Hasil untuk "Information technology"

Tawfiq Abu-Raqabeh

Today’s swiftly changing technology, globalization, and integration of corporations has created a need for the introduction of IAS to higher education institutes. This study explores and examines the introduction of IAS to the higher education institutes. The readiness of the institutes, the problems they face to incorporate the IAS to the curriculum. The criteria utilized by ABET focuses on content and delivery of curriculum within the IS discipline. The advantages of incorporating the IAS in the curriculum for students and faculty.

David Berdik, Safa Otoum, Niko Schmidt et al.

Abstract Blockchain technologies have grown in prominence in recent years, with many experts citing the potential applications of the technology in regard to different aspects of any industry, market, agency, or governmental organizations. In the brief history of blockchain, an incredible number of achievements have been made regarding how blockchain can be utilized and the impacts it might have on several industries. The sheer number and complexity of these aspects can make it difficult to address blockchain potentials and complexities, especially when trying to address its purpose and fitness for a specific task. In this survey, we provide a comprehensive review of applying blockchain as a service for applications within today’s information systems. The survey gives the reader a deeper perspective on how blockchain helps to secure and manage today information systems. The survey contains a comprehensive reporting on different instances of blockchain studies and applications proposed by the research community and their respective impacts on blockchain and its use across other applications or scenarios. Some of the most important findings this survey highlights include the fact that blockchain’s structure and modern cloud- and edge-computing paradigms are crucial in enabling a widespread adaption and development of blockchain technologies for new players in today unprecedented vibrant global market. Ensuring that blockchain is widely available through public and open-source code libraries and tools will help to ensure that the full potential of the technology is reached and that further developments can be made concerning the long-term goals of blockchain enthusiasts.

C. Avgerou

A. Dillon, Michael G. Morris

B. Santos, K. Peffers, D. Mauer

M. Broadbent, P. Weill, D. S. Clair

Heming Jia, Marjan Kordani, Iman Ahmadianfar et al.

Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices using advanced machine learning methods. The approach combines the least squares support vector machine (LSSVM) with the Sherman–Morrison–Woodbury (SMW) formula and local weighting techniques to improve the model's capacity to identify local trends and nonlinearities. The hybrid model, SMW-LSSVM-R, integrates the advantages of SMW-LSSVM with ridge regression to provide a balanced and resilient predictive framework. The model parameters are improved by a self-adaptive teaching-learning-based differential evolution (SATLDE) method, attaining optimal performance. Additionally, SATLDE is combined with a ridge feature selection model to identify the key input factors and boost accuracy. The model also employs optimized multivariate variational mode decomposition (OMVMD) using SATLDE algorithm to more effectively assess complex data patterns. When the models were tested at two Iranian stations, Farisat and Molasani, the SMW-LSSVM-R model with a testing R value of 0.975 and an RMSE of 0.990, exhibited better performance than the basic and OMVMD-enhanced models. These findings demonstrate the potential of the proposed hybrid model to offer valuable insights into environmental monitoring and management.

Emmanouil M. Athanasakos, Nicholas Kalouptsidis, Hariprasad Manjunath

This paper introduces a methodology based on Euclidean information theory to investigate local properties of secure communication over discrete memoryless wiretap channels. We formulate a constrained optimization problem that maximizes a legitimate user's information rate while imposing explicit upper bounds on both the information leakage to an eavesdropper and the informational cost of encoding the secret message. By leveraging local geometric approximations, this inherently non-convex problem is transformed into a tractable quadratic programming structure. It is demonstrated that the optimal Lagrange multipliers governing this approximated problem can be found by solving a linear program. The constraints of this linear program are derived from Karush-Kuhn-Tucker conditions and are expressed in terms of the generalized eigenvalues of channel-derived matrices. This framework facilitates the derivation of an analytical formula for an approximate local secrecy capacity. Furthermore, we define and analyze a new class of secret local contraction coefficients. These coefficients, characterized as the largest generalized eigenvalues of a matrix pencil, quantify the maximum achievable ratio of approximate utility to approximate leakage, thus measuring the intrinsic local leakage efficiency of the channel. We establish bounds connecting these local coefficients to their global counterparts defined over true mutual information measures. The efficacy of the proposed framework is demonstrated through detailed analysis and numerical illustrations for both general multi-mode channels and the canonical binary symmetric wiretap channel.

Said Varlioglu, Hazem Said, Murat Ozer et al.

