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Summarization: Alternative tourism has gained considerable interest among consumers and enterprises in the tourism sector, creating new opportunities for providing high quality and innovative tourist services, while contributing to sustainable development. The Internet plays a key role as a tool for promoting alternative tourism. Motivated by this, this article presents an original research on the evaluation of the content features of the websites of enterprises involved with alternative tourism services in the region of Chania, Greece. The sample consists of 28 websites evaluated over 11 performance criteria through the ELECTRE III multicriteria method. The websites are ranked in a benchmarking context and the results of the evaluation are used to identify the most successful practices, as well as the website features that require improvements for the enterprises to take advantage of the opportunities that arise in the new era of technological innovation in the tourism sector. Presented on: IEEE Transactions on Engineering Management

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

While addressing grey fabric quality in a renowned circular weft knitting mill of Bangladesh, the authors experienced some questionable approach practiced by knitters. The subjective nature of defect detection by knitters/inspectors often time causes wrong emphasizing on frequently occurring defect(s) instead of focusing on influential defect(s) and subsequently, employing wrong quality control approach to minimize the grey fabric defects. Knit fabric defects (e.g., hole, stain, press-off, gout, miss knit, barrè, tucking, etc.) should be assessed by type, fault coverage, gravity and the frequency of occurrence instead of focusing only on frequency of occurrence in the fabric. In this study, grey weft-knitted fabric quality is investigated based on influential defects instead of frequently occurring defects. Quality data of single jersey, fleece and 1X1 rib were gathered and analyzed from an established knitting factory in Bangladesh over three months duration. A fabric inspection machine and 4-point inspection method were employed in this study. Gout was found as the most frequently occurring defect for each fabric type but not influential for rib fabric. For a significant amount of knitted fabrics, totaling of 55,524.91 m2 inspected fabric, the most occurring defects were ranked as gout, press-off, hole, miss knit, stain, and tucking and influential defects (based on inspection points) were ranked as gout, press-off, hole, stain, miss knit, and tucking (highest to lowest). In the inspection report, the knitter/inspector mistakenly categorized gout as the most occurring as well as the most influential defect for 1X1 rib fabrics and suggested remedies accordingly.

Housing in Algeria is experiencing a multi-dimensional crisis: crisis in design (standardization of housing), construction, financing, management, etc., which has a negative impact on user comfort, poor housing quality and high energy consumption.This article focuses on the study of the impact of different thermal insulation str ategies on energy consumption (heating and cooling) of a residential building situated in semi arid climate (Constantine northeast of Algeria) using thermal dynamic simulation tool (TRNSYS 16)

Kalinyamat Wetan is one of the villages located in Tegal City, Central Java, Indonesia. Based on observations made by the community partnership program of Pancasakti Tegal University there are problems faced by our partners, namely: 1) the uneven economy of the residents of Kalinyamat Wetan Village. In general, the economy of The People of Kalinyamat Wetan is good enough, but there are some poor people in Kalinyamat Wetan who need capital assistance and skills courses so that it can be used for entrepreneurship, 2) The problem of lack of knowledge about innovations in the field of Industry. The level of education is still relatively low in Kalimat Wetan which leads to a lack of information and knowledge about the latest innovations to developments in the field of Industry. To answer the problem, we provide solutions through providing training to batik craftsmen to improve the quality of their batik packaging. This is done so that there is an increase in the turnover of batik sales in order to increase their revenue. The implementation of community partnership program activities in the village of Kalinyamat Wetan Tegal city consisting of planning, action and evaluation in general went lancer and successful. Residents in the village of Kalinyamat Wetan, especially batik craftsmen have been able to make a variety of batik packaging designs and are able to provide product descriptions in attractive batik packaging. Residents responded to this PKM activity with great enthusiasm. Keywords: Batik Tegal, Package Inovation, Marketing, Kalinyamat Wetan, SMEs

Changes in product characteristics on the extensive margin (the addition of new features and the removal of old ones) are an important and hitherto neglected dimension of quality change. Standard techniques for adjusting price indices for new goods cannot handle such changes satisfactorily, and this leads to an economically and statistically significant bias in the measurement of prices and real output. We combine insights from the theories of exact index numbers and demand for characteristics to develop a new method for incorporating changes on the extensive characteristic margin. Applied to U.K. data on new car sales, our method leads to revisions in estimated inflation rates for this commodity group that are both plausible and quantitatively important.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasets Geolife and Chengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed ~18% under evaluation metric perimeter and improve ~0.72% under evaluation metric ADTS.

We examine the effectiveness of a quality assurance mechanism that relies on a third-party’s reputation. We focus on the market for fine and rare wines where auction houses intermediating between sellers and buyers have also claimed expertise to assess product authenticity. An auction house’s reputation can sustain product quality in this market only if consumers absorb relevant information and punish houses who fail at screening out counterfeits. We test the existence of such reputational effects by studying the market responses to two disclosures of houses having auctioned fake wine. We find that one house experienced negligible losses after a 2008 event involving a limited number of counterfeit bottles. However, three houses associated with a 2012 incident involving thousands of bottles are found to have suffered reputational losses. These losses are demonstrated over the following year by an average 4.5% decrease in equilibrium sales prices of all wines, and a slight decrease in sales quantities of older wines, relative to other houses.

