At present there is a renewed interest in theories of ”modified” gravity. Here, under a more drastic modification enforced by Galilei group, we obtain a completely new gravitational structure, and which exists in addition to the already available general relativity of today. Correlated with this, we show that in addition, there is a new ”modified” quantum mechanics, in as much as it exists as an independent and new ”pure” non-relativistic quantum me- chanics, and which has no relativistic counterpart. This is in addition to the present quantum mechanics, where the relativistic and non-relativistic structures are counterparts of each other. The above holds, firstly due to the correlation between Galilei group and quantum mechanics. These math- ematical conclusions are consolidated by the fact that there exists a physical Majorana interaction between each neutron- proton pairs in nuclei. Galilei invariance of Majorana exchange in Majorana interaction, shows that the mass here is of pure gravitational nature, and which is immune to the other three forces. This makes an amazing connection between the gravitational force and the quantum mechanics. This pure gravitational mass would man- ifest itself as dark matter of the universe. It is our new modified gravity that generates the dark matter.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

Purpose: The pandemic, named COVID-19, which shook global economy, production and trade has equally affected the social and domestic lives of ours. In this study the COVID-19’s impact has been investigated on lack of social interaction, financial stress and how it influences the domestic violence in a developing country like Pakistan. The study focuses on some of the important factors that are root causes of increased domestic violence in Pakistan. Design/methodology/approach: The study is quantitative and survey questionnaire has been conducted for structural equation modelling (SEM) to evaluate the hypotheses. The modified scale was validated using confirmatory factor analysis, the Purposive and snowball sampling technique is used for collecting data from 200 respondents of Punjab Pakistan for data analysis through SPSS and SmartPLS. Findings: It is revealed that Covid 19 has a significant effect on lack of social interaction and financial stress. Moreover, lack of social interaction and financial stress also enhances domestic violence. Finally, lack of social interaction and financial stress mediates COVID-19 and domestic violence. Originality/value: This study appears to be frontier research which determines the impact of COVID-19 on lack of social interaction, financial stress and domestic violence particularly in a developing country like Pakistan.

Objective: The influence of gender on the number and pattern of co-morbid psychiatric illness was archivally compared between patients with and without an Alcohol Use Disorder (AUD) among psychiatrically treated individuals drawn from a large Danish birth cohort. Method: By age 45, fifteen percent of the Copenhagen Perinatal Birth Cohort (N=8,109) had been treated in a Danish psychiatric facility. Of those 1,247 patients, 368 were assigned an AUD diagnosis (29.5%). All psychiatric diagnoses assigned to these patients, over all admissions, were classified into one of 14 broad ICD-10 F categories for comparative purposes. Results: More of the patients with an AUD diagnosis were male. Patients with an AUD diagnosis were significantly more likely to receive two or more non-AUD psychiatric diagnoses than patients without an AUD diagnosis. The number of psychiatric diagnoses was greater for female than male AUD patients, a difference not found among male and female patients without an AUD diagnosis. For patients with no AUD diagnosis, the influence of gender on the pattern of psychiatric disorders was similar to that commonly reported. In contrast, the influence of gender on the number and pattern of psychiatric disorders for patients with an AUD diagnosis indicated fewer gender differences. Several diagnostic categories that discriminated male from female patients without an AUD-related diagnosis, no longer discriminated male from female patients with an AUD-related diagnosis. Conclusions: Our findings suggest that gender differences associated with psychiatric comorbidity that have been reported in large heterogenous study, should not be generalized to specific diagnostic groups without further study.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Increasing demand for electricity and modernization of power systems within competitive markets induce power systems to operate close to their stability limits. Therefore, a power system continuous monitoring and control by using voltage stability indices are known exigence. This is the first-ever effort, examined more than 40 voltage stability indices thorough an exhaustive study based in on their formulation, application, performance, and assessment measures. Which is conducted based on a logical and chronological order considering the most recent worldwide applied voltage stability indices. However, the generalizability of such an immense diversity of these indices in term of multivariable objectives subjects to a certain limitation to summate into a deciphered outcome. Despite all these obstacles, this study systematically reviews available indices in the literature within the past three decades to set out an integrated knowledge with an insight up-to-date exposition. That followed with a comparative analysis in term of their similarity, functionality, applicability, formulation, merit, demerit, and overall performances. Also, a broad categorization of voltage stability indices is addressed. This study with an immediate implication in practice as an exhaustive roadmap can be counted a planning and operation reference in the context of voltage stability for students, researchers, scholars, and practitioners.

The Metaverse of Things (MoT) is an advanced technology that has the potential to revolutionise urban living in the present era. This article explores the advantages, uses, and transformative outcomes of MoT in smart cities. The covered subjects encompass sustainability, urban planning, citizen participation, infrastructure management, and various more. MoT integrates the Internet of Things (IoT) with metaverse technologies. The ultimate objective is to develop virtual environments that are highly interactive, interconnected, and immersive while maintaining a high level of fidelity to reality. The IoT utilises virtual reality (VR), augmented reality (AR), and other digital technologies to gather data, facilitate communication, and automate certain processes, thereby enhancing several elements of urban living. It is all a component of the metaverse. The IoT will bring about a profound transformation in the way cities gather and utilise data to enhance services and optimise efficiency. Cities that can efficiently distribute this data can enhance public safety, optimise energy usage, regulate traffic, and manage waste properly. MoT apps that utilise immersive technologies and the IoT can be used to generate more intelligent and captivating cityscapes. The implementation of MoT can greatly enhance the quality of life for residents of smart cities through improvements in transportation, healthcare, education, and community engagement. The study's author examined how smart cities utilise the IoT to enhance the daily experiences of their inhabitants. This study examines the technical structure, possible advantages, and difficulties of implementing MoT in urban settings, aiming to enhance the resilience, responsiveness, and adaptability of cities. The findings emphasise the importance of robust legislative frameworks, stringent security requirements, and well-developed infrastructure to facilitate the extensive use of MoT technology. These factors are crucial for establishing a highly interconnected and efficient urban environment.

Spectral photon counting cone-beam CT imaging is challenged by individual pixel response behaviour, which leads to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform post-processing ring artefact correction of sinogram data or scan reconstructions, without taking the pixel response explicitly into account. Here we present a novel post-processing method (DAC-shifting), which explicitly measures the current pixel response using flat field images, and subsequently corrects the projection data. The DAC-shifting method was evaluated with a repeat series of spectral photon counting imaging (Medipix3) of a phantom with different density inserts, and in iodine K-edge imaging. The method was also compared against PMMA based STC. The DAC-shifting method was shown to be effective in correcting individual pixel response, and was robust against detector instability. On the contrary, STC correction showed varying results, which was almost as good as DAC-shifting when data was acquired just after STC calibration, but worse with larger time differences. In K-edge imaging, DAC-shifting provides a sharper attenuation peak, and more uniform CT values, which is expected to benefit iodine concentration quantification.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

MPC (Magneto-Photonic Crystal) Optimisation is a feature-rich Windows software application designed to enable researchers to analyze the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. A set of computational algorithms aimed at enabling the design optimization and optical or magneto-optical (MO) spectral analysis of 1D magnetic photonic crystals (MPC) is reported, together with its Windows software implementation. Relevant material property datasets (e.g., the optical spectra of refractive index, absorption, and gyration) of several important optical and MO materials are included, enabling easy reproduction of the previously published results from the field of MPC-based Faraday rotator development, and an effective demonstration-quality introduction of future users to the multiple features of this package. We also report on the methods and algorithms used to obtain the absorption coefficient spectral dispersion datasets for new materials, for which the film thickness, transmission spectrum, and refractive index dispersion function are known.

A study of photovoltaic solar window technologies is reported, focusing on their structural features, functional materials, system development, and suitability for use in practical field applications, e.g. public infrastructures and agricultural installations. Energy generation performance characteristics are summarized and compared to theory-limit predictions. Working examples of pilot-trial solar window-based installations are described. We also report on achieving electric power outputs of about 25 W_p/m² from clear and transparent large-area glass-based solar windows.

We present a review of the current state of the field for a rapidly evolving group of technologies related to solar energy harvesting in built environments. In particular, we focus on recent achievements in enabling the widespread distributed generation of electric energy assisted by energy capture in semi-transparent or even optically clear glazing systems and building wall areas. Whilst concentrating on the cutting-edge recent results achieved in the integration of traditional photovoltaic device types into novel concentrator-type windows and glazings, we compare the main performance characteristics reported with these achievable using more conventional (opaque or semi-transparent) solar cell technologies. A critical overview of the current status and future application potential of multiple existing and emergent energy harvesting technologies for building integration is provided.

Amorphous ferrite-type rare-earth (RE) substituted garnets and garnet-oxide nanocomposite layers are prepared on clear glass substrates by using RF magnetron sputter-deposition process. By using a combination approach employing custom-built spectrum-fitting software in conjunction with Swanepoel’s envelope method, the spectral dispersion function of optical constants and the layer thicknesses are derived accurately from the transmission spectra of the as-deposited samples. The effects of excess metal-oxides added to the base material systems during the co-deposition process are found to affect the refractive index and the optical absorption coefficients of garnet-oxide composites. A number of optical constant datasets are presented, enabling the experimentalists to design nanophotonic or integrated-optics devices employing these functional materials.

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.

The mHealth is a term that is used for mobile health supported by smart devices such as mobile phones, tablets, and wearable smart devices, etc. The smart devices strengthen the efficiency and effectiveness of interaction with patients, physicians, and specialists. Patients nowadays would like to be intimately involved in their diagnosis as well as to make more informed decisions concerning their care. It has begun to measure the success of the quality of treatment. This was a reason that patients trust mHealth to provide them with consistency in their communications with the physicians. Most wireless strategies do not measure up to this standard so that patient engagement ultimately ended up decreasing. The blockchain can boost mHealth through storing and sharing electronic data securely and transparently. It can enhance the accessibility of patient information in real-time. The Internet of Things (IoT) provides a unique identification number to every connected device such as mobile devices, medical devices, and wearable devices. This framework uses the blockchain and IoT technologies together to provide quick help to the patients, monitor remotely, reduce the cost and unnecessarily hospitalization physically and find the real diagnosis. In order to increase patient involvement, mHealth framework with blockchain and IoT technologies has built with the key objective of providing patients with full information on their treatment and diagnosis.

