Synthesis of activated carbon from waste biomass is of current interest towards sustainability. The properties of biomass derived activated carbon largely depends on the carbonization process. This study reports preparing extremel high surface area mesoporous activated carbon from hemp bast fibre using hydrothermal processing. Processing in hot water (390-500^oC), then activation using KOH and NaOH was investigated at different loading ratios. The described approach was found to enhance the mesoporosity (centered at 3.0 to 4.5 nm) of the hemp derived activated carbon (HAC) from activation (confirmed by BJH pore size distribution and TEM imaging). BET results showed that the product has an extremely high surface area (2425 m²/g) while the surface functional groups (-OH, COOH, C=C/C-C) were confirmed and quantified by XPS and FTIR results. Increasing KOH concentration was found to enhance the surface area with an optimum biochar to KOH ratio of 1:3. The crystallite domain size of HAC was determined using Raman spectroscopy of different wavelengths. The procedure described in this study is an environmentally friendly scalable route for the mass production of activated carbon using hemp fiber.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a field-programmable gate array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6ms and a power consumption of 4.66mW for the classification of the MNIST test dataset consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency—surpassing existing models by more than a factor of 100. In conclusion, the paper demonstrates the substantial advantages of FPGA-implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical. The study’s reproducible results and corresponding code are available on GitHub at the following link: https://github.com/mahmad2005/CVNNonFPGA

This research investigates the implementation of complex exponential-based neurons in FPGA, which can pave the way for implementing bio-inspired spiking neural networks to compensate for the existing computational constraints in conventional artificial neural networks. The increasing use of extensive neural networks and the complexity of models in handling big data lead to higher power consumption and delays. Hence, finding solutions to reduce computational complexity is crucial for addressing power consumption challenges. The complex exponential form effectively encodes oscillating features like frequency, amplitude, and phase shift, streamlining the demanding calculations typical of conventional artificial neurons through levering simple phase addition of complex exponential functions. The article implements such a two-neuron and a multi-neuron neural model using Xilinx system generator and Vivado design suite, employing 8-bit, 16-bit, and 32-bit fixed-point data format representations. The study evaluates the accuracy of the proposed neuron model across different FPGA implementations while also providing a detailed analysis of operating frequency, power consumption, and resource usage for the hardware implementations. BRAM-based Vivado designs outperformed Simulink regarding speed, power, and resource efficiency. Specifically, the Vivado BRAM-based approach supported up to 128 neurons, showcasing optimal LUT and FF resource utilization. Such outcomes accommodate choosing the optimal design procedure for implementing spiking neural networks on FPGAs.

Background and objectives: The hepatoprotective activity of vitamin E and C is evident due to their ability of modulating the antioxidant pathway. In this study, we have evaluated the effects of α-tocopherol and ascorbic acid on paracetamol induced liver damage with offsetting various levels of drug treatment following an in vivo experimental protocol on Wistar albino male rats. Materials and Methods: The level of lipid peroxidation as well as histological examination of liver tissues were observed among 50 Wistar albino male rats to evaluate hepatoprotective effect of α-tocopherol and ascorbic acid on hepatocytes. The experiment was divided into 5 groups (10 rats in each group)- Basal control group (Group-I, with propylene glycol), Paracetamol treated control group (Group –II), α-tocopherol pretreated & paracetamol treated group (Group –III), Ascorbic acid pretreated & paracetamol treated group (Group –IV) and Ascorbic acid pretreated & paracetamol treated group (Group –IV). Results: The mean (± SD) Malondialdehyde (MDA) concentration were significantly reduced in α-tocopherol pretreated and paracetamol treated group (P<0.001), Ascorbic acid pretreated and paracetamol treated group (P≤0.05) and combined α-tocopherol with ascorbic acid pretreated & paracetamol treated group (P<0.001). Statistically significant differences in histological findings of rat liver were observed in paracetamol treated control group (P<0.001), ascorbic acid pretreated and paracetamol treated group (P<0.001). The serum alanine aminotransferase (ALT) level was also significantly higher in paracetamol treated group (P<0.001), α-tocopherol pretreated plus paracetamol treated group (P≤0.05) and in ascorbic acid pretreated plus paracetamol treated group (P<0.001). Conclusion: The combined pretreatment of α-tocopherol & ascorbic acid have better hepatoprotective effects than α-tocopherol or ascorbic acid alone against paracetamol induced liver damage. The decrement of free radicals produced by vitamin E could be a better hepatoprotective antioxidant than vitamin C in paracetamol induced toxicity.

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory infections in young children worldwide. RSV-associated deaths in children are underreported in Bangladesh. We analyzed hospital-based surveillance data on severe acute respiratory infections (SARI) in under-five children before (August 2009–February 2020) and during the COVID-19 pandemic (March 2020–March 2022). Using the World Health Organization definition, we identified SARI cases in 14 tertiary-level hospitals. Nasopharyngeal and oropharyngeal swabs were collected for real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) testing of six respiratory viruses, including RSV. SARI deaths during the pandemic (2.6%, 66) were higher than pre-pandemic (1.8%, 159; p<0.001). Nearly half of pandemic deaths (47%) had underlying respiratory viruses, similar to the pre-pandemic rate (45%). RSV detection in deaths was consistent pre-pandemic (13%, 20/159) and during the pandemic (12%, 8/66). Children aged <6 months constituted 57% (16) of RSV-related deaths. Evaluating interventions like maternal vaccination and infant monoclonal antibody prophylaxis is crucial to address RSV, a major contributor to under-five SARI deaths.

The application of electrode-based microfluidic devices in biological entity often imposes a problem due to joule heating. The strong applied potentials or micro channels having narrow cross sections generate undesirable temperature inside the microfluidic channels leading to strong thermal distribution inside the micro channel. When intrinsic distribution of temperature, if not fix with threshold value, causes device damage or cell loss. In this work, we investigate the effects of temperature generated due to joules heating effects and we attempt to address the design constraints for minimizing the joule heating effects in the microfluidic device for developing effective microfluidic device. The device reliability was analyzed under different parametric constraints for various types of substrate materials (PDMS, PMMA, Polyimide and glass). We also attempt to investigate the effects of cell reliability due to strong temperature gradients generated through different applied potentials on different cell types. Furthermore, the response of the device performance due to different electrode configuration and different conductivity of the medium was also studied. Our investigation will eventually provide guidelines for microfluidic researchers to fabricate efficient electrode based microfluidic device which will ultimately help to choose a critical channel dimensions, threshold potentials, and conductivity of solutions in order to avoid device damage and cell loss.

Diarrhea and fever are prevalent childhood illnesses with potentially severe consequences, especially when they co-occur. This study investigates the prevalence and determinants of diarrhea, fever, and their coexistence among children under-five in Bangladesh. Data from the 2017-18 Bangladesh Demography and Health Survey (BDHS) were analyzed using multivariable stepwise logistic regression with backward selection. This study found that 5.0% for diarrhea, 34.0% for fever, and 3.0% for the coexistence of both illnesses. Common factors associated with childhood diarrhea and fever included the mother's education, her attitude toward wife-beating, and the child’s age (12-23 months). Diarrhea was associated with households with improved water sources and children in the Barisal division, while fever was linked to underweight children and those from more affluent backgrounds. The coexistence of both was significantly linked to underweight children, higher birth orders, and children from the Rajshahi division. Notably, child illnesses were associated with parental education, higher socio-economic status, and access to improved drinking water sources. Diarrhea affects one in 20 children, fever affects one in three, and the coexistence of both conditions affects one in 35 children in Bangladesh. The findings need further research and policy reviews to develop effective interventions and improve child health in Bangladesh.

Purpose: When it comes to improving citizen service delivery standard and processes, or broadly, management in government (MIG), many reform programs and improvement efforts are ongoing across different ministries, departments, and agencies. This paper is part of a larger empirical research where we are interested to know what those programs and projects are, and how they are doing, and what the gaps are. Design/methodology/approach: Analyzing the publicly available data on the government portal and donors’ website, this article provides an overall idea of current reforms in various places of government. In parallel, field research was conducted through visiting agency offices, observing office environment and mechanisms, and interviewing public managers who are working at district and sub-district level offices of directorates or agencies.Findings: As a trend, state-led development process and the traditional mode of administration have moved to New Public Management based quality and performance approach. All donors have their own strategy framework document like ‘Country Assistance strategy’, ‘Country Operations and Business Plan’, Logical Framework Document, Focus or Priority Streams, etc. Many of the projects are sectoral and agency or ministry specific and have an inherent limitations, due to ‘cadre’ based organizational structural arrangement. So, it is common ‘not to own the projects’, rather ‘using the fund as much as possible anyway within the given project duration’. When the fund ends, implementing units, either government or NGOs, abandon the projects. That is why many projects are actually half done without significant and sustained direction.Originality/value: We can conclude that the typical ‘top-down’ or ‘trickle down’ conception is still dominant in design and implementation of reforms programs and service quality efforts. So, how to reach systemically at the bottom where public managers provide services to citizens, needs even more deliberation.

Nowadays, more than 180 million people believe that the Holy Qur'an was sent by the Almighty Allah through Prophet Muhammad in 610-632 A.D in Mecca and Medina. However, the word Mecca (Q: 48:24) is mentioned only once in the entire Holy Qur'an, which is not related to the birth of the Prophet or any worship. Conventional Islam is mostly based on the hearsay of some Islamic narrators published in the eighth and ninth centuries. To think deeply and conduct research on the verses of the Holy Quran has been mentioned (special urge) directly and indirectly by this Holy Scripture itself many times but it would not be wrong to say that there is no scholarship on the verses of the Quran based on the research method described in the Holy Quran until the present century. Therefore, this paper has been dared to carry out by combining the methods of research followed in the Holy Quran and the academically accepted research methodology. The analysis shows that the results of almost fourteen hundred years of mythological history of conventional Islamic narrators are opposites. First of all, the proper name of the Prophet revealed by the Holy Quran is Ahmed, but Muhammad is an adjective and there are many such names. Secondly, this prophet was not born in Mecca, KSA, nor in 570/571 A.D, but in the Beqaa Valley of Lebanon, between the third and fourth centuries. Thirdly, the Muslim Hajj (pilgrimage) of Mecca is celebrated entirely according to the Lahwal Hadith (idle tales) (Q: 31:6) in the language of the Holy Quran. Originally the place designated for pilgrimage was in the Beqaa Valley, Lebanon. This article will have a double influence on the Muslim world, that is, Islamic scholars will think again deeply and research new ways of scholarship of the Holy Quran, instead of based on the hearsay of idle tales (Lahwal Hadith). On the contrary, those who are stubborn Islamic narrators, since they have already rejected the Holy Quran, will not hesitate to violently attack the researchers and they will not be positively motivated by this paper. This article specifically urges Islamic scholars to conduct in-depth studies on the most important issues of Islam using the Holy Quranic research method.

