No single evolutionary event has been identified as the cause for the final emergence of our species. I propose that a mutation on CD155 receptor gene occurred to establish a symbiosis with poliovirus, which exerted its beneficial impact via RNA dependent non-genetic transgenerational inheritance, which caused a qualitative enhancement of cognitive functions. I posit that this mutation occurred in what we call, Anatomically Modern Humans, our immediate ancestor species and that the disruption of this symbiosis causes autism spectrum disorder. Positive selection of CD155 to the extent of becoming a species defining characteristic, the chronology of autism spectrum disorder prevalence increase and continued increase, the multigenerational nature of RNA inheritance, the universal infection of humans by poliovirus and a very low associated mortality rate, and several other factors support this hypothesis. Specific genetic, epidemiological and sperm miRNA content studies are suggested to test this hypothesis.

Cadmium(II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic cadmium(II) ions from synthetic solutions. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium(II) ion is obtained at pH 6. The thermodynamic parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd(II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

In the past decade, considerable efforts have been devoted to the development of semitransparent organic solar cells. Numerous materials and architectures have been proposed for the future commercialization of these devices. Among these, the use of ternary active layers demonstrated a great promise for the development of efficient semi-transparent organic solar cells with the potential for future applications including self-powered greenhouses and powered windows. Researchers seek alternative solutions to the trade-off between device power conversion efficiency (PCE) and average visible transmittance (AVT), with photoactive materials being the key parameters that govern both (PCE) and (AVT) as well as device stability. Several new organic materials, including polymers and small molecules, were synthesized, and used in conjunction with a variety of techniques to achieve semitransparent conditions. In this review paper, we will look at the working principle and key parameters of semi-transparent organic solar cells, as well as the methods that have been used to improve the performance of ternary-based semi-transparent organic solar cells. The main approaches were concluded to be spectral enhancement and transparency increment of the active layer through bandgap tuning, utilizing novel organic semiconductors, and design architecture of the active layers.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

Organic solar cells are contributing extensively to the production of future large area and cost-effective sources of renewable energy with a compact environmental impact. Moreover, due to tunable optical properties of the active medium, the semi-transparent organic solar cell can be fabricated and become a promising nominee for building Integrated Photovoltaics (BIPV), vehicles, and mobile electronic devices. Therefore, three newly synthesized polymers used as were host materials for the active medium and blended with beetroot dye. Hence, based on BHJ structure two ternary systems have been constructed and utilized to fabricate semitransparent solar cells and characterized. The results show that the natural dye has a crucial impact on solar cell characteristic parameters. Consequently, large value of V_OC close to 1V were obtained for ternary systems and acceptor material named PINDOLE exhibits potentials for electron transport layer (ETL) and shows an improvement in both fill factor and device efficiency.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

Dysferlinopathies comprise a spectrum of muscular dystrophies characterized by progressive muscle weakness and degeneration due to mutations in the DYSF gene, which encodes the dysferlin protein critical for muscle membrane repair. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We explore the phenotypic heterogeneity of dysferlinopathies, highlight the incomplete understanding of genotype-phenotype correlations, and discuss the implications of various DYSF mutations. Furthermore, we examine the utility of animal models in elucidating disease mechanisms and the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease's progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. By culminating the complexities inherent in dysferlinopathies, this article emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

Fibrodysplasia Ossificans Progressiva (FOP) is an enigmatic, ultra-rare genetic disorder characterized by progressive heterotopic ossification, wherein soft connective tissues undergo pathological transformation into bone structures. This incapacitating process severely limits patient mobility and poses formidable challenges for therapeutic intervention. Predominantly caused by missense mutations in the ACVR1 gene, the disorder has hitherto defied comprehensive mechanistic understanding and effective treatment paradigms. This write-up offers a comprehensive overview of the contemporary understanding of FOP's complex pathobiology, underscored by advances in molecular genetics and proteomic studies. We shed light on targeted therapy, spanning genetic therapeutics, enzymatic and transcriptional modulation, stem cell therapies, and innovative immunotherapies. We also focused on the intricate complexities surrounding clinical trial design for ultra-rare disorders like FOP, addressing fundamental statistical limitations, ethical conundrums, and methodological advancements essential for the success of interventional studies. We advocate for the adoption of a multi-disciplinary approach that converges bench-to-bedside research, clinical expertise, and ethical considerations to tackle the challenges of ultra-rare diseases like FOP and comparable ultra-rare diseases. Overall, this article serves a dual role: as a definitive scientific resource for ongoing and future FOP research and as a call to action for innovative solutions to address methodological and ethical challenges that impede progress in the broader field of medical research for ultra-rare conditions.

Hepatitis B and C are major health issues in developing countries such as Pakistan and Afghan-istan. The aim of this study was to determine the prevalence and effectiveness of a screening program for hepatitis B and C in a region with no existing programs, and to estimate their prev-alence in the general population in Khyber Pakhtunkhwa Province, Pakistan, as well as in Afghan refugees or migrants. A prospective cohort study was done in the general population of Peshawar and its adjacent districts, as well as migrants from neighboring provinces of Afghani-stan, who presented to our tertiary-care health facility. A Microsoft Excel registry was created for data collection, which were analyzed using IBM SPSS via descriptive analysis, normal distribution curve histograms, and chi-squared tests. A total of 9563 individuals were screened for hepatitis (5894 males and 3669 females), including Afghan migrants in Peshawar and surrounding districts. 876 individuals tested positive for hepatitis, with 538 positive for hepatitis B (383 males and 155 females) and 330 positive for hepatitis C (198 males and 134 females). Eight individuals had a hepatitis B and C co-infection. Among the study population, the prevalence rates were 2.2% for hepatitis B and 2.3% for hepatitis C, of which the Afghan migrants accounted for 2.7% and 0.5%, respectively. According to the gender-based distribution, hepatitis B was more prevalent in males, whereas there was no significant gender-based difference for hepatitis C. Our results highlight the need for a comprehensive approach to control hepatitis B and C in Pakistan and Afghanistan. Increased awareness, improved healthcare, and preventive measures such as screening and elimination programs to prevent severe liver diseases and eradicate hepatitis are necessary.

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

The world is facing an unprecedented socio-economic crisis caused by the novel coronavirus infection (COVID-19). It is also spreading through the import and export food supply chains. The Chinese authorities have discovered the COVID-19 virus in various imported frozen meat packages. Traceability plays a vital role in food quality and food safety. The Internet of Things (IoT) provides solutions to keep an eye on environmental conditions, product quality, and product traceability. These solutions are traditionally based on the centralized architecture, which does not guarantee tamper-proof data sharing. The blockchain is an emerging technology that provides tamper-proof data sharing in real-time. This article presents Hyperledger Fabric-based blockchain use case and a quick reference guide to develop the blockchain network for tracking and tracing the supply chain to minimize the risk of COVID-19 in the frozen meat supply chain.

In sheep raised for meat production, the relationship between external udder conformation, colostrum quality, and lamb growth rates has not received much attention. We hypothesize that ewes with a more desirable udder conformation at lambing have greater colostrum quality and increased growth rates in lambs. Fifty Suffolk ewes were used in this study. Within 6-8 hours of parturition, colostrum samples from both halves of the udder were collected and a visual scoring of the udder was conducted. Colostrum quality was measured for total protein using both optical and Brix refractometers. On day 2, day 45, and day 60 after parturition, lamb weights were recorded, and udder conformation measurements were repeated. A visual scoring system evaluating udder floor (scale 1-4), udder depth (scale 1-9), teat placement (scale 1-9), teat lesions (present or absent), and the presence of wool (present or absent) was used to assess external udder conformation. Normal udder parameters include udder depth scores of 5 or 6, udder floor score of 1 or 2, teat placement scores 4, 5, and 6, and the absence of teat lesions and wool. All ewes not meeting normal parameters were considered to have an abnormal udder. The data was analyzed using the GLM procedure. Mean total colostrum total protein was greater (p = 0.03) in ewes displaying ‘normal’ udder conformation compared with those with ‘abnormal’ (14.82±0.5 and 13.31±0.3mg/dl). Mean Brix values were also greater (p = 0.03) for ewes with a ‘normal’ udder compared to an abnormal udder confirmation, respectively (21.70±0.8 and 19.54±0.5). On day 2 after parturition, mean lamb body weight was not different between ewes with ‘normal’ and abnormal udder (5.38±0.26 vs 5.46±0.15). No difference (p > 0.05) in lamb weights were detected between ewes with normal and abnormal udder conformations on day 45 and 60 after parturition. This data provides evidence of increased colostrum total protein values and increased Brix values present in ewes with ‘normal’ udder conformation and no difference in lamb weights between udder conformations.

Rice is the grain feeds more than half of the world population. It is an important staple food provides twenty percent of the calories consumed worldwide. Food safety affects the health of consumers. Establishing an effective traceability system is one of the primary measures to protect rice quality and food safety. Blockchain-based traceability system has the characteristics of data tamper-proofing, decentralization, and co-governance. This paper proposed a rice traceability scheme based on blockchain technology that selects the critical nodes in the rice industry chain to construct a blockchain network to achieve transparent data sharing. It is meaningful for both enterprises and consumers. It can let enterprises get the real quality information of rice and related raw materials. Consumers can get the true information of rice and rice products to buy safe and healthy food. The scheme is proved feasible by simulating the traceability of the rice supply chain.

