Monitoring of groundwater quality in today's scenario is very much important. Due to urbanization and population pressure regular monitoring of groundwater for drinking as well as irrigation purposes need a major concern. With this aim, a study has been carried out consisting 26 groundwater samples in May 2017, to access the physiochemical characteristic, water quality index (WQI) of groundwater by using GIS software and to find out the groundwater suitableness for drinking as well as for irrigation purpose. The pH is slightly alkaline and the TDS is much more than prescribed limits of BIS. The trend of cations in groundwater are Ca2+>Na+>Mg2+>K+ while anions trend is HCO3->SO42->Cl->NO3->CO32->F-. The Ca-Mg-HCO3 and Na-K-Cl-SO4 types of groundwater facies were dominant. Generally, the chemical changes in groundwater are administered by the evaporation process with ion exchange, and mixing of particles is the significant source of the solute acquisition process. WQI of the study area suggested that the 15% sample is unsuitable, 69% is poor and remaining is good for drinking uses. The potential salinity of the groundwater sample is nearly high although the majority of the sample is suited for irrigation activities.

This study explores the potential of Eisenia fetida (Savigny) in vermicompost production within vermipits during winter months in the Rampur region of Uttar Pradesh, India. The vermipits were inoculated with 0.33 kg of E. fetida per pit, against the recommended rate of inoculation for assessment of real-time potential of earthworm on seasonal and regional grounds. The results revealed a significant increase in earthworm population, with three times production (4.1 kg) of the inoculated number (1.0 kg). The productivity dynamics of compost vs E. feida revealed a notable threefold and fourfold effectiveness, respectively. This dual advantage signifies the success of composting process, with the increased worm population indicating enhanced decomposition. The study attributes the combination of regional and edaphic factors viz., sandy loam soil with high humus content and good water passage, facilitating earthworm survival, reproduction and decomposition of agricultural waste. The loose and perforated soil structure allowed easy penetration of earthworms, transforming the soil into fertile compost. The research emphasizes the importance of soil moisture of semi-arid ecosystems thus highlighting the seasonal significance governing earthworm activity. The study provides insights into the mechanical ability of earthworms, its sensitivity to soil compaction and the influence of high hydration of the region. Inoculation during the winter period indicates increased earthworm activity setting the stage for enhanced compost production in the upcoming hot periods of summer months. The findings suggest that optimizing vermicomposting practices with altered feeding stocks, could further improvise compost and earthworm production and productivity in subsequent seasons. The study contributes for sustainable utilization of E. fetida in composting, its regional and seasonal efficacy turns trash to treasure for enhanced agricultural efficacy.

COVID-19 pandemic has caused a large-scale havoc in almost every country across the globe, putting major challenges for the healthcare system in many parts of the world. Several of the laboratories are running in the race with undying efforts for developing potential vaccine, drugs or therapeutics to treat or prevent the infection. However, with the limited time window and high rate of infection, the task is very big for humanity to find a cure. With hundreds of genomic data of SARS-CoV-2 virus isolates from humans are being submitted almost every day, it is coming into knowledge that virus is mutating, slower in countries with sporadic cases, but higher in countries experiencing large outbreak. These types of mutations in virus may bring challenges in vaccine or therapeutic development for use in each and every country, as each hotspot region may have their own pattern of mutations in virus with ongoing outbreak. In our current study, we retrieved non-synonymous mutation data of around 12,225 SARS-CoV-2 virus samples isolated from humans globally, and discovered all mutations that are collectively happening in antibody epitope regions of the virus country-wise. We found a few numbers of epitope regions in SARS-CoV-2 that are highly conserved collectively in all variants and may be used for epitope-based vaccine development for whole world. We also found epitope regions that are conserved collectively in SARS-CoV-2 variants country-wise and can be used for customized epitope-based vaccine development in each different country.

Hyperlipidemia is an identified risk factor for metabolic syndrome, obesity and cardiovascular complications characterized by elevated levels of lipids in blood. The present study aimed to inhibit HMG-CoA reductase with 15-oxoursolic acid through in-vivo and in-silico assessments. HFD-induced hyperlipidemia in rats was developed with a marked increase in total cholesterol (58.34 ± 0.21 mg/dl), triglycerides (45.45 ± 0.39 mg/dl), LDL-c (66.12 ± 0.10 mg/dl), VLDL-c (19.34 ± 0.16 mg/dl), and a decrease in HDL-c (10.34 ± 0.18 mg/dl). The oral administration of 15-oxoursolic acid at a concentration of 30 mg/Kg b.w significantly reduced the serum concentration of total cholesterol (42.65 ± 0.54 mg/dl), triglycerides (32.33 ± 0.16 mg/dl), LDL-c (50.34 ± 0.15 mg/dl) and VLDL-c (13.15 ± 0.45 mg/dl) respectively and an increased in the serum HDL-c (18.56 ± 0.34 mg/dl). 15-oxo ursolic acid also caused a decrease in the coronary risk index and atherogenic risk index. In-silico studies of 15-oxoursolic acid with HMG-CoA reductase showed significant interaction capability with binding energy (-9.26805 Kcal/mol). It is concluded that 15-oxoursolic acid could significantly reduce the total cholesterol, triglycerides and LDL levels in HFD-induced hyperlipidemia rats which might lead to new medication with significant hypolipidemic potential.

The COVID-19 outbreak has severely affected the social and economic conditions across this globe. Little is known about the relationship of COVID-19 with countries’ economic and socio-demographic status. Publicly available data on COVID-19 test rate, attack rate, case fatality rate, and recovery rate were analyzed in relation to country’s economic status, population density, median age, and urban population ratio. We also conducted multinomial logistic regression analysis to predict the influence of countries’ social and economic factors on COVID-19. The results revealed that the median age had significant positive correlation with attack rate (r=0.2389, p=0.003), case fatality rate (r=0.3207, p=0.000) and recovery rate (r=0.4847, p=0.000). The urbanization has positive significant correlation with recovery rate (r=0.1957, p= 0.016). The multinomial logistic regression analysis revealed low-income countries are less likely to have an increased recovery rate (p=0.000) and attack rate (p=0.016) compare to high-income countries. The lower-middle-income and upper-middle-income countries are less likely to have an increased recovery rate (p=0.000 and p=0.001, respectively) compared to high-income countries. Based on the result of this study, these economic and socio-demographic factors should consider in designing appropriate preventive measures as a next step. The low and lower-middle-income countries should invest more in health care services to lower the case fatality rate and increase test and recovery rates as part of pandemic preparation like COVID-19. As the number of COVID-19 attacks, death and recovery rates are constantly changing; however, the intensive study is required to obtain a clear picture.

The prominent cultivation of lemongrass relies on the pharmacological incentives of its essential oil. The lemongrass essential oil (LEO) has a significant amount of citral (mixture of geranial and neral), isoneral, isogeranial, geraniol, geranyl acetate, citronellal, citronellol, germacrene-D, and elemol in addition to numerous other bioactive compounds. These components confer various medicinal activities to LEO including antifungal, antibacterial, antiviral, anticancer, and antioxidant properties. These attributes are commercially exploited in pharmaceutical, cosmetics, and food preservations industries. Furthermore, the employment of LEO in the treatment of cancer opens a new vista in the field of therapeutics. Although different LEO components have shown promising anticancer activities in vitro, these effects have not been assessed yet in humans. Further studies on the anticancer mechanisms exerted by lemongrass components are required. The present review intends to provide a timely discussion on the relevance of lemongrass extracts in cancer and health treatment, and in food industry applications.

AI applications are transforming the technology and digital sector, seamlessly integrating into the daily interactions of individuals with the digital world. Key entry points to the internet, such as search engines, are now complemented by recommendation systems that curate content on social networks, streaming platforms, and online marketplaces. Additionally, virtual assistants have become ubiquitous across various platforms. While these groundbreaking changes are gradually extending to several industries, there remains significant untapped potential for the application of artificial intelligence in diverse domains. The renewable energy sector, in particular, continually reinvents itself through research and innovation to meet the escalating demand. Positioned at the forefront of technological evolution, this industry is well-placed to effectively adopt and tailor the innovations of artificial intelligence to its specific needs. Presently, both emerging and established tools leverage elements of AI to enhance processes and introduce functionalities that were previously unattainable. If the current trajectory persists, AI-enhanced tools have the potential to assume a role as crucial to the future of engineering as computer-aided design (CAD) software is in contemporary times.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

The paper presents a thorough examination of the burgeoning field of utilizing manufacturing waste for sustainable energy generation, aligning with the global imperative for resource efficiency and clean energy solutions. With the manufacturing sector being a significant contributor to waste generation, this study explores innovative approaches to transform waste materials into valuable resources, thereby mitigating environmental impacts and contributing to a circular economy. This work critically reviews current trends, emphasizing technological advancements, policy interventions, and market dynamics shaping the manufacturing waste utilization landscape. Noteworthy developments, such as advanced sorting technologies and the integration of digital solutions, are discussed in detail, showcasing their role in enhancing the efficiency of waste management processes. The study outlines future trends in the field, anticipating a shift towards closed-loop systems guided by circular economy principles. This comprehensive review aims to contribute to the discourse on sustainable waste management and energy generation by providing a holistic perspective on the field's current state and offering insights into the future trajectories that will propel manufacturing waste toward a more sustainable and circular future. The synthesis of technological innovation, collaborative efforts, and evolving regulatory frameworks presents a compelling case for the continued exploration of manufacturing waste as a valuable resource in the pursuit of a more sustainable and energy-efficient world.

A well-known result of Posner’s second theorem states that if the commutator of each element in a prime ring and its image under a nonzero derivation is central, then the ring is commutative. In the present paper, we extend this bluestocking theorem to an arbitrary ring with involution involving prime ideals. Further, apart from proving several other interesting and exciting results, we establish *-version of Vukman’s theorem [48, Theorem 1]. Precisely, we describe the structure of quotient ring A/L, where A is an arbitrary ring and L is a prime ideal of A. Further, by taking advantage of the *-version of Vukman’s theorem, we show that if a 2-trosion free semiprime ring A with involution admits a nonzero *-centralizing derivation, then A contains a nonzero central ideal. This result is in a spirit of the classical result due to Bell and Martindale [19, Theorem 3]. As the applications, we extends and unify several classical theorems proved in [6],[25,[42], and [48] . Finally, we conclude our paper with a direction for further research.

