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    Huizhou Central People’s Hospital Reports Findings in Artificial Intelligence (A rtificial intelligence software for assessing brain ischemic penumbra/core infar ction on computed tomography perfusion: A real-world accuracy study)

    57-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Huizh ou, People’s Republic of China, by NewsRx correspondents, research stated, “With the increasingly extensive application of artificial intelligence (AI) in medic al systems, the accuracy of AI in medical diagnosis in the real world deserves a ttention and objective evaluation. To investigate the accuracy of AI diagnostic software (Shukun) in assessing ischemic penumbra/core infarction in acute ischem ic stroke patients due to large vessel occlusion.” Our news editors obtained a quote from the research from Huizhou Central People’ s Hospital, “From November 2021 to March 2022, consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy (MT) post-Shu kun AI penumbra assessment were included. Computed tomography angiography (CTA) and perfusion exams were analyzed by AI, reviewed by senior neurointerventional experts. In the case of divergences among the three experts, discussions were he ld to reach a final conclusion. When the results of AI were inconsistent with th e neurointerventional experts’ diagnosis, the diagnosis by AI was considered ina ccurate. A total of 22 patients were included in the study. The vascular recanal ization rate was 90.9%, and 63.6% of patients had mod ified Rankin scale scores of 0-2 at the 3-month follow-up. The computed tomograp hy (CT) perfusion diagnosis by Shukun (AI) was confirmed to be invalid in 3 pati ents (inaccuracy rate: 13.6%). AI (Shukun) has limits in assessing ischemic penumbra.”
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    Study Findings on Robotics Detailed by a Researcher at China University of Petro leum (Design and analysis of a pipe robot based on metamorphic mechanism)

    58-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on robotics is now availab le. According to news reporting from Beijing, People’s Republic of China, by New sRx journalists, research stated, “Addressing the challenges of limited passabil ity and adaptability encountered by existing pipe robots in navigating through t ee and four-way junction pipes, This article designs a pipe robot based on the p rinciple of metamorphism.” Funders for this research include National Key Research And Development Program of China; Science Foundation of China University of Petroleum, Beijing; National Natural Science Foundation of China.
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    University of Science and Technology of China Reports Findings in Machine Learni ng (Predicting the performance of lithium adsorption and recovery from unconvent ional water sources with machine learning)

    59-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Hefei, People’s Republ ic of China, by NewsRx correspondents, research stated, “Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothe rmal brines, and rejected seawater desalination brines) using inorganic lithium- ion sieve (LIS) materials can address Li supply shortages and distribution issue s. However, the development of high-performance LIS materials and the optimizati on of recovery-related operating parameters are hampered by the variety of produ ction methods, intricate procedures, and experimental expenses.” Our news journalists obtained a quote from the research from the University of S cience and Technology of China, “Machine learning (ML) techniques offer potentia l solutions for enhancing LIS material development. We collected literature data on Li adsorption, categorizing 16 parameters into adsorbent parameters, operati ng parameters, and solution components. Three tree-based algorithms-Random Fores t (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting ( XGBoost)-were used to evaluate the impact of these parameters on lithium adsorpt ion. The grouped random splitting method limited data leakage and mitigated over fitting. XGBoost demonstrated the best performance, with an R² of 0.98 and a roo t-mean-squared error (RMSE) of 1.72. The SHAP values highlighted that operating parameters were the most influential, followed by adsorbent parameters and coexi sting ion concentrations. Therefore, focusing on optimizing operating parameters or making targeted improvements on LIS based on operating conditions will enhan ce LIS performances in UWS.”
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    Researchers from Chengdu University of Traditional Chinese Medicine Report on Fi ndings in Machine Learning (Machine Learning-assisted Colorimetric Sensor Array for Rapid Identification of Adulterated panax Notoginseng Powder)

    60-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting out of Chengdu, Peopl e’s Republic of China, by NewsRx editors, research stated, “Panax notoginseng (P N) is a popular functional food worldwide, yet adulterated PN powders are preval ent in the commercial market. Herein, a method combining a low-cost colorimetric sensor array and machine learning is proposed to rapidly identify and quantify adulterated PN powder.” Funders for this research include National Natural Science Foundation of China ( NSFC), Xinglin Scholar Research Promo-tion Project of Chengdu University of Trad itional Chinese Medicine, Sichuan Province Science and Technology Activities Fun ding for Returned Overseas Scholars.
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    Heinrich-Heine-University Dusseldorf Reports Findings in Anxiety Disorders (Pred iction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets)