Cotton (Gossypium L.) is a major source of natural fibre for the world's textile industry and is also an important oil plant. Cotton fibre is the main source of textiles, the seeds are used for oil and the rest of the bagasse is a high-protein animal feed. In addition, more than 50 industries use cotton's so-called short fibre. Breeding studies in cotton are generally based on crosses between the best genotypes in terms of yield and fibre quality. However, the narrow genetic diversity of cultivars has a negative impact on cotton breeding programmes. For this reason, the identification of genetic resources and the disclosure of genetic diversity are important. Herein, high-resolution capillary gel electrophoresis was used to determine the genetic diversity of G. hirsutum and G. barbadense genotypes. Using 19 EST-SSR markers, a total of 47 genotypes were screened. The PIC values of the markers used ranged from 0.268 to 0.889. The mean PIC value was calculated to be 0.603. Concerning the clustering, PCoA and population structure analyses gave similar results, and the genotypes could be divided into 3 main groups. Genetic admixture with G. hirsutum was found in some genotypes of G. barbadense species. We can conclude that i) the EST-SSR markers used in this study are effective in the determination of genetic diversity, ii) The genetic diversity should be increased through the collection of genetic resources iii) genic EST-SSR markers in this study should be considered in breeding programmes by using them in QTL studies.

Artificial Intelligence (AI) in healthcare has ushered in a revolutionary era by transforming the way we provide care and reduce adverse consequences. This paper explores the field of AI applications in healthcare, including side effect management, streamlined administrative procedures, and customized treatment programs.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

The prevalence of internet gaming disorders (IGD) is considerably high among youth, especially with social isolation imposed by the ongoing COVID-19 pandemic. IGD adversely affects mental health, quality of life, and academic performance. The Internet Gaming Disorder Scale (IGDS9-SF) is designed to detect IGD according to DSM-IV diagnostic criteria. However, inconsistent results are reported on its capacity to diagnose IGD evenly across different cultures. To ensure the suitability of the IGDS9-SF as a global measure of IGD, this study examined the psychometric properties of the IGDS9-SF in a sample of Sri Lankan university students (N = 322, mean age =17.2 ± 0.6, range = 16-18 years, 56.5% males) and evaluated its measurement invariance across samples from Sri Lanka, Turkey, Australia, and the USA. Among Sri Lankan students, a unidimensional structure expressed good fit, invariance across different groups (e.g., gender, ethnicity, and income), adequate criterion validity (strong correlation with motives of internet gaming, daily gaming duration, and sleep quality), and good reliability (alpha = 0.81). Males and online multiplayers expressed higher IGD levels, greater time spent gaming, and more endorsement of gaming motives (e.g., Social and Coping) than females and offline players. Across countries, the IGDS9-SF was invariant at the configural, metric, and scalar levels, albeit strict invariance was not maintained. The lowest and highest IGD levels were reported among Turkish and American respondents, respectively. In conclusion, the IGDS9-SF can be reliably used to measure IGD among Sri Lankan youth. Because the scale holds scalar invariance across countries, its scores can be used to compare IGD levels in the studied countries.

Microelectromechanical System (MEMS) vibrating gyroscope design considerations are always intriguing due to their microscale mechanical, electrical, and material behavior. MEMS vibrating ring gyroscopes have become important inertial sensors in inertial measurement units (IMU) for navigation and sensing applications. The design of a MEMS vibrating ring gyroscope incorporates an oscillating ring structure as a proof mass, reflecting unique design challenges and possibilities. This paper presents a comprehensive design analysis of the MEMS vibrating ring gyroscope from the mechanical, electrical, and damping perspectives. The mechanical design of the MEMS vibrating ring gyroscope investigates the various frame designs of the vibrating ring structure, as well as the various beam structures, including curved, rectangular, and semicircular beam structures, which are analyzed using mathematical models and finite element analysis (FEA) simulations that provide an in-depth analysis of the stiffness and deflection of the vibrating structures. The electrical designs of the MEMS vibrating ring gyroscope were analyzed using various electrode configurations, electrostatic actuation, and capacitive detection mechanisms, respectively. The design analysis of various forms of damping, including viscous, structural, thermoelastic, and anchor damping, has been discussed. The variety of design structures investigated for MEMS vibrating ring gyroscopes' mechanical, electrical, and damping performance.

Although numerous articles have been published to address drivers or barriers of corporate social responsibility (CSR), some parts of the world have received less attention. In this study, I reviewed the literature, from 2010 to 2021, to identify drivers and barriers of CSR in the the Middle East and North Africa (MENA) region and compare them with the findings in western countries. (2) Methods: For this study, I used a structured literature review method. By setting the inclusion and exclusion criteria, only 28 articles remained from selected database; (3) Results: The findings revealed some CSR drivers, such as leadership styles, profitability, reputation, moral commitment, and environmental conservation are common in both regions. There are also some differences between CSR drivers, for example religious beliefs, low concentration of ownership, and company characteristics are some of the drivers in the MENA region. Maintaining social license to operate, avoiding the risks of community opposition, pressure from the government, and consumer demand tend to be more important in western countries. Common barriers in both regions are lack of financial resources, cost, lack of CSR knowledge and awareness, and ownership concentration. This review also highlighted that lack of law enforcement, lack of stakeholder communication, lack of management commitment, lack of interests, corruption, and financial debts are some of the addressed barriers of CSR in the MENA region, while cost/benefit ratio, lack of customer interest, and lack of scientific frameworks are the special barriers in western countries. (4) Conclusions: While researchers in western countries have more focus on energy sector, there is a lack of research about drivers and barriers of CSR in the MENA region in several industries, including oil and gas.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Background: Qazvin province has suffered a lot of natural disasters (earthquakes) for a long time. so, the establishment of the first industrial city in Iran, has always been a source of concern. One of these problems is the possibility of chlorine gas leaking from the chlorination system of drinking water and treatment plant the existing ones are on the health of the inhabitants of this region.Material and method: This analytical study was carried out in Novorossiysk district of Alborz in December 2012. Initially, chlorine gas leakage scenarios from these facilities were simulated by ALOHA and WISER software, and then using the Emergency Level System, according to the CCPS Consultative Center's Chemical Process Safety Advice, the Emergency Response Program was presented. Results: ALOHA software, in comparison with WISER software, has more precision to identify and address the consequences of chlorine gas leakage hazards and according to the simulation of this software in the event of a leak from the well and water resources unit at 8240 m from the area and at night 7520 meters will be affected. Due to the speed of delivering results through the WISER software, this software can be used at times when the operating speed is considered (incident time).Conclusion: According to the CCPS model, the emergency conditions were classified at levels 3 and 4, therefore, the relevant crisis management agencies (Qazvin Crisis Management Committee, Alborz City Health Center, Red Crescent and Firefighting Authority) should provide training maneuvers At Alborz city level, and informing people of cities and industrial companies, many efforts have been made to reduce human and environmental damage in case of possible leakage.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

Methamphetamine (METH) has the potential to disrupt the activities of neurotransmitters in the Central Nervous System (CNS) and cause neurotoxicity through various pathways. These pathways include increased production of reactive nitrogen and oxygen species, hypothermia, and induction of mitochondrial apoptosis. In this study, we investigated the long-term effects of METH addiction on the structural changes in the amygdala of postmortem human brains, as well as the involvement of the CREB/BDNF and Akt-1/GSK3 signaling pathways. We examined ten male postmortem brains, comparing control subjects with chronic METH users, using immunohistochemistry, Real-time PCR (to measure levels of CREB, BDNF, Akt-1, GSK3, and TNF-α), Tunnel assay, stereology, and assays for ROS, GSSG, and GPX. The findings revealed that METH significantly reduced the expression of BDNF, CREB, Akt-1, and GPX, while increasing the levels of GSSG, ROS, RIPK3, GSK3, and TNF-α. Furthermore, METH was found to induce inflammation and neurodegeneration in the amygdala, with ROS production mediated by the CREB/BDNF and Akt-1/GSK3 signaling pathways.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

Background: Cyber security is used to save the important data from getting hacked, took by some unknown access. It makes our environment a safe place for us to work out or share our information and since privacy is almost everyone’s top priority, there must be the surety of saving the private information. Methods: In today’s world cyber security is really important as there are now many devices, websites, new technology which makes it a lot easier for hackers to get into anyone’s files which stores crucial data. However, many steps can be reserved to protect the data such as; educating your employees with how to prevent your files or computers from getting hacked, avoid clicking on websites that don’t seem safe, use firewalls, antimalware system and most importantly make sure to keep passwords that are hard to guess and try to go for a face recognition instead of pins and passcodes. Results and Conclusion: This all does not only makes using technology safe but also favors your business for the safety of employees working within the organization. The customers being assured about their data being safe, improves productivity since viruses can slow down computers which may trigger the focus of staff during the working hours. It could save your system from adware; the links of websites are also prevented from slow internet speeds. There is no denying at the fact that cyber security is a major requirement for everyone who is indulged with technology today.

Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.

Background: The rise of cloud computing has led to the increasing number of organizations that rely on it for various tasks and services, such as education, healthcare, and e-commerce. Unfortunately, many security threats can be caused by the sudden use of cloud platforms. Objective: This paper aims to provide a comprehensive overview of these threats and how they can be mitigated. Many companies are moving toward cloud computing to sustain their business growth and provide their employees with the best possible work environment. Results: Due to the rise of cyber security threats and the unprecedented number of breaches of data, small and medium-sized enterprises are also starting to take a huge leap. The outbreak of COVID-19 has affected the lives of people all around the world. Conclusion: Due to the seriousness of the situation, the WHO has declared the COVID-19 pandemic a public health emergency. To minimize the spread of the virus, the entire world has started adopting social distancing

Background: Smartphones have become an indispensable part of the daily lives of adolescents in the 21st century, which is characterized by a highly digitized modern world. Besides their many advantages, smartphones might pave the way to compulsive usage and addictive experiences. To remedy this problem, this study proposes an authentic approach which integrates consumer behavior theories and techniques such as unhook and gamification. An education program has been designed based on these approaches to decrease the problematic smartphone use. Method: The participants of the education program consisted of 305 students (48.2% girls and 51.8% boys) with a mean age of 14.57 (SD = 0.74). The Demographic Form and Smartphone Addiction Scale for Adolescents (SASA) were conducted before the education program and three weeks after the education. Results: The results of the paired sample t-test analysis before and after the education program revealed that the SASA total scores decreased significantly (p < 0.01). There are significant differences in terms of gender, mothers’ education and class levels. Conclusion: This research emphasizes the role of an interdisciplinary approach to the addiction problem. The content used in the education program includes strategies that originally aimed at increasing consumption. The effectiveness of the program can be enhanced further in the future along with self-regulatory additions.