Quercetin (QUA), a flavonoid compound, is ubiquitously found in plants and has demonstrated a diverse range of biological activities. The primary objective of the present study is to assess the potential antiemetic properties of QUA using an in vivo and in silico approach. In this experiment, emesis was induced in 4-day-old chicks by orally administering copper sulfate pentahydrate (CuSO4.5H2O) at a dose of 50 mg/kg (orally). Domperidone (6 mg/kg), ondansetron (5 mg/kg), and hyoscine (21 mg/kg) were treated as positive controls (PCs), and distilled water was employed as a negative control (NC). QUA was administered orally at two distinct doses (25 and 50 mg/kg). Additionally, QUA (50 mg/kg) and PCs were administered separately or in combination to assess their antagonistic or synergistic effects on the chicks. The binding affinity of QUA and referral ligands towards 5HT3, D2, D3, and M1–M5 receptors was estimated, and ligand-receptor interactions were visualized through various computational tools. In vivo findings indicate that QUA (25 and 50 mg/kg) has a significant effect on reducing the number of retches (16.50 ± 4.65 and 10.00 ± 4.19 times) and increasing the chick latency period (59.25 ± 4.75 and 94.25 ± 4.01 seconds), respectively. Additionally, QUA (50 mg/kg) in combination with domperidone and ondansetron exhibited superior antiemetic effects, reducing the number of retches and increasing the onset of emesis-inducing time. Furthermore, it is worth noting that QUA demonstrated the strongest binding affinity towards the D2 receptor with a value of -9.7 kcal/mol through the formation of hydrogen and hydrophobic bonds. In summary, the study found that QUA exhibited antiemetic activity in chicks, potentially by interacting with the D2 receptor pathway.

Contact tracing has become a vital tool for public health officials to effectively combat the spread of new diseases, such asthe novel coronavirus disease COVID-19. Contact tracing is not new to epidemiologist rather, it used manual or semi-manualapproaches that are incredibly time-consuming, costly and inefficient. It mostly relies on human memory while scalabilityis a significant challenge in tackling pandemics. The unprecedented health and socio-economic impacts led researchersand practitioners around the world to search for technology-based approaches for providing scalable and timely answers.Smartphones and associated digital technologies have the potential to provide a better approach due to their high level ofpenetration, coupled with mobility. While data-driven solutions are extremely powerful, the fear among citizens is thatinformation like location or proximity associated with other personal data and can be weaponised by the states to enforcesurveillance. Low adoption rate of such apps due to the lack of trust questioned the efficacy and demanded researchers tofind innovative solution for building digital-trust, and appropriately balancing privacy and accuracy of data. In this paper,we have critically reviewed such protocols and apps to identify the strength and weakness of each approach. Finally, wehave penned down our recommendations to make the future contact tracing mechanisms more universally inter-operable andprivacy-preserving.

Digital marketing adoption in the multinational Fast-Moving Consumer Goods (FMCG) sector presents both challenges and opportunities that significantly impact organizational strategies and market competitiveness. This qualitative study explores the complexities surrounding digital marketing adoption through interviews with 45 participants from multinational FMCG companies. The research investigates key barriers such as organizational resistance, technological constraints, and concerns over data privacy and security, while also examining the benefits of enhanced consumer engagement, global market reach, and data-driven decision making. Findings reveal that organizational resistance remains a formidable barrier, driven by senior management's cautious approach to digital transformation and concerns over return on investment (ROI) amidst rapidly evolving digital landscapes. Technological challenges, including outdated systems and limited budgets for technology upgrades, further hinder comprehensive digital integration. Moreover, navigating regulatory frameworks and addressing consumer trust in data privacy emerge as critical considerations for FMCG companies seeking to leverage digital strategies effectively. Conversely, digital marketing adoption offers substantial opportunities. Enhanced consumer engagement through personalized and interactive campaigns allows companies to strengthen brand-consumer relationships and foster loyalty. The global reach facilitated by digital platforms enables multinational FMCG firms to expand market presence, penetrate new markets, and adapt marketing strategies to diverse consumer preferences worldwide. Data-driven decision making emerges as pivotal, leveraging analytics to optimize marketing strategies, measure campaign effectiveness, and anticipate consumer behaviors in real-time. Strategies for overcoming barriers emphasize building internal digital capabilities through training, recruitment, and cross-functional collaboration. Aligning digital strategies with overarching business objectives and integrating digital efforts with traditional marketing channels are crucial for creating cohesive and effective campaigns. Embracing innovation, flexibility, and agility in adapting to technological advancements are essential for maintaining competitiveness and relevance in a rapidly evolving digital landscape. Looking forward, emerging trends such as AI and machine learning integration for predictive analytics, sustainability-focused marketing initiatives, and the evolution towards omnichannel strategies are poised to reshape digital marketing practices in the FMCG sector. By aligning strategies with these trends, FMCG companies can harness digital technologies to drive sustainable growth, enhance market competitiveness, and meet evolving consumer expectations in a digitally-driven global marketplace.

In this study an adjusting post-processing approach is implemented for improving intra-hourly forecasts of solar power and ramp events of PV solar power systems at different locations in the United States. This study also serves as an out-of-sample test to evaluate the performance of the adjusting approach with different locations and timescales. Thus, various individual intra-hourly forecasts of solar power are combined and adjusted by applying the adjusting approach. Both point and probabilistic forecasts of solar power are included. After that, solar power ramp event forecasting by the adjusting approach is carried out.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

The paper provides a comprehensive review of the recent advancements and methodologies in Human-Robot Collaboration (HRC) applied to the manufacturing assembly process. In modern manufacturing, the assembly process involves intricate and time-consuming operations, often necessitating flexible manual interventions. However, the cost and stability issues associated with manual labor highlight the need for collaborative solutions integrating humans and robots. HRC, as a viable solution, involves the joint effort of humans and robots in manufacturing tasks, presenting advantages in terms of precision, reproducibility, and cycle time. This review categorizes and discusses methodologies such as task allocation, reinforcement learning, and Cyber-Physical Systems (CPS)-based planning approaches that facilitate HRC in the assembly process. It also explores experiments and future trends to address challenges and enhance efficiency in manufacturing assembly through intelligent collaboration between humans and robots. The objective of this research is to provide insights and directions for further research in HRC to optimize manufacturing processes. By analyzing the existing state-of-the-art and presenting future prospects, this paper aims to guide researchers and practitioners toward more effective implementations of HRC in manufacturing assembly, ultimately leading to improved operational efficiency and productivity.

The paper delves into an extensive exploration of the integration of the circular economy paradigm within the realm of additive manufacturing (AM). The objective is to comprehensively investigate existing methodologies for structuring the symbiotic relationship between circular economy and additive manufacturing, while meticulously analyzing the current research gap concerning the implementation of circular economy principles in additive manufacturing practices. A thorough review focusing on the sustainability of additive manufacturing within the circular economy framework was conducted. This review aims to recognize and delineate pertinent aspects related to post-use material valuation in AM, recyclability of materials, and the environmental footprint associated with these processes. Emphasis was placed on the significance of examining circular economy facets concerning additive manufacturing processes to establish a holistic understanding. The overarching goal of this review is to augment knowledge regarding the potential advantages and benefits derived from the seamless integration of circular economy principles within AM. By elucidating the activities essential to attain compliance with the Sustainable Development Goals, this study endeavors to illuminate the pathway toward promoting sustainable development through the harmonious marriage of additive manufacturing and the circular economy.

This paper examines a persuasion game between two agents with one-sided asymmetric information, where the informed agent can reveal her private information prior to playing a Battle-of-the-Sexes coordination game. We find that in the presence of strategic uncertainty in coordination there exists an equilibrium where there is no 'unraveling' of information. We provide a purification argument for this mixed strategy equilibrium to strengthen the central result, which is robust to several extensions, including both-sided asymmetric information and imprecise information revelation.

This is a case study in the domain of public sector management or management in government, with particular reference to education as a sector as a whole. Bangladesh has shown persistent upward trends in development indicators during the last four decades. In the education sector also, many indicators improved - literacy, the female proportion of literacy, number of educational institutions, etc. Quantity wise investment and indicators increased and improved at all levels and dimensions like primary, secondary, tertiary, technical, and so on. But qualitative improvement remained mysteriously low, despite government intention to increase quality of teaching, learning, and administration. This paper aimed to draw an overall picture of qualitative scenario of the education sector. Analyzing secondary sources like public media, and primary sources like citizen interactions, this qualitative study consolidated the problems of the education sector in a coherent whole. It argues that many problems of education sector are not education problems or budget problems, rather purely and simply ‘management’ problems, and so, can effectively be solved with basic but thorough understanding of the basic technicalities of management as a discipline.

This study intends to study a distinct typology of vernacular architecture built by the Manipuri communities of Bangladesh. The Manipuris are one of the ethnic diasporic communities in Bangladesh commonly known for their diverse cultural practice, including their dance form. This research aims to reveal the cultural entity of Manipuri that has been transformed into their living environment and household architecture. Architectural elements adapted by the Manipuris are assessed here as a part of cultural symbols to have a rigorous view of the philosophy of living. This study is a documentation of Manipuri habitat culture through the intervention of their living environment, which will attract any future working on this issue. This research shows that despite a rapid socio-economic change of context, the Manipuri housing practice is deeply connected to their socio-cultural and religious values. As the authors used an observational and ethnographical approach to studying vernacular architecture for this research.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

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 $

Objective: SARS-Cov-2 infection or COVID-19 is a global pandemic. From the time of identification to till, multiple clinical symptoms and parameters have been identified by the researchers of various countries and regions regarding the diagnosis and presentations of COVID-19 disease. In this manuscript, we investigated the primary symptoms and basic hematological presentations of SARS-CoV-2 infection among the Bangladeshi patients. Methodology: We have collected the disease history of mild to moderate degree of COVID-19 patients; hematological and biochemical on admission reports of moderate degree COVID-19 patients. All of them were tested positive for SARS-CoV-2 by RT-PCR in different institutes in Bangladesh. Results: According to this study though COVID-19 patients in Bangladesh commonly presented with fever, cough, fatigue, shortness of breath, and sore throat, but symptoms like myalgia, diarrhea, skin rash, headache, Abdominal pain/cramp, nausea, vomiting, restlessness, and a higher temperature of >100⁰F have a greater presentation rate and more frequent than other published studies. CRP and Prothrombin time was found to increase in all the patients. Serum ferritin, ESR, SGPT, and D-Dimer were found increased among 53.85%, 80.43, 44%, and 25% patients respectively. 17.39% of the patients had leukocytosis and neutrophilia. 28.26% of patients presented with lymphocytopenia. 62.52% of patients had mild erythrocytopenia. Conclusion: Despite some similarities, our study has evaluated a different expression in presenting symptoms in the case of COVID-19 patients in Bangladesh. CRP, Prothrombin time, serum ferritin, ESR, SGPT, D-Dimer, erythrocytopenia, and lymphocytopenia can be initial diagnostic hematological findings and assessment for prognosis COVID-19 disease. Also, gender variations have a different scenario of clinical and laboratory appearance in this region.

Counterfeiting and theft have always been problems that incur high costs and results in considerable losses for the international markets. In this research paper, we will address the issue of counterfeiting while using RFID technology in retailer systems or other industries by presenting a new anti-counterfeiting and anti-theft system for the retailer market. This system will address the two above mentioned issues and provide a solution that can save the retailer systems millions of dollars yearly. This proposed system will achieve the objective of preventing or minimising the counterfeiting and theft of tagged products. At the same time, it will provide a strong indication for suspiciously sold or obtained items. Furthermore, we conducted a security analysis to prove the correctness of our protocol on the basis of the strand spaces.

