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In this paper, we introduce, model and solve the pickup and delivery problem with time windows, multiple stacks, and handling operations (PDPTWMS-H). In the PDPTWMS-H, a fleet of vehicles based at a depot is used to complete a set of requests which consist of transporting items from a pickup location to a delivery location. The vehicles have multiple stacks operated using last-in-first-out (LIFO) loading which requires the vehicle to be rear-loaded and items can only be unloaded if they are closest to the back door. In the PDPTWMS-H, additional handling operations, referred to as rehandling, are allowed and an additional handling time might be incurred when rehandling items (by unloading and reloading items). The problem consists of determining the number of vehicles and the vehicle routes needed to complete the set of requests at minimal cost while respecting the possible handling operations. We model the PDPTWMS-H with a set-partitioning formulation and resort to branch-price-and-cut (BPC) for its solution. To solve the pricing problem, we derive a unified labeling algorithm that is able to cope with the different rehandling possibilities. The labeling algorithm keeps track about the information of on-board items such that symmetries with respect to both stacks and item positions are reduced. Extensive tests are performed on benchmark instances to assess the performance of the proposed BPC methodology and to provide insights on the impact of the rehandling flexibility on solution quality and time.

In this paper, we propose an improvement in the preprocessing stage of the reversible data hiding method based on contrast enhancement applied to grayscale digital images. In conventional proposals, the preprocessing stage plays a fundamental role in the performance of the data hiding algorithm in terms of capacity and visual quality. Thus, this work presents two improvements to the preprocessing stage based on the analysis, merging, and shifting the image histogram, which, in conjunction with the data hiding stage, maximize the embedding capacity while keeping the visual distortion at a minimum. The proposed algorithm was implemented on two image datasets and compared with several related works previously reported in the scientific literature. The experimental results show that the visual quality can be preserved after data have been concealed when employing different embedding rates. Either of the preprocessing techniques presented in this paper can be adopted considering the application scenario and the priority of the requirements of the capacity and visual quality needed.

Prior research on collaboration and creativity often assumes that individuals choose to collaborate to improve the quality of their output. Given the growing role of collaboration and autonomous teams in creative work, the validity of this assumption has important implications for organizations. We argue that in the presence of a collaboration credit premium—when the sum of fractional credit allocated to each collaborator exceeds 100%—individuals may choose to work together even when the project output is of low quality or when its prospects are diminished by collaborating. We test our argument on a sample of economists in academia using the norm of alphabetical ordering of authors’ surnames on academic articles as an instrument for selection into collaboration. This norm means that economists whose family name begins with a letter from the beginning of the alphabet receive systematically more credit for collaborative work than economists whose family name begins with a letter from the end of the alphabet. We show that, in the presence of a credit premium, individuals may choose to collaborate, even if this choice decreases output quality. Thus, collaboration can create a misalignment between the incentives of creative workers and the prospects of the project.

Learning from demonstration (LfD) assists robots to derive a policy from a training set to execute a task. The training set consists of training demonstrations collected from a human who executes the same task. However, due to the human’s varied skill level, the quality of the training set may be bad, which will affect the accuracy of the derived policy. To solve this problem, this paper proposes a novel method to improve the quality of the training set. This method includes two steps, namely detecting and handling bad training demonstrations in the training set. In the detecting step, a reference set containing reference demonstrations is provided by a human teacher. Based on the reference set, we calculate the influence of each training demonstration on the policy derivation. If the influence is negative, the corresponding training demonstration is bad. Afterward, in the handling step, we calculate the proportion of the negative influence with respect to the overall influence and reduce the proportion by iteratively removing bad training demonstrations until it is less than a threshold. The results show that the accuracy of a policy derived from the improved training set increases with up to 19.30%, which verifies the effectiveness of our method.

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including “smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

Crowd-sourcing of information has become popular in the years since James Surowiecki published The Wisdom of Crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. In sports, crowd-sourced estimates of players’ values and abilities are common, particularly in football where salary information is generally unavailable. The analysis here first considers the characteristics of a good crowd-sourced value then turns to an empirical analysis which applies those characteristics and their implications to assess the quality of the commonly used crowd-sourced values from Transfermarkt. Our empirical results show systematic influences from some obvious factors indicating that the crowd-sourced transfer fees are biased as predictors of the true market determined fees. The findings are useful because they address the question of whether these values can reasonably be used as proxies for unknown salary in academic research. Additionally, because Transfermarkt values are often used in negotiations between clubs and players, it is useful to both parties to know the accuracy and the bias of the crowd-sourced values.