The feral mink population in Denmark consists of two groups of animals; mink born in the wild and mink that have recently escaped from farms. The aims of this study are; 1) to estimate the reproduction and mortality of wild-born and captive-born mink and 2) to estimate the age of mink based on the width of pulp cavity (% of tooth width) in the canine teeth. During 2018, 247 wild caught mink were sent for necropsy at the Danish National Veterinary Institute. Of these mink, 112 were determined as captive-born and 96 were determined wild-born. The mean litter size ± SE of wild-born females was 7.6 ± 0.9 (range: 5-11 kits) and for captive-born females 5.9 ± 0.9 (range: 1-10 kits). The best fitting regression line for mink age (in months) based on pulp width was y=0.42x2-11.52x+104.7, R² = 0.77, p< 0.0001. Individuals with a pulp cavity width <35% was found to be younger than one year. The turnover of mink caught in nature was estimated to 66% and the yearly mortality to 69%, therefore the population is slightly declining. In conclusion, a feral reproducing mink population in Denmark persists, besides the continues influx of captive-born mink escaped from farms.

Currently, the COVID‐19 has directly affected the millions of humans lives. The symptoms of the disease involving fever, malaise, chest infection, and breathing difficulties, were identified, and its existence is continuously becoming restructured. The World Health Organization (WHO) had mentioned the wide diagnostics test besides COVID-19 that would also assist medical facilities to recognize infectious diseases as well as currently focusing efficiently on preventing and afterward defeating this viral disease. The infection is usually transmitted among human beings in direct contact, greatest through the liquid bubbles generated through cough, sneeze, or speaking. This paper reviews the COVID 19 pandemic, its history, current updates, contact tracing applications, and use of emerging technologies like the Internet of Things (IoT) and Blockchain for stopping the spreading and provide service online to the patient from a distance.

Online class now is the demand of the day as little scopes are to find out alternatives to online class in these unprecedented days caused by corona pandemic across the globe. The study was qualitative in approach and data were collected from secondary sources i.e. different newspapers and journals in the recent times along with a mini interview with students of private universities studying in different subjects over mobile phone by the researcher. Findings of the study show that though online education has a number of challenges faced by two main stakeholders; students and teachers, handling all these challenges carefully can have the chance to create a positive atmosphere in the field of education as an alternative teaching learning resulting in positive outcomes in all regards.

Blockchain (BC) in the Internet of Things (IoT) is a novel technology that acts with decentralized, distributed, public and real-time ledger to store transactions among IoT nodes. A blockchain is a series of blocks, each block is linked to its previous blocks. Every block has the cryptographic hash code, previous block hash, and its data. The transactions in BC are the basic units that are used to transfer data between IoT nodes. The IoT nodes are different kind of physical but smart devices with embedded sensors, actuators, programs and able to communicate with other IoT nodes. The role of BC in IoT is to provide a procedure to process secured records of data through IoT nodes. BC is a secured technology that can be used publicly and openly. IoT requires this kind of technology to allow secure communication among IoT nodes in heterogeneous environment. The transactions in BC could be traced and explored through anyone who are authenticated to communicate within the IoT. The BC in IoT may help to improve the communication security. In this paper, I explored this approach, its opportunities and challenges.

Seaweeds are regarded as one of the valuable coastal resources because of their usage in human food, cosmetics, and other industrial items. They also play a significant role in providing nourishment, shelter, and breeding grounds for fish and many other sea species. This study introduces a newly developed seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave infrared (SWIR) of Landsat 8 satellite data. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using SEI, and its performance was compared with other commonly used indices - Normalized Difference Vegetation Index (NDVI) and Floating Algae Index (FAI). The accuracy of the mapping results obtained from SEI, NDVI, and FAI was checked with field verified seaweed locations. The purpose of the field surveys was to validate the results of this study and to evaluate the performance of SEI with NDVI and FAI. The performance of SEI was found better than NDVI and FAI in enhancing submerged patches of the seaweed pixels what other indices failed to do.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient's condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

At present, the world is now passing a very far different time than normal situation due to the COVID-19 pandemic crisis. The global life-style and human civilization is currently progressing with down-stream that affecting almost every sectors necessary for human civilizations except the current environmental situation. To control the COVID-19 spreading, most of the countries are following lockdown process that reduces human mobility, thus reducing the CO2 emission to the environment. Though the COVID-19 pandemic is a blessing for the present environment, however, the post-COVID world will face a massive thrust of energy and only conventional energy resources may not be enough to mitigate the energy demands. Solar power generation technology mainly the photovoltaic (PV) systems and their advancement can be the leading possibilities to minimize the gap between the power demand and generation. It is now time to think how we can improve the PV power generation in future and the post-COVID world. In this encyclopaedia communication, we report on Nano-technological approach to improve the conversion efficiency of GaAs solar cells. We have designed and optimized several types of nano-structured assemblies that can be implemented to reduce the front surface incident light reflection losses thus can assist to improve the conversion efficiency of GaAs solar cells.

Many empirical and theoretical studies indicate that Brownian motion and diffusion models as its mean field counterpart provide appropriate modelling techniques for individual insect movement. However, this traditional approach has been challenged and conflicting evidence suggests that an alternative movement pattern such as Lévy walks can provide a better description. Lévy walks differ from Brownian motion since they allow for a higher frequency of large steps, resulting in a faster movement. Identification of the ‘correct’ movement model that would consistently provide the best fit for movement data is challenging and has become a highly controversial issue. In this paper, we show that this controversy may be superficial rather than real if the issue is considered in the context of trapping or, more generally, survival probabilities. In particular, we show that almost identical trap counts are reproduced for inherently different movement models (such as the Brownian motion and the Lévy walk) under certain conditions of equivalence. This apparently suggests that the whole ‘Levy or diffusion’ debate is rather senseless unless it is placed into a specific ecological context, e.g. pest monitoring programmes.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

Cervical cancer is mostly diagnosed cancer of women worldwide and the second most common cancer among middle-aged women in Bangladesh. Knowledge and perception of women are considered one of the major components of prevention strategy. We carried out this survey to assess the existing knowledge level of students and their perception to the role of vaccination in preventing cervical cancer. The participants were female undergraduate students studying Public Health in a university in Chattogram city of Bangladesh. A total of 275 female students were interviewed through prescribed questionnaire possessing questions on basic information of cervical cancer, its causal agent, risk factors, prevention methods, and vaccination. We marked the responses of participants with zero for wrong answer and one for every correct answer and ranked the knowledge level of students. The mean age of study participants was 20.68 years. In total, 69.1% students possessed poor knowledge whereas, 30.9% had good knowledge. The country of students has significant association with their level of knowledge. Most of the students got information on cervical cancer from traditional media (49.8%) and doctor (30.9%) and the effect of these two sources was statistically significant on the knowledge level of students. Students mostly identified vaccination (21.1%) and Gynecological examination (14.39%) as preventing methods. However, 65.5% of them acknowledge that vaccines cannot prevent this cancer and 33.4% believe in different risks of vaccine. Although students have heard of cervical cancer the overall knowledge was insufficient and perceived negative concepts about cancer vaccine. Therefore, we recommend the initiation of educational programs on basic reproductive health and health seeking behaviour targeting young women to successfully control and prevention of this life-threatening health problem.

We report on the development of several different thin-film functional material systems prepared by RF magnetron sputtering at Edith Cowan University nanofabrication labs. We conduct research on the design, prototyping, and practical fabrication of high-performance magneto-optic (MO) materials, oxide based sensor components, and heat regulation coatings for advanced construction and solar windows.

Metallic thin-film materials and nanoparticles (mainly Silver (Ag)-based) are recently being used in many nano-technological applications including sensors, reflective heat-mirror coatings, and antibacterial coatings as well. The physical vapor deposition technique attracts significant attention for Ag-based nanocomposites with tailoring the structural and optical properties of metallic thin films thus allowing further improvements and application possibilities in various existing fields namely electronics, catalysis, magnetics, optics in alongside the environment and health and new emergent fields in, particularly thin-film coatings. This study highlights the preparation, characterization, properties, and possible future application directions of several types of Silver (Ag)-based nanocomposite thin films prepared by using physical vapor deposition techniques.

With the dramatic increase in world population, continued advances in modern greenhouse agriculture and plant growth practices are expected to help overcome the global problem of future food shortages. Next generation greenhouse design practices will need to address a range of issues, ranging from energy and land use efficiency to providing plant-optimised growth techniques. In this paper, we focus on investigating the optimum irradiation spectra matched to the lettuce species (Lactuca sativa, L.), which is commonly grown in greenhouse environments, in order to develop low-emissivity glass panes that maximize the biomass productivity of glass greenhouses. This low-emissivity glass passes the solar spectral components needed for crop growth, while rejecting other unwanted radiations, leading to significant energy savings and other beneficial effects related to greenhouse climate control, in a range of climates. This is due to reducing both the solar heat gain and photosaturation, which can raise the temperature of the crops to harmful levels. Experimental results show that substantial biomass productivity improvements in lettuce (up to ~14.7%) can be attained using spectrally optimized illumination, compared with white light irradiation. We also report on the development of advanced metal-dielectric thin-film filters that produce the optimum illumination spectrum when exposed to sunlight.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Data integrity and security are among the most essential things in this digital era performance. Digital certificates can be one solution to this problem. This research will develop the architecture of digital certificate verification and validation using blockchain. The blockchain concept for digital certificates uses three stages: requirements planning, workshop design, and implementation. Digital certificate files will be tested based on a scenario in which there could be any changes to those files, such as compressing files, changing data of files, inserting images in the file, and rotating files. This system architecture will detect any changes that could occur to the digital certificate file by using a digital signature, Keccak256 hash algorithm, and blockchain. This research shows that change detection accuracy for digital certificate files is 100%. However, there is a digital signature nature that could reduce the accuracy of change detection, and several solutions can be resolved. The average time to generate a signature using blockchain technology is 2.11 seconds, faster than 3.23 seconds when it does not use blockchain. The average time to validate a signature is 2.11 seconds without using blockchain, which is reduced to 0.22 seconds using blockchain technology. This research concludes that the architecture for digital certificate verification and validation using blockchain technology has successfully improved the security of digital certificates. Testing showed that even minor changes to a digital certificate could affect the signature, ensuring the certificate's integrity. For further research, evaluating the performance of various hash algorithms within the blockchain network is recommended.

