Hasil untuk "Information technology"

D. Alberts, John J. Garstka, F. Stein

Patrick Y. K. Chau, P. H. Hu

A. Poon

Tourism, the world s fastest growing industry, is now entering a more mature phase. During the 1970s and 1980s mass tourism, with its rigid, standardized packages, developed rapidly and provided many consumers with their first experiences of international travel. Today, a complex and multi-faceted industry, tourism faces growing pressures - consumer demand for more individually tailored holidays, an increasingly competitive operational environment, opportunities provided by new technology and growing environmental concerns. This book analyzes the major challenges facing tourism today. The author highlights the central role of information technology in creating mass tourism by the mid-1970 s, and how this technology and innovation is creating a new best practice of flexibility, market segmentation and diagonal integration within tourism. The book demonstrates how companies in the industry can enhance their competitiveness in the market place. Aimed at both academics and industry practitioners, this original and challenging work will attract a wide readership."

A. Barua, C. Kriebel, Tridas Mukhopadhyay

R. Nelson, P. Todd, B. Wixom et al.

Patrick Y. K. Chau

M. Castells

David Constant, S. Kiesler, L. Sproull

P. Checkland, Sue Holwell

O. Hanseth, K. Lyytinen

R. Law, Dimitrios Buhalis, C. Çobanoğlu

Purpose – The purpose of this paper is to establish the progress of information and communication technology (ICT) based on a review of papers published in tourism and hospitality journals between 2009 and 2013. Design/methodology/approach – Based on three major databases, 107 journal papers were retrieved and reviewed. The papers were grouped into two major categories, consumer and supplier, which generally comprise the key players in the industries. Findings – A content analysis showed that hospitality and tourism industries use ICT in different functional units and for different applications. This, in turn, indicates their wide adoption in the industry. Industrial implications are discussed. Practical implications – On the basis of the content analysis, industry practitioners can learn about up-to-date practices and decide how to take advantage of recent technological developments. Originality/value – A major contribution of this paper is the comprehensive review of recently published papers in tourism and hospitality journals from the perspectives of consumer and supplier.

Weiyin Hong, F. Chan, J. Thong et al.

Yameng Qiao, Wenzheng Liu, Fanzhen Wang et al.

Individual recognition of Hu sheep is a core requirement for precision livestock management, significantly improving breeding efficiency and fine management. However, traditional machine vision methods face challenges such as high annotation time costs, the inability to quickly annotate new sheep, and the need for manual intervention and retraining. To address these issues, this study proposes a solution that integrates automatic annotation and transfer learning, developing a sheep face recognition algorithm that adapts to complex farming environments and can quickly learn the characteristics of new Hu sheep individuals. First, through multi-view video collection and data augmentation, a dataset consisting of 82 Hu sheep and a total of 6055 images was created. Additionally, a sheep face detection and automatic annotation algorithm was designed, reducing the annotation time per image to 0.014 min compared to traditional manual annotation. Next, the YOLOv10n-CF-Lite model is proposed, which improved the recognition precision of Hu sheep faces to 92.3%, and the mAP@0.5 to 96.2%. To enhance the model’s adaptability and generalization ability for new sheep, transfer learning was applied to transfer the YOLOv10n-CF-Lite model trained on the source domain (82 Hu sheep) to the target domain (10 new Hu sheep). The recognition precision in the target domain increased from 91.2% to 94.9%, and the mAP@0.5 improved from 96.3% to 97%. Additionally, the model’s convergence speed was improved, reducing the number of training epochs required for fitting from 43 to 14. In summary, the Hu sheep face recognition algorithm proposed in this study improves annotation efficiency, recognition precision, and convergence speed through automatic annotation and transfer learning. It can quickly adapt to the characteristics of new sheep individuals, providing an efficient and reliable technical solution for the intelligent management of livestock.

Andika Fadilla Siagian, Suendri Suendri

Retail businesses, particularly hardware stores, often encounter challenges in order management such as delayed deliveries, inaccurate stock tracking, and limited information transparency factors that hinder operational efficiency and customer satisfaction. This study proposes a web-based order management system utilizing Progressive Web Apps (PWA) technology, developed with the Next.js framework. The Periodic Review System (PRS) method is implemented to calculate reorder points based on actual demand and safety stock levels. System development follows the Waterfall model, with data collected through observation, semi-structured interviews, and literature review. Testing confirms that the application enhances stock accuracy, minimizes delivery delays, supports offline access, and meets SEO performance standards. The implementation significantly improves operational efficiency and holds promise for boosting customer loyalty. The study concludes that PWA-based digital systems are practical, scalable solutions for the MSME sector, with future potential for integration of AI, CRM, and real-time analytics.

