Search Results (10)

This study explores the novel integration of data sonification into hydraulic engineering by translating transient pressure fluctuations in a hydraulic system into sound. Using a simple hydraulic model built in KYPipe, a pump connected to a reservoir and a tank was simulated to trip, causing transient pressure changes. These pressure variations were mapped onto the C-major scale using Microsoft Excel, creating an auditory representation. The methodology included generating a sound library using recorded piano samples and applying VBA code to link pressure values with musical notes. The results demonstrated that sonification provides an innovative means of presenting transient hydraulic phenomena, enabling users to identify critical events such as pressure spikes audibly. While the study highlights challenges, such as computational limitations and resolution trade-offs in mapping, it opens pathways for employing auditory data representation in engineering contexts. Future work could focus on expanding audio sample libraries and optimizing computational methods to improve resolution and usability. Full article

Background: Personalization of medical care is a significant topic of interest. Precision medicine denotes customized medical treatments based on individual genetic, molecular, and/or biomarker data. We conducted a scoping review to identify studies exploring precision medicine in breast reconstruction. Objectives: (1) To map the existing literature, (2) to identify key concepts, and (3) to discuss current and future clinical implications of precision medicine in breast reconstruction. Eligibility criteria: Indexed journal articles (primary research studies) relating to precision medicine in breast reconstruction written in the English language. Sources of evidence: Medline (via Pubmed), Web of Science, and the Cochrane Library. Charting methods: Data charting of selected studies was performed independently by two reviewers using Microsoft Excel. Any discrepancies in data charting were addressed through inter-reviewer discussion and/or expert review. Results: Of 321 initial records, 9 studies that were published between 2011 and 2025 were included in the final review. Eight studies focused predominantly on genomics, and one study focused predominantly on targeted therapies. Genomic-based studies were frequently implemented to evaluate patient risk and inform clinical decision-making, while targeted therapies were used to optimize reconstructive outcomes through cell-based therapies. Conclusions: There is a limited but emerging body of literature on precision medicine in breast reconstruction. Genomic data are the driving force of precision medicine in breast reconstruction, and multiple potential avenues exist to achieve translational applications in the short-term period. Future efforts should focus on translating known genomic data into real-time clinical applications and investing in precision-based research for targeted therapies and regenerative medicine in breast reconstruction. Full article

Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system. Full article

(1) Background: This study aimed to integrate an augmented reality (AR) image-guided surgery (IGS) system, based on preoperative cone beam computed tomography (CBCT) scans, into clinical practice. (2) Methods: In preclinical and clinical surgical setups, an AR-guided visualization system based on Microsoft’s HoloLens 2 was assessed for complex lower third molar (LTM) extractions. In this study, the system’s potential intraoperative feasibility and usability is described first. Preparation and operating times for each procedure were measured, as well as the system’s usability, using the System Usability Scale (SUS). (3) Results: A total of six LTMs (n = 6) were analyzed, two extracted from human cadaver head specimens (n = 2) and four from clinical patients (n = 4). The average preparation time was 166 ± 44 s, while the operation time averaged 21 ± 5.9 min. The overall mean SUS score was 79.1 ± 9.3. When analyzed separately, the usability score categorized the AR-guidance system as “good” in clinical patients and “best imaginable” in human cadaver head procedures. (4) Conclusions: This translational study analyzed the first successful and functionally stable application of the HoloLens technology for complex LTM extraction in clinical patients. Further research is needed to refine the technology’s integration into clinical practice to improve patient outcomes. Full article

The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI’s ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes. Full article

Contributing to achieving sustainability in South Africa’s energy sector, this study probes financial sustainability and its relationship to the environmental sustainability of Eskom. This is because, over the past three financial years (FY2018–2019 to FY2020–2021) of Eskom’s generating plants’ performance, the energy availability factor (EAF) has taken a deep dive, reaching an extremely low generation availability year-end performance of 64.2%, translating to approximately an average of 29,800 MW available generation capacity out of a nominal generation capacity of 46,466 MW in FY2020–2021. Therefore, the study employed a quantitative research methodology, where the relevant financial records were analysed, and the necessary energy calculations made using descriptive analysis in Microsoft Excel. The findings show that the volumes (GWh) produced by the OCGTs during this period far exceed the regulatory approved volumes, thus attracting substantial costs, amounting to ZAR 25.9 bn instead of ZAR 8.9 bn, that could have been spent on the OCGTs if the level of efficiency achieved in FY2016–2017 and FY2017–2018 was maintained. The analysis also revealed that the OCGTs’ long-term financial and environmental sustainability could be achieved through switching from diesel to natural gas, thus resulting in lower fuel costs and lower emissions. Further, potential savings of approximately ZAR 27 bn (excluding capital expenditure) at a 10% load factor can be realised over a ten-year period when the natural gas price is sitting at ZAR 85/GJ (minimum). Finally, in order to attain financial and environmental sustainability, it is recommended that both Eskom’s and the independent power producers’ (IPPs) OCGTs must switch fuel from diesel to natural gas and be run at a 10% load factor, allowing the OCGTs to be run as mid-merit generators. Full article

The paper aims to capitalize on the new features that are offered by the Microsoft Excel calculation program for reliability modeling, using the Median Ranks estimator that is calculated directly with the BETA.INV function, not estimated by various algebraic estimators, as is generally the case. Starting from this first step, a method of modeling reliability is elaborated through the three-parametric Weibull model that is based exclusively on this software, which is accessible to anyone and can be used even in the case of online learning, which is widespread in recent years due to the pandemic situation. The probability plotting method is applied, using the Median Ranks estimator that is calculated directly with the BETA.INV function for a probability equal to 0.5. A flowchart is made for the proposed method, which could be easily translated into a calculation program. By representing in logarithmic coordinates, we determined the Weibull models for different values that were initially adopted for the location parameter: using as a criterion the coefficient of determination that was obtained using the trendline function for the linear model, it was possible to identify, by successive tests, the optimal value of the location parameter—for which the three-parametric model has a good likelihood. By the proposed method, this value can be found following this iterative process. So, based on the current facilities of the Microsoft Excel program, a precise and easy-to-apply method has been achieved, through which an appropriate three-parametric Weibull model can be identified. Full article

Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F₁score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI. Full article

In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accomplish this dynamic task, a formula related to the velocity of the conveyor and the moving speed of the robot is implemented. The average error of 6D pose information between our system and the practical measurement is lower than 7%. The performance of the proposed method and algorithm has been appraised on real experiments of a specified stamping workpiece. Full article

Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results. Full article