The quality of parental support is recognized as a crucial factor in the early stages of a student’s development, and particularly in instrumental music education. At the start of 2020, the outbreak of a global pandemic crisis posed new and unprecedented challenges to education, forcing families to stay at home to prevent contagion. This investigation was conducted during the period of a COVID pandemic lockdown in Portugal. We explored whether parental support, provided during the lockdown period, was associated with their child’s achievement as reported by their instrumental music teacher. For this study, 39 parent–teacher dyads of first-grade students of an instrument music course were recruited from two public music conservatories. Parents supplied information on the frequency in which they provided student-support-related attitudes and actions in the home context. Simultaneously, teachers provided information about the student’s achievement during the lockdown compared with the previous in-person performance period. Results indicate a strong relationship between parental support and musical achievement, with students who received higher levels of supportive parental involvement performing better than before the pandemic crisis. The findings are discussed in relation to the importance of parental involvement in a child’s instrumental music education.

Denture cleaning is indispensable to the maintenance of good oral and systemic health for denture users. Nowadays people often consult YouTube about health-related topics, including denture care. However, the quality of the information about denture care presented on YouTube is unknown.The purpose of this study was to evaluate the usefulness and quality of popular videos about denture care shared by different uploaders on YouTube and to evaluate the demographic characteristics of the videos.Google Trends showed that "denture cleaning" was the most searched keyword on the topic. This keyword was used to search YouTube videos. Of the 200 most-watched videos, 109 videos were selected for analysis. Included videos were analyzed for their demographic data, including number of views; number of likes, dislikes, and comments; days since upload; duration and number of subscribers; an 8-point usefulness score system, a global quality scale (GQS); video sources; target groups; and primary purposes of the videos. Statistical analyses were conducted by using the Kruskal-Wallis test, post hoc Mann-Whitney U test, Pearson chi-squared test, and Spearman correlation analysis, which was used to investigate the relationship among total content score, GQS score, and video demographics. Cohen kappa statistics was used to measure the reliability of the investigator's evaluations of the videos (α=.05).Based on the usefulness score, 59.6% of the videos were classified as poor, 32.1% as moderate, and 8.2% as good. No statistically significant differences were found among usefulness scores according to the video demographics, except lower GQS score of poor content videos (P.001). The overall mean ±standard deviation GQS score was 1.92 ±1.0 out of 5. Videos were primarily uploaded (38.5%) from commercial companies. No statistically significant difference was found between video sources and usefulness scores (P.05). The number of videos with poor content was significantly higher than the number with moderate and good content among the videos primarily intended for the education of health professionals (P.001).The majority (59.6%) of YouTube videos on denture care received poor content quality ratings, independent of video demographics. Therefore, YouTube is not suitable as the only source of information on denture care. Dentists and prosthodontists should take more responsibility for enriching the content of video-sharing platforms because this content can affect the behavior of patients and their attitudes about denture care.

More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain data quality and inflexible pricing interface. To resolve them, potential solutions could be to launch data demonstrations and release free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data for enabling flexible pricing. However, their economic benefits are still not clear. In this paper, we design the optimal data selling mechanisms for IoT data exchange, and derive the following results. First, whether to deploy data demonstrations and how much free sampling data to release depend on the extent of data consumers inaccuracy perceptions for data quality. We found that data vendors have no incentive to conduct these strategies if data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for real-time and streaming IoT data exchange, it brings less economic benefits to data vendors compared with fixed pricing schemes. We evaluate the optimal selling mechanisms on a real-world Taxi GPS dataset. Evaluation results verify the insights derived from our theoretical analysis.

Introduction. To represent our national goods at the Western market it is necessary to ensure, first of all, the competitiveness of local products through complex mechanization of technological processes, labour costs reduction, increasing yields and quality of received products. The aim of the research is to provide the comparative analysis of technology and machines for potato harvesting, to determine the main factors that influence the agricultural technical indicators of harvesting equipment, to develop technological process patterns and to examine the strategies for improving the potato root-and-harvest machines. Methods and methodology. The research was carried out on the basis of technological and constructional analysis of technology and machines for potato harvesting. The methods of comparison and mathematical modelling of technological processes were used in the study. The research studies of Ukrainian and foreign scientists on technology and machines for potato harvesting were used as information base of our paper. Results. A comparative analysis of technology and machines for potato harvesting made it possible to determine the main factors that influence the agricultural performance of harvesting machinery and to develop the technological process for a digger, a double-loader digger and a harvester (harvesting machines). A theoretical model of the technological processes of harvesting machines includes both uncontrolled and controlled factors that influence the choice of a potato harvesting technology of a digger, a double-loader digger and a harvester. Nevertheless, the first factors are connected, primarily, with the conditions of potato growth and its biological properties, and the second factors deal with the organizational and technical measures, including the improvement of the working bodies of machines (cutting, dirt digging through, top separation, longitudinal hump with cut roller and other constructive and kinematic parameters), quality of received products, length and terms of potato storage, available farming human resources, free vehicle in the period of harvesting, farm potato storage, farm equipment for potato cleaning and sorting.

