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Forecasts of demands or prices become increasingly unreliable as the future becomes more distant. It is, therefore, beneficial to show that optimal decisions during an initial time interval are either partially or wholly independent of the forecasted data from some future time onwards. Using a commodity trading model as an example, we obtain conditions that allow us to make optimal buying and selling decisions for a commodity in some initial time interval without knowing its price forecast beyond some future time. Such an initial time interval is called a decision horizon and the time up to which the forecasted data is required to make the optimal decisions during the decision horizon is called a forecast horizon. We use the maximum principle to solve the example and show that the decision and forecast horizons in the problem arise from lower and upper bounds imposed on the on-hand inventory of the commodity. [ABSTRACT FROM AUTHOR]

In this study, the metallic alloy nanoparticles of silver and zinc oxide (Ag-ZnO) were biosynthesized by Capparis zeylanica aqueous extract. The characterization of biosynthesized Ag-ZnO NPs was performed by scanning electron microscopy (SEM) and revealed that they were with irregular shape, whereas the Fourier transform infrared spectroscopy (FTIR) confirmed the role of secondary metabolites as capping and stabilizing agents on biosynthesized nanoparticles. The antioxidant activity of Ag-ZnO NPs was evaluated through 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging test, hydroxyl radical scavenging test, and reducing power assays. A DPPH free radical scavenging activity value of 340 (IC50 µg/ml) was exhibited by Ag-ZnO NPs which is found to be higher than that of the ascorbic acid, i.e., 289.74 (IC50 µg/ml). The hydroxyl radical scavenging activities of Ag-ZnO NPs and ascorbic acid were 344.35 (IC50 µg/ml) and 316.10 (IC50 µg/ml), respectively. In the case of reducing power assay, unlike DPPH and hydroxyl radical scavenging assays, Ag-ZnO NPs exhibited a slightly lower value (0.498) than that of the standard ascorbic acid (0.511). The reducing power assay displays a much less variation between Ag-ZnO NPs and standard (ascorbic acid), a dose-dependent effect was clearly observed in the reducing power for both sample and standard. The results obtained in the present study reveal that this biologically synthesized alloy nanoparticle, Ag-ZnO can be used as a potent antioxidant. [ABSTRACT FROM AUTHOR]

The present work deals with analyzing the effect of silicon dioxide on mechanical and thermal stability of hybrid polymer composite. Compression molding is adopted to develop the hybrid composite with constant 0.5 wt.% of graphene and varying wt.% of silicon dioxide (2, 4 & 6 wt.%). Modified hummers method is used to synthesis graphene by chemical exfoliation of graphene from graphite flakes. Mechanical properties of developed composite is investigated using tensile, flexural, impact and interlaminar shear testing follow by fracture surface analysis of tested samples. Thermal stability is examined by thermogravimetric analysis and functional group confirmation is carried out using Fourier transform infrared spectroscopy (FTIR). Investigation over mechanical properties of fabricated composite reveals that addition of silicon dioxide up to 4 wt.% depicted higher flexural and tensile strength. Increment in silicon dioxide percentage improves the toughness of composite. FTIR results confirms the presence of asymmetric stretching vibrations due the after effect of hybrid fillers. Thermogravimetric analysis results depict that incorporation silicon dioxide increases the activation of energy of matrix from ~ 24.2 to 31.5 kJ/mol. [ABSTRACT FROM AUTHOR]

New Flexible Advertising Model Unveiled: Enhancing Advertising Strategies Researchers have generalized the classical Sethi advertising model (1983) to provide a flexible yet tractable approach to optimizing advertising strategies. Unlike traditional methods, the generalized Sethi advertising model is designed to adapt to different market conditions in various media and markets, offering advertisers greater flexibility. By assuming a constant-returns-to-scale relationship between advertising expenditure and untapped market potential, the generalized Sethi advertising model provides advertisers with more options without sacrificing tractability. The research team has made important breakthroughs by developing practical formulas for determining the best advertising strategies for both individual companies and groups of companies. They have also conducted analyses to better understand how different factors influence these strategies. The generalized Sethi advertising model opens up new opportunities for advertisers to fine-tune their campaigns and reach more customers. This research marks a significant step forward in advertising optimization, giving businesses a valuable tool to achieve better results and make informed decisions in their advertising efforts. We propose a flexible yet tractable dynamic advertising model called the generalized Sethi model to capture different market penetration rates across various media and markets via advertising. Specifically, the generalized Sethi model employs a Cobb–Douglas production function of advertising expenditure and the untapped market share with constant returns to scale. It encompasses some standard dynamic advertising models as particular cases. Moreover, the model's flexibility does not compromise its tractability. We demonstrate it by showing single- and multifirm advertising problems involving Nash and Stackelberg games to admit closed-form expressions for the firms' optimal advertising strategies and value functions under the generalized model. Sensitivity analysis of the model parameters also yields novel economic insights regarding the firms' optimal advertising strategies and value functions. Funding: A. P. Kennedy acknowledges the Hong Kong University Grant Council for their support of him pursuing his PhD at the Chinese University of Hong Kong. S. P. Sethi acknowledges financial support from the Eugene McDermott Chair Professorship. C. C. Siu acknowledges financial support from the Research Grants Council of Hong Kong [Grant "Generalized Sethi Advertising Model and Extensions" (Project UGC/FDS14/P02/20)]. S. C. P. Yam acknowledges financial support from HKGRF-14301321 [Project "General Theory for Infinite Dimensional Stochastic Control: Mean Field and Some Classical Problems] and General Research Fund by the Research Grants Council of Hong Kong-14300123 [Project "Well-Posedness of Some Poisson-Driven Mean Field Learning Models and Their Applications"]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0717. [ABSTRACT FROM AUTHOR]