Many recent studies in procedural content generation (PCG) are based on machine learning. One of the promising approaches is generative models, which have shown impressive results in generating new pictures and videos from existing ones. However, it is usually costly to collect sufficient content for training on PCG. To address this issue, we consider reinforcement learning (RL), which does not need to collect training data in advance but learns from its interaction with an environment. In this work, RL agents are trained to generate stages, which we define as series of events in turn-based role playing games (RPG). It is a challenging task since several events in a stage are usually highly correlated to each other. We first formulate the stage generation problem into a Markov decision process. A handcrafted evaluation function, which simulates players’ enjoyment, is defined to evaluate generated stages. Two RL algorithms are selected in the experiments, which are deep Q-network (DQN) for discrete action space and deep deterministic policy gradient (DDPG) for continuous action space. The generated stages from both models receive evaluation values indicating good quality. To solve the delayed reward problem and further improve the quality of the stages, we employ virtual simulations to give rewards to intermediate actions and get stages with higher average scores. In addition, we introduce noise to avoid generating similar stages while trying to keep the quality as high as possible. The proposed methods succeed in generating good and diverse stages.

Motivated by collective emotions theories that propose emotions shared between individuals predict group-level qualities, we hypothesized that co-experienced affect during interactions is associated with relationship quality, above and beyond the effects of individually experienced affect. Consistent with positivity resonance theory, we also hypothesized that co-experienced positive affect would have a stronger association with relationship quality than would co-experienced negative affect. We tested these hypotheses in 150 married couples across 3 conversational interactions: a conflict, a neutral topic, and a pleasant topic. Spouses continuously rated their individual affective experience during each conversation while watching video-recordings of their interactions. These individual affect ratings were used to determine, for positive and negative affect separately, the number of seconds of co-experienced affect and individually experienced affect during each conversation. In line with hypotheses, results from all 3 conversational topics suggest that more co-experienced positive affect is associated with greater marital quality, whereas more co-experienced negative affect is associated with worse marital quality. Individual level affect factors added little explanatory value beyond co-experienced affect. Comparing co-experienced positive affect and co-experienced negative affect, we found that co-experienced positive affect generally outperformed co-experienced negative affect, although co-experienced negative affect was especially diagnostic during the pleasant conversational topic. Findings suggest that co-experienced positive affect may be an integral component of high-quality relationships and highlight the power of co-experienced affect for individual perceptions of relationship quality. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Landscape and quality attributes are major ecosystem characteristics closely associated with soil conservation service (SCS). However, the intrinsic mechanisms by which these two attributes influence SCS are still unclear. Therefore, this study quantitatively analyzed the landscape pattern, ecological quality, and SCS in the Lianshui River watershed (a typical soil and water loss area of red soil in southern China) and its sub-watersheds in 2019. The boosted regression tree model was used to explore the influence of 15 factors (i.e., landscape and quality attributes) on SCS at the sub-watershed scale. According to the results, compared with the landscape attribute, the quality attribute of the watershed ecosystem could better explain the spatial heterogeneity of SCS across 66 sub-watersheds. The overall degree of influence of five quality factors on SCS reached 57.81%, with the highest being the normalized differential build-up and bare soil index (NDBSI), at 25.11%. Among 10 landscape factors, aggregation had the greatest influence on SCS, at 28.64%. The relationships between key influencing factors and SCS were non-monotonic and non-linear, with threshold effects. For example, NDBSI values of 0.18–0.41 had a positive influence on SCS, while NDBSI values of 0.41–0.65 had a negative influence on SCS. The findings broaden our understanding of the response of SCS to changes in landscape and quality attributes at the sub-watershed scale, and could offer comprehensive support for soil erosion management in the watershed ecosystem.

In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration paths. One of them is L 2 -sense OMP and the other is SSIM -sense OMP (iOMP). Both paths intersects only at the starting point and gives different quality levels after each iteration. This distinction enables us to select the coefficients of the best quality and to avoid the uncertainty issue of iOMP. From the point of view of the SSIM levels, the conducted experiments proved that, the proposed methodology works better than iOMP and OMP. Also, the performance of this method is checked for significance by the t-test, and the obtained results proved that the method works well especially for large images and the data-independent based dictionary.

Digital twin technology is being invested by many countries around the world and actively used in many areas from aircraft engines to spacecraft. A digital twin is a digital replica virtual representation that allows modeling the state of a physical entity or system, and it is created by digitalizing data collected from physical entities through sensors, so various predictions could be made by understanding the behavior of the physical entity. The importance of digital twin increases even more in critical systems such as the military field with low fault tolerance. In the military field, digital twin technology stands out with the advantages it offers in terms of producing cost-effective solutions and increasing efficiency and quality. In this study, the current applications of digital twin technology in the military field and the advantages to be gained by using this technology will be evaluated, and then the future predictions will be discussed

Academic medical centers have a duty to serve as hospitals of last resort for advanced cardiac surgical care and therefore manage patients at elevated risk of post-operative morbidity and mortality. They must also meet state and professional quality targets devised to protect the public. The tension between these imperatives can be managed by a multi-dimensional quality improvement program which aims to manage risk, optimize outcomes and exclude futile operations. We here share our approach to this process, its impact on our institution and discuss pertinent issues relevant to institutions in a similar situation.

Costs from switching suppliers can affect prices by discouraging buyer movements from high- to low-cost sellers. This paper uses confidential data on U.S. importers and their Chinese exporters to investigate these costs. I find barriers to supplier adjustments: nearly half of importers keep their partner over time. Importers switch less if their supplier offers higher quality or provides lower prices. I propose and structurally estimate a dynamic discrete choice model to compute switching costs. Cost estimates are large, heterogeneous across products, and matter for trade prices: halving switching costs reduces the U.S.-China Import Price Index by 7.6%.