Facial Emotion Recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This paper investigates the impact of histogram equalization, data augmentation and model optimization strategies on three well-known FER datasets using pre-trained VGG models. Additionally, this paper showcases the effectiveness of different regularization techniques, callbacks, and learning schedulers in enhancing model performance by conducting extensive experiments. The model evaluation is discussed in terms of the following metrics: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG models exhibit state-of-the-art performance compared to more complex conventional transfer learning models resulting in accuracies of 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Consumption of milk contaminated with antibiotic residues above the maximum residue limit (MRL) causes direct toxicity to humans and the development of superbugs that leads to the failure of antibiotic therapy and threatens human life. Besides, long-duration exposure might alter the nature of gut microflora results in the enhancement of many diseases. Therefore, we examined 300 raw and processed packet milk samples using thin layer chromatography (TLC) and ultra high performance liquid chromatography (UHPLC) method against five veterinary antibiotics and assessed the risk for consumers in Chattogram, Bangladesh. Risk analysis was calculated by using hazard quotient on the basis of 165 ml per capita milk consumption. We found a total of 7% prevalence of antibiotic residues in raw milk samples which were higher (8%) in individual samples than the pooled samples (4%). However, we did not find any processed packet milk samples as positive. The mean concentration of oxytetracycline residue was detected 61.29 µg/l and amoxicillin was 124 µg/l in individual milk samples. Risk analysis showed the hazard quotient values of 0.0056 for oxytetracycline and 0.0017 for amoxicillin residue which was confirmed that, no significant health risk associated with the consumption of milk produced and marketed in the study area. Our study might fill the gap of knowledge to measure the safety status of milk regarding public health issues.

In recent years, many researchers have begun to shift their focus onto the synthesis of nanomaterials as this field possesses immense potential that may provide incredible technological advances in the near future. The downside of conventional synthesis techniques, such as co-precipitation, sol-gel and hydrothermal methods, is that they necessitate the use of toxic chemicals, produce harmful by-products and require a considerable amount of energy; therefore, more sustainable fabrication routes are sought after. Biological molecules have been previously utilised as precursors to nanoparticle synthesis, thus eliminating the negative factors involved in traditional methods. In addition, transition-metal nanoparticles possess a wide scope of applications due to their multiple oxidation states and large surface areas; thereby allowing for a higher reactivity when compared to their bulk counterpart and rendering them an interesting research topic. However, this field is still relatively unknown and unpredictable. Therefore, this review aims to obtain a better understanding on the plant-mediated synthesis process of the major transition-metal and transition-metal oxide nanoparticles, and how different parameters affect their unique properties.

Modernizing the retail sector is essential to societal progress toward creating a smart city. The retail sector is crucial to the development of a smart city because of its adaptability and capacity for innovation. However, due to their perishability, supermarkets throw away a lot of fruits and vegetables. This article discusses the design of a sub-optimal process for processing sorted fruits using a reverse logistics approach, demonstrating how productivity can be increased despite constraints on processing capacity. This paper presents the results of a case study conducted in a Qatari supermarket that offers a variety of regular fruit processing techniques. In order to maximize output and cut down on waste, this project aimed to develop a reverse logistics system for imperfect fruit.

Currently, many manufacturing industries use machine learning, artificial intelligence, robotics, and blockchain technologies as process-streamlining instruments of industry 4.0. Before implementing industry 4.0 tools in any process of a manufacturing industry's supply chain, it is essential to evaluate their adaptability. In this paper, the adaptability of industry 4.0 procurement tools to an oil and gas-related manufacturing company's procurement process is evaluated to determine the company's level of maturity. The active participation of participants is evaluated to determine the level of maturity of the main procurement process areas. The results identified the areas of the procurement process that can be improved by implementing the suggested industry 4.0 tools and techniques.

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

In Bangladesh, family businesses play a significant role in the country's business landscape because so few private companies are set up as large-scale, non-family ownership firms. Thus, it is vital to comprehend family businesses in order to comprehend Bangladeshi company dynamics. In this paper, authors attempted to survey the existing research in the area of family business in Bangladesh. It was found that the area as a research is totally an open field and a lot of new research can be done in the said area. Although a good number of studies can be classified as focused on the SME (small and medium enterprise) domain, a major shortcoming of those SME studies is that the SMEs themselves vary greatly in terms of size, scope, and technology. Whereas most of the family businesses can be predominantly put in the SME category, the particular issue of family business and family dynamics is not very common in those studies. This paper suggests that a good amount of research or studies can be done focusing on family business constitution, family members' participation norm, recruitment and promotion norm, family business succession rules and succession planning, training for family business leadership and management, training for board and directorial supervision, and so on.

This article aims to reflect upon the relevance of Decolonization methodologies with the Theses on Feuerbach. Somehow, all the Indigenous scholars started from new Marxist like Paulo Freire, Frantz Fanon, but not from classic Marx. To us, the German Ideology of young Marx only resembles the pioneering sources of Indigenous methodology. This discussion is thus a reflection of our studies and a philosophic endeavor to talk about the marginal people of the world, and the scholars who engaged in and with the oppressed. However, we are not prepared to turn our attention away from all the vastness of Marx to a collection of potentially equally relevant to Indigenous methodology. This article concludes that the Theses on Feuerbach is the core of Marxist archaeology of knowledge or philosophy as a whole and has been wading in the Indigenous paradigm.

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface —will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics such as Click-through rates, Buy-through rates, and Conversion revenue.

Malnutrition is the main cause of child mortality in developing nations, especially in Sub-Saharan Africa and South Asian countries. This undernutrition status is predominantly higher among primitive tribal (ST) children under 5 years of age. The nutritional status of tribal children is a serious concern in India. For this, there should be intends to examine the prevalence and determinants of primitive tribal children's nutritional status. To investigate the effect of household, child’s & maternal characteristics on biological children under 5 years of age. The study aims to find out the association between some selected determinants on scheduled tribe child’s nutritional status. The study used some selected variable of scheduled tribe mothers, household characteristics and their children anthropometric indices from DHS datasets of India, which was published by the NFHS datasets of India. In this study People datasets and Children (Under 5 years of age) of interviewed women file datasets of NFHS-IV & V is taken. The datasets of West Bengal, ST mothers, and their child into consideration as per the research interest. Then to analyze the determinants of ST children (U5 age) nutritional status, some determinant factors are taken from mothers, children and household characteristics. Children’s stunting, wasting and underweight as dependent variable and the mothers, child’s and household’s characteristics as independent variable. The statistical analysis was done by using Pearson Chi square test used to analyze the association between dependent and independent variables. The study results shows that the mother’s educational status, child’s age, birth order, size of child at birth, type of residence had significant associations with stunting. Mother's anemia status and mother age had significant associations with wasting. Mother education, currently breastfeeding, child age, sex of the child, birth order, size of child at birth, type of residence, wealth, toilet facility, and cooking fuel shows a significant association with underweight. The results found that many determinant factors had a significant role among ST children's (U5 age) nutritional status. Thus, the findings will help to achieve tentative resource allocation, and utilization through various programs and policies to improve the ST children (U5 age) nutritional status. There is a need to take a multisectoral and multidimensional approach to develop for those primitive tribal children.

The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.

Background: Currently, limited number of therapeutic options are available to treat Diabetes mellitus, and to find a potential candidate, laboratory work takes time and also needs animal studies. So, the aim of this study was to determine the efficacy of some natural phytocompounds with the aid of molecular docking, bioinformatics and in silico drug design approaches. Method: Two proteins (Human CYP3A4 linked to metformin and Human dipeptidyl peptidase-IV) were selected and molecular docking studies were conducted using Pymol, AutoDock Vina, PyRx, and Discovery Studio. Different important pharmacokinetic parameters like ADME and toxicity data were obtained from online databases SwissADME and pkCSM program. Results: It was found that human dipeptidyl peptidase-IV (PDB ID: 4A5S) has exhibited a maximal affinity of -9.7 Kcal/mol for bryonolic acid and hesperidin, but only -6.7 kcal/mol for metformin hydrochloride. Similarly, the highest affinity of hesperidin for human CYP3A4 bound to metformin (PDB ID: 5G5J) is -10.7 Kcal/mol compared to metformin hydrochlor (-6.3 Kcal/mol). Besides, all the compounds have been documented outstanding ADMET profile, and accepted by drug-likeness or Lipinski rule. Conclusions: The present study suggested that these compounds can be further investigated in vitro and in vivo to establish them as lead compounds against Diabetes mellitus..

Aquaculture is one of the fastest-growing animal food-producing agricultural industries in the world and proper performance of fish in morphological, physiological and immunological aspects is important for fish production and sustainable expansion of aquaculture. But several inhibitors like disease, pathogen, and adverse environment can overpower these performances. At present, antibiotics in preventing these inhibitors have been seen as becoming favorable to those inhibitors. So, Bacillus, an important group of probiotic bacteria can be an alternative to these antibiotics in aquaculture. Bacillus has been seen used in different experiments, mainly as a supplement in feed at various concentrations. Bacillus showed effective results like improved growth with minimum cost, improvement in reproduction, hematology, improved immune response and disease, and stress resistance as well as better proximate composition in different fish species. Application of Bacillus strains has proven efficient in improving water quality by reducing ammonia and nitrite toxicity, harmful algal blooms and utilization of H⁺ ion. Larger application of probiotic Bacillus instead of the hazardous synthetic chemicals would promote eco-friendly low-input sustainable aquaculture for food and nutritional security of the increasing world population. So many more experiments should be conducted in commercially important fishes for better growth and health of fishes which will certainly increase fish

Lead Acid Batteries (LABs) are used for starting, lighting, and igniting, as well as in air conditioning systems and to supply power to electric engines in transport vehicles (TVs). However, the application of LABs for TVs has faced a number of market challenges, mounted by the upcoming high energy density and long lifespan batteries, such as lithium ion. LABs, on the other hand, are inexpensive. The key research question is, how can the lifespan of LABs used in automotive industries be increased, while still ensuring a low cost solution? Thus, integrating LABs with the supercapacitor (known as an electric double layer capacitor—EDLC) is likely to outperform the competing alternative batteries for TVs. This paper proposes a multiple stage approach to hybrid lead acid batteries and a supercapacitor system for TVs that is capable of maintaining the battery state-of-charge (SOC) at statistically high limits, ranging between 90% and 95%. This SOC target will likely ensure that the lifespan of the hybrid battery system can be elongated (extended) more than its competitors. In this study, the multiple stage approach of concatenated converters has been designed in order to satisfy all energy storage requirements for different characteristics of LABs and the supercapacitor. The designed hybrid system has been simulated using Matrix Laboratory (MATLAB/Simulink (version R2016a, MathWorks, Natick, MA, USA)). The simulated results show that high transient currents from the direct current (DC) bus of LABs, caused by the regenerative braking or deceleration of the TVs, reduce the battery lifespan and induce mechanical stress. The supercapacitor reduces the stress on the LAB by absorbing high transient currents. This, in turn, keeps the LABs’ SOC between 90% and 96% and the voltage at 12 V. As indicated by the simulated results, the hybrid battery SOC is maintained at 90–96% and the terminal voltage is approximately 12 V.

The transportation sector stands out as California's primary contributor to greenhouse gas emissions, necessitating urgent action from government agencies to promote the adoption of low-carbon transportation (LCT) within the heavy-duty vehicles (HDV) and off-road equipment (ORE) sectors. This study conducts content analysis to delve into the impact of behavioral factors, specifically awareness and impression, on LCT adoption within these sectors. The analysis results are honed using a generative artificial intelligence (AI) tool. Positive associations emerge between the potential for LCT adoption and awareness, as well as factors linked to LCT (government incentives, environmental considerations). Notably, the study reveals that financial barriers and incentives hold greater significance for small-fleet organizations compared to their large-fleet counterparts. These findings offer actionable insights for government agencies to formulate targeted strategies, including mandates and incentives, tailored to the unique needs of organizations within the HDV and ORE sectors.