Globally, lung cancer contributes significantly to the public health burden–associated mortality. As this form of cancer is insidious in nature, there is an inevitable diagnostic delay leading to chronic tumor development. Non-small cell lung cancer (NSCLC) constitutes 80–85% of all lung cancer cases, making this neoplasia form a prevalent subset of lung carcinoma. One of the most vital aspects for proper diagnosis, prognosis, and adequate therapy is the precise classification of non-small cell lung cancer based on biomarker expression profiling. This form of biomarker profiling has provided opportunities for improvements in patient stratification, mechanistic insights, and probable druggable targets. However, numerous patients have exhibited numerous toxic side effects, tumor relapse, and development of therapy-based chemoresistance. As a result of these exacting situations, there is a dire need for efficient and effective new cancer therapeutics. De novo drug development approach is a costly and tedious endeavor, with an increased attrition rate, attributed, in part, to toxicity-related issues. Drug repurposing, on the other hand, when combined with computer-assisted systems biology approach, provides alternatives to the discovery of new, efficacious, and safe drugs. Therefore, in this review, we focus on a comparison of the conventional therapy-based chemoresistance mechanisms with the repurposed anti-cancer drugs from three different classes—anti-parasitic, anti-depressants, and anti-psychotics for cancer treatment with a primary focus on NSCLC therapeutics. Certainly, amalgamating these novel therapeutic approaches with that of the conventional drug regimen in NSCLC-affected patients will possibly complement/synergize the existing therapeutic modalities. This approach has tremendous translational significance, since it can combat drug resistance and cytotoxicity-based side effects and provides a relatively new strategy for possible application in therapy of individuals with NSCLC. [ABSTRACT FROM AUTHOR]

In the digital age, operations can be improved by a wise use of information and technological tools. During the COVID-19 pandemic, governments faced various choices of vaccines possessing different efficacy and availability levels at different time points. In this article, we consider a two-stage vaccine ordering problem of a government from a first and only supplier in the first stage, and either the same supplier or a new second supplier in the second stage. Between the two stages, potential demand information for the vaccine is collected to update the forecast. Using dynamic programming, we derive the government's optimal vaccine ordering policy. We find that the government should select its vaccine supplier based on the disease's infection rate in the society. When the infection rate is low, the government should order nothing at the first stage and order from the supplier with a higher efficacy level at the second stage. When the disease's infection rate is high, the government should order vaccines at the first stage and switch to the other supplier with a lower efficacy level at the second stage. We extend our model to examine (i) the value of blockchain adoption and (ii) the impact of vaccines' side effects. [ABSTRACT FROM AUTHOR]

Jumping spiders (Salticidae) are a diverse group of non‐web‐building predators and the most species‐rich spider family. The Salticidae Tribe Nannenini consists of a small group of Oriental jumping spiders that are very poorly known. The use of explicit phylogenetic analyses to establish its affinities has so far remained limited. The Nannenini genus Epidelaxia has a peculiar morphology, making its systematic position uncertain. In this study, we present a comprehensive dataset combining molecular and morphological data for Nannenini, including exemplars from all genera. This dataset comprises DNA sequences of approximately 2400 bp, including nuclear genes (18S, 28S and H3) and the mitochondrial gene CO1, sequenced for 40 taxa (20 ingroups and 20 outgroups), along with 61 morphological characters. Notably, both DNA sequences and morphological traits were analysed separately and concurrently for the first time. The Nannenini clade is recovered with high support. Further, Epidelaxia is monophyletic and sister to Tubalaxia gen. n. This is the first hypothesis on the internal phylogenetic structure of Epidelaxia and its placement within the Tribe Nannenini. A new genus Tubalaxia gen. n. and the following new species are described: E. bharathi sp. n., E. somasundaram sp. n., T. castanea sp. n., T. aurea sp. n. The following new combination is proposed: Tubalaxia minuta (Prószyński, 1992) comb. n. Epidelaxia albostellata, E. albocruciata and E. obscura are redescribed. It is worth noting that the newly described species face endangerment due to their limited distribution and small population sizes. [ABSTRACT FROM AUTHOR]

Most women, in general, have an ovarian cyst, which causes a variety of disorders. Cervical cysts occur when multiple cysts appear in or on top of the uterus. This is especially true for women who have a good reason for having a baby. Related to menstrual problems and cyst problems in women during pregnancy. Ultrasound imaging techniques are used to detect ovarian cysts. Doctors have many difficulties identifying these types of tumors that are not clearly visible from ultrasound images and what type of ovarian cyst they are To make these problems more useful to the doctors, the system of automatic detection of various cyst type has been implemented. The cyst detection and classification method are implemented using the features extracted from the ultrasound image. Automated detection methods and various ovarian cyst classification are implemented using a 2D Convolutional Neural network, and the proposed prediction model has yielded 99.37% accurate results. [ABSTRACT FROM AUTHOR]

Perovskite solar cells have gained more popularity in recent years because of their high efficiency and low cost. The most widely employed active layer in perovskite solar cells is FAPbI3, which possesses higher stability, efficiency, and smaller bandgap value. However, its sensitivity to light and humidity makes it challenging to prepare under normal conditions in an economical manner. In this paper, we tried to control not only humidity during the annealing stage but also light intensity conditions of FAPbI3 samples. Here, we synthesize samples in four different ways: (i) Without dark annealing; (ii) Vacuum-assisted without dark annealing; (iii) Dark annealing; and (iv) Vacuum-assisted dark annealing. Among these methods, Dark annealing and Vacuum-assisted dark annealing are two effective methods where we controlled light during the annealing stage. The morphological, structural, and optical characteristics of the samples were investigated. The FTIR data showed that the relative humidity of the sample is favourably reduced for the samples prepared using vacuum-assisted methods. X-ray analysis confirmed the α phase of FAPbI3, which is a favourable phase for solar cell applications. The FESEM analysis confirmed the defect-free morphology of the prepared sample. The prepared α- FAPbI3 accounts for a bandgap value of 1.51 eV, which is quite close to the ideal bandgap value. The α phase of FAPbI3 is also conformed from TEM analysis. Thermal stability of sample was analysed by TGA/DTA and got a decomposition temperature of 442 °C. [ABSTRACT FROM AUTHOR]

When it comes to the causes of dementia, Alzheimer's disease is the most mysterious. There is no central genetic component connected to Alzheimer's disease. Previous approaches and tools for determining Alzheimer's disease genetic risk factors are unreliable. The brain images provided the bulk of the available information. In contrast, large-scale approaches in bioinformatics have seen significant development in recent years. It has encouraged efforts to identify the hereditary risk factors for developing Alzheimer's disease. A large amount of data on the brain's prefrontal cortex as a consequence of recent studies has allowed for the creation of classification and prediction models for Alzheimer's disease. Using the OASIS-4 dataset, which suffers from High Dimension Low Sample Size (HDLSS) problems, a Deep belief network with a Restricted Boltzmann Machine (RBM)-based classification model for processing multimodal data has been constructed. The multi-layer feature selection procedure that took into account both the technical and biological aspects of the characteristics to solve the HDLSS problem has been proposed. In molecular-level information, in the first stage of the two-tiered feature selection method, abnormal places in the dataset are found. Second, combining multiple different feature selection methods is used to refine the set of candidate genes. The principal component analysis is used for dimensionality reduction in MRI, and well pre-processed cognitive assessment scores like MMSE and ADA-cog are considered. Deep belief networks with multiple RBM are used to do unsupervised feature learning. Fivefold cross-validation has been used in all classification studies. [ABSTRACT FROM AUTHOR]