Since 2007, at the Electron Science Research Institute (ESRI) nano-fabrication laboratories, Edith Cowan University, Australia, we have devoted research efforts to the synthesis and characterization of bismuth-containing ferrite-garnet-type thin-film magneto-optic (MO) materials of different compositions. We report on the development and properties of highly bismuth-substituted iron-garnet thin films prepared by using radio frequency (RF) magnetron sputtering. We study the process parameters associated with the RF magnetron sputter deposition technique and investigate the results of optimizing process parameters and implementing several special techniques including the fabrication of co-sputtered nanocomposite films, all-garnet multilayer structures, applying oxygen plasma treatment on amorphous garnet layers just after the deposition process, and designing modifications of the annealing crystallization process and regimes for achieving the best MO properties. We demonstrated significant improvement in MO properties of Bi-containing ferrite-type garnet thin-film materials, including record-high MO figures of merit and improved conventional and un-conventional hysteresis loops of Faraday rotation. The attractive optical, magnetic, and magneto-optic properties obtained in highly bismuth-substituted iron garnet thin-film materials of multiple composition types are relevant in the context of manufacturing next-generation ultra-fast optoelectronic devices, such as light intensity switches and modulators, high-speed flat panel displays, and high-sensitivity sensors.

We report on the field testing datasets and performance evaluation results obtained from a commercial property-based visually-clear solar window installation site in Perth-Australia. This installation was fitted into a refurbished shopping centre entrance porch, and showcases the potential of glass curtain wall-based solar energy harvesting in built environments. In particular, we focus on photovoltaic (PV) performance characteristics such as the electric power output, specific yield, day-to-day consistency of peak output power, and the amounts of energy generated and stored daily. The dependencies of the generated electric power and stored energy on multiple environmental and geometric parameters are also studied. An overview of the current and future application potential of high-transparency, visually-clear solar window-based curtain wall installations suitable for practical building integration is provided.

Over the last decades, the coated glass products, and especially the low-emissivity coatings have become a common building material used in modern architectural projects. More recently, these material systems became common in specialized glazing systems featuring solar energy harvesting. Apart from achieving the stability of optical parameters in multilayer coatings, it is also important to have improved control over the design of visual color properties of the coated glass. We prepare metal-dielectric composite (MDC) based multilayer thin-film structures using the RF-magnetron sputtering deposition and report on their optical and chromaticity properties in comparison with these obtained using pure metal-based Dielectric/Metal/Dielectric (DMD) trilayer structures of similar compositions. Experimentally achieved Hunter L, a, b values of MDC-based multilayer building blocks of coatings provide a new outlook on the engineering of future-generation optical coatings with better color consistency and developing approaches to broaden the range of achievable color coordinates and better environmental stability.

We report on the study of energy-harvesting performance in medium-size (400 cm²) glass-based semitransparent solar concentrators employing edge-mounted photovoltaic modules. Systems using several different types of glazing system architecture and containing embedded diffractive structures are prepared and characterized. The technological approaches to the rapid manufacture of large-area diffractive elements suitable for use in solar window-type concentrators are described. These elements enable the internal deflection and partial trapping of light inside glass-based concentrator windows. We focus on uncovering the potential of pattern-transfer polymer-based soft lithography for enabling both the improved photon collection probability at solar cell surfaces, and the up-scaling of semitransparent solar window dimensions. Results of photovoltaic characterization of several solar concentrators employing different internal glazing-system structure and diffractive elements produced using different technologies are reported and discussed.

This research project focuses on the optimization of the hybrid energy system together with the assistance of thin-film coatings aiming to achieve self-sustainable food and crop storage facilities which will run effectively with its own generated energy. An infrastructure will be designed and constructed that will comprise a hybrid power generation system accompanied by thin-film coated semitransparent and non-transparent construction materials for energy saving. Thin-film low emissivity (Low-E) type coatings will assist the transparent or semitransparent construction materials to reflect most of the infrared (IR-mostly heat) and UV spectra of sunlight without interrupting the visible spectrum and will lead to saving energy consumption by reducing the heat and lighting during day time

Microplastics, which are plastic particles smaller than 5 mm in size, are a growing environmental concern since they have been found in oceans, rivers, lakes, and even on land. These tiny particles can escape water filtration systems and end up in water bodies as well as through the degradation of the larger plastic products causing serious environmental and food safety concerns. This research represents the magnitude of this pollution, characterization of microplastic contamination, its type of polymer, and possible sources of this pollution in the rivers around Dhaka, Bangladesh. Quantitative analyses were done following the method suggested by the NOAA and other conventional methods. Among the rivers, Buriganga has the highest microplastic pollution with 3.654 items/L in surface water and 43.125 items/kg in sediment. The concentration in Shitalakshya and Turag rivers are 3.011 items/L and 2.051 items/L in surface water and 34.875 and 17.875 items/kg in sediment respectively. ANOVA test revealed a significant difference between the concentration level of microplastic in the surface water of the Burigana and Turag rivers. A positive correlation between the concentration level of microplastic in the surface water and the sediment was also found. On average 58% of the microplastics were found to be fibrous in surface water. Qualitative analyses were done to find the characteristics and chemical composition of the microplastics. Through the use of FTIR spectroscopy, the study identified different kinds of synthetic polymers in the sample including polyamide (Nylon), polyethylene (PE), polystyrene (PS), polypropylene (PP), polyethersulfone (PES), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) and polyethylene were the most abundant polymer in the samples. This study involved the investigation of three distinct rivers located in the vicinity of Dhaka city

In terms of sustainability, cities become smart when they provide intelligent services to the inhabitants using information and communication technologies without threatening the future of the environment, economy or the society. However, the process of developing such sustainable smart services has certain challenges, especially in understanding the real needs of the people living in the city. Inhabitants of the city or the citizens are the key stakeholders in case of smart services in a city. Active involvement of the people throughout the process is a way to design such services. On the other hand, integrating sustainability, for example including environmental data to the smart city services has been found challenging. Therefore, this research discusses an approach on combining environmental data with regular smart city services and to engage city inhabitants in the process, the approach that is adapted from the concept of living lab methodology. Finally, an application has been developed to represent a smart city service following this method.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Malignant mesothelioma is a rare proliferative cancer that develops in the thin layer of tissues surrounding the lungs. Malignant mesothelioma is associated with an extremely poor prognosis and the majority of patients do not show symptoms. The epidemiology of mesothelioma is important for the identification of disease. The primary aim of this study is to explore the risk factors associated with mesothelioma. The dataset consists of healthy and mesothelioma patients but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease’s factors; asbestos exposure, duration of asbestos exposure, duration of symptoms, erythrocyte sedimentation rate and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with mesothelioma may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with mesothelioma which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.

This work is devoted to physical vapor deposition synthesis, and characterisation of bismuth and lutetium-substituted ferrite-garnet thin-film materials for magneto-optic (MO) applications. The properties of garnet thin films sputtered using a target of nominal composition type Bi_0.9Lu_1.85Y_0.25Fe_4.0Ga₁O₁₂ are studied. By measuring the optical transmission spectra at room temperature, the optical constants and the accurate film thicknesses can be evaluated using Swanepoel’s envelope method. The refractive index data are found to be matching very closely to these derived from Cauchy’s dispersion formula for the entire spectral range between 300-2500 nm. The optical absorption coefficient and the extinction coefficient data are studied for both the as-deposited and annealed garnet thin-film samples. A new approach is applied for accurately deriving the optical constants data simultaneously with the physical layer thickness, using a combination approach employing custom-built spectrum-fitting software in conjunction with Swanepoel’s envelope method. MO properties, such as specific Faraday rotation, MO figure of merit and MO swing factor are also investigated for several annealed garnet-phase films.

To mitigate the spread of the COVID-19 coronavirus, some countries have enforced more stringent non-pharmaceutical interventions in contrast to those widely adopted (for e.g. the state of Kuwait). In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies such as the total confinement of highly populated areas has also been implemented. In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host may be infected if in close spatial proximity of the infectious host. Our focus is only on the short-time scale prior to the infectious period, so that no further transmission is assumed. We find that the level of infection depends heavily on the population dynamics, and increases in the case of slow population diffusion, but remains stable for a high or super-diffusive population. Also, we find that the confinement of homogeneous or overcrowded sub-populations has minimal impact in the short term. Finally, we discuss the possible implications of our findings for total confinement in the context of the current situation in Kuwait.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Punica granatum (PG) or Pomegranate is a bush, having many trunks and often get taller 1.8 to 4.7m long and their leaves are shiny and fleshy, approximately 7 to 8cm in length. Traditionally, it is used in diarrhea and dysentery due to its strong constricting property. Punica Granatum covering used in dysentery, diarrhea, piles, bronchitis. It is also used to minimize the risk of heart disease. The peel extract of Punica Granatum contain methanol and their dried peels has antidiarrheal venture, also help in wound healing activity. Peels of the pomegranate also holds around 60% of the fruit and they contain different types of ingredients including ellagitannins and proanthocyanidin compounds and minerals such as calcium, phosphorus, magnesium, potassium, flavonoids and sodium. Some animals were found in different local areas with both acute and chronic diarrhea. The condition of these animals were quite miserable because of dehydration and loss of electrolytes in the body cells of the animals. They were unable to move due to seriousness of the disease. 8 grams of PG Peel Powder mixed with 1 liter of water, 2 g of PG Peel Powder mixed with 250 ml water and 8 g of PG Peel Powder mixed with 1 liter of water were administrated orally by using sterilized bottles to the selected three cases. 3 days after the administration of different doses, all cases of diarrheal animals show a significant recovery. The condition of the animals seem to be healthier. Before administration of the pomegranate peel powder extract, all three cases had weak body structure due to diarrhea. Watery stool that may be yellow in color and mucus was also evident in the stool, due to this reason the patient calf was weak and depressed. In sometimes they also show unconsciousness and blood glucose levels seem to be reduced from the normal level.