Synthesis of new series of 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea derivatives and its in vitro antiproliferative activities against NCI-60 human cancer cell lines of nine different cancer types are described. Fourteen compounds 5a-n have been synthesized with three different hydrogen bondable moieties (4-hydroxylmethylpiperidinyl and trimethoxyphenyloxy and 4-hydroxyethylpiperazine) attached to the core structure 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea. Different substituents with different π and σ values were added on the terminal phenyl group. Compounds with 4-hydroxymethylpiperidine moiety showed higher mean percentage inhibition values over the 60-cell line panel at 10-µM concentration. They showed broad-spectrum antiproliferative activity over many cell lines of different cancer types. For instance, compound 5a elicited some lethal rather than inhibition effects on SK-MEL-5 melanoma cell line, 786-0, A498, RXF 393 renal cancer cell lines, and MDA-MB-468 breast cancer cell line by 146.1, 108.7, 136.2, 134.8, 116.6 % at 10 µM, respectively. Compounds 5a-e exhibited superior antiproliferative activity than Paclitaxel and Gefitinib against the most sensitive cell lines. Two compounds, 5a and 5d showed promising mean growth inhibitions and thus were further tested at five-dose testing mode to determine their IC₅₀ values. The data revealed that 5a and 5d urea compounds are the most active derivatives with significant efficacies and superior potencies than Paclitaxel in 21 different cancer cell lines, belonging particularly to renal cancer and melanoma cell lines. Moreover, 5a and 5d had superior potencies than Gefitinib in 38 and 34 cancer cell lines, respectively; belonging particularly to colon cancer, breast cancer and melanoma cell lines.

Escherichia coli or E. coli is a member of the fecal coliform group and is a more specific indicator of fecal contamination than other fecal coliform species, its presence indicate possibly presence of harmful bacteria which will cause diseases and it also suggests the extent as well as the nature of the contaminants. E. coli bacteria able to survive in water for 4 – 12 weeks and at present, it appears as an indicator to provide the accurate bacterial contamination of fecal matter in drinking water, because of the availability of simple, affordable, fast, sensitive and exact detection techniques. According to the laboratory experiment based techniques, 24 - 48 hours are required for the bacterial concentration to be reported. So, there is a necessity for continuous monitoring. Techniques for detection of many pathogenic bacterial strains are not yet available, sometimes days to weeks are required to get the results. To overcome the difficulties, expensive and time-consuming techniques are required to detect, count and identify the presence of specific bacterial strain. Public health relies on online monitoring of water quality that depends majorly on examination of fecal indicator bacteria, thus protection of health requires fecal pollution indicator so that it is not required to analyze drinking water to overcome the problems associated with waterborne diseases. This paper will brief the classification, sources, survival of E. coli bacteria and its correlation with basic water quality parameters in water sources.```

The increasing demand for water resources in urban areas, such as Bangladesh, due to population growth is a significant concern. One potential solution under consideration is the use of air conditioning (AC) condensate water. A study conducted at the European University of Bangladesh (EUB) focused on assessing the quality and quantity of AC condensate water from various systems. The results indicate that the collected water generally adhered to the quality standards established for drinking and household use in Bangladesh. Parameters such as pH (averaging 6.8), turbidity (1.08 NTU), total dissolved solids (TDS) (averaging 219 mg/L), iron content (0 mg/L), alkalinity (averaging 41.67 mg/L), arsenic (0 mg/L), chemical oxygen demand (COD) (averaging 3.67 mg/L), biochemical oxygen demand (BOD) (averaging 1.33 mg/L), chloride content (averaging 30.77 mg/L), and other factors were evaluated. Moreover, the AC units at EUB, varying in cooling capacity (1 ton, 2 tons, and 4 tons), produced substantial monthly volumes of 96, 177, and 354 liters of condensate water, respectively. This underscores the potential of AC condensate water as a valuable resource for addressing urban water scarcity. As a result, there is a pressing need for local decision-makers and policymakers to establish well-defined guidelines for the effective utilization of AC condensate water to mitigate water scarcity issues in urban areas.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

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.

Abstract: In recent years, the construction engineering sector has undergone a transformative shift towards the integration of machine learning (ML) techniques, particularly in predicting the properties of steel fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, a persistent challenge remains – their opacity, lacking transparency and real-world relevance for practi-tioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) Boosting algorithm, crafting a comprehensive ap-proach. Grounded in a meticulously curated database drawn from 43 seminal pub-lications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of Partial Dependence Plots (PDPs), revealing intricate relationships between input parameters and con-sequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against inde-pendent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thri-ughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

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.

In this paper, it is defined that how the IPOs are affected by the underwriter reputation. What will happen to IPOs who are backed by reputed underwriter and who is not? And evaluate the IPOs short-run and long-run performance. Also try to explain the relationship between IPOs and underwriter. Also tried to compare some of the international return and Pakistani returns. Also determine the relation of the investor to the underwriter. There is a set of investors who have there own relationship with the underwriters how they will affect on the IPOs in the market.

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.

Asthma causes chronic coughing, wheezing, dyspnea, and chest pressure. This study assessed asthmatic patients' knowledge, attitudes and awareness of bronchial asthma and proper education on its meaning, risk factors, symptoms, diagnosis, management, and prevention practice. We performed a cross-sectional interventional asthma KAP survey in Erbil, Iraq. We adapted a validated study questionnaire from KAP studies in other nations to the Erbil situation and culture. In Erbil, Kurdistan, Iraq, two major hospitals' asthma clinics were studied. We chose 250 asthmatic ICU patients from October 2018 to July 2019. Health education was comprehensive. The health education program used a Kurdish PowerPoint with a printout. 25 groups got two-weeks of one-hour health education pre-intervention. Each group was questioned before, 2 and 12 weeks after health education. All data were analyzed by SPSS v26. The mean age of the respondent was 37.52±15.16 with 48.7 % respondent having a positive family history of asthma. After 2weeks of health education intervention, respondent have a higher knowledge score, positive attitudes compared to pre-education and after 12 weeks of education with a significant difference (P < 0.001) with improvised prevention practice. Health education programs led to considerable improvements in asthmatic patients' knowledge, attitudes, and practices regarding their condition. After receiving health education for a period of two weeks, the majority of the participants answered correctly regarding asthma, its causes, and the elements that trigger asthma attacks.

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.

Cadmium Sulfide nanoparticle (CdS-LA hybrid nanoparticles) was synthesized here by a green approach using the precursor cadmium acetate and sodium sulphide along with the extract of a plant Lathyrus aphaca L. containing the phytochemicals which were responsible for surface modification of nanoparticles. The nanoparticle was used to evaluate their inhibition potential against species of bacteria and fungi. The nanoparticles were characterized by XRD which demonstrates the hexagonal crystal structure. SEM confirms the homogenous surface appearance of the CdS-LA hybrid crystalline structure. EDX analysis confirms surface modification of nanoparticles by phytochemicals. FTIR confirmed the Cd-S linkage laterally with the related functional groups and the presence of metabolites on the surface of nanoparticles. The UV-visible spectroscopy confirmed the peak at the characteristic wavelength range but a slight shift occurred in the peak of the CdS nanoparticles due to the presence of the phytochemicals. This study particularly provides an environment-friendly strategy to synthesize the CdS nanoparticles capped by Lathyrus Aphaca L. extract that are biologically active due to the mediation of the plant extract. CdS-LA hybrid nanoparticles have shown inhibition potential against various species of bacteria and fungi and realize the biological importance of the green synthesis of nanoparticles especially mediated with the plant extract.

This study aims to formulate binary cementitious systems containing silica fume (SF) and micro quartz (MQ) to improve cementitious concrete's durability and mechanical properties. In this investigation, we examined the effects of different essential variables, such as the level of SF and MQ replacement and the ratio of the water-binder (w/b). In this study, the w/b ratios were 0.25, 0.3, 0.35, and 0.4. Replacement levels for SF were 8, 10, and 12%, while replacement levels for MQ were 5, 8, 10, 15, 25, and 35%. The porosity and permeability decreased with an increase in replacement levels, regardless of the type of fine material used or the amount of replacement. Meanwhile, the strength increased markedly with SF replacement, reaching its maximum MQ of 25% at the SF level. The strength of the SF mixing was the highest in all mixes, and it remained the highest until 15% of MQ was replaced. The ultrafine size of MQ particles contributes to the improvement of compressive strength, porosity, and permeability in a similar way to SF particles, despite their high crystalline structure. Furthermore, in this study, analysis of variance (ANOVA) was employed to verify the influence of each variable on the studied response.

