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\"This book presents the latest research on the software crowdsourcing approach to develop large and complex software in a cloud-based platform. It develops the fundamental principles, management organization and processes, and a cloud-based infrastructure to support this new software development approach. The book examines a variety of issues in software crowdsourcing processes, including software quality, costs, diversity of solutions, and the competitive nature of crowdsourcing processes. Furthermore, the book outlines a research roadmap of this emerging field, including all the key technology and management issues for the foreseeable future. Crowdsourcing, as demonstrated by Wikipedia and Facebook for online web applications, has shown promising results for a variety of applications, including healthcare, business, gold mining exploration, education, and software development. Software crowdsourcing is emerging as a promising solution to designing, developing and maintaining software. Preliminary software crowdsourcing practices and platforms, including Apple's App Store and TopCoder, demonstrate the advantages of crowdsourcing in terms of software ecosystem expansion and product quality improvement.\"--Back cover.

In recent decades, Arctic surface warming has reached its maximum (minimum) during the cold season (summer) because of increased seasonal ocean heat storage (SHS) release (formation). Reanalysis data suggest that increased land ice melt dominates summer Arctic surface warming. The absence of land ice melt decreases Arctic surface warming in Phase 6 Coupled Model Intercomparison Project (CMIP6) simulations. SHS formation/release in the Arctic mainly occurs in the upper layer of the ocean. The additional SHS release is completed mainly via enhanced vertical diffusion and dominates Arctic surface warming during cold seasons. Although the trigger of SHS release is that surface air becomes colder than the sea surface, sea ice exhibits a high correlation coefficient with Arctic surface warming during the cold season since SHS release is enhanced via the strengthening of vertical diffusion and air‒sea heat exchange. In the CMIP6 historical simulations, vertical diffusion and the mixed layer depth (MLD) greatly differ, although the MLD and SHS are highly correlated in most CMIP6 members. A greater MLD during the cold season suggests greater additional SHS release and a warmer Arctic surface, which in turn results in a warmer springtime Arctic surface and greater sea ice melt in the next year. As a result, a deeper MLD is more sensitive to climate change and results in overestimated future climate estimates.

هذا العمل الجماعي المشترك، كما يشير عنوانه : (التاريخ المديني الحضري للصين) هو كتاب حضاري بامتياز. كتاب ‏يستعرض جوانب أساسية من حضارة الشعب الصيني القديمة-المتجددة الراسخة، الضاربة جذورها عميقا في تربة التاريخ ‏البشري الشامل.‏‎ يعد الكتاب سفر حضاري قيم وبحث متصل رصين حول التطور التاريخي العمراني في المدن الصينية وهو إذ ‏يتناول بشكل أساسي، فن العمارة والبناء والتصميم وتخطيط المدن والشوارع والأحياء والأسواق والمقار الحكومية ‏والحدائق والمعابد والساحات ومسالك الملاحة النهرية والري، فإنه لا يغفل في الوقت نفسه عن ما يتصل به هذا ‏الموضوع، من عشرات المواضيع الحضارية والثقافية والمعيشية الأخرى، مثل : العبادات والاعتقادات والتقاليد ‏والعادات والآداب والطقوس والحرف والفنون والنظم الاجتماعية والصراع السياسي والغزو الخارجي والتنظيم ‏الحكومي والقبلي والاجتماعي والطبقي والعسكري والإداري والإنمائي والعمراني والإسكاني والتعليمي والتجاري ‏والأدبي والفني والموسيقي والفولكلوري. إنه كتاب جميل يقدم العديد من اللوحات الرشيقة الرحبة المتقابلة المتلاحقة ‏المتكاملة التي ترحل بنا بين القصور والأنهار والأسوار والأحياء والمعاهد والمعابد وهياكل عبادة الأجداد ومواقع ‏التنقيب والساحات والحدائق والميادين ودواوين الشعر وسجلات التاريخ ولا يخلو كل ذلك من جولات من البحث ‏المقارن في غير أمر من هذه الأمور ولن نطيل عليكم في الإضاءة على مواضيع هذا الكتاب لأكثر من ذلك، آملين ‏لكم سلاسة القراءة ومتعة الاكتشاف.

Formalistic tasks are widely utilized in modern companies due to their ability to increase productivity and contribute to the achievement of corporate goals at a lower cost. However, these tasks are often meet with resistance from individuals because they do not provide direct short-term rewards for their efforts. Drawing on social cognitive theory, this study examined the influence of individual quality and organizational attachment on the completion of formalistic tasks. To address this, the study conducted a questionnaire survey to collect data from 602 Chinese respondents and built a structural equation model for data analysis. Through empirical research, the study confirmed the positive role of individual quality, including knowledge and personality, in the completion of formalistic tasks. Furthermore, the study proved that avoidant attachment could significantly weaken the effect of some components of individual quality on formalistic task completion. This paper is the first to reveal the influence of individual and environmental factors on individuals’ completion of formalistic tasks, progressing from bottom to top. The implications of these results are discussed.