Beyond merely satisfying customers, companies are increasingly striving to build deeper and more meaningful customer relationships characterized by strong customer-company identification. However, whereas previous research has solely focused on symbolic drivers of identification, it remains unclear whether, when, and how managers can build on core functional company characteristics (i.e., quality, innovativeness, and price) to establish customer–company identification. The present study addresses these questions by developing a theoretical framework based on theoretical notions of social identity theory and the cue diagnosticity framework. Evidence from two field studies and one experimental study shows that functional company characteristics are not effective in creating customer–company identification per se, but that their influence depends on whether they match with a self-definitional need that is important to the customer (i.e., self-continuity, self-distinctiveness, or self-enhancement). The findings also reveal the underlying mechanism of this contingency by showing that a self-definitional need fosters customer–company identification because it strengthens the symbolic value of a matching functional characteristic. By identifying specific characteristic–need matches, this research offers novel insights into how managers can leverage functional company characteristics in their targeting and communication efforts to establish meaningful long-term relationships with customers.

Family caregivers provide the majority of care for older and disabled family members living with an illness or disability. Although most caregivers want to provide high-quality care, many report providing care that is potentially harmful. We apply the Stress Process Model to review the preponderance of literature implicating quality of the relationship between caregivers and care recipients as a factor contributing to quality of family caregiving. In drawing together literature on caregiving relationships and caregiving quality, this commentary identifies potentially modifiable intervention targets to develop programs to support high-quality caregiving to older adults living with a chronic illness or disability.

Background Agencies and clinical practices are beginning to provide trauma-informed care (TIC) to their clients. However, there are no measures to assess clients’ perceptions of and satisfaction with the TIC care they have received. A 20-item questionnaire, the TIC Grade, was developed, based on the National Center for Trauma-Informed Care principles of TIC, to assess the patient or client perception of the TIC provided in settings that serve adolescents and emerging adults. Objective The goal of this project was to evaluate the psychometric properties of the TIC Grade instrument and to make recommendations for use of the full measure and its short form—an overall letter grade. Study Design The TIC Grade questionnaire was administered to youth over the age of 18 years from four community partners providing care to vulnerable young adults. Potential participants were offered questionnaires at the end of their visit. Those interested in participating left their completed anonymous questionnaire in a locked box to maintain confidentiality. Questionnaires were collected from 100 respondents; 95 were complete enough to include in analyses for psychometric evaluation. Results The findings of this project support the reliability and usability of the 20-item TIC Grade measure to assess youth’s perceptions of the quality of TIC they received. Conclusions This TIC-specific, behaviorally worded client report measure can assist service delivery organizations to assess their success at implementing TIC and to identify areas where further staff training and support are needed.

Due to the high consumption of resources and energy in the construction sector, the development of resource-efficient and sustainable construction solutions is gaining increasing attention. The awareness of sustainability and resource conservation results in the interest of using natural and renewable materials in contemporary architecture. Timber construction methods offer both constructive and ecological potential for sustainable solutions. From a building physics perspective, the acoustic performance of lightweight buildings, such as those made of timber, presents a challenge. Even if standard requirements are met, the increased low-frequency sound transmission typical for light-weight construction can cause discomfort and is already the subject of questions in building physics, which are currently increasingly extending to timber construction. Within the framework of a holistic approach, this paper compares the problem of acoustic properties, design optimizations and the ecological properties of timber-frame and solid timber construction components. The comparison with heavy materials, such as concrete, shows the relation of acoustic optimization with the change of the environmental profile. In order to establish the interaction between acoustic quality of wooden ceiling constructions and their ecological characteristics, this article aims to demonstrate the potential of materials used in the building sector under ecological aspects considering a life cycle analysis., Deutsche Forschungsgemeinschaft

The article analyzes the state guarantee of the right of citizens to quality medical care in terms of vaccination from COVID-19. It is determined that in today's conditions it is extremely important to ensure the proper functioning of health care, the provision of quality medical services which in turn will guarantee proper medical care. The state must provide the citizens of Ukraine not only with the vaccine, but also with reliable information about the need for vaccination. It turns out that the small number of vaccinated in Ukraine is not only due to the slow pace of procurement and vaccination, but also the lack of awareness of citizens about all the benefits of the need for vaccination. The main indicators of vaccination in the World and Ukraine as of mid-April 2021 have been determined. It is concluded that the positive in today's conditions are: the efforts of the legislator still not to influence the subjective choice of every resident of Ukraine on the need for vaccination; regulatory support of the Center for Public Health, which in turn will strengthen the network of regional Centers for Disease Control and Prevention (CCP); creation of a Roadmap for the introduction of a vaccine against acute respiratory disease COVID-19 caused by the coronavirus SARS-CoV-2. Proposals are being made to focus the attention of public authorities and local governments on the information component of ensuring the right of citizens to quality medical care in the context of vaccination against COVID-19.