Salmonella sp. is a globally prevalent organism responsible for causing salmonellosis, a foodborne illness. Salmonella Typhimurium represents a type of Salmonella sp. not associated with typhoid fever but capable of causing stomach and intestinal inflammation or severe infection. In the present study, 150 uncharacterized proteins of S. Typhimurium were randomly selected from UniProtKB and analyzed using TMHMM, PROSITE, STRING, DEG, and BLASTp. Results indicated that 32 uncharacterized proteins (21%) were predicted to be transmembrane proteins involved in various biological pathways. Among them, 30 transmembrane proteins were predicted to be essential and non-host homologous. This study's findings suggest their potential as drug targets against salmonellosis.

Background and Research Highlights: Despite all the concerns and initiatives, natural resources like forests, as well as biodiversity are decreasing at an alarming rate worldwide. Conservation is considered as one of the major tools to prevent such loss and rapid degradation. Evidence around the world shows the adverse effects of conservation laws and policies on indigenous peoples and other local communities. Objectives: This study was conducted in one of the forest-dependent communities situated in Sundarban (world’s largest mangrove forest) to understand the impact of conservation laws and policies on their livelihood. Materials and Methods: A qualitative methodology was designed to collect data, using focus group discussions and case study with community people, and individual interviews with the personnel from NGOs and relevant government departments. Findings: Strict conservation policies and restrictions in accessing forest resources made lives and livelihoods of the local community insecure and unstable, thus putting the community in a vulnerable situation. The had to leave their traditional mode of income and look for alternative livelihood options. Almost no evidence was found in relation to upkeeping their rights in conservation activities. Prohibited movement, provision of punishment for entering into the forest without proper permission and struggles in everyday life were some of the highlighted issues. They had no participation in conservation activities, management of alternative livelihood options, and even they were not sensitized before putting restrictions. Although they had a history of emotional and physical attachment with the forest, existing activities did not consider these issues. In addition, corruption and abuse of power by law enforcement agencies towards the local community intensified the sufferings. Conclusion: This study argues that the realization of human rights in conservation activities and the sensitization of the implementing stakeholders are prerequisites for ensuring the sustainability of both biodiversity and the affected people.

Worldwide, diarrhoea in children under-five years of age is the second leading cause of death. Despite having high morbidity and mortality, diarrhoeal diseases can be averted by simple and cost-effective interventions. The Integrated Management of Childhood Illness (IMCI) has proposed the use of Oral Rehydration Salt (ORS) and zinc together with adequate food and fluid intake for the management of acute non-dysenteric watery diarrhoea in children. In the past, few studies examined the determinants of adherence to diarrhoea case management. Therefore, this study measured the determinants of therapeutic and dietary adherence to diarrhoea case management using the third and fourth wave of Pakistan Demographics & Health Surveys (PDHS) datasets. Data from 4,068 children between 0 to 59.9 months with positive history of diarrhoea were included, while data on children with dysentery, severe dehydration, and co-morbid condition was excluded. This study reported therapeutic adherence in less than 10% of children in Pakistan, while dietary adherence was reported in 39.2% of children (37.7% in 2012-2013 ~ 40.7% in 2017-2018). A significant improvement in therapeutic (0.8% in 2012-2013 ~ 8.1% in 2017-2018) and dietary adherence (37.7% in 2012-2013 ~ 40.7% in 2017-2018) was reported in the 2017-2018 survey, compared to the 2012-2013 survey. In general, children over the age of one year (compared to children <1 year) and of the richer/richest socioeconomic class (compared to poorest/poorer socioeconomic class) have showed higher therapeutic and dietary adherence. Therapeutic and dietary adherence among diarrhoeal children can be improved by increasing the awareness and accessibility of ORS, zinc, and essential foods.

The software industry has adopted component-based software development (CBSD) to rapidly build and deploy large and complex software systems with significant savings at minimal engineering effort, cost, and time. However, CBSD encounters issues on security trust, mainly with respect to dependability attributes. A system is considered dependable when it can produce the outputs for which it was designed with no adverse effect on its intended environment. Dependability consists of several attributes that imply availability, confidentiality, integrity, reliability, safety, and maintainability. Dependability attributes must be embedded in a CBSD model to develop dependable component software. Motivated by the importance of these attributes, this paper pursues two objectives: to design a model for developing a dependable system that mitigates the vulnerabilities of software components, and to evaluate the proposed model. The model proposed in this study is labelled as developing dependable component-based software (2DCBS). To develop this model, the CBSD architectural phases and processes must be framed and the six dependability attributes embedded according to the best practice method. The expert opinion approach was applied to evaluate 2DCBS framing. In addition, the 2DCBS model was applied to the development of an information communication technology (ICT) portal through an empirical study method. Vulnerability assessment tools (VATs) were employed to verify the dependability attributes of the developed ICT portal. Results show that the 2DCBS model can be adopted to develop web application systems and to mitigate the vulnerabilities of the developed systems. This study contributes to CBSD and facilitates the specification and evaluation of dependability attributes throughout model development. Furthermore, the reliability of the dependable model can increase confidence in the use of CBSD for industries.

More than 110 countries including 500 cities worldwide have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, the heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision-makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution based Multi-Criteria Decision Making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the final ranking. The robustness of the proposed method is verified by 3 sensitive analyses from different aspects. Air to water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide the decision-makers apply this ranking guideline with their practical feasible situations.

Abstract: Background: Exercise Training at work has the potential to improve employees’ produc-tivity, health, and well-being. However, exercise interventions to healthcare workers in hospitals may be challenged by a high time pressure and the ongoing workflow with patient care. Objective: The aim was to identify barriers and facilitators for participation in exercise training during work in a hospital department. Methods: Eight semi-structured interviews were conducted with hospital employees of different staff groups, who participated in 12 weeks exercise twice weekly. The data analysis was a thematic approach based on the Theoretical Domains Framework and the COM-B factors in the Behavior Change Wheel. Results: Barriers and facilitators varied between different groups. Barriers included limited structure, busyness, and a discouraging culture. Facilitators in-cluded gaining a feeling of community, psychological and physical well-being. Seven contextual subthemes were vital for successful implementation of exercise in a hospital setting. Conclusions: The informants appreciated exercise training during work and wished to participate. Inpatient departments’ informants had difficulties participating in the intervention, whilst those with more administrative tasks found it easier. This study identified barriers and facilitators vital for a successful implementation of an exercise training intervention in a hospital department to improve health, wellbeing, and productivity amongst hospital employees. It explains how future interventions can improve reach, adoption, and implementation of exercise training interventions for hospital staff.

Magneto-optic (MO) imaging and sensing are at present the most developed practical applications of thin-film MO garnet materials. However, future component and system-level sensors and imagers technology improvements are still necessary in order to improve sensitivity for a range of established and also the forward-looking applications. These improvements are expected to originate from new material system development. We propose a set of technological modifications for the RF-magnetron sputtering deposition and crystallization annealing of magneto-optic bismuth-substituted iron-garnet films and investigate the improved material properties. Results show that standard crystallization annealing for the as-deposited ultrathin (sputtered 10 nm thick, amorphous phase) films resulted in more than a factor of two loss in the magneto-optical activity of the films in visible spectral region, compared to the liquid-phase grown epitaxial films. Results also show that an additional 10 nm-thick metal-oxide (Bi2O3) protective layer above the amorphous film results in ~2.7 times increase in the magneto-optical quality of crystallized iron-garnet films. On the other hand, the effects of post-deposition oxygen (O2) plasma treatment on the magneto-optical (MO) properties of Bismuth substituted iron garnet thin films material is investigated. Results show that, in the visible part of the electromagnetic spectrum (at 532 nm), the O2 treated (up to 3 minutes) garnet films retain higher specific Faraday rotation and figure of merit than those of non-treated garnet films.

This study was performed to evaluate the efficacy of nicotinic acid against Streptococcus pneumoniae biofilm. The experimental results indicate that S. pneumoniae forms biofilms, particularly at the liquid-air interface, as evidenced by the pellicle assay. Nicotinic acid demonstrated significant inhibition of S. pneumoniae biofilm formation at 6 h and 18 h, with higher concentrations leading to lower biofilm biomass, suggesting a concentration-dependent response. However, minimal biofilm inhibition was observed at 12 h. Treatment with nicotinic acid induced FTIR spectral changes in protein biomolecules of S. pneumoniae biofilm at 6 h and 18 h, evidenced by alterations in spectral peaks at 750 cm-1, 906 cm-1, 1540 cm-1, 1548 cm-1, and 1644 cm-1. Notably, no FTIR spectral changes were detected in 12 h S. pneumoniae biofilm following exposure to nicotinic acid. These findings suggest that nicotinic acid inhibits biofilm formation in a time and concentration-dependent manner, possibly by altering the structure of protein biomolecules within the biofilm.

The occurrence and ability of Salmonella typhimurium to withstand the effects of various antibiotics has been extensively recorded. This highlights the necessity for alternative methods to combat Salmonella infections. The current investigation aimed to ascertain the impact of benzoic acid on the extent of surface coverage and biomass of S. typhimurium biofilm after 48 and 72 hours. The findings indicated that all the different doses of benzoic acid (ranging from 3.125% to 50%) resulted in a decrease in the extent of biofilm surface coverage. Benzoic acid exhibited a substantial inhibitory effect on the biofilm biomass at all concentrations. Higher concentrations of benzoic acid led to a decrease in biofilm biomass, suggesting a concentration-dependent response. This finding implies that benzoic acid is effective in managing Salmonella infections.

