Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility of wearable activity trackers in cystectomy patients to monitor for postoperative complications

Abstract Background: To determine the feasibility of using wearables in patients undergoing radical cystectomy to monitor postoperative heart rate and activity and attempt to correlate these factors to complications and readmissions. Materials and methods: We conducted a prospective study of 20 patients undergoing radical cystectomy for bladder cancer between June 2017 and March 2018. Each patient was provided with a Garmin Vívofit heart rate (HR) activity tracker and instructed to wear it on their wrist for 30 days postoperatively. Heart rate, steps, and sleep data were collected during this time. Patients were called at 10-day intervals and surveyed for complications and device compliance. Univariable mixed effects logistic regression models were used to compare daily activity tracker measures with occurrence of an adverse event. Odds ratios, 95% confidence intervals, and p-values were reported. Results: Median age was 65 (interquartile range 61–74) years. Patients had usable data for a median of 59.3% (interquartile range 25–71.7%) of the time. Five patients experienced a postoperative event (1 readmission for sepsis from urinary tract source, 1 inpatient rapid response called for tachycardic event, 3 unscheduled visits related to dehydration), where event data was recorded over a total of 17 days. Higher step count was associated with reduced odds of an adverse event (odds ratio 0.31, 95% confidence interval 0.10–0.98 per 1000 steps, p = 0.047). Conclusions: Postoperative activity and heart rate monitoring in cystectomy patients is feasible though current wearables are not well suited for this task.
Design of a 128-channel transceiver hardware for medical ultrasound imaging systems

In this work, the design and development of a 128-channel transceiver hardware for medical ultrasound imaging systems and research is presented. The proposed hardware solution integrates the analog front-end (AFE) sections, high voltage transmit pulser sections, field programmable gate array (FPGA)-based transmit beamforming and control logic, time gain compensation (TGC) and continuous (CW) Doppler functional circuits, and the necessary power supplies (high voltage (HV) and low voltage (LV)) into a single board. In addition, it integrates pervasive segments like power, clock tree sections, and power management and debugger logic. The developed transceiver solution helps to advance the research in medical ultrasound imaging techniques and technologies. To prototype an ultrasound imaging system, the developed hardware can be interfaced with a 128-channel ultrasound transducer array and an FPGA-based signal processing module. As the transceiver hardware is designed with commercially available chipsets, it provides the flexibility to programme the ultrasound AFE signal chain, transmit beamforming and the arbitrary transmit wave pattern. Besides, compared to the commercial open ultrasound research scanners, the flexibility to interface FPGA-based signal processing module helps to investigate the performance of hardware realisation of various ultrasound signal processing algorithms. Moreover, the work realises a single-board transceiver solution for multichannel ultrasound system fulfilment.
Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.
SWAT Model Adaptability to a Small Mountainous Forested Watershed in Central Romania

This study aims to build and test the adaptability and reliability of the Soil and Water Assessment Tool hydrological model in a small mountain forested watershed. This ungauged watershed covers 184 km2 and supplies 90% of blue water for the Brașov metropolitan area, the second largest metropolitan area of Romania. After building a custom database at the forest management compartment level, the SWAT model was run. Further, using the SWAT-CUP software under the SUFI2 algorithm, we identified the most sensitive parameters required in the calibration and validation stage. Moreover, the sensitivity analysis revealed that the surface runoff is mainly influenced by soil, groundwater and vegetation condition parameters. The calibration was carried out for 2001–2010, while the 1996–1999 period was used for model validation. Both procedures have indicated satisfactory performance and a lower uncertainty of model results in replicating river discharge compared with observed discharge. This research demonstrates that the SWAT model can be applied in small ungauged watersheds after an appropriate parameterisation of its databases. Furthermore, this tool is appropriate to support decision-makers in conceiving sustainable watershed management. It also guides prioritising the most suitable measures to increase the river basin resilience and ensure the water demand under climate change.
Morphological study of the particle-to-fiber transition threshold during electrohydrodynamic processing of chitosan solution

Abstract In this work, solutions of low molecular weight chitosan with the concentration of 4 and 5 wt. % were subjected to electrohydrodynamic forming at the feed rate of 0.1 and 0.2 ml/h. Using a statistical analysis of the electron scanning microscopy data, it was shown that at the concentration of 4 wt. %, spherical particles of two types are formed: most of the particles are with the average diameter about of 260 nm, and particles with a larger diameter of about 480 nm. A transition through the particle-to-fiber threshold was observed with an increase in the concentration of the solution to 5 wt. %. This led to the formation of both spherical particles with a monomodal distribution and average diameter of 207 nm and elongated particles of a fiber-like shape with diameter of 157 nm and length of 1401 nm. At the same time, an increase in the solution feed rate resulted in a strong decrease in the average diameter of spherical particles to 150 nm and an elongation of fiber-like objects by 1.5 times. The particle-to-fiber threshold can be observed when the dynamic viscosity of the solution changes in the range of about 10–14 cP. Graphical Abstract
Demonstration of a Consistent Relationship Between Dual-Frequency Reflectivity and the Mass-Weighted Mean Diameter in Measurements of Frozen Precipitation from GCPEX, OLYMPEX, and MC3E