The extrusion process of plastic transformation involves a series of variables and components which must be associated with the concept of reliability to achieve an expected performance in operational and quality terms. This paper describes the reliability framework of an extruder of a packaging company located in Brazil through the following mathematical representations: probability density function f(t), reliability curve R(t) and function rate of failure h(t). Through a statistical software the set of representations was able to develop the reliability framework of the machine, which can be used to support a FMEA analysis. Besides that, it was evidenced that in the current operating conditions of the machine, approximately 50% of the failure observations happen on the hour 100 of operating time. The main outcome is that it is recommended a systematic preventive maintenance based on 100h cycles. It is expected that this work may act as a guide for future implementation of improvement activities.

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.

Despite numerous measures, bacterial sepsis associated with the transfusion remains a major threat. The incidence of septic events induced by platelets transfusion is approximately 10 times higher than with transfused red blood cells due to their storage temperature. This caused new Standard that implements the methods for the detection and reduction of bacteria in the platelet concentrates (PC). The aim is to consider the possibility of wider application of this tests in order to extend the shelf-life of PC. Sterility testing of PC is done once or twice per month using BacT/Alert BPA and BacT/Alert BPN bottles. If positive, all products from the initial unit were tested to confirm or deny the status. During six years period, 67236 PC units were made and 872 of them were tested. Only two were found initially positive. After testing the other products from the same initial unit, results were negative so, final results proclaimed false positive. Pretransfusion bacterial detection is an important potential method for reducing the risk of bacteriemia and transfusion-associated septic reactions. In addition to routine measures, Mirasol PRT pathogen inactivation system, could be included. This allows certain amount of PC to be inactivated during the first 32 hours. Untreated PC units would be stored in standard conditions and for given time (three days) potentially present bacteria would reach a detectable level. This way the quantity of samples for sterility testing could be reduced, taking only 2 ml of each of four units of PC. Samples would be planted at the same vial-aerobic bottle, which would also, double the capacity in BacT/Alert 3D automated system.

A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated crossdomain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed with insufficient theoretical support. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is a series of MAP-based cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized.We prove the general multiplicative inapproximability, and thus propose two heuristics, which, respectively, enjoy an -additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are also empirically validated, with one of the datasets extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations manifest the importance of community information in enhancing privacy inferencing.

Although quite natural for human beings to communicate based on their own personality in daily life, it is rather challenging for neural dialog systems to do the same. This is because the general dialog systems are difficult to generate diverse responses while at the same time maintaining consistent persona information. Existing methods basically focus on merely one of them, ignoring either of them will reduce the quality of dialog. In this work, we propose a two-stage generation framework to promote the persona-consistency and diversity of responses. In the first stage, we propose a persona-guided conditional variational autoencoder (persona-guided CVAE) to generate diverse responses, and the main difference when compared with general CVAE-based model is that we use additional dialog attribute to assist the latent variables to encode the effective information in the response and further use it as a guiding vector for response generation. In the second stage, we employ persona-consistency checking module and the response rewriting module to mask the inconsistent word in the generated response prototype and rewrite it to more consistent. Automatic evaluation results demonstrate that the proposed model is able to generate diverse and persona-consistent responses.

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility.

In recent years, there has been an increasing interest in the development of arthroplasty registries, therefore, in our country, the Italian Arthroplasty Registry (RIAP), was issued by the National Law No. 221/2012. In the last decade, however, some European countries -namely Sweden, Denmark, Norway, and Germany (in development)- have introduced another nationwide orthopaedic registry than arthroplasty registers: the fracture registry. The development of this new tool aims to improve quality and safety in fracture management, thus trying to provide a better postoperative quality of life in trauma patients. Based on these findings, the AO-Trauma Italy Council encouraged the development of a national fracture registry in Italy. The present study aims to (1) provide an overview of the fracture registries in Europe and (2) to develop, for the first time, a pilot Italian Fracture Registry (RIFra). Thirteen AO-Trauma Italy members, chairmen of Level-I orthopaedic and trauma centres, diffused throughout Italy, were involved in the RIFra project. The RIFra form, developed between November 2019 and March 2020, consists of 5 main sections, namely: epidemiologic data, previous surgical procedure (if any), patient and fracture features, surgical procedure, surgical implant details. This study constitutes the first step to start, in future years, the bureaucratic procedure leading to the final establishment of a RIAP-like fracture registry in Italy.

A major priority of the Toyota Production System is to satisfy the customers by providing high value. The use of continuous flow, pull systems, the Andon system, standard work, visual controls, the 5S system, and reliable technology are critically important. Optimizing one's workforce is also very important by developing leaders and teams, using consensus in decision making, and becoming a learning organization. To fully understand the problems, it is also important that leaders go to the workplace to talk to workers and see what is going on.