Capital structure decision plays an imperative role in firm’s performance. Recognizing the importance, there has been many studies inspected the rapport of capital structure with performance of firms and findings of those studies are inconclusive. In addition, there is relative deficiency of empirical studies examining the link of capital structure with performance of banks in Bangladesh. This paper attempted to fill this gap. Using panel data of 22 banks for the period of 2005-2014, this study empirically examined the impacts of capital structure on the performance of Bangladeshi banks assessed by return on equity, return on assets and earnings per share. Results from pooled ordinary least square analysis show that there are inverse impacts of capital structure on bank’s performance. Empirical findings of this study is of greater significance for the developing countries like Bangladesh because it will call upon concentration of the bank management and policy makers to pursue such policies to reduce reliance on debt and to accomplish optimal level capital structure. This research also contributes to empirical literatures by reconfirming (or otherwise) findings of previous studies.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

Non-woody forest products play a vital role in the economic uplifts of the local communities living in forest areas. This study was conducted in the Shishikoh Valley, district Chital to identify the economic importance of the Chilghoza nuts. The study was based on interviews and questionnaires, and a total of 50 questionnaires were collected from the whole valley. The results showed that before 2003, the people were unaware of the high economic value of the Chilghoza nuts, they used to sell the Chilghoza nuts at a very low price (US$ 1.7 to 2.4 per bag: approximately 5 Kg) and harvest it for fuelwood purposes. After the IPRP interventions, training and awareness, the local people started conservation of Chilghoza trees and sustainable cone harvesting. The total collection and sell out of Chilghoza nuts in the Shishikoh valley jumped to 87.9 tons and US$ 7.64/kg in 2005 from 32.7 tons and US$ 0.34-0.48/kg, respectively. This study concludes that IPRP interventions improved the economic values of Chilghoza nuts as well as the living standards of the local people.

The concept of friction damper is widespread technique and used to reduce the structural vibrations in many industrial applications. These friction dampers are generally coated with low wear rate materials to reduce the fretting wear. This paper investigates the use of physical vapor deposition (PVD) nitride based AlTiN coating material applied over the stainless steel friction damper to enhance the damping capacity and to reduce the fretting wear. A friction test rig has been developed to measure the dynamic hysteresis characteristics of friction damper at high temperature (700 °C). The damping capacity and fretting wear analysis is carried out at room temperature, 300 °C and 600 °C. The force versus displacment characterisitcs curve is used to predict the damping capacity and fretting wear analysis is carried out at microscopic level for the comparision. It is observed that at room temperature AlTiN coated friction damper has not much influence to improve the damping capacity and to reduce the fretting-wear. However, at high temperature it has better damping capacity and less wear in comparision to uncoated friction damper. Wear behaviour in coated and uncoated damper is different at room temperature and at elevated temperature.

The effect of transition metal substitution for Fe and the structural and magnetic properties of Gd2Fe16Ga0.5TM0.5 (TM=Cr, Mn, Co, Ni, Cu, and Zn) compounds were investigated. Rietveld analysis of X-ray diffraction indicates that all the samples crystallize in the hexagonal Th2Ni17 structure. The lattice parameters: a, c and unit cell volume show TM ionic radii dependence. Both Ga and TM atoms show preferred site occupancy for 12j and 12k sites. The saturation magnetization at maximum room temperature was observed for Co, Ni, and Cu of 69, 73, and 77 emu/g, respectively while minimum value was observed for Zn (62emu/g) doping Gd2Fe16Ga0.5TM0.5. Highest Curie temperature of 590 K was observed for Cu doping which is 15% and 5% and thesehigher than Gd2Fe17 and Gd2Fe16Ga compounds, respectively. The hyperfine parameters viz. hyperfine field and isomer shift, show systematic dependence on the TM atomic number. The observed magnetic and Curie temperature behavior in Gd2Fe16Ga0.5TM0.5 is explained on the basis of Fe(3d)-TM(3d) hybridization. The superior Curie temperature and magnetization value of Co, Ni, and Cu doped Gd2Fe16Ga0.5TM0.5 compounds as compared to pure Gd2Fe17 or Gd2Fe16Ga makes Gd2Fe16Ga0.5TM0.5 a potential candidate for high-temperature industrial magnet applications.

Mangrove vegetation plays a vital role in habitat and nursing ground for different organisms and prevents coastal erosion caused by wave and tide action. In recent years the mangrove vegetation in Chattogram coast, Bangladesh, has been interrupted by other infrastructural development, which has a destructing effect on the surrounding environment. Land surface temperature analysis of an area helps learn about different environmental conditions, weather, and climate. It is also essential to monitor the rising temperature and global warming, the biggest threat to humanity. NDBI and NDVI are the efficient process for monitoring vegetation and build up areas of a geographical location. This study focused on those analyses to understand the importance of mangrove vegetation in the Salimpur area and surrounding coastal areas of Chattogram by studying the relationship between NDVI and NDBI, NDVI and LST, NDBI, and LST. The outcome indicates that a higher vegetation index results in lower land surface temperature during different periods, negatively correlated. This study also found a strong positive correlation between buildup index (NDBI) and land surface temperature (LST), which means Land Surface temperature was found higher in Buildup areas. The vegetation areas are greatly affected by the buildup areas. The correlation between buildup areas and vegetation areas was strongly negative, which means an increase of NDBI decreases NDVI, and a decrease of NDBI increases NDVI.

Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

The study examined the learner autonomy in EFL learning at the undergraduate level in the context of Bangladesh. It investigated the effectiveness of learner autonomy on EFL learning. Besides, it explored the factors affecting autonomous learning in English language class. This study adopted a mixed- method approach. Data were collected from 60 students who had just completed their second semester at a private university in Dhaka city. The respondent students attended the compulsory English foundation course including their two semesters. Data were also collected from 12 ELT teachers teaching at the tertiary education level. Five EFL classes were observed following a sample of classroom observation schedule. Both open-ended and closed-ended questionnaires were used by the researchers for collecting the data from the students and teachers. Major findings of the study showed that lack of confidence, teachers’ dependency, lack of motivation, short-term goals in learning, huge confusions and confinement in memorization, tendency of using mother tongue and unawareness to self-evaluation of students affected learner autonomy in EFL learning of the undergraduate students. However, the study revealed that motivated students hardly faced any difficulties in achieving maximum outcomes with autonomous learning in the EFL class. Finally, the researchers gave a number of recommendations to bring out effective EFL learning with learner autonomy.

Artificial intelligence (AI) is a versatile term that is a conclusive remedy to solve the problem using past rational data after deep contemplation using these terms i-e basic statistics, carving data, familiarity with common AI algorithms. Seafood especially tiger prawn export as a busi-ness will provide enormous foreign exchange to any country if the farmers overcome the corre-lated vulnerabilities in prawn farming. This research is elucidating lacking in Tiger prawn (TP) farming like curbing of Oxygen, pH, water temperature, and nutrients, etc. Moreover, hatchery statistics in terms of juveniles will depict this study's clear picture of curbed aquaculture. For normative decisions, the Analytical Hierarchical Process (AHP) is used. The problem which has been faced by local prawn farmers that there is a stagnant TP growth in ponds, the reason is the predominant sensitivity factor in TP. For this reason, they need indemnification of thirteen fac-tors with natural resources to get the plausible results to get calmness in their lives. This study will solely focus on the TP growth model, and the monitoring effect will be established by the Artificial Intelligence algorithm. This study will employ the AHP, 0-1 scaling method, data cura-tion techniques, and ecological statistics. The life of Tiger Prawn (TP) depends upon these factors mainly, a) Physical and b) Chemical parameters. Physical parameters contain environment (E) provided to TP like season (S) and temperature (T) etc. whereas the quality of Ammonia NH3 (N) from fish waste, Oxygen level (O), and water quality hard & soft (W) lies in chemicals do-main. This research will Elucidate the factors which cause conceptual muddles in the aquamarine life of TP, for this reason, Statistical tools will assess the current result, forecast the gap. AHP will analyze the domain inputs, circumspect ramification which will depict visceral factors, later results depict which pond suits the TP. In curtail, these factors will be curbed to improve the growth of TP in a control conditioned environment.

Tables in the document image are one of the most important entities since they contain crucial information. Therefore, accurate table detection can significantly improve information extraction from tables. In this work, we present a novel end-to-end trainable pipeline, HybridTabNet, for table detection in scanned document images. Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. Moreover, we replace conventional convolutions with deformable convolutions in the backbone network. This enables our network to detect tables of arbitrary layouts precisely. We evaluate our approach comprehensively on ICDAR-13, ICDAR-17 POD, ICDAR-19, TableBank, Marmot, and UNLV. Apart from the ICDAR-17 POD dataset, our proposed HybridTabNet outperforms earlier state-of-the-art results without depending on pre and post-processing steps. Furthermore, to investigate how the proposed method generalizes unseen data, we conduct an exhaustive leave-one-out-evaluation. In comparison to prior state-of-the-art results, our method reduces the relative error by 27.57% on ICDAR-2019-TrackA-Modern, 42.64% on TableBank (Latex), 41.33% on TableBank (Word), 55.73% on TableBank (Latex + Word), 10% on Marmot, and 9.67% on UNLV dataset. The achieved results reflect the superior performance of the proposed method.

