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Stock market plays a key role in economical and social organization of a country. Stock market forecasting is highly demanding and most challenging task for investors, professional analyst and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. Prediction of stock market is a crucial task and prominent research area in financial domain as investing in stock market involves higher risk. However with the development of computational intelligent methods it is possible to reduce most of the risk. In this survey paper, our focus is on application of computational intelligent approaches such as artificial neural network, fuzzy logic, genetic algorithms and other evolutionary techniques for stock market forecasting. This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods. In this article, the selected papers are organized and discussed according to six main point of view: (1) the stock market analyzed and the related dataset, (2) the type of input variables investigated, (3) the pre-processing techniques used, (4) the feature selection techniques to choose effective variables, (5) the forecasting models to deal with the stock price forecasting problem and (6) performance metrics utilized to evaluate the models. The major contribution of this work is to provide the researcher and financial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques.

Carbon dioxide (CO2) emissions from burning fossil fuels play a crucial role in global warming/climate change. The effective removal of CO2 from the point sources or atmosphere (CO2 capture), its conversion to value-added products (CO2 utilization), and long-term geological storage, or CO2 sequestration, has captured the attention of several researchers and policymakers. This review paper illustrates all kinds of CO2 capture/separation processes and the challenges faced in deploying these technologies. This review described the research efforts put forth in gas separation technologies. Recent advances in the existing gas separation technologies have been highlighted, and future directives for commercial deployment have also been outlined.

Duchenne muscular dystrophy (DMD) is one of the most severe forms of inherited muscular dystrophies. It is caused by a mutation in the dystrophin gene, leading to progressive muscular degeneration and weakness. It is inherited as an X-linked recessive trait, mostly located on chromosome Xp21; however, approximately 30% of cases are de novo mutations. Mutations lead to limited production of the dystrophin protein, which results in loss of myofibres and progressive replacement of muscle with connective tissue and fat. Children with DMD have difficulty walking, whereas increased steroid intake can result in depressed mood or attention deficit hyperactivity disorder.

The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. We propose an adaptive particle swarm optimization (PSO)-optimized ensemble learning framework tailored to address these challenges in modern IDS applications. Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. The proposed framework is also benchmarked against state-of-the-art IDS approaches, including ASRL and PSOGSA. Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies.

BackgroundThere is a need for new approaches in agriculture to improve safety of final products as well as to increase environmental acceptability. In this paper, the biodynamic preparation 501 (horn silica) was sprayed on Vitis vinifera (L.) cv Garganega plants in two vineyards located in Veneto region, North-East Italy. Leaf samples were collected on the day of 501-treatment and 11 days later, and berries were sampled at harvest time. Leaves and berries samples were analysed combining targeted and untargeted measurements related to primary metabolism (pigment, element and amino acid contents) and to secondary metabolism. Chlorophyll content in leaves, and amino acid and element (C, N, S) analysis in berries were combined with untargeted UPLC-QTOF metabolomics.ResultsThe discriminant compounds related to the 501-treatment were annotated on the basis of accurate MS and fragmentation and were identified as secondary metabolites, namely phenolic constituents belonging to the shikimate pathway. The level of most of the identified compounds increased in plants treated with 501 preparation.ConclusionsResults highlight the prominent value of the metabolomic approach to elucidate the role of the 501 applications on grapevine secondary metabolism.

In this paper, we presented a long short-term memory (LSTM) network and adaptive particle swarm optimization (PSO)-based hybrid deep learning model for forecasting the stock price of three major stock indices such as Sensex, S&P 500, and Nifty 50 for short term and long term. Although the LSTM can handle uncertain, sequential, and nonlinear data, the biggest challenge in it is optimizing its weights and bias. The back-propagation through time algorithm has a drawback to overfit the data and being stuck in local minima. Thus, we proposed PSO-based hybrid deep learning model for evolving the initial weights of LSTM and fully connected layer (FCL). Furthermore, we introduced an adaptive approach for improving the inertia coefficient of PSO using the velocity of particles. The proposed method is an aggregation of adaptive PSO and Adam optimizer for training the LSTM. The adaptive PSO attempts to evolve the initial weights in different layers of the LSTM network and FCL. This research also compares the forecasting efficacy of the proposed method to the genetic algorithm (GA)-based hybrid LSTM model, the Elman neural network (ENN), and standard LSTM. Experimental findings demonstrate that the suggested model is successful in achieving the optimum initial weights and bias of the LSTM and FC layers, as well as superior forecasting accuracy.