In a sample of 28 individuals cohabiting with a partner in NYC, Boston, or Chicago, this study tested whether implementation of stay-home orders to combat the spread of COVID-19 disrupted physical activity and whether high-quality romantic relationships buffered adverse effects. Participants provided FitBit data between February and October, 2020. Stay-home orders were associated with a reduction in daily step counts, B = −1595.72, p = 0.018, increased sedentary minutes, B = 33.75, p = 0.002, and reduced daily minutes of light and moderate physical activity, B = –25.01, p = 0.011; B = –0.72, p = 0.021. No moderation effects emerged.

The use of equipment with a high non-linear load causes a decrease in the quality of power (Power Quality) at Malang City Point Apartment. Power quality analysis is an important requirement in the electrical system and needs special attention to save energy and costs. Savings can be achieved by reducing or even eliminating losses resulting from the use of non-linear loads. This thesis aims to present the results of an analysis of power quality conditions at Malang City Point Apartments. This analysis includes voltage, current, voltage and current imbalance, power factor, current THD and measured voltage. The system is designed using Web - Based Power Quality Meter (PM 5110) as a measurement tool, then the results are analyzed and compared with existing standards to evaluate power quality in Malang City Point Apartments. Among the various power quality problems measured, it was found that the main problems of power quality in Malang City Point Apartments were the occurrence of overvoltages and current harmonic distortion. So it is necessary to do repairs on this to overcome the overvoltage by reducing the operation of equipment that has a large load and repairing neutral conductors. Furthermore, to reduce the system harmonics, it is designed by designing a single tuned passive filter and simulated using the ETAP software version 16.0.0.

Agriculture is an important sector that plays an essential role in the economic development of a country. Each year farmers face numerous challenges in producing good quality crops. One of the major reasons behind the failure of the harvest is the use of unscientific agricultural practices. Moreover, every year enormous crop loss is encountered either by pests, specific diseases, or natural disasters. It raises a strong concern to employ sustainable advanced technologies to address agriculture-related issues. In this paper, a sustainable real-time crop disease detection and prevention system, called CROPCARE is proposed. The system integrates mobile vision, Internet of Things (IoT), and Google Cloud services for sustainable growth of crops. The primary function of the proposed intelligent system is to detect crop diseases through the CROPCARE -mobile application. It uses Super-Resolution Convolution Network (SRCNN) and the pretrained model MobileNet-V2 to generate a decision model trained over various diseases. To maintain sustainability, the mobile app is integrated with IoT sensors and Google Cloud services. The proposed system also provides recommendations that help farmers know about current soil conditions, weather conditions, disease prevention methods, etc. It supports both Hindi and English dictionaries for the convenience of the farmers. The proposed approach is validated by using the PlantVillage dataset. The obtained results confirm the performance strength of the proposed system.

Drawing on insights from organizational theory, in this article, we examine the paradoxical tensions that reside between quality and safety and impact construction organizations and their projects. Rather than adopting the dualistic, reductionist, and ineffective “either/or” framing of quality and safety, we suggest confronting them as a duality. The corollary is balancing the competing demands of quality and safety, thus putting managers in a position to optimally reduce rework and improve project safety at the same time. Managers also need to focus on balancing the paradoxical tensions of error prevention and error management that can influence a project's performance. However, the offsetting of these tensions has been overlooked in practice. In this article, we proffer that if construction organizations are to make headway to improve quality and safety simultaneously, then managers should espouse ambidexterity, move from the “tyranny” of quality or safety to “genius” of quality and safety and, thus, synergize these two competing demands while managing the paradoxical tensions of error prevention and error management.

Given the high demand for low-cost housing by the low-income earners, coupled with the tropical climate experienced in sub-Saharan Africa, new-built housing stock needs utmost attention to cost, durability, and efficiency. With the walls accounting for a substantial proportion of the total building cost, choice of a wall system for use in building construction is critical. This choice usually depends on how durable, comfortable, ecological, and economical a given system is, to meet both the quality standards and low-cost aspects of housing. Although the earthbag building system allows for the construction of strong, affordable, and sustainable housing, it is not widely known. As such, its sustainable characteristics have gone unrecognised throughout the building industry. This paper examines and compares the thermal properties and total Life Cycle Costs (LCC) of earthbag walls with the commonly used burnt brick walls - based on the Degree-Days method and Life Cycle Costing analyses of building walls located in one of the hottest regions in Uganda. In-situ measurements of temperature and heat flux were conducted in accordance with ISO 9869 and the annual energy requirements obtained. The total LCC were calculated based on the initial construction costs and annual energy costs attributed to the building wall systems. Earthbags housing was found to be thermal-physically better than the brick wall with a lower U-value resulting in lower annual energy requirements and a huge saving in annual energy costs of up to 83.2%. This saving, coupled with low initial construction costs made the earthbag unit 68.7% cheaper than the burnt brick unit over a 30-year period. Therefore, this study findings suggest earthbag walling system as an economically viable and technically feasible low-cost construction option for rural areas and low-incomers earners’ housing in warm climatic conditions, a characteristic of sub-Saharan countries – so as to promote regional development.