In this paper, we investigate a new subclass of univalent functions defined by a generalized differential operator, and obtain some interesting properties of functions belonging to the class R^m λµ ( α, β, γ, υ ).

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

Coronary no-reflow (CNR) is a frequent phenomenon that develops in patients with ST-segment elevation myocardial infarction (STEMI) following reperfusion therapy. CNR is highly dynamic and develops gradually (over hours) and persists for days to weeks after reperfusion. Microvascular obstruction (MVO) developing as a consequence of myocardial ischemia, distal embolization and reperfusion-related injury is the main pathophysiological mechanism of CNR. The frequency of CNR or MVO after primary PCI differs widely depending on the sensitivity of the tools used for diagnosis and timing of examination. Coronary angiography is readily available and most convenient to diagnose CNR but it is highly conservative and underestimates the true frequency of CNR. Cardiac magnetic resonance (CMR) imaging is the most sensitive method to diagnose MVO and CNR that provides information on the presence, localization and extent of MVO. CMR imaging detects intramyocardial hemorrhage and accurately estimates the infract size. MVO and CNR markedly negate the benefits of reperfusion therapy and contribute to poor clinical outcomes including adverse remodeling of left ventricle, worsening or new congestive heart failure and reduced survival. Despite extensive research and the use of therapies that target almost all known pathophysiological mechanisms of CNR, no therapy has been found that prevents or reverses CNR and provides consistent clinical benefit in patients with STEMI undergoing reperfusion. Currently the prevention or alleviation of MVO and CNR remain unmet goals in the therapy of STEMI that continue to be under intense research.

Intracortical microelectrodes that can be implanted have the ability to capture fast-changing neuron action potentials in the living brain. Despite this potential, many obstacles must still be overcome for reliable, long-term, high-quality recordings and accurate analysis of brain activity. These challenges include improving recording quality, enhancing recording stability, increasing recording capacity, and adding multi-functional features. One potential approach to enhance implantable microelectrodes involves the advancement of materials, refinement of implantation methodologies, and augmentation of the number of sites for recording. However, these challenges are still being actively researched, and advancements in microelectrode technology are underway. The difficulties with implantable microelectrodes are evaluated, and solutions are presented from different perspectives. The latest advancements in microelectrode technology are reviewed and condensed, and future possibilities are explored.

This paper is devoted to the study of reproducing kernels on 2-inner product Hilbert spaces. We focus on a new structure to produce reproducing kernel Hilbert and Banach spaces. According to multi-variable computing, this structures can be useful in electrocardiographs, machine learning and economy

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

The concept of a metaverse, a shared virtual space that brings together the physical and digital worlds, has been a topic of interest for decades. With recent advancements in technology, particularly in virtual reality (VR), augmented reality (AR), and blockchain, the metaverse has become a rapidly evolving and promising ecosystem. This paper provides a review of the metaverse, exploring its history, evolution, and current state of development. The paper delves into the different types of the metaverse, including fully immersive VR metaverses, hybrid metaverses that blend the physical and virtual worlds, and decentralized metaverses that are powered by blockchain. Additionally, the paper examines the impact of web 3.0 on the metaverse, as well as the various technologies used to create and sustain the metaverse, including VR, AR, and blockchain. Furthermore, the paper explores the concept of digital ownership and the potential for marketing and commerce within the metaverse. This paper serves as a review of the metaverse and its various components, providing insights into its history, development, and prospects, and is intended to provide a foundation for further research into this exciting and rapidly evolving ecosystem.

This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

Coastal areas are influenced by natural coastal hazards such as hurricanes, storm surges and tsunamis. For centuries coastal structures such as seawalls, detached breakwaters, groins, and revetments have been constructed to protect coastal properties. However, their effectiveness diminishes with time, and they are not adaptable to changing coastal conditions. Ecosystem-based approaches offer protection against erosion and the creation or restoration of coastal habitats. However, the protection properties and the sustainability provided by these systems are not well understood. Salt marshes have been known as one of the ecosystem-based protection systems. They protect coasts and lands by dissipating energy, stabilizing sediments and producing organic matter through blow ground production. Stems of plants dissipate waves propagating over salt marsh and plant roots stabilize soils and sediments. The dissipation rate varies with wave frequency; the low-frequency swell wave is dissipated less on the edge of the marsh than wind-sea waves. Salt and wetlands can also be used for reducing coastal flooding and storm surges, but a wetland is required to successfully attenuate storm surges. A salt marsh can be resilient to sea-level rise in certain conditions. Large sediment supply and gentle upland slope increase salt marsh resiliency to relative sea-level rise. Connecticut marshes, like other marshes in the world, are vulnerable to anthropogenic and climate change effects. However, an assessment of current sea level rise and average marsh accretion rates in Connecticut demonstrates sea level rise is not the main vulnerable factor for salt marshes loss. The study on the feasibility of developing an ecosystem based on two coastlines in Connecticut, Guilford and Stratford, shows that both coastlines, like other coastlines in Connecticut, have limited wave energy, which is a positive factor for marsh growth. The available data assessment represents that sediment supply is the most important parameter to guarantee the resilience and sustainability of a newly developed salt marsh system in Connecticut. In Stratford, conditions for establishing a new ecosystem seem to be better, as the fetch length is pretty small, and there is some sediment supply for the ecosystem. In Guilford, wave energy is limited, but it is more than in Stratford case. In addition, sediment availability is low and the coastline experienced a large erosion during Hurricane Sandy and has not recovered yet.

Alkali-silica reactivity (ASR) is one of multiple reactions responsible for premature loss in concrete infrastructure service life. ASR is a deleterious reaction initiated when highly reactive silicious content of aggregates reacts with alkali hydroxides content within portland cement in the presence of moisture. ASR results in the formation of expansive, white-colored gel-like material which results in internal stresses within hardened concrete. ASR induced stresses result in concrete cracking, spalling, and increased reinforcement steel corrosion rates. The main objective of this research is to improve the conditions of concrete infrastructure projects conditions by mitigating ASR damaging effect. The expansion of accelerated mortar bars poured using fine aggregates collected from different sources is measured versus time to evaluate aggregate’s reactivity. Different percentages of supplementary cementitious materials (SCMs) including class c fly ash, micro-silica, were used in remixing mortar bars to evaluate the efficiency of different types of SCMs in mitigating mortar bar expansion. Research findings showed that SCMs can mitigate ASR, thus, decrease the mortar bar expansion. The efficiency of SCMs in ASR mitigation is highly dependent on the incorporated SCM percentage and particle fineness. Silica fume, having the least particle size, displayed higher rates of ASR mitigation followed by fly ash, respectively. The outcomes of this research will assist design engineers in avoiding future losses due to ASR cracking in concrete infrastructure projects, and reduce the excessive need to maintenance, repair, and replacement activities.

This paper discusses the use of fly ash in concrete construction industry. Fly ash is incorporated as a supplementary cementitious material (SCM) in ordinary Portland cement (OPC) concrete in partial replacement of cement, and is recently used as a geopolymer cement in the development of geopolymer concrete (GPC) mixes. Class C and Class F fly ash high aluminosilicate content, and fine granular size contributes to concrete improved workability, lower permeability, and reduced heat of cement hydration. Due to its chemical properties, the use of fly ash in producing OPC and GPC results in increased compressive strength, higher tensile strength evaluated by measuring hardened concrete modulus of rupture (MOR), and higher modulus of elasticity (MOE). The fine size of fly ash particles increases the concrete mix packing order, and reduce the ingress of moisture, and mitigates the impact of aggressive environmental attacks through the reduction of sulfates and chlorides rate of concrete penetration. Thus, fly ash improves concrete resistivity to alkali-aggregate reactions (AAR), and reduces the corrosion of reinforcing steel, and prestressing strands. Fly ash as an economic byproduct of coal industry results in reduced material cost, increased durability, and a higher sustainability of concrete construction projects.

Background: One of the most common occupational health hazard and serious health concern, now a days, recognized is Musculoskeletal disorders (MSDs). The high prevalence among students and teachers is a major cause of decline in their health related quality of life and work performance. This study aimed to identify the prevalence of musculoskeletal disorders among teachers and students during online education. Methods: A cross-sectional survey was conducted among university faculty members and students who were involved in online education during COVID-19. Data collection was carried out electronically through a validated Arabic version of the Nordic Musculoskeletal Questionnaire (NMQ) which is self reported. A total of 175 respondents responded to the questionnaire (response 60%). Results: The prevalence of any part of the body was 90.3%. The commonest site of MSDs for past 12 months was the low back (65.7%), the neck (58.3%), and shoulders (57.7%). Females (80%) suffered more than males (20%). The students reported slightly higher MSDs as compared to teachers (93.1% vs 90.3). Binominal regression analysis showed association of females with low back pain “OR: 2.24 (95% CI): 1.04-4.83; p = 0.03” and neck pain “OR: 2.7 (95% CI): 1.24–5.84; p = 0.012)” while bad posture was associated with pain in upper back “OR: 3.46 (95% CI): 1.73-6.93; p = 0.001”. Conclusion: The faculty members and students are prone to high MSDs during online classes and significant measures should be taken to reduce the prevalence by addressing associated risk factors.