Acemella oleracea is known as toothache plant belongs to the family of Asteraceae. It is treated as a medicinal remedy like tuberculosis, illness, cough, rheumatism, and illness. This study was concentrated on the antioxidant, cytotoxic, antimicrobial & thrombolytic activities of methanol extract of Acmella oleracea leaves using in vitro model. The antioxidant activity was estimated as trolox equivalent antioxidant capacity utilizing the DPPH and reducing power tests. The plant extract was tested for its cytotoxic action using a brine shrimp lethality bioassay, thrombolytic activity using clot disruption, and antibacterial activity using a disc diffusion assay technique against four distinct gram-positive and gram-negative bacteria. IC₅₀ value of standard ascorbic acid for DPPH was 7.8µg/ml and the IC50 of the methanol extract of Acmella oleracea leaves was 198.34µg/ml that was moderate effect at all compare with ascorbic acid. Thrombolytic assay of Streptokinase as a positive control showed 88.49% where the extract shows 18.69% lytic activity shows the assay. In this study, the sample of LC₅₀ results of cytotoxicity assay was 1.431 µg/mL which can be treated as less activity. Moreover, the extracts showed low to moderate antibacterial activity against both gram-negative and gram-positive bacterial stains (zone of inhibition-10-26 mm). Vibrio Mimicus bacteria stains exhibited the highest level of activity, with a range of 23 for the diameter of the growth inhibition zone. The current review shows leaf extracts of Acmella oleracea may be used as a source of antioxidant and thrombolytic activity, as well as a significant source of antibacterial and anticancer substances. Further research is required to evaluate in-vivo the pharmacological activity of Acmella oleracea leaves in order to identify the essential metabolites and potential mechanisms.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Beta thalassemia is an inherited blood disorder that results in inefficient erythropoiesis due to genetic mutation that leads to reduction or absence of the hemoglobin beta-globulin protein. Approximately 8.5% of UAE residents suffer from β-thalassemia, a significant health and financial problem. The treatment options available for β-Thalassemia major are limited and associated with a wide range of complications. β-thalassemia gene therapy is emerging as a potential novel treatment option that eliminates the complications caused by the current long-term treatment modalities and the associated economic burden. This paper reviews the scientific literature related to emerging gene-therapy for β-Thalassemia by analyzing all the articles published from January 2010 to December 2023 in the English language on Databases like PubMed, Scopus, ProQuest, and CINAHL. The use of gene therapy has demonstrated promising outcomes for a permanent cure of β-Thalassemia. ZYNTEGLO (Betibeglogene autotemcel) is the only cell-based gene therapy which was approved by the FDA on 19th August, 2018. To conclude, Gene therapy is an innovative solution. It demonstrates a promising future, but does come with its own set-backs and is something that must be tackled in-order to revolutionize it in the medical world. FDA approved ZYNTEGLO is a potentially one-time curative treatment for β-Thalassemia. Although cutting-edge, its use is limited because of the high cost; a price of $2.8million per patient.

Berberine (BBR) is an isoquinoline that inhibits the proliferation of transformed cells in vitro, but due to its poor solubility and bioavailability, it has only moderate therapeutic potential in vivo. Increasing evidence indicates that BBR specifically targets several metabolic, signaling and gene transcription events in transformed cells, altering their progression through the cell cycle and decreasing their metabolic rate and proliferation. In order to further develop BBR as a therapeutic, its mode of cellular internalization and localization within the cell needs to be further examined. BBR’s molecular targets and interactions with kinases, transcription factors and some enzymes are discussed in an attempt to better understand BBR’s role in these important pathways and how they may lead to changes in metabolism. Lastly, this review examines some of the benefits and challenges of using BBR as an inhibitor in cancer cell proliferation in breast cancer and glioblastoma, as two examples. BBR is a potent drug with multiple targets and it is this multiplicity of function that makes BBR such a promising drug for targeting cell metabolism and proliferation.

High-grade gliomas are extremely fatal tumors, marked by severe hypoxia and therapeutic resistance. Autophagy is a cellular degradative process that can be activated by hypoxia, ultimately resulting in tumor advancement and chemo-resistance. Our study aimed to examine the link between autophagy markers expression in low (LGG) and high-grade gliomas (HGG). In 39 glioma cases, we assessed protein expression of autophagy markers LC3B, p62, and DRAM by immunohistochemistry (IHC) and mRNA expression of autophagy genes; PI3K, AKT, m-TOR, PTEN, ULK1, ULK2, UVRAG, Beclin 1 and VPS34 using RT-qPCR. Livak’s method was used to analyze the relative changes in gene expression and multi-variate Anova test and Spearman's correlation tests were performed for statistical significance. LC3B, p62, and DRAM expression were positive in 64.1%, 51.3%, and 28.2% of glioma cases respectively. The expression of LC3B and p62 was notably higher in HGG compared to LGG. VPS34 exhibited a significant differential expression and increased fold change and demonstrated strong positive correlations with Beclin1 (rs=0.768), UVRAG (rs=0.802), and ULK2 (rs=0.786) in HGG compared to LGG, thus highlighting the potential link between autophagy and glioma progression. We Provide preliminary data for functional analysis of autophagy using cell culture model and to identify potential targets for therapeutic interventions

In addressing the challenges of data scarcity in biometrics, this study explores the generation of synthetic palmprint images as an efficient, cost effective, and privacy preserving alternative to real-world data reliance. Traditional methods for synthetic biometric image creation primarily involve orientation modifications and filter applications, with no established method specific to palmprints. We introduced the utilization of the “Style-based generator”, StyleGAN2-ADA, from the StyleGAN series, renowned for generating high-quality images. Furthermore, we explore the capabilities of its successor, StyleGAN3, which boasts enhanced image generation, facilitating smooth and realistic transitions. By comparing the performance of StyleGAN3 on public dataset, we aim to establish the most efficient generative model for this purpose. Evaluations were conducted using the SIFT (Scale-Invariant Feature Transform) algorithm into our evaluation framework. Preliminary findings suggest that StyleGAN3 offers superior generative capabilities, enhancing equivariance in synthetic palmprint generation.

The global COVID-19 pandemic has affected all spheres of human life, resulting in millions of deaths and overwhelming medical facilities. Moreover, the world has witnessed great financial hardship because of job losses resulting in economic havoc. Many sections of society have contributed in different ways to slow the spread of the virus and protect public health. For example, medical scientists are praised for their efforts to develop COVID-19 vaccines. Clinical trials have shown that the COVID-19 vaccines are highly effective in preventing symptomatic COVID-19 infections. However, many people around the world have been hesitant to get vaccinated. Vaccine misconceptions have emerged and increased due to a combination of factors, including the availability of information on the Internet and the influence of celebrities and opinion leaders. In this context, we have analyzed ChatGPT responses to relevant queries on vaccine misconceptions. The positive responses and supportive opinions provided by the AI chatbot could be instrumental in shaping people’s perceptions of vaccines and in encouraging users to get vaccinated and reduce misconceptions.

A flat-plate unglazed solar water heater (SWH) with polymer thermal absorber was developed and tested. Polymer thermal absorber as a viable alternative to metal thermal absorber that is found in most SWH systems. The performance of this polymer SWH system is measured based on inlet and outlet water temperature, water flowrate, ambient air temperature and solar irradiance. The polymer thermal absorbers are hollow Polyvinyl Chloride (PVC) pipes with 20mm external diameter and 3 mm thick, painted in black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward-outward opposing-flow (RSioof). The collector pipes are placed in a 1 m2 square enclosure with bottom insulation and reflective surface for better radiation capture. Water circulation is a closed loop with a 16-liter uninsulated storage tank, driven by a pump and controlled by valves to maintain a mass flow rate of 0.00031 to 0.00034 kg/s. The test was conducted under partially clouded sky from 9 am to 5 pm, with solar irradiance between 105 and 1003 W/m2 and 27-36oC ambient air temperature. The SHW produced outlet hot water at 65oC by mid-day and maintained the storage temperature at 63oC until the end of test duration. Photo-thermal energy conversion was recorded with 23% maximum value. Results indicate that a flat plate solar water heater with polymer thermal absorber in RSioof design can be an effective alternative to the metal thermal absorber solar water heater and can be further optimized with glazing and tube sizing.

Silver eel were sampled in 1981 and 1983 in River Brede, Denmark, with outlet to the North Sea. Yellow eel in River Køge-Lellinge, Denmark, with outlet to the Baltic Sea were sampled 1965-1968. Silver eel were aged by burning the otoliths. Silver male ages varied from 4 to 25 years, lengths 30.8 to 45.3 cm, and female silver eel varied from 7 to 25 years, lengths 42.3 to 77.3 cm. von Bertalanffy trajectories of length-at-age of male and female yellow eel were calculated in both rivers. Younger yellow eel had significantly higher annual growth rate compared to older age groups, and females grew significantly faster than males. Two models for natural mortality M were used to estimate number of glass eel needed to produce number of silver eel for each sex and silver age group. Annual silver eel production from River Brede Å was 49.2 kg ha-1, demanding 2,894 glass eel ha-1 and in River Køge-Lellinge, the silver eel production was 48.5 kg ha-1, demanding 5,570 glass eel ha-1. It was calculated that one (0.29 g) thousand glass eel contribute to 8.8 kg silver eel in River Køge-Lellinge, and 17.0 kg silver eel in River Brede. To fulfill the Danish EMP in rivers requires annual stocking of 33 tons or 9.4 million reared on-grown eel (3.5 g) to compensate for 183 tons lost silver eel.