Veterans Health Administration (VHA) continues to expand the mental health (MH) workforce to meet increasing demand for services. In the present study, longitudinal unbiased recursive partitioning models (conditional inference trees) were created to identify optimal cutoffs for outpatient staffing ratios associated with success on VHA's measures of quality, access, and satisfaction. Quarterly Staff-to-Patient Ratios (SPRs), defined as the number of full-time equivalent providers per 1,000 veterans receiving outpatient mental health care, were calculated for 12 quarters from fiscal years 2016-2018. Associations between VHA metrics associated with quality, access, and satisfaction were evaluated in relation to the overall outpatient SPR. The root node identified an overall outpatient SPR of 7.39 as the split for optimal MH performance. Root nodes associated with metrics addressing population coverage, continuity of care, and experience of care identified SPRs of 7.87, 6.81, and 7.42, respectively. In all analyses, the lowest SPRs were associated with the lowest performance on VHA MH metrics, while the highest SPRs were associated with the highest performance. Analyses support VHA's current recommended minimum outpatient SPR of 7.72 as a reasonable target to provide high-quality care, access, and satisfaction. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Health Information Exchanges (HIEs) are designed to improve the efficiency and quality of healthcare through enhanced information sharing between disparate health entities. The objective of this study is to systematically examine the impact of HIE use in emergency departments (EDs) on quality and efficiency of medical care. We focus on two dimensions of healthcare quality, namely length of stay and 30-day readmission rate. From hospitals’ efficiency perspective, we examine if HIE access reduces the likelihood that more than one doctor would be consulted for a treatment. Drawing on complementarity theory, we adopt a contingency framework and examine the moderating role of patient-specific prior information (breadth of focal patient’s information and prior interaction between a focal patient and attending doctor) and doctor’s experience with the HIE on the effectiveness of HIE access on quality and efficiency of medical care. To accomplish our objectives, we leverage a unique panel data of actual HIE access by physicians who practice in a hospital system that participate in an HIE. The individual patient-level dataset comprises detailed medical provider information, patient-level medical information, and various other information related to procedures that were performed. Leveraging this unique and comprehensive data on physicians’ access of HIE system, we examine the effect of HIE use on the three outcome variables. After controlling for a battery of patient-specific, doctor-specific, and hospital-specific control variables, our results show that HIE access in EDs results in reduction in length of stay and 30-day readmission rate, and reduces the likelihood that more than one doctor would be consulted. We find that the benefits of HIE access are greater with greater breadth of patient information, greater experience of the attending physician with the HIE, and prior interaction between the focal patient and focal attending doctor. We account for endogeneity due to self-selection in HIE use and perform additional tests to ensure that our results are robust. Based on our results, we offer insights to practitioners and academicians alike on how adopting and sustaining HIE can yield better patient-level and provider-level outcomes.

This paper investigates how the pricing policy of a revenue-maximizing monopolist is influenced by the social learning dynamics of customers who use online reviews to estimate the quality of the product. A salient feature of our problem is that the customers’ willingness to pay, and hence the demand function, evolves over time in conjunction with the online reviews. The monopolist strives to maximize its total expected revenue over a finite horizon by adjusting prices in response to these dynamics. The revenue maximization problem is studied using two different review models: a quality-based review model, where customers report their experienced quality, and a value-based review model, where reviews internalize experienced quality as well as the purchase price. To formulate the problem in tractable form, we derive a fluid model that provides a good approximation of the system dynamics when the volume of sales is large. This formulation lends itself to key structural insights into the interactions between optimal pricing policies and review dynamics. In particular, we identify critical time scales and social learning regimes that sharply separate the efficacy of dynamic pricing vis-à-vis fixed-price strategies. Furthermore, we demonstrate the impact of the quality-based and value-based review models on key structural properties of the optimal pricing policies. These structural insights are also elucidated in an illustrative simulation study based on data from an online marketplace. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2022.4387 .

Objectives: The objective of this study is to assess quality of the most popular cholesteatoma videos on YouTube using recognized scoring systems and to determine if video quality metrics correlated with video popularity based on likes and views Design: Cross sectional survey of available data Setting: Metadata acquisition using YouTube searches using Australian IP addresses Participants: Three independent neuro-otologists partaking in scoring videos Main outcome measures: Each video was viewed and scored by three independent assessors using both a novel tool to score the usefulness of the video as well as the validated DISCERN scoring tool. Popularity metrics were analyzed and compared to video popularity. Results: A total of 90 YouTube videos were analyzed with an average 55,292 views per video with an average of 271 likes and 22 dislikes. The inter-rater correlation was moderate with Fleiss-kappa score 0.42 [P < 0.01] using a novel scoring tool for cholesteatoma and inter-rater correlation coefficient was 0.78 [95% CI = 0.58 - 0.90] indicating good reliability for DISCERN scores. The overall video quality was poor with higher DISCERN scores found in videos uploaded from Academic Institutions. Conclusions: Informative video quality on YouTube on cholesteatoma is overall of poor quality. Videos with unclassified sources or more dislikes correlated poorly with video quality. Given the increase in patients turning to the internet for information regarding their health conditions, otology and otolaryngology societies should be encouraged to publish high quality YouTube videos on cholesteatoma and other ear conditions.