To compare maternal and neonatal outcomes in women with a previous history of pregnancy loss and/or preterm delivery who underwent ultrasound-indicated cerclage (UIC) or history-indicated cerclage (HIC). PubMed, Web of Science, Scopus, and Embase databases were searched for observational studies and randomized controlled trials (RCT) from inception to 30 April 2024. Eligible studies should have compared the outcomes of women with singleton pregnancies who underwent UIC or HIC. STATA version 15.0 was employed, and the analysis was done using a random effects model and unadjusted effect sizes from the included studies. Of 25 included studies (n = 3909), most (n = 18) were retrospective cohort studies. Compared to women who underwent HIC, UIC was associated with higher risk of having a preterm birth (<37 weeks of gestation) (OR 1.48, 95% CI: 1.17, 1.88; N = 15), low birth weight (<2500g) (OR 1.78, 95% CI: 1.32, 2.41; N = 6) and admission to neonatal intensive care unit (OR 1.70, 95% CI: 1.27, 2.27; N = 6,). Women with UIC also had a higher risk of chorioamnionitis (OR 2.34, 95% CI: 1.36, 4.04; N = 4). The risk of having a low APGAR score (5-minute score of less than 7), fetal death and preterm premature rupture of membrane (PPROM) was comparable among the two groups. Our results demonstrate that UIC is associated with higher risks of adverse pregnancy outcomes compared to HIC. However, our evidence emanates from observational studies and is prone to biases, particularly because the findings were unadjusted for potential confounders. More clinical trials are needed to confirm our observations. PROSPERO CRD42024544181.

Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n  = 43), acute lympholastic leukemia (ALL; n  = 23) and non-Hodgkin lymphoma (NHL; n  = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission ( n  = 24) and those in partial remission ( n  = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations. The success rate of chimeric antigen receptor T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here authors show that therapeutic response in multiple myeloma, acute lymphoblastic leukemia and non-Hodgkin lymphoma, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

This study aimed to develop and validate an interpretable radiomics-based machine learning model using contrast-enhanced T1-weighted imaging (CE-T1WI) to differentiate glioblastoma (GB) from primary central nervous system lymphoma (PCNSL), while comparing the performance of models using high-order versus low-order features. A retrospective analysis was conducted on 383 patients with histopathologically confirmed diagnoses (226 GB cases with 226 samples; 157 PCNSL cases with 232 samples). Radiomic features were extracted from CE-T1WI sequences using PyRadiomics, including both low-order and high-order features. A sequential feature selection pipeline combining variance thresholding, minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) was used to identify the most informative and stable radiomic features for model building. Ten machine learning algorithms, including LightGBM, logistic regression, and random forests, were utilized to construct classifiers. Model performance was evaluated based on area under the curve (AUC), accuracy, specificity, sensitivity, negative predictive value (NPV) and positive predictive value (PPV). A comparison of the average performance metrics across all ten models was conducted between the high-order and low-order feature models. Interpretability was provided through SHapley additive exPlanations (SHAP). Statistical analyses were conducted with SPSS version 25.0 and Python 3.10.16. The sum of 1316 high-order features were extracted, and after feature reduction and selection, 17 optimal features were retained for machine learning models. Additionally, 107 low-order features were reduced to 20 discriminative features. The models, particularly those based on high-order features, demonstrated exceptional diagnostic performance, with AUC values exceeding 0.95 in 9 out of 10 models in the test sets. Among the ten classifiers evaluated, the LGBM model emerged as the most robust performer, achieving a test set AUC of 0.955 and demonstrating the smallest discrepancy (0.001) between the training and test AUC values. High-order features significantly outperformed low-order features, with improvements in AUC, sensitivity, and NPV (p < 0.05). The SHAP provided an in-depth interpretation of the LGBM model’s predictions, identifying key features such as original_firstorder_Kurtosis and exponential_GLDem_DependanceVariance as significant contributors, while offering both global and sample-specific perspectives. The study demonstrates the potential of using a CE-T1WI-derived radiomics approach combined with machine learning for distinguishing GB from PCNSL with high accuracy and interpretability. The model provides a practical, non-invasive diagnostic approach to support preoperative decision-making, particularly when biopsy or pathological sampling is challenging or uncertain. This approach has strong potential for clinical application in neuro-oncology.

Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students’ learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student’s learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

Dysregulation of heat shock protein DNAJA2 induces genomic instability and was consequently hypothesized to promote tumorigenesis. However, DNAJA2 knockout mice do not develop cancer but exhibit neonatal lethality and the underlying mechanism remains unknown. Here, we demonstrate that DNAJA2 maintains homeostatic glucose metabolism by regulating insulin signaling. Mechanistically, DNAJA2 binds to the insulin receptor (IR) and prevents adaptor protein 2 (AP2)-mediated spontaneous IR endocytosis by inhibiting the IR-AP2 interaction. Thus, DNAJA2 defects lead to reduced IR localization on the plasma membrane and suppression of the insulin-stimulated signaling cascade, thereby inhibiting glycogen synthesis and storage in the liver during embryogenesis, further resulting in neonatal lethality of DNAJA2-deficient mice. Analysis of public datasets reveals a strong association between DNAJA2 and metabolic phenotypes, including type 2 diabetes mellitus (T2DM) and obesity, in both humans and mice. In conclusion, our study elucidates the mechanism by which DNAJA2 regulates IR endocytosis, insulin signaling and glucose metabolism, shedding light on the pathogenesis of metabolic disorders. Insulin signaling pathway is critical for glucose homeostasis maintenance. Here, the authors identify a role for DNAJA2 in regulating insulin signaling and glucose metabolism, loss of which causes insulin resistance and type 2 diabetes phenotypes.

The scattering of guided waves propagating through pipe bends is studied by means of normal mode expansion. First, the bi-orthogonality relationship for normal modes in pipe bends is derived, based on which the displacement and stress fields at the interfaces between the straight and curved parts are expanded with the normal modes in both parts. Then, based on the displacement and stress field continuity principle, the scattering problem is regarded as an eigenproblem of a transfer matrix, the solution of which gives the mode conversions at the interfaces. A case study is presented of the low-frequency longitudinal mode incident on a pipe bend, and it is found that the dominant mode conversions are L(0,1) reflection and mode conversion from L(0,1) to F(1,1). Finite element simulations and experiments are also conducted. L(0,1) bend reflection and mode-converted F(1,1) are clearly observed, which agrees well with the theoretical predictions.