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The disruption in a production system occurs due to labor problem, machines breakdown, strikes, political issue, and weather disturbance, etc. This leads to delay in the supply of the products, resulting customer to approach other dealers for the products. This paper is an attempt to develop an economic production quantity model using optimization method for deteriorating items with production disruption. We obtained optimal production time before and after the system gets disrupted. We have also devised the model for optimizing the shortage of the products. This research is useful to determine the time for start and stop of the production when system gets disrupted. The optimal production and inventory plan are provided, so that the manufacturer can reduce the loss occurred due to disruption. Finally a graph based simulation study has been given to illustrate the proposed model.

Purpose: The main factor that causes cataracts is the increased oxidative stress and imbalance of an antioxidant defense mechanism, which leads to significant changes in the lens microarchitecture. Senile cataract is the most common type of acquired cataracts due to aging. Methods: We carried out a case–control study in the biochemistry department to examine the antioxidant status (catalase and total antioxidant capacity [TAC]) and lipid peroxidation marker, that is, malondialdehyde (MDA) in human lens epithelial cells (HLECs) of different grades of senile cortical, nuclear, and posterior subcapsular cataracts. We collected 150 samples from patients aged 50–90 years. These included 50 samples of cortical cataracts, 50 of nuclear cataracts, and 50 samples of posterior subcapsular cataracts. We measured catalase activity by the Beer method, TAC by the Benzie and Strain method, and protein by the Bradford method. We also estimated TAC in the aqueous extract of HLECs by the ferric reducing ability of plasma (FRAP) method and MDA by the thiobarbituric acid assay method. Results: The results of this study showed that the level of catalase enzyme was higher in the first grade of nuclear, posterior subcapsular, and cortical cataracts than in other grades. This suggests that the catalase enzyme activity drops sharply in the second and third grades of these types of cataracts. The same pattern was observed for TAC, which was higher in the first grade of nuclear, posterior subcapsular, and cortical cataracts than in other grades. There were significant differences between catalase and TAC in different grades of cataracts, indicating that as the grading increases, both catalase and TAC decrease. Conclusion: The results of this study showed that the levels of MDA were higher and the levels of catalase and TAC were lower in patients with more severe cataracts compared to the healthy controls.

Background: Positive patient care attitudes for mental illness cases must be developed in the early stages of medical education since those affected by mental illness or having some mental disorder suffer negative attitudes from other people in the community. Earlier studies identified several factors that affect medical students' attitudes toward patients with mental illnesses. Purpose: The purpose of the study was to determine the attitude of medical students toward patients of mental illness. Methods: In this cross-sectional study using convenience sampling, 3rd and 4th year medical students (220) were assessed for attitudes toward mental illness using a pretested and pre-designed questionnaire which included details of socio-demographic variables such as age, gender, year of study, personal experience with mental illness, family history of mental illness or having direct contact with mental illness cases and knowledge about mental illness. Percentage and proportion were calculated, and the chi square test was applied to assess significance of the results. Results: The factors significantly associated with medical students’ attitudes toward mental illness were age, year of study, and knowing or having direct contact with mental illness patients. Those knowing mental illness cases in their family or friends had good attitude toward such patients. Conclusion: Education and direct contact with cases of mental illness serve as intervention strategies to reduce negative attitudes associated with mental illness among medical students.

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

Introduction Shisha tobacco use is gaining popularity around the world, especially among young people and college students. Shisha users are exposed to many of the same harmful substances as cigarette smokers but at much higher levels, which could have more serious adverse health impacts. The aims of this study were to: 1) determine the patterns of shisha smoking among university students in the Northern Province of Saudi Arabia; 2) identify the reasons for using shisha tobacco; and 3) ascertain whether usage of shisha smoking differs by their sociodemographic characteristics. Methods A cross-sectional survey was conducted among students of seven different colleges at a public university between October and December 2022. A modified version of the Global Youth Tobacco Survey questionnaire was used to collect information specifically on shisha smoking. After adjusting for confounding variables, a logistic regression analysis was used to determine the related factors. Results A total of 418 participants were included in the study. Of the studied population, shisha smoking was seen in 73.7% (n=308). Out of 308 shisha smokers, 208 (67.5%) had their first session of shisha smoking within the past two years; 34.4% of participants had used it in the past 30 days (current users), of which 27.4% had smoked shisha from 1 to 7 days during the past 30 days. The majority of respondents (63.6%) reported having friends that smoke. The most popular flavor among respondents (24.6%) was apple. For quitting attempts, 46.4% reported a 24-hour quit attempt in the previous year. Stress (23.0%) was reported as the main reason for beginning shisha use. Shisha smoking was significantly associated with age (Ref. 18–20 years; 24–26 years, AOR=0.08; 95% CI: 0.02–0.33, p

