Search

Digital platforms share their customers’ data with social planners, who may utilize it to improve socioeconomic infrastructure. This may benefit customers because of the experience of improved infrastructure. On the contrary, it may lead to privacy concerns among them (as these data sets may include sensitive information). In this paper, we analyze the game-theoretic model to characterize the granularity of data sharing between firms and the social planner and the investments by the social planner to improve public infrastructure. In order to analyze the impact of regulation on data sharing strategy, we consider the cases when data sharing is regulated (decided by the social planner) and unregulated (strategically decided by firms). Our analysis reveals that the firms as well as the social planner decrease the granularity of data with an increase in privacy concerns among customers. To analyze the impact of regulation, we compare the granularity of data shared under unregulated and regulated scenarios. We find that when the firm is monopolist, it shares data with a higher level of granularity in the unregulated scenario. Interestingly, we find that under market competition, the data granularity may be higher or lower compared with the regulated scenario. Specifically, we find that if firms jointly determine the granularity of data to be shared, they share data with higher granularity under the unregulated scenario; however, if they do not collaborate and individually decide on data sharing, we find that regulation leads to higher granularity of data to be shared. Finally, we find that firms’ payoffs and customer surplus are higher under the unregulated data-sharing setup if they jointly determine the granularity of data; however, if they do not collaborate on data sharing, their payoffs, as well as customer surplus, are higher under regulation.Supplemental Material:The online appendix is available at https://doi.org/10.1287/serv.2022.0052.

This abstract describes a Wi-Fi-based drone with optimized battery life for longer flying times because many UAVs (Unmanned Aerial Vehicles) are also utilized by people for daily purposes in metropolitan areas. The drone has a Wi-Fi module that enables communication with a base station, which serves as the drone's command center. The base station is in charge of controlling the drone and keeping track of its battery life. The drone has been optimized so that it can stay in the air for longer, making it perfect for a variety of tasks like surveillance, search and rescue, and aerial photography.

Recently, e-commerce platforms like Amazon and Flipkart have started collaborating with sellers to introduce the product in competition with an existing seller on the platform. The collaborating seller and the platform firm make significant investments that determine the quality of the product. Considering such collaboration dynamics, we investigated should the platform invest early in the product development or wait for the collaborating seller? We adopted the game-theoretic approach to solve the above problem. Our analysis revealed that under the scenario where sellers do not have wholesale pricing power and quality improvement effectiveness in codeveloping the product is low, the platform makes a higher profit when all players simultaneously decide the efforts. Otherwise, the platform earns a higher profit when it invests after observing the efforts of both sellers. Interestingly, when the sellers strategically decide wholesale prices, the platform always makes a maximum profit when it invests after observing investments by the sellers.

Family building is a human right. The high cost and lack of insurance coverage associated with fertility treatments in the United States have made treatment inaccessible for many patients. The universal uptake of “add-on” services has further contributed to high out-of-pocket costs. Expansion in access to infertility care has occurred in several states through implementation of insurance mandates, and more employers are offering fertility benefits to attract and retain employees. An understanding of the economic issues shaping fertility should inform future policies aimed at promoting evidence-based practices and improving access to care in the United States.

Newspapers are a rich informational source. A headline of an article sparks an interest in the reader. So, news providing agencies tend to create catchy headlines to attract the reader's attention onto them, and this is how sarcasm manages to find its way into news headlines. Sarcasm employs the use of words that carry opposite meaning with respect to what needs to be conveyed. This leads to the need of developing methods by which we can correctly predict whether a piece of text, or news for that matter, truthfully means what it says or is simply being sarcastic about it. Here, the authors have used a dataset containing 55,329 tuples consisting of news headlines from The Onion and the Huffington Post, which was taken from Kaggle, on which they applied feature extraction techniques such as Count Vectorizer, TF-IDF, Hashing Vectorizer, and Global Vectorizer (GloVe). Then they applied seven classifiers on the obtained dataset. The experimental results showed that the highest accuracies among the ML models were 81.39% for LR model with Count Vectorizer, 79.2% for LR model with TF-IDF Vectorizer, and 78% for SVM model with Count Vectorizer. They also obtained the best accuracy of 90.7% using the Bi-LSTM Deep Learning Model. They have trained the seven models and compared them based on their respective accuracies and F1-Scores.