This research aims to comprehensively examine the clearness index (KT), total ozone column (TOC), and ultraviolet A (UVA) and ultraviolet B radiation (UVB) over Lumbini, Nepal (27°28' N, 83°16' E, and 150 m above sea level) throughout the 11 years of solar cycle 24 (2008 to 2018). The Lumbini, a highly polluted region, is important in advancing the identification and analysis of TOC variations across regions with similar geographical and climatic attributes. Data from the Ozone Monitoring Instrument (OMI) of the EOS-AURA satellite of NASA were used to analyze the daily, monthly, seasonal, and annual trends in the clearness index (KT), ultraviolet A (UVA), ultraviolet B (UVB), and TOC from the Comprehensive Environmental Data Archive (CEDA). The study found that the yearly averages for KT, TOC, UVA, and UVB were 0.55 ± 0.13, 272 ± 14 DU, 12.61 ± 3.50 W/m2, and 0.32 ± 0.11 W/m2, respectively. These values provide insights into the long-term variations in atmospheric parameters at Lumbini. The study also applied the continuous wavelet transform (CWT) to analyze KT, TOC, UVA, and UVB temporal variations. The power density peak of 35,000 DU2 in the TOC was observed from the end of 2010 to the end of 2011, within 8.5 years, underscoring the significance of analyzing TOC dynamics over extended durations to understand atmospheric behavior comprehensively. [ABSTRACT FROM AUTHOR]

The marine fish oil obtained during fishmeal production and shrimp shells through whole shrimp processing have less commercial importance, mostly considered as waste and are disposed to environment resulting in pollution. The current research focuses on exploiting fish industry wastes such as Fenneropenaeus indicus shrimp shell and marine fishmeal industry (MFMI) oil with a perspective of achieving sustainable biodiesel production and overcome disposal pollution issues. The physicochemical properties of MFMI oil indicated its suitability to be used as feedstock for biodiesel production. The catalyst characterization by X-ray diffraction (XRD), Fourier transform infrared spectroscopy, Scanning electron microscopy (SEM), and Brunauer–Emmett–Teller analysis revealed that calcination of shrimp shells resulted in the formation of crystalline mesoporous solid catalyst primarily composed of CaO. As per the central composite design of response surface methodology, the maximum biodiesel yield of 86.56wt% was obtained from MFMI oil at an oil to methanol molar ratio of 1:12 (mol/mol), catalyst concentration of 5 wt% of oil, reaction temperature of 65 °C, and reaction time of 120 min. Further, based on flask level optimized condition, the biodiesel production was scaled to a 50 L oil volume batch and achieved a good yield of 88.74 wt%. Further, the physicochemical properties and cold flow property of MFMI oil biodiesel ensured its application as fuel. Thus, MFMI oil and calcined shrimp shell catalyst could be utilized as promising feedstock and catalyst for sustainable biodiesel production. [ABSTRACT FROM AUTHOR]

The present review primarily focuses on the perspectives and state-of-the-art of heterogeneous catalysts, nanocatalysts, biocatalysts, bifunctional catalysts, metal-organic frameworks (MOF), and covalent organic frameworks (COF) for biodiesel production. The environmental concern associated with nonrenewable fossil fuels has led to finding alternative energy sources that can be used to meet global energy demands. Biofuels such as biodiesel are one of the energy sources that could replace fossil fuels. The homogeneous acid and base catalysts are generally used for commercial biodiesel production. However, homogeneous catalysts have downsides such as toxicity, corrosion, soap formation, high wastewater output, and non-reusability. Consequently, heterogeneous acid and base catalysts have been introduced that are less sensitive to moisture and free fatty acids (FFAs), easily separated and recovered, and reusable. Recently, novel catalysts such as waste biomass-derived mesoporous heterogeneous catalysts, chemically synthesized heterogeneous catalysts, metal ion-doped heterogeneous catalysts, bifunctional acid-base catalysts, and carbonaceous char-supported hetero catalysts, nanocatalysts, MOF and COF catalysts have potential to replace homogeneous base catalysts, aid in sustainable and cost-effective biodiesel production. This review provides insights into the recent advancement of various catalysts, catalyst preparation and operations, type of catalysts and suitability, catalyst efficiency, life cycle assessment, catalyst-associated challenges, and prospects for sustainable biodiesel production. [ABSTRACT FROM AUTHOR]

Drowning is considered to be the leading cause of death in water and the third most common cause of accidental death worldwide, with the highest drowning rates in developing countries. The WHO defines drowning as the process of experiencing respiratory impairment from submersion/immersion in liquid. For forensic experts, death in water is a multifaceted issue presenting a challenge to them. Since all the circumstances from natural death to homicide are possible in the water, critical examination and interpretation of all abnormal findings found during autopsy are of great importance. Drowning is a diagnosis of exclusion, based on ruling out all other causes of death via complete autopsy and toxicology. Drowning causes hypoxemia, loss of consciousness, apnea and ultimately cardiac arrest. Fluid aspiration results in lung injury and acute respiratory distress syndrome. Acute Respiratory Failure is the main component of drowning pathophysiology. Near drowning results in multiple complications including aspiration pneumonia which is often life-threatening. Hence the histopathological changes in the lungs provide good corroborative evidence of death due to drowning. Nevertheless, for the examined drowning signs, no high diagnostic certainty could be observed. However, these findings can increase their diagnostic value if forensic doctors take influencing factors into consideration. From the central record section of the Department of Forensic Medicine, Siddhartha Medical College, Vijayawada, Andhra Pradesh, about 80 medicolegal postmortem reports of death due to drowning in undecomposed bodies were selected in which one whole lung was sent for histopathological examination during the past two years i.e, 2020 - 2022. Parameters like Age, Sex and Marital Status were also studied. Histopathological examination of drowned lungs showed that congestion was seen in 92% of cases, oedema in 96% cases, dilatation of alveoli in 86% of cases, thinning of septa was seen in 84% of cases rupture of alveoli seen in 64% of cases and hemorrhageis seen in 46% of cases. Mostly males are affected and drowning deaths are seen more in 26 - 40 years age group. The histopathological investigations must be performed on all the organs of non-putrefied bodies with the aim of making the difference between death by drowning and other causes of death. The lung examinations can show over-distension of the alveoli, thinning of the alveolar septa and compression with narrowing of the capillary network. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) with potent anti-listerial activity were characterized from a novel marine Bacillus velezensis FTL7. A Box-Behnken statistical experimental design was used to study the combined impact of culture conditions on the production of AMPs by B. velezensis FTL7. The conditions optimized by statistical experimental design were 34.5 °C incubation temperature, 23 h incubation time, and 7.6 initial pH of the medium. AMP purification was performed by ammonium sulphate fractionation and butanol extraction followed by reversed-phase C18 solid-phase extraction. Tricine-SDS-PAGE analysis revealed a peptide with a molecular mass of ~ 6.5 kDa in an active AMPs fraction, whereas the mass spectrometry (MS) analysis showed the presence of AMPs in the mass range of 1–1.6 kDa, along with a 6.5 kDa peptide. Both MS and MS/MS analysis confirmed the AMPs as lipopeptides including surfactin, fengycins and iturin A and a circular bacteriocin amylocyclicin. The minimum inhibitory concentration of these AMPs against L. monocytogenes Scott A was 2.5 µg/mL. Further, the in-silico docking studies showed that the AMPs from B. velezensis FTL7 have high binding energy and stable binding patterns towards L. monocytogenes target proteins. Thus, this new combination of AMPs can serve as an effective food bio-preservative. [ABSTRACT FROM AUTHOR]