Fruits and vegetable diagnosis and inspection is a crucial part of quality control and health safety. From harvesting to retailing, stakeholders encounter tons of food losses due to the lack of monitoring of the fruit condition in retail. The problem gets worse because it is being handled by many people, including retailers and customers. We propose a transparent fiberglass-made vertical fruit showcase, which provides 80 percent more visibility, takes lesser shop space, and prevents human contact. It is convenient for the retailer and customer to use. Moreover, the controlled fruit environment is easy to integrate with IoT, which will help to generate consensus data for fruit management decisions in machine learning.

For a considerable length of time synthetic composts are utilized to satisfy the dirt necessity of supplements and yield, however huge measure of these substance manures are hazardous for condition, advantageous microorganisms, creatures, and people also. In this way, natural inviting and savvy biofertilizers are utilized. Biofertilizer are the substances which contain microorganisms those microorganisms might be growths, microscopic organisms, and protozoa which have capacity to build ripeness of soil by Nitrogen obsession, Phosphorous solubilization, and Iron sequestration. These cycles convert insoluble type of supplements into solvent structure and make it accessible to the foundations of plant which effectively take them up and use them. There are assortment of the yields whose profitability can be expanded by applying biofertilizer, for example, rice, oat, and other grain crops. In this audit we experience the method of utilization of biofertilizers, and how the assistance the plants and in which they help.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

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.

In the wake of the novel coronavirus, SARS-CoV-19, the world has undergone a critical situation in which grave threats to global public health emerged. Among human populations across the planet, travel restraints, border enforcement measures, quarantine, and isolation provisions were implemented in an attempt to control and limit the spread of the contagion. Decisions on how to implement and enforce various control policies should be determined based on available real-world evidence and theoretical prediction. In this study, we propose a novel exportation- importation epidemic model associated with the quarantine and hospitalized-isolation policies by considering the two-body system: a source country of a contagious disease and a neighboring country that is initially disease-free. The model is first applied to the original COVID-19 data in China, Italy, and the Republic of Korea (ROK) and observed through consistent fitting results with equivalent goodness-of-fit. Then, the data are estimated per the fitting parameters. Driven by these parametric settings and considering the normalized population, the numerical analysis, and epidemiological exploration, this work further elucidates the substantial impact of quarantine policies, healthcare facilities, and the public counter-compliance effect.

1) Background: A significant proportion of child laborers are compelled to work in exploitative environments, experience both deteriorating health and financial loss. The present study sought to determine the factors affecting child labour and the characteristics of their working environment. 2) Methods: A questionnaire survey was conducted with 80 child labourers aged 5 to 17 years. Alongside descriptive statistics, a newly devised technique known as Influencing Causes Index (ICI) was administered and tested. 3) Results: The startling demographic findings reveal that peak share of child labourers are young children (12-14 years) and 32.5% child laborers had never attended school. The thorough assessment of determinants reflects that not only poverty, but schooling expenses and lack of access opportunity to primary schools are also the top-ranked push factors to trigger children towards labour. Around 72.5% of children work for over 8 hours a day. A significant proportion of participants received no leave, training, or access to hygiene facilities. The existing pattern of employment and working conditions resulted in musculoskeletal pain and dermatological infections among child labourers (p&lt;0.05). 4) Conclusion: This research suggests that income measures for households, and an education programme for both children and parents would expedite the abolition of child labour.

With the advent of digitization, the integration of advanced technologies in operations and projects across all sectors is in progress. Accordingly, efforts are being made to utilize cutting-edge technologies to improve the circularity of the built environment. This study aimed to review the existing applications and limitations of building information modeling (BIM) tools for circular economy (CE) implementation from this perspective. A literature review was conducted to provide an overview of the use of BIM tools for conducting life cycle assessments, energy analysis, waste management, and formulation of material passports for buildings. It was found that BIM tools, which are available across all life cycle phases of building, have a great potential to improve the circularity of the sector. The overview provided on the use of various BIM tools may help stakeholders of the built environment understand the role of BIM for CE adoption in the sector.

High antioxidants level in food is gradually becoming popular because of the enhanced risk of oxidative stress in humans. Bread wheat is rich in vital antioxidants but its major bioactive compounds are not available for the human. This study was conducted with the aim to enhance the phytochemical constituents and antioxidative activity of wheat grains by fermenting it with the use of Bacillus subtilis KCTC 13241. The antioxidative potential was determined by DPPH (2,2-diphenyl-1-picryl- hydrazyl) and ABTS (3-ethyl-benzothiazo- line-6-sulfonic acid) radical scavenging assay as well by the concentration of amino acids, flavonoids, minerals, carbohydrates, and phenolic compounds. Different varieties showed different free radical scavenging potential on fermentation, which was significantly high with respect to their corresponding unfermented wheat varieties. The highest potential was found in a fermented wheat variety named Namhae and this combination can be used in the pharmaceutical and food industries.

Sustainable development and education in sustainable development (ESD) are crucial and interconnected topics under wide discussion today. Young adults need to be educated in how to help bring about change. Thus, the dynamic relationship between school and home is important. In education, use could be made of English language classes, especially since English is a global language that enables access to the sustainability literature and facilitates discussion in global conferences and meetings. Consequently, English as a foreign language (EFL) students could gain confidence from being able to understand and use the terminology of, for example, sustainability. In this paper, the use of competence-based language teaching (CBLT) is proposed as a means of introducing sustainability topics into EFL writing classes at a university in Saudi Arabia. Students’ competence in sustainable development is therefore considered based on their ability to write about suggested sustainable development topics, measured on a scale for five dimensions of ESD proposed by Michelsen and Fischer (2017). The results demonstrate that the students possessed competence in sustainable development and intended to participate in its implementation. Thus, recommendations are made for the stakeholders in the education sector to include sustainable development topics as an initial offering in the curricula of various specialties using the CBLT approach.

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Hydrolysate prepared from water hyacinth biomass, containing a considerable amount of solubilised carbohydrate and nutrients, was utilised as a medium for the cultivation of two strains of Chlorella sorokiniana. These strains were isolated from an oxidation pond using two different media, i.e., BG-11 and Knop's media maintained at pH-9. Different light intensities, light-dark cycles, and various concentrations of external carbon sources (monosaccharides and inorganic carbon) were used to optimise the microalgal growth. It was observed that in the presence of organic carbon (glucose), biomass productivity increased significantly (~300 mgL-1day-1) as compared to that in the presence of only inorganic carbon (~100 mgL-1day-1). For the accumulation of stress products (lipids and carbohydrates), the microalgal strains were transferred to nutrient-amended media (N-amended and P-amended). The combined effects of glucose, inorganic carbon, and a 12h:12h light-dark cycle proved to be optimum for biomass productivity. For Chlorella sp. isolated from BG-11, maximum carbohydrate content (22%) was found in the P-amended medium, whereas high lipid content (17.3%) was estimated in the N-amended medium. However, for Chlorella sp. isolated from Knop's medium, both the lipid (17%) and carbohydrate accumulation (12.3%) were found maximum in the N- amended medium. Kinetic modelling of the lipid profile revealed that kinetic coefficients obtained for strain isolated from BG-11 media were statistically significant from each other.

Regeneration of lost tail is of great importance to lizards. Anolis carolinensis, a green lizard, is capable of regenerating its tail efficiently after autotomy. Hence, it is considered as a model organism in regeneration study. A. carolinensis shed its tail in order to distract the predator’s attention and thus makes a way to escape. Restoring of the amputated tail takes several days and the mechanism is currently clearly understood. Although save its life, tail regeneration is associated with the impairment of several vital functions in Anoles. In addition, various differences have been observed between original and regenerated tail in terms of mechanism and structure. To date, very little work has been conducted on tail autotomy and regeneration at molecular and genetic level. The genes responsible for regeneration in anoles are identified recently. These genes are evolutionarily conserved through all tetrapod vertebrates. They are, however, in a state of ‘switched-off’ in other vertebrates including humans. Consequently, a throughout study of these so called ‘switched-off’ genes may provide a way of restoring lost organs in human, and thus could revolutionize the modern medical science.

Different materials prepared by appropriate synthesis methods and applying different post syn-thesis processes could be used as hole transport layer in perovskite solar cells. In the present work annealing was employed to enhance physical properties of copper oxide (CuO) thin films fabricated via spin coating technique and the effects of annealing on their optical, morphological, electrical and structural properties were investigated. The XRD findings revealed the polycrystalline structure with large crystallite size. Optical spectra showed larger transmission beyond the visible region, reduced absorption, and a decrease in bandgap energy from 1.93 eV to 1.81 eV. The Ag phonon modes at 299.22 and 304 cm-1 shown by the Raman shift confirmed the development of the CuO pure phase. FTIR spectra showed the Cu-O stretching band at 759, 764 and 446 cm−1. SEM revealed the agglomeration of nano crystallites with large grains on the surface. The carrier type conductivity of annealed CuO thin films was determined via two-point hot probe technique. Low resistivity values were determined via four-point probe method. These findings indicate that CuO thin films may be successfully used as a companion with an n-type layer in p-n junction solar cells, and a hole transport layer in perovskite solar cells.

The present article summarises Earth Observation (EO)-based rice mapping strategies since 1979, with a focus on data, methodologies, and methods based on 3,700 research publications across global literature and its comparison with the Vietnamese Mekong Delta (VMD). Various quan-titative analyses were conducted through bibliometric analysis using the VOS viewer and Scopus database. Optical images, particularly MODIS and Landsat time series datasets, were found to be the most commonly utilized. Landsat data had the highest share in the global context, while MODIS data research dominated in the VMD, while Sentinel series data and the Google Earth Engine (GEE) platform became more popular in recent years. The research on rice mapping using UAVs has been gradually creeping into global rice mapping research but is a loophole yet to be implemented in the VMD. The most widely used approaches for rice mapping globally were Random Forest, Support Vector Machine, and Principal Component Analysis. Indices like EVI, NDVI, and RVI were commonly used for rice mapping and monitoring. The findings underscore the critical role of EO-based rice mapping studies in the VMD in addressing sustainability and food security chal-lenges.

Product counterfeiting is an on-going problem in supply chains and retail environments, Recently an anti-counterfeiting protocol to address this issue via cost-effective use of auto-identification technologies such as radio-frequency identification (RFID) was proposed by researchers.Yet the use case of re-selling the same product was not been fully addressed which might cause serious problem for the exciting and proposed schemes and transactions. This paper proposes an extended RFID-based anti-counterfeiting to address the use case of the original buyer reselling the same item to a second buyer. The extended scheme will be followed by a formal security analysis to show that the proposed protocol satisfies the requirements of security correctness and is resistant to compromise through security attacks.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

This study examines the influence of digital marketing on brand awareness within the multinational Fast-Moving Consumer Goods (FMCG) sector, focusing on how various digital strategies contribute to overall marketing effectiveness. Utilizing a sample of 277 respondents, the research employs descriptive statistics, correlation, and regression analyses to explore the relationships between digital marketing strategies, brand awareness outcomes, and perceived digital marketing effectiveness. The findings reveal that digital marketing strategies significantly enhance brand awareness and overall marketing effectiveness, underscoring the pivotal role of activities such as social media engagement, content marketing, and search engine optimization. Specifically, the regression analysis shows that digital marketing strategies have a positive and statistically significant impact on overall effectiveness, indicating that companies investing in these areas are better positioned to achieve higher levels of brand recognition and marketing success. However, the study also uncovers a complex relationship between brand awareness outcomes and overall effectiveness, where increased brand awareness does not directly correlate with enhanced digital marketing effectiveness. This suggests that while brand awareness is critical, its influence on marketing success is nuanced and may involve diminishing returns or additional mediating factors. The regression model explains a modest portion of the variance in marketing effectiveness, highlighting the need for a holistic evaluation approach that incorporates a broader set of performance metrics and contextual factors. The high reliability of the measures used affirms the robustness of the findings, which emphasize the importance of a strategic and integrated approach to digital marketing in the FMCG sector. Companies must continuously adapt and optimize their digital marketing strategies to align with dynamic market conditions and achieve sustainable success.