Knowledge Management (KM) is one of the most important systems for any biopharmaceutical company. KM is considered to be a vital connection between other management subsystems in an organization. This [...]

Process validation is required by cGMP regulations to ensure that process consistency and the ensuing product conforms to product requirements. A risk-based approach to process validation helps to identify crucial [...]

(Abstracted from JAMA Network Open2023;6(1):e2251739)Multiple gestation is one of the most common risks associated with fertility treatment, such as in vitro fertilization (IVF). Compared with singleton pregnancies, pregnancies with multiples are at increased risk of maternal and neonatal morbidity and mortality as well as higher health care costs.

Over the past few years, the awareness and popularity of Mental health have been on a rapid rise and people are becoming more aware of the surrounding problems. This has helped for mental illnesses like Depression to become recognized and be treated appropriately. Social media has played an integral part in this uproar due to its increased popularity and ease of use. This has allowed people to spread awareness, seek help and vent out their emotions. Our paper is a comparative study of different models for detecting depression with real-time Twitter data and proposing the best performing model. For depression detection, a collection of tweets per user spread over time was used. The data was augmented and then passed through the deep learning model to identify depression in Twitter users based on their Time-Distributed tweets. The proposed model achieved an accuracy of over 90%.

In the modern era, social networking platforms play a vital role in day-to-day life. It is used by personalities, bureaucrats, and common people alike. It provides a platform for everyone to express their opinions, share their experiences with their family, friends, and the world. The advent of such platforms has completely changed the outlook of the world. Sexual harassment has been a problem for a long time, but with the advancement in technology, some users have taken the harassment to the digital level. It is common these days to find users posting derogatory remarks, uploading sensual and private content of others in order to sexually harass them. This is not all. Some targeted audience might even take some wrong steps. This article presents a way to detect such derogatory and harassing remarks using Twitter as a database. The classification part is done with the help of two deep learning models, and at the end, the performance of both models is evaluated on various parameters.

The paper here suggests a new intrusion detection method that is applied using an improved Lasso Regularization Metaheuristic Optimization. A comparison with other metaheuristic algorithms used to test the current method alongside the associated works is part of the proposed analysis herein. Because of the high processing capacity, network traffic in the intrusion detection system (IDS) has erratic behavior. The size of the device increases; the vast number of features must then be explored. However, the undesirable features and (or) any noisy data have a significant effect on the performance of the IDSs. Lasso regression implements L1 regularization, applying a penalty proportional to the absolute value the coefficient magnitude. Sparse formulas with few coefficients can result in this form of regularization; certain coefficients can become negative and be removed from the model. The algorithm for particle swarm optimization (PSO) is added to the selective features that increased the IDS' detection rate and accuracy.

AbstractNatural language processing (NLP) is an area in artificial intelligence that deals with understanding, interpretation and development of human language for computers to carry out tasks such as sentiment analysis, summarization of text in a document, developing conversational agents, machine translation and speech recognition. From conversational agents called catboats deployed on various websites that interact with consumers digitally to understand the needs of the consumers to reading summarized content delivered through apps in smartphones, NLP has had some major achievements in transforming the digital world that is increasingly gearing towards artificial intelligence. One area that has seen remarkable growth in recent times is language modelling, a statistical technique to compute the probability of tokens or words in a given sentence. In this paper, we attempt to present an overview of various representations with respect to language modelling, from neural word embeddings such as Word2Vec and GloVe to deep contextualized pre-trained embedding such as ULMFit, ELMo, OpenAI GPT and BERT.

In pandemics or epidemics, public health authorities need to rapidly test a large number of individuals without adequate testing kits. We propose a testing protocol to accelerate infection diagnostics by combining multiple samples, and in case of positive results, re-test individual samples. The key insight is that a negative result in the first stage implies negative infection for all individuals. Thus, a single test could rule out infection in multiple individuals. Using simulations, we show that this protocol reduces the required number of testing kits, especially when the infection rate is low, alleviating a key bottleneck for public health authorities in times of pandemics and epidemics such as COVID-19. Our proposed protocol is expected to be more effective when the infection rate is low, which suggests that it is better suited for early stage and large-scale, population-wide testing. However, the managerial trade-off is that the protocol has costs in additional time for returning test results and an increased number of false negatives. We discuss applications of pooled testing in understanding population-wide testing to understand infection prevalence, to diagnose infections in high-risk groups of individuals, and to identify disease cold spots.