The advancement of bioconcrete over cementitious composites has brought us to the application of microbes in the field of construction materials. Certain microbes like bacteria, algae, and fungi have been discussed in the review. The purpose of applying these microbes in the matrix is mainly to enhance the concrete’s strength and other properties such as durability, resistance, and self-healing ability. As these microbes are able to induce calcite biomineralizations, the process is also known as Microbiologically Induced Calcite Precipitation (MICP). Some known microorganisms with their mentioned ability are Bacillus subtilis and Bacillus cohnii (bacteria), Chlorella vulgaris and Spirulina platensis (algae), and Trichoderma reesei, Aspergillus niger, and Neurospora crassa (fungi). The paper provides a “state-of-the-art” review of research into the effects of bioconcrete and discusses the overall methodologies of every medium with their physiological, physicochemical and bioengineering properties in the light of recent researches done so far in the same field.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). An example is given here of Type II contextuality involving a simple cooperative game. This arises due to the cooperative nature of the game which results in sign intransitivity for expectation values while individual random variables remain consistently connected. This shows that the conditions attributed uniquely to quantum systems may occur in classical settings as well. Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of intransitivity, these simulated colonies came close to but failed to violate Dzhafarov’s inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required

Myeloperoxidase (MPO) belong to the family of heme containing peroxidases, produced mostly from polymorphonuclear neutrophils. The active enzyme (150 kD) is the product of MPO gene located on long arm of chromosome 17. The primary gene product undergoes several modifications like removal of introns and signal peptide and leads to the formation of enzymatically inactive glycosylated apoproMPO which complexes with chaperons, producing active proMPO by the insertion of heme moiety. The active enzyme is a homodimer of heavy and light chain protomers. This enzyme is released into extracellular fluid after oxidative stress and different inflammatory responses. MPO is the only type of peroxidase using H2O2 to oxidize several halides and pseudohalides to form different hypohalous acids. So the antibacterial activities of MPO involve the production of reactive oxygen and reactive nitrogen species. Controlled MPO release at the site of infection is of prime importance for its efficient activities. Any uncontrolled degranulation exaggerates the inflammation that can also lead to tissue damage even in absence of inflammation. Several types of tissue injuries and pathogenesis of several other major chronic diseases like rheumatoid arthritis, cardiovascular diseases, liver diseases, diabetes and cancer have been reported to be linked with myeloperoxidase derived oxidants. So the enhanced level of MPO activity is one of the best diagnostic tool of inflammatory and oxidative stress biomarkers among these commonly occurring diseases.

Precipitation gauges are critical for measuring precipitation rates at regional and global scales and are often used to calibrate precipitation rates estimated from other instruments such as satellites. However, precipitation measured at the gauges is affected by gauge-undercatch that is often larger for solid precipitation. In the present work, two popular gauge-undercatch correction factors are assessed: one utilizes a dynamic correction model and is used in the Global Precipitation Climatology Centre (GPCC) Monitoring product and the other one employs a fixed climatology and is used in the Global Precipitation Climatology Project (GPCP) product. How much the choice of correction factors can impact the total estimate of precipitation was quantified over land at seasonal, annual, regional, and global scales. The correction factors are also compared as a function of the environmental variables used in their development, among those are near-surface air temperature, relative humidity, wind speed, elevation, and precipitation intensity. Results show that correction factors can increase the annual precipitation rate based on the gauges by ~9.5 % over the global land (excluding Antarctica), although this amount can vary from ~6.3% (in boreal summer) to more than 10% (in boreal winter), depending on the season and the method used for gauge-undercatch correction. Annual variations of correction factors can also be large, so the use of the fixed climatology correction factors requires caution. Given their magnitudes and differences, selection of appropriate correction factors can have important implications in refining the water and energy budget calculations.

In recent years, numerous topologies of single phase and three phase Field Excited Flux-Switching Machine (FEFSM) have been developed for several applications. Comparative study of three types of single-phase low-priced Field Excited Flux-Switching Machine (FEFSM) is presented in this paper. Both the conventional 8S/4P sub-part rotor design and 6S/3P salient rotor design have an overlapped winding arrangements between armature coil and field excitation coil that depicts high copper losses as well as results in increased size of motor. Additionally, FEFSM with salient structure of the rotor have high flux strength in the stator-core that has much impact on high iron losses. Copper consumption and iron loss being a crucial proportion in total machine losses. Therefore a novel topology of single phase modular rotor field excited FSM with 8S/6P configuration is proposed, which enable non-overlap arrangement between armature coil and FEC winding that facilitates devaluation in the copper losses. The proposed modular rotor design acquires reduced iron losses as well as reduced active rotor mass comparatively to conventional rotor design. It is very persuasive to analyze the best range of speed for these rotors to avoid cracks and deformation, the maximum tensile strength (can be measured with principal stress in research) of the rotor analysis is conducted using JMAG. A deterministic optimization technique is used to enhance the performance of 8S/6P modular rotor design. The electromagnetic performance of conventional sub-part rotor design, F1-A3-3P design and proposed novel-modular rotor design are analyzed by 3D-Finite Element Analysis (3D-FEA), includes flux linkage, flux distribution, flux strength, back-EMF, cogging torque, torque characteristics, iron losses and efficiency.

Renewable energy research becomes increasingly necessary as it grows in importance. Wind turbines are an excellent method of harnessing wind energy, which is one of the most important renewable energy sources. In light of the importance of studying wind turbine performance, this study describes the background and principles of wind turbine operation. Computational fluid dynamics (CFD) was used to evaluate the hydrodynamic performance of two types of offshore and onshore wind turbines. Mechanical power and thrust force are calculated using the CFD model for both types of turbines in a steady state based on the number of blades and rotors. The results of three-dimensional simulations are compared with those of experimental testing. Based on the method and assumptions used, the offshore turbine's mechanical torque showed an average deviation of about 4%. Increased wind speed increased mechanical power and thrust, while turbulence caused irregular pressure distributions on surfaces as a result of turbulence intensity. Furthermore, as the free wind speed increases, the blade tip and hub speed will also increase, expanding the vortices created by that flow.

It is imperative to examine the performance of wind turbines both in steady and unsteady states due to their significant role in generating renewable energy by utilizing wind power. The computational fluid dynamics method (CFD) is one of the most efficient tools in this field, and the unsteady state has a great deal of importance when predicting turbine performance. Using the CFD method, the hydrodynamic performance of two types of offshore and onshore horizontal-axis wind turbines in the unsteady state was examined. Offshore wind turbines were modeled using the SST turbulence model and onshore wind turbines were modeled using the large eddy simulation (LES). Three-dimensional results of the simulation were compared and validated with experimental results, and good agreement between the two results indicates that the assumptions and method were accurate. Vortices were also studied in relation to the blade tip and their formation and development. Increasing the number of blades results in a reduction in torque, as well as a rise in vortices caused by the tips of the rotor blades. Due to the intensity of turbulence, the distribution of pressure on the surfaces will become irregular as the wind speed increases.

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.

We investigate the behavior of thermal quantum coherence in the Heisenberg XXX model for a two-qubit system placed in independently controllable Inhomogeneous magnetic fields applied to two qubits respectively. We discuss the behavior of quantum coherence by systematically varying the coupling parameter, magnetic field, and temperature for both ferromagnetic and antiferromagnetic cases. The results show the interesting behavior of quantum coherence in a certain range of parameters. Generally, it is observed that quantum correlations decay with temperature, but in the ferromagnetic case with uniform magnetic interaction, it rises with temperature up to a certain threshold value and ultimately it decreases its value to zero. Moreover, it is observed that preserving the quantum coherence for small temperatures is very hard with the increasing magnetic field because, at small temperatures, quantum coherence decays sharply with the increase in magnetic field whereas at larger temperatures it decays completely at fairly large values of the magnetic field. The variation of quantum coherence with uniform magnetic field in the antiferromagnetic case is observed to be Gaussian for larger temperature but at zero or nearly zero temperature, it behaves as a constant function for uniform magnetic field up to a threshold value and then decays to zero with an infinite slope. This shows the signature of quantum phase transition from quantum nature to classicality.

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.

This comprehensive critical review shows how green buildings and energy optimization solutions improve indoor air quality as well as environmental quality (IEQ) and human health. Electricity-efficient green buildings create a better indoor atmosphere and save electricity. Green buildings improve occupant well-being through IAQ, thermal comfort, acoustics, and lighting. Renewable energy, green construction materials, and intelligent technologies increase sustainability and reduce environmental impact. Environmental hazards, architectural design, and societal variables can improve indoor air quality and subsequently reduce health risks linked with poor air quality. The evaluation highlights the pros and cons of low-cost IAQ sensors and equipment. These devices provide valuable insights, but calibration and validation using reference equipment are necessary to ensure accuracy and reliability. Architects, engineers, environmentalists, and policymakers are essential in creating green buildings with high IEQ. Prioritizing occupant comfort, health, and productivity ensures a sustainable and healthy future for future generations. However, continual monitoring and research are needed to improve green building practices and overcome construction problems. Intelligent models for continual on-field calibration of low-cost sensors may improve performance. Green buildings benefit occupants and the environment by optimizing energy use and using IAQ monitoring technologies.

This article investigates the features of heat and mass transfer for the steady two-dimensional Williamson nanofluid flow across an exponentially stretched surface depending on suction/injection. The boundary conditions incorporate the impacts of the Brownian motion and thermophoresis boundary. The analysis of heat transfer is carried out for the two cases of prescribed exponential order surface temperature (PEST) and prescribed exponential order heat flux (PEHF). The ongoing flow problem is mathematically modeled under the basic laws of motion and heat transfer. The similarity variables are allowed to transmute the governing equations of the problem into a similarity ordinary differential equation (ODEs). The solution of this reduced non-linear system of ODEs is supported by the Homotopy analysis method (HAM). The combination of HAM arrangements is acquired by plotting the h-curve. In order to evaluate the influence of several emergent parameters, the outcomes are presented numerically and are plotted diagrammatically as a consequence of velocity, temperature and concentration proles.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Our environment has been significantly impacted by man-made pollutants, primarily due to industries make substantial use of synthetic chemicals, resulting in significant environmental consequences. In this research investigation, the co-precipitation approach was employed for the synthesis of cellulose-based ferric oxide (Fe₂O₃/cellulose) and copper oxide nanoparticles (CuOx-NPs). Scanning electron microscopy (SEM) analyses were conducted to determine the properties of the newly synthesised nanoparticles. Furthermore, the synthesized nanoparticles were employed for eliminating chromium from aqueous media under various conditions, including temperature, contact time, adsorbent concentration, adsorbate concentration, and pH. Additionally, the synthesised materials were used to recover Cr(VI) ions from real samples, including tap water, seawater, and industrial water, and the adsorptive capacity of both materials evaluated under optimal conditions. The synthesis of Fe₂O₃/cellulose and CuOx-NPs proved to be effective, as indicated by the outcomes of the study.