Gold- and MnO₂- nanozymes are well-known for their enzyme-like activity. In this regard, initially, gold- and MnO₂- nanozymes were synthesized by simple and green methods. Afterward, the kinetic studies were performed using the Michaelis–Menten model for both gold- and MnO₂- nanozymes. The kinetic parameters including K_m and V_max were calculated via the construction of the linear plot of Lineweaver–Burk for both nanozymes. The results showed a V_max and K_m of 185 nM sec^-1 and 47 nM sec^-1 for the gold- and MnO₂- nanozymes, in order. The ratio of V_max(gold)/V_max(MnO₂) was found to be about 4.0 which pointed that the catalytic efficiency of gold-nanozymes is 4.0-fold higher than the catalytic efficiency of MnO₂- nanozymes. The K_m value was found to be 0.72 mM and 1.6 mM for the as-prepared gold- and MnO₂- nanozymes, respectively, and the K_m of MnO₂-nanozymes is 2.2-fold higher than that of gold nanozymes. Since the Km shows the affinity of substrate for binding to nanozyme active nodes (lower K_m=higher affinity), it is consultable that the substrate affinity toward MnO₂-nanozymes is 2.2-fold lower than that of the gold-nanozymes. Considering the above results, the as-prepared gold nanozymes are very stronger peroxidase-like mimics than the metal oxide MnO₂-nanozymes.

Nitrogen and phosphorous support the ecosystem by supplying nutrients to algae and aquatic plants. Having them in excess results in the eutrophication of waters creating quality problems. In the past, nitrogen has been widely investigated for wells in the context of groundwater flow. However, a national-scale nitrogen assessment in rivers and streams has not received enough attention. In this research, the Wilcoxon rank sum test, as a non-parametric hypothesis testing method, has been applied to nitrogen concentration in the form of nitrate-nitrogen and nitrite-nitrogen in rivers and streams of the Contiguous United States. This approach was particularly selected because of the non-normal and positively skewed nitrogen levels occurring in the surface flow. This method was able to identify the impaired body of waters as well as quantify the confidence, significance, and errors involved. The Northern Appalachians (NAP), Northern Plains (NPL), and Xeric (XER) ecoregions were worsening in the nitrogen-nitrate condition with NAP, and XER needed immediate actions. The nitrite-nitrogen condition did not pose an immediate threat, so mitigation plans should focus more on nitrate-nitrogen remediation. It was shown that the method was superior to the two-sample t-test by yielding lower type II errors.

The use of nitrogen (N) fertilizers can have detrimental effects on the environment due to nitrate leaching. This study aims to examine the impact of N fertilizer source and application timing on wheat yield and nitrate leaching in various soil textures. A randomized complete block design (RCBD) was employed to conduct the experiment. The treatments consisted of two N fertilizer sources: ammonium nitrate (AN) and urea (NN), applied at three different times throughout the year, with a control treatment. The three application timings were total application before sowing (T₁), total application in spring (T₂), and half the amount before sowing and the other half in spring (T₃). The experiment was conducted in three different soil textures (sandy loam, silty clay loam, and silty clay), with three replications over three growing seasons: 2018-19 (Y₁), 2019-20 (Y₂), and 2020-21 (Y₃). The results indicated significant effects of different soil textures on grain yield. Specifically, the silty clay loam soil differed significantly from the sandy loam and silty clay soils in terms of grain yield. The interaction effects between the N fertilizer source and application timing were also found to be significant for all soil textures (p<0.01). In sandy loam and silty clay soils, the grain yield increased when using the T₃ application timing compared to the silty clay loam soil. Notably, the silty clay loam soil with the T₂ application timing exhibited the highest grain yield, with a yield of 6863.6 kg ha^-1. Furthermore, the study revealed that N fertilizers and their application timing significantly influenced the protein content in the silty clay and sandy loam soils. The protein percentages were 10.76 and 10.93 for the silty clay and sandy loam soils, respectively, following the T₂ treatment. Additionally, ammonium nitrate (AN) demonstrated a noticeable effect on the percentage of grain protein compared to urea (NN). In conclusion, the application timing of N fertilizers had an impact on grain yield and protein content in different soil textures. Moreover, nitrate concentration increased throughout the experiment, with the highest soil nitrate concentration observed in both fertilizer treatments, particularly in the sandy loam soil.

MnO₂ nanoparticles are considered nanozymes with intrinsic peroxidase-like activity and there are several reports on their application in the field of nanozyme-based sensing and detection, however, up to now, there is no report on the investigation of their biochemical and kinetics properties toward enzyme-mediated oxidations. Hence, in this work, the MnO₂ nanozymes were synthesized and characterized with different characterization methods. Thereafter, their biochemical properties including pH stability, thermal stability, and salt stability as well as their kinetics perfromances were evaluated toward n-electron irreversible oxidation of 3,3’-diaminobezedine. The results showed that the as-prepared nanozymes reveal their maximal enzyme-like activity over a wide pH range of 3.0-6.0 at a temperature of 23-25 ℃ toward oxidation of 3,3’-diaminobezedine. Besides, the salt stability studies exhibited that the as-mentioned nanozymes can save their maximal activity over a wide range of high salt concentrations over 3-7 M of NaCl. Moreover, the kinetics studies revealed a K_m as low as 1.6 mM and a V_max as high as 47 nM sec^-1 for the MnO₂ nanozymes toward irreversible oxidation of DAB to produce the brown-colored polyDAB.

In this study, considering the peroxidase-like activity of the SiO₂@AuNPs nanocomposite, their biochemical properties including, pH stability, cycling stability, shelf stability, and kinetic parameters were investigated. The results revealed that the as-mentioned SiO₂@AuNPs nanocomposite reveal its maximal activity at pH=4.0 along with saving 83.3% of its maximal activity at pH=5.0. Besides, the reusability and shelf-storage studies exhibited that the SiO₂@AuNPs nanocomposite retained 90% and 100% of its initial activity after 5 operational cycles and 30 days of storage, in order. The kinetic parameters of the as-prepared nanozymes were calculated using Menten kinetic model, revealing a V_max of 1.35 µM min^-1 and a K_m as low as 0.06 mM for the SiO₂@AuNPs nanocomposite. Based on the results of this work, the as-prepared SiO₂@AuNPs nanocomposite with intrinsic peroxidase-like activity shows high substrate affinity and catalytic efficiency along with excellent cycling- and shelf-stability, making them suitable for application in peroxidase-mediated reactions instead of the native enzyme.

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

This study focuses on predicting traffic speed using the simple Multilayer Perceptron (MLP) model, despite the availability of various models for traffic prediction, with artificial intelligence demonstrating superior predictive capabilities. Emphasizing that the quality of the data holds greater significance than the model itself, the research underscores the challenges posed by data containing significant errors and fluctuations. Unlike relying solely on criteria such as Mean Absolute Deviation (MAD) or coefficient of determination (r2), this study advocates for the use of MAD/range as a more robust metric for prediction accuracy, especially when dealing with data exhibiting substantial order.The study sets the speed limit and traffic value at 0.65 and 0.88, respectively. Notably, both criteria yield identical accuracy results of 13% for MAD/R in predicting speed and 14% for traffic. This parity suggests that the artificial intelligence model performs equally well for both variables, highlighting the importance of considering alternative evaluation metrics in the face of data complexities.

In this contribution, unmodified silver nanoparticles were synthesized and characterized for their size and morphological properties. Thereafter, their peroxidase-like activity as the common catalytic property of silver nanoparticles was investigated by catalyzing the oxidation of 3,3’,5,5’-tetramethyl-benzidine (TMB) as peroxidase substrate, exhibiting, a specific activity as high as 5.4 µM min^-1 for the as-prepared unmodified silver nanoparticles. The stability of the catalytic activity of the as-prepared nanozymes was also checked upon their storage at ambient temperature within 7 days at different storage conditions. The results revealed that the peroxidase-like activity of unmodified silver nanoparticles was approximately retained at about 75%, and 63% after 7 days exposing daylight and air oxygen, in order. The shelf-life (storage stability) of the as-prepared nanozymes was also investigated at usual storage conditions (i.e., 4 ℃ under dark), revealed that the nanozymes saved their activity about 96% of their initial activity after 10 days of storage at 4 ℃ under dark conditions.

Open System Interconnection (OSI) model is the best means to understand the way the internet works and the communication of computers over a network system. It is considered as the standard model for network communication. This article describes the OSI model with an example and provides a brief description of the seven layers of the OSI model.

It is well-known that the peroxidase enzyme catalyzes the oxidation of different substrates such as 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine, however, the affinity of different substrates to the enzyme and consequently and catalytic performances of the enzyme toward different substrates is not same, resulting in different kinetics indexes. As the BSA-gold nanozymes are considered artificial peroxidase enzymes, hence, the kinetics indexes of these nanozymes should also be varied by varying their substrate. Hence, in this work, a comparative study was performed on the kinetics performances of BSA-gold nanozymes for enzyme-mediated oxidations of 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine. The results showed that the Km value of BSA-gold nanozymes was 0.03 mM and 0.72 mM toward TMB and DAB, in order, to reveal the higher affinity (lower Km) of TMB for binding to nanozyme active nodes compared to its alternative substrate, DAB. In contrast, the Vmax was found to be 263 nM sec^-1 and 185 nM sec^-1 for nanozmye-mediated oxidation of TMB and DAB, respectively. The higher Vmax of the nanozyme-mediated oxidation of TMB revealed that the catalytic efficiency of BSA-Au nanozymes toward TMB oxidation is higher (about 1.5-fold) than that of the DAB oxidation. The difference between the kinetic indexes of TMB and DAB may be related to their different oxidation pathways and their different reactivity. In fact, the DAB oxidizes via an n-electron irreversible oxidation pathway to produce an indamine polymer. While TMB nanozyme-mediated oxidation has occurred upon a 2-electron reversible mechanism for the production of a cation radical. These different pathways resulted in different kinetic performances.