Introduction: Numerous case series have reported on the baseline characteristics and in-hospital mortality of patients with COVID-19, however, these studies included patients localized in a specific geographic region. The purpose of our study was to identify differences in the clinical characteristics and the in-hospital mortality of patients with a laboratory-confirmed diagnosis of COVID-19 internationally. Methods: A comprehensive search of all published literature on adult patients with laboratory-confirmed diagnosis of COVID-19 that reported on the clinical characteristics and in-hospital mortality was performed. Groups were compared using a Chi-square test with Yates correction of continuity. A two-tailed pvalue of less than 0.05 was considered as statistically significant. Results: After screening 516 studies across the globe, 43 studies from 12 countries were included in our final analysis. Patients with COVID-19 in America and Europe were older compared to their Asian counterparts. Europe had the highest percentage of male patients. American and European patients had a higher incidence of co-morbid conditions (p< 0.05 for all variables). In-hospital mortality was significantly higher in America (22.23%) and Europe (22.9%) compared to Asia (12.65%) (p< 0.0001), but no difference was seen when compared with each other (p= 0.49). Conclusions: There is a significant variation in the clinical characteristics in patients diagnosed with COVID-19 across the globe. In-hospital mortality is similar between America and Europe, but considerably higher than Asia.

Supplemental Digital Content is available in the text.

Table of Contents Abstract Introduction Materials And Methods Results Discussion References Abstract Objective: To determine whether moderate conscious sedation (using a benzodiazepine and opioid) provides adequate pain control during in [...]

Supplemental Digital Content is available in the text.

Application Abstract for Service Spotlights

The present study focuses on the investigation of 2,2′,2″-nitrilotrisethanol (triethanolamine) as an entrainer to effect the separation of an isopropanol–water mixture. The vapor–liquid equilibrium (VLE) data were measured for the binary systems isopropanol + triethanolamine and water + triethanolamine using an improved Othmer still at 101.3 kPa. Experimental data for the ternary mixture isopropanol + water + triethanolamine were also studied for different entrainer concentrations. Three initial triethanolamine concentrations, 5 mol %, 10 mol %, and 15 mol %, were examined in the feed solution for VLE experiments. The results demonstrated that the addition of triethanolamine in the isopropanol + water mixture produces a considerable increment in the relative volatility, which could eliminate the azeotrope successfully. Hence, triethanolamine was found to be an effective entrainer for breaking the isopropanol + water azeotrope. Further, the NRTL model was used for fitting the experimental VLE data, and the binary interaction parameters were regressed. The estimated values for binary and ternary systems agreed well with the experimental data. The overall average deviations for the vapor-phase mole fraction and equilibrium temperature between the experiment and correlation were found to be 0.09 and 0.40 K, respectively. Moreover, the thermodynamic consistency check of experimental VLE data was performed by using the modified McDermott–Ellis test and Wisniak’s L–W test.

Low transition temperature mixtures (LTTMs) are versatile alternatives to ILs. They share many properties with ILs, so they become a suitable choice for entrainers in extractive distillation processes. In this study, glycolic acid and choline chloride in a 3:1 molar ratio (GC3:1) were synthesized and explored as entrainers for separation of acetonitrile + water azeotropic mixtures. Isobaric vapor–liquid equilibrium data for the pseudobinary mixtures of ACN + GC3:1 and water + GC3:1 were measured at atmospheric pressure (101.32 kPa). For the pseudoternary system ACN + water + GC3:1, also VLE data were measured at different GC3:1 mole fractions of 0.05, 0.1, and 0.15. The thermodynamic modeling of these systems was performed using the nonrandom two-liquid (NRTL) model. Furthermore, a study was conducted for synthesized GC3:1 recoverability. A good agreement were found between experimental data and predicted values for these systems. Results showed that LTTM (GC3:1) eliminated the acetonitrile + water azeotrope by manipulating the relative volatility of the acetonitrile + water mixture. Therefore, LTTM (GC3:1) can be concluded as an efficient entrainer for the separation of an acetonitrile + water azeotropic mixture by extractive distillation.