Drawing on social exchange theory and institutional theory, this article extends the literature on sustainable operations by examining the mediating and moderating effects of buyer–supplier relation and institutional context on the relationships among internal green practice, external green practice, and environmental performance. This article analyzes data from 440 firms in three categories of market (the institutional context): industrialized Western, emerging Western, and emerging Asian markets. Both internal green practice and external green practice have positive direct impacts on firms’ environmental performance. More specifically and interestingly, external green practice partially mediates the relationship between internal green practice and environmental performance, and this process is moderated by the quality of the buyer–supplier relation. A higher quality of buyer–supplier relation helps to achieve a better outcome. In addition, the examined relationships vary between the emerging market and developed market: that is, they depend on the institutional context. The findings have implications for both practitioners and policymakers on how to improve environmental performance through leveraging green practice and buyer–supplier relation and what policies on green supply chain management might best be used in different markets.

This paper uses a uniquely granular online retail dataset that spans Russia's enormous currency depreciation in late 2014 to show that firms choose to offer lower quality products in response to rising input costs. We show that this reallocation is not driven by an income shock or "flight from quality". To explain quality downgrading, we construct a model of demand and firm dynamics where consumers value low prices and high qualities, and firms draw product cost and quality from a joint distribution on entry. We argue that a positive correlation between marginal cost and quality can explain quality downgrading. After verifying this correlation in the data, we calibrate the model and simulate a counterfactual cost shock. The model is able to generate a reallocation in offerings towards low cost, low quality products, and yields starkly different welfare implications following an exchange rate shock than a model with only cost heterogeneity.

As a rising star of social apps, short video apps, e.g., TikTok, have attracted a large number of mobile users by providing fresh and short video contents that highly match their watching preferences. Meanwhile, the booming growth of short video apps imposes new technical challenges on the existing computation and communication infrastructure. Traditional solutions maintain all videos on the cloud and stream them to users via contend delivery networks or the Internet. However, they incur huge network traffic and long delay that seriously affect users' watching experiences. In this paper, we propose an edge-assisted short video sharing framework to address these challenges by caching some videos highly preferred by users at edge servers that can be accessed by users via high-speed network connections. Since edge servers have limited computation and storage resources, we design an online algorithm with provable approximation ratio to decide which videos should be cached at edge servers, without the knowledge of future network quality and watching preferences changes. Furthermore, we improve the performance by jointly considering video fetching and user-edge association. Extensive simulations are conducted to evaluate the proposed algorithms under various system settings, and the results show that our proposals outperform existing schemes.

Various treatments of waste water include primary, secondary & tertiary methods. For improved and efficient treatment technologies, advancements are continuously being developed against traditional treatment technologies. The paper provides details of development in the field of nano technology for waste water treatment. Solution of Water crisis is up most priority for technocrats currently. Reasons for crisis are increase in population, climate changes, and deterioration in quality of waste water. This is high time to find new technologies for sustainable water management. Application of Nano technology has great potential in improving treatment efficiency and to supply safe water through unconventional sources. In this paper overview of nanotechnology application for waste water treatmentis presented. By under- standing characteristics and applying its use in advancement for existing treatment methods, sustainability can be increased to supply good quality of water to people.

Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the population. India may find it difficult to achieve this service level target, owing to several infrastructural deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed as critical nodes and must be overhauled structurally to make the implementation of strategic inventory policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to identify critical nodes and structurally overhaul them later for efficiently achieving service level targets.

Video conferencing applications have become an integral part of today’s world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code; a video/audio conferencing feature using Peer JS; a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches.

The rapid progress of artificial intelligence expands its wide applicability in internet of things (IOT). Meanwhile, data insufficient and data source privacy are key supply chain challenges facing IOT especially in the healthcare industry. To address this problem in healthcare IOT, we propose a skin cancer detection model based on federated learning integrated with deep generation model. First, we employ dual generative adversarial networks to address the problem of insufficient data. In addition, to improve the quality of generated images, we synchronously optimize the sharpness of images, Frechet inception distance, image diversity, and loss using knee point-driven evolutionary algorithm (KnEA). Then, we protect patient information privacy by training federated skin cancer framework. Finally, we employ the ISIC 2018 dataset to test the performance of the proposed training model under different situations, including using identically distributed data, nonidentically distributed data, a sparse convolutional neural network, and a fully connected convolutional neural network. The experiment results demonstrate that the accuracy and area under the curve reach 91% and 88%, respectively. This model can help resolve problems of insufficient data in smart medicine of IOT and protect the privacy of user data while also providing an excellent detection rate.

