Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a simple unstructured kinetic and cost of goods models to support T‐cell therapy manufacture

Manufacturing of cell therapy products requires sufficient understanding of the cell culture variables and associated mechanisms for adequate control and risk analysis. The aim of this study was to apply an unstructured ordinary differential equation‐based model for prediction of T‐cell bioprocess outcomes as a function of process input parameters. A series of models were developed to represent the growth of T‐cells as a function of time, culture volumes, cell densities, and glucose concentration using data from the Ambr®15 stirred bioreactor system. The models were sufficiently representative of the process to predict the glucose and volume provision required to maintain cell growth rate and quantitatively defined the relationship between glucose concentration, cell growth rate, and glucose utilization rate. The models demonstrated that although glucose is a limiting factor in batch supplied medium, a delivery rate of glucose at significantly less than the maximal specific consumption rate (0.05 mg 1 × 106 cell h−1) will adequately sustain cell growth due to a lower glucose Monod constant determining glucose consumption rate relative to the glucose Monod constant determining cell growth rate. The resultant volume and exchange requirements were used as inputs to an operational BioSolve cost model to suggest a cost‐effective T‐cell manufacturing process with minimum cost of goods per million cells produced and optimal volumetric productivity in a manufacturing settings. These findings highlight the potential of a simple unstructured model of T‐cell growth in a stirred tank system to provide a framework for control and optimization of bioprocesses for manufacture.
Book Review: Future of Tourism Marketing edited by Seongseop (Sam) Kim and Dan Wang (Routledge; 1st Edition (15th June 2021); Pages: 128)

The rapid emergence and vast adoption of information and communication technologies (ICT) have transformed tourism marketing strategies from a conservative, static marketing model to an emerging, transformative conception. The transformative power of ICT has created a paradigm shift in the tourism industry by providing an opportunity for tourism service providers and tourists to co-create value. Moreover, recent technological developments have paved the way for new markets, product/ service innovations, management practices, and competitive strategies, thus forming a complex socio-technical tourism ecosystem. Meanwhile, postmodern, digitally savvy tourists expect tourism service providers to be more active in digital platforms. Consequently, a burgeoning research stream has started looking into these new developments, capabilities, and changes driven by ICT advancement in the tourism and hospitality industry in recent times. Moreover, to better understand and anticipate these fundamental changes, novel theoretical lenses, approaches, concepts, themes, and even research methods have emerged lately. Future of Tourism Marketing, edited by two renowned contemporary tourism scholars, Seongseop (Sam) Kim and Dan Wang, adds to this growing research stream by emphasizing how ICT developments have revolutionized conventional tourism marketing strategies. The reviewer used the academic approach as applied by Gamage (2021) and Peiris et al. (2020) in reviewing this book.
Wide-range routing method for lunar exploration rovers using multi-objective optimization

This paper presents a wide-range routing method for multiple rovers deployed for a lunar polar exploration mission. In the mission scenario considered in this paper, multiple exploration points are divided into several sets of points to be assigned to multiple rovers. Subsequently, routes between the exploration points are generated for each rover. This assignment and routing problems are well known as the multiple Traveling Salesman Problem (mTSP). The scenario aims to minimize two objective functions: the sum and standard deviation of operation time among the multiple rovers. This problem is then subject to the multi-objective mTSP (MOmTSP). Therefore, the proposed method is composed of a three-step procedure for solving the MOmTSP. First, the k-means algorithm divides an exploration area into several regions for the number of rovers. Subsequently, a routing algorithm with the space-filling curve and 2-opt algorithm determines the order of exploration points in each region. A local path planner then generates a feasible path between two exploration points. The simulation results using the proposed method with 80 cases are statistically analyzed. We confirm that the proposed method solves the multiple optimization problems by finding the routes that can equalize the rover's operation time and minimize their deviations. GRAPHICAL ABSTRACT
Spatial structure and dispersal dynamics in a house sparrow metapopulation.