Background:

Objectives: To evaluate the diagnostic performance of contrast-enhanced F18 FDG PET-CT with conventional modality (contrast-enhanced computed tomography) in restaging of cervical carcinoma. Study Design: A retrospective analysis of histopathological proven patients of carcinoma cervix (n= 386; age range 32–82 years; mean age 51.4 years) between April 2017 and July 2022 was done. All the patients were treated either in combination or alone with surgery, radiotherapy or chemotherapy. The patients were referred for F18 FDG PET-CT on the basis of clinical and imaging suspicion of residual or recurrent disease and for post-therapy monitoring. Histopathological examination and clinical or imaging follow-up were taken as gold standard. Results: Of 386 patients, underwent PET-CT examination for post-therapy monitoring. F18 FDG PET-CT detected abnormal FDG uptake in 258 patients. Uptake at the primary site was detected in 112/258 (43.41%) patients, and only regional pelvic lymph nodes and distant metastatic lesions were in 18/258 (~ 6.96%) and 128/258 (49.4%) patients, respectively. On lesion-based analyses, distant metastases, lymph nodes, lung, liver and skeleton were noted in 128, 50, 18 and 30 patients respectively, either isolated or combined. Fourteen patients had distant metastases at rare sites including kidney, muscle, ascites, pleural effusion and gut (4, 4, 2, 2, 2). Additionally, metachronous primaries were also detected in 18 patients and confirmed on histopathology (lung-6, pharynx-4, breast-4, lymphoma-2 and rectum-2). After a median follow-up period of 30 months (± SD 20.8), 226 patients having residual or recurrence disease and FDG PET-CT had the sensitivity, specificity, PPV, NPV and accuracy as 89.38, 65, 78.29, 81.25 and 80%, respectively, while conventional imaging (CI) had the sensitivity, specificity, PPV, NPV and accuracy of 66.42, 40.68, 71.77, 34.78 and 70%, respectively. On ROC curve analysis, SUVmax > 4.4 had sensitivity and specificity 78.8 and 83.7%, respectively, for lesion detection on F18 FDG PET-CT. There was a significant correlation between SUVmax and patient outcome (r =  ~ 0.0585). Conclusions: This study concluded that F18 FDG PET-CT has high diagnostic accuracy as compared to conventional imaging for detection of suspected residual/recurrent disease in cervical cancer patients and also useful in detection of additional metachronous second primary.

Purpose: The aim of this project is to investigate the usefulness of the absolute liver lesion ADC value and ratio of Apparent diffusion coefficient (ADC) values of a liver lesion and liver parenchyma to discriminate between a benign and malignant lesion.

•ADCratiois a reliable and reproducible tool in quantification of diffusion restriction for clinically significant PCa foci.•Comparing an experienced and a new MRI reader, Inter-reader reliability in the calculation of ADCratiowas excellent.•ADCratiohas potential to replace the current practice of visual analysis of ADCtumourreduction, and provide an objective tool.

The present article presented a 47-year-old woman with areas of nodular swelling and discoloration involving bilateral lower limbs since 12 months. Biopsy from skin lesions revealed erythema nodosum. As erythema nodosum can be secondary to multiple etiologies, whole-body 18F-FDG PET/CT was performed to assess the disease distribution and guide the site of biopsy. 18F-FDG PET/CT revealed metabolically active mediastinal lymphadenopathy in addition to the skin lesions. Endobronchial ultrasound–guided biopsy from subcarinal lymph node revealed tuberculosis.

Currently under construction for Sterlite Industries, the new copper smelter in Maharashtra state will use Isasmelt and Isa Process copper refining Brisbane-based MIM Holdings Limited is providing the core technologies [...]

Graphene and other two-dimensional materials offer a new class of ultrathin membranes that can have atomically defined nanopores with diameters approaching those of hydrated ions. These nanopores have the smallest possible pore volumes of any ion channel, which, due to ionic dehydration and electrokinetic effects, places them in a novel transport regime and allows membranes to be created that combine selective ionic transport with ultimate permeance and could lead to separations and sensing applications. However, experimental characterization and understanding of sub-continuum ionic transport in nanopores below 2 nm is limited. Here we show that isolated sub-2 nm pores in graphene exhibit, in contrast to larger pores, diverse transport behaviours consistent with ion transport over a free-energy barrier arising from ion dehydration and electrostatic interactions. Current–voltage measurements reveal that the conductance of graphene nanopores spans three orders of magnitude and that they display distinct linear, voltage-activated or rectified current–voltage characteristics and different cation-selectivity profiles. In rare cases, rapid, voltage-dependent stochastic switching is observed, consistent with the presence of a dissociable group in the pore vicinity. A modified Nernst–Planck model incorporating ion hydration and electrostatic effects quantitatively matches the observed behaviours.