The tyre could be among the most critical and complicated elements of automobile dynamics and roadway pressure analysis as it serves just about only connection among road surface as well as the automobile. Substantial study and growth efforts on automobile dynamics and tyre models are being reported in last centuries, but the implementations and parameter recognition involved with both of these tyre models remain elusive. The goal of this research paper is to research and analysis the impact on tyre performance of the different operational conditions of the tyre the parameters of tire operation. Experimentally checking tire performance parameters allows the designer to compare parameter relationships and plan the tire, so it's necessary to test the tire. Information of tires ' Vibrant properties are essential for any type of automobile dynamics study and growth activities. The purpose is to distinguish the characteristics of the tyre from vehicle, reach a good degree of validity and minimise prices. This analysis study offers details on the work of numerous researcher in conducting inter-laboratory tyre development work with tyre performance characteristics, but this review article also assist to explain the variables on which tyre quality factors rely.

In this digital world, all the developing countries' growth has improved drastically with farmers' influence and innovative farming processes. Generally, the farming process includes ancient traditional methodologies for maintaining the crops' quality and yields. Their farming was developed and has given more profit only with the quality of the soil and the nutrition used on land. But the drawback is they were spending much time to get their yields from their land, and the nutrition level was not maintained at all times. Moreover, more space was used for farming, with huge manpower required to maintain the entire land. Most countries are moved to smart farming concepts with IoT platforms to optimize the time and techniques. In that hydroponic, the best innovative idea to produce more crops, vegetables, and fruits without soil. Rockwool is used for farming processes with water contaminants at regular intervals will provide huge productions as well as no need to wait for a long time for cultivation. This method was implemented in most of the countries that were doing smart farming with less manpower and low cost. The hydroponic farming methodology is implemented with IoT sensors for monitoring crop's status and health continuously. Once their nutrition level or water level has decreased, it will provide all at constant time intervals to the entire system effectively. A few years ago, hydroponic farming was horizontally implemented on smaller spaces for the regular water flow. But now a day it is implemented on a vertical surface to reduce the space, and water flow is only at the time of need. This technology is used to increase the productivity of the crops with a small space of land and less manpower. Perhaps the cost of the entire system has been taken into consideration by small-scale unit farmers; vertical hydro farming provides better results when compared with previous classical methods. This research paper has given the design and implementation of automated vertical hydro farming techniques with IoT platforms, and their analytics will be done using big data analytics.

The founder of a start‐up (principal) who has a project with uncertain returns must retain and incentivize an agent using promise of future payments and information gathering. The agent's effort incrementally advances production and such advance is a prerequisite for gathering new information. The principal decides how much information to gather based on these incremental advancements. The principal faces cash constraints. The agent's outside option is large relative to his effort cost. Equilibrium features one of two outcomes: immediate learning, whereby the agent's compensation is low, learning is immediate and retention is possible only conditional on the project being of high quality; or gradual learning, whereby the agent's compensation is high, learning is gradual, the agent never quits and effort is inefficiently high.

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the real-world applications are being developed at the moment. This article discusses machine learning and artificial intelligence applications in agriculture, healthcare, management, and social studies. Disease Prediction, water irrigation optimization, Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.

Worker reliability estimation is fundamental for crowdsensing applications. This paper studies a robust feedback rating approach to estimate worker reliability. In this approach, the requester provides a feedback rating to reflect the quality of the sensor data submitted by each worker. The aggregation of each worker's historical feedback ratings serves as a reliability estimate. The challenges are: (1) Feedback ratings are subjected to noise or bias; (2) Workers cognitive bias in task selection leads to higher sensor data quality variations. We develop a mathematical model to quantify the degree of rating bias and degree of cognitive bias. We also derive sufficient conditions, under which the aggregate rating is asymptotically accurate in estimating worker reliability, via stochastic approximation techniques. These conditions identify a class of asymptotically accurate rating aggregation rules for crowdsensing applications. We further derive the minimum number of ratings needed to guarantee a given reliability estimation accuracy, via martingale theory. Via extensive experiments: (1) We reveal fundamental understandings on how various factor such as rating bias influence the minimum number of ratings needed to achieve certain accuracy; (2) Our feedback rating approach improves air quality index estimation accuracy by as high as 50% over the URP algorithm.

Machines have to operate effectively and free from faults in order to enhance productivity, increase quality, and decrease costs. With automated and flexible action, this is far more likely. An accurate identification of the underlying cause Causes of failure that need repairs and the time for restoration include: A lot of Various monitoring methods are available to track the experimentally-recorded machine health. Maintaining instruments and sensors. The vibration monitoring methods in this list. It is the right fit, and it has shown itself over time. Measuring vibrations in this job three different locations resembling the stock of a gun: headstock, tailstock. When conducting turning operation with mild steel, the tool post of the lathe machine is made of metal. Data that is gathered through sensors is sent straight to the cloud in this case, such as Ubidots IoT platform. via way of nodeMCU. Whether a task is impacted or a tool is impacted, mission failure has occurred. Thus, in either instance, early warning is provided by a signature, thus the development does not progress as quickly machining. If the tail-stock seems to be broken, and this appears to be a consistent problem, then the whole batch must be inspected for errors to be inspected closely is anything that has been created under such circumstances. An optimal decision-making procedure light may be thrown on CM so processes may be machine-wise instituted in a shop there. Tools while talking about lathes that are large, medium, or tiny. Overall it is a method which is easy but yet capable of benefitting people at all levels, from the absolute beginner to experts Floor productivity is aided by training elements that are almost unnecessary.