An opinion dependent cross sectional survey was conducted among charland peoples of Noakhali, Bangladesh with a view to identify the factors that affect green economy. Nijhumdwip Island and Tamaruddi union are highly affected by cyclone and soil salinity. Unpredictable rainfall is the most acute in Nijhumdwip. Lack of information the main problem in Nijhumdwip Island. Farmers are found less interest in integrated farming and crop diversification. Few farmers from Sonadia Union are involved in homestead gardening. Regression analysis have shown a negative relationship (p<0.001) between education of stockholders and decrease of crop production. On the other hand education level of stockholders is to be found positively (p<0.05) varied with decrease of food insecurity. So it can be said that educated farmers are more adaptive against climate change.

Photonic sensors are innovative devices that leverage the principles of light-matter interaction to enable precise and efficient detection of various physical parameters. These sensors operate by exploiting the interaction between photons, the fundamental particles of light, and matter. The light propagates through an optical waveguide, and a portion of the electromagnetic field extends beyond the waveguide surface, forming the evanescent field. This evanescent field interacts with the surrounding medium, allowing for highly sensitive detection of changes in the refractive index or properties of nearby substances. The underlying concept involves the modulation of light properties, such as intensity, wavelength, or phase, when it interacts with a target substance or undergoes changes in the surrounding environment. The advancements in photonic sensor technology have paved the way for enhanced sensitivity, selectivity, and miniaturization, making them invaluable tools in diverse industries. The ability to harness light-matter interactions for sensing purposes not only facilitates highly sensitive measurements but also allows for non-intrusive and remote monitoring, contributing to the evolution of smart and connected systems. In this overview, the material platforms and waveguide structures are explored that can make highly sensitive photonic devices applied to gas and biosensing applications.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

The effect of the COVID-19 pandemic in a developing country like Bangladesh is enormous. A research conducted by South Asian network of Economic Modelling predicted that the pandemic could double the poverty. But it is not that only the socioeconomic condition is dropping in Bangladesh, the impact of COVID-19 pandemic is manifold. The poor condition of Bangladesh's health sector has also been exposed due to the pandemic. People are not getting proper treatment due to lack of isolation beds, oxygen, ICU etc. The health sector of Bangladesh is not much developed and now with this pandemic it has become impossible to provide treatment facility for all the patients. Education sector, which is the backbone of a country,has also been greatly affected by the pandemic. We know that different types of cultural occasions are an inherited tradition of Bangladesh, COVID-19 have not even spared these traditions, all the cultural programes and festivals have been cancelled due to this pandemic.In this paper, our aim is to present the present status of all these sectors.

The consumption and sale of fresh-cut products and salads have been growing tremendously in the present era. Therefore, the microbial safety of such products is of great concern. In the current study, a survey of general microbiological safety of fresh-cut produce and salads at quick-service restaurants (QSR) was undertaken across the Kingdom of Saudi Arabia. These findings were compared with microbiological criteria for foodstuffs by Saudi standards, metrology, and quality organization SASO-GSO-1016. Of the 82 samples of fresh-cut produce, 7% of samples were found to be unsatisfactory or beyond the acceptable limits. TPC count was unsatisfactory at 22%, coliform at 48%, and Staphylococcus aureus at 4%. For 108 samples for fresh salads, 11% of samples were found to be unsatisfactory or beyond the acceptable limits,13%, 27%, 4%, and 27% of samples showed an unsatisfactory range of TPC, coliforms, S. aureus, and Escherichia coli, respectively. The fresh-cut produce and salads were microbiologically safe in the central region compared to the eastern region followed by the western region. The relatively higher count was found in green pepper, mixed vegetables, and lettuce followed by fresh-cut onions and coleslaw salads. No Salmonella was detected in both fresh-cut produce and salads. The restaurants should be more stringent in their processing to ensure the consumer safety. Washing and sanitization of produce is the only way to reduce the diffusion of food borne pathogens.

Background: The incidence of spinal metastatic disease has risen with improved cancer survivorship. Separation surgery, the current standard surgical treatment, does not fully address spinal deformity. Advances in operative management warrant reevaluating maximal tumor resection and anterior column reconstruction with corpectomy. Methods: An IRB-approved retrospective single-center review of 63 patients was performed. Demographics, tumor characteristics, clinical and radiographic data, treatments, and neurological and survival outcomes were analyzed. Results: The mean age at presentation was 63.5 ± 9.6 years (range, 37-84 years) and 28 patients (44.4%) were female. Functional outcomes, measured by KPS, RAND-36 survey scores and Oswestry Disability Index all improved postoperatively (pre- vs postop: 70 to 80 (p=0.0056), 26.5 to 46.4 (p=0.0081), and 28.9 to 16.9 (p=0.0613), respectively). Spinal alignment also improved, with Cobb angle measurements showing a reduction in spinal kyphosis from -5 to 0 (p=0.021). Nine patients (14.5%) had non-fatal postoperative complications. The median OS for this cohort was 14.4 months (range, 0.6-145.0 months) with median follow-up time of 15.5 months. Conclusion: In oligometastatic spinal disease patients with good functional status, corpectomy for decompression of neural elements, deformity correction, and spinal stabilization is associated with improvements in pain and functional outcomes without overwhelming morbidity.

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second most common cause of death due to cancer. About 30% of patients with PCa who have been castrated develop a castration-resistant form of the disease (CRPC), which is incurable. In the last decade, new treatments that control the disease have emerged, slowing progression and spread, and prolonging survival while maintaining the quality of life. These include immunotherapies; however, we do not yet know the optimal combination and sequence of these therapies with the standard ones. All therapies are not always suitable for every patient due to co-morbidities or adverse effects of therapies or both, so there is an urgent need for further work on new therapeutic options. Advances in cancer immunotherapy with an immune checkpoint inhibition mechanism (e.g., ipilimumab, an anti-CTLA-4 inhibitor) have not shown a survival benefit in patients with CRPC. Other immunological approaches have also not given clear results, which has indirectly prevented breakthrough for this type of therapeutic strategy into clinical use. Currently, the only approved form of immunotherapy for patients with CRPC is a cell-based medicine, but it is only available to patients in some parts of the world. Based on the experience gained from recently completed clinical research on immunotherapy with dendritic cell-based immunohybridomas, the aHyC dendritic cell vaccine, for patients with CRPC, we highlight the current status, and possible alternatives that should be considered in the future.

City zones have become increasingly overcrowded as a result of the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a major issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication scheme for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed schemes support minimizing the end-to-end (E2E) delay and route request, in addition to reducing link failure. Our schemes’ leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The schemes are centred on short-range peer-to-peer (P2P) wireless communication in a 400-meter radius cluster. This includes innovative P2P wireless communications on VANET using minimized resources. The proposed schemes deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes, and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

Background: Psychological sufferings are observed among dental students during their academic years, which had been intensified during the COVID-19 pandemic. Objectives: This study assessed the levels and identified factors associated with psychological distress, fear and coping experienced by dental undergraduate students in Bangladesh. Methods: A cross sectional online survey was conducted during October-November, 2021. The Kessler Psychological Distress Scale (K-10), Fear of COVID-19 Scale (FCV-19S) and Brief Resilient Coping Scale (BRCS) were used in order to assess psychological distress, fear and coping strategies respectively. Results: A total of 327 students participated; the majority (72%) were 19-23 years old and females (75%). One in five participants were infected with COVID-19 and 15% reported contact with COVID-19 cases. Negative financial impact (AOR 3.72, 95%CIs 1.28-10.8), recent or past COVID-19 infection, contact with COVID-19 cases were associated with higher levels of psychological distress; but being a 3rd year student (0.14, 0.04-0.55) and being satisfied about current social life (0.11, 0.03-0.33) were associated with lower levels of psychological distress. Being a 3rd year (0.17, 0.08-0.39) and a 4th year student (0.29, 0.12-0.71) were associated with lower levels of fear. Health care service use and feeling positive about life were associated with medium to high resilience coping. Conclusions: This study iden-tified dental students in Bangladesh who were at higher risk of psychological distress, fear and coping during the ongoing pandemic. Development of mental health support system within dental institution should be considered in addition to the academic and clinical teaching.

The COVID-19 pandemic has affected Bangladesh's economic and social well-being in many ways. During the lockdown, many households lost their full income. The day labors and the slum dwellers including rickshaw pullers, CNG drivers, housemaids, hawkers, and street vendors were the worst victims of this pandemic. Besides the voluntary organizations, the city corporation, being instructed by the central government took the initiative to distribute necessary food as well as financial assistance to the city dwellers to survive during the lockdown. The objective of this study is to identify the satisfaction level of the slum dwellers with the assistance received from the City Corporation and get an idea of how far these supports were enough for them to lead their livelihood. It is a mixed-method research consisting of both qualitative and quantitative approaches. Researchers conducted a field survey in Sylhet City Corporation, the northeastern part of Bangladesh, and collected primary data from eighty respondents through a semi-structured questionnaire along with an interview schedule. Samples have been selected by applying a multistage sampling technique. Researchers analyzed quantitative data using SPSS software and qualitative data using the thematic analysis method. Study findings reveal that, although this aid was very inadequate compared to the needs of the slum dwellers, it was able to alleviate food shortages to some extent in times of extreme hardship. However, some dwellers got deprived of the aid due to having different political ideologies and corruption. Proper aid management, well coordination, and community participation are indispensable for dealing with any crisis.

Over the years, sustainability, impact on the environment, as well as the operation expenditure have been a major concern to the deployment of mobile cellular base stations worldwide. This is because the mobile cellular base stations are known to consume a high percentage of power within the mobile cellular network. Such energy consumption contributes to the emission of Greenhouse Gases (GHG) through the use of conventional diesel generating a set. As a result, the mobile cellular operators are faced with the dilemma of minimising the power consumption, GHG emission, and the operation cost, while improving the Quality of Service of the networks. In attempting to find a solution, this study presents the feasibility and simulation of a solar photovoltaic (PV) with battery hybrid power system (HPS) as a predominant source of power for a specific mobile cellular base station site situated in Soshanguve area of the city of Pretoria, South Africa. It also presents the technical development, showed the environmental advantage and cost benefits of using a solar PV-battery HPS to power a base station site of a 24 hrs daily load of 241.10 kWh/d and the peak load of 20.31 kW as compared to using the HPS of solar PV-diesel generating set-battery. The solar resource pattern for the city of Pretoria was collected from The National Aeronautics and Space Administration and modelled statistically. Thus, the statistical modelling done using solar radiation resource exposure characteristic patterns of Pretoria, South Africa, revealed an average annual daily solar radiation of 5.4645 Wh/m2/d and 0.605 clearness index. The simulation and the design were done using the Hybrid Optimization Model for Electric Renewables and Matlab/Simulink software. The simulation finding shows that the HPS of solar PV-battery combination has about 59.62 % saving on Net Present Cost, Levelized Cost of Energy, and 80.87% saving on Operating cost as against conventional BS powered with Gen Set-Battery.