Information Technology (IT) is recognized as an independent and unique research field. However, there has been ambiguity and difficulty in identifying and differentiating IT research from other close variations. Given this context, this paper aimed to explore the roots of the Information Technology (IT) research domain by conducting a large-scale text mining analysis of 50,780 abstracts from awarded NSF CISE grants from 1985 to 2024. We categorized the awards based on their program content, labeling human-centric programs as IT research programs and infrastructure-centric programs as other research programs based on the IT definitions in the literature. This novel approach helped us identify the core concepts of IT research and compare the similarities and differences between IT research and other research areas. The results showed that IT differentiates itself from other close variations by focusing more on the needs of users, organizations, and societies.

Faridokht Salahshoori, Majid Jangi, Ebrahim Sadeghi‐demneh et al.

ABSTRACT Background and Aim Telerehabilitation has emerged as a promising solution to address accessibility, cost‐effectiveness, and continuity of care for patients requiring long‐term rehabilitation, like cerebral palsy (CP) patients. This study aimed to qualitatively explore the perceptions of clinical specialists, and CP patients regarding the feasibility, barriers, and facilitators of home‐based telerehabilitation. Methods This qualitative study conducted a thematic analysis approach. Participants include two groups: 17 medical informatics and rehabilitation professionals and 13 CP patients and/or their caregivers. Participants were selected via expert and snowball sampling. Interviews were semi‐structured, transcribed, and analyzed using the Braun‐Clarke thematic analysis technique and MAXQDA software. Results Thematic analysis revealed four dominant themes including feasibility, barriers, facilitators, and advantages. Feasibility was affected by technological infrastructure (internet connectivity, data security), human resources (availability of multidisciplinary specialists), legal aspects (patient data privacy), and financial sustainability. Key barriers included low digital literacy, limited access to specialized rehabilitation technologies, cultural resistance, legal regulations, and financial constraints. The findings also highlighted several advantages of home‐based telerehabilitation, including cost savings, improved accessibility to rehabilitation, and enhanced patient engagement in therapy. Conclusion The current study suggested that home‐based telerehabilitation can be a feasible alternative for CP patients in Iran, but a variety of technological, financial, legal, and cultural barriers must first be addressed for its successful development. Overcoming such barriers requires targeted investments, supportive policies, and cultural education for successful implementation.

Myeong-Jun Kang, Jaesun Park, Hyuk Heo et al.

Abstract This paper presents novel MIMO microstrip patch antennas with dimensions of 40 × 80 × 1.6 mm³ incorporating a decoupling and pattern correction structure (DPCS) designed to mitigate mutual coupling and radiation pattern distortion, operating within 3.6–3.7 GHz. Using characteristic mode analysis (CMA), two key modes affecting coupling and pattern degradation are identified, with the DPCS strategically positioned to address these issues. Unlike other decoupling techniques, the DPCS requires no additional space or structural complexity, making it suitable for 5G MIMO systems. The proposed design achieves isolation up to 90 dB and enhances the realized gain of Port 2 by 3 dB at boresight in simulations. Fabricated antennas were measured, achieving peak isolation of 80 dB in an anechoic chamber. Additionally, measurements in a noisy environment confirmed the robustness of the design under realistic conditions. Measured radiation patterns verified the DPCS’s ability to correct the radiation pattern. Key MIMO performance metrics, including ECC (2 × 10⁻⁴), DG (≈ 10), CCL (< 0.2 bits/s/Hz), MEG (≈ -7 dB), and TARC (< -12 dB), affirmed the design’s superior performance. The proposed structure can be applied to a variety of applications such as high-density urban wireless networks and IoT systems, where maintaining high isolation and reliable communication are critical requirements.