Aluminium matrix composites (AMCs) play a vital role in constructing aircraft, marine and automobile components. The major issue in manufacturing of AMCs through stir casting method is a localized ceramic particles agglomeration. In current research, metal castings have been prepared with 97.5 wt% of aluminium alloy (AA 7075) and 2.5 wt% of nano-alumina (n-Al2O3) particles through a modified matrix encapsulated feeding technique. The tensile strength, microhardness and dry sliding wear behaviour of n-Al2O3 reinforced AA 7075 composites were measured following ASTM standards. The ultimate tensile strength of the developed AMCs varies from 280 to 315 MPa, which is 12.5% higher than unreinforced AA 7075 alloy. Yield strength increases from 140 to 158 MPa, whereas microhardness of the composites increases from 138 to 210HV. Due to homogeneous distribution of n-Al2O3, the overall wear resistance of n-Al2O3 reinforced AA 7075 composites increases up to 90% as compared to unreinforced AA 7075 alloy. Also, the addition of n-Al2O3 particles marginally reduces the network of microcracks and deep furrows in the worn surface. The microstructural and elemental studies are also performed through scanning electron microscopy and X-ray diffraction. [ABSTRACT FROM AUTHOR]

This study investigates the use of evolutionary computation for mining high-value patterns from benchmark datasets. The approach employs a fitness function to assess the usefulness of each pattern. However, the effectiveness of evolutionary algorithms heavily relies on the chosen strategy parameters during execution. Conventional methods set these parameters arbitrarily, often leading to suboptimal solutions. To address this limitation, the research proposes a method for dynamically adjusting strategy parameters using temporal difference approaches, a machine learning technique called Reinforcement Learning (RL). Specifically, the proposed IPSO RLON algorithm utilizes SARSA learning to intelligently adapt the Crossover Rate and Mutation Rate within the Practical Swarm Optimization Algorithm. This allows IPSO RLON to effectively mine high-utility itemsets from the data. The key benefit of IPSO RLON lies in its adaptive control parameters. This enables it to discover optimal high-utility itemsets when applied to various benchmark datasets. To assess its performance, IPSO RLON is compared to existing approaches like HUPEUMU-GRAM, HUIM-BPSO, IGA RLOFF, and IPSO RLOFF using metrics like execution time, convergence speed, and the percentage of high-utility itemsets mined. From the evaluation it is observed that the proposed IPSO RLON perfroms better than the other methodology. [ABSTRACT FROM AUTHOR]

This document is an editorial note for a special issue of the International Journal of Logistics: Research & Applications. The special issue focuses on enhancing supply chain resiliency in an era of reglobalization. The note highlights the challenges and disruptions faced in the reglobalization era and the importance of supply chain resilience. It also provides a brief overview of the research included in the special issue, which covers factors affecting supply chain resilience, assessment of disruption risk, and strategies to improve supply chain resilience. The note concludes by expressing gratitude to the authors, reviewers, and editorial team for their contributions and encourages readers to explore the research presented in the special issue. [Extracted from the article]

The booming platform economy has made it very convenient for consumers to communicate with each other through social media whenever and wherever they want. As industry and academia have repeatedly confirmed, such social communications are now significantly changing firms' business and management practices. This paper studies how social communications affect an upstream firm's product line design in the platform economy when an online platform makes strategic contract choices. We show that for both wholesale and agency contracts, social communications increase the product line length while decreasing the product price and quality. We also identify the conditions under which social communications provide a firm with fewer incentives for product line extension under a wholesale contract than under an agency contract. On the platform side, we show that an agency contract can be preferred in the presence of social communications over a wholesale contract if and only if the commission rate is sufficiently high or both the commission rate and the product line extension fee are moderate. [ABSTRACT FROM AUTHOR]

Due to critical vibration and excess weight in the frame structure of the automobile, the automobile acceleration effects are the primary problems. This paper describes the material selection based on its weight, vibration reduction, and increasing vehicle performance. The front cabins heavy-duty van frame was considered for this research work and designed according to the frame dimension. Generally, the frame is made of mild steel and replaced by high-strength extrusion magnesium alloy. The frame structures crash and impact analysis was investigated using nonlinear buckling and transient analysis. The stress, vibration and nonlinear buckling analysis was obtained under different loading conditions. The bending performance of the frame was analytically investigated and verified with the FEA code (ANSYS 15.0). [ABSTRACT FROM AUTHOR]

