no difference on the fluorescence intensity of the MPMC-Cu2+ system apparently, that is, MPMC displayed a good anti-interference ability. Job’s plot of MPMC and copper ions indicated the detection limit was 15.16 nM (R2=0.9612) for the assayed actions, with a stoichiometric ratio of 1:1 for MPMC and Cu2+. Additionally, the color of MPMC probe solution changed from nearly colorless to yellow in the presence of Cu2+ in visible light, which the color change could be observed by the naked eye. Similarly, the color resolved bright yellow into blue in ultraviolet light. Also reusability studies indicated that MPMC probe was reusable. The pH effect of probe MPMC to Cu2+ had a broad range of pH detection 4.0 to 11.0. The response time of MPMC probe for determining Cu2+ was within 1 min. The recognition of Cu2+ by MPMC performed on pre-treated paper under sunlight and UV light both had a distinct colour change. Thus, the solid-state method for detecting Cu2+ with the naked-eye, was both economical and convenient.

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side.