Abstract Background Docetaxel (DTX) finds extensive use in treating various cancers, but its limited solubility, side effects, and multi-drug resistance (MDR) hinder its effectiveness. To enhance DTX's properties, the study aimed to formulate DTX-loaded mixed micelles (MMs) and evaluate their anticancer potential using Quality by Design (QbD) approach. Using solvent evaporation, DTX-loaded MMs were prepared and optimized via a 32 full factorial design. Results The optimized formulation (R5) displayed a % entrapment efficiency (%EE) of 74.81 ± 4.27%, % drug loading capacity (%DLC) of 29.27 ± 0.70%, and mean particle size (MPS) of 71.4 ± 1.24 nm. TEM images confirmed well-dispersed spherical MMs. Analytical studies (IR, DSC, and P-XRD) showed no adverse drug-excipient interactions. The MMs were converted into vacuum foam-dried (VFD) products for enhanced stability. The optimized VFD products exhibited low residual moisture, rapid reconstitution, consistent drug content, and high %EE. Notably, sustained drug release from the VFD product reduced hemolysis and in vitro cytotoxicity against B16F10 melanoma cells. Conclusion This study creatively tackled DTX's challenges through targeted MM development, transformed them into VFD products, demonstrating the potential for melanoma treatment. The QbD approach ensures the formulation’s safety, efficacy, and quality, underscoring the promising VFD technology and multifunctionality of mixed micelles. Graphical abstract

Introduction: Subarachnoid Block (SAB) with Hyperbaric Bupivacaine (HB) is the most common anaesthetic technique for Lower Segment Caesarean Section (LSCS). Levo-bupivacaine, an enantiomer of racemic bupivacaine, has been developed to provide anaesthesia with the same effectiveness but with better haemodynamic stability. Aim: To compare the effectiveness of hyperbaric levo-bupivacaine to HB in achieving sensory and motor blocks in Caesarean Section(CS) using equivalent doses. Materials and Methods: Eighty parturients aged 18-38 years with no co-morbidities were randomly divided into two groups receiving equivalent doses of HB and hyperbaric levo-bupivacaine for SAB. The effectiveness of the two drugs was compared in terms of the time taken to achieve sensory and motor blocks, as well as the time for block regression for two segments for sensory block and the return of motor block assessed by the ability to flex the ankle joint. Adverse events such as a fall in Systolic Blood Pressure (SBP) and the dose of vasopressor were noted for the two groups. Results: The time taken to attain a T6 dermatomal block level was 2.43±1.00 and 2.80±1.51 (p-value 0.08) for the bupivacaine and levo-bupivacaine groups, respectively. Complete motor block of the lower limb was achieved in 4.85±1.67 and 5.15±1.82 (p-value 0.53). However, the time to 2-segment regression for sensory block was significantly faster in the levo-bupivacaine group than in the bupivacaine group (125.9±28.56 minutes and 109.13±28.84 minutes, respectively, p-value 0.009). Regression from motor block was also found to be highly statistically significant (158.38±34.92 minutes for bupivacaine and 138.75±25.71 minutes for the levo-bupivacaine group, p-value 0.006). Spinal-induced hypotension was comparable in both groups, but the bupivacaine group needed a much higher repetition of dose of vasopressor than the levo-bupivacaine group. Conclusion: Levo-bupivacaine is comparable to its racemic isomer bupivacaine in achieving anaesthesia when administered Intrathecally (IT) for CS. However, with equivalent doses, the duration of action is significantly shorter with hyperbaric levo-bupivacaine. Dose adjustment might be required with hyperbaric levo-bupivacaine based on the duration of the surgery. Haemodynamic stability is also similar with both drugs.