Automobile manufacturing industry is at recent trend because people all over the globe requires automobile to support transportation. Due to increase in demand for automobile vehicles, forecasting its sales has become quite significant. Devoiding of sales forecasting results in excess raw material procurement, delay in product delivery anf excess production. To address these issue, sales demand forecasting model must be developed for predicting the future sales of the product. However the exisiting sales demand forecasting model fails to achieve accurate prediction of future sales due to process complexity. So, this current research focus on designing an automated sales demand forecasting model using statistical and neural network approach for forecasting future sales in the car industry. In order to forecast furture sale of cars, past sales record is essential. Initially, the past sales record is collected from three car industries. Using this past sales information, the future sales is predicted in two scenarios. The first scenario is a prediction of linear series of data using the Seagull Optimization-based Holt Winter (SO-HW) method. Then, in the second scenario, the prediction of nonlinear data series is achieved using Quantile Regression Neural Network (QRNN). The performance of the proposed sales demand forecasting model is estimated using parameters such as RMSE, MSE, MAE and MAPE. The value of RMSE and MSE for proposed forecasting model is 0.5%, 0.6% and 0.3%, 0.4% respectively. Based on this analysis, it is suggested that error metrics are minimal for the proposed sales demand forecasting model, and this proves that forecasting of seasonality, level and trends of car sales can be attained effectively. [ABSTRACT FROM AUTHOR]

The current paper discusses the effect of gravity assisted thermosyphon heat pipe cooling on the performance of solar photovoltaic panels. In this experimental study two identical photovoltaic modules along with two thermosyphon heat pipes have been manufactured. Heat pipes have been used to passively cool one of the modules, in order to reduce its operating temperature. The other module, without heat pipes has been used as the reference module. Distilled water is used as the working fluid in two wickless heat pipes. The two modules have been installed close to each other at Indian Institute of Technology, Bombay, India. For the comparative performance analysis of both the modules, experimental parameters such as solar irradiance, ambient temperature, current and voltage from each module along with their temperatures have been monitored through a data logger. Minute-wise values of the various parameters have been recorded and stored. The actual test duration for this study has been considered as 9 AM to 5 PM, during which the solar irradiance is prominent and the data is tabulated at intervals of 15 minute. A comprehensive performance analysis has been carried out for the various dates and has been presented in this paper. An optimum temperature drop of almost 6 oC is achieved due to thermosyphon heat pipe cooling, which corresponds to around 10 % decrease compared to the reference module temperature. Consequently the conversion efficiency and the power output of the module cooled with thermosyphon heat pipes are fairly improved as compared to the reference module. [ABSTRACT FROM AUTHOR]

We consider an e‐tailer's upstream supplier who wants to encroach into retailing to earn additional revenue. The supplier needs to decide whether or not to enter the retail market by either selling to consumers on the e‐tailer's platform by paying commission fees (agency encroachment) or opening an independent online/offline retail store (direct encroachment). The e‐tailer has private demand information and decides whether or not to share it with the supplier. Two leadership scenarios—the supplier‐leads (i.e., the supplier selects the channel before the e‐tailer decides whether to share information) and the e‐tailer‐leads (i.e., the supplier selects the channel after the e‐tailer decides whether to share information)—are examined. Our main findings are as follows. First, we show that the e‐tailer has no incentive to share information under no encroachment and direct encroachment. Interestingly, this result holds in both leadership scenarios. Second, a medium commission rate gives rise to an equilibrium of agency encroachment with information sharing by the e‐tailer. This equilibrium is more likely to sustain in the supplier‐leads scenario than in the e‐tailer‐leads scenario. Third, agency encroachment brings the supplier the highest sales volume (at retail in the encroaching channel plus on wholesale to the e‐tailer) when the two parties compete in quantity while direct encroachment does so for a price competition. Fourth, supplier encroachment always improves consumer surplus, but it is not necessarily welfare‐improving. Last, we find that the e‐tailer is more willing to share information to induce the supplier to encroach through his agency channel if he has a significant selling cost advantage over the supplier or can endogenously determine the commission rate. [ABSTRACT FROM AUTHOR]

Electrical load prediction aids electrical producing and allocation firms in planning capacity and management to ensure that all customers get the energy they need on a consistent basis. Despite the fact that numerous prediction models have been created, none of them can be applied to all market trends. As a result, this article provides a practical technique for predicting customer power usage. To address the troubles of power utilization surveying, CRF-based energy utilization choosing strategy conditional random field based powered consumption prediction (CRF-PCP) is proposed. A convolutional brain organization (a technique in view of artificial intelligence) joined with a contingent irregular field is utilized to prepare and foresee the energy consumption (EC) of the districts. The training model's features are extracted using a spatiotemporal texture map (STTM). Supervisory control and data acquisition (SCADA) is utilized to gather and keep up with information on the power utilization of local purchasers. The information given in the cloud is sent to the power circulation framework. Additionally, power utilization expectation utilizing a convolution neural network (CNN) with profound conditional random field (CRF) provides an outcome of 98.9% precision, which is far superior to prior research in the same area. The acquired result demonstrates that the employed machine learning methods are performing at their peak. [ABSTRACT FROM AUTHOR]

Here we report a cost effective solar harvestor based on parabolic trough collector (PTC) technology with three remarkable characteristics. First, unlike the expensive (~USD 170/m2‐aperture) state‐of‐the‐art PTCs which use large curved reflectors, this reflector is composed of a plurality of long rectangular mirrored strips of glass placed on laser‐cut parabolic ribs to yield an accurate reflecting shape giving a concentration ratio of 38 (comparable to the state‐of‐the‐art). Second, the entire assembly is on bolts. It can be delivered in boxes, and can easily be erected on desired site: in stark contrast to state‐of‐the art PTCs, which need to be fabricated in a specially equipped workshop and installed using cranes. Third, by incorporating several innovations guided by comprehensive optimization using finite element analysis and ray tracing, the cost of the system has been brought below USD 82/m2‐aperture (as per the recommendation of NAE). Taken together, this solution promises more cost‐effective base load electricity than Photovoltaics, hence goes a long way toward meeting the UN Sustainable Development Goal of Affordable Energy (SDG‐7). Further, it can be fabricated using industrial equipment that is readily available in the developing regions of the Sun‐belt and installed using unskilled labor. Hence, it can form the core of a massive, decentralized, micro‐CSP industry that can provide dignified employment to large numbers of people in developing regions (SDG‐8). We have termed this technology Harvest of the Sun. [ABSTRACT FROM AUTHOR]

