This longitudinal, quasi-experimental study investigated students’ cognitive personality type using the Myers-Briggs personality Type Indicator (MBTI) in Internet-based Online and Face-to-Face (F2F) modalities. A total of 1154 students enrolled in 28 Online and 32 F2F sections taught concurrently over a period of fourteen years. The study measured whether the sample is similar to the national average percentage frequency of all 16 different personality types; whether specific personality type students preferred a specific modality of instructions and if this preference changed over time; whether learning occurred in both class modalities; and whether specific personality type students learned more from a specific modality. Data was analyzed using regression, t-test, frequency, and Chi-Squared. The study concluded that data used in the study was similar to the national statistics; that no major differences in preference occurred over time; and that learning did occur in all modalities, with more statistically significant learning found in the Online modality versus F2F for Sensing, Thinking, and Perceiving types. Finally, Sensing and Thinking (ST) and Sensing and Perceiving (SP) group types learned significantly more in Online modality versus F2F.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Privacy: Our analog parents’ dating preferences were considered a most private of matters. For our digital children, their dating preferences is a wholesale harvesting opportunity for marketing purposes. How did this terrifying shift come to be?

I believe the first big harvester of dating preferences was the innocent-looking site hotornot.com 18 years ago, a site that more seemed like the after-hours side work of a frustrated highschooler than a clever marketing ploy. It simply allowed people to rate their subjective perceived attractiveness of a photograph, and to upload photographs for such rating. (The two founders of this alleged highschool side project netted $10 million each for it when the site was sold.)

Then the scene exploded, with both user-funded and advertising-funded dating sites, all of which cataloged people’s dating preferences to the smallest detail.

Large-scale pornography sites, like PornHub, also started cataloging people’s porn preferences, and contiously make interesting infographics about geographical differences in preferences. (The link is safe for work, it’s data and maps in the form of a news story on Inverse, not on Pornhub directly.) It’s particularly interesting, as Pornhub is able to break down preferences quite specifically by age, location, gender, income brackets, and so on.

Do you know anyone who told Pornhub any of that data? No, I don’t either. And still, they are able to pinpoint who likes what with quite some precision, precision that comes from somewhere.

And then, of course, we have the social networks (which may or may not be responsible for that tracking, by the way).

It’s been reported that Facebook can tell if you’re gay or not with as little as three likes. Three. And they don’t have to be related to dating preferences or lifestyle preferences — they can be any random selections that just map up well with bigger patterns.

This is bad enough in itself, on the basis that it’s private data. At a very minimum, our digital childrens’ preferences should be their own, just like their favorite ice cream.

But a dating preferences are not just a preference like choosing your flavor of ice cream, is it? It should be, but it isn’t at this moment in time. It could also be something you’re born with. Something that people even get killed for if they’re born with the wrong preference.

It is still illegal to be born homosexual in 73 out of 192 countries, and out of these 73, eleven prescribe the death penalty for being born this way. A mere 23 out of 192 countries have full marriage equality.

Further, although the policy direction is quite one-way toward more tolerance, acceptance, and inclusion at this point in time, that doesn’t mean the policy trend can’t reverse for a number of reasons, most of them very bad. People who felt comfortable in expressing themselves can again become persecuted.

Genocide is almost always based on public data collected with benevolent intent.

This is why privacy is the last line of defense, not the first. And this last line of defense, which held fast for our analog parents, has been breached for our digital children. That matter isn’t taken nearly seriously enough.

Privacy remains your own responsibility.

Global concerns over obesity, diabetes, and cardiovascular disease will usher in the new year’s health and wellness initiatives. Consumers will engage in the annual effort to seek out food and beverage choices with more moderate amounts of nutritive sweeteners (i.e., sucrose and fructose), lower amounts of “undesirable” fats, and fewer calories. 

Abstract Through the lens of evolutional psychology, mate preferences are posited into a three “G” framework (good genes, good resources, and good persons/parents/partners) that captures genetic quality, resource acquisition, and personality and caregiving qualities. Previous research acknowledged that adult children had different mate preferences from their parents, but had no consensus on how such differences […]

The post Parent–child discrepancies in mate preferences: A three‐level meta‐analysis was curated by information for practice.

The post How Do Financial Attitudes and Preferences Vary among Latine Consumers? was curated by information for practice.