Many assumptions exist about online learning and its impact on college students. Hitherto, the views of those meant to be the beneficiaries of this technology have been given little consideration despite the fact that students use cyberspace for academic work and beyond. This qualitative case-study report is based on research conducted by college students at a private university in the Eastern Province of Saudi Arabia. The aim was to examine the online learning experiences of their peers during the first wave of the coronavirus global pandemic, with a view to understand how prepared their university is for an academic genre located in cyberspace. The findings are based on the perspectives of 2,298 college students responding to a survey administered to the entire student population comprising around 9,000 individuals. They suggest that increasing opportunities for cyberlearning could have positive effects on students. Also provided is cautionary advice about the need to improve teaching pedagogies and combat academic dishonesty.

We describe a 90-year-old male presenting with disequilibrium, loss of balance and difficulty walking for three days prior to initial presentation. Interestingly, he denied cough, fever or dyspnea prior to arrival. Over the course of 48 hours, the patient developed acute respiratory distress syndrome (ARDS) requiring intubation, diagnosed with COVID-19 infection and was treated in the intensive care unit where he died. Since the initial cases in Wuhan China in Dec 2019, the medical and epidemiological communities have learned much about the presenting features, symptomatology, epidemiology, transmission and common physical, laboratory and radiological findings of this disease. Although common symptoms are already established, it is very important to learn and record atypical symptoms or presentations of this highly contagious disease. By doing so, we will be able to recognize earlier atypical symptoms and prevent the environmental exposure to Health care workers and future patients as well. We report that Central disequilibrium may be such as initial presenting sign and symptom of impending respiratory failure from SARS-CoV-2 virus. These atypical findings such as presyncope may precede common respiratory complications of SARS-CoV-2.

In the previous work, the pure fatigue behavior of AM60 magnesium alloy (PF-AM60) was compared with the corrosion fatigue behavior of these specimens (CF-AM60). In this research, in continuation of the previous job, the pure fatigue behaviors of AM60 with polylactic acid (PLA) coating (PF-AM60-PLA) and also, corrosion fatigue behaviors of magnesium alloy with PLA coating (CF-AM60-PLA) were evaluated. Polymer coating was made by fused deposition modeling (FDM) with a 3D printer and attached to standard fatigue test specimens with a glue. Then, they were immersed in the simulated body fluid (SBF) for 27 days. In the end, a high-cycle bending fatigue test was performed on samples. The fracture surface of the samples was also observed using the field emission scanning electron microscopy (FESEM). Due to corrosion, the weight of the specimens redcued by an average of 35%. The corrosion rate decreased in the first 7 days and then increased. PF samples with coating had an average of 49% increase in fatigue lifetime. Regarding the CF samples, despite the use of a 10 times stronger solution, the fatigue lifetime of these samples decreased by only 35%. FESEM results also showed cleavage plates and striations. In addition, the separation of the glue from the coating and Mg was observed. Corrosion products including microcracks and holes were seen on the fracture surface of CF specimens, which caused stress concentration and crack growth. Holes caused by the release of gases were also observed in polymer coatings.

Background: The Internet has become an essential component of college students' lives, serving as a tool for academic pursuits, daily activities, and social interactions with individuals and the global community. Productive internet use is associated with student success, while pathological internet use can negatively impact psychosocial performance. This study aimed to assess the impact of internet addiction on the cognitive function, mood, and physical activity of college students. Study design: The study was based on a correlational observational study design. Settings: The study’s participants were taken using a convenience sampling method from the Manav Rachna International Institute of Research and Studies campus, Faridabad, Haryana, having a student strength of more than two thousand. Methods: With a response rate of 69.93%, one hundred male and female college students who could understand the self-administered questionnaires and obtained the internet addiction test (IAT) scores equal to or above twenty participated (aged 18-25 years) in this study. The participants were assessed for the study’s outcomes, such as the internet addiction (viz. average, moderate, and excessive) cognition, mood, and physical activity level, using an internet addiction test questionnaire (IATQ), cognitive failure questionnaire (CFQ), the profile of mood state questionnaire (POMS), and international physical activity questionnaire (IPAQ), respectively. A categorical analysis with mean scores was carried out for all the outcomes. The correlation between internet dependency and cognitive abilities, emotional state, and levels of physical activity among the participants was ascertained using Pearson's correlation coefficient. The threshold of significance was set to 95%, meaning any p-value < 0.05 was considered significant. Results: The study’s outcome variable- internet addiction, revealed a moderate positive correlation with cognition (r=0.793; p=0.001) and mood (r=0.703; p=012), while a moderate negative correlation with physical activity (r=-0.681; p=003). Conclusion: The study concluded that internet addiction is positively linked to cognition and mood disruption; however, it negatively affects physical activities among college students. An awareness program should be executed on “Internet addiction and its effects on college students’ Cognition, Mood, and Physical Activity” and advocating Internet use in a limited manner for getting aroused, feeling better, and avoiding physical inactivity.

Additive manufacturing (AM) are used in several industries such as automotive, aerospace, and medical sciences. One of the most common devices used in additive manufacturing is fused deposition modeling (FDM) 3D printers. This fabrication method has different inputs that affect the quality of the parts. In this research, the bending fatigue properties of polylactic acid (PLA) biomaterial made with a 3D printer are investigated. To demonstrate the influence of printing parameters on fatigue lifetime, standard specimens with nozzle diameters of 0.2-0.6 mm, extruder temperature of 180-240°C, and print speed of 5-15 mm/s were printed. After performing fully-reversed bending fatigue tests, it was found that printed specimens at 180°C have the best fatigue lifetime in most cases. Accordingly, fatigue behavior improved by reducing the nozzle diameter. Printing at lower temperatures also improved fatigue lifetime. The printing speed affected the slope of the S-N diagram, known as the fatigue strength exponent.

Text book ought to be viewed as one of the numerous sources educators can attract after making a powerful exercise and may offer a system of direction and introduction. The analysis of textbook forms a necessary part in academics so as to devise the most appropriate content. The present study made a comparative account of textbooks for Grade 5 Social studies taught in public (i.e., Punjab textbook) and private sector (Oxford textbook) of Pakistan. The comparison was made on key features and on themes suggested by National Curriculum Council (2017) as a standard for textbook publishers. The findings demonstrated that both books contain activities and are clearly written, however, PTB is cheaper, possess low paper quality than OTB and was revised in 2018 whereas OTB has not revised since 2010. It is also suggested that both textbooks do not contain enough information as per NCC’s standard. It is suggested that government should take notice about it and publishers should be bound to deliver only that material in education sector that is according to the devised set of standards.

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

In this paper, a two-stage stochastic programming modelling is proposed to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involves both strategic and tactical decision levels. The first stage deals with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. At the second stage tactical decisions such as base stock level as an inventory policy is determined. The generic introduced model consists of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of proposed model is its applicability to various industries. The problem is formulated as a mixed-integer linear programming model and is solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities are added to the master problem. The proposed accelerated BD is evaluated through small-, medium-, and large-sized test problems. Numerical results reveal that proposed solution algorithm increases convergence of lower bound and upper bound of BD and is able to reach an acceptable optimality gap in a convenient CPU time.

Among various metal-based nanoparticles, silver nanoparticles (AgNPs) manifest superior inhibitory effect against several microorganisms. In fact, the AgNPs based therapy has been reported to inhibit both sensitive as well resistant isolates of bacteria and other disease causing microbes with equal propensity. Keeping this fact into consideration, we executed bio-mediated synthesis of AgNPs employing Hibiscus rosa sinensis flower extract. The as-synthesized AgNPs were evaluated for their potential to inhibit Streptococcus mutans (S. mutans), one of the main causative bacteria for dental caries. Beside several other reasons, orthodontic appliances have also been reported to facilitate infliction of oral cavity with a range of microbes including S. mutans. To determine the growth inhibitory and anti-adherence activity of AgNPs on orthodontic appliance, we performed microbiological assays employing AgNPs adsorbed on to the orthodontic wires. Topographic analysis of orthodontic wires was executed by scanning electron microscopy. In addition to antimicrobial and antibiofilm activity against oral S. mutans, the as-fabricated AgNPs demonstrated significant inhibitory and anti-biofilm properties against other biofilm forming bacteria such as Escherichia coli and Listeria monocytogenes as well.

An efficient design of conservation tillage trencher (CTT) saves time, natural resources, and conserves soil health by avoiding vigorous soil disturbance of tillage operations. A two-wheel tractor operated CTT has constructed with modified rotary shafts, toolbar frames, depth control bars, bed shapers and chisel ploughs for conservation tillage operation of sugarcane plantation. The performance evaluation of developed CTT was tested for comparing the suitability with small-scale farmer’s practice (SFP) and large farm practice (LFP). Finally, the CTT is applied for preparing sugarcane farmland in different conservation tillage methods of i) bed cum trenching (BT), ii) zero-tillage trenching (ZT) and iii) strip-tillage (ST). The performance of effective field capacities of CTT were 0.14 ha h-1, 0.12 ha h-1 and 0.14 ha h-1 for BT, ZT and ST, respectively. Developed CTT saved time 80% and 26-37% over SFP and LFP, and required 96% and 26-36% less labour than SFP and LFP, respectively. Conservation tillage required 7-10 l ha-1diesel whereas SFP and LFP required 25 l ha-1and 65 l ha-1, which reduced CO2 emission by 60-72% and 85-89% compared to SFP and LFP, respectively. Ultimately, newly developed CTT saved sugarcane land preparation costs by 86% to 90% compared to conventional tillage systems.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. Therefore, the primary objective of this study was to evaluate the impact of crossbreeding Bangladeshi local cattle with the exotic Holstein breed on their body characteristics and milk quality. To achieve the goal, data pertaining to body traits and milk samples were gathered from a total of 981 cows from 19 dairy farms located in the northwestern region of Bangladesh. A trained evaluator measured body condition score (BCS), udder score, locomotion score, and body conformation traits. Milk yield information was acquired from official records, while milk composition details were determined through milk analysis. The average measurements for body traits were as follows: wither height (WH) at 138 cm, body length (BL) at 155 cm, and heart girth (HG) at 203 cm. Notably, crossbred cows (Holstein x Local cattle) exhibited greater values for WH, BL, HG, BCS, and udder score than their Bangladeshi local counterparts. Furthermore, the average daily milk yield of sampled cows was recorded at 8.7 kg, with fat and protein contents of 3.78% and 3.67%, respectively. It is worth noting that crossbred cows produced a higher volume of milk compared to Bangladeshi local cattle. However, milk from crossbred cows displayed lower fat and protein content, although their somatic cell score (SCS) remained similar. Additionally, milk from crossbred cows exhibited a longer coagulation time when compared to that of Bangladeshi local cattle. A more comprehensive assessment of the crossbreeding scheme, particularly in comparison to Holstein, would necessitate further research, including an examination of its impact on cheese yield traits, health, fertility, and longevity.