The objective of the study was to establish the actors’ roles and functions in improved rice varieties innovation system in the Eastern Zone of Tanzania. The target population samples for the study comprised of household heads (n = 340) and other key identified actors (n = 99). The research design was a convergent parallel mixed method. Face-to-face interviews, focus group discussions and documentary review were used to collect the data. The findings indicate weak or inability of Agricultural Seed Agency, TANSEED International Ltd, Quality Declared Seed producers in seed multiplication and distribution and agro-dealers were not selling quality rice seeds, thereby resulting in low availability and high prices of the same. Also, the findings show weak participation of farmers, agro-dealers, traders and millers in the functions of guidance of the search and rice varieties development, and the limited number of market actors and weak credit mobilization for farmers to invest in IRVs. The study recommends for the Government of Tanzania to create enabling environment that enhances participation of all key actors in the processes of guidance of the search and rice varieties development; linking farmers with external markets; and enhancing the farmers’ access to credit facilities by working on the obstacles hampering farmers from accessing loans from financial institutions.

Continuous improvement of service quality is one of the important keys in facing the era of globalization which implies that services must be carried out according to standards and meet quality rules that are oriented to the interests of consumers. With these programs and facilities, it is hoped that the community will no longer be reluctant to seek treatment at the Puskesmas. The purpose of this study was to analyze the effect of the quality of health services on the satisfaction of inpatients at the Sei Mencirim Health Center.This research is a survey research, conducted at the Sei Mencirim Health Center. The population was all patients who were hospitalized at the Sei Menistrim Health Center in January-July 2021 totaling 74 people and the entire population was taken as a sample. Primary data obtained from interviews and secondary data in the form of inpatient data. The hypothesis was tested using the Multiple Regression test.Based on Bivariate analysis revealed that there was no effect of physical evidence on patient satisfaction. There is an effect of reliability, responsiveness, assurance and empathy on patient satisfaction.The most dominant variable affecting patient satisfaction is empathy. Considering that empathy is the dominant variable affecting patient satisfaction, puskesmas need to improve quality individual service and patient family time for consultation is met.Keywords: Service Quality, Patient Satisfaction, Puskesmas Sei Mencirim

A high-reliability organization must have high-performing teams. Core teams in healthcare should include the patient and, where possible, the patient's family. Everyone on the team should understand what is expected of them in terms of expertise, leadership, communication, mutual support, and awareness of the situation. One test of the excellence of a clinical team is having high-quality handoffs of patients.

Introduction. The use of high-quality general and specialised dictionaries is indispensable for professional translators and other users to work effectively with foreign languages. A lack of such dictionaries affects the opportunities of cross-cultural communication and is an evidence of an inferior condition of national lexicography. The issues of the quality of Ukrainian bilingual dictionaries have been paid minor attention in scientific research. While they are used widely, no analysis has been carried out to justify whether such dictionaries are appropriate in terms of quality.The purpose of the article is to analyse the lack of originality and use of plagiarism in English-, French-, and Italian-Ukrainian dictionaries, emphasising how these factors precondition the dictionaries’ inferior quality.Methods. Secondary research was used for collecting necessary data. Comparative analysis allowed revealing the original sources from which Ukrainian dictionaries copy information. Translation analysis was performed to illustrate the dictionaries’ inferior quality. Deductive thinking was used to investigate the implications for translators and the public.Results. The findings of the research testify that all major English-, French-, and Italian-Ukrainian dictionaries are based on the use of plagiarism and direct copying of information from Russian dictionaries. The authors resort to Russian-Ukrainian translation, often with the use of automated translation software, to present the results as authentic Ukrainian bilingual dictionaries. These academically dishonest practices result in the dictionaries’ systemically inferior quality: incorrect terminological translations, domination of Russian calques, and violation of orthographic standards. Both professional translators and the public are thus deprived of any opportunity to use high-quality and genuine Ukrainian bilingual dictionaries.Conclusion. The most widely used English-, French-, and Italian-Ukrainian dictionaries are rooted in copying from Russian dictionaries, which is the main factor preconditioning their inferior quality. This approach testifies the poor condition of Ukrainian lexicography today and illustrates the relevance of the issue.

Implemented in 2018, MiFID II changed sell-side analyst incentives in Europe, forcing analysts to justify the value they add. While the number of analysts decreases, the average stock return synchronicity with the market also decreases, implying an improvement in price informativeness. The decrease in synchronicity is larger for firms that are more important for the analysts and brokers covering them. It is also asymmetric and substantially larger for downside market movements. Our results suggest that, by changing incentives, MiFID II not only improves the quality of individual analyst work, but also achieves an improvement in the aggregate stock price informativeness.