The post CfP: Patient preferences in psychotherapy (Deadline for expressions of interest: Oct 31) was curated by information for practice.

Panelists in a discussion on ‘Mobility Megatrends 2050’, highlighted that in the next decade, with electrification, autonomous driving, smart and connected infrastructure, modal diversity, and mobility that is integrated, resilient, shared, and sustainable – powered by disruptive business models, will transform and shape up of the automotive industry. The industry is racing towards a new world, driven by sustainability and changing consumer behaviour, encompassing electric vehicles, autonomous cars, mobility fleet sharing, and always connected.

Continuing Medical Education for Nurse Practitioners, Physician Assistants & Physicians

In this episode, Thomas Domville introduces us to the new “per voice settings” introduced with iOS 17.

These can be found by navigating to Settings > Accessibility > VoiceOver > Speech, and then selecting the language and desired TTS voice. After selecting the voice, flick up or down with one finger, or press space with dot 3 or dot 6 on a Braille display, to access the per voice settings.

The configurable parameters available vary by voice. For example, Vocalizer voices can tweak sentence pause and timbre, while for Alex you can adjust pitch range and words per minute.

Women between the ages of 26-55 account for the majority of trading, thus, dispelling the notion that most trading is done by college students, data from the FYERS platform indicates

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

Employees today prefer personalised gifts, and India Inc is taking cognisance

Cheltenham, UK ; Northampton, MA : Edward Elgar Publishing, [2023]

An extended analysis of structural ensembles obtained from small-angle X-ray scattering data reveals subclass-specific conformational preferences of IgG antibodies, which are largely determined by the hinge-region structure.

Agri-environment schemes (AES) are widely researched; some important issues, however, remain unstudied. Researchers have investigated some of these issues using a sample of olive growers in southern Spain. Their study reveals the level of monetary incentive needed for farmers to accept an ‘ecological focus area’, and a general unwillingness to participate collectively. These results could help policymakers design more cost-effective AES.

The Natura 2000 network is the backbone of nature conservation in the EU, and agri-environment schemes (AES) are an important tool to protect biodiversity on European farmland. A recent study, which investigated the effectiveness of AES in relation to grassland birds in Poland, found that AES were not associated with species richness of target species, and proposed a number of reasons for this. The researchers recommend that AES management regimes should be diversified and customised to provide optimal habitat for a wider range of bird species.

#SettingPrefs #PreferencesMacros Preference Settings A preference setting lets you define a simple macro that will be expanded in your output. A preference setting ... (last changed by ProjectContributor)

CBM Wiki Site Level Preferences This topic defines site level settings that apply to all users and webs on this site. Note: This topic should be regarded as read only ... (last changed by AdminUser)

Researchers find that breastfed children were exposed to more food flavors and were therefore more likely to be healthier eaters than their non-breastfed peers.

New equipment, technology, and marketing efforts have allowed Alaska's wood products producers to consider opportunities previously unavailable to them. Until recently, the primary product produced by Alaska firms was rough, unseasoned lumber sold primarily within local markets. Given the purchase and installation of new drying and planing equipment, Alaska producers can now enter domestic and export markets for a variety of secondary wood products. Previously underutilized species, such as red alder (Alnus rubra Bong.), paper birch (Betula papyrifera Marsh.), and Alaska yellow-cedar (Chamaecyparis nootkatensis (D. Don) Spach) are also gaining in popularity and market potential. A detailed knowledge of species preferences for Alaska lumber, across business types and geographic regions, will be essential if Alaska producers are to be competitive.

In embodiments of the present invention improved capabilities are described for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of (1) ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; (2) determining the internet based social interactive graphical representation for the plurality of known users; and (3) inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation.

A failure to look upward when parking by the river at a favourite roost for local pelicans has left a Port Macquarie man red-faced and his car disgraced.

Cyclists and noncyclists have a wide range of perceptions of on-street bicycling facility designs — including sharrows, bike lanes, and buffered bike lanes — along a variety of roadway types, with and without curbside automobile parking. The TRB National Cooperative Highway Research Program's NCHRP Research Report 941: Bicyclist Facility Preferences and Effects on Increasing Bicycle Trips provides insights from communities where on-road cycling for transportation is less common, particularly in the South...