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.

Topological defects such as magnetic monopoles that may have formed during cosmological phase transitions in the early universe. The Kibble mechanisms govern the formation of this defects and their abundance is influenced by the correlation length and the temperature-dependent Higgs mass. The Magnetic monopoles are particularly interested but their excessive production poses the monopole problem which is challenge to the standard model. Many solutions including symmetry breaking patterns and thermal pair production have been proposed to mitigate the monopole problem. Detecting monopoles would offer valuable insights into high-energy physics and the early universe. Detection of (even) a single monopole could revolutionize our current knowledge of the universe.

In this work, generalized geometry of second-order tangent groups and affine configuration complexes is proposed. Initially, geometry for higher weights n=4 and weights n=5 is presented through some interesting and suitable homomorphisms, finally, this geometry is extended and generalized for any weight n.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

This paper is given to the investigation of warm inflation using Modified Chaplygin gas in the background of locally rotationally symmetric Bianchi Identity type I. We find out the field equations and perturbations parameters such as; scalar power spectrum, scalar spectral index, scalar potential and tensor to scalar ratio under slow roll approximation. We find out these parameters in directional of Hubble parameter during the Logamediate inflationary regime in weak and strong case. These comological parameters shows that the anisotropic model is also compatible WMAP$7$ with recent observational data Planck $2018$.

Culinary herbs and spices are widely used in daily diets. Pakistan flora is enriched with phytochemicals due to a diverse range of land. Phytochemicals, including volatile and non-volatile compounds, have captured much interest due to their numerous health advantages and significance in daily diet. The present study aimed to conduct in-depth metabolomic profiling of Pakistani-grown fenugreek leaves (Trigonella foenum-graecum), fennel seeds (Foeniculum vulgare), mint leaves (Mentha royleana), coriander seeds (Coriandrum sativum) and basil leaves (Ocimum basilicum) by using liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography–mass spectrometry (GC-MS). The first study was conducted to optimize extraction using different solvents (methanol, ethanol, chloroform, acetone, and water). Total phenolic content (TPC), total flavonoid content (TFC), and total condensed tannins (TCT) were quantified along with the antioxidant and anti-diabetic activities. The highest TPC (125.42 ± 10.89 mg GAE/g) and the highest antioxidant and anti-diabetic potential were quantified in mint. Seventy-one phytochemical metabolites were identified using LC-MS/MS, while forty-nine volatile constituents were identified using the GC-MS. A positive correlation was identified between phenolic contents and their biological activities. Furthermore, molecular docking helped to find drug molecules with more excellent anti-diabetic activity based on their binding affinities. This study suggests that selected medicinal herbs from Pakistan have significant nutraceutical and phytopharmaceutical potential. This study could further help in drug discovery.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

Renewable energy especially insights into solar energy are crucial in the energy business cycle. In this paper, an impact study of meteorological variables on solar energy production is conducted. The paper discusses the pipeline of data analysis specific to the case study and exploits open-source machine-learning algorithms to build predictive models. RandomForestRegressor and XGBoosting regressors are used to build the predictive model. The application code is integrated into an analytic software service (Power BI) and tested on Solar energy Open Data of Saudi Arabia. The solar energy production is found to be high between 800-1000 barometric pressure locations. Global Horizontal Irradiation is found to increase when the atmospheric temperature is more than 34∘ C. The impact of relative humidity is that the produced energy is high at lower humidity points. The wind speed effect is that the produced energy is significant between 2 to 4 m/s and reaches its maximum at 2.4 m/s. The predictive models produce nearly real-time predictions with Random Forest with R²: 0.909. The paper provides Global Horizontal Irradiation estimation equations using meteorological coefficients generated by the trained regressor. The paper presents implementation methodologies and records various instances of test results. It highlights the efficacy of machine learning in predictive modeling for renewable energy applications.

In many Bayesian computations, first, we obtain the expression of the joint distribution of all the unknown variables given the observed data. In general, this expression is not separable in those variables. Thus, obtaining their marginals for each variable and computing the expectations are difficult and costly. This problem becomes even more difficult in high dimensional quandaries, which is an important issue in inverse problems. We may then try to propose a surrogate expression with which we can do approximate computations. Often a separable expression approximation can be useful enough. The Variational Bayesian Approximation (VBA) is a technique that approximates the joint distribution $p$ with an easier, for example separable, one $q$ by minimizing Kullback–Leibler Divergence $KL(q|p)$. When $q$ is separable in all the variables, the approximation is also called Mean Field Approximation (MFA) and so $q$ is the product of the approximated marginals. A first standard and general algorithm is alternate optimization of $KL(q|p)$ with respect to $q_i$. A second general approach is its optimization in the Riemannian manifold. However, in this paper, for practical reasons, we consider the case where $p$ is in the exponential family and so is $q$. For this case, $KL(q|p)$ becomes a function of the parameters $\thetab$ of the exponential family. Then, we can use any other optimization algorithm to obtain those parameters. In this paper, we compare three optimization algorithms: standard alternate optimization, a gradient-based algorithm and a natural gradient algorithm and study their relative performances on three examples.

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.

Bangladesh has a history of species introduction from different geographic regions. The country was a major trade route during the early-modern era and was under British colonial rule until 1947s. Many species of plants and animals are either domesticated or cultivated at different times that were brought into the country by the settlers, seamen, and traders. The deliberate preferences of fast-growing, high-yielding exotics in recent decades also threaten the existence of native species and their genetic resources in the country. Here we provide an overview of the invasive alien species in Bangladesh, likely pathways of their introduction, their impacts on ecosystem and people, and strategies for their effective management and regulation. Many exotic plants and animals both terrestrial and aquatic have found to be invasive in the country with negative impacts on local ecosystems, biodiversity, and livelihoods. Collectively, those species possess serious threats to country’s agriculture, forestry, and fisheries sector. Although initially introduced to favor primary productivity or other environmental benefits, many of these are now regarded as obnoxious pest or weed in Bangladesh. A comprehensive list of invasive species both for native and exotics and a framework to characterize them is also absent in the country. We recommend a separate statutory body and appropriate rules and policies for the introduction, monitoring, and management of alien species in the country. Community awareness, advocacy, surveillance, capacity building of relevant government staff and agreement with neighboring countries for transboundary management of invasive alien species is also necessary.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Matrix metalloproteinases (MMPs) are critical enzymes involved in a variety of cellular processes. MMPs are well known for their ability to degrade the extracellular matrix (ECM) and their extracellular role in cell migration. Recently, more research has been conducted on investigating novel subcellular localizations of MMPs and their intracellular roles at the respective locations. In this review article, we focus on the subcellular localization and novel intracellular roles of two MMPs closely related: membrane-type-1 matrix metalloproteinase (MT1-MMP) and matrix metalloproteinase-2 (MMP-2). Although MT1-MMP is commonly known to localize on the cell surface, the protease also localizes to the cytoplasm, caveolae, Golgi, cytoskeleton, centrosome, and nucleus. At these subcellular locations, MT1-MMP functions in cell migration, macrophage metabolism, invadopodia development, spindle formation and genes expression, respectively. Similar to MT1-MMP, MMP-2 localizes to the caveolae, mitochondria, cytoskeleton, nucleus and nucleolus, and functions in calcium regulation, contractile dysfunction, genes expression and ribosomal RNA transcription. Our particular interest lies in the role MMP-2 and MT1-MMP serve within the nucleus, as it may provide critical insights into cancer epigenetics and tumor migration and invasion. We suggest that targeting nuclear MT1-MMP or MMP-2 to reduce or halt cell proliferation and migration may lead to the development of new therapies in cancer and other diseases.

1. Background and Objective: Spontaneous Intracranial Hemorrhage (ICH) is a catastrophic medical condition associated with significant morbidity and mortality. As the accumulation of unbound iron following ICH contributes to secondary brain injury, deferoxamine, an approved chelation drug, has become the center of attention. At the same time, its therapeutic remains a matter of dispute. This double-blinded Randomized Controlled Trial (RCT) aims to investigate the therapeutic potential of deferoxamine in neurological and radiological aspects. 2. Methods and Materials: The study enrolled 42 participants diagnosed with spontaneous ICH and confirmed by CT scan, randomly assigning them to either the deferoxamine treatment group or the placebo control group. While the control group received the routine treatment plus a placebo, the treatment group received routine treatment conjugated with 7.5 mg/kg of deferoxamine per hour intravenously over the first three days. The study compared hematoma and edema volume, GCS score, and mortality rates between the two groups. Our study employed rigorous randomization and blinding procedures to ensure unbiased results. 3. Results: There was a significant (p<0.05) improvement in patients' GCS up until the fourth day, but no significant difference was noted afterward. In addition, the volume of both edema and hematoma was significantly lower in the intervention group, as well as the length of stay, intubation requirement, and mortality. 4. Discussion: Deferoxamine administration can, at least in a short timeframe, improve neurological and radiological parameters.

This study will lead to the development of a numerical data set that will assist in the design of Savonius wind turbines. A major objective of this study is to develop a better design for Savonius turbine blades in order to increase their torque coefficients, rotational speeds, and pressure coefficients. As part of the experimental design methodology, a full dataset was generated by simulating three-dimensional scale models utilizing computational fluid dynamics (CFD) simulations that were validated by wind tunnel data. This process is multi-objective optimization for optimizing turbine performance. The twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. The group method of data handling (GMDH) algorithm was utilized to model objective functions based on input-output data. The Pareto fronts were plotted using polynomial models obtained from the evolutionary Pareto-based optimization approach (modified NSGA-II), and the optimal commercial points were determined using TOPSIS. A comparison of three- and two-objective optimization data revealed that two-objective optimization data lie within the boundaries of a three-objective optimization problem. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively, using multi-objective optimization. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.