This paper focuses on hyperglycemic hyperosmolar syndrome (HHS), a unique hyperglycemic state requiring divergent diagnosis and treatment approaches from diabetic ketoacidosis (DKA), despite some shared characteristics. We introduce the mnemonic DI-FF-ER-EN-CE-S to encapsulate unique HHS management and complications. 'DI' emphasizes the need to delay and reduce initial insulin therapy until serum glucose decline is managed by fluid resuscitation alone. 'FF' and 'ER' stress the importance of fluid and electrolyte replacement due to severe dehydration and imbalances. 'EN' denotes the potential for encephalopathy and the requirement for a controlled serum osmolality reduction. 'CE' indicates that cerebral edema is rare but necessitates aggressive fluid therapy. 'S' signifies systemic multiorgan failure. We categorize the associated risks into three mnemonic groups: the 3Rs (renal failure, respiratory distress, rhabdomyolysis), the 3Hs (heart failure, hypercoagulation, hyperthermia), and AP (arrhythmias, pancreatitis) to facilitate awareness and screening of HHS.

Numerous genes expression lead to inflammation in the individuals’ lungs that have chronic obstructive pulmonary disease (COPD) may be affected by epigenetic alteration. Important epigenetic processes include methylation of DNA and different histones post-translational changes, including ubiquitination, phosphorylation, methylation, SUMOylation and acetylation. Smoking can trigger the enzymes that control these epigenetic changes. According to the majority of publications, both environmental and genetic variables have a substantial role in the development of COPD. Although, the information about COPD epigenetic is not much but, a better perception of the disease pathophysiology and identifying new markers to create novel therapeutics for patients can be achieved via a better understanding of the epigenetic processes involved.

Sorghum has become increasingly popular for diverse applications, including food, feed, and bioenergy. The present study aimed to perform a bibliometric and content analysis on sorghum research in the industrial-agricultural sector to evaluate global research patterns, emerging topics, and future trends. The bibliometric analysis of the Scopus bibliographic metadata was done using Bibliometrix and Vosviewer. The results of this investigation show the descriptive analysis underscored fluctuation in research output and citation impact over the years. The identified four patterns through co-occurrence analysis encompassed central themes of sorghum research, including genetic expression and plant stress cluster, cereal crop and yield cluster, biomass and microbial bioprocessing, and metabolism and antioxidant cluster. The thematic map portrays a comprehensive view of cereal crops and yields, biomass and microbial bioprocessing, metabolism and antioxidant research, environmental remediation and sustainable agriculture practices, and the declining theme of starch. The findings provide valuable insights into the global pattern of sorghum research and emerging trends, thus directing future multidisciplinary research efforts for enhanced knowledge and sustainable practices in the industrial-agricultural sector.

CrHZSM-5 was placed in an electric field with appropriate strength in a quartz packed bed reactor with CO2 as oxidant to analyze its catalytic activity. Olefin yield increases with decrease in band gap since lattice oxygen mobility increases by reducing band gap. Fermi level change at the catalyst surface affects the catalytic activity. One way to change Fermi level is use electric field. In high voltage electric field, energy band was curved, bending of the energy band promoted the activity and Fermi level position is increasing. The CCD experiments were carried out with Design-Expert 7.3 software to determine the interaction between four operating variables, namely: temperature, electrical current, gap distance and metal loading. The levels of the independent variables were: temperature (550-700 °C), electrical current (0-12 mA), gap distance (6-14 mm), metal loading (0.5-7.5 %wt.). The conversion of LPG (Liquefied petroleum gas) was greatly increased by weak and effective application of an electric field to the catalyst bed. The obtained results indicated that the maximum yield value (46.94%) can be achieved under 673.66 °C, input electrical current of 11.01 mA, gap distance of 6.55 mm and metal loading of 3.98 wt.%.

To close the nutrient-related yield gaps in maize, balanced nutrition using primary and secondary macronutrients as well as micronutrients is recommended. Multi-nutrient diagnostic field trials were conducted in Guinea Savanna of Nigeria to assess the interactive effects of macronutrients and micronutrients on maize grain yields, nutrient uptake as well as N, P and K use efficiencies. The treatments consisted of a control (zero fertilizer applied), an NPK treatment and 10 other treatments in which macronutrient (Mg, S) and/or micronutrients (B, and Zn) were added to the NPK. The experiment was laid out in Randomized Complete Block Design with 3 replications. Data collected were subjected to mixed model with nutrient management strategy as fixed effects while replication nested in location and interaction between location and nutrient management strategy as random effects. The results revealed significant effects (P < 0.05) of nutrient management strategy on maize yield, nutrient uptake and nutrient use efficiencies of N, P and K. The study revealed that yield advantage over the recommended NPK fertilizer as a result of application macronutrients and micronutrients were highest with Mg in Lere (2.4 t ha⁻¹), S + B + Zn in Faskari (2.8 t ha⁻¹), S + B in Doguwa (1.5 t ha⁻¹) and S + Zn in Toro (2.4 t ha⁻¹). Addition of Mg, S and B significantly increases macronutrient uptakes over the recommended NPK only. Agronomic use efficiency, internal utilization efficiency, apparent recovery efficiency and partial factor productivity were significantly increased with the addition of S, Mg, and B but were not improved with Zn application. It was concluded that nutrient limitations to maize in the Guinea Savanna go beyond N, P and K. This study recommends that S, Mg, and B are needed to improve maize productivity and engender improve the use efficiency of NPK fertilizers.

This study investigated the potential of electrospun collagen/Hyaluronic Acid nanofibers in biomedicine and skincare. Needleless electrospinning technology employed in the production process resulted in nanofibers with consistent fiber diameters and improved mechanical properties. The composite nanofibers demonstrated impressive tensile strength and elongation capabilities, indicating a favorable interaction between collagen and HA. Cellular assays revealed that the collagen/HA scaffolds facilitated superior cell adhesion and proliferation compared to the control scaffolds, indicating their biocompatibility and potential for tissue engineering. This biomaterial offers an environment-friendly alternative to conventional skincare products by eliminating the need for preservatives and reducing waste. Although the initial costs are high, the long-term benefits of material durability and active ingredient efficacy provide a compelling case for industrial adoption. Our findings suggest that collagen/HA nanofibers have the potential to be next-generation materials for regenerative medicine and skincare, and further research is needed to optimize their production and assess the economic feasibility of skincare and tissue engineering applications.

The aim of this study was to investigate in-vitro antioxidant properties and in-vivo protective effects of different concentrations of Hypericum triquetrifolium Turra. (HT) seed methanol extracts against acute hepatotoxicity, myelotoxicity and hematotoxicity in rats exposed to overdose of cyclophosphamide (CP). HT seed methanol extracts were tested in view of its in-vitro antioxidant activities as total phenolic contents and DPPH free radical-scavenging activity. To investigate in-vivo protective effects of HT seed methanol extracts on rat tissues; tested animals were divided into nine groups. Three groups only were treated with HT extracts (25, 50 and 100 mg/kg HT) for 6 days. Three groups were pre-treated with the extract of HT (25, 50 and 100 mg/kg HT) for 6 days and on the last day they were injected with single dose of CP (150-mg/kg body weight). Two groups were used as control groups and one group was only treated with CP (150 mg/kg) on the 6th day. The toxic effects of CP and protective effects of HT extracts on the nucleated cells which were produced by bone marrow and serum alanine transaminase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), oxidative stress index (OSI) levels were investigated biochemically. Additionally, liver tissue samples were examined histopathologically. Our results show that HT seed methanol extract has high total phenolic content and antioxidant activity. Over dose CP administration caused hepatotoxicity, myelotoxicity and hematotoxicity on rat. Whereas, 25, 50 and 100 mg/kg HT plus CP administered groups showed significant protective effects on nucleated cells. And 25, 50, 100 mg/kg HT plus CP treated groups showed an important decrease on serum ALT, ALP, LDH and OSI levels when compared with CP treated group. Our results showed that the administration of different HT doses with high doses of CP significantly reduced hepatotoxicity, myelotoxicity and hematoxicity on rats.

This review paper explores auditory tactile (AT) synesthesia, a rare neurological condition where sounds evoke tactile sensations. The paper provides a historical overview of the condition and discusses its epidemiology, with a prevalence of less than 1% of the general population. The neurological basis of AT synesthesia is explored, including the role of cross-modal processing and hyperconnectivity within the brain. The paper also describes the phenomenology of the condition, including the range of tactile sensations that can be experienced in response to different sounds. The occurrence of AT synesthesia in the present-day world is discussed, including its relationship to music and art. Various hypotheses surrounding the development and maintenance of AT synesthesia are reviewed, focusing on genetic and environmental factors. The implications for clinical practice are explored, including potential benefits for individuals with sensory processing disorders. Finally, the paper concludes with a discussion of future directions for research in this field, including the need to explore further the underlying neural mechanisms of AT synesthesia and potential therapeutic interventions.

This study investigates the failure of a roof with steel truss construction of a factory building in Tekirdag in North-western part of Turkey. The failure occurred under hefty weather conditions including thunderbolt, lightning strikes, heavy rain and fierce winds. In order to interpret the reason for the failure, the effects of different combinations of factors on the design and dimensioning of the roof are checked. Therefore, finite element analysis is performed several times under different assumptions and considering different factors aiming to determine the dominant ones that are responsible for the failure using the commercial software Abaqus (Dassault Systèmes, Vélizy-Villacoublay, France). Each loading condition gives out a characteristic form of failure. The scenario with the most similar form of failure to the real collapse is considered as the most likely scenario of failure. Also, the factors included in this scenario are expected to be the responsible factors for the partial collapse of the steel truss structure.

