Developing entrepreneurial competences in biotechnology early career researchers to support long-term entrepreneurial career outcomes
توسعه صلاحیت های کارآفرینی در محققان حرفه ای اولیه بیوتکنولوژی برای حمایت از نتایج شغلی کارآفرینانه طولانی مدت-2020
This paper explores how early career biotechnology researchers develop entrepreneurial competences through participation in a bespoke entrepreneurship education competition and whether this affects their longer-term entrepreneurial actions. Specifically, we discuss the pedagogy and evaluate the short- and long-term impact of a long-running entrepreneurship competition, where biotechnology doctoral and postdoctoral researchers address societal and environmental challenges through hypothetical new venture creation. We present evidence regarding the efficacy of this experiential education, where online mentoring is blended with a team-based residential competition utilising inspirational speakers, practitioner support and peer learning in encouraging ECRs to consider commercialising their research. We conclude that long-term entrepreneurial career outcomes can be fostered through tailored short-term interventions.
Keywords: Entrepreneurship | SET | STEM | Entrepreneurship education | Evaluation | Commercialisation | Biotechnology | careers
Democratization of AI, Albeit Constrained IoT Devices & Tiny ML, for Creating a Sustainable Food Future
دموکراتیک سازی هوش مصنوعی ، دستگاه های محدود IoT و Tiny ML ، برای ایجاد آینده غذایی پایدار-2020
Abstract—Big Data surrounds us. Every minute, our smartphone collects huge amount of data from geolocations to next clickable item on the ecommerce site. Data has become one of the most important commodities for the individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only this is a huge missed opportunity for the big data companies, it is one of the significant obstacle in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the paper is to develop a framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution and start contribution to the digital economy and empowers the world through the data to create a sustainable food for our collective future.
Keywords: edge | IoT device | artificial intelligence | Kalman filter | dairy cloud | small scale farmers | hardware constrained model | tiny ML| Hanumayamma | cow necklace
Financing paths, firms’ governance and corporate entrepreneurship : Accessing and applying operant and operand resources in biotechnology firms
مسیرهای تأمین مالی ، حاکمیت شرکت ها و کارآفرینی شرکتی : دسترسی و استفاده از منابع عملیاتی و عملگرایی در شرکتهای بیوتکنولوژی-2020
This study investigates the systemic relationship between financing paths used by early-stage biotechnology firms, the accessed resources, the subsequent reconfiguration of management and governance structures, and their effect on the level of corporate entrepreneurship. Adopting a qualitative methodology based on an inductive approach, in 2018 and 2019 we observed 12 UK biotechnology ventures that accessed private, corporate or crowdfunding equity investments. We collected primary data through open-ended and semi-structured interviews with CEOs and board members of these firms. Findings were interpreted applying a resource-based perspective, which unveiled the role and importance of operant and operand resources for organizational coordination and functioning. The way in which the controlled operant resources are used to improve the management and governance structures, and their functional interdependence, ultimately determines an optimal level of corporate entrepreneurship for effectively exploiting the accessed operand resources. The results provide useful insights regarding the systemic interdependence between financing paths, organizational resources, management team, governance bodies, and corporate entrepreneurship, that can enhance the understanding and performance of managers, shareholders and policy-makers involved in biotechnology business.
Keywords: Equity financing paths | Operand and operant resources | Governance and management structures | Corporate entrepreneurship | Systemic perspective
Historical AIS Data-Driven Unsupervised Automatic Extraction of Directional Maritime Traffic Networks
استخراج خودکار بدون نظارت داده های AIS از شبکه های ترافیکی دریایی جهت دار-2020
Vessel experience route analysis can provide empirical support for maritime traffic management. Recently, the application of the Automatic Identification System (AIS) provides multi-dimensional data about voyages and vessels. However, traditional route extraction methods do not take into account information such as the vessel traffic pattern and the density distribution in the channel. The experience routes obtained is not accurate enough. This paper proposes an unsupervised method for extracting vessel experience routes from historical AIS data. The method consists of three parts: vessel traffic pattern extraction, channel boundary extraction, channel triangle network construction and hottest route extraction. The method comprehensively considers the spatiotemporal information and density distribution of the vessel trajectories, and constructs a directional maritime traffic network which can effectively convert historical data into information supporting decision-making.