The present study was designed to check the prevalence of cancer in residents of District Bannu, Khyber Pakhtunkhwa. Patients' data were collected from three consulted Institutes during the year 2005-09. The data was analyzed for different parameters; year-wise, age-wise prevalence, gender-wise, occupation wise and marital status wise prevalence, addiction status, type of cancer, site affected, diagnostic tests performed and treatment of cancer. Results revealed that during 2005, a high number of cancer patients were present. A total of 172 patients were in the age group of 51-60 years, while only 11 patients were in the age group of 80 plus years. Female patients were more as compared to males. Married people were highly affected than unmarried. Patients addicted to smoking were highly affected than non-addicted patients. Malignancy was high in patients as compared to benign cancer. The most affected site was the blood system. The most affected people occupation wise were housewives followed by the farmer. A biopsy was mostly conducted for the diagnosis of cancer. Most of the patients were treated with chemotherapeutic agents. It has been revealed that the number of cancer patients decreased per annum. Moreover, the study should be conducted on the general population of Bannu as well as of the Khyber Pakhtunkhwa to sort out the total number of patients. The government should launch preventive, diagnostic programs so that prevalence may be minimized.

Background: The novel coronavirus (COVID-19) infection is primarily transmitted through respiratory droplets from an infected person and contact with tainted surfaces or items. Social distancing became the primary measure of preventing the spread of COVID-19, with the principal aim being to reduce the probability of contact with an already infected person. Social distancing can be distressing, as it is against the community's way of life. Saudi Arabia imposed a curfew and suspended all religious, entertainment, sporting, and mass congregations. The daily updates on COVID-19 infection and mortality are upsetting and further, aggravate depression and anxiety. With this background, this study was planned with the following objectives. To determine the sociodemographic profile of the study participants, to find the prevalence of psychological morbidity, and to measure the association between the sociodemographic details, COVID-19 associated factors, and psychological effects among Abha citizens during the COVID-19 pandemic. Methodology: A descriptive cross-sectional community-based study was conducted in the Abha city population from August 2021 to July 22. This study followed snowball sampling, the data was collected using an online Google form the link was shared in WhatsApp groups and through emails to contacts, asking friends to share it with their friends, until reaching the adequate sample size the responses were accepted. Results: A total of 530 participated, and their age ranged from 19 -70 years. Many of the participants (34.5%, n=183) belonged to 36-45 years old. Most of the responders were 69.8% married and Females (69.2%, n=367). Half of the participants had been quarantined (49.6%), and 51.5% were scared from fear of COVID-19. 64.8% of the participants had depression; 14.2%, 20.8%, 13.6%, and 16.2% had mild, moderate, severe, and extremely severe depression, respectively. Around 58.2% of participants suffered from anxiety; 5.8% experienced mild anxiety, 21.7% moderate anxiety, and 30.7% severe anxiety. The proportion of stress among the participants was 45.0%; 10.8% had mild stress, 9.4% had moderate stress, and 24.5% had severe to extremely severe stress. Conclusions: COVID-19 poses mental health problems among individuals and communities. There is a need to find the more susceptible individuals for mental health issues or other psychological disorders. Mental health and psychological problems can be resolved with health education, counseling, and appropriate interventions to curb the negative health impacts of COVID-19.

The rapid development in industries manufacturing plastic bags is taking serious consideration to save the environment and human health, one way or the other. This study examines the composition of degradable and non-degradable plastic bags collected from the markets of different sectors of Islamabad. Hundred samples of both Degradable and non-degradable plastic bags were collected from the open market. The concentrations, proportion, and patterns of different heavy metals (additives) were used in the production of both degradable and non-degradable plastic bags by using the standard method with the help of Hands Held X-Ray Fluorescent (HHXRF) instrument. Samples results of the study showed Titanium, Calcium and Copper used in massive amounts, some of the toxic metals i.e., Arsenic, Lead, Cadmium, Chromium and Mercury were also detected. This study also reveals that degradable plastic bags might be more hazardous than non-degradable plastic bags because due to the Photolytic properties of additives used in degradable plastic bags when the chains of polymers break, the heavy metals are released in environment and become the part of it. So, concern department like Ministry of climate change and Pakistan Environmental Protection Agency should take serious step to control these serious issues.

Coronavirus that is also known as COVID-19 disease is produced by SARSCoV-2. This causative agent is highly contagious and can cause potentially fatal pneumonia worldwide with serious public health concerns. In the beginning among infected individuals, most of them were those who were mainly shown to the wet animal market in a big city of China known as Wuhan. So, it was suggested that this was almost certainly the zoonotic source of COVID-19illness. The transitional source of origin and their mode of transmission to humans were not known obviously. Conversely, from human to human rapidly transformation have been confirmed generally. Currently, there is no availability of FDA approved clinically antiviral drugs and/or vaccines to be used against the COVID-19. Afterward, SARS-CoV and MERS-CoV, the occurrence of SARS-CoV-2 has been manifested as the third sketch of an enormously pathogenic coronavirus into human population globally. In this review, we provide a brief overview of the history of COVID-19 in Pakistan up-to 18^th weeks after beginning, current situation, epidemiology, and its impact on the human population. Moreover, we focused on physiological variation during the incubation period, genome analysis of SARS-CoV2, supportive treatment approaches, and safety measures in the Pakistani population, which may be supportive for combating the risk of COVID-19 epidemic. We also reviewed the future approaches for the development of therapeutic interventions and vaccines to cope with the COVID-19 epidemic.

Plant Growth Promoting Rhizobacteria (PGPR) is beneficial microorganisms that inhabit the rhizosphere of plants and play a crucial role in enhancing plant growth and development. This review provides an overview of the significant role of PGPR in sustainable agriculture. The mechanisms that promote plant growth, such as nitrogen fixation, phosphate solubilization, and growth hormones, are discussed. The potential of PGPR in improving plant tolerance to biotic and abiotic stresses, reducing the use of chemical fertilizers and pesticides, and enhancing nutrient availability, soil fertility, and uptake is also highlighted. Moreover, the ecological and economic benefits of using PGPR for sustainable agriculture include improving crop productivity, reducing environmental pollution, and ensuring food security.

The Ali-Cesaro Stolz theorem, introduced in this paper, extends the classical Cesaro-Stolz theorem by addressing cases where the latter does not yield results or provides a "Does Not Existe (DNE) form. This novel theorem leverages the properties of limits and Z-transforms, offering a robust framework for analyzing sequences and series. The theorem is particularly effective in scenarios where traditional approaches fail, providing new insights and tools for mathematical analysis. This paper presents a detailed formulation of the Ali-Cesaro Stolz theorem, including proofs, applications, and a discussion on its implications and advantages over existing methods.

The growth of social media has changed the face of many aspects of marketing such as online, digital, etc. It also has changed the way modern human communicates and connects with others. Moreover, the behavior on this platform could not and should not be justified with strategies of other marketing channels and media. Due to the nature of social media, they are rich in precise and lean data, but processing these data and extracting knowledge and insights from them are problematic. Evaluating the effectiveness of a marketing endeavor is also a task related to these data. The current research attempts to assess the effectiveness of an advertising campaign on Instagram via advertising cost and sentiment classification of audience opinion regarding the campaign. The methodology used in this research is the standard process of data mining, i.e., CRISP-DM. Furthermore, multiple machine learning models and approaches were studied to train a prediction model based on data. In order to find the most accurate algorithm, grid search was used among the trained models and different algorithms with different combinations of hyper-parameters. The obtained results revealed that although the number of not profitable advertising media was higher than the profitable media, the overall status of the campaign was profitable, both in the cost-effectiveness approach and sentiment analysis approach. The other valued outcome of this research was important general and specific insights which can be used to shape a better-performing and effective advertising campaign on Instagram.

Membrane transporters (MTs) are mainly localized at the plasma membrane (PM), tonoplast and vacuolar membrane (VM) of cells in all plant organs. Their work is to maintain the cellular homeostasis by controlling ionic movements across PM channels from roots to upper plant parts, xylem loading and remobilization of sugar molecules from photosynthesis tissues in the leaf (source) to roots, stem and seeds (sink) via phloem loading. The plant’s whole source-to-sink relationship is regulated by multiple transporting proteins in a highly sophisticated manner and driven based on different stages of plant growth and development (PG&D), and environmental changes. The MTs play a pivotal role in PG&D in terms of increased plant height, branches/tiller numbers, enhanced numbers, length and filled panicles per plant, seed yield and grain quality. Dynamic climatic changes disturbed the ionic balance (salt, drought and heavy metals) and sugar supply (cold and heat stress). Due to poor selectivity, some of the MTs also uptake toxic elements in the roots that negatively impact on PG&D, later on also exported to upper parts and then deteriorate the grain quality. As an adaptive strategy, in response to salt and HMs plants activated PM and VM localized MTs that export toxic elements into vacuole, and also translocate in the root’s tips and shoot. However, in case of drought, cold and heat stresses, MTs increased the water and sugar supply to all organs. In this review, we mainly reviewed recent literature from Arabidopsis, halophytes, and major field crops such as rice, wheat, maize and oilseed rape to argue on the global role of MTs in PG&D and abiotic stress tolerance. We also discussed the gene expression level changes and genomic variations within a species as well as within a family in response to developmental and environmental cues.