Drinking water supplies in the developing world often serve as a biosphere for various organisms. Viral gastroenteritis is a neglected area of research in Pakistan, there is no data for the prevalence of enteric viruses in drinking water of the largest city of Karachi. The present study aimed to provide a survey of the existence of enteric viruses: human adenovirus (HAdV), human enteroviruses (hEV), and genotype A rotavirus (GARV) in tap water. Using a simple PCR approach, we detected 20%, 43%, and 23% of HAdV, hEV, and GARV in tap water respectively. We have also shown an overall quality of tap water at the pumping station and consumer tap. Moreover, we assessed the efficiency of small-scale water treatment methods for the removal of viruses.

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

Use of antibiotics and other chemicals to combat disease outbreaks have been a bottleneck for the sustainable growth of shrimp industry. Among various replacement proposed, organic acid (OA) and their salts (OS) are commonly used by farmers and feed millers. However, in free forms, their requirement is very high (2-3 kg/MT) as they tend to disassociate before reaching the hindgut. The dosage can be reduced by microencapsulation of the ingredients. In this study, a 63-day trial was conducted to assess the effects of OA and OS (COMP) microencapsulated (ENCAP) with fat (HF), fat + alginate (HA), wax esters – (WE), and HA and WE (HAWE) on performance, digestive enzyme, immune, and resistance to Vibrio parahaemolyticus. A positive control (PC, 200 g/kg fishmeal - FM) and a negative control (NC, 130 g/kg FM) diet were formulated. Eight other diets were formulated supplementing NC diet with microencapsulated OA (OAHF, OAHA, OAWE, OAHAWE) and OS (OSHF, OSHA, OSWE, OSHAWE). Among the ENCAPs, significant difference was observed in serum malondialdehyde (P = 0.026) where HF showed the lowest level (6.4 ±0.3 mmol/L). Significant interactions between COMP and ENCAP were observed in lipid deposition (P = 0.047), serum alkaline phosphatase and acid phosphatase (P < 0.0001), and hepatopancreatic and serum phenol oxidase (P < 0.0001). Despite no differences, 96-h mortality during pathogenic Vibrio parahaemolyticus challenge in all treatment diets (45% - 56%) was lower compared to the NC diets (63%). In conclusion, use of HF microencapsulated OA diets could provide improved performance and disease resistance that could contribute to the reduction of antibiotic use by the shrimp industry.

Construction 4.0 is witnessing exponential growth in Digital Twin (DT) technology developments and applications, revolutionizing the adoption of Building Information Modelling (BIM) and other emerging technologies used throughout the lifecycle of the built environment. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, DT enhances this with real-time data for cyber-physical integration, enabling live asset monitoring and better decision-making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study presents a holistic review of the existing research focusing on the critical components responsible for developing DT applications in the construction industry. The study identifies five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, nontechnical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices.

Gas sensing is crucial for detecting hazardous gases in industrial environments, ensuring safety and preventing accidents. Additionally, it plays a vital role in environmental monitoring and control, helping to mitigate pollution and protect public health. Integrated photonic gas sensors are important due to their high sensitivity, rapid response time, and compact size, enabling precise recognition of gas concentrations in real-time. These sensors leverage photonic technologies, such as waveguides and resonators, to enhance performance over traditional gas sensors. Advancements in materials and fabrication techniques could further improve their efficiency, making them invaluable for environmental monitoring, industrial safety, and healthcare diagnostics. In this review, we delved into photonic gas sensors that operate based on the principles of evanescent field absorption (EFA) and wavelength interrogation methods. These advanced sensing mechanisms allow for highly sensitive and selective gas detection, leveraging the interplay of light with gas molecules to produce precise measurements.

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

Biogeochemical models estimate soil organic carbon (SOC) sequestration, crop growth, and yield. The DeNitrification and DeComposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice-wheat rotation under organic/inorganic fertilizations with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC under-predicted harvestable grain C-biomass of rice by 29.22% to 42.14% and over-simulated grain C-biomass of wheat by 55.01% with equal amounts of NPK and animal manure applied under CT. However, after calibration by adjusting default values of soil/crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (only -2.81% to -6.17% less). DNDC also predicted effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha-1). DNDC simulated SOC trends for rice-wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice-wheat systems.

The efficiency of a new composite material of the layered double hydroxide (LDH) of ZnMgAl and rice husk biochar (RHB) for the removal of Cu(II) and Pb(II) ions from synthetic wastewater was investigated in this study. The images of scanning electron microscope showed extremely fine crystalline LDH particles decorated on the rough surface of the RHB while the succesful formation of the composite adsorbent (LDH/RHB) was confirmed by the corresponding energy dispersive X-ray and the Fourier-transform infrared spectroscopy. An equilibrium contact time of 30 and 15 min for Cu2+ and Pb2+, respectively, was proposed for the optimum performance of the batch adsorption process. The dose of the LDH/RHB adsorbent was optimized at 0.4 g yielding maximum adsorption capacities of 117 and 124 mg g-1 for Cu2+ and Pb2+, respectively, with corresponding maximum removal efficiencies of nearly 94% and 99%. The initial meta concentrations was optimized at 50 or 60 mg L-1 corresponding to maximum removal efficiencies or adsorption capacities. For the changing initial metal’s concentration, the removal efficiencies remained unchanged for up to 50 mg L-1 and decreased by about 50% by increasing the initial concentrations from 50 to 100 mg L-1. The adsorption capacities observed a linear increasing trend by increasing the initial concentrations from 5 to 60 mg L-1 and remained unchanged afterward up to 100 mg L-1. A solution pH of 6.0 yielded optimum results with an increasing trend in adsorption capacities and percentage removal by changing the solution pH from 2.0 to 7.0. Based on the best-fit of the pseudo second-order kinetic model to the experimental data, the chemisorption was suggested to be the controlling mechanism of adsorption. The fitting of the Langmuir model suggested a monolayer sorption of Cu2+ and Pb2+ in addition to a heterogeneous adsorption as supported by the fitting of the Temkin isotherm. The application of the Dubinin–Radushkevich isotherm proposed a physical adsorption (mean free energy of adsorption less than 8 kJ mol−1) of both heavy metal ions on the surface of the LDH/RHB adsorbent.

Bifenthrin is a synthetic pyrethroid insecticide. It is used worldwide against different agricultural pests. Nervous system (NS) is the primary target organ for the pyrethroid group of insecticide which causes hyper excitation. The aim of this study to determine the effects of bifenthrin on the brain of rabbits. The animals were treated for 14 days with three different doses (1/5th, 1/10th and 1/15th of LD50) of bifenthrin. Neurotransmitters and Histopathological alterations were observed at the end of the experiments. Neurochemical parameters were checked 24 hours after the last dose. Significant changes in the level of biogenic amines were observed. The levels of epinephrine (EPN), dopamine (DA), 3, 4 dihydroxyphenylacetic acid (DOPAC) were decreased as compared to controls group with the increase in insecticide. However, norepinephrine (NE) and homovanillic acid (HVA) levels were found to be significantly higher than those of the controls. A significant reduction in the activity of the acetyl cholinesterase enzyme was found in the frontal cortex’s erythrocyte, plasma and brain of rabbit with the increase in administered bifenthrin. The oxidative stress showed significantly decrease in the activity of glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT) and glutathione S- transferase (GST) with the increase in dose of bifenthrin. Histopathological study of brain tissues showed normal GC (glial cells) and NC (neuronal cells) in the frontal cortex of brain tissue of control groups while the treated groups showed evacuation (odema), elongation of pyramidal nucleus, glial cell damage and brain haemorrhage.

A total of five protoclones were successfully cultured on PDA medium out of regenerated twenty two colonies of Termitomyces protoplast and further studied. Liquid MYG grown mycelial tissue is used for protoplast isolation by enzymatic digestionin a mixture containing Lysing enzyme 2% and Cellulase R10 2% in 0.6 M mannitol. The incubation conditions like temperature, shaking and time were standardized at 24ºC, 60 rpm and 10 hours, respectively for healthy protoplasts liberation. The purified protoplasts showed an average yield of 1.2 × 10⁷ cells/gm tissue with 31.60 ± 9.31% regeneration efficiency on specific medium and 77.12 ± 2.72% viability by FDA test. Four ISSR primers were used in this study resulting a total of 27 reproducible bands with mean value of 6.75. They showed similar banding pattern in all the lines with zero percent polymorphism ranged from 280 bp–2700 bp. The amplified rRNA-ITS gene showed ~600 bp size in gel and found a single restriction site for enzyme HaeIII in all the protoclones and parent with similar fragment size in all.

The comfort of human life depends on the quality, size, and reliability of the infrastructure projects. In the infrastructure systems, rapid growth is found, where the economic and sustainable impact has become a topic of significant concern for policies and government officials. To achieve con-straints of sustainable development, all the policies and actions over the infrastructure project's life cycle must be assessed. Decision-makers have adopted approaches for economic, social, and en-vironmental initiatives through Life Cycle Assessment (LCA) and Life Cycle Cost Analyses (LCCA) of infrastructure projects. The purpose of this review is to highlight the impact of per-forming LCA and LCCA in infrastructure projects. To achieve this goal, a systematic literature review methodology is adopted in which renowned databases, i.e., Web of Science, Science Direct, Emerald and Scopus were selected to extract the relevant literature. Using the PRISMA approach, 1251 publications were identified which were then filtered and 55 documents were included in the final review. In the extracted publications most, researchers were biased toward LCA and LCA individually, whereas few focused on integrated LCA and LCCA. The researchers assessed the costs and impact associated with the infrastructure project while there were less focused on the environmental cost. Besides this, techniques of economic, social, and environmental growth of infrastructure projects have been emphasized during the design phase because of substantial relations between infrastructure design and operation management. Moreover, a conceptual framework has been developed that will assist the decision-makers to consider the effects of LCA and LCCA on various aspects of the infrastructure project and how it impacts sustainability. In the last, a case study was performed to assess the developed framework with the incorporation of environmental impact cost.

Cyber security plays an important role to protect our computer, network, program and data from unauthorized access. Intrusion detection system (IDS) and intrusion prevention system (IPS) are two main categories of cyber security, designed to identify any suspicious activities present in inbound and outbound network packets and restrict the suspicious incident. Deep neural network plays a significant role in the construction of IDS and IPS. This paper highlights a novel IDS using optimized convolution neural network (CNN-IDS). An optimized CNNIDS model is an improvement over CNN which selects the best weighted model by considering the loss in every epoch. All the experiments have been conducted on the well known NSL-KDD dataset. Information gain has been used for dimensionality reduction. The accuracy of the proposed model is evaluated through optimized CNN for both binary and multiclass categories. Finally, a critical comparison has been performed with other general classifiers like J48, Naive Bayes, NB tree, Random forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolution Neural Network(CNN). All the experimental results demonstrate that the optimized CNN-IDS model records the best recognition rate with minimum model construction time.

Blockchain innovation stands out enough to be noticed and adopted in various countries and organizations around the world. Many businesses, including finance, medical services, inventory networks, security, libraries, and the internet of things, are currently under attack. For the benefit of the blockchain, many businesses incorporate blockchain technology into their frameworks. Despite its solidarity, blockchain faces a few challenges in security, protection, adaptability, and other areas. This paper examines the forward leap in blockchain innovation, as well as its applications and challenges. While many blockchain papers focus on digital currencies, IoT, and security, this paper focuses on the overall best in a class of blockchain innovation, its new twists and turns, and choices, particularly in areas other than cryptographic forms of money. The investigators' goal is to provide a thorough audit of the cryptography underlying blockchain to better understand the innovation. The examiners also conduct general research on people and venture blockchains, as well as future exploration opportunities and their implications for blockchain innovation.