Keywords : AIS | Traffic Pattern | Triangular Network | Directional Maritime Traffic Network
Fumbling to the future? Socio-technical regime change in the recorded music industry
دست و پا می زنید به آینده؟ تغییر رژیم اجتماعی - فنی در صنعت موسیقی ضبط شده-2020
In this paper, I draw on the institutional entrepreneurship and sociotechnical imaginaries literature to develop a prospective and actor-centric approach to understanding technological transitions. Empirically, I examine the initiatives that newcomers and incumbents engaged in between 1990 and 2005 to transition the socio-technical regime associated with recorded music. My account reveals the limited ability of these actors to effectively migrate the regime despite initiating several efforts to do so – a pattern of behavior I term the fragility of in- stitutional entrepreneurship. I identify underlying factors for why this is the case and suggest that these can contribute to a regime remaining in a state of flux for an extended period of time. I also demonstrate the emergence of provisional regimes or temporary settlements between actors that either gain traction or are themselves transformed over time. In specifying the micro-processes that unfold as part of such transitions, I provide a complementary perspective to the current theorizing around socio-technical regime migration, and contribute fresh insights to the institutional entrepreneurship and sociotechnical imaginaries literature.
Keywords: Socio-technical regime transition | Technological change | Institutional entrepreneurship | Sociotechnical imaginaries | Digital music
Projection of spatiotemporal variability of wave power in the Persian Gulf by the end of 21st century: GCM and CORDEX ensemble
پیش بینی تغییر پذیری مکانی و قدرت موج در خلیج فارس تا پایان قرن بیست و یکم: GCM و CORDEX-2020
This study investigates future variability of wave power in the Persian Gulf. The contribution of this paper is twofold: (a) to evaluate spatiotemporal resolutions, downscaling techniques and global circulation model (GCM) selection impacts running multi-climate models, and (b) to project wave energy resources and its variability by the end of 21st century using RCP4.5 and RCP8.5 as two different representative concentration pathways (RCPs). The SWAN (Simulating Waves Nearshore) model forcing with near surface wind components was employed for wave simulation. The numerical wave model was calibrated and validated using wave measurements by two buoys prior to wave energy computations. The results of wave models obtained from different climate models showed a wide range of variety for different climatic resources associated with GCM selection, temporal and spatial resolutions and downscaling approach. Outputs of the wave model forcing with 3 hourly wind data of CMCC-CM and CORDEX-MPI (Max Plank Institute) with daily temporal resolution were recognized as the models with the best performance. Using a weighted average of these two models, the wave characteristics were obtained and wave energy were computed for the historical and future periods. Temporal distribution of energy shows highly intra-annual and seasonal variability when the mean wave power for the strongest month exceeds 1000Watt per meter that is 10 times higher than the mean wave power in the weakest month. Similarly, a strong spatial variability in wave power distributionwas revealed where the middle part of the Gulf has found to have the highest energy and the eastern and northwestern regions have the lowest energy. The projections illustrated a decreasing trend on future wave energy up to 40% in the Iranian coastlines and lower rate of changes in the southern stripe of the study area.
Keywords: Renewable energy | Climate change | CORDEX | Representative concentration pathways | Energy management
به سمت لبه هوشمند: ارتباطات بی سیم به یادگیری ماشین میرسد
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 31
احیای هوش مصنوعی در اواخر (AI) تقریباً در هر شاخهای از علم و فناوری، انقلابی ایجاد کرده است. با توجه به گجتهای تلفن همراه هوشمند و همه جا حاضر و دستگاههای اینترنت اشیا (IoT)، انتظار میرود که اکثر برنامههای هوشمند را بتوان در لبهی شبکههای بی سیم استقرار داد. این روند باعث شده است، تمایل قوی برای تحقق «لبه هوشمند» ایجاد شود تا از برنامههای کاربردی مجهز به AI در دستگاههای لبه مختلف استفاده شود. بر این اساس، یک حوزهی پژوهشی جدید به نام یادگیری لبه به ظهور رسیده است که از دو رشته عبور میکند و انقلابی در آنها ایجاد میکند: ارتباطات بی سیم و یادگیری ماشین. یک موضوع اصلی در یادگیری لبه غلبه بر قدرت محاسباتی محدود و همچنین دادههای محدود در هر دستگاه لبه است. این امر با استفاده از پلت فرم محاسبات لبه تلفن همراه (MEC) و استخراج دادههای عظیم توزیع شده در تعداد زیادی دستگاه لبه محقق شده است. در چنین سیستمهایی، یادگیری از داده توزیع شده و برقراری ارتباط بین سرور لبه و دستگاهها دو جنبهی حیاتی و مهم است و همجوشی آنها، چالشهای پژوهشی جدید و زیادی را به همراه دارد. این مقاله از یک مجموعه جدید از اصول طراحی برای ارتباطات بی سیم در یادگیری لبه پشتیبانی میکند که در مجموع ارتباطات یادگیری محور نامیده میشوند. مثالهای گویایی ارائه شدند تا اثربخشی این اصول طراحی مشخص شوند و برای این منظور فرصتهای تحقیقاتی منحصر به فردی شناسایی شدند.