Healthcare monitoring systems have been one of the most important system in the last decade, and they have become increasingly technological. Humans are facing various issue and untimely death due to multiple illness, due to a lack of medical treatment for patients. To overcome this issue a real time health monitoring system has proposed based on IOT. The proposed system consists of mobile application and GSM for continuous wireless monitoring of patients. The main aim is to create a dependable patient management system based on IoT so that healthcare professionals can monitor their patients who are either hospitalized or at home using an IOT based integrated healthcare system to ensure quality patient care. Sensors are used to track vital parameters, and the data collected by the sensors is sent to the cloud via a WiFi module. A wireless healthcare monitoring system has created that can provide real time online information about a patients conditions. The system is made up of sensors, a data acquisition unit, a microcontroller (i.e. ESP32) and software. The patients temperature, heart beat rate, ECG, blood pressure and SPO2 are regularly monitored, displayed and stored by the system and the same has been sent to the doctors mobile containing the application. Additionally a message is sent to the doctors mobile if any of the parameter crosses the threshold value. Thus, IOT based real time health monitoring system systematically monitor the condition of patients health and save their life on time.

In the animation industry, automatically predicting the quality of cartoon images based on the inputs of general distortions and color changes images is an urgent task, while the existing no-reference (NR) methods usually measure the perceptual quality of the natural images. In this paper, based on the observation that structure and color are the main factors affecting cartoon images quality, we proposed a new NR quality prediction metric for cartoon images, which fully takes gradient and color information into account. The experimental results on our newly constructed NBU-CIQAD dataset with color changes and other existing cartoon image dataset demonstrate that the proposed method significantly outperforms existing no-references methods for the task of cartoon image quality assessment.

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machine learning models for the complexity and diversity of data in the real-world applications are being developed at the moment. This article discusses machine learning and artificial intelligence applications in agriculture, healthcare, management, and social studies. Disease Prediction, water irrigation optimization, Sales growth, profit maximization, sales forecast, inventory management, security, fraud detection, and portfolio management are some of the major uses.

Transitions to a new principal are common, especially within urban public schools, and potentially highly disruptive to a school's culture and operations. We use longitudinal data from New York City to investigate if the effect of principal transitions differs by whether the incoming principal was hired externally or promoted from within the school. We take advantage of variation in the timing of principal transitions within an event-study approach to estimate the causal effect of principal changes. Changing principals has an immediate negative effect on student test scores that is sustained over several years regardless of whether hired internally or externally. However, externally hired principals lead to an increase in teacher turnover and a decline in perceptions of the school's learning environment, whereas transitions to an internally promoted principal have no such effects. This pattern of results raises important questions about leadership transitions and the nature of principal effects on school quality.

Modular projects can be of higher quality, a safer, faster and more predictable construction process, and less environmental nuisance compared to a traditional on-site project. High-rise projects seem to be particularly suitable for modular building methods, however there are still some process-related barriers. Research so far has not focused on collaboration forms for modular high-rise projects. This paper examines which collaboration form fits best for a modular high-rise project by conducting an international case study research. From the case studies it is debatable whether the current PDMs meet the need for modular concepts; modular buildings will benefit more from a long-term collaboration with fixed partnerships. This requires a complete different approach to the construction industry. Until then, the best match should be sought by matching the customer profile to the PDM characteristics. The most suitable PDM is dependent on the client profile that can be determined by 17 selection criteria.

Nowadays, the additive manufacturing process had significant attention due to its great advantages that are being used for developing products and producing prototypes. This technology overcomes the various drawbacks of existing techniques and it becomes the most efficient approach. In the industrial sector, this technology is most important for individual designing products and extended with different functions. The performance of additively manufactured parts, on the other hand, is largely influenced by their surface integrity and dimensional accuracy. The surface integrity is an important issue because the additively manufactured products are affected by staircase effects resulting from layer-by-layer manufacturing. The rapid prototyping contains multiple methods such as VAT Photopolymerization and Poor surface quality is caused by the staircase effect in all additive manufacturing processes so therefore, the various post-processing efforts and controlling different process parameters of additive manufacturing are necessary on the produced component for improving surface quality and reducing surface roughness. To focus on improving the finish of an additively manufactured part's surface, there are numerous methods presented. This research described the various existing approaches of surface finishing with various efficacies such as reduction of surface roughness value, mechanical strength, and dimensional accuracy. Current post-processing methods for improving the surface characteristics of additively manufactured parts are discussed, with a classification based on the processes' mechanisms.