Search

Research misconduct refers to deliberate or accidental manipulation or misrepresentation of research data, findings, or processes. It can take many forms, such as fabricating data, plagiarism, or failing to disclose conflicts of interest. Data falsification is a serious problem in the field of medical research, as it can lead to the promotion of false or misleading information. Researchers might engage in p-hacking - the practice of using someone else's research results or ideas without giving them proper attribution. Conflict of interest (COI) occurs when an individual's personal, financial, or professional interests could potentially influence their judgment or actions in relation to their research. Nondisclosure of COI can be considered research misconduct and can damage the reputation of the authors and institutions. Hypothesis after results are known can lead to the promotion of false or misleading information. Cherry-picking data is the practice of focusing attention on certain data points or results that support a particular hypothesis, while ignoring or downplaying results that do not. Researchers should be transparent about their methods and report their findings honestly and accurately. Research institutions should have clear and stringent policies in place to address scientific misconduct. This knowledge must become widespread, so that researchers and readers understand what approaches to statistical analysis and reporting amount to scientific misconduct. It is imperative that readers and researchers alike are aware of the methods of statistical analysis and reporting that constitute scientific misconduct. Keywords: Conflict of interest, data fabrication, misconduct, p-hacking, plagiarism, Author(s): Karthik N. Rao (corresponding author) [1]; Manish Mair [2]; Ripu D. Arora [3]; Prajwal Dange [3]; Nitin M. Nagarkar [4] Introduction Research misconduct refers to the deliberate or accidental [...]

Dermatofibrosarcoma protuberans (DFSP) is a rare, locally aggressive cutaneous sarcoma with a propensity for recurrence. Its management, particularly in the head and neck (H&N) region, presents unique challenges. This study aimed to evaluate the effectiveness of Mohs micrographic surgery (MMS) compared to wide local excision (WLE) in treating H&N DFSP and its impact on recurrence rates and tissue preservation. A comprehensive search was conducted in PubMed/MEDLINE, yielding 29 relevant studies. We included studies comparing MMS and WLE in adult patients with H&N DFSP and reporting local recurrence outcomes. Data were analyzed using random effects analysis, with a meta-analysis performed for comparative studies. Analysis of studies demonstrated a lower recurrence for MMS. Comparative analysis of five studies involving 117 patients showed a significantly lower recurrence rate in the MMS group (2%) compared to the WLE group (19%). Margin status varied between studies, with some achieving negative margins at shorter distances. In the management of H&N DFSP, MMS has emerged as a superior surgical technique, consistently associated with reduced recurrence rates and the potential for tissue preservation. The adoption of MMS should be considered for its capacity to achieve negative margins with fewer processing steps, particularly in anatomically complex regions like the H&N. [ABSTRACT FROM AUTHOR]

Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to  { 1 , − 1 } . The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 × 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise  N C  is 3.1614  ×   10 − 20 . The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication. [ABSTRACT FROM AUTHOR]