The quality of covert communications is determined by the choice of communication channels and the design of data embedding schemes. Recently, the Bitcoin system is prevalent as a covert communication channel. The consensus mechanism requires participants to spread their found valid blocks under an adjustable difficulty, which provides a stable periodic broadcast channel. Moreover, senders and receivers are difficult to be traced, because the Bitcoin system is pseudonymous. However, since the historical data in the ledger cannot be removed from the Bitcoin system, the openness and the persistent storage of the ledger in the Bitcoin system post new challenges when designing data embedding schemes. More concreteness, most traditional data embedding schemes either design by heuristic or empirical algorithms or use a fixed field to embed data in the transactions. Therefore, the covert data can be recognized once the algorithm is leaked or the pattern is explored. In this article, we first propose a Hash Chain-based Covert Data Embedding (HC-CDE) Scheme. The embedded transactions are difficult to be discovered. We further propose an Elliptic Curve Diffie-Hellman Chain-based Covert Data Embedding (ECDHC-CDE) Scheme to enhance the security of the HC-CDE scheme. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed schemes.

As one of the most important applications of AI, machine translation has always been the hot topic among scholars in linguistics, computer science, cognitive science and other areas. This article made an assessment of translations of 4 selected major online machine translation platforms from perspectives of efficiency, operating mode and condition. The outputs of machine and human were compared by employing new “6-4” table and comprehensive error rate. The assessment shows that although the quality of machine translation is improving, the gap still exists between the quality of machine translation and human translation. Based on the research findings, the author predicts that machine translation cannot possibly replace human translation and the two will continue to coexist in the foreseeable future.

Within the context of a theoretical model where an increase in the proportion of foreign-invested firms is interpreted as increase in foreign direct investment (FDI), this paper shows that, through its impact on firm capability and revenue, FDI can affect the quality of goods produced by domestic firms in host economies. We show that, in the presence of positive productivity spillovers, an increase in the proportion of foreign-invested firms lowers the cut-off capability of domestic firms, which allows some relatively inefficient domestic firms to enter the industry thereby contributing to a decrease in product quality. An increase in the proportion of foreign-invested firms affects firm revenue through two channels. On the one hand, an increase in the proportion of foreign-invested firms enhances productivity, which contributes to an increase in firm revenue. On the other hand, entry of new domestic firms also increases the level of competition within the industry, which puts a downward pressure on firm revenue. We conclude that, in the presence of positive productivity spillovers, an increase in FDI decreases the quality of goods produced by domestic firms in host economies only if its impact on firm revenue is sufficiently small (or negative). Using data from China’s beverage manufacturing industry, employing the nonlinear seemingly unrelated regression technique on firm level data and without explicitly measuring quality, we estimate the impact of an increase in the proportion of foreign-invested firms on the product quality of domestic firms. Our empirical analysis suggests that a 1% increase in the proportion of foreign-invested firms decreases the product quality of domestic firms by more than 0.6259%.

Objectives The objective of the study was to examine the quality of communication across the different medical specialties. Prior research suggests that the quality of communication between doctors and patients influences the quality of medical care and adherence to treatment regimens, but little is known about factors that contribute to successful interactions between doctors and patients. Setting A survey questionnaire was undertaken at large metropolitan based hospital in Brisbane, Australia. Primary and secondary outcomes In this initial study, we surveyed 67 doctors across various specialties on a range of social cognition and personality measures. We then rated each of the specialties on the degree to which they rely on both procedures and diagnostics, as well as the extent to which they involve patient communication. Results A regression analysis using SPSS 26.0 was undertaken to ascertain if doctor's satisfaction with their communication was related to these three aspects of the various specialties. Results revealed that communication satisfaction was negatively related to the degree to which the specialty relies on diagnostics. No relationship emerged between reliance on procedures and communication satisfaction. Finally, communication frequency was positively related to communication satisfaction. Conclusions We propose two possible accounts for this finding regarding diagnostics: 1) the cognitive demands of diagnosis disrupt communication and/or 2) diagnoses are interpreted by patients as opinions with which they are sometimes motivated to disagree. Further research is needed to replicate these findings and establish the underlying mechanism. This article is protected by copyright. All rights reserved.

This article leverages a unique field experiment to characterize who in the crowd is willing and able to solve complex engineering design problems for the prospect of a prize. Contrary to popular skepticism, we find that the crowd is highly capable of not only providing quality point solutions but also contributing valuable engineering insight. However, although a diverse population of more than 9000 registrants showed initial interest in the competitions, of the 255 judgeable solutions received, the majority of good solutions came from solvers with prior within-discipline experience. Of those, more of the good solutions came from solvers who perceived the relevant problem as “at the boundary” of their expertise. This suggests that while complex engineering design problems do require some threshold of relevant training and skills, prize competitions still play an important role in attracting solvers who perceive themselves to be adjacent to the problem. Furthermore, the submissions revealed a role for solvers in the crowd in augmenting traditional concept generation and tradespace exploration processes, by leveraging the independence of solvers in the crowd. These findings contribute an empirical basis for resolving how and when the crowd can be used most effectively, as part of an innovation toolkit.