Exchange rate volatility is the most important determinant influencing a country's economic health. A rise in exchange rate risk increases transaction costs and reduces the gains accruing from international trade. The economy of a country is closely dependent on its foreign exchange system. This paper reviews various research studies conducted in recent years on factors responsible for exchange rate volatility and its impact on exports, international trade, trade volumes, FDI, etc. The review reveals that government expenditure, changes in supply of money, domestic and external debts, trade, openness, interest rates, domestic and foreign money supply, inflation, changes in terms of trade, bank intervention, productivity, etc. are some of the factors that make exchange rate volatile. Studies pertaining to relationship between exchange rate volatility and trade present ambiguous views. The review reveals that earlier studies found an insignificant and negative relationship, while recent literature showed a positive relationship between exchange rate volatility and trade, due to advancement in the estimation techniques adopted, like autoregressive distributive lag (ARDL) model, error correction models, etc. The review concludes that there is no unanimity in results, as the magnitude of the effect depends on a number of factors and varies from country to country. [ABSTRACT FROM AUTHOR]

The supply chain is vulnerable to disruptions, which can lead to operational failure. Therefore, it is crucial to investigate and monitor the risks associated with such disruptions. Epidemics, including the profound impact of COVID‐19, exemplify disruptive risks that engender complexities throughout the value chain. COVID‐19 has caused significant disruptions to the global supply chain due to the preexisting fragile supply chains, resulting in the unavailability of essential raw materials, halts in production, and imbalances between supply and demand. It has forced the supply chain community to rethink their risk mitigation strategies. Despite its significance, there are a few studies on the existing options and strategies to mitigate the risks associated with epidemics such as COVID‐19. This special issue explores state‐of‐the‐art knowledge of various risks and their mitigation strategies, presenting optimized models and solutions. This editorial seeks to introduce valuable insights and illuminate promising avenues for future research by reviewing existing studies. The comprehensive understanding derived from this editorial and the accompanying articles of this special issue will benefit both researchers and practitioners, enabling them to navigate the multifaceted landscape of epidemic‐induced disruptive risks in the supply chain and adopt suitable mitigation strategies. Furthermore, this special issue lays a solid foundation for implementing advanced methods for managing and mitigating the disruptive risks that epidemics pose to the supply chain. [ABSTRACT FROM AUTHOR]

Consideration of standard landing cases from technical guideline document is the traditional design practice followed to ascertain aircraft landing loads. A rationale approach on landing load generation and its impact on flexible airframe dynamic response is explored, considering multi-variate landing conditions for the first time on land-base high performance aircraft. Methodology on nonlinear landing dynamic response simulation is developed by amalgamating inputs such as nonlinear nose and main landing gear dynamic models, aircraft rigid body dynamics, low speed aerodynamic and thrust forces. True representation of landing gear nonlinear oleo-pneumatic strut and tire characteristics, variable geometric strut structural stiffness and nonlinear spin-up friction resulted in satisfactory correlation of vertical and longitudinal dynamic response simulation with experimental drop test results. The adequacy of lateral dynamic model is established from the compatibility condition of landing gear and tire dynamics. A down selection methodology is worked out considering total probability and first order sensitive multi-variate landing parameters such as pitch, roll and vertical descent speed, taking into account the plausible statistical variation. Transient dynamic response simulation is then performed for the critical landing cases, on a flexible airframe finite element model by inputting landing load time history as base excitation motion. The dynamic amplification of acceleration response on flexible fuselage nose location from the simulation, is verified with flight data accelerometer frequency response. The application of flexible dynamic responses in ensuring the structural integrity of airframe is elaborated. The proposed methodology on landing load generation is pragmatic, in comparison with the traditional design practice and provides insight during early design phase thereby achieving landing gear subsystem development in-unison with airframe structural clearance aspects. [ABSTRACT FROM AUTHOR]

An interface between a DC supply and an electric vehicle's drive fed by an inverter is a bidirectional DC-DC converter. In this research, a topology for an electric vehicle based on an induction motor that integrates a high voltage gain bidirectional non-isolated DC/DC converter with a three-phase inverter is proposed. This study compares a bidirectional DC to DC converter inverter system controlled by fuzzy logic (FL), and fractional order proportional integral derivative (FOPID). The suggested converter runs in discontinuous-current mode (DCM), with all switches and diodes switching at zero current. It is possible to operate across a wide duty cycle range while maintaining high output voltage gain, low switching stress, minimal switching losses, and high efficiency. The proposed converter's size and weight are decreased so as to support a wide range of duty cycle operations, maintain lower voltage stress on all devices, ensure equal current sharing among inductors, are simple to control, and require a more compact inductor. The converter also uses a constant input current which offers a choice for various applications. MATLAB Simulink is used to construct, model, and simulate open loop system, closed loop FL and FOPID. The results of these simulations are then reported. The investigations show that FOPID controlled DC-DC converter performed better response. [ABSTRACT FROM AUTHOR]

We consider a dynamic Stackelberg game between a manufacturer and a retailer facing a randomly changing market environment (boom–bust cycle) modeled as a Markov process over time. The manufacturer announces the national advertising effort and a cooperative (co‐op) advertising subsidy, and the retailer sets the local advertising effort. We derive the optimal feedback equilibrium advertising strategies and show that the channel members are forward‐looking in their advertising decisions. In the single‐crisis case, both the necessary and sufficient conditions for a positive subsidy reveal that the subsidy decision hinges on the relative profit margins before and after the crisis. Our comparative statics analysis indicates that the presence of national advertising enriches the members' advertising behaviors significantly. Furthermore, the pre‐crisis (post‐crisis) aggregate advertising contribution of the supply chain generally decreases (increases) in the damage rate and the crisis likelihood, as in the single‐firm case. But the individual advertising behaviors of the members may exhibit richer structures, depending on the comparative advantages of the members in each state. In addition, our numerical studies demonstrate that channel members can suffer sizable losses when they adopt myopic advertising strategies over their regime‐switching counterparts. Finally, we extend our analysis to incorporate retail and brand competition and show that the channel members' advertising behaviors are reasonably consistent with those without competition. [ABSTRACT FROM AUTHOR]