Purpose: To study the impact of different optimization methods in dealing with metallic hip implant using intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques. Materials and Methods: A cohort of 16 patients having metallic implants was selected for the study. Three sets of IMRT and VMAT plans were generated. Set 1 IMRT (IM_Base), VMAT (VM_Base) without any restrictions on beam entry and exit, set 2 (IM_ENT and VM_ENT) optimizer restricts the beam entry and set 3 (IM_EXT+ENT), neither entry nor exit doses were allowed toward the metallic implant. Results: There was no significant difference in target (D95%) and organ-at-risk doses between IM_Base and IM_ENT. There were significant (P = 0.002) improvements in planning target volume (PTV) V95% and homogeneity from IM_EXT+ENT to IM_ENT. There was no significant difference in plan quality between VM_Base and VM_ENT. There were significant (P = 0.005) improvements in PTV, V95%, homogeneity from VM_EXT+ENT to VM_ENT. V40Gy, V30Gy for bladder, rectum, bowel, and bowel maximum dose decreases significantly (P < 0.005) in IM_ENT compared to IM_EXT+ENT, but not significant for VMAT plans. Similarly, there was a significant decrease in dose spill outside target (P < 0.05) comparing 40%, 50%, 60%, and 70% dose spills for IM_ENT compared to IM_EXT+ENT, but variations among VMAT plans are insignificant. VMAT plans were always superior to IMRT plans for the same optimization methods. Conclusion: The best approach is to plan hip prosthesis cases with blocked entry of radiation beam for IMRT and VMAT. The VMAT plans had more volumetric coverage, fewer hotspots, and lesser heterogeneity.

Objectives: The objective of this study is to assess and correlate adverse drug events following coronavirus disease (COVID) vaccination with blood group and dietary style. Methodology: This is a cross-sectional study carried out from May 2021 to July 2021. A prevalidated Google questionnaire containing demographic details, dietary style, blood group, preexisting diseases, and adverse events of the COVID vaccine was circulated to all health-care professionals of our institution through mail/WhatsApp. Informed consent was obtained (in Google Forms) from all the participants after describing the purpose of the study and the assurance to maintain anonymity and confidentiality. A total of 102 responses were collected, out of which 100 (n = 100) responses were analyzed and interpreted (two responses were excluded since participants are not vaccinated). The descriptive statistical method is applied for the assessment of adverse events. The Chi-square test is applied to assess the correlation between adverse events with blood group and dietary style. P < 0.05 is considered statistically significant. Results: The majority of the participants had comorbidities (80%) and were not infected with COVID (90%) before vaccination. Pain at the injection site is very frequently experienced followed by body aches, fatigue, fever, and weakness of the arm. The Chi-square correlation test showed that nonvegetarians had a significantly higher incidence of pain at the injection site compared to vegetarians [χ2 = 7.799, P < 0.004]. However, the study did not find a significant association between other adverse events with blood group and dietary style of the participants (P > 0.05). Conclusion: The present study concludes that study participants experienced minor adverse events following Covishield and Covaxin; pain at the injection site, myalgia, and fever are more frequent. Moreover, there is a higher incidence of injection site pain in nonvegetarians compared to vegetarians. However, there is no significant association between other adverse events with blood group and dietary style of the participants.

Aim: The aim of this study was to compare the effectiveness of rotary instrumentation over manual instrumentation with ultrasonic irrigation on incidence, duration, and intensity of postendodontic pain (PEP). Subjects and Methods: Eighty patients, with asymptomatic irreversible pulpitis in maxillary anterior teeth, were selected and treated with single-visit endodontic treatment. Patients were randomly divided into 2 groups (40 each), Group A (K files using step-back technique) and Group B (ProTaper Next using crown-down technique) along with passive ultrasonic irrigation. Patients were recalled, examined, and asked to fill up questionnaire after 24 h, 48 h, and 7 days. On the basis of response given in the feedback forms, incidence, duration, and intensity of PEP were evaluated. Results: Statistical analysis of the data was carried out using Chi-square test, and level of significance (P < 0.05) was evaluated. More incidence of pain was noticed in Group A when compared with Group B. Significant difference found between two groups (χ2 = 22.759; P = 0.001). There was also statistically significant difference between two groups at different time intervals. Conclusion: Both instrumentation techniques under investigation cause PEP. The incidence of pain was more in manual technique than rotary technique. The duration of pain was higher in manual group than rotary group at different time intervals.

Acmella oleracea is a promising cosmetic, nutraceutical, and pharmaceutical ingredient, and plants with high levels of active compounds are needed in the market. Cultivation can be valuable if sufficient levels of alkylamides are present in plant material. In this regard the application of biostimulants can be an innovative approach to increase yield of cultivation or bioactive compound levels. A. oleracea plants were cultivated in Northern Italy in an experimental site using three different types of biostimulants, triacontanol-based mixture (Tria), an extract from plant tissues (LL017), and seaweed extract (Swe). Plants were grown in the field in two different growing seasons (2018 and 2019). After treatments inflorescences were harvested and the quali-quantitative analysis of alkylamides and polyphenols was performed. Treated and control plants were compared for yields, morphometric measurements, quali-quantitative composition in secondary metabolites. Overall results show that both triacontanol-based mixture and the LL017 positively influenced plant growth (Tria >+ 22%; LL017 >+ 25%) and flower production (Tria >+ 34%; LL017 >+ 56%). The amount of alkylamides and polyphenols in flowers were between 2.0–5.2% and 0.03–0.50%, respectively. Biostimulant treatments ensure higher cultivation yields and allow maintenance of the alkylamide and polyphenol levels based on % (w/w), thus offering an advantage in the final quantity of extractable chemicals. Furthermore, data revealed that samples harvested in late season show a decrease of polyphenols.