This paper studies the relationship between logistics, intermodality and airport efficiency. To this end, using Data Envelopment Analysis (DEA), the efficiency of the 21 largest European airports has been estimated over the 2009–2014 period. In a second stage, this research analyses the effect of Logistics and Modal Integration on airport efficiency. The airport's ability to compete globally depends, greatly, on the passengers and cargo’ access to efficient networks. The results show evidence of the positive impact of the quality of the national logistics system on Airport efficiency. The effect of Logistics on overall airport efficiency is estimated at 0.15% for every 1% increase in the Logistic Performance (LPI), ceteris paribus. Furthermore, airports that have a direct link to a High-Speed Rail (HSR) network are expected to perform, on average, 27 per cent above the rest of the airports. Finally, airports that offer more facilities to optimise the interface between airport and aircraft are expected to achieve higher efficiency levels.

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we call socio-technical motif congruence (STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC is not related to project quality measures -- software bugs and churn -- in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere., 29 pages, 21 figures. To appear in IEEE Transactions on Software Engineering

In this article, we aim to address the following research question: How can a construction organization reduce and contain errors in its projects and mitigate rework and failures? We adopt an organizing sense-making perspective to acquire a sense of order of quality (e.g., rework) and to understand its relationship with safety (e.g., unsafe behavior) in construction. We undertook semistructured interviews from a range of employees involved with delivering a construction organization's projects. Also, documentary sources were accessed to supplement the rework and safety incidents that were referenced during the interviewing process. We found that the construction organization's prevailing culture focused on error prevention, which stymied its ability to learn and reduce rework in projects. The organization consciously enacted a trade-off between quality and safety. The upshot of this either/or framing of competing values was the suppression by senior management of nonconformances, which then led to deviant behavior manifesting in projects. We also revealed that safety incidents tended to arise when people engaged in unsafe behaviors while performing rework. The empirical evidence supports our call for construction organizations to engage in error management so that they can cultivate mindfulness where individuals and project teams can improvise and better handle errors, so they are not repeated.

An airport and its region have a mutual interaction. Thus, the airport's performance is crucial for its region. Moreover, airport's performance is influenced by air transport network. The measurement of the quality and capacity of the air transport network is multifaceted. The study assesses an airport's network in terms of its type, accessibility, and connectivity. It focusses on 12 Tier-II Indian cities having medium-hub international airports because of their huge growth potential and under-addressed assessment. Furthermore, it is in coherence with the current policies of boosting regional air connectivity. Specific analyses for Tier-II cities' airports is important as a comparison with the bigger metropolises will be skewed. This exploration puts special emphasis on their relationship with the Tier-I cities as the connectivity of Indian airports is majorly complementary than competitive. Measures used for assessment are concentration indices, graph theory-based connectivity and accessibility indices (shortest path length and quickest time length), and partial accessibility indicators (daily accessibility, potential indicator, location indicator, and network efficiency). Modifications and synthesis of partial indicators are also attempted. Annual data sets for the financial year 2017–18 and real-time data from 25–July 31, 2018 are used, and the analysis was carried out from April–August 2018. The study identifies future potential (hubs/feeders) among the Tier-II cities' airports and proposes to align policies accordingly. The results illustrate the need for more strong hubs in northern India and reveal the potential of Amritsar, Calicut, and Trichy of becoming future regional hubs.

In this article, we define four technological strategies by analyzing two dimensions in the exploitation versus exploration choice: knowledge base and technical application. The relationships of technological strategies with the quality of invention are investigated. The operationalization of strategies through patent data is provided, using knowledge field combinations as the unit of analysis. Patents filed by worldwide top R&D spending technology hardware and equipment companies are analyzed. A 2 × 2 matrix is suggested, defining four strategies: strengthening, enrichment, resettlement, and venturing. Mixed effects regression models are used to investigate the effects of such strategies on invention quality, providing positive relationships in relation to the first two strategies.

In this article, the role of individual and team-level factors in promoting female entrepreneurs' participation in accelerator and incubator programs is explored. Using data collected from 65 teams enrolled in a team-based entrepreneurship program run by a university located in the U.S. Midwest region, the results showed that female and male program participants differed in several entrepreneurial metrics. The results further indicate that female students are less likely to participate in accelerator or incubator programs than their male counterparts. Furthermore, a higher proportion of women in a team was positively related to women's' subsequent entrepreneurial actions, namely joining accelerator and/or incubator programs. Although female students’ entrepreneurial self-efficacy and intention scores were initially lower, their scores consolidated toward the end of the program. Lastly, the results indicate that the quality of students’ group experiences positively influenced their participation in accelerator and incubator programs.

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources over the communicating users. In this scheme, classification learning is used for identifying replicas and access granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.

A principal (e.g., a bond issuer) contracts with a researcher of unknown quality (e.g., a credit rating agency) to conduct a costly experiment, either to inform the principal or to persuade a decision maker. This contracting problem has a novel feature that captures the difference between the form of an experiment and the strength of its results: researchers face a problem of information design, rather than optimal effort. Using a novel comparative static for Bayesian persuasion settings, I characterize the optimal contract and show how experimentation is distorted by adverse selection. Moreover, I show that there is no loss from contracting on the experiment’s result rather than the experiment itself. This optimal results-based contract takes the form of reimbursement for contractible costs, at a rate which is lower for stronger results, plus a (piecewise) two-part tariff.