The retrieval of the mass-weighted mean diameter (Dm) is a fundamental component of spaceborne precipitation retrievals. The Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) satellite is the first satellite to use Dual Wavelength Ratio measurements - the quotient of radar reflectivity factors (Z) measured at Ku and Ka wavelengths – to retrieve Dm. While it is established that DWR, being theoretically insensitive to changes in ice crystal mass and concentration, can provide a superior retrieval of Dm compared to Z based retrievals, the benefits of this retrieval have yet to be directly observed or quantified. In this study, DWR-Dm and Z-Dm relationships are empirically generated from collocated airborne radar and in-situ cloud particle probe measurements. Data is collected during nine intensive observation periods (IOPs) from three experiments representing different locations and times of year. Across IOPs with varying ice crystal concentrations, cloud temperatures, and storm types, Z-Dm relationships vary considerably while the DWR-Dm relationship remains consistent. This study confirms that a DWR-Dm relationship can provide a more accurate and consistent Dm retrieval than a Z-Dm relationship, quantified by a reduced overall RMSE (0.19 mm and 0.25 mm, respectively) and a reduced range of biases between experiments (0.11 mm and 0.32 mm, respectively).
The Theory and Practice of Mediation: A Reply to Professor Susskind

This article details the theory and practice of mediation. The mediation process can be characterized as follows: it is (1) a noncompulsory procedure in which (2) an impartial, neutral party is invited or accepted by (3) parties to a dispute to help them (4) identify issues of mutual concern and (5) design solutions to these issues, (6) which are acceptable to the parties. The mediator’s role is to convey each party’s proposals in a language that is both faithful to the desired objectives of the party and formulated to ensure the highest degree of receptivity by the listener. The article then looks at the profile of a mediator and explores mediator strategies for building a settlement. It also considers environmental dispute mediation.
Transferring Cognitive Talent Across Domains: The Case of Finance

We consider Theory of Mind, the ability to correctly predict the intentions of others. The skill requires abstraction from one’s own particular circumstances. Here, we posit that such abstraction can be transferred successfully to other, non-social contexts. We consider the disposition effect, which is a pervasive cognitive bias whereby investors, including professionals, improperly take their personal trading history into account when deciding on investments. We design an intervention policy whereby we attempt to transfer Theory of Mind skills, subconsciously, to personal investment decisions. In a within-subject repeated-intervention laboratory experiment, we record how the disposition effect is reduced by a very significant 85%, but only for those with high Theory of Mind skills. No such transfer is observed in subjects who score well only on the emotional dimension of interpersonal skills. Our findings open up a promising way to exploit cognitive talents in one domain in order to alleviate cognitive deficiencies elsewhere.
Learning ethnographically during the year abroad: modern languages students in Europe and Latin America

ABSTRACT This paper reports findings from a longitudinal qualitative study that explored the ethnographic learning processes of 10 modern languages students who spent one full academic year abroad, having first completed successfully an Introduction to Ethnography course in the UK. It begins from the argument that although significant attempts have been made to integrate ethnography into modern languages undergraduate degree programmes, relatively little is known about its actual impact on modern languages sojourners. Drawing on active interviews and reflective diaries that were designed to investigate this impact from shortly before participants embarked on their year abroad to the moment they returned, the thematic and critical discourse analysis of the data focuses attention on two key themes: students’ perceptions of their host cultures and the impact of ethnography on students’ perceptions. Findings from the first theme reveal that participants’ perceptions were derogatory and that they used a number of mitigating discourse strategies to avoid creating a negative impression on the researchers. Findings from the second theme suggest that ethnography made little impact on most participants, given that their derogatory perceptions of the “foreign other” remained almost intact throughout the year abroad. The paper discusses possible reasons for these findings, arguing that the contextual nature of ethnographic inquiry does not always enable modern languages sojourners to dismantle the cultural generalisations they may make.
Landsat-5 Time Series Analysis for Land Use/Land Cover Change Detection Using NDVI and Semi-Supervised Classification Techniques

Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative index (NDVI) and semi-supervised image classification (SSIC) integrated with high-resolution Google Earth images of the Kuantan River Basin (KRB) in Malaysia. The Landsat-5 (TM) images for the years 1993, 1999, and 2010 were selected. The results from both classifications provided a consistent accuracy of assessment with a reasonable level of agreement. However, SSIC was found to be more precise than NDVI. Overall accuracy was 82% for 1993 and 1999, and 80% for 2010, with the kappa values ranging from 0.789 to 0.761. Meanwhile, NDVI accuracy was attained at 64% with kappa value at 0.527 for 1999. In addition, 70% and 72% accuracy were obtained for 1993 and 2010 with estimated kappa values of 0.651 and 0.672, respectively. The study is anticipated to assist decision makers for better emergency response and sustainable land development action plans, thus mitigating the challenges of rapid urban growth
Lexical data augmentation for sentiment analysis

Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic‐based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution‐based data augmentation methods, one of which is thesaurus‐based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST‐2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM‐AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well‐designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms.
What about Data-Driven Business Models? Mapping the Literature and Scoping Future Avenues

The paper aims to perform an assessment of the literature at the intersection of data and business models, examining the extent to which the data-driven business model (DDBM) is considered in the current literature and how it is characterised in terms of approaches, benefits and barriers. A systematic literature review (SRL) of the available body of knowledge on these topics was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The SRL reveals limited but rapidly growing coverage of the cutting-edge phenomenon on the part of scientific studies. In problematising the extant literature, competitive, cultural and strategic approaches are proposed together with the relative enablers fostering the adoption of each approach. Benefits and barriers to the implementation of a DDBM are also discussed across technical, organisational and financial dimensions. The insights derived from a critical review of the DDBM literature point out gaps, which may itself inform future research and theory development in this area, as well as support practitioners’ decision-making on the datatisation of business models.
Mitogenome phylogeny reveals Indochina Peninsula origin and spatiotemporal diversification of freshwater crabs (Potamidae: Potamiscinae) in China

The Asian potamid subfamily Potamiscinae is the most species‐rich group of primary freshwater crabs in the world, and a large number of these (318 species in 52 genera) are found in China where they exhibit a remarkably high rate of endemism. However, the evolutionary history and phylogenetic relationships of this subfamily is still poorly understood and the hypothesis that the Chinese potamids originated in the Indochina Peninsula has yet to be tested using a modern phylogeographic reconstruction and molecular dating methods. Here we analyze the phylogenetic relationships and biogeographic history of the Chinese potamiscines based on whole mitogenomes of 72 species of Asian freshwater crabs representing 65% of the potamiscine genera found in China, including new mitogenome sequences for 57 species. The mitogenomic phylogeny generated four separate clades: Southwest China (SWC), Indochina‐Southwest China (ISWC), Central China (CC), and South China‐adjacent Islands (SCI). Our results suggest that the Chinese potamiscine fauna most likely originated in Indochina during the Eocene. The establishment of the East Asian monsoon during the Eocene/Oligocene resulted in major climate changes that coincide with the local diversification of the CC and SCI clades. In addition, diversification of two other clades (ISWC and SWC) may have been driven by past large scale environmental changes taking place at that time such as increased local precipitation and expanding freshwater habitats associated with the uplifting of the Hengduan Mountains. In addition, our phylogenies did not support monophyly for six currently recognized potamiscine genera, highlighting the need for further taxonomic revisions of this fauna. The broad taxonomic sampling and well‐supported molecular phylogenies used here allow for the updating of our understanding of the evolutionary origins and diversification of the potamiscine freshwater crabs in China.
Biodegradable and Highly Deformable Temperature Sensors for the Internet of Things

Recent advances in biomaterials, thin film processing, and nanofabrication offer the opportunity to design electronics with novel and unique capabilities, including high mechanical stability and biodegradation, which are relevant in medical implants, environmental sensors, and wearable and disposable devices. Combining reliable electrical performance with high mechanical deformation and chemical degradation remains still challenging. This work reports temperature sensors whose material composition enables full biodegradation while the layout and ultrathin format ensure a response time of 10 ms and stable operation demonstrated by a resistance variation of less than 0.7% when the devices are crumpled, folded, and stretched up to 10%. Magnesium microstructures are encapsulated by a compostable‐certified flexible polymer which exhibits small swelling rate and a Young's modulus of about 500 MPa which approximates that of muscles and cartilage. The extension of the design from a single sensor to an array and its integration onto a fluidic device, made of the same polymer, provides routes for a smart biodegradable system for flow mapping. Proper packaging of the sensors tunes the dissolution dynamics to a few days in water while the connection to a Bluetooth module demonstrates wireless operation with 200 mK resolution prospecting application in food tracking and in medical postsurgery monitoring.
Factors Predicting Consumers’ Continuance Intention to Use Mobile Wallets: Evidence from Kerala, India

Mobile wallets are novel and is commanding attention globally as an alternative means of payment. As far as any technology is concerned, continued usage of the system is far more important than its initial adoption, on which prior literature has concentrated upon. The purpose of this study is to examine the factors that predict a mobile wallet user’s usage continuance intention. The study, based on a valence framework, analyses the effect of the various dimensions of valence, namely the effect of positive valence (hedonic value, utilitarian value, monetary value and social value) and negative valence (perceived risk) on the usage continuance intention of mobile wallet users in Kerala. Data was collected from 134 participants through a self-administered online questionnaire, and variance based PLS-SEM was used for analysis. The results of the study indicate that both positive and negative valence significantly predicted the usage continuance intention.