Sara S. Alhasan, Reem A. Alnanih

The increasing adoption of Artificial Intelligence (AI) in several industries has created a demand for user-centered explanations that align with how users think and understand concepts. This paper presents EXACT (EXplainable AI with Cognitive Theories), a novel framework that combines cognitive theories that explain how people think and understand with cognitive functions, focusing on perception, memory and language abilities, to improve users&#x2019; comprehension of and engagement with artificial intelligence technologies. By aligning cognitive functions with the design principles of Human-Computer Interaction (HCI), which promote user-centered intuitive systems. the framework addresses challenges related to making AI understandable to users with various levels of cognitive abilities. As a proof-of-concept, a self-diagnosis tool was created to demonstrate the framework&#x2019;s effectiveness. Then, 60 participants were divided into a control group and an experimental group. Participants completed six tasks designed to evaluate their perception, memory, and language-related cognitive functions. The experimental group outperformed the control group across all tasks, demonstrating significantly improved performance. Subjective metrics also supported these findings: the experimental group reported higher levels of understanding (4.60 vs. 2.87), confidence (4.67 vs. 3.07), and clarity (4.87 vs. 2.80) compared to the control group. These findings suggest that EXACT framework significantly enhances user&#x2019;s functions when using AI systems. However, further research is needed to explore its broader applicability in other contexts and utilize other cognitive functions.

R. Fichman, Wallace E. Carroll

Siripen Pongpaichet, Boonyapat Sukosit, Chitchaya Duangtanawat et al.

Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted to utilize online news articles as a reliable and timely source for information on crime activity. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86&#x0025;, 51&#x0025;, and 67&#x0025; F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.

Li Yizhan, Dong Lu, Fan Xiaoxiao et al.

Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

Anastasiia Kudriavtseva, Stefan Jarić, Nikita Nekrasov et al.

Graphene-based materials are actively being investigated as sensing elements for the detection of different analytes. Both graphene grown by chemical vapor deposition (CVD) and graphene oxide (GO) produced by the modified Hummers’ method are actively used in the development of biosensors. The production costs of CVD graphene- and GO-based sensors are similar; however, the question remains regarding the most efficient graphene-based material for the construction of point-of-care diagnostic devices. To this end, in this work, we compare CVD graphene aptasensors with the aptasensors based on reduced GO (rGO) for their capabilities in the detection of NT-proBNP, which serves as the gold standard biomarker for heart failure. Both types of aptasensors were developed using commercial gold interdigitated electrodes (IDEs) with either CVD graphene or GO formed on top as a channel of liquid-gated field-effect transistor (FET), yielding GFET and rGO-FET sensors, respectively. The functional properties of the two types of aptasensors were compared. Both demonstrate good dynamic range from 10 fg/mL to 100 pg/mL. The limit of detection for NT-proBNP in artificial saliva was 100 fg/mL and 1 pg/mL for rGO-FET- and GFET-based aptasensors, respectively. While CVD GFET demonstrates less variations in parameters, higher sensitivity was demonstrated by the rGO-FET due to its higher roughness and larger bandgap. The demonstrated low cost and scalability of technology for both types of graphene-based aptasensors may be applicable for the development of different graphene-based biosensors for rapid, stable, on-site, and highly sensitive detection of diverse biochemical markers.

Mikhail Ivanenko, Damian Wanta, Waldemar T. Smolik et al.

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified.

FENG Jianghua, TAN Bo, DOU Zechun et al.

With the continuous advancement of China's "dual carbon" goals and the ongoing optimization of the energy mix, the electrification of transportation equipment, as a low-carbon and environmentally-friendly approach, has become an important development trend in the transportation industry. This paper presents the exploration in high-power-density electrification technologies for transportation equipment, focusing on those in the chain-type key technology routes encompassing devices, components, equipment, systems and architectures. Taking products supplied by CRRC Zhuzhou Institute Co., Ltd. as an case study, detailed investigations were made into five key technologies for high-power-density design: high-frequency converters, customized devices, silicon-based equipment, structural integration, and diversified networking, and three high-power-density generic technologies: thermal management, electromagnetic compatibility, and reliability, highlighting their key roles in improving the performance, efficiency, and reliability of transportation equipment. The study summarizes the current research status concerning the development of transportation equipment towards higher power, lighter weight, and smaller size. For future development in high-power-density electrification technologies, this paper suggests a focus on continuous innovation and development in four areas: the innovation chain, intelligent systems, new power semiconductor device technologies, and safety. The research outcomes provide strong support for the green transformation and sustainable development of the transportation industry.

Geneviève Bassellier, I. Benbasat

Kieran A. Murphy, Dani S. Bassett

Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy. Finding an optimal measurement is challenging, and has generally required intimate knowledge of the dynamics in the few cases where it has been done. We establish an equivalence between a perfect measurement and a variant of the information bottleneck. As a consequence, we can employ machine learning to optimize measurement processes that efficiently extract information from trajectory data. We obtain approximately optimal measurements for multiple chaotic maps and lay the necessary groundwork for efficient information extraction from general time series.