Corporate bond prices are slow to respond to default risk and interest rate shocks, as proxied by firm-level stock returns and Treasury returns, respectively. Furthermore, the underreaction is risk-specific: bonds with better credit quality underreact more to default risk, while those with worse quality underreact more to interest rates. The underreactions imply substantial out-of-sample return predictability, and investors appear to be leaving too much money on the table. The results are consistent with behavioral inattention models in which investors endogenously allocate more attention to payoff-relevant (or salient) risks, and they are not explained by traditional trading friction mechanisms.

Enhancing visual qualities for underexposed images is an extensively concerned task that plays important roles in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we in this work develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to solve the underexposed image correction task. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. Additionally, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What's more, we employ our method to single image haze removal whose experimental results further demonstrate its superiorities., Submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS). Project page: http://dutmedia.org/HPNDP/

The freight transportation activities having an impact on the freight logistics process. The environmental and social sustainability-related processes are poorly managed compared to the economic process. The quality of environmental and social sustainability (EnSoS) practices ought to be enhanced by measuring current sustainability practices, and at the same time, obstacles to sustainability have to be identified. Some of the previous work try to develop a sustainability framework, but less attention is paid to develop the EnSoS assessment framework for the freight transport industry. To bridge this research gap, this study develops an EnSoS framework based on an integrated multi-criteria decision-making (MCDM) method, i.e., fuzzy best-worst method (FBWM) and fuzzy logic approach. The proposed framework evaluates the current sustainability performance with an index and highlights the obstacles to the sustainable freight transportation system. The proposed framework presents two sustainability enablers, nine dimensions, and sixty-three sustainability attributes, including ten new attributes. The FBWM method is used to compute the fuzzy importance weight of attributes, whereas the fuzzy logic is used to assess the performance of each attribute. The proposed framework is validated with the case of the Indian freight industry. The fuzzy transportation EnSoS index (FTEnSoSI) is computed as (3.511, 6.366, 8.418), and close to ‘very environmental and social sustainable (VESS)’ label. The fuzzy performance importance index (FPII) is computed to assess the performance of each attribute. Based on FPII value, thirty attributes are identified as obstacles to transport sustainability. The result analysis presented proposed policy measures for improving EnSoS obstacles.

Product failure resulting from sourcing supplier’s defective component has compelled a brand owner to enhance quality management, especially when the supplier has informational advantage. In this article, we examine a brand owner’s problem of screening a certain supplier’s inherent quality level with an attempt to induce supply chain partners’ quality efforts using the warranty contract based on information acquired from inspection technology. A supplier’s inherent quality level is herein determined by the private information held by the supplier and is typically characterized as an uncertain variable. The optimal warranty contracts and the expected profits of the brand owner and the supplier are derived from four different scenarios under the framework of uncertainty theory and principal–agent theory. We find that under the condition of pure double moral hazard or pure adverse selection, the first-best outcomes can be achieved without incurring agency cost under the designed contract. However, double moral hazard combined with adverse selection often leads to underinvestment in quality efforts as the supplier can shirk by misreporting her type. Consequently, we present the menu of warranty contracts to screen the supplier’s private information. Finally, we provide empirical managerial recommendations on mitigating potential adverse impacts caused by information asymmetry, supported with numerical investigations.

Managerial relevance: In this competitive business world, social media penetrates online sales very rapidly. Businesses operating online are required to provide a safe and secured environment for customers to ensure their satisfaction. Our research results confirm that in addition to social media advertising, guaranteed 'e-safety' for customers will help establishing trust between the customer and the online businesses; it is also viewed important for present and future sales. The social media reviews need to be incorporated in managerial decision making. We try to analyse how these reviews are changing over the period of time and what are the ways to enhance customer satisfaction using information on customer expectations. This paper can help service companies to position their websites to compete in the market. Abstract In this era of social media, products and services are sold globally using a few simple clicks online. In such online purchases, trust and familiarity are considered two important driving forces of consumer decision making. While online sales advocate high levels of flexibility and choices for consumers, they also hold the online service provider responsible for ensuring the security of the online user's data. Using a Structural Equation Model (SEM) with data collected from the online service industry, we test the direct effects of 'social media-induced purchase intention' on customer satisfaction. We also test the mediating role of e-commerce/online sales (e-advertisement, e-safety and e-information) on customer satisfaction. In addition to social media advertising and information sharing, we find that a new factor-'e-safety'-mediates the relationship between customer purchase intention and customer satisfaction. Our analysis indicates that online e-trust can be established between the customer and the service company when online purchases are made. At the same time, the quality of online information and e-safety of online payments make the service company trustworthy for future purchases. We relate data analysis directly to managerial decision making to avoid any delay in online customer services in the era of social media.

Large-scale power systems are composed of regional utilities with assets that stream sensor readings in real time. In order to detect cyberattacks, the globally acquired, real-time sensor data needs to be analyzed in a centralized fashion. However, owing to operational constraints, such a centralized sharing mechanism turns out to be a major obstacle. In this article, we propose a blockchain-based decentralized framework for detecting coordinated replay attacks with full privacy of sensor data. We develop a Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well as empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip-driven approaches in terms of accuracy, timeliness, and scalability.