The present study examined the volatile composition and antimicrobial and gastrointestinal activity of the essential oils of Elettaria cardamomum (L.) Maton harvested in India (EC-I) and Guatemala (EC-G). Monoterpene were present in higher concentration in EC-I (83.24%) than in EC-G (73.03%), whereas sesquiterpenes were present in higher concentration in EC-G (18.35%) than in EC-I (9.27%). Minimum inhibitory concentrations (MICs) of 0.5 and 0.1 mg/mL were demonstrated against Pseudomonas aeruginosa in EC-I and EC-G, respectively, whereas MICs of 0.125 and 1 mg/mL were demonstrated against Escherichia coli in EC-I and EC-G, respectively. The treatment with control had the highest kill-time potential, whereas the treatment with oils had shorter kill-time. EC-I was found to be more potent in the castor oil-induced diarrhoea model than EC-G. At 100 and 200 mg/kg, EC-I exhibited 40% and 80% protection, respectively, and EC-G exhibited 20% and 60% protection, respectively, in mice, whereas loperamide (positive control) exhibited 100% protection. In the in vitro experiments, EC-I inhibited both carbachol (CCh, 1 µM) and high K+ (80 mM)-induced contractions at significantly lower concentrations than EC-G. Thus, EC-I significantly inhibited P. aeruginosa and E. coli and exhibited more potent antidiarrheal and antispasmodic effects than EC-G.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

The COVID-19 has caused gigantic negative effects on populace wellbeing, society, education, and the economy in Bangladesh. The aim is to deliver a comprehensive overview of the observed and the possible impacts that could appear in the coming days. The study is based on secondary information. During the early period, due to a lack of accurate facts about the case affected and death tension up-and-down among the nations. The total number of confirmed cases is increasing following geometric patterns in Bangladesh. Dairy farmers, vegetable producers, pharmaceuticals, poultry farmers are in deep crisis due to lower prices. Also, the pandemic has seriously affected educational systems, banking, FDI, ready-made garments, remittances, etc Finally, it is not possible to mitigate the effects of pandemic individually but the integrated effort from the state authority as well as concern people of all sectors need to come forward.

Whole genome duplication (WGD) or polyploidization can occur at the cellular, tissue, and organismal levels. At the cellular level, tetraploidization has been proposed as a driver of aneuploidy and genome instability, and correlates strongly with cancer progression, metastasis, and development of drugs resistance[1–6]. WGD is also a key developmental strategy for regulating cell size, metabolism, and cellular function[1,7–10]. In specific tissues, WGD is involved in normal development (e.g. organogenesis), tissue homeostasis, wound healing, and regeneration[10–17]. At the organismal level, WGD propels evolutionary processes such as adaptation[18], speciation[19], and crop domestication[20]. An essential strategy to further our understanding of the mechanisms promoting WGD and its effects is to compare isogenic strains that differ only in their ploidy. Caenorhabditis elegans (C. elegans) is emerging as an animal model for these comparisons, in part because relatively stable and fertile tetraploid strains can be produced rapidly from nearly any diploid strain[7]. Here we review the use of Caenorhabditis polyploids as tools to understand important developmental processes (e.g. sex determination, dosage compensation, and allometric relationships)[21–27] and cellular processes (e.g. cell cycle regulation, chromosome dynamics during meiosis)[28–32]. We also discuss how the unique characteristics of the C. elegans WGD model will enable significant advances in our understanding of mechanisms of polyploidization and its role in development and disease.

This study reports fractal analysis of one-year radon in groundwater disturbances from five stations in China amid the catastrophic Wenchuan (Mw=7.9) earthquake of May 12, 2008 (day 133). Five techniques are used (DFA, fractal dimensions with Higuchi, Katz, Sevcik methods, power-law analysis) in segmented portions glided throughout each signal. Noteworthy fractal areas are outlined in the KDS, GS, MSS station data, while the portions were non-significant for PZHS and SPS. Up to day 133 critical epoch DFA-exponents have 1.5<=a<2.0 with several above 1.8. Fractal dimensions exhibit Katz’s D around 1.0-1.2, Higuchi’s D between 1.5-2.0 and Sevcik’s D between 1.0-1.5. Several power-law exponents are above 1.7 and numerous above 2.0. All fractal results of KDS-GS-MSS stations are further analysed using a novel computerised methodology which locates the exact out-of-threshold fractal areas and combines the outcomes of different methods per five, four, three, and two (maximum 13 combinations) versus nineteen Mw<=5.5 earthquakes of the area. Most different-technique coincidences are before the great Wenchuan earthquake and its post-earthquakes. This is not only with one method, but with 13 different methods. Other interpretations are also discussed.

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

Mechanical robustness and high energy efficiency of composite materials are immensely important in modern stretchable, self-powered electronic devices. However, the availability of these materials and their toxicities are challenging factors. This paper presents the mechanical and energy-harvesting performances of low-cost natural rubber composites made of stearic acid-modified diatomaceous earth (mDE) and carbon nanotubes (CNTs). The obtained mechanical properties were significantly better than those of unfilled rubber. Compared to pristine diatomaceous earth, mDE has higher reinforcing efficiencies in terms of mechanical properties because of the effective chemical surface modification by stearic acid and enhanced filler-rubber interactions. The addition of a small amount of CNT as a component in the hybrid filler systems not only improves the mechanical properties but also improves the electrical properties of the rubber composites and has electromechanical sensitivity. For example, the fracture toughness of unfilled rubber (9.74 MJ/m3) can be enhanced by approximately 484% in a composite (56.86 MJ/m3) with 40 phr (per hundred grams of rubber) hybrid filler, whereas the composite showed electrical conductivity. At a similar mechanical load, the energy-harvesting efficiency of the composite containing 57 phr mDE and 3 phr CNT hybrid filler was nearly double that of the only 3 phr CNT-containing composite. The higher energy-harvesting efficiency of the mDE-filled conductive composites may be due to their increased dielectric behaviour. Because of their bio-based materials, rubber composites made by mDE can be considered eco-friendly composites for mechanical and energy harvesting applications and suitable electronic health monitoring devices.

At present, sustainability and emerging technology are the most expressed issues in any supply chain management (SCM) sector. At the same time, pandemic makes consumers more concerned regarding health, and safe food with a sustainable way to access the current market. Thus, supervision and monitoring of product quality with symmetric traceability information in fresh food and fisheries SCM is significant. Research on food safety and traceability systems based on blockchain, internet of service (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provides the solution of constancy from production to consumption. This review focused on the RFID-based traceability systems in fisheries SCM, which have been employed globally in the last fifteen years to ensure fish quality and security. Additionally, a summarized comparison study has presented different sectors’ traceability systems using RFID and their advantages over real-time applications. The outcome of this study will help future researchers to solve the crisis in terms of trust between consumers and the fisheries SCM. Thus, this review would be a guideline and solution for enhancing the reliability of RFID-based traceability in food SCM systems to ensure the integrity and reducing the opacity and asymmetry in the product information.

Population growth and increasing global demand for freshwater have raised a serious challenge for the depleting sources of freshwater in the 21st century. Desalination technologies can be a reliable technique for providing freshwater. Capacitive deionization is one of the innovative desalination methods that has received increasing interest. Flow-electrode capacitive deionization (FCDI) (a new architecture of capacitive deionization) is one of the efficient, cost-effective, and environmentally-friendly desalination methods for freshwater production. In this experimental research, the performance of an FCDI system was investigated and the influence of important parameters such as flow rate of flow-electrodes, electrolyte salt concentration of flow-electrodes, and initial feed water concentration will be assessed on the efficiency of desalination operation. In this study, the flow-electrodes operated in short-circuited closed-cycle operation (SCC) mode, and also the feed water operated similarly to the flow-electrodes in closed-cycle. Moreover, in all the experiments, the salt adsorption capacity (SAC) and salt removal efficiency (SRE) was calculated. Herein, by optimizing the above mentioned parameters, the salt removal efficiency of 83% and a SAC value of 29.12 mg/g dry carbon were achieved in 5 hours.

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

MCrAlYHf bond coats are employed in jet and aircraft engines, stationary gas turbines, and power plants, which require strong resistance to oxidation at high temperatures. The oxidation behavior of a free-standing CoNiCrAlYHf coating was investigated in this study. Isothermal oxidation tests were performed in air at 1050 °C on the specimens with various levels of surface roughness to investigate the oxidation kinetics. X-ray diffraction, focused ion beam, scanning electron microscopy (SEM), and scanning transmission electron microscopy were used to characterize the surface oxides. The surface roughness was examined using a contact profilometer and SEM. An empirical model was proposed to elucidate the impact of surface roughness on the oxidation behavior based on the correlation between the surface roughness level and oxidation rates.

Depth maps produced by LiDAR based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the traditional approaches focus on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have sub-divided the literature into two major categories; traditional approaches and backbone-based approaches. The latter is further sub-divided into two-branch, and spatial propagation approaches. The two-branch approaches still have a sub-category named guided-kernel approaches. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review and detail different state-of-the art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

Using the above results from RMS analysis the optimum values were predicted for the independent significant variables (Figure 3) the optimized levels of these variables in combination with other media variables the maximum production was predicted to be 199.90 U/mL. The predicted data were validated through confirmatory experiments performed in triplicates. A 1.29-fold increase in amylase activity against un-optimized (OVAT) medium was achieved in the present study authenticating the efficacy of RSM in process optimization (Figure 4). 2.6 Model validation and scale-up at laboratory scale (5L) bioreactor Once the parameters were standardized in the shake-flasks culture, the experiment was scaled-up to a laboratory-scale bioreactor (5 L). The yield of amylase increased by 1.01 fold (205.69 U/mL), it could be possible because the enzyme production in a bioreactor is higher than in shake-flasks culture as the various critical variable factors such as the dissolved oxygen (DO) and the pH can be optimally controlled at the desired levels [22].

Salmonella typhimurium is the predominant serovar identified in cases of salmonellosis. It is recognised for adhering to various surfaces and creating biofilm, leading to several foodborne outbreaks and antibiotic resistance. Actinomycetes are thought to possess a wide array of antibacterial characteristics that can combat different diseases. Currently, it is unclear whether it has antibiofilm properties against S. typhimurium. This study aimed to assess the antibiofilm efficacy of actinomycetes extracts against S. typhimurium ATCC 14028 by the Crystal Violet assay. Four actinomycetes isolates, EAS5, EAS7, EAA141, and EAA11, were obtained from BRIS soil in Terengganu. The actinomycetes isolates were extracted using ethyl acetate (EA). S. typhimurium was exposed to extracts for 6, 12, and 18 hours. The results showed that all EA extracts effectively prevented the S. typhimurium biofilm at 12 and 18 hours. EAAA11 was the most effective against the 12-hour biofilm, whereas EAS5 was the most effective against the 18-hour biofilm among these extracts. In conclusion, actinomycetes isolated from the BRIS soil in Terengganu show potential for controlling Salmonella infection.

