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Purpose: Nowadays, many studies discuss scholarly publishing and associated challenges, but the problem of hijacked journals has been neglected. Hijacked journals are cloned websites that mimic original journals but are managed by cybercriminals. The present study uses a topic modeling approach to analyze published papers in hijacked versions of medical journals. Methods: A total of 3384 papers were downloaded from 21 hijacked journals in the medical domain and analyzed by topic modeling algorithm. Results: Results indicate that hijacked versions of medical journals are published in most fields of the medical domain and typically respect the primary domain of the original journal. Conclusion: The academic world is faced with the third-generation of hijacked journals, and their detection may be more complex than common ones. The usage of artificial intelligence (AI) can be a powerful tool to deal with the phenomenon.
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At the moment, the academic world is faced with various challenges that negatively impact science integrity. One is hijacked journals, a second, inauthentic website for indexed legitimate journals, managed by cybercriminals. These journals publish any manuscript by charging authors and pose a risk to scientific integrity. This piece compares a journal's original and hijacked versions regarding authority in search engines. A list of 16 medical journals, along with their hijacked versions, has been collected. The MOZ Domain Authority has been used to check the authority of both original and hijacked journals, and the results have been discussed. It indicates that hijacked journals are gaining more credibility than original ones. This should alarm academia and highlights a need for serious action against hijacked journals. The related policies should be planned, and tools should be developed to support easy detection of hijacked journals. On the publishers' side, the visibility of journals' websites must be enhanced to address this issue.
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Periodicals as Topic , Humans , Publishing , Internet , Search EngineABSTRACT
Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals.
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OBJECTIVES: Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. METHODS: The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. RESULTS: The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. CONCLUSIONS: This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal.
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Artificial Intelligence , Peer Review , HumansABSTRACT
In the present work, the role and the effect of platinum and gold on the catalytic performance of ceria supported tri-metallic Pt-Pd-Au catalysts have been studied. The optimum composition of these tri-metallic supported catalysts has been discovered using methods and tools of combinatorial catalyst library design. Detailed catalytic, spectroscopic and physico-chemical characterization of catalysts in the vicinity of the optimum in the given compositional space has been performed. The temperature-programmed oxidation of methane revealed that the addition of Pt and Au to Pd/CeO2 catalyst resulted in higher conversion values in the whole investigated temperature range compared to the monometallic Pd catalyst. The time-on-stream experiments provided further evidence for the high-stability of tri-metallic catalysts compared to the monometallic one. Kinetic studies revealed the stronger adsorption of methane on Pt-Pd/CeO2 catalysts than over Pd/CeO2. XPS analysis showed that Pt and Au stabilize Pd in a more reduced form even under condition of methane oxidation. FTIR spectroscopy of adsorbed CO and hydrogen TPD measurements provided indirect evidences for alloying of Pt and Au with Pd. CO chemisorption data indicated that tri-metallic catalysts have increased accessible metallic surface area. It is suggested that advantageous catalytic properties of tri-metallic Pt-Au-Pd/CeO2 catalysts compared to the monometallic one can be attributed to (i) suppression of the formation of ionic forms of Pd(II), (ii) reaching an optimum ratio between Pd0 and PdO species, and (iii) stabilization of Pd in high dispersion. The results also indicate that Pd0 - PdO ensemble sites are required for methane activation.
Subject(s)
Cerium/chemistry , Combinatorial Chemistry Techniques , Gold/chemistry , Methane/chemistry , Palladium/chemistry , Platinum/chemistry , Adsorption , Carbon Monoxide/chemistry , Catalysis , Kinetics , Oxidation-Reduction , Sensitivity and Specificity , Spectrophotometry , Spectroscopy, Fourier Transform Infrared , Surface Properties , Temperature , Time Factors , X-RaysABSTRACT
Different MCM-41 samples containing framework iron were prepared and tested in CO oxidation showing unprecedented high activities after reduction in hydrogen above 773 K.
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The effect of pharmacological doses of zinc oxide (1000; 2500; 5000 mg per kg diet) and two levels of dietary protein on pancreatic and intestinal hydrolase activity in rats were studied. It was hypothesized that ZnO would increase intestinal and pancreatic hydrolase enzyme activity. Male Wistar rats, averaging 64 g body weight, were randomly allocated to dietary treatments (chow diets- meeting all NRC requirements) containing 10% or 15% protein supplemented with additional ZnO (above 100 mg/kg ZnSO(4)) as follows: 0.0; 0.1; 0.25; 0.5% w/w. Water and food were provided ad libitum. Animals were fed the diets for 10 days and body weights were recorded; after decapitation blood and organ samples were collected. Amylase, lipase, trypsin, and total protease activity of pancreatic homogenates and small intestinal contents were determined. ZnO supplementation dose dependently increased the plasma Zn concentration and significantly increased amylase, lipase, trypsin and total protease activity in pancreatic homogenates and small intestinal contents. The statistical analysis showed significant protein and ZnO interaction on the activity of amylase in the pancreas, and amylase, trypsin and total-protease in the small intestinal content. Therefore ZnO at high dietary concentration may influence the digestion of nutrients via increased hydrolase activity.