There is an urgent unmet medical need to develop therapeutic options for the ~50% of depression patients suffering from treatment‐resistant depression, which is difficult to treat with existing psycho‐ and pharmaco‐therapeutic options. Classical psychedelics, such as the 5HT2A agonists, have re‐emerged as a treatment paradigm for depression. Recent clinical trials highlight the potential effectiveness of 5HT2A agonists to improve mood and psychotherapeutic growth in treatment‐resistant depression patients, even in those who have failed a median of four previous medications in their lifetime. Moreover, microdosing could be a promising way to achieve long‐term alleviation of depression symptoms without a hallucinogenic experience. However, there are a gamut of practical barriers that stymie further investigation of microdosing 5HT2A agonists, including: low compliance with the complicated dosing regimen, high risk of diversion of controlled substances, and difficulty and cost administering the long‐term treatment regimens in controlled settings. Here, we developed a drug delivery system composed of multilayered cellulose acetate phthalate (CAP)/Pluronic F‐127 (P) films for the encapsulation and interval delivery of 5HT2A agonists from a fully biodegradable and biocompatible implant. CAPP film composition, thickness, and layering strategies were optimized, and we demonstrated three distinct pulses from the multilayered CAPP films in vitro. Additionally, the pharmacokinetics and biodistribution of the 5HT2A agonist 2,5‐Dimethoxy‐4‐iodoamphetamine (DOI) were quantified following the subcutaneous implantation of DOI‐loaded single and multilayered CAPP films. Our results demonstrate, for the first time, the interval delivery of psychedelics from an implantable drug delivery system and open the door to future studies into the therapeutic potential of psychedelic delivery. [ABSTRACT FROM AUTHOR]

Nowadays, continuous human activity recognition is being studied broadly by investigators for diverse applications. However, the studies based on physiotherapy action tracking from the physiotherapy video dataset are limited. Hence, the physiotherapy dataset has been considered in this present study. Moreover, deep learning-based (DL) neural networks have promoted the enhancement of activity detection study to become an essential technique. DL-based neural networks, such as long short-term memory, can automatically acquire the significant features from the physiotherapy video of sub-activity and main activity. Nevertheless, some physiotherapy videos are inappropriate and correspond to insignificant actions. Consequently, these insignificant actions can cause the recognition of continuous movements. Therefore, a novel strawberry-based recurrent neural framework is proposed to address this issue. Here, a physiotherapy video is taken as the input, and this dataset consists of several actions. Consequently, all the steps are done by one single person. So, the proposed design initially identifies all sub-activities based on that sub-activities, and the main physiotherapy actions were classified. After that, repeated action counts and their starting and ending times are evaluated. Finally, the present study's design is considered in terms of performance metrics. Three things are mentioned in this article. First the class determines whether the human body is static, dynamic, or transitional, which class indicates the position of action. To recognize the main activity, it is important to first identify all sub-activities in the physiotherapy video. Then, you should count the number of times each sub-activity was performed and how long it took overall. The proposed model was implemented using the Python platform, and the results were compared with the existing models. The proposed model shows higher recognition accuracy in comparison. [ABSTRACT FROM AUTHOR]

A series of hybrid heterocycles was synthesized from methylenebis(thiazolidinone carboxylic acids) by treating with propargyl bromide and p-chlorophenyl azide under microwave irradiation at 65°C by using glucose as catalyst. Anticancer activity of the prepared compounds was evaluated by in vitro screening technique against different cancer cell lines DU145, A549, HELA, MCF. Compounds with the 4-Cl, 4-NO2, 4-F, and 4-CH3 groups showed significant anticancer activity against all cell lines examined. [ABSTRACT FROM AUTHOR]

Very little is known about factors determining the assemblage structure of megadiverse polyphagous‐herbivore scarab chafers in the tropics (Coleoptera: Scarabaeidae). Here, we examined the composition of Sri Lankan chafer assemblages and investigated whether it is influenced more by the general ecoclimatic situation, macrohabitat, or indetermined stochastic biotic and abiotic factors of each locality. We also explored the influence of the latter on separate lineages and general body size. Based on dedicated field surveys conducted during the dry and wet seasons, we examined 4847 chafer individuals of 105 species sampled using multiple UV‐light traps in 11 localities covering different forest types and altitudinal zones. Assemblages were assessed for compositional similarity, species diversity, and abundance within four major eco‐spatial partitions: forest types, elevational zones, localities, and macrohabitats. Our results revealed that assemblages were shaped mainly by locality stochastics (i.e., multi‐factor ensemble of all biotic and abiotic environmental conditions at local scale), and to a minor extent by ecoclimatic conditions. Macrohabitat had little effect on the assemblage composition. This was true for the entire chafer assemblage as well as for all single lineages or different body size classes. However, in medium and large species the contrasts between localities were less pronounced, which was not the case for individual lineages of the assemblage. Contrasts of assemblage similarity between localities were much more evident than those for forest types and elevation zones. Significant correlation between species composition and geographic distance was found only for the assemblage of small‐bodied specimens. Seasonal change (dry–wet) in species composition was minor and only measurable in a few localities. The strong turnover between examined localities corroborates with the high degree of endemism in many phytophagous chafers, particularly in Sericini. Connected with their hypothetic poor habitat specificity and polyphagy, this might also explain why so many chafer crop pests in the Asian tropics are endemics. [ABSTRACT FROM AUTHOR]

The field of energy harvesting is expanding to power various devices, including electric vehicles, with energy derived from their surrounding environments. The unique mechanical and electrical qualities of composite materials make them ideal for energy harvesting applications, and they have shown tremendous promise in this area. Yet additional studies are needed to fully grasp the promise of composite materials for energy harvesting in electric vehicles. This article reviews composite materials used for energy harvesting in electric vehicles, discussing mechanical characteristics, electrical conductivity, thermal stability, and cost-effectiveness. As a bonus, it delves into using composites in piezoelectric, electromagnetic, and thermoelectric energy harvesters. The high strength-to-weight ratio provided by composite materials is a major benefit for energy harvesting. Especially important in electric vehicles, where saving weight means saving money at the pump and driving farther between charges, this quality is a boon to the field. Many composite materials and their possible uses in energy harvesting systems are discussed in the article. These composites include polymer-based composites, metal-based composites, bio-waste-based hybrid composites and cement-based composites. In addition to describing the promising applications of composite materials for energy harvesting in electric vehicles, the article delves into the obstacles that must be overcome before the technology can reach its full potential. Energy harvesting devices could be more effective and reliable if composite materials were cheaper and less prone to damage. Further study is also required to determine the durability and dependability of composite materials for use in energy harvesting. However, composite materials show promise for energy harvesting in E.V.s. Further study and development are required before their full potential can be realized. This article discusses the significant challenges and potential for future research and development in composite materials for energy harvesting in electric vehicles. It thoroughly evaluates the latest advances and trends in this field. [ABSTRACT FROM AUTHOR]

