Temporal Variations in Activity Network Using Smart Card Data

This study explores temporal variations in activity networks for four million passengers, differentiated as workers and non-workers, using public transport based on a large-scale smart card dataset generated over 105 days in Beijing. We aim to capture their day-to-day transition and cumulative temporal expansion in activity network using transit over days, weeks, and months. Particularly, workers and non-workers are automatically identified based on their different daily routines, whose activity networks are characterized by six features concerning space coverage, distance coverage, and frequency coverage in two ways, namely, on a per-day transition and with an accumulation of days. The transition features of the networks are statistically analyzed and compared by time, while how the expansion features evolve with time are modeled. Results show that, on weekdays, workers are more likely to travel longer (have larger distance coverage), but cover less area (have smaller space coverage) than non- workers. While opposite patterns occur on weekends. Traveling in the ‘North-South’ direction is weakly correlated with traveling in the ‘East-West’ direction. Workers on weekdays, as well as non-workers on weekends, make longer ‘North-South’ trips. Manhattan distance, trip count, and perimeter present a ∩ shape in their probability density functions, while the remaining features decline dramatically, with probability density functions fit by the exponential distribution. The distance coverage expands faster than that of space coverage. Most passengers increase coverage of space and distance when time expands (obviously no one decreases coverage over time, but some don’t change). The research enables findings on temporal load-balancing, long-term cumulative expansion in travel demands of workers and non-workers, re-balancing the distribution of existing workplace and residential location opportunities, and constructing transit-oriented developments with mixed functions over time.Chinese Scholarship Council TransportLa

＜書評＞大谷栄一（編著）『ともに生きる仏教――お寺の社会活動最前線 』

書評筑摩書房, 2019年4月, 新書判, 254

ボウモリ ガ カンガエル ジイン ノ シャカイテキ ヤクワリ ホクリク チイキ ニ オケル グループ インタビュー チョウサ カラ ミル

日本における仏教寺院の衰退要因を探求する際に、寺院が変化する時代に応じて社会的役割を果たしていないことが判明された。よって、現代社会における寺院の社会的役割を理解するにあたって、寺院運営者の視点が欠かせないと考えた。また、事実上寺院管理者である寺庭婦人はあまり研究されていないことから、本研究は彼女らの考えと思いを明らかにしようとした。その際に、北陸地域における3つの真宗寺院の坊守を研究対象とし、彼女らが認識している自坊の運営状況、坊守像、地域における寺院像について、グループインタビューの手法を用い、調査を行った。その結果、坊守たちにとって、寺院の持つ社会的役割は関係者のネットワークと地域社会の接点 であり、かかわってくる人の「心の拠り所」であることがわかった。While exploring the causes of the decline of Japanese Buddhist temples, the need for the social role of temples to change with the changing times has been noticed. On top of that, the viewpoints of Bōmori, those who have not yet been fully researched. This study aims to clarify their perceptions of the management of their temples, the ideal of Bōmori, and the ideal of temples in their communities through group interviews with Bōmori of three Shinshu temples in the Hokuriku region. The result indicates that for Bōmori, Buddhist temples serve as place to connect with others in local communities and provide a source of comfort for people who are involved.論

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer

Analysis of Key Aroma Components of Three Representative Oolong Tea Varieties by Stir Bar Sorptive Extraction Combined with Gas Chromatography-Olfactory-Mass Spectrometry

Stir bar sorptive extraction (SBSE) combined with gas chromatography-olfactory-mass spectrometry (GC-O-MS) was used to identify and describe the key aroma components of three representative oolong tea varieties, Huangdan, Tieguanyin and Jinguanyin. Comparative analysis was conducted in terms of odor activity value (OAV), aroma character impact (ACI) value and sensory evaluation. The sensory evaluation showed that each variety showed obvious aroma characteristics. Huangdan oolong tea had an obvious floral aroma as well as a slight milky aroma. Tieguanyin oolong tea had a strong floral aroma. Jinguanyin oolong tea had a sweet fruity aroma as well as a slight woody aroma. According to the results of OAV and GC-O-MS analysis, geraniol, phytol, methyl jasmonate, trans-nerol tertiary alcohol, 2-nonone, and phenyl ethanol were identified as key aroma components in Huangdan oolong tea, which provided it with clean and high floral aroma and obvious milky aroma characteristics. In Tieguanyin oolong tea, linalool, 3,5-octylodiene-2-one, linalool oxide, cis-jasmonone, dehydrolinalool, and α-terpineol showed diverse floral aromas, which were closely related to the characteristic aroma of Tieguanyin oolong tea. The key aroma components identified in Jinguanyin oolong tea included linalool, canalaldehyde, geranyl acetone, cis-jasmonone and isoeugenol, which were responsible for the characteristic sweet floral and woody aromas of Jinguanyin oolong tea

Preparation and photodynamic therapy application of NaYF4:Yb, Tm-NaYF4:Yb, Er multifunctional upconverting nanoparticles

E-mail Addresses: [email protected]; [email protected] preparation, characterization and application of NaYF4:Yb3+, Tm3+-NaYF4:Yb3+, Er3+ core-shell upconversion nanocrystals (UCNPs) with multiple emission peaks (e.g. 539, 654 and 802 nm) have been demonstrated in this work. The monodisperse nanocrystals were prepared via a modified thermal decomposition synthesis. The resulting UCNPs were similar to 31 nm in diameter with the lanthanide ions Tm3+ and Er3+ doped in the core and the shell, respectively. Under the laser diode excitation at 980 nm, these core-shell nanocrystals give strong upconversion emissions from the visible to near-infrared (NIR) region. By coating a PEG-phospholipid (PP) layer on the surface of the nanocrystals, the as-prepared UCNPs were favorably endowed with good water solubility for the potential biological applications. Here, a photosensitizer drug of Chlorin e6 (Ce6), which has maximum absorption that overlaps with the red emission of UCNPs, was loaded on these PP-coated UCNPs (UCNP@PP) by physical adsorption. The activity of the Ce6-loaded UCNP@PP (UCNP@PP-Ce6) in photodynamic therapy of cancer cells in vitro has been fully investigated in this work. Our results indicated that these multifunctional UCNP@PP-Ce6 nanoparticles have efficient NIR-to-NIR upconversion luminescence and photodynamic therapy capabilities, which could be potentially employed as a theranostic platform for cancer treatment.National Natural Science Foundation of China 21101131 21021061 20925103 Natural Science Foundation of Fujian Province 2012J01056 Fundamental Research Funds for the Central Universities 2010121015 Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry NFFTBS J121001

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

UNBOUND

Unbound showcases the graduating class from the fashion design school at Fanshawe College. Unbound describes the creative spirit and achievements of our twenty-seven emerging Canadian fashion designers. Unbound 2014 is a professional collaboration between Fanshawe College, Community and Professionals in the Fashion Industry. As you turn the pages, admire their accomplishments - the results of three years of passion, hard work, and dedication.https://first.fanshawec.ca/famd_design_fashiondesign_unbound/1003/thumbnail.jp