Hasil untuk "Information technology"

S. Azhar

M. Knight, C. Muir

Ritu Agarwal, J. Prasad

G. Scappucci, Christoph Kloeffel, F. Zwanenburg et al.

In the effort to develop disruptive quantum technologies, germanium is emerging as a versatile material to realize devices capable of encoding, processing and transmitting quantum information. These devices leverage the special properties of holes in germanium, such as their inherently strong spin–orbit coupling and their ability to host superconducting pairing correlations. In this Review, we start by introducing the physics of holes in low-dimensional germanium structures, providing key insights from a theoretical perspective. We then examine the materials-science progress underpinning germanium-based planar heterostructures and nanowires. We go on to review the most significant experimental results demonstrating key building blocks for quantum technology, such as an electrically driven universal quantum gate set with spin qubits in quantum dots and superconductor–semiconductor devices for hybrid quantum systems. We conclude by identifying the most promising avenues towards scalable quantum information processing in germanium-based systems. Germanium is a promising material to build quantum components for scalable quantum information processing. This Review examines progress in materials science and devices that has enabled key building blocks for germanium quantum technology, such as hole-spin qubits and superconductor–semiconductor hybrids.

Ching-Hsing Wang, Ping Zhang

Erin E. Kenneally, D. Dittrich

Wonhee Ko, Seoung‐Hun Kang, Qiangsheng Lu et al.

Abstract Quantum materials with novel spin textures from strong spin‐orbit coupling (SOC) are essential components for a wide array of proposed spintronic devices. Topological insulators have a necessary strong SOC that imposes a unique spin texture on topological states and Rashba states that arise on the boundary, but there is no established methodology to control the spin texture reversibly. Here, it is demonstrated that functionalizing Bi2Se3 films by altering the step‐edge termination directly changes the strength of SOC and thereby modifies the Rashba strength of 1D edge states. Scanning tunneling microscopy/spectroscopy shows that these Rashba edge states arise and subsequently vanish through the Se functionalization and reduction process of the step edges. The observations are corroborated by density functional theory calculations, which show that a subtle chemical change of edge termination fundamentally alters the underlying electronic structure. Importantly, fully reversible and repeatable switching of Rashba edge states across multiple cycles at room temperature is experimentally demonstrated. The results imply Se functionalization as a practical method to control SOC and spin texture of quantum states in topological insulators.

Aftab Ahmed, Sara Ilyas, Pervaiz Ali Channar et al.

Abstract Human Carbonic Anhydrase inhibitors (CAIs) have been clinically used to treat a variety of disorders, such as cancer, obesity, haemolytic anaemia, glaucoma, retinopathy, and epilepsy. To develop a Carbonic Anhydrase inhibitor, Iminothiazoline analogue ((Z)-N-(3-([1,1'-biphenyl]-2-yl)-4-heptyl-4-hydroxythiazolidin-2-ylidene)-4-bromobenzamide) was synthesized and characterized. Single crystal X-Ray diffraction studies and Hirshfeld surface analysis (HSA) were conducted to find the exact molecular structure as well as intermolecular interactions. DFT Calculations indicated the soft and reactive nature of molecule. In-Vitro carbonic anhydrase inhibition studies showed the excellent inhibition potential of (Z)-N-(3-([1,1'-biphenyl]-2-yl)-4-heptyl-4-hydroxythiazolidin-2-ylidene)-4-bromobenzamide (IC50 value of 0.147 ± 0.03 µM). Four hydrogen bonds and a multiple hydrophobic interactions were observed between synthesized molecule and the enzyme during Molecular docking studies. Molecular dynamic simulation studies showed that Protein–ligand complex generally remained stable throughout the time. ADMET studies suggested the need of structural modification for the drug like behavior of synthesized molecule.

Weiwei Wang, Elena Blanc-Betes, Madhu Khanna et al.

Abstract Energy crops will be critical for scaling up production of Sustainable Aviation Fuel in the United States and reducing greenhouse gas emissions. Here we examine the economic incentives for the extent and type of land conversion needed to scale up fuel production from a mix of cellulosic feedstocks and quantify its greenhouse gas intensity. We show that even with the availability of marginal non-cropland, there will be incentives for converting cropland to produce energy crops as the price of sustainable aviation fuel increases. But contrary to expectations, we find that scaling up fuel production by converting more cropland and more non-cropland from existing uses to energy crops lowers its net greenhouse gas intensity, due to high soil carbon sequestration rate of energy crops, even after considering land use change emissions.The potential savings in emissions are larger than the foregone soil carbon accumulation benefits from keeping that land in current uses.