Introduction: Rabies is a zoonotic disease that can be fatal and continues to impose a significant financial burden on developing countries. Recently, there have been advancements in the introduction of cell culture vaccines and immunoglobulin, as well as the approval of Intradermal (ID) schedules for vaccine administration. Aim: To assess the knowledge of doctors in Rewa District regarding the National Guidelines for Rabies Prophylaxis in 2019. Materials and Methods: A cross-sectional study was conducted, involving doctors from tertiary healthcare centres, district hospitals, and private practices. Data were collected over a period of one month from 206 doctors using a Google form, which included information about their field of practice and their knowledge of recent guidelines on Rabies prophylaxis. Data analysis was performed using the Chi-square test. Results: Out of the 206 doctors surveyed, 92 (44.7%) were male. A 56.7% of the doctors were aware of the regimen and dosage for ID administration of the Anti Rabies Vaccine, (ARV) and 44.6% were knowledgeable about the modification of Post-Exposure Prophylaxis (PEP) to Pre-Exposure Prophylaxis (PrEP) in the Essen regimen. Furthermore, 70.9% of the doctors were familiar with the dosage of Human Rabies Immunoglobulin (HRIG), while 42.7% knew how to manage animal bites in immunocompromised patients. Conclusion: The study revealed a lack of sufficient knowledge on various aspects of rabies management among the doctors surveyed. This underscores the need for reorientation programs and Continuing Medical Education (CMEs) training to be provided to doctors to enhance their knowledge of rabies and improve the effective management of animal bites.

Objective: To compare the accuracy of working length determination between cone beam computed tomography (CBCT) and electronic apex locator by measuring the actual working length of teeth. Material and Methods: A total of 150 single-rooted tooth assessed by radiograph undergoing root canal therapy were selected. The process was repeated to obtain a buccolingual and mesiodistal section of all teeth. The measurement line was considered from the reference occlusal plane following the center of the canal to the terminus. All information regarding the accuracy of cone-beam computed tomography and apex locator was noted in a pre-designed proforma. Results: CBCT consistently demonstrated high accuracy across all tooth types in both jaws. The electronic apex locator exhibited varying precision, with greater accuracy observed in the mandible. Statistical analyses revealed significant differences in electronic apex locator accuracy among tooth types in the maxilla (p=0.042), emphasizing the importance of specific clinical considerations. Conclusion: Cone beam computed tomography emerges as a reliable diagnostic tool for accurate working length determination, especially in complex cases, while the electronic apex locator remains valuable with careful consideration of potential variations in accuracy. An individualized approach, considering tooth type, jaw location, and clinical context, is crucial for precise working length determination in endodontic practice.

Type 2 Diabetes (T2D) exerts a substantial impact on mortality rates. According to 2023 statistics, more than half a billion individuals are experiencing the effects of T2D, making it one of the top 10 leading contributors to worldwide deaths. Multiple factors contribute to the onset of T2D, such as obesity, poor diet and lifestyle, the mutation in specific genes and many more. Among the various factors that contribute to the development of T2D, genetics is a pivotal aspect. Due to the significant influence of genes in the initiation and advancement of various phases of T2D, our focus lies on exploring the association between T2D and genes. In the present article, we have curated Standard disease gene association data which contains evidence or reference sentences which contain this disease gene association information, which is further classified into 4 classes: Yes, No, Ambiguous and X each pertaining to Positive, Negative, Ambiguous and Not related disease-gene associations respectively. For the purpose of this work, we downloaded T2D related abstracts from PubMed using EDirect and further pre-processed this abstract data to extract Reference Sentences Data. This data was later double-fold manually validated to compile this disease gene association data. The data produced in this article serves as reference data for the training text mining-based biological literature classifiers. Classifiers will further be used to predict classes of published literature, not just for T2D, but can also be expanded beyond to encompass a wide range of disease and their complications. The compilation of positively linked genes derived from these predictions can then be utilized for in-depth system-level analysis of T2D.

Abstract India is the world’s largest cotton producer and the only country that grows all four cultivated cotton species. There have been very few studies on the diversity and abundance of natural enemies of cotton insect pests in these cultivated cotton species. Therefore, the current study (2016–2018) was conducted to assess the diversity and abundance of natural enemies that cultivated cotton species harbour. Phule Dhanwantari, Suraj, Suvin, RCH-2, and DCH-32 were the five genotypes used in the study, each with a distinct genetic background. Using the adiv 2.0.1 and vegan R packages, we identified significant differences in natural enemies in terms of species diversity, richness, evenness, and abundance. Analysis of Similarity (ANOSIM) and Non-metric Multidimensional Scaling (NMDS) indicated substantial differences in the natural enemy community structure among the examined genotypes. A total of 17,279 natural enemies were collected and identified across genotypes from seven predatory families and five parasitoid families. The percentage share of these natural enemy families across genotypes and years, in descending order, is Coccinellidae (28.23%)