1. The effects of spatial structure on metapopulation dynamics depend upon the interaction between local population dynamics and dispersal, and how this relationship is affected by the geographical isolation and spatial heterogeneity in habitat characteristics. 2. Our aim is to examine how emigration and immigration of house sparrows, Passer domesticus, in a Norwegian archipelagic metapopulation are affected by key factors predicted by classic metapopulation models to affect dispersal: spatial and temporal variation in population size, inter-island distance, local demography and habitat characteristics. 3. This metapopulation can be divided into two major habitat types: (1) islands closer to the mainland where sparrows breed in colonies on farms, and (2) islands without farms, situated farther away from the mainland where sparrows are exposed to harsher environmental conditions. 4. Dispersal was spatially structured within the metapopulation; there was proportionally and numerically less emigration and immigration involving farm islands, as compared to non-farm islands. Furthermore, emigration and immigration occurred mostly between nearby islands. Moreover, emigration in response to spatial differences in mean population size differed between the habitat types, but populations with large mean received more immigrants in both habitat types. The number of emigrants and immigrants was negatively related to long-term recruit production, which was not the case in non-farm islands. The proportion and number of emigrants was positively related to temporal increases in recruit production on farm islands, however, not on non-farm islands. 5. Our results demonstrate that spatial heterogeneity in environmental conditions influences how spatial variation in long-term mean population size, and temporal and spatial variation in recruit production, affects dispersal dynamics. The spatial structure of this metapopulation is therefore best described by a spatially explicit model in which the exchange of individuals within each habitat type is strongly affected by the degree of geographical isolation, population size and recruit production. However, these relationships differed between the two habitat types, with the non-farm islands showing similarities to a mainland-island model type of structure, whereas dispersal on the farm islands showed features more associated with source-sink or balanced dispersal models. Such differential dispersal dynamics between habitat types is expected to have important consequences for the ecological and evolutionary dynamics within this metapopulation.
A UN SDGs-Based Sustainability Evaluation Framework for Globally Important Agricultural Heritage Systems (GIAHS): A Case Study on the Kuaijishan Ancient Chinese Torreya Community

The Food and Agriculture Organization of the United Nations (FAO) launched the conservation initiative of the Globally Important Agricultural Heritage Systems (GIAHS) in 2002, providing a new solution to current global problems such as widening gap between rich and poor, environmental pollution, biodiversity destruction and degradation of agro-ecological functions. However, since the implementation of GIAHS, few studies have evaluated the role of GIAHS in regional sustainable development. To fill this research gap, this paper attempts to build an evaluation index system that integrates economic, social and environmental sustainability by matching the 169 subgoals and 232 indices of the UN Sustainable Development Goals (SDGs) with five FAO-GIAHS selection criteria and selecting the indices. The index weights are determined through Delphi method (expert scoring) and Analytic Hierarchy Process (AHP). Then the GIAHS site of the Kuaijishan Ancient Chinese Torreya Community in Shaoxing City, Zhejiang Province is taken as an example for evaluation to enhance the empirical study. The results show that the comprehensive score for this heritage site is 77.90 points, of which economic, social and environmental sustainability scores 88.27, 86.12 and 69.71 points respectively, showing a descending trend. Thus, ecological protection of the Kuaijishan Ancient Chinese Torreya Community should be strengthened and be given prominence in the future. As for the contribution to further research, this SDGs-based GIAHS sustainability evaluation framework can serve as an important guide for assessing the approaches and effects of GIAHS in promoting the achievement of the SDGs. It can also be used for future international GIAHS sustainability assessment and comparison and provide a basis for adjusting the development model of GIAHS sites.
Effect of Pilates Mat Exercise on Myoelectric activity of cervical Muscles in Patient with Chronic Mechanical Neck Pain: Randomized Clinical Trial

Objective: The goal of this study is to detect the efficacy of Pilates mat exercise and conventional therapy on cervical flexor and extensor myoelectric amplitude in subjects with chronic mechanical neck pain. Methods: This trial included 91 participants from both genders (42 males and 49 females) who were diagnosed by an orthopedist with chronic mechanical neck pain and referred to the physiotherapy clinic. The participants were randomly assigned to two groups: Group A, which is an experimental group, included those receiving Pilates mat exercise in conjunction with conventional therapy, and Group B, which is a control group, included those receiving only conventional therapy. The intervention was delivered to both groups three times a week for 12 weeks. In this study, the variables pain, function, and muscle activity were measured using the visual analog scale, Arabic neck disability index, and Electromyography respectively. All variables were assessed before and after the 12 weeks. Results: To detect the impact of treatment and time on all measured variables, MANOVA was used and showed a statistically significant effect (p < 0.05) in both groups in treatment and time with favor to Pilate's group. Conclusion: Pilates mat exercise combined with conventional therapy and conventional therapy alone are effective techniques for refining neck pain, function, and muscle amplitude in subjects with chronic mechanical neck pain, with further advantages shown in the experimental group that received the Pilates mat exercise treatment.
Revisión teórica de modelos de gestión logístico para el sector artesanal Macanero Gualaceo- Ecuador