Periodontal diseases and Thalassemia Major (TM), which are two chronic inflammatory diseases, are associated with increased levels of proinflammatory cytokines in bio-fluids. Interleukin (IL)-6 and IL-8 have been identified as potent proinflammatory biomarkers in periodontal diseases as well as in patients with β-thalassemia (TM-β). This suggests that patients with TM-β and gingival inflammation have two chronic inflammatory conditions, each of which may affect the other. The objective of this pioneer research was to evaluate the effect of oral hygiene maintenance and care on serum levels of IL-6 and IL-8. 31 participants selected after screening and were given Non-surgical periodontal therapy(NSPT). IL-6 and IL-8 were evaluated by enzyme linked immunohistochemistry assay (ELISA) at baseline and compared at 6 weeks after NSPT. Statistical analysis was done with SPSS v23. The comparison from the baseline to 6-weeks follow-up for both IL-6 and IL-8 was done using Wilcoxon Signed rank test. The descriptive statistics are represented as median and inter quartile range (IQR). Highly significant improvement (p-&lt;0.000) was found for IL-6 and IL-8 when compared at 6 weeks from baseline. Results provide a gateway to new modality in lowering systemic burden of the disease, thereby reducing morbidity, via local measures or oral care.

Zinc nanoparticles (ZnNPs) open new opportunities for biomedical applications as a strategy against microbes and dye removal in an efficient way. The composite ZnNPs using Rhododendron arboreum (R. arboreum) stem bark were synthesized and characterized for UV-visible spectroscopy (UV-vis), Fourier transform infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The synthesized nanoparticles showed zones of inhibition of 23±0.09, 18±0.1 and 16±0.05 mm, against the Klebsiella pneumoniae (K. pneumoniae), Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli strains. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against K. pneumoniae, S. aureus, and E. coli were found to be 34±0.21 and 72.71±0.47, 47±0.11 and 94.86±0.84 and 94±0.18 and 185.43±0.16 µg/mL, respectively. The biosynthesized ZnNPs resulted in significant eradication of the outer and inner membranes of the bacterial cells. Likewise, the synthesized ZnNPs showed time-dependent photocatalytic degradation activity and revealed 65% methyl orange dye degradation with an irradiation period of 6 hours. The findings of this study suggest the suitability of the novel R. arboreum stem bark-based ZnNPs as an effective ameliorant against bactericidal activities and photocatalysts for the removal of hazardous water contaminants.

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.

The interaction between endocannabinoid (eCB) system with in key brain structures such as hippocampus, amygdala and prefrontal cortex and sleep deprivation (SD)-induced psychosis has been less studied. The present hypothesis revolves around the question whether altered chemical dynamics within the eCB system with the resultant impact on cannabinoid receptors in key cortical hubs would impact SD-induced psychosis-like symptoms. Having this investigated research is expected to pave the path towards identifying newer drug targets namely for schizophrenia.

Highly realistic media created through Artificial Intelligence and Deep Learning, commonly known as deepfakes, presents a serious risk to political stability and the integrity of information. We are entering an era where images and videos, which were once considered reliable evidence, are now being altered to spread false information and incite unrest. This threat is especially heightened in developing nations, where misinformation and manipulation of public sentiment could lead to the collapse of the fragile democracies. This study delves deep into the increased vulnerability of these regions to deepfakes due to slower technological advancement and widespread lack of awareness. This study provides a detailed analysis and sets the stage for future investigations, proposing a combined approach to counteract the consequences of deepfakes. This includes combining technical methods for detection of deepfakes with public awareness campaigns and legal frameworks. Reducing the harmful effects of deepfakes requires cooperation among a diverse group of experts.

Abstract Natural disasters such as earthquakes have affected urban historical centers severely. Considering the limitations of the existing methods in conservation and preservation of these sensitive areas, and recent advances in technological devices and techniques such as GIS and 3D printers, this article aims to propose a new methodology utilizing available knowledge and resources. The authors of the present research propose an innovative, technological, and highly accurate method to assess the seismic vulnerability of historical contextures rectifying the existing obstacles and limitations. Through this methodology, the level of trial and error is substantially reduced and the output is stronger, more optimized and more conservative as compared with commonly used methods. Using high tech principles and theoretical foundations (i.e. sustainability principles) this method achieves updated analyses, practical plans, and economic optimization.

Large-span post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited literature exists on their demolition. In a case study, this paper addresses the unique challenge of demolishing large-span bonded post-tensioned beams due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous pre-stressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, offering insights from the design perspective of PT systems. The paper details an innovative method for safely demolishing large-span bonded PT beams, using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety.

Enteropathogenic microorganisms like Escherichia coli cause severe intestinal problems by disrupting the gut homeostasis. The live microorganisms, when given in adequate quantities provide several beneficial effects to the host are known as probiotics. One of the pronounced benefits conferred by probiotic is to antagonize the growth of enteropathogens competing for adherence to the intestinal epithelium. Bifidobacterium is the major genera of human especially infant are intestinal microbiota. In current study, Bifidobacterium bifidum was isolated from the infant stools and probiotic potential was assessed using prescribed tolerance tests against low pH, gastric juices and bile salts. Anti-infectious activity of probiotic Bifidobacterium bifidum against enteropathogenic E. coli was checked both in vitro and in vivo using agar well diffusion assay and mice model respectively. Mice feces were evaluated for both Bifidobacterium bifidum and E. coli counts in all groups and analyzed statistically. In vitro results showed Bifidobacterium bifidum possess marked antibacterial activity against E. coli. There was significant decrease in enteropathogenic E. coli burden in the mice group fed with Bifidobacterium bifidum before and after challenge. In conclusion, the endogenous Bifidobacterium bifidum have excellent probiotic potential and can be used prophylactic and treatment option against enteropathogens.

This study uses the Scopus and Web of Science databases to review the forecast of electricity consumption from 2015 to 2024. Based on the keywords used in the article title, which are associated with electricity consumption forecasts, the study retrieved 821 docu-ments for additional analysis using various techniques. For frequency analysis, we used Mi-crosoft Excel; for data visualisation, we used VOSviewer; and for citation metrics and analysis, we used Harzing's Publish or Perish. Standard bibliometric variables, including publication growth, authorship patterns, collaboration, prolific authors, national contribution, most active institutions, favourite journals, and highly cited articles, are used in this study's reporting of the findings. Our analysis indicates that over ten years, starting in 2015, publications on the forecasting of electricity consumption have continued to increase. China was the country that contributed the most to research on electricity consumption. Most of the research publications on electricity consumption were published in the Journal of Energy.

Self-compacting concrete is essential for vertical structures due to high reinforcement and usage of pumps for transporting the concrete at the working site. SCC is mainly influenced by self-curing for arid areas. To explore the SCC (Self-Compacting Concrete) mixture and condition influencing the self-curing condition were determined using the experimental procedure. Determination of various parameters was done using T50 slump flow, V-funnel, L-box-box, J ring test, compressive strength, splitting tensile strength, and flexural strength test. After the experimental investigation, it shows that if the dose if the dosage of superplasticizer (SNF%) increases, it will improve the concrete mixture's different properties, such as flowability and ability to pass. Higher water-to-cement ratios (w/c) generally result in enhanced flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) demonstrates favourable results across all parameters, indicating excellent flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) also shows favourable flowability and stability characteristics. Lower concentrations, such as 0.5%, may have a positive effect up to a certain point but might not provide sustained improvement over the curing period. On the other hand, concentrations of 1% and 2% demonstrated consistent and significant enhancement in strength throughout the 28 days. It is found that the higher concentrations of PEG 4000 may be more effective in improving the material strength.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 Snow Telemetry (SNOTEL) sites in Alaska are used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018-2019) of the high resolution radar/rain gauge data (Stage IV) product was also utilized to add insights into scaling differences between various products. The outcomes were also used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range in which other products can be assessed. Time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tend to overestimate snow accumulation in the study area, while current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that corrections factors applied to rain gauges are effective in improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill in capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill in capturing the geographical distribution of snowfall and precipitation accumulation, so bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Studying Active Transportation (AT) is widespread in America and Europe's research and stud-ies that include Latent variables (LV) are growing to identify exact results of finding what to do to increase the utility of AT. LVs help us to have more accurate research. LVs are defined as psy-chological factors like feeling safe while you ride at night and thus they are not subjective and hard to understand but very important to increase the utility of using AT modes. In this study, most of the previous studies about bicycling have been reviewed. In this research, different var-iables including subjective and LVs are conducted for maximizing the use of bicycle utility and introduced to have better sight for future researchers to deal with modeling AT mode choice. Results represent the importance of having 'will' for using a bicycle, especially in difficult situa-tions and cultural barriers that affect the cycling of women.

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

Let G be a finite group. The prime graph Γ(G) of G is defined as follows: The set of vertices of Γ(G) is the set of prime divisors of |G| and two distinct vertices p and p' are connected in Γ(G), whenever G has an element of order pp'. A non-abelian simple group P is called recognizable by prime graph if for any finite group G with Γ(G)=Γ(P), G has a composition factor isomorphic to P. In [4] proved finite simple groups ²D_n(q), where n ≠ 4k are quasirecognizable by prime graph. Now in this paper we discuss the quasirecognizability by prime graph of the simple groups ²D_2k(q), where k ≥ 9 and q is a prime power less than 10⁵.

Omega-3 polyunsaturated fatty acids (ω-3 PUFAs, n-3 PUFAs), including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and alpha-linolenic acid (ALA), are essential polyunsatu-rated fats primarily obtained from fatty fish and plant-based sources. Compelling evidence from preclinical and epidemiological studies consistently suggests beneficial effects of ω-3 PUFAs on bone health and healthy aging processes. However, clinical trials have yielded mixed results, with some failing to replicate these benefits seen in preclinical models. This contraindication is mainly due to challenges such as low bioavailability, potential adverse effects with higher doses, and susceptibility to oxidation of ω-3 fatty acids, hindering their clinical effectiveness. This review provides an in-depth analysis of recent findings, focusing on clinical perspectives and encom-passing all relevant published studies to date, including preclinical and epidemiological research, with an emphasis on the role of omega-3 fatty acids in promoting bone health and supporting healthy aging. Additionally, it explores strategies to improve ω-3 PUFA efficacy, including na-noparticle encapsulation and incorporation of specialized pro-resolving mediators (SPM) derived from DHA and EPA, to mitigate oxidation and enhance solubility, thereby improving therapeutic potential. By consolidating evidence from various studies, this review underscores current in-sights and future directions in leveraging ω-3 PUFAs for therapeutic applications.