Background: Obesity and diabetes are two metabolic disorders linked by an inflammatory process named insulin resistance (IR). Various research on the role of gut microbiota in developing obesity and its associated disorders has led to the growing interest in probiotic supplementation. Considering the life-threatening complications of diabesity this mini-review explored the effects of probiotic supplementation on IR in obesity-associated diabetes. Method: This review is based on recent articles from 2005-2020, studying the role of probiotic supplementation on glucose and insulin parameters in healthy and diabetic mouse models. Result: Probiotic supplementation altered the gut microbiota composition, increased short-chain fatty acid production, and decreased pro-inflammatory cytokines. Additionally, they decreased intestinal permeability, circulating lipopolysaccharide, and metabolic endotoxemia hence improved insulin sensitivity and reduced obesity. Although multi-strain probiotic supplementation showed greater benefits than single strain interventions, variations in the concentration of probiotics used and the duration of treatment also influenced the results. Conclusion: Probiotic supplementation could manipulate the gut microbiota by reducing intestinal permeability, inflammation and ameliorate IR and obesity-associated diabetes in animal models which requires further long-term clinical studies in humans.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

With the ever-increasing rate, obesity has become an epidemiological problem throughout the globe comprising about 39% of the world population as of now. Among several reasons, disruption of the gut microbial ecosystem might contribute to the pathogenesis of metabolic disorders, including obesity, metabolic syndrome, type 2 diabetes, and other associated comorbidities. Though the mechanisms related to dysbiosis are unclear, diet might play a modulating role where different dietary approaches manipulate microbial richness and abundance as well as stability. For instance, shifting of Firmicutes and Bacteroidetes ratio in the gut might have a role in association with the dietary approaches and ingestion duration. Along with altered gut microbial composition, microbial metabolites such as short-chain fatty acids (SCFA) after ingestion of non-digestible dietary starches may have an impact on host metabolism by regulating lipogenesis, gluconeogenesis, and inflammation with potential associations to health and obesity. The dietary approaches like carbohydrates, fibre, protein, and/or fat diet at various arrangements can make a shift in the composition of gut microbiota if introduced for a short period. However, the unique pattern of the gut microbes usually remains the same along with the longer period of habitual diet. Though the short-term dietary intervention or circadian rhythm influences a transient change in gut microbes, other than habitual diet, the understanding related to long-term dietary change-induced permanent alterations is minimum. Alternatively, the usage of prebiotics, probiotics as well as postbiotics could be beneficial to overcome dysbiosis. This review highlights the current knowledge and the interaction between the human intestinal microbiota and diet as a modifying factor, in obesity allowing the scientists to uncover novel targets and tools to use as customized therapy.

Increased awareness of the multiple roles of RNA molecules has led to the realization that, in addition to their structural and functional roles, RNAs can be drug targets for small molecular therapy. The aim of this study was to identify multivalent amikacin specific RNA aptamers that can be a new target sites for aminoglycoside antibiotics, including amikacin using the systemic evolution of ligands by exponential enrichment (SELEX) method. Amikacin, a member of the aminoglycoside group of antibiotics, binds to specific sites in bacterial 16S ribosomal RNAs (rRNAs) and interferes with protein synthesis, leading to cell death. Here, we used the SELEX method to isolate high affinity RNA fragments (aptamers) that bind to amikacin. After five rounds of SELEX selection, in which a linear N25 DNA template was used for the first selection cycle, the resulting RNA was cloned and sequenced. Among the 38 clones generated, five groups of sequences (groups A through E) containing nine conserved motifs were identified. The sequences of groups A and B were almost identical, indicating that the selected RNA was enriched. Subsequently, the Basic Local Alignment Search Tool program was used to search for the conserved motifs in bacterial 16S rRNA sequences. Strikingly, no sequence homology was observed, suggesting that the conserved sequences (motifs) identified in this study may be novel target sites for amikacin.

Governance is one of the most essential instruments for environmental management. Biodiversity is in the core field of environmental governance. Yet environmental authorities are persistently challenged the loss of biodiversity as a very important global issue for several years due to high dependent exposure to risks. The study attempts to relook at the key governance tools that strengthen policies towards managing biodiversity within and around the national park’s survey in Moulvibazar district. The study showed that biodiversity related legislation amended was the highest in Bangladesh for the period of 2010 to 2016. The growth of policy instruments maximized at but in low environmental governance services within the same period. The study assessed that the existing environmental policy instrument is inadequate and sluggish for effective conservation, compared with several others governance tools and various performances are still below par. Governance knowledge is indispensable for biodiversity management but such knowledge is poorly identified. These results reflect the importance of effective governance for transparency that the State provides. The research is to represent a dynamic and adaptable framework that can be applied for collective governance relevant to policy integration, participation and enforcement in order to foster environmental conservation sustainability.

The mutation databases have vital importance in detecting genetic mutation and their corresponding phenotypes. The emergence of next generation sequencing genetic technologies has accelerated this process, and the growth of genetic disease identification has created a need for quality control and documentation of mutation-related information. Ethnic-specific mutation databases offer valuable information for researchers, and the article evaluates the homogeneity and effectiveness of these databases. The aim to raise awareness of NEMDBs to healthcare professionals, the general public, and researchers studying genetic disorders. The article also recommendations to improve the effectiveness of these databases.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.

The present study investigates the decision-making process of investors on the Pakistan Stock Exchange with regard to portfolio construction, explicitly focusing on the incorporation of ESG concerns. The present study employed SmartPLS and structural equation modeling (SEM) to quantitatively assess data received from a sample of 393 out of 400 respondents. Based on the available data, investors participating in the Pakistan Stock Exchange are notably impacted by ESG aspects. The findings of this study hold significance for emerging economy firms, regulators, and investors, both in terms of theoretical and practical ramifications. The study's findings demonstrate a clear indication of investors' significant emphasis on ESG matters. Furthermore, it provides valuable insights for stakeholders who are involved in the ever-evolving realm of sustainable finance within developing countries

Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. Early detection and intervention can help to minimise the damage caused by a stroke, reduce the risk of long-term complications, and enhance the general quality of life for people who have survived a stroke. Additionally, research in stroke prediction can help to identify risk factors and improve understanding of the underlying causes of stroke, which can lead to the development of better prevention strategies. Research on brain stroke prediction is ongoing and has led to the development of various models and tools for predicting the risk of stroke and detecting it early. However, the implementation and use of these tools in clinical practise vary depending on several factors, such as the availability of resources, the specific healthcare system, and the level of awareness and acceptance of these tools among healthcare providers and patients. In general, risk prediction models may be used to quickly identify individuals at high risk of stroke and target them for preventive interventions, such as lifestyle changes, medication management, and screenings. Early detection tools can be used to quickly identify stroke symptoms and initiate appropriate treatment, which can improve outcomes for stroke patients. However, it is important to note that research and development of these models and tools are ongoing, and their use in clinical practise is constantly evaluated and updated. It may take time for these tools to be widely adopted in clinical practise and to see their real-world impact. This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Bernoulli Naive Bayes (BNB), and a Voting Classifier. The main emphasis of this research was to compare the effectiveness of oversampling techniques, as the data was significantly imbalanced. We have used evaluation metrics in this research to assess the accuracy, precision, recall, and other key performance indicators of a model's predictions. Area under the Receiver Operating Characteristics Curve (AUC), minority class accuracy, and majority class accuracy were used to assess the approaches.

The complex systems that require safety are the Safety Critical Systems. Maintaining these systems is a big challenge. Now a days, safety is a very critical requirement for the latest systems. Safety critical systems must be safe. Different approaches to ensure quality and safety in safety critical systems has been discussed in this paper. A comparison is also conducted between these various approaches. Safety critical systems must remain more influential in future.

Estimating peak flow for a catchment is commonly undertaken using the design event method, however this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of stream flow at the outlet of the catchment. With this data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a one in 100 year annual exceedance probability peak flow, the peaks over threshold method (333m3/s ±50m3/s) was significantly less uncertain than the annual maximum method (427m3/s ±100m3/s). For the one in 100 year annual exceedance probability, the median peak flow from the peaks over threshold method (333m3/s) produced an outcome comparable to the design event method peak flow (328m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, the paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.

The study presents the synthesis and characterization of dual-layer sulfonated polyphenylenesulfone (SPPSu) nanocomposite hollow fiber nanofiltration membranes incorporating titanium dioxide (TiO2) nanoparticles using the phase inversion technique. The newly developed membranes were systematically characterized using advanced tools and methods to evaluate their properties and performance. The study focused on investigating the effects of TiO2 addition in the SPPSu inner layer on pure water permeability, and salt rejection. The nanocomposite hollow fiber nanofiltration membranes exhibited a significant enhancement in pure water permeability, with a three-fold increase compared to the pristine membranes, achieving a flux of 5.4 L/m2h.bar. The addition of TiO2 also led to an improvement in the mechanical properties of the membranes, as evidenced by the increase in the expected tensile strength from 2.4 to 3.9 MPa. To evaluate the salt rejection performance, rejection tests were conducted using a laboratory-scale filtration setup in recycle mode. Aqueous solutions containing different divalent ions, specifically magnesium sulfate (Mg2SO4) and sodium sulfate (Na2SO4) at a concentration of 500 ppm, were used as the feed solution. The modified dual-layer hollow fiber nanocomposite membranes exhibited a maximal rejection of 95% for Mg2SO4, demonstrating their effective separation capabilities for divalent ions. The findings of this study highlight the successful synthesis and characterization of dual-layer SPPSu nanocomposite hollow fiber nanofiltration membranes incorporating TiO2 nanoparticles. The incorporation of TiO2 resulted in improved pure water flux, mechanical strength, and salt rejection performance. These enhanced properties make the developed nanocomposite membranes promising candidates for applications in nanofiltration processes, particularly for the selective separation of divalent ions from aqueous solutions.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.