Shunan Zhang, Xiangying Zhao, Tong Zhou et al.

Abstract Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency.

Andrzej Klimczuk, Delali A. Dovie, Agnieszka Cieśla et al.

Li Jiaheng, Hu Mengjin, Li Hongkui et al.

Based on the high-resolution satellite image data, the information mining technology of the available surface elements of PV is studied, and the investigation of the available surface elements of PV in 98 counties and cities of South Hebei grid is realized. Based on the large-scale and high-resolution remote sensing data obtained by multi-source remote sensing data fusion technology, the depth-learning-based surface feature recognition technology for photovoltaic development is studied. Based on the method of automatic identification and artificial combination of depth-learning, it can identify the available ground elements (roof, water surface, road surface, dry beach, etc.), the available surface elements of PV in 98 counties and cities of Hebei South Grid were obtained. From the overall point of view, the photovoltaic land, the building occupies the main position, in the four cities are relatively high, are in the 6% ~ 15%

Kim Nordmann, Stefanie Sauter, Marie-Christin Redlich et al.

Background Germany's healthcare system provides high-quality, universal health coverage to almost all residents. However, a major challenge lies in the strong separation of healthcare structures, which hinders efficient interprofessional and intersectoral communication and collaboration. The mandatory nationwide implementation of the telematics infrastructure may offer a solution to enhance healthcare professionals’ communication and collaboration. Objective Our study aims to elicit participants’ perceptions of and attitudes towards the implementation and usage of the telematics infrastructure in fostering interprofessional communication and collaboration between home-care nursing services and general practitioner practices. Methods We conducted interviews with seven members of general practitioner practices and 10 in home-care nursing services. Using thematic content analysis, we identified five themes, of which four along with 10 subthemes were integrated into Greenhalgh et al.'s ‘nonadoption, abandonment, scale-up, spread and sustainability’ framework. Results Participants recognised the potential of digital technology to enhance interprofessional communication and collaboration. However, this potential largely depended on individual healthcare actors’ willingness to seek information, invest and adapt. Attitudes towards the telematics infrastructure varied widely from hopeful confidence to outright rejection. Home-care nursing services generally viewed the telematics infrastructure with optimism, while general practitioners expressed reservations, particularly due to technological disruptions, lack of user-friendliness, and organisational structures. Conclusion Our findings highlight the potential of digital technology to enhance interprofessional communication. Successful implementation of technological innovations, however, goes beyond technological aspects and involves social, political and organisational processes. Future implementation strategies for such innovations in healthcare should involve users early and ensure clear communication.

Qin Zeng, Wen-Ru Wang, Yi-Han Li et al.

ObjectivesGalactose-deficient IgA1 (Gd-IgA1) is a critical effector molecule in the pathogenesis of IgA nephropathy (IgAN), a leading renal disease without noninvasive assessment options. This updated systematic review aimed to determine the diagnostic and prognostic value of Gd-IgA1 assessment in biological fluids in patients with IgAN.MethodsPRISMA guidelines were followed in this review. We searched PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, VIP Information/China Science and Technology Journal Database, and WANFANG for studies published between database inception and January 31, 2023. Eligible studies that evaluated aberrant IgA1 glycosylation in IgAN patients relative to controls were identified, and random effects meta-analyses were used to compare Gd-IgA1 levels in different groups. The quality of the evidence was assessed using the Newcastle-Ottawa Scale. This study was registered on PROSPERO (CRD42022375246).FindingsOf the 2727 records identified, 50 were eligible and had available data. The mean Newcastle-Ottawa Scale score was 7.1 (range, 6–8). Data synthesis suggested that IgAN patients had higher levels of blood and/or urine Gd-IgA1 compared with healthy controls (standard mean difference [SMD]=1.43, 95% confidence interval [CI]=1.19−1.68, P<0.00001), IgA vasculitis patients (SMD=0.58, 95% CI=0.22−0.94, P=0.002), and other kidney disease patients (SMD=1.06, 95% CI=0.79−1.33, P<0.00001). Moreover, patients with IgAN had similar levels of serum Gd-IgA1 compared to first-degree relatives (SMD=0.38, 95% CI= -0.04−0.81, P=0.08) and IgA vasculitis with nephritis patients (SMD=0.12, 95% CI= -0.04−0.29, P=0.14). In addition, ten studies demonstrated significant differences in serum Gd-IgA1 levels in patients with mild and severe IgAN (SMD= -0.37, 95% CI= -0.64−-0.09, P=0.009).ConclusionsHigh serum and urine Gd-IgA1 levels suggest a diagnosis of IgAN and a poor prognosis for patients with this immunological disorder. Future studies should use more reliable and reproducible methods to determine Gd-IgA1 levels.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022375246, identifier CRD42022375246.