Spatial evaluation of flood-prone areas at the drainage basins is one of the basic strategies in the field of flood risk management. The present study aims to investigate the efficiency of the CN logistic and hydrological regression model for predicting and zoning floods. In the first stage, 13 runoff parameters, hydrologic soil groups (HSGs), slope, lithology, drainage density (DD), land curvature, elevation, distance to waterways/rivers, topographic wetness index (TWI), stream power index (SPI), rainfall, land use, and NDVI were employed. In the SCS-CN model of the drainage basin, the infiltration rate (S) and runoff amount (Q) were determined. The weights of the used layers were weighted by the AHP. Also, a flood zoning map of the drainage basin with different 5, 15, 25, and 50 year return periods was drawn by applying the weights of the layers. To ensure the accuracy of the zoning map with the logistic regression model, the ROC curve, and the area below the curve were used. The results showed that for the prediction rate, the AUC is 0.81%, indicating that the model has acceptable accuracy. The most important factors affecting flood are geological index; distance to waterways/rivers; and NDVI in the logistic regression model, and slope, DD, rainfall, and land use in the SCS-CN model respectively. 30 to 46% of the drainage basin area during 5 to 50 year periods has moderate flood potential, and 28 to 34% has high potential.

A claim management office (CMO) is a new intra-organisation fit based on an organisational project management (OPM) approach and deals specifically with improving claim performance. However, CMO is either misunderstood or often overlooked in construction companies, mainly due to ignorance of the OPM context but further exacerbated by a lack of real case study research on how to adopt CMO in these organisations. This study addresses this prevailing knowledge gap by investigating the nature of the construction industry from the CMO viewpoint and developing an extensive framework through which to improve organisational claims performance. Organisational ambidexterity theory integrated with X-inefficiency theory is adopted for claims performance outcomes, due to organisation intra-firm irrational decisions when managing such claims. Data was obtained and analysed from a international large construction company (which used CMOs) and its internal focus group discussion (as a multi-method approach). The reference framework provides new perspectives on how construction-related companies can adopt a CMO structure, which enables them to improve claims performance by planning in three interrelated activities viz. function-, process- and performance-based-. Furthermore, findings contribute to researchers and practitioners by providing a true understanding of the CMO-related mechanism and plausible roadmap for future work.

Keywords: Barriers; Facilitators; Sepsis; Sepsis Six Guidelines; Nurses.

The global rising demand for exploiting limited shared groundwater resources, coupled with significant water deficit, especially in arid and semi-arid regions, has led to escalating conflicts among stakeholders. As such, using Bankruptcy Theory methods can be an appropriate response to the reallocation of resources. This study introduces a novel approach to weighted Bankruptcy, where the relative importance of stakeholders is determined by their contributions to sustainable development, and the impact of their claims on the shared groundwater resources is considered. The framework is implemented in a two-level bankruptcy process including the plains and their beneficiaries (agriculture, drinking and industry). The proposed two-level weighted bankruptcy process applies to the Neyshabour-Ataiyeh-Sabzevar plains in Iran. The process is implemented for two scenarios including the Baseline scenario related to the present status of demands and the Future scenario associated with the estimated demands for the year 2041. To compare the weighted proposed method with the other weighted methods such as Proportional (WPRO), Constrained Equal Awards (WCEA), Pinile (WPIN), Talmud (WTAL), Constrained Equal Losses (WCEL), Modified Constrained Equal Losses (MWCEL), two categories including the deficit-based and resource-based approaches are considered. According to the stability analysis using the Bankruptcy Allocation Stability Index (BASI) method, among the deficit-based methods, the novel approach is chosen as the preferred method for the beneficiaries’ reallocation like the Sabzevar’s beneficiaries. Accordingly, it allocates 86.95%, 10.86% and 2.17% of the shared resource to the agricultural, drinking, and industrial demands, respectively. Among the resource-based methods, the WPIN method is selected for reallocation between the stakeholders and their beneficiaries in each level and scenario of reallocation. The novel approach offers a promising solution to the water resource reallocation problem, ensuring a more equitable and sustainable management of shared groundwater resources.

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

Background: Hepatitis B virus (HBV), a lethal form of viral hapatitis, is an extra burden on the socioeconomic status of a country. Objectives: The population-based studies to quantify the incidence of hepatitis B. Data sources: in the North Waziristan, formally known as EX-FATA (Federal Administrated Tribal Area), Pakistan, are still rare. Study eligibility criteria: 943 individuals were selected on the basis of signs and symptoms to determine the epidemiology of HBV in the study population. Tests: Hepatitis B virus (HBV). Methods of data synthesis: Among these, 626 were male and 317 were female. All the HBV suspected individual were screened through ICT (Indirect Coombs Test) , ELISA (enzyme-linked immunosorbent assay) and RT-PCR (Re-verse transcription polymerase chain reaction). Results: The results obtained from the screening tests for HBV revealed that RT-PCR based results are more reliable and it indicated that HBV infection is more common in male 41 (6.54%) than female 15 (4.73%). The age range selected for the study was 1-75 years. Age wise analysis indicated that HBV is more prevalent among individuals of age group between 16 to 45 years. This age range also showed higher number of patients having either mild, moderate or elevated viral loads. The HBV genotype D was the most common, present in 62.5% patients. However, 16.07 % patients were having genotype U, 12.5 % genotype C, 8.92 % genotype A, and 7.14% had genotype B. Discussion: The questionnaire based investigation revealed that HBV was most prevalent among married individuals (64.6%), whereas least prevalent among individuals having HBV positive family member (1.4 %). The other risk factors contributed were history of dental procedures (57.8%), ear, nose, piercing, or tattooing and uses of un-disposable syringes (56.5%), transfusion of blood or plasma (42.9%), surgery (41.8%), frequent visits to barber shops (39.45%), and addiction of injectable drugs (38.1%). The current study is providing a clear picture of the alarming situation HBV transmission and emphasis on the need for more effective planing, strategies and policies for the control and prevention of HBV.

Background: In today’s competitive market, predicting customer churn with high accuracy is crucial for enterprises to maintain growth and profitability. Traditional predictive models often lack in accuracy due to the complexity of customer behavior.Objective: This research aims to improve the accuracy of predicting customer churn by utilizing the Particle Swarm Optimization (PSO) algorithm for optimizing the hyperparameters of a composite deep learning model. The performance of this enhanced model is evaluated against traditional models such as LSRM_GRU, LSTM, GRU, and CNN_LSTM to demonstrate the effectiveness of PSO in hyperparameter tuning.Methods: A composite deep learning approach was employed, integrating various neural network architectures to leverage their strengths in modeling complex customer interactions. The PSO algorithm was used to optimize the model’s hyperparameters. Customer transaction and interaction data from different business operations served as the dataset for testing and analyzing the model’s performance. Evaluation metrics including accuracy, precision, recall, F1 score, and ROC AUC were utilized for a detailed comparison with established models.Findings: The PSO-enhanced composite deep learning model showed superior performance across all metrics, significantly outperforming the LSRM_GRU, LSTM, GRU, and CNN_LSTM models. Notably, improvements in ROC AUC and F1 score highlight the robustness and balanced precision-recall trade-off of the proposed model, demonstrating its effectiveness in identifying potential churners.Conclusion: The integration of PSO for hyperparameter optimization in composite deep learning models for customer churn prediction has proven to significantly enhance predictive accuracy and performance metrics over conventional models. This underscores the potential of evolutionary algorithms in improving deep learning applications. Future research should explore the scalability of this approach across various sectors and further refine predictive accuracy with evolving customer data.

Mind-body interaction and its manifestations at the brain level has been studied extensively in the field of consciousness research. Fara-darmani Consciousness Field, as claimed by Mohammad Ali Taheri (the founder), is a method of connecting with the Cosmic Consciousness Network through human mind and his brain has a detective role in this process. As a result of this connection, the scanning process of the state of a being, e.g., the health status of the cells and consequently organs is performed. This study was conducted to evaluate the effects of the Fara-darmani Consciousness Field connection on electroencephalogram (EEG) features as an important biomarker of the brain functioning. The results showed that there was a significant increase in the gamma2 frequency band (35-40 Hz) power in the frontal lobe in medial frontal gyrus (BA6) and paracentral lobule (BA31) of the brain during the task condition compared to the rest condition in a Fara-therapist population. Considering the cortical electrical activity of Fara-therapist’s brain during Fara-darmani Consciousness Field connection, characterizing increase in the power of gamma wave and the activity of the areas affecting on memory, attention, perception and default mode network intrinsic activity. This manifestation distinguishes Fara-darmani Consciousness Field connection from other known methods dealing with the mind-body interaction criterion mainly different types of mediation.

This study aimed to identify and categorize the influencing factors on the purchase of dairy products among customers of dairy companies in Mashhad city. This study is a mixed research (qualitative and quantitative), conducted with the aim of identifying and categorizing the influencing factors on consumers’ purchase. Therefore, the theoretical foundations were examined in the initial step, after which the customers and their needs and demands were addressed in terms of the company and its products. In the qualitative part, interviews were conducted in the focus group (23 people in 5 groups) to identify and extract these factors using the theme analysis technique. The reliability of the interviews was confirmed by retesting and the reliability between two encoders. Sixty themes (factors) that affected the purchase of dairy products were collected. In quantitative part, 517 questionnaires were collected from 13 regions of Mashhad city to perform statistic tests. A questionnaire was designed using the extracted themes in the previous (qualitative) stage to assess customers’ feedback. The validity of the questionnaire was confirmed by content and structure validity (exploratory and confirmatory factor analysis), and its reliability was checked by Cronbach’s alpha. Fourteen components were obtained by exploratory and confirmatory factor analyses, after which the variables were labeled as intrinsic, psychological, and personality factors, the identity of the company, production power of the company, competitive power of the company, competitive prices, social awareness, and store capabilities. The construct validity was confirmed using confirmatory factors.