In both public and private sectors, critical technology-based tasks, such as innovation, forecasting, and road-mapping, are faced with unmanageable complexity due to the ever-expanding web of technologies which can range into thousands. This context cannot be easily handled manually or with efficient speed. However, more precise and insightful answers are expected. These answers are the fundamental challenge addressed by tech-mining. For tech-mining, discovering the associations among them is a critical task. These associations are used to form a weighted directed graph of technologies called “association tech-graph” which is used for technology development, trend analysis, policymaking, strategic planning, and innovation. In this article, we present a novel method to build an artificial intelligence (AI) agent for automatic association discovery among technologies in a way that matches the quality of the human experts. To this end, neural network-based word embedding methods are exploited to represent technology terms as vectors, and their associations are calculated using similarity measures. To increase the accuracy of the vectors, several crawlers are built to acquire more appropriate training data. Furthermore, we introduce a validation method to measure the accuracy of the AI agent compared to human intelligence, which allows us to discuss the drawbacks of both approaches.

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.

To reinforce competitiveness, companies must focus on competitive priorities including cost, quality, delivery, and flexibility. The competitiveness also depends on the adherence to environmental requirements and the ability of companies to survive unforeseen disruptions. Lean, agile, resilient, and green (LARG) paradigm is a hybrid supply chain paradigm, which can enhance competitiveness in today's complex markets. In this article, we propose a new conceptual decision-making model to evaluate LARG supply chain performance in medical equipment companies. In this regard, supply chain operations reference model attributes were utilized as performance evaluation criteria. Furthermore, the appropriate practices of LARG supply chain are selected through fuzzy Delphi method. Also, an extension of decision making trial and evaluation laboratory-based analytic network process method is proposed to uncover network relationships and obtain the weights for dimensions and elements. According to the results, the agile paradigm, responsiveness, and quick introduction of new products are determined as the most prioritized elements in their corresponding dimensions, which emphasize the importance of the customer satisfaction. Finally, the extended hesitant fuzzy linguistic VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) is applied to rank LARG performance of Iranian needles and syringes companies via qualitative group decision-making process. The results of this study are conformed to conventional selection results of needles and syringes suppliers conducted by Iran's Ministry of Health.

Advanced machine learning techniques have recently demonstrated outstanding performance when applied to Power Quality Disturbance (PQD) classification. Nevertheless, a possible problem is that power experts may find it hard to trust the results of such algorithms, if they do not fully understand the reasons for their outputs. In this light, this work suggests a method that explains the outputs of PQD classifiers, using Explainable Artificial Intelligence (XAI). The method operates as follows: first, various XAI techniques and classifiers are combined and scored based on their explanations during the validation step. Then, the best combination of classifier and XAI technique for each disturbance is used on the testing set, such that the classifier outputs are more transparent. To accomplish these steps, a definition for the correct explanation in PDQ is given. Also, to determine the quality of an explanation for a certain output, we propose an evaluation process to measure the explainability score for each XAI technique and classifier. By means of this approach, the PQD classifier outputs are optimized to be both accurate and easy to understand, allowing experts to make informed and trustworthy decisions.

The locus of innovation has shifted from mechanical advances to digital solutions. By emphasizing the importance of user needs, Design Thinking is apt to develop human-centered innovation, including digital solutions. Using two representative examples from 21 Design Thinking projects spanning the gamut of mechatronic to fully digital solutions, we report on critical incidents as opportunities and challenges of applying Design Thinking in a digital context. In the case of mechatronic solutions, we identified opportunities related to improved collaboration and higher quality prototyping as well as in innovative business models, which in turn created challenges in managing stakeholders. In the fully digital context, we observed opportunities in improved needfinding and the ability to offer individualized products. Conversely, we uncover difficulties in imagining digital features, estimating their feasibility, and correctly setting the fidelity of prototypes. Based on these observations, we discuss the intangibility of digital artifacts as enabler and inhibitor of Design Thinking in a digital context.

A presente pesquisa tem como objetivo geral, debater conceitualmente sobre os sistemas de informação como elementos viabilizadores do desenvolvimento sustentável. Como justificativa para a escolha do tema conceba sobre sua contemporaneidade, além da expectativa de contribuir para o campo acadêmico. A metodologia empregada é de natureza qualitativa, com pesquisa bibliográfica. Dentre os principais achados, foi possível concluir que as relações entre sociedade e meio ambiente surgem cada vez mais atreladas e evidentes no cotidiano, considerando as preocupações implicadas nas práticas humanas e seus impactos ambientais, bem como a necessidade de conscientização ambiental. Sendo assim, é necessário aplicar um dos maiores benefícios humanos a favor do desenvolvimento sustentável, momento em que as tecnologias da informação, por meio dos sistemas de informação, se tornam coerentes para promover uma gestão ambiental mais eficiente, efetiva e de qualidade na manutenção do desenvolvimento sustentável