Dwi Kurnia Utami, Novica Irawati, Sumantri Sumantri

The Family Hope Program (PKH) is a program that provides cash assistance to Very Poor Households (RSTM) which are required to fulfill requirements related to efforts to improve the quality of human resources. In selecting residents to be recipients of the Family Hope Program (PKH) in Pulau Rakyat Tua Village, the problem that often arises is that the provision of Family Hope Program assistance is often considered not to be on target. In addition, errors often occur because the selection is still done manually and requires a long time in selecting participants, which can be influenced by the objective assessment of PKH companions. The research objective is to apply the k-means clustering algorithm in selecting prospective beneficiaries of the Family Hope Program (PKH). The method used uses the application of data mining with the k-means clustering algorithm. Based on the results of applying the k-means clustering algorithm, the results of the system being built can make it easier to select potential recipients of Family Program assistance. The results of the k-means clustering algorithm test produced Cluster 1 in the Eligible category totaling 29 PKH beneficiary data and Cluster 2 in the Ineligible category totaling 1 PKH beneficiary data.

Olesia Barkovska, Dmytro Mohylevskyi , Yuliia Ivanenko et al.

The paper is devoted to the actual problem of classifying textual documents of the collection by characteristic features, which is used for classifying news, reviews, determining the emotional tone of the text, as well as for forming catalogs of scientific, academic and research works. The paper proposes an approach for determining the significant words of a document for their further use as a feature vector in the classification process. In the course of the work, the author's keywords were identified, a partial dictionary was built, and the correlation between the author's keywords and the list of ordered words of the frequency dictionary based on the TF method, which also includes the author’s keywords, was analyzed. The determination of the range and percentage of significant words allows for further classification of scientific and research papers when forming thematic catalogs even in the absence of a list of author's keywords that can be used for classification. The results show that the use of the entire input range of frequency dictionary words is redundant and leads to a longer classification time.

Chun-Hui Lin, Cheng-Jian Lin, Yu-Chi Li et al.

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.

Jun Cheng, Mazhar Sadiq, Olga A. Kalugina et al.

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users’ sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.

Xu, Wenge, Liang, Hai-Ning, He, Qiuyu et al.

BackgroundAlthough full-body seated exercises have been studied in a wide range of settings (ie, homes, hospitals, and daycare centers), they have rarely been converted to seated exergames. In addition, there is an increasing number of studies on immersive virtual reality (iVR) full-body gesture-based standing exergames, but the suitability and usefulness of seated exergames remain largely unexplored. ObjectiveThis study aimed to evaluate the difference between playing a full-body gesture-based iVR standing exergame and seated exergame in terms of gameplay performance, intrinsic motivation, and motion sickness. MethodsA total of 52 participants completed the experiment. The order of the game mode (standing and sitting) was counterbalanced. Gameplay performance was evaluated by action or gesture completion time and the number of missed gestures. Exertion was measured by the average heart rate (HR) percentage (AvgHR%), increased HR%, calories burned, and the Borg 6-20 questionnaire. Intrinsic motivation was assessed with the Intrinsic Motivation Inventory (IMI), whereas motion sickness was assessed via the Motion Sickness Assessment Questionnaire (MSAQ). In addition, we measured the fear of falling using a 10-point Likert scale questionnaire. ResultsPlayers missed more gestures in the seated exergame than in the standing exergame, but the overall miss rate was low (2.3/120, 1.9%). The analysis yielded significantly higher AvgHR%, increased HR%, calories burned, and Borg 6-20 rating of perceived exertion values for the seated exergame (all P<.001). The seated exergame was rated significantly higher on peripheral sickness (P=.02) and sopite-related sickness (MSAQ) (P=.004) than the standing exergame. The score of the subscale “value/usefulness” from IMI was reported to be higher for the seated exergame than the standing exergame. There was no significant difference between the seated exergame and standing exergame in terms of intrinsic motivation (interest/enjoyment, P=.96; perceived competence, P=.26; pressure/tension, P=.42) and the fear of falling (P=.25). ConclusionsSeated iVR full-body gesture-based exergames can be valuable complements to standing exergames. Seated exergames have the potential to lead to higher exertion, provide higher value to players, and be more applicable in small spaces compared with standing exergames. However, gestures for seated exergames need to be designed carefully to minimize motion sickness, and more time should be given to users to perform gestures in seated exergames compared with standing exergames.