It is well established in literature that the public debt and economic growth bear positive and non-linear relationship. However, in recent literature, evidence of no causal relationship is found when accounted for endogeneity in case of advanced economies (Panizza & Presbitero, 2014). Chudik, Mohaddes, Pesaran, & Raissi, (2017) analyse the data on forty countries and find no evidence of universally applicable threshold effect in the relationship between debt and growth. These advancements in the debt-growth literature provides the motivation to re-explore the relationship between public debt and economic growth under non-linearity and endogeneity in context of developing economies of South Asia including Pakistan, India, Bangladesh and Sri-Lanka for the period 1980-2014. There exists a significant, positive but nonlinear relationship between the public debt and economic growth for the selected set of developing countries when accounted for endogeneity and non-linearity. The negative association between the public debt and economic growth for SAARC region is found when the debt level is higher than 61% of GDP which is quite lower than developed economies (90% of GDP). Individual threshold levels for debt-to-GDP ratio divulge that Sri Lanka, Pakistan and India need to control their public borrowings as their current debt levels are higher and/or around the respective threshold levels.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

ackground: The swift assimilation of Artificial Intelligence (AI) across multiple industries is altering the worldwide economic terrain. Unprecedented opportunities and difficulties are brought about by this revolutionary technology, which has an impact on innovation, trade, labor markets, and security. Objective: The goal of this study is to thoroughly investigate the economic effects of AI by exploring how it may affect labor markets, innovation, trade internationally, and security. The goal is to offer insights that support strategic decision-making and policy formation by examining current trends, opportunities, and difficulties. Results: The research highlights the dual character of artificial intelligence's influence, highlighting both its potential to spur economic expansion and its drawbacks, including the loss of jobs, a lack of standards, and security issues. The study highlights how crucial cooperation is to overcoming these obstacles and realizing AI's full potential. Conclusion: The balancing opportunities and risks becomes critical as AI continues to change the economic landscape. The report promotes proactive steps including ethical AI design guidelines, ongoing worker retraining, and flexible regulatory strategies. As we shape an AI future that puts inclusion, creativity, and responsible governance first, collaboration emerges as a key theme.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

This review paper delves into using micro-credentials in higher education ecosystems as a digital enablers. Micro-credentials, which are digital credentials that attest to a learner’s mastery of a specific skill or knowledge area, are becoming more popular in higher education. The paper examines the successful implementation of micro-credential frameworks in higher education, using case studies to demonstrate the advantages of micro-credentials. The review emphasizes the agility and flexibility of microcredentials, which enable learners to acquire new skills quickly and respond to changes in the job market. In addition, the paper discusses the digital nature of micro-credentials and how they allow institutionsto provide targeted, skills-based training that isrelevant to employers. It also explores how micro-credentials are delivered through online platforms, making them convenient and easily accessible for learners. The review underscores the significance of digital infrastructure, connectivity, and public utility for promoting micro-credentials. The paper argues that micro-credentials function as a digital enabler for higher edu- cation ecosystems, allowing learners to acquire targeted training and enabling institutions to expand their offerings and reach more students. The paper concludes by highlighting the potential for micro-credentials to help bridge the skills gap and equip learners with the skills necessary to succeed in today’s digital economy.

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

The observed data of COVID-19 progression in Pakistan for first 50 days from the first patient been reported has shown quite an unusual trend which is in opposition to clear exponential spread pattern of any infectious disease. The data of positive cases of 50 days of disease progression has been collected from COVID-19 dashboard of Pakistan and analyzed to see the graphical trend and to forecast the behaviour of disease progression for next 30 days. Mathematical equations regarding exponential growth are used to analyse the disease progression and different possible trajectories are plotted to understand the approximate trend pattern. The possible projections estimated 20k-456k positive cases within 80 days of disease spread in Pakistan. Although, the disease progression pattern is not perfectly exponential, it is still threatening a major fraction of susceptible population and demands effective strategic planning and control.

The production of concrete and the manufacturing process of cement results in a significant carbon footprint, contributing to a large portion of global emissions in structures such as buildings, bridges, roads, and tunnels. Although concrete is an ideal building material that is durable and long-lasting, it can be susceptible to micro-cracks. These micro-cracks in concrete can allow water and chlorine ions to penetrate the structure, leading to the degradation of concrete and corrosion of the reinforcement, posing an unacceptable level of structural risk. Self-healing concrete is not a new material in the construction industry but can be characterized by the capability of concrete to repair its cracks autogenously or autonomously. Recent advancements in concrete research and technology have given us a better understanding of concrete's healing properties. Self-healing concrete combines durability with sustainability while offsetting the high carbon output of concrete manufacturing and production and associated life-cycle costs. Technologies such as microbially-induced calcite (calcium carbonate) precipitation (MICP), shape memory polymers, encapsulation methods, hydration, and swelling agents can potentially reduce carbon emissions while enhancing resilience and longevity. This paper examines these technologies and their applications in the construction industry by comprehensively reviewing the literature and available case studies. The study concluded that there are promising advancements and innovations in concrete, particularly when improving upon the autogenous healing properties. The recommendations for future research include exploring more ways to bring the concrete industry and cement manufacturing toward net zero carbon emissions.

This study explores several global and country cluster sustainable development (herein SD) models. It jointly examines constructs from the “Neoliberal-turn” ((e.g., Institutional Enhancers (e.g., business freedom, property rights, government integrity, and judicial effectiveness), Financial Enhancers (e.g., government spending and monetary, trade, investment, and financial freedoms), Foreign Direct Investment, Global Competitiveness-institutions, and Global Competitiveness-innovation-sophistication)), and the “Social-turn” (five SD pillars (e.g., Planet, People, Peace, Prosperity, and Resources), which synthesize the 17 United Nations SD goals); and Happiness and Life Satisfaction (herein H&amp;LS). The study examines a large and diverse set of relationships encompassing 11 years of data of 108 social, economic, and environmental indicators from 129 countries at the global, country cluster, and country levels. We analyze data using structural equation modeling. Remarkably, Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the "neoliberal-turn" constructs and H&amp;LS. Results show model configurations and scale effects. Furthermore, findings reveal both synergies between neoliberal and the five SD pillars as well as both negative and non-significant relationships among them. The diversity of results calls for further model integration and specificity. Diverse findings entail caution and difficulties in generalizing knowledge. Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) and or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

This study compares the effects of lecture-based instruction and process-oriented guided inquiry learning (POGIL)-based instruction on the self-efficacy performance of Grade 12 students. A quasi-experimental, pretest-posttest design was adopted to investigate and assess the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes; Knowing, Applying and Reasoning (KAR). Two government high schools in Alain were selected as research sites, one for the boys and one for the girls. The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys), and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a circular motion unit in physics using POGIL-based instruction, while the control group was taught the unit using lecture-based instruction. The study's findings showed statistically significant differences between students of the control group and the treatment group in favour of the latter regarding their science performance, self-efficacy and science-related attitudes. Moreover, positive correlations were found between participants’ performance at the KAR test, self-efficacy and scientific attitudes towards scientific inquiry after the intervention. In conclusion, it is recommended to shift teaching towards POGIL-based instruction due to its positive impact on students’ performance and self-efficacy. It is also suggested to replicate the study to include government and private schools, elementary and high schools, teachers and advisors.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

In this paper, we study the weighted reproducing kernels. We introduce a generalization of weighted reproducing property and extend it to relative reproducing property on Banach spaces. This generalization will develop the parallel computing and it can be used as a base of parallel learning for machines especially in the case that there are non-homogenous weighted hypothesis. This methods formulate learning and estimation problems in a relative reproducing kernel Banach space (RRKBS) of functions defined on the data domain, expanded in terms of a kernel.

ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

Although most people are infected with Epstein-Barr Virus (EBV) during their lifetime, only a minority of them develop an EBV-associated malignancy. EBV acts in both direct and indirect ways to transform infected cells into tumor cells. There are multiple ways in which the EBV, host, and tumor environment interact to promote malignant transformation. This paper focuses on some of the mechanisms that EBV uses to transform the tumor microenvironment (TME) of EBV-associated gastric cancer (EBVaGC) for its benefit, including overexpression of IDO1, synergism between H pylori and EBV coinfection, and M1 to M2 switch. In this review, we expand on different modalities and combinatorial approaches to therapeutically target this mechanism.

Steam boiler has a significant role in the industrial sector. The failure in boiler tubes significantly reduces the availability of the plant. Furthermore, deposits on the inner tube wall contribute to such failure by changing the thermal resistance of the wall, which causes a significant increase in the tube wall's temperature and, consequently, lower allowable stress. The presented thermal-fluid model is developed for the water tube. The model is implemented using ANSYS FLUENT to estimate the wall maximum water tube wall temperature considering the deposits layer and then determine the minimum thickness of the tube wall via ASME standards to assure safe operation. The model is verified via a mesh independence test. The minimum tube thickness is recommended and justified given the thermo-mechanical performance of the water tubes at several operating loads of 50, 75, and 100 %. These loads are implemented by different convective heat transfer coefficients of flue gases under different deposit thicknesses, thermal conductivity, and flue gas temperature. The present work contributes to the literature by recommending the minimum thickness of the water wall tubes and highlighting the thermo-mechanical effect of the deposits on water tube resilience under different operating conditions.