The recent outbreak of Coronavirus disease 2019 (COVID‐19) has posed serious threats and challenges to global supply chain management (GSCM). To survive the crisis, it is critical to rethink the proper setting of global supply chains and reform many related operational strategies. We hence attempt to reform the GSCM from both supply and demand sides considering different pandemic stages (i.e., pre, during, and post‐pandemic stages). In this research paper, we combine a careful literature review with real‐world case studies to examine the impacts and specific challenges brought by the pandemic to global supply chains. We first classify the related literature from the demand and supply sides. Based on the insights obtained, we search publicly available information and report real practices of GSCM under COVID‐19 in nine top global enterprises. To achieve responsiveness, resilience, and restoration (3Rs), we then propose the "GREAT‐3Rs" framework, which shows the critical issues and measures for reforming GSCM under the three pandemic stages. In particular, the "GREAT" part of the framework includes five critical domains, namely, "government proactive policies and measures," "redesigning global supply chains," "economic and financing strategies under risk," "adjustment of operations," and "technology adoption," to help global enterprises to survive the pandemic; "3Rs" are the outputs that can be achieved after using the "GREAT" strategies under the three pandemic stages. Finally, we establish a future research agenda from five aspects. [ABSTRACT FROM AUTHOR]

The current research work on the effect of exhaust gas recirculation on the diesel engine for the analysis of performance and emission characteristics with corn oil methyl ester blends. The engine was tested with three fuels are 100 diesel, 20 B (20% COME + 80% Diesel) and 40 B (40% COME + 60% Diesel) with EGR condition of 0% and 20% rate. The engine was evaluated with performance characteristics such as thermal efficiency, brake specific fuel consumption, exhaust gas temperature and emissions such as carbon dioxide, hydrocarbon, oxides of nitrogen and particulate matter. The analyzed results of the brake thermal efficiency were reduced with increased brake specific consumption and the exhaust emissions are decreased with the increase of biodiesel blend and increased EGR rate. [ABSTRACT FROM AUTHOR]

Recent studies have focused heavily on tumor-oriented nanocarriers mediated by cellpenetrating peptides (CPPs). However, the loss of CPPs in normal tissues and enzymatic degradation in circulation frequently prevented the use of CPPs in vivo. To alleviate these limitations, CPPs needed to be kept immobilized before they arrived at the intended target for receptor-mediated endocytosis (RME). In this study, we developed CAR/DOX-Liposomes with doxorubicin hydrochloride (DOX) entrapped in the hydrophilic core of liposomes using a thin film hydration method, and CAR peptide was subsequently conjugated through SPDP chemistry on the surface of liposomes as targeting moiety to develop and improved targeted cancer chemotherapy. The prepared liposomes were characterized and evaluated for different parameters which were recorded to be vesicle size 275.2±5.65 nm, polydispersity index 0.260±0.85, Zeta-potential -33.90±2.42 mV, and % entrapment efficiency 83.96±2.56 %. In addition, Transmission electron microscopy (TEM), and Atomic Force Microscopy (AFM) studies were conducted to assess morphology and in vitro drug release performed. Further, the Cell line study of the CAR/DOX-Liposomes was studied over the lung cancer cell line HOP-62 and the comparison IC50 values were determined. The study establishes that CAR/DOX-Liposomes offer specific delivery of DOX to the heparan sulfate receptor(s) exclusively overexpressed on the cancer cells. [ABSTRACT FROM AUTHOR]

Fixed costs of ordering items or setting up a production process arise in many real‐life scenarios. In their presence, the most widely used ordering policy in the stochastic inventory literature is the (s,S)$(s,S)$ policy. Optimality of (s,S)$(s,S)$ policies and (s,S)$(s,S)$‐type policies have been examined for various inventory models, including those with the inventory level being reviewed in every period or continuously, finite and infinite horizons, discounted‐cost and average‐cost criteria, backlogging and lost‐sales practices, standard and generalized demands and/or costs, deterministic and stochastic lead times, single‐product and multi‐product settings, and coordinated pricing‐inventory decisions. We comprehensively survey the vast literature accumulated over seven decades in two papers. This paper is devoted to discrete‐time models, and the companion paper, also published in this journal issue, reviews continuous‐time models. We go over model specifications, proof techniques, specific results, and limitations of the articles published in the literature. We conclude each paper by providing corresponding suggestions for extensions and directions for future research. [ABSTRACT FROM AUTHOR]

Fixed costs of ordering items or setting up a production process arise in many real‐life scenarios. In their presence, the most widely used ordering policy in the stochastic inventory literature is the (s,S)$(s,S)$ policy. Optimality of (s,S)$(s,S)$ policies and (s,S)$(s,S)$‐type policies have been examined for various inventory models, including those with the inventory level being reviewed in every period or continuously, finite and infinite horizons, discounted‐cost and average‐cost criteria, backlogging and lost‐sales practices, standard and generalized demands and/or costs, deterministic and stochastic lead times, single‐product and multi‐product settings, and coordinated pricing‐inventory decisions. We comprehensively survey the vast literature accumulated over seven decades in two papers. This paper is devoted to continuous‐time models, and the companion paper, also published in this journal issue, reviews discrete‐time models. We go over model specifications, proof techniques, specific results, and limitations of the articles published in the literature. We conclude each paper by providing corresponding suggestions for extensions and directions for future research. [ABSTRACT FROM AUTHOR]

The objective of the paper is to propose a Machine Learning based model for incident management. Incident management is a key activity during the operations phase. It's essential to address the incidents in an optimal time so that there is no disruption to the operations. As the number of incidents increase, it's essential to have algorithms which allocate the incident to the right bot or concerned department through which the incident gets resolved or closed. The paper takes machine-first, smart, automation focused operations approach for incident management. The machine first approach proposes various tools, methods and processes that use machine learning to optimize the overall ticket management process. The Machine First Incident Management (MFIM) approach improves the productivity as well as quality. We elaborate the detailed method for smart ticket management, pro-active failure prediction, smart problem management, proactive maintenance and smart shift left in this paper. [ABSTRACT FROM AUTHOR]

Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose an alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient (c1 and c2) and Inertia Weight (ω). The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations. [ABSTRACT FROM AUTHOR]

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Operations management (OM) has evolved to its current status due to the contributions of researchers in many fields including economics, finance, behavioral sciences, operations research/management science, mathematics, statistics, and computer science. Many of these contributors are Nobel laureates—winners of the highest academic award. The Nobel Memorial Prize in Economic Sciences is awarded to those with monumental works in economics. Yet, some winners have also made contributions to operations management, the field of this journal. Others have developed important concepts and techniques that have made an enormous impact on OM research. Here, we describe the OM contributions as well as the impactful concepts of these Laureates. [ABSTRACT FROM AUTHOR]