    61-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Mental Health Diseases and Conditions - Anxiety Disorders is the subject of a report. According to new s reporting from Dusseldorf, Germany, by NewsRx journalists, research stated, “D epressive symptoms are rising in the general population, but their associated fa ctors are unclear. Although the link between sleep disturbances and depressive s ymptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.” The news correspondents obtained a quote from the research from Heinrich-Heine-U niversity Dusseldorf, “Using three population-based datasets (N = 1813), we trai ned the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and fu nctional) measurements on DSS, then we tested our models’ performance in two ind ependent datasets (N = 378, N = 334) to test the generalizability of our finding s. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66 ). In addition, we performed a mediation analysis to identify the role of anxiet y and brain measurements on the sleep quality and DSS association. Sleep quality could predict individual DSS (r = 0.43, R = 0.18, rMSE = 2.73), and adding anxi ety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar re sults. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model’s performance but also mediated the link between sleep quality and DSS. The study is supported by Helmholtz Imaging Platform gran t (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundat ion)-Project-ID 431549029-SFB 1451, the programme ‘Profilbildung 2020’ (grant no .”
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    Shandong University Researchers Have Provided New Data on Symmetric Cryptology ( Perfect Monomial Prediction for Modular Addition)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on sy mmetric cryptology. According to news reporting out of Shandong, People’s Republ ic of China, by NewsRx editors, research stated, “Modular addition is often the most complex component of typical Addition- Rotation-XOR (ARX) ciphers, and the division property is the most effective tool for detecting integral distinguishe rs. Thus, having a precise division property model for modular addition is cruci al in the search for integral distinguishers in ARX ciphers.” The news reporters obtained a quote from the research from Shandong University: “Current division property models for modular addition either (a) express the op eration as a Boolean circuit and apply standard propagation rules for basic oper ations (COPY, XOR, AND), or (b) treat it as a sequence of smaller functions with carry bits, modeling each function individually. Both approaches were originall y proposed for the twosubset bit-based division property (2BDP), which is theore tically imprecise and may overlook some balanced bits. Recently, more precise ve rsions of the division property, such as parity sets, threesubset bit-based divi sion property without unknown subsets (3BDPwoU) or monomial prediction (MP), and algebraic transition matrices have been proposed. However, little attention has been given to modular addition within these precise models. The propagation rul e for the precise division property of a vectorial Boolean function f requires t hat u can propagate to v if and only if the monomial pu(x) appears in pv(f). Bra eken and Semaev (FSE 2005) studied the algebraic structure of modular addition a nd showed that for x y = z, the monomial pu(x)pv(y) appears in pw(z) if and only if u + v = w. Their theorem directly leads to a precise division property model for modular addition. Surprisingly, this model has not been applied in division property searches, to the best of our knowledge. In this paper, we apply Braeke n and Semaev’s theorem to search for integral distinguishers in ARX ciphers, lea ding to several new results. First, we improve the state-of-the-art integral dis tinguishers for all variants of the Speck family, significantly enhancing search efficiency for Speck-32/48/64/96 and detecting new integral distinguishers for Speck-48/64/96/128. Second, we determine the exact degrees of output bits for 7- round Speck-32 and all/16/2 output bits for 2/3/4-round Alzette for the first ti me. Third, we revisit the choice of rotation parameters in Speck instances, prov iding a criterion that enhances resistance against integral distinguishers.”
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    University of Mannheim Reports Findings in Artificial Intelligence (The global g eography of artificial intelligence in life science research)

    63-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Mannheim, Germa ny, by NewsRx correspondents, research stated, “Artificial intelligence (AI) pro mises to transform medicine, but the geographic concentration of AI expertize ma y hinder its equitable application. We analyze 397,967 AI life science research publications from 2000 to 2022 and 14.5 million associated citations, creating a global atlas that distinguishes productivity (i.e., publications), quality-adju sted productivity (i.e., publications stratified by field-normalized rankings of publishing outlets), and relevance (i.e., citations).” Our news journalists obtained a quote from the research from the University of M annheim, “While Asia leads in total publications, Northern America and Europe co ntribute most of the AI research appearing in high-ranking outlets, generating u p to 50% more citations than other regions. At the global level, i nternational collaborations produce more impactful research, but have stagnated relative to national research efforts.”
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    New Intracranial Vasospasm Findings from University of California Los Angeles (U CLA) Discussed (Machine Learning Predicts Cerebral Vasospasm In Patients With Su barachnoid Haemorrhage)

    64-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ce ntral Nervous System Diseases and Conditions - Intracranial Vasospasm. According to news reporting originating in Los Angeles, California, by NewsRx journalists , research stated, “Cerebral vasospasm (CV) is a feared complication which occur s after 20-40 % of subarachnoid haemorrhage (SAH). It is standard p ractice to admit patients with SAH to intensive care for an extended period of r esource-intensive monitoring.” Financial supporters for this research include NIH National Center for Advancing Translational Sciences (NCATS), NIH National Heart Lung & Blood I nstitute (NHLBI), National Institutes of Health (NIH) - USA.
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    Transilvania University Researcher Yields New Data on Machine Learning (Assessin g Various Scenarios of Multitemporal Sentinel-2 Imagery, Topographic Data, Textu re Features, and Machine Learning Algorithms for Tree Species Identification)

    65-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news originating from Transilvania University by N ewsRx correspondents, research stated, “Accurate information about forests, incl uding the identification of tree species, can be achieved by utilizing combinati ons of various datasets, analyzed over different temporal scales, and employing advanced classification algorithms. Free Sentinel-2 (S-2) satellite imagery, alo ng with other auxiliary data, can serve as valuable sources of geospatial data.” Funders for this research include Anelis Plus Consortium Based on An Agreement B etween Anelis Plus Consortium And Institute of Electrical And Electronics Engine ers.
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    Southern Medical University Reports Findings in Gliomas (Prediction of Glioma en hancement pattern using a MRI radiomics-based model)

    66-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Gliomas is the subject of a report. According to news reporting originating in Guangzhou, P eople’s Republic of China, by NewsRx journalists, research stated, “Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate predic tion of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients.” The news reporters obtained a quote from the research from Southern Medical Univ ersity, “This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologicallyproven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid att enuated inversion recovery images, which were divided into enhancing and non-enh ancing groups. Predictive radiomics models based on machine learning with 6 diff erent classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation coho rt (n = 99). Receiver-operator characteristic curve was used to assess the predi ctive performance of these radiomics models. This study demonstrated that the ra diomics model comprising of 15 features using the Gaussian process as a classifi er had the highest predictive performance in both the training cohort and the in ternal validation cohort, with the area under the curve being 0.88 and 0.80, res pectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.8 2, 0.76 and 0.96, respectively, in the external validation cohort.”
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