کلمات کلیدی: سرورها | مدل سازی جوی | هوش مصنوعی | پایگاه های داده توزیع شده | ارتباطات بی سیم | یادگیری ماشین | مدل سازی محاسباتی
|مقاله ترجمه شده|
Community health education re-envisioned: The value of partnership with the local food bank
آموزش بهداشت جامعه مجددا پیش بینی شده است : ارزش مشارکت با بانک مواد غذایی محلی-2020
Baccalaureate-prepared nurses must be prepared to fulfill expanding role and skill expectations in community settings to improve population health. The use of non-traditional community clinical sites provides opportunities for students to learn and use a broad skill set such as leadership, communication, research, teaching, project management, and critical thinking. Students explore utilization of epidemiological systems and nursing theories to assist groups and communities to meet their nursing and community health needs. An example of a successful non-traditional partnership was established between our school and the regional food bank. This symbiotic relationship provides two-way benefits: students are afforded some flexibility and creativity in completion of required course assignments, and the food bank gains additional workers to help accomplish their strategic goals. It is a community health clinical resource that is accessible to schools of nursing in every community.
Keywords: Community health | Baccalaureate nursing education | Community partnership | Food bank | Clinical placement
A different insight in hair analysis: Simultaneous measurement of antipsychotic drugs and metabolites in the protein and melanin fraction of hair from criminal justice patients
بینش متفاوت در تجزیه و تحلیل مو: اندازه گیری همزمان داروهای ضد روان و متابولیت های موجود در پروتئین و ملانین در مو از بیماران عدالت کیفری-2020
Background: Previous studies have postulated that four structural compartments may be differentiated in hair: surface protein domain, water-accessible protein domain, water-inaccessible protein domain, and melanin. Drugs contained in blood, sweat, sebum, and environment would be deposited in the first two domains, with primarily drugs in blood being incorporated in the latter two domains during hair synthesis. Drugs in the first two domains would be removed by washing procedures. Use of enzymatic extraction procedures and evaluation of hair for damage from harsh cosmetic treatments might help to separately identify and quantify the drugs incorporated in the second two domains. Aims: a) Development of an UPLC-MS/MS method for the simultaneous quantification of the following 19 antipsychotic drugs and metabolites in hair: amisulpride, aripiprazole, chlorpromazine, clotiapine, clozapine, desmethylclozapine, desmethylolanzapine, haloperidol, norchlorpromazine, 7-OH-quetia- pine, 9-OH-risperidone, olanzapine, pimozine, pimpamperone, quetiapine, risperidone, sertindole, sulpride, and tiapride; b) evaluation of measurement of patient adherence to prescribed medication use, c) determination of the influence of biochemical individuality effects on hair drug content, d) evaluation of relative binding of antipsychotic drugs to protein and to melanin hair structures. Method: Approximately 10 mg of intact hair were decontaminated with isopropanol and phosphate buffer, and then enzymatically digested overnight with dithiothreitol. After centrifugation, the supernatant digest (protein fraction) was separated from the remaining melanin hair pellet (melanin fraction). Melanin fraction was washed with water, and the drugs were extracted with dimethyl sulfoxide with ball-mill pulverization. Both fractions were purified with solid-phase cation exchange cartridges and injected in the UHPLC-MS/MS system. Results and discussion: Validation of the method was carried out on the protein fraction following international guidelines. The limits of quantification ranged from 1.6–40 pg/mg. The method was applied to 59 head hair samples from prisoners from an antipsychotic compliance study in the criminal justice system in US. The patients were under chlorpromazine, haloperidol, risperidone, olanzapine, or quetiapine multiple antipsychotic treatment, during incarceration. The first head hair centimeter, closest to the scalp, was analyzed. The results were evaluated in relation to the type of hair, colour, hair damage, drug melanin affinity, and prescribed dose. In general, no good correlation between the prescribed dose/ concentration in hair was obtained. A wide range of antipsychotic concentrations were observed ‘dose mg/day (d); pg/mg protein fraction (A)’: chlorpromazine (d:50-400;A:
Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement
احراز هویت سریع و مجوز پیشرو در اینترنت اشیا با مقیاس بزرگ: نحوه استفاده از هوش مصنوعی برای تقویت امنیت-2020
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.