DOI: https://doi.org/10.33262/concienciadigital.v4i3.2.1842 Introduction: The globalizing processes accompanied by technological and communicational advances to which organizations are forced to incorporate to sustain themselves in the market, has made the conditions of the business environment become complex, mainly affecting activities such as artisanal whose characteristics are high vulnerability but that require incorporating logistical processes to sustain the activity. Objectives: Faced with this reality, the need arises to determine a logistics management model of the supply chain for the Macanero artisan textile activity of the GualaceoEcuador Canton. Methodology: This research is framed within a bibliographic design of a documentary type at a historical level to understand the evolution of concepts, practices and models for the implementation of logistics management in organizations. Results: There have been several contributions made on this subject, Velásquez (2003) developed various tools and strategies to promote a change from some SMEs in a natural state to innovative small and medium-sized companies, so that they fit into the current business world but adjusted to their context and their capacities without losing the horizon of being part of organizations of the social and solidarity economy. Conclusions: There are problems in the logistics performance of SMEs in Latin America mainly structurally, 1 Universidad Internacional IberoamericanaMéxico(UNINI), Aspirante a doctor en Derecho Económico y de la Empresa, Campeche-México, mrosalesn@ucacue.edu. ec, https://orcid.org/0000-0002-3240-1146 ISSN: 2600-5859 Vol. 4, N°3.2, p. 19-47, septiembre, 2021 Diseño Educativo Página 20 www.concienciadigital. org alternatives are proposed that allow these production structures to overcome the limitations through an efficient, efficient and effective logistics administration, incorporating the proper management of the supply chain, technological innovation and permanent training.
Apollo: A Classical Piano Composer Using Long Short-Term Memory

With Deep Learning becoming more accessible than ever, music generation has become a domain that has piqued the interest of computer scientists and musicians’ with a scientific bent. Recent advances in Deep Learning have made it possible to compose music almost indistinguishable from those fashioned by humans. However, any musical model that proposes to produce a sequence of harmonious music necessitates that the model should have the potential to recollect past details and have a sound understanding of the musical structure. Hence, in this paper, we propose a Deep Learning architecture to generate piano symphonies by selecting a list of notes and using the power of Long Short-Term Memory (LSTM) to predict the next note based on the discography of the given composers, such as Ludwig van Beethoven and Wolfgang Amadeus Mozart.
Impact of a multiple-test strategy on breeding index development for the Australian dairy industry

Context A high level of acceptance and use of breeding indices by farmers and breeding companies that target a National breeding objective is an effective strategy to achieve high rates of genetic gain. Indices require maintenance to ensure that they reflect current economic and genetic trends and farmer preferences. Often, indices are tested on an average herd on the basis of, for example, milk composition and calving pattern. However, this strategy does not differentiate the impact on breeds. Australian dairy farmers routinely make breeding decisions by using the balanced performance index (BPI) or the health weighted index, published by DataGene. Aims The aim of the present study was to test new selection indices on the most popular breeds to better understand the genetic progress that each breed is expected to make. Existing economic models were updated to reflect changing trends in input costs and milk income. Consultative processes identified opportunities to improve alignment between farmer preferences and Australia’s National Breeding Objective. In response, more than 20 selection index options were developed and options were discussed with industry. Methods Indices were evaluated on three breeds in the following three ways: (1) expected response to selection from the use of each index, (2) index and trait correlations, and (3) relative trait emphasis. Key results Farmer trait preferences varied by breed and this information was considered in the development of economic weights. The updated BPI has primary emphasis on production traits (44% in Holstein, 49% in Reds), secondary emphasis on health and fertility (35% in Holstein, 29% in Reds), tertiary emphasis on type, workability and feed saved. The equivalent index for Jerseys is similar, but following stakeholder feedback to multiple tests, it was decided to remove emphasis on the feed saved estimated breeding values, so that the percentage emphasis on trait groups in Jerseys is 51% production, 32% health and fertility and the remainder on type and workability. Implications Understanding trait preferences and testing indices on different breeds can change the decisions that are made during index development. Conclusions Developing a better understanding of the differences among breeds had a positive impact on farmer engagement and resulted in a modified BPI for the Jersey breed.
Impacts of Holding Area Policies on Shared Autonomous Vehicle Operations

Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand, which may lead to an unbalanced fleet displaced in cities thus unable to serve requested trips. Strategically siting holding areas (depots for dispatching and relocating fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel, and the insights from this paper can inform service providers on how to site holding areas for improved performance. We develop a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-h period. We then integrate four holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering results in superior SAV fleet performances (average passenger waiting times reduced by about 20% compared with the worst performing policy), with increased served demand and reduced deadheading. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand (producing worst results); this method may suffice for operating SAV services within a small geofence with high trip densities.
Bioactive Composition and Nutritional Profile of Microgreens Cultivated in Thailand

Microgreens are young and tender leafy vegetables that have gained wider consumer acceptance. This is attributed to their low caloric composition and rich micronutrient and antioxidant composition. The present study investigated the bioactive composition and proximate analysis of fourteen microgreens belonging to Brassicaceae, Fabaceae, Pedaliaceae, Polygonaceae, Convolvulaceae, and Malvaceae. All the microgreens showed low calories (20.22 to 53.43 kcal 100 g−1) and fat (0.15 to 0.66 g 100 g−1), whilst mung bean and lentil microgreens showed considerable amounts of carbohydrate (7.16 g 100 g−1) and protein (6.47 g 100 g−1), respectively. Lentil microgreens had the highest total chlorophyll (112.62 mg 100 g−1) and carotenoid (28.37 mg 100 g−1) contents, whilst buckwheat microgreens showed the highest total phenolic content (268.99 mg GAE 100 g−1) and DPPH• scavenging activity (90.83 mM TEAC g−1). The lentil microgreens also presented high ascorbic acid content (128.70 mg 100 g−1) along with broccoli, Chinese kale, purple radish, and red cabbage microgreens (79.11, 81.33, 82.58, and 89.49 mg 100 g−1, respectively). Anthocyanin content was only detected in purple radish (0.148 mg CGE 100 g−1) and red cabbage (0.246 mg CGE 100 g−1). The results provide basic information and highlight the benefits of utilizing genetic biodiversity to obtain microgreens with the desired nutrients and antioxidants.
Stock Prediction Based on Technical Indicators Using Deep Learning Model

: Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 th of Nov 2008 to the 15 th of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%,and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
Layered Metal‐Halide Perovskite Single‐Crystalline Microwire Arrays for Anisotropic Nonlinear Optics

Hybrid organic–inorganic metal‐halide perovskites with diverse structure tunability are promising for nonlinear‐optical (NLO) applications, such as frequency conversion and electro‐optic modulation. For integrated NLO devices, single‐crystalline perovskite micro‐ and nanostructures with high quality and multifunctionality are in high demand. However, the fabrication of single‐crystalline perovskites arrays is still challenging in regulating liquid dynamics and crystal growth simultaneously. Herein, a capillary‐bridge‐manipulated strategy is established to steer the dewetting process of microdroplets and provide spatial confinement for crystal growth. These 1D perovskite microwire arrays show regulated geometry, pure orientation, and single crystallinity. Chiral ammonium molecules are introduced into the metal‐halide octahedral quantum wells to break the centrosymmetry of the perovskite, allowing the perovskite to exhibit excellent second‐order NLO properties. The as‐prepared microwire arrays also demonstrate linearly polarized second harmonic generation and two‐photon fluorescence. Microwire arrays exhibit higher second harmonic conversion efficiency compared with their polycrystalline thin‐film counterparts. It is believed that this strategy for the fabrication of chiral perovskite microstructure arrays holds great promise for NLO integrated applications and opens up an avenue to explore multifunctional chiral perovskites.