Abstract:Salmonella species (spp) and Escherichia coli (E. coli) are the most common infectious pathogens in poultry. Antimicrobials were given either for the treatment or growth promoters that can increase the possibility of emergence of bacterial resistance towards antimicrobials. The aim of this study was to determine the prevalence of antimicrobial resistant (AMR) Salmonella spp and E. coli isolated from a sample of broiler farms in East Coast Malaysia from 2018-2019. A total of 384 cloacal swabs were collected from broilers farms in Kelantan, Terengganu, and Pahang. The bacteria were isolated and confirmed by bacteriological and serological methods. Following that, confirmed isolates were subjected to antimicrobial susceptibility test. Salmonella spp and E. coli were recovered from the cloacal swabs samples with the overall prevalence of 6.5% and 51.8% respectively. In Kelantan, Terengganu and Pahang, the prevalence of Salmonella spp were 7%, 6.5% and 5.8% respectively, while the prevalence for E. coli were 50%, 48.3% and 58% respectively. Salmonella spp and E. coli displayed resistance towards the following antimicrobials: erythromycin (100% for both pathogens), chloramphenicol (76.2%, 84.5%), tetracycline (62%, 94.6%), ampicillin (47.7%, 87%), sulfamethoxazole/trimethoprim (42.9%, 83.3%), ciprofloxacin (4.8%, 23.8%), nalidixic acid (9.6%, 60.7%), streptomycin (19%,66%), and kanamycin (28.6%,57%), cephalotin (0%, 11%), gentamicin (0%, 20.2%) respectively. No resistance were recorded towards colistin for both pathogens. Multidrug resistance (MDR) was recorded in 82% of Salmonella spp and 100% of E. coli. These findings demonstrate the high prevalence of MDR Salmonella spp. and E. coli in broiler farms in East coast Malaysia. This could be attributed to the excessive use of antimicrobial agents by the poultry farm owners. Enhanced control measures and a strong monitoring system should be urgently implemented to reduce the emergence of antimicrobial resistance that is harmful to public health.

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.

As formally is known, nanozymes are the nanomaterials or nanoscale materials which reveal enzyme-like properties. These nanomaterilas can be utilzied instead of the natural enzymes for simulation of enzymatic reactions in harsh environmental conditions. Among different nanoscale enzyme-like materials, the majority of them reveal peroxidase-like activity, due to the high catalytic efficiency and stability of peroxidase-like nanozymes, they had been widely utilized for sensing and detection via constructing the colorimetric and fluorescence-based analytical and bioanalytical sensors. There are several high impact nanozymatic sensors which reported in the literature. Although the review articles published in this field, however, the current reviews focus on the introducing nanozyme field not description on the best articles published in this filed. Hence, this article aimed to present a brief introduction on the nanozyme-based chemistry with emphasizing on the historical overview of recent nanozymatic sensors. In this regard the best nanozymatic sensors which published over 2016-2023 were briefly described and overviewed.

Considering higher stability and lower cost of nanozymes than native enzymes, the nanozymatic systems had been utilized for several practical application, especially for sensing and detection. Most of common nanozymatic sensors are single-nanozyme based systems, however, recently a new generation of nanozyme-based systems called “multinanozyme system’ was introduced by Hormozi Jangi et al. (2020). Since the first report of multinanozyme systems, several multinaozyme systems have been developed and utilized for highly sensitive and selective sensing aims. The main advantages of multinaozyme systems compared of common nanozymatic sensors are their impact on simultaneous enhancing selectivity and sensitivity of sensor in a well-designed detection process. Since, the principles of design and detection mechanism of this new generation is not well-described in the literature, the aim of this article is the fast review of the principles of design of this new generation of nanozyme-based sensing and detection.

Influence of reaction time on silver nanoparticles-catalyzed oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) was evaluated via probing the oxidation process by recording the UV-Visible absorbance of the resulting colored products of TMB oxidation by hydrogen peroxide in the presence of silver nanoparticles as peroxides mimics. The time curve of the oxidation process was constructed, revealed that the concentration of the oxidation product was increased by increasing the reaction time and then levled off because the nanozyme active nodes were saturated after a certain reaction time and consequently the steady-state condition can be observed. The maximal activity of the silver nanozymes was achieved after a very short reaction time as short as 3.0 min and after this time, the color intensity of the oxidation products remained constant, revealing saturation of active nodes of nanoparticles with nanozyme-substrate.

In this contribution, time course studies were performed toward oxidation of different peroxidase substrates in the presence of silver nanozymes. In this regard, two common peroxidase substrates, TMB and DAB, were selected as model chromogens. The process of the oxidation reaction was probed by recording the absorbance of the colored products using UV-Vis spectrophotometer. Thereafter, the nanozymatic activity was calculated and used as an index for comparing the performance of the silver nanozymes against different substrates in a certain reaction time. The time course curves, revealed that the maximal activity of the silver nanozymes were achieved after a very short time of 3.0 min toward TMB oxidation while regarding DAB, the maximal activity was observed after 25.0 min which is 6.5-fold slower than that of the TMB oxidation. The difference between the DAB and TMB can be assigned to their different reaction pathways and different reactivity. In fact, the oxidation of DAB has occurred via n-electron irreversible pathway while the TMB oxidation has occurred through a simple 2-electon reversible pathway. Besides, DAB is a high stable and less relative compound compared of TMB, resulting different oxidation times, different affinity for binding to nanozyme active nodes which lead to different nanozymatic activities.

In this work, a high throughput nanozymatic method was developed for green nanozyme-mediated biodegradation of organic dyes in real water media. The nanozymes were synthesized and characterized for their size and morphology by TEM and DLS analysis. The nanozymatic properties of the as-prepared nanomaterials were evaluated by standard enzyme activity assay, revealing high peroxidase-like performances for the as-prepared nanozymes. Therefore, for exploring more precise, the kinetics behavior of the as-prepared nanozymes was evaluated by the standard Lineweaver-Burk method, revealing a V_max as high as 0.263 µM sec^-1 and a K_m as low as 0.03 mM, revealing high catalytic efficiency and affinity of the as-prepared nanozymes. Hence, the as-prepared nanozymes were utilized for organic dye degradation in real water media using methylene blue as a model organic dye. The effective factors on the dye degradation including pH, ionic strength, degradation time, nanozyme amount, etc. were optimized by the one-factor-at-a-time optimization method. At optimal experimental conditions, the as-prepared nanozymes can degrade about 99.0% of the organic dye within a degradation time as low as 7 min. The developed method was finally employed for the nanozyme-mediated degradation of methylene blue from real water media such as pool, mineral, river, and tap water samples. The results of this study revealed an excellent biodegradation yield of over 94.3%-99.0% for the different real samples, proving the suitability of the developed method for dye degradation in real water media.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

The laser host materials undergo relatively small changes in their intrinsic properties due to various sources during an experiment. These sources are often related to temperature change or applied stress, which may cause change in optical properties of the material under study. Consequently, the crystal experiences an optical anisotropy, which may affect the final outcome of the experiments. One way to reduce probable noises is to predict the change in optical properties with respect to load. To predict the changing pattern of the refractive index in a crystal, one needs to know both the elastic and photoelastic constants of the material. In this study, we utilized density functional perturbation theory (DFPT) to extract the photoelastic tensor of Y$_2$SiO$_5$ and Eu-doped Y$_2$SiO$_5$ crystals. Using the photoelastic and elastic constants calculated, a Finite Element (FE) model was developed, which allowed us to apply load and then post-process the results. This methodology enabled us to observe the variation in refractive index ($n$), and consequently, the shift in the resonance frequency of the cavity. The results obtained were in agreement with experimental measurements, falling within a 3\% discrepancy across a temperature range spanning from cryogenic to room temperature. This correlation suggests the feasibility of using the current workflow as a predictive tool for evaluating variations in refractive indices over a specific interval.

Supplementary cementitious materials (SCMs) are increasingly used in precast concrete industry to enhance concrete strength and durability. SCMs of large market share in precast concrete in-dustry include silica fume, also known as micro-silica and class C fly ash. The main objective of this research is to develop non-proprietary high strength concrete mixes for precast/prestressed concrete industry. Two SCMs are used simultaneously in partial replacement of portland cement to develop ternary mixes. Class c fly ash and silica fume incorporation in the mix design, in ad-dition to low water-to-powder ratio resulted in the successful development of high strength mixes with minimum 24-hour strength in excess of 70 MPa and a final compressive strength ex-ceeding 100 MPa at 28 days. Mechanical properties testing of developed concrete mixes showed that SCMs incorporation resulted in increased values for hardened concrete modulus of elasticity (MoE) and modulus of rupture (MoR). The enhanced concrete properties, mainly high early strength, are highly required in precast concrete industry as it allows for increased productivity of precast facilities and the possibility of pouring high strength slender sections for prestressed concrete applications

Breakwaters influence coastal wave climate and circulation by blocking and dissipating wave energy. Accurate representation of these effects is essential to the determination of coastal circulation and wave processes. MIKE21SW and SWAN are two third-generation spectral wave models which are used widely in coastal research and engineering applications. Recent improved versions of the models are able to consider the influence of breakwater structures. In this study, we used available observations to evaluate the accuracy of model simulations of waves in New Haven Harbor, Connecticut, USA, an estuary with three detached breakwaters near the mouth. We then compare the accuracy and computational efficiency of MIKE21SW and SWAN. Both models were executed on the same unstructured triangular grid. The boundary conditions were derived from a bottom mounted ADCP on the offshore side of the breakwaters. Wind forcing was applied using data from the Central Long Island Sound buoy. We find that both models are largely consistent with observations during storms. The MIKE21SW significant wave height and wave direction simulations were slightly superior; however, SWAN is more efficient and faster due to its implementation of a fully implicit technique for time integration.