In this paper, we consider a supply chain where two buyers compete by investing in in-house capacity and outsourcing the remaining part of its production requirements to a common supplier to serve their individual markets with uncertain demand. The supplier has the option to sell capacity to these buyers and to an alternative market with random demand. We determine the buyers’ optimal sourcing and in-house production capacity decisions under such scenario.

Abstract:This paper demonstrates that the social institutions of lineage maintenance, patrilocality, and joint families have a significant role in explaining sex differences in survival and health outcomes in rural India. Tests using panel data from rural households support this explanation, which accounts for 7 percent of excess female mortality in Haryana and Rajasthan and 4 percent in Punjab. An institutional explanation suggests limits on the role for public policy in addressing large sex differences in health and mortality outcomes.

Cannabinoids are part of an endogenous signaling system consisting of cannabinoid receptors and endogenous cannabinoids as well as the enzymatic machinery for their synthesis and degradation. Depolarization-induced suppression of excitation (DSE) is a form of cannabinoid CB1receptor–mediated inhibition of synaptic transmission that involves the production of the endogenous cannabinoid 2-arachidonoyl glycerol (2-AG). Both diacylglycerol lipase α(DAGLα) and DAGLβcan produce 2-AG in vitro, but evidence from knockout animals argues strongly for a predominant, even exclusive, role for DAGLαin regulation of 2-AG–mediated synaptic plasticity. What role, if any, might be played by DAGLβremains largely unknown. Cultured autaptic hippocampal neurons exhibit robust DSE. With the ability to rapidly modulate expression of DAGLαand DAGLβin these neurons with short hairpin RNA, they are well suited for a comparative study of the roles of each isoform in mediating DSE. We find that RNA interference knockdown of DAGLαsubstantially reduces autaptic DSE, shifting the “depolarization-response curve” from an ED50value of 1.7 seconds to 3.0 seconds. Surprisingly, DAGLβknockdown diminishes DSE as much or more (ED506.4 seconds), suggesting that DAGLβis also responsible for a portion of 2-AG production in autaptic neurons. Similarly, the two DAGLs both contribute to the production of 2-AG via group I metabotropic glutamate receptors. Our results provide the first explicit evidence for a role of DAGLβin modulating neurotransmission.

The aim of this study was to evaluate the clinical utility of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET-CT) in staging and restaging of patients with malignant primary salivary gland tumours.

Pulmonary rehabilitation (PR) improves anxiety and depression in patients with chronic obstructive pulmonary disease. However, little is known regarding its ability to produce clinically meaningful improvements in these outcomes.

68Ga-labeled prostate-specific membrane antigen ligand PET-CT has emerged as a promising technique to evaluate the extent of disease in patients with prostate carcinoma. We are reporting a 63-year-old man with prostatic carcinoma subjected to 68Ga–prostate-specific membrane antigen ligand PET-CT for initial staging. In addition to the radiotracer uptake in known primary site (prostate), focal increased radiotracer uptake was also noticed in the left breast. Subsequent biopsy of the breast lesion revealed PASH (pseudoangiomatous stromal hyperplasia), which is a benign mesenchymal proliferative lesion that may present clinically as palpable mass requiring further evaluation to rule out malignancy.

AbstractAutomation of lead compound design in silicogiven the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz.sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.

Metastatic jugular paraganglioma are rare tumors and account for less than 1% of the cases of head and neck tumors. We report a 40-year-old woman of jugular paraganglioma, presenting with right-sided neck swelling, hearing loss, and pulsatile tinnitus. Contrast-enhanced CT temporal bone revealed a mass in the right jugular foramina and extending inferiorly to internal jugular vein. 68Ga DOTANOC PET/CT was performed, which revealed somatostatin receptor expressing lesion in the right internal jugular vein and extension into sigmoid sinus and additional metastatic focus in the sacrum.