ZeYang Fan, JunMin Lin, Tian Zhang et al.

Abstract Programmable photonic integrated circuits can realize analog matrix multiplication to accelerate computing disruptively in various fields. However, a major challenge is the precise voltage configuration of the circuit to deal with the universal static error derived from manufacturing. Here, we propose a complete off-chip method based on the combination of gradient descent and genetic algorithms to find the optimal configuration for an arbitrary matrix, enabling imperfect circuits to achieve excellent performance. In the simulation, we demonstrated that our method implements an arbitrary matrix with an average fidelity of 0.992 on a Mach–Zehnder-interferometer-based circuit with up to 28 input ports. Experimentally, we demonstrated superior performance on the circuit with 4 input ports, including training a theoretical model that characterized the experimental imperfections of the fabricated chip and obtaining the optimal configuration for permutation matrices with near-one fidelity and for 100 unitary matrices with a 0.985 average fidelity.

Kiselev Gennady G., Zolkin Alexander L., Bityutskiy Alexey S. et al.

The article discusses the need to improve the efficiency of freight cars operation by introducing modern technologies in the maintenance process. To improve the quality of technical inspection of rolling stock at the maintenance point, it is offered to introduce a robotic complex capable of autonomously conducting technical inspection and diagnostics of rolling stock. Structurally, the robotic complex includes: chassis; manipulator; automation, control and data transmission system; diagnostic equipment; power unit. The possibility of using various diagnostic tools, vision systems, non-contact scanning sensors is considered. The functions of the complex are to pass through the inspection positions, identify malfunctions, accumulate information about the technical condition of the car with the transfer of information to the fleet operator or maintenance point.

Raenald Syaputra, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto

Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.

Lei Zhang, Yiru Huang, Leiming Yan et al.

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.

Jorge Enrique Díaz Pinzón

To establish the degree of correlation between the gender of the students and their video game consumption habits, a survey of closed items was applied to a sample of 330 students from the General Santander Educational Institution in the municipality of Soacha, Cundinamarca. Five survey questions related to video game consumption habits were analyzed and five research hypotheses were proposed, which were contrasted using the chi-square statistic. Pearson's correlation coefficients were also calculated for the variables under study. The correlational analysis regarding the gender of the students indicated that there is a relationship between this and the consumption habits of video games. In addition, a significant correlation between the variables studied was largely evidenced. It was concluded that adolescent men spend more time playing video games than women.

Eric Balki, Niall Hayes, Carol Holland

BackgroundDuring the COVID-19 pandemic, government-mandated social distancing prevented the spread of the disease but potentially exacerbated social isolation and loneliness for older people, especially those already vulnerable to isolation. Older adults may have been able to draw from their personal resources such as psychological resilience (PR) and technology use (TU) to combat such effects. Educational attainment (EA) or early-life EA may potentially shape later-life personal resources and their impact on the effects of the pandemic lockdown on outcomes such as loneliness. The developmental adaptation model allows for the supposition that social isolation, TU, and PR may be affected by early EA in older adults. ObjectiveThis study examined the indirect impact of EA on pandemic-linked loneliness in a sample of older adults. The developmental adaptation model was used as the conceptual framework to view EA as a distal influence on loneliness, social isolation, PR, and TU. We hypothesized that EA would predict TU and PR and have a moderating impact on social isolation and loneliness. We also hypothesized that PR and TU would mediate the effect of EA on loneliness. MethodsThis was a cross-sectional observational study, in which data were gathered from 92 older adults aged ≥65 years in the United Kingdom from March 2020 to June 2021, when the country was under various pandemic-linked social mobility restrictions. The data captured demographic information including age, gender, ethnicity, and the highest degree of education achieved. The University of California Los Angeles Loneliness Scale, Connor–Davidson Resilience Scale, Lubben Social Network Index, and Technology Experience Questionnaire were used as standardized measures. Pearson correlation, moderation, and mediation regression analyses were conducted to investigate the hypotheses. ResultsWe found a higher prevalence of loneliness in older adults than in prepandemic norms. EA was correlated with greater TU and PR and moderated the impact of social isolation on loneliness. PR mediated and TU partially mediated the relationship between EA and loneliness. ConclusionsEarly-life EA was confirmed as a distal resource for older adults and played an indirect role in affecting loneliness levels during the pandemic. It has an impact on present-day personal resources, such as PR and TU, which affect loneliness and also moderate the impact of social isolation on loneliness. Policymakers should be aware that older adults with low levels of EA may be more vulnerable to the harmful impacts of loneliness when isolated by choice.