Meningitis is a severe disease associated with death in children under five with highest rates of infections under age of one. Vaccines for Neisseria meningitides and Haemophilus influenza are used to prevent the main causative agents of meningitis. Administration of H. influenzae type b (Hib) vaccine is recommended at 2, 4 and 6 months with a booster dose at 18 months. N. meningitidis has two commercially available vaccines, the pure polysaccharide is recommended at 24 months meanwhile the protein-conjugated vaccines at 12 months. We sought in this study to examine if coadministering the vaccines for the two main meningitis causing bacteria might be synergistic as a preliminary step towards the possibility of shuffling immunization schedule. So, we coadministered Hib vaccine with commercially available vaccines either quadrate (ACWY) polysaccharide meningococcal (Men) or conjugated meningococcal (Nim) vaccines in Balb/C mice (n = 6/group) and compared to each vaccine administered separately and controls. Thirty-five days post immunization, we measured specific antibodies titers. Hib vaccine increased Men antibody titers significantly for serotypes Y and W. When Hib vaccine was coadministrated with Nim, antibody titer for Y, W and A significantly increased. For serotype C, there was no significant difference in antibody titers among immunized groups. As for effect of meningococcal vaccines on Hib, Men significantly increased Hib antibody titers while Nim had no effect. Collectively, our data suggested that coadministration of Hib and Men or Nim vaccines was safe and had synergistic effect on immune responses elicited to both vaccines. Further studies are needed before immunization schedule modifications. Such immunization schedule recommendation should provide better protection against this life-threatening disease in young children.

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.

How do sensor research and technologies grow over time? This paper applies the network analysis with a new computational approach to map the structure and evolution of sensor research and technologies over a 30-year time frame (1990-2020).The goal of this study is to analyze the evolution of sensor research for forecasting emerging scientific and technological trajectories. Results show that the scientific interaction within ecosystem (represented with networks) of sensor generates a co-evolution of scientific fields supporting the accelerated growth of different technological tra-jectories, such as: wireless sensors, fiber optic and optical sensors, gas sensors and biosensors. These results suggest main theoretical implications that explain the evolution of sensor research with critical aspects of innovation management to support R&D investments towards new technological trajectories having a high potential of growth.

Oral squamous cell carcinoma often arises from an oral potentially malignant disorder called oral leukoplakia (OL). With this work we aimed to develop a novel data-driven predictive model based on gene expression profiles to distinguish OL patients who underwent malignant transformation from those who did not. We used the Tree Augmented Naïve (TAN) Bayes classifier to predict the posterior probability of having oral cancer given the data. 86 patients were included with a median follow-up of 7.11 years. Fifty-one patients (51/86; 59%) underwent malignant transformation. We found that 16 genes were predictors of oral cancer in patients with OL and these included SLC7A11, SPINK6, SERPINA12, VIT, ATP1B3, CST6, FLRT2, ELMOD1, AZGP1, RNASE13, DIO2, ECM1, CYP4F11, SYTL4, AKR1C1, and AKR1C3. In conclusion, we showed that Bayesian gene networks are a data-driven approach which could be used also in other predictor models in oncology.

The ozonation reaction in a bubble column reactor (BCR) has been widely used in the removal of phenol from wastewater, but the phenol removal efficiency in this type of reactor is limited because of low ozone solubility and reactivity in the system. In the present study, the phenol degradation was enhanced using α-Al2O3 as a packing material and a ZnO nanocatalyst in the BCR. The reactor diameter and height were 8 cm and 180 cm, respectively. The gas distributor was designed to include 52 holes of 0.5-mm diameter. Also, the gas holdup, pressure drop, and bubble size were measured as a function of the superficial gas velocity (i.e., 0.5, 1, 1.5, 2, 2.5, and 3 cm/s). The evaluation of the hydrodynamic parameters provided a deep understanding of the ozonation process to select the optimal operating parameters in the reactor. It was found that the best superficial gas velocity was 2.5 cm/s. A complete (100%) phenol removal was achieved for phenol concentrations of 15, 20, and 25 ppm at reaction times of 80, 90, and 100 min, respectively, using α-Al2O3 packing material and a ZnO nanocatalyst in the BCR. Additionally, the results provided a clear reaction mechanism describing the ozonized BCR. Finally, the applied treatment method can be used efficiently to remove phenol from wastewater at a low cost, with little consumed energy, and a simple operation.

The aim of article is to design a calm water resistance predictor based on Machine Learning Tools and development of a systematic series for battery-driven catamaran hull forms. Regression Trees (RT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) regression models are applied for dataset training on developed systematic series of catamarans. A hullform optimization was implemented for various catamarans including dimensional and hull coefficient parameters based on resistance and structural weight reduction and battery performance improvement. This paper provides a diverse database of catamaran hullform. Hence, an automated Matlab program was coded for geometry generation and cost function evaluation. Design distribution based on Lackenby transformation fulfills all design space and sequentially a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on generated data of case study. This study shows that ANN algorithm correlates well with the measured resistance. Accordingly, a general and unique tool is proposed for optimized and desired design in first design stage.

A challenging problem in the aquaculture industry is bacterial disease outbreaks which results in the global reduction of fish supply and foodborne outbreaks. Biofilms in marine pathogens protect against antimicrobial treatment and host immune defence. Zeolites are minerals of volcanic origin made from crystalline aluminosilicates which are useful in agriculture and in environmental management. In this study, silver ion-exchanged zeolite A of four concentrations; 0.25M (AgZ1), 0.50M (AgZ2), 1.00M (AgZ3)and 1.50M (AgZ4) were investigated for biofilm inhibition and antimicrobial properties against two predominant marine pathogens V. campbelli and V. parahemolyticus by employing the Minimum Inhibitory Concentration(MIC), Crystal Violet Biofilm Quantification assays as well as Scanning Electron Microscopy. In the first instance, all zeolite samples AgZ1-AgZ4 showed antimicrobial activity for both pathogens. For V. campbellii AgZ4 exhibited the highest MIC at 125.00 µg/ml while for V. parahaemolyticus the highest MIC was observed for AgZ3 at 62.50 µg/ml. At sublethal concentration, biofilm inhibition of V. campbelli and V. parahemolyticus by AgZ4 were observed at 60.2% and 77.3% inhibition respectively. Scanning electron microscopy exhibited profound structural alteration of the biofilm matrix by AgZ4. This is the first known study that highlights the potential application of ion-exchanged Zeolite A against marine pathogens and their biofilms.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

Brain stem cells (neural stem cells or NSCs) and neurons of a chosen kind reprogramming is a potential technique for cell therapy. It is possible to reprogram non-neuronal cells, for example, by using a predetermined group of factors, nuclear transfer, and the induced transcriptional factors (TFs) expression in a related lineage of cells, and non-coding microRNAs (miRNAs). Researchers have additionally been attempting to improve reprogramming methods, whether it is by employing unique sets of biomolecules and particular TFs or by delivering relevant miRNA and Biomolecules. The technique of miRNA mediated is intriguing for its capability to quickly create a range of biologically desirable cell types for therapy from different lineages of cells. Current findings have made significant advancements towards changing the somatic cells to diverse particular neuronal subgroups with greater efficiency, using reprogramming of miRNA-mediated neural cells, despite the fact that the precise processes need to be discovered. To further understand how miRNAs might direct somatic cells to become neural, we need to look at the latest research on their function in neural reprogramming over the differentiated cells. Recent findings on the role of miRNAs in the initiation of cell reprogramming and the determination of the neuronal subtype's destiny are the primary focus of this comprehensive overview. Furthermore, we cover the far more latest results concerning certain miRNAs' activity in controlling different phases of neuronal differentiation, which contributes in comprehending the interaction network of miRNAs and their receptors.

Herd immunity happens when a relatively large proportion of a population becomes infected by an agent, subsequently recovers, and attains immunity against the same agent. That proportion thus indirectly protects the naïve population by preventing the spread of the infection. Herd immunity has been suggested to interrupt and control the COVID-19 pandemic. However, relying on establishing herd immunity can be catastrophic considering the virulence and lethality of SARS-CoV-2. Meanwhile our understanding of the pathogenesis, case-fatality rate, transmission routes, and antiviral therapy for COVID-19 remains limited now. Interrupting or slowing the COVID-19 transmission seems more opportune than vaccination, antiviral therapy, or herd immunity, all of which will take some time to yield. Thus, social distancing, face-masking, and hygiene are the most appropriate immediate countermeasures. Because the social fabrics, economic implications, and local demands of various nations are unique, early relaxation of restrictions may seem hasty particularly when fatality rates are high, or when the healthcare systems could be inadequate or become inundated. Conclusively, avoiding any overwhelmingly risky approach in fighting the pandemic is prudent.

Globally, football as a sport has recorded the highest rate of injury morbidity compared with other sports due to the high degree of contact between the players. Coaches play an important role in reducing injuries among the players. The objective of this study was to explore the pattern of football-related sports injuries among junior high school footballers in Dhaka, Bangladesh. A descriptive cross-sectional study was conducted between January 2019 to March 2019 in 20 junior high schools in Dhaka Metropolitan city. We observed 368 boys in the age range of 10 to 18 years old. A pre-structured questionnaire was provided to six trained junior physiotherapists to conduct the face-to-face interview with the boys in the school setting. Most students were from middle-income families. The injury prevalence of defenders was the most (157, 42.7%) followed by mid-fielders (132, 35.94%), forwards (63, 17.1%), and goalkeepers (16, 4.3%). Tackling was the main cause of injury in 21.1% of cases followed by foul play in 19.1% of the cases. In July, there were more injuries (69.0%) and associated muscle strain. Injured footballers did not visit sports physiotherapists as much as they did other health profession and the association was significant. When considering scientific knowledge, students were aware of fitness, flexibility & endurance (25.5%), sports massage (24.5%), the relation of body structure with sports Injury (21.2%), warm-up and/or cool down (19.6%). Based on the study, it was seen that students had no scientific knowledge of sports. As sports is a key activity for school-going children, comprehensive sports injury preventive knowledge is needed for students and sports teachers and coaches. Access to sports physiotherapists is also needed to prevent and manage sports injury at the field level and for rehabilitation.