The novel coronavirus (COVID-19) has inundated the whole world by causing severe acute respiratory syndrome in humans, thus posing serious public health concerns. The oral and olfactory systems are significantly impacted by the COVID-19 condition, in addition to the respiratory and digestive systems. Oral symptoms of COVID-19 can be caused by a loss in oral hygiene, immune system suppression, a decline in general health, and candidiasis due to prolonged antibiotic use. Because many aerosols and droplets are produced during various dental operations, dental professionals and dentists are at a significant risk of contracting the COVID-19 infection. Few precautionary measures include performing a pre-appointment health screening for all patients, performing aerosol-producing procedures in a designated space or operatory, using proper personal protective equipment and disinfecting the operatory between patients, and limiting the number of dental procedures at one time. Against this background, the purposes of this article are to explore the oral presentations of COVID-19 infection and to emphasize the hazards to dental professionals during COVID-19 pandemic. Additionally, few preventive measures are also highlighted.

Private Fifth Generation (5G) Networks can quickly scale coverage and capacity for diverse industry verticals by using the standardized 3rd Generation Partnership Project (3GPP) and Open Radio Access Network (O-RAN) interfaces that enable disaggregation, network function virtualization, and hardware accelerators. These private network architectures often rely on multi-cell deployments to meet the stringent reliability and latency requirements of industrial applications. One of the main challenges in these dense multi-cell deployments is the interference to/from adjacent cells, which causes packet errors due to the rapid variations from air-interface transmissions. One approach towards this problem would be to use conservative modulation and coding schemes (MCS) for enhanced reliability, but it would reduce spectral efficiency and network capacity. To unlock the utilization of higher efficiency schemes, in this paper, we present our proposed machine-learning (ML) based interference prediction technique that exploits channel state information (CSI) reported by 5G User Equipments (UEs). This method is integrated into an in-house developed Next Generation RAN (NG-RAN) research platform, enabling it to schedule transmissions over the dynamic air-interface in an intelligent way. By achieving higher spectral efficiency and reducing latency with fewer retransmissions, this allows the network to serve more devices efficiently for demanding use cases such as mission critical Internet-of-Things (IoT) and extended reality applications. In this work, we also demonstrate our over-the-air (OTA) testbed with 8 cells and 16 5G UEs in an Industrial IoT (IIoT) Factory Automation layout, where 5G UEs are connected to various industrial components like automatic guided vehicles (AGVs), supply units, robotics arms, cameras, etc. Our experimental results show that our proposed Interference-aware Intelligent Scheduling (IAIS) method can achieve up to 39% and 70% throughput gains in low and high interference scenarios, respectively, compared to a widely adopted link-adaptation scheduling approach.

The world is becoming increasingly urbanized, with migration rates often exceeding the infra-structural capacity in cities across the developing world. As such, many migrants must reside in informal settlements that lack civil and health protection infrastructure, including air quality monitoring. Here, geospatial inverse distance weighting and archived Central Pollution Control Board (CPCB) air quality data for neighboring stations from 2018 to 2021 were used to estimate air conditions in five informal settlements in Delhi, India, spanning the 2020 pandemic lockdown. The results showed that WHO limits for PM2.5 and NO2 were exceeded regularly, although air quality improved during the pandemic. Air quality was always better during the monsoon season (44.3 ± 3.47 and 26.9 ± 2.35 μg/m3 for PM2.5 and NO2, respectively) and poorest in the post-monsoon season (180 ± 15.5 and 55.2 ± 3.59 μg/m3 for PM2.5 and NO2). Differences in air quality among settlements were explained by the proximity to major roads and places of open burning, with NO2 levels often being greater near roads and PM2.5 levels being elevated near places with open burning. Field monitoring was performed in 2023 at three settlements and local CPCB stations. Air quality at settlements and their closest station were not significantly different (p < 0.01). However, field data showed that on-site factors within settlements, such as cooking, ad hoc burning, or micro-scale industry, impact air quality on local scales, suggesting health risks are greater in informal settlements because of greater unregulated activity. City-scale models can estimate mean air quality concentrations at unmonitored locations, but caution is needed because such models can miss local exposures that may have the greatest impact on local health.