As urban populations and transport demand expand, Pakistani metropolitan centers face traffic congestion and environmental challenges. This research aims to assess carpooling (CP) to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city with a high percentage of personal cars in Pakistan, to assess the travelers’ perceptions, and public acceptance of CP and determine the effects on traffic congestion, fuel consumption, and CO2 emissions. The investigation of travelers' perceptions towards CP was conducted through a stated preference questionnaire survey. A total of 700 responses from one million population of Islamabad were collected as per Sloven's Formula. SPSS and Smart PLS software were used for data analysis. Various factors influencing peoples’ travel perceptions, mode choice, and tendency to carpool, were categorized to evaluate the relationship between travelers’ perception and the tendency to carpool in the presence of mode choice as a mediating variable. Confirmatory factor analysis was conducted in SMART PLS to refine the number of scale items for each variable. One-way ANOVA, step-wise and mediated multiple regression analysis was conducted in SPSS to check the direct and mediating relationship between different variables. Moreover, the probability of reducing traffic congestion and assessing fuel reduction resulting from CP was also analyzed. The findings imply that travelers’ mode choice acts as a mediator between the relationship between travelers’ perception and tendency to carpool. Furthermore, results predicted that CP might reduce 0.25 million cars on the road, about 33.6% of the total private vehicles. Thereby, saving up to 546,810 barrels per year, about 3.42% of Pakistan's total petrol consumption. The findings of this study could be helpful for transport planners, environmentalists, and policymakers to implement carpool systems. Thus, reducing traffic congestion and harmful CO2 emissions and maintaining a sustainable environment.

The oviparous blow fly Calliphora vicina has shown potential as a managed pollination species to support honeybee (Apis mellifera) usage in Australian horticulture. This blow fly species lays eggs onto carrion soon after death and the resultant larvae that hatch are necrophagous and feed on the decomposing tissues of the dead animal. Newly hatched larvae of this fly were provided with plant-based meals (soya bean and canola) and compared with animal-derived meatmeal to determine if plant-based meal could be used to mass rear this blow fly species. Both soyabean and canola meal media did not support the survival of any larvae through to adult eclosion. The addition of only 10% whole egg powder to the plant-based meals enabled survival to eclosion of 39% and 13% on soyabean and canola based medias respectively compared with 76% on animal-based meatmeal with 10% whole egg powder. Larvae fed livestock meatmeal with 10% whole dried egg powder had the fastest development to the pupal stage, the highest pupation rate, the heaviest pupae and the highest subsequent adult eclosion. This study concluded that the use of plant-based meals in the mass rearing of the blow fly C. vicina was neither viable, nor economically viable.

A new class of nano-cube core-shell heterostructures containing Ag coating on the top of WO₃ was fabricated. Physical vapor deposition was used to produce WO₃ based nano-heterostructures. All kind of wet toxic chemical process was avoided to make the process simple and contaminant free. Sputtering of WO₃ and a subsequent thermal annealing process was done to create nano-cubes of WO₃. After that, sputtering of Ag was performed to form the Ag-WO₃ core-shell nano-heterostructures (CSNH). The CSNHs were characterized using field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD) and UV-vis spectroscopy. The morphologies, elemental analysis, interfaces, crystallinity, phases, and chemical compositions were analyzed. The bottom-up growth of WO₃ nanocubes was studied using different time periods at 900°C. Ag coating was also studied before and after annealing. Finally, an optical property (band gap) was also analyzed using Tauc plot derive from absorption spectra. The tailoring the band gap of WO₃ from ~2.9eV to ~ 2.45 eV was observed while Ag-WO₃ CSNH formed.

Medical waste management is crucial issues for keeping our life safe and safety. Without effective measures for waste management, we cannot keep ourselves safe from different types of contagious diseases; viruses, bacteria. Knowledge sharing assists healthcare staff in identifying and mitigating hazards on appropriate handling, segregation and disposal medical waste. The aim of the study is to identify a multifaceted analysis of knowledge sharing, training, communication and attitudes for effective medical waste management. This study selected 153 medical workers randomly, including nurses, lab technologists, and cleaners, from hospitals, diagnostic centers, and clinics in the northern part of Bangladesh. The study design of this research is mixed-methods. This research adopts a survey and interview as a research strategy. The samples are collected randomly. SPSS software packages, MS Excel Office, and PLS-SEM are used to analyze data. Most of the respondents always share their knowledge face-to-face according to the findings. The result also shows that there are positive relationship between sources of knowledge (p: 0.046), awareness and knowledge (p: 0.049), attitudes of waste management (p: 0.025) and effective medical waste management. However, there are no relationships between communication and collaboration (p: 0.943), the practice of waste management (p: 0.155), supervision and staff monitoring (p: 0.527), training and education (p: 0.656) and effective medical waste management. Its focus on the northern part of Bangladesh introduces regional specificity, potentially limiting the applicability of results to other regions. The knowledge-sharing practices for waste management among medical staff are examined within this research, along with the relationship between attitude, awareness, and knowledge, staff supervision and monitoring, practice, training, and education and effective medical waste management that have not been studied before home and abroad. This analysis highlights the critical need for an integrated approach to medical waste management, emphasizing the importance of continuous education, open communication, and cultivating a culture that prioritizes environmental sustainability. The insights from this study offer practical recommendations for healthcare institutions to improve their medical waste management practices, ultimately leading to enhanced health outcomes and better environmental protection.

Social networking applications such as Twitter have increasingly gained significance in terms of socio-economic, political, and religious as well as entertainment sectors. This in turn, has witnessed a wide gamut of information explosion in the social networking realm that can tend to be both useful as well as misleading at the same point of time. Spam detection is one such solution that caters to this problem through identification of irrelevant users and their data. However, existing research has so far laid primary focus on user profile information through activity detection and relevant techniques that may underperform when these profiles exhibit characteristics of temporal dependency, poor reflection of generated content from the user profile, etc. This is the primary motivation for this paper that addresses the aforementioned problem of user profiles by focusing on both profile information and content-based spam detection. To this end, this work delivers three significant contributions. Firstly, exhaustive use of Natural language processing (NLP) techniques has been rendered towards creation of a new comprehensive dataset with a wide range of content-based features. Secondly, this dataset has been fed into a customized state-of-art hybrid machine learning model that has been exclusively built using a combination of both machine learning and deep learning techniques. Extensive simulation based analysis not only records over 98% accuracy but also establishes the practical applicability of this proposal by proving that modeling based on the mixed profile and content-generated data is more capable of spam detection in contrast to each of these standalone approaches. Finally, a novel methodology based on logistic regression is proposed and supported by analytical formulations. This paves the way for the custom-built dataset to be analyzed and corresponding probabilities to be obtained that differentiate legitimate users from spammers. The obtained mathematical outcome can henceforth be used for future prediction of user categories through appropriate parameter tuning for any given dataset. This makes our method a truly generic one capable of identifying and classifying different user categories.

Blockchain technology has become one of the most popular technologies for maintaining digital transactions. From the foundation of Bitcoin to the now predominant smart contract, blockchain technology promises to induce a shift in thought about digital transactions in many fields, such as energy, healthcare, Internet of Things, cybersecurity, financial services and the supply chain. Despite blockchain technology offers many cryptography advantages such as immutability, digital signature and hashing; it has suffered from several critical cybersecurity threats and vulnerabilities. In this paper, we build upon the previous studies on vulnerabilities and investigates over 60 real cybersecurity incidents that have been happening on the blockchain networks between 2009 and 2019. We categorise those incidents against the key cybersecurity vulnerabilities in blockchain technologies; and have developed a taxonomy that captures five types of cybersecurity threats and vulnerabilities based on five main players in blockchain. The outcome of this research prompted concerns and research direction in developing countermeasures to alleviate these risks.

Spontaneous parametric downconversion At the heart of all quantum optics experiments is called as Spontaneous parametric downconversion. This is a downconversion process when the output frequency is smaller than the input frequency. The whole process is Spontaneous. Experimentally we have to downconvert the photons and convert them into pairs of a photon. We use the Beta-barium borate(BBO) crystals to downconvert the coming violet-blue laser. We find the maximum number of counts on detectors and their coincidence. When the BBO crystals is well aligned, we find no difference in detecting vertically or horizontally polarized photons because they are orthogonal to each other. Proof of existence of photons We want to investigate the properties and verification of quantization of light. Experimently we want to establish the existence of single photon which shows the grainy-ness of individual photon quantum mechanically and shows wave like behaviour classically. We get the coincidences counts on detectors with addition of detector B’ by using PBS in the path of signal beam. Experimentally we prove the Quantum as well as classical nature of light by the measurement of second-order degree coherence in light g²(0). For classically the g²(0)≥1 and quantum mechanically g²(0)=0.When we add the delay time interval of 1ns after reflected and transmitted photons from PBS signal beam. The delay generator did not create a significant difference in the coincidence counts detection in the value of g²(τ ). Single photon interference and quantum eraser we want to observe the interference by making the single photons pass through the interferometer, and we will see the interference of the single photons. If the light passes through the interferometer, the visibility of the interference pattern to which extent these fringes are observed is dependent on the extent to which the ’which-way’ or ’which-path’ information is available to the experimenter. If the observer knows the information about the ’which-way’ of the light path, then we cannot observe the inference pattern. Because we only can make an interference pattern if we erase the which-way information of the light path. If you erase the information about the’ which-way,’ we can observe a very high interference pattern. We can erase the information about the light path’s ’which-way’ or ’which-path’ by using a polarizer and orienting at 45^o. In this way, we can observe a very high pattern. If we introduce the angle of the polarizer at 0^o or 90^o, we see very low interference.

The gut microbiome offers numerous advantages to the hosts, through a variety of physiological mechanisms like firming up gut integrity or shaping the intestinal epithelium. Studies showed 2172 species are identified and isolated from human beings, ordered into twelve different phyla, of which 93.5% belonged to Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria. Out of the 12, three phyla are identified. Human microbial communities are affected by the host external factors like dietary patterns, lifestyle factors, antibiotic usage, internal factors gender, host genotype, age and race. To analyze the dysbiosis and bacterial variation in different areas of District Faisalabad 10,000 individuals of age 25–35 years were recruited from the two different rural and urban localities. The oral swab and fecal sample were collected according to prescribed manner. The collected samples from demographically and geographically distinct regions of District Faisalabad were subjected to DNA extraction and quantification. The amplification of 16s rRNA was carried out by PCR and visualized through a transilluminator. The results revealed significant elevated, % abundance of fecal microbial profile Blautia spp, Firmicutes, Ruminococcaceae, in rural population however non-significant difference % abundance of total Bifido, E. coli, Faecali bacteria, total Lactobacillus were identified in both demographically distinct regions. The pathogenic Gamma Protebacteria, Fusobacterium, Campylobacter coli were not detected in both study areas. Out of five orally detected microbial profiles, elevated % abundance of Firmicutes was detected in rural population however non-significant difference was observed in % abundance of total Bifido and E. coli in both populations. Total Lactobacillus and Fusobacterium were not detected in oral sample of both urban and rural population. Conclusively, this research accomplished the potential effects of demographic and geographical induced dysbiosis in the normal microbiome of the oral cavity and intestine that might cause dysbiosis associated disorders.

Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

There are many pollution and environment problem in the human ecosystem. There are different methods are used to removal of sulfur from sour gases for example Basic Claus process and Modified Claus process . There are different chemical software are used for simulation and optimization of Claus process for example Aspen Plus and Chemcad ECT. The Gibbs free energy method is introduced and model of Claus process. There are new parameter are introduced in reaction furnace to reduce the error from 33% to 7 %. The waste heat boiler is installed at the reaction furnace in which high pressure stream is produced and study the decomposition the hydrogen sulphide. The new rate of reaction is introduced of the enhancement of H2 production in chemical process. The simulation of reaction furnace in Aspen plus software is the maximum utilization of process. Due to suitable operating condition of reaction furnace is caused the maximum destruction of ammonia gas in the reactor. When we are increasing the oxygen concentration and temperature of feed is causing decreasing the ammonia production in reaction furnace. It is below than acceptance value of ammonia is 150 ppm in the reaction furnace. The presence of oxygen components, Sulfur oxide, hydroxide components are effect on decreasing the amount of ammonia in furnace and temperature is about at 1350⁰C. It is noted that when the production of sulfur recovery is decrease in Claus process and the production of carbon monoxide is increase in the thermal section at the existence. Now we are work on parametric studies of furnace that could be causes the production of ammonia destruction and CO emission in the Claus process. Due to optimize the reaction furnace parameter are help to get large of sulfur production, ammonia gas destruction, increased the catalyst life and decreased of dangerous gases.

This study investigated the ability of a HYDRUS 1D model for predicting the vertical distribution of potassium iodine (200 ppm) in soil columns after amendment with five different common remediation materials (gypsum, lime, fly ash, charcoal and sawdust) at a rate of 2.5% (w/w), relative to an unamended control soil. Results shows that relative to the unamended soil, iodine leaching was decreased by all amendments but that the magnitude of the decreases varied with the soil amendment applied. Iodine content was highest in the upper layer of the soil columns and decreased progressively with soil depth. The model was evaluated via comparison of the model simulated values with measured values from the soil column studies. The results showed that the HYDRUS 1D model efficiency was near to 1, indicating that the stimulated results were near to the measured values. Therefore, this study showed that iodine leaching through a soil could be ascertained well using a HYDRUS 1D model. The model over predicted iodine leaching, resulting to a weak correspondence between the simulated and the measured results for iodine leaching. This suggests that the HYDRUS-1D model does not explain accurately different organic and inorganic amended soil and the preferential flow that occurs in these columns. This may be due to the fact that Freundlich isotherm, which is part of the transport equations, does not sufficiently describe the mechanism of iodine adsorption onto the soil particles. This study would help to select amendments for an effective management strategy to reduce exogenous iodine losses from agro-ecosystems. This would also improve understanding of iodine transport in soil profile.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

Social, cultural, environmental and economic factors closely regulate the selection, allocation and consumption of maternal diets. We developed a nutrition behaviour change intervention to promote a balanced diet in pregnancy through practical demonstration in rural Bangladesh and tested the impact with a cluster randomised controlled trial. This paper presents the findings of the process evaluation and describes the strategies that worked for intervention compliance. We conducted in-depth interviews with pregnant women, women who birthed recently, and their husbands; focus groups with mothers and mothers-in-law; key-informant interviews with community health workers, and observation of home visits. We identified six key areas within the intervention strategy that played a crucial role in achieving the desired adherence. These included practical demonstration of portion sizes; addressing local food perceptions; demystifying animal-source foods; engaging husbands and mothers-in-law; leveraging women’s social networks; and harnessing community health workers’ social role. Practical demonstration, opportunity to participate and convenience of making of the plate with the food available in their kitchen or neighbours’ kitchen were the most commonly mentioned reasons for acceptance of the intervention by the women and their families. The balanced plate intervention helped women through practical demonstration to learn about a balanced meal by highlighting appropriate portion sizes and food diversity. The women needed active involvement of community health workers in mobilising social support to create an enabling environment essential to bring changes in dietary behaviours. Programs to promote a nutritious maternal diet should focus on encouraging the use of healthy foods through practical demonstration of portion sizes and engagement of the women and family instead of replicating the traditional information-based counselling.

During December 2019, a novel coronavirus named SARS-CoV-2 has emerged in Wuhan, China. The human to human transmission of this virus has also been established. The virus has so far infected more than 2 million people and spread over 200 countries. The World Health Organization (WHO) has declared COVID-19 a global health emergency due to its spread well beyond China. It has been established that this virus originates from bats and uses an intermediate host for transfer to humans. The knowledge about the intermediate host is important to find the virus shuttle mechanism to stop future outbreaks. For this, the genetic and structural analysis of coronaviruses spike proteins was performed using a computer-assisted approach.To conduct the In silico analysis, 43 sequences of spike protein belong to different species were retrieved from the NCBI nucleotide database. Pairwise and multiple sequence alignments were performed to check the similarities and differences of the retrieved sequences. Moreover, to highlight relationships among different species, phylogenetics analysis was performed using the MEGA software tool. In the end, protein structure alignment (superimposition) was performed against the reference structure by UCSF Chimera software. The results highlighted that the maximum similarity of human protein was found against Bat and Pangolinsequences. Moreover, among Bat and Pangolin, the highest similarity was found against pangolin based on phylogenetics analysis. These results suggest that SARS-CoV-2 transfers from bats to humans through pangolins.

This article is a continuity of prevouse two reserch in daylight harvastion control that were deigned using classical control apoproach and then fuzzy logic technique, In this article ANN controller is designed and its response is compared with the performance of the other two controllers. Detailed compression for the different error type is illustrated, however, the results show that the three controller operate with satisfactory accuracy.

This review chronicles the indirect transmission method which seems to be overlooked by most people and makes attempts to clearly document the various transmission ways with a hope that such information may strengthen the knowledge base of researchers towards methods of eradicating the pandemic. Current knowledge of transmission and exposure of SARS-CoV-2 has been explained. Various researchers have put forward different ways of exposure and transmission. Literature does not reveal whether the indirect transmission route is the dominant one. However total lockdown could be a veritable means to reduce both direct and indirect transmission routes. In many countries where the indirect transmission has been reduced, the scourge of the virus is less. The work creates awareness on the need to watch out for those routes of transmissions that may not be popular and suggested key knowledge gaps that needs to filled.

Hearing impairment is an immensely diagnosed genetic cause, 5% of the total world population effects with different kind of congenital hearing loss (HL). In third-world countries or countries where consanguineous marriages are more common the frequency rate of genetic disorders are at its zenith. Approximately, the incidence of hearing afflictions is ostensibly 7-8:1000 individuals whereas it is estimated that about 466 million peoples suffer with significant HL, and of theses deaf cases 34 million are children’s up to March, 2020. Several genes and colossal numbers of pathogenic variants cause hearing impairment, which aided in next-generation with recessive, dominant or X-linked inheritance traits. This review highlights on syndromic and non-syndromic HL (SHL and NSHL), and categorized as conductive, sensorineural and mixed HL, which having autosomal dominant and recessive, and X-linked or mitochondrial mode of inheritance. Many hundred genes involved in HL are reported, and their mutation spectrum becomes very wide. Mapping of pathogenic genes in consanguinity family is facilitated to understand the disease history. Review presents the bases of HL and also focused on various genetic factors that cause deafness like the basics of genetic inheritance, and classic and well-characterized inherited factors of it. It also overviews the application of linkage analysis, SNPs genotyping and whole exome sequencing methods, in mapping and identification of new locus, causative genes and their variants in families inherited with HL. Conclusively, this review supports researchers in understanding the location of chromosome, the causative genes and specific locus which causing deafness in humans.

Climate change is impacting the spread/intensity of vector-borne diseases, including malaria, and accelerating evolutionary/adaptive changes in vector species. These changes including chromoso-mal inversions and overexpression and/or changes in allele frequencies of thermotoler-ance-associated genes, may facilitate insecticide resistance through pleiotropy. This study investi-gated the impact of thermotolerance on pyrethroid resistance in four populations of malaria vector An. gambiae, from savanna/sub-Sahel of northern Nigeria. Anopheles coluzzii and An. gambiae were the only malaria vectors found, sympatric in all the sites, with the former species predominant. High thermotolerance was observed, with no mortality at 38°C, and LT50 of ~44°C. Significantly high permethrin resistance was observed (mortality <50%) in heat-hardened (44°C) larvae from two sites, BUK and Pantami, compared with control, and heat-hardened adult females from Auyo (mortality = 3.00%±1.20, χ2 = 5.83, p<0.01) compared with control (12.00%±4.65). The 2La chromosomal inver-sion was detected at ~50% in larvae and 58% in adult females. Significant association was observed (OR = 7.2, p<0.03) between permethrin resistance and 2La/+a rearrangement compared with 2L+a/+a, in BUK larvae. For all sites permethrin resistance correlated with 2La/a homozygosity in adult fe-males [OR = 5.02, p=0.01). qRT-PCR identified 6 genes commonly induced/overexpressed, including heat shock protein 70 (AGAP004581) which was 2468x and 5x overexpressed in heat-hardened and permethrin-resistant females, respectively, trehalose-6-phosphate synthase (AGAP008227), and ionotropic glutamate receptor genes, IR25a (AGAP010272) and IR21a (AGAP008511). This study highlights challenges associated with insecticide-based malaria vector control, and the epidemiological significance of taking climate variables into account for design/choice of control measures.

Biomedical implants are the need of this era due to the increase in number of accidents and follow-up surgeries. Different types of bone diseases such as osteoarthritis, osteomalacia, bone cancer, etc. are increasing globally. Mesoporous bioactive glass nanoparticles (MBGNs) are used in biomedical devices due to their osteointegration and bioactive properties. In this study, silver (Ag) and strontium (Sr) doped mesoporous bioactive glass nanoparticles (Ag-Sr MBGNs) were prepared by a modified Stöber process. In this method, Ag+ & Sr2+ were co-substituted in pure MBGNs to harvest the antibacterial properties of Ag ions, as well as pro-osteogenic potential of Sr2 ions. The effect of the two ion concentration on morphology, surface charge, composition, antibacterial ability, and in-vitro bioactivity was studied. Scanning electron microscopy (SEM), X-Ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) confirmed the doping of Sr and Ag in MBGNs. SEM and EDX analysis confirmed the spherical morphology and typical composition of MBGNs, respectively. The Ag-Sr MBGNs showed a strong antibacterial effect against Staphylococcus carnosus and Escherichia coli bacteria determined via turbidity and disc diffusion method. Moreover, the synthesized Ag-Sr MBGNs develop apatite-like crystals upon immersion in simulated body fluid (SBF), which suggested that the addition of Sr improved in-vitro bioactivity. The Ag-Sr MBGNs synthesize in this study can be used for the preparation of scaffolds or as a filler material in the composite coatings for bone tissue engineering.

A reliable and affordable energy supply is a fundamental prerequisite for reducing poverty, promoting investment, and boosting economic growth in the developing world. Among the different challenges that developing countries face, chronic energy crises are harrowing. The crises result from the unsatisfactory state of the central grid, a misguided energy mix, and ill-informed policies, among other things. The possibility of solving energy crises through a variety of alternative solutions is worth exploring. This review discusses two paths of energy development side by side: a traditional “hard” path of energy development (i.e., central grid extension powered by fossil fuels and nuclear energy expansion) and a relatively recent “soft” path of energy development, which is based on energy conservation and the deployment of renewable energy resources. This paper focuses on one central axis of the discussion: centralization vs. decentralization. This discussion, in turn, has technological, economic/business, and political dimensions. Finally, the paper discusses the significance of the debate from meeting the developing world’s energy demands. The paper intends not to prefer one or another path of energy development, nor it gives recommendations on diffusing or adopting those development paths. Instead, it explores the literature’s central arguments that might help frame the questions for further research. While this debate could be used to ask interesting questions that might help solve the energy crisis in the developing world, the discussion informs countries to advance policies specific to their circumstances under the umbrella of a sound and thoughtful energy productivity policy framework.