Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an eDNA‐based survey method for urban fish markets

Fish and fishery products are among the most highly traded commodities in the world, and over‐exploitation continues to threaten the biodiversity and sustainability of global stocks. The state of knowledge for many fish species is limited by current monitoring techniques, which rely on labour‐intensive visual or genetic surveys of individual specimens (often at inconsistent or coarse taxonomic resolution). To address the need for more efficient methods that effectively monitor trade, we developed a novel application of eDNA‐based metabarcoding that can identify a broad range of fish taxa from effluent water draining from urban fish markets. Using samples collected at three Hong Kong fish markets over a 5‐day period, we tested two DNA capture protocols (filtration vs. precipitation) with the goal of ensuring that methods were relatively easy to implement without sacrificing species coverage relative to each other and to a standard visual survey. eDNA‐based survey methods were able to overcome many of the challenges associated with visual identification, such as resolving morphologically similar species and accounting for butchered fish products. However, rare and/or quickly transiting species occasionally went undetected. The two variations of eDNA isolation methods performed almost identically, emphasizing the relative robustness of eDNA metabarcoding, and allowing for user flexibility depending on the turbidity of collected waters. Our results demonstrate the ability of a few spatial and temporal replicates to generate an efficient and accurate market‐wide species inventory that can complement visual inspections and guide targeted investigations of illegal trade. This research provides important baseline information on the capabilities of eDNA metabarcoding as a non‐invasive survey tool, with the potential to complement conventional surveys. To our knowledge, this study is the first proof of concept using eDNA metabarcoding to monitor wildlife trade and also highlights the potential for eDNA metabarcoding to be applied to investigations of urban ecology.
Raptor Research in India: Inadequate Data and Species' Status Uncertainty for Many Species

Abstract. Despite the recent surge in ornithological research in South Asia, raptors remain poorly studied. Inadequate information on the regional status and ecology of birds of prey hinders development of effective conservation measures. Our meta-analysis that focused on trends of scientific research on diurnal raptors in India found 478 published articles on 78 raptor species. We used the online databases of Google Scholar, Scopus, and the Journal of the Bombay Natural History Society to develop regional research and conservation priority indices (RCPI) for raptors, using the tool originally developed by Buechley et al. (Diversity and Distributions 25:856–869, 2019) for the raptors of the world. Published literature on raptors in India was highly skewed; most were either anecdotal reports about common species, or studies focused on vultures, whose local extirpations received significant attention. Twenty-one raptor species, of which two are near threatened and three vulnerable, were specifically studied in fewer than five publications. Even many common species are similarly poorly studied. The regional raptor RCPI was highest for the Andaman Serpent-Eagle (Spilornis elgini; 0.71) and lowest for Black Kites (Milvus migrans; 0.11). Several species that received high priority scores are vulnerable to extirpation due to factors such as habitat loss, poisoning, electrocutions, and other threats. We conclude that assessment of the species-bias in published studies will help researchers identify and address the knowledge gaps. This will ultimately help conservationists and managers devise effective regional and country-specific management strategies. We recommend further regional contextualization of the RCPI.
Engaging Frontline Physicians in Value Improvement: A Qualitative Evaluation of Physician-Directed Reinvestment

Purpose Physicians can limit upward trending healthcare costs, yet legal and ethical barriers prevent the use of direct financial incentives to engage physicians in cost-reduction initiatives. Physician-directed reinvestment is an alternative value-sharing arrangement in which a health system reinvests a portion of savings attributed to physician-led cost reduction initiatives back into professional areas of the physicians’ choosing. Formal evaluations of such programs are lacking. Methods To understand the impact of Stanford Health Care’s physician-directed reinvestment in its first year (2017–2018) on physician engagement, adherence to program requirements around safety and fund use, and factors facilitating program dissemination, semi-structured qualitative interviews with physician participants, non-participants, and administrative stakeholders were conducted July-November 2019. Interview transcripts were qualitatively analyzed through an implementation science lens. To support contextual analysis of the qualitative data, a directional estimation of the program’s impact on cost from the perspective of the health system was calculated by subtracting annual maintenance cost (derived from interview self-reported time estimates and public salary data) from internal cost accounting of the total savings from first year cohort to obtain annual net benefit, which was then divided by the annual maintenance cost. Results Physician participation was low compared with the overall physician population (n=14 of approximately 2300 faculty physicians), though 32 qualitative interviews suggested deep engagement across physician participants and adherence to target program requirements. Reinvestment funds activated intrinsic motivators such as autonomy, purpose and inter-professional relations, and extrinsic motivators, such as the direction of resources and external recognition. Ongoing challenges included limited physician awareness of healthcare costs and the need for increased clarity around which projects rise above one's existing job responsibilities. Administrative data excluding physician time, which was not directly compensated, showed a direct cost savings of $8.9M. This implied an 11-fold return on investment excluding uncompensated physician time. Conclusion A physician-directed reinvestment program appeared to facilitate latent frontline physician innovation towards value, though additional evaluation is needed to understand its long-term impact.
How Well is the Implementation of CAPM in Condition of Market Anomaly? Case in Market Overreaction Anomaly at Indonesia Stock Exchange

This study aims to test the occured of the market anomaly, iemarket overreactions in Indonesian stock market at the Covid-19 pandemic. If the event occurs then be tested whether a contrarian investment strategy is relevant to be implemented. It will also be tested whether the CAPM's market risk factor will affect the returns. The data from the Indonesia Stock Exchange (IDX) used are the stock price in periods of January 2019-December 2020, which during the pandemic were have the potential to be profitable or detrimental. Through the formation of the portfolios that called as as the winner and the losers, testing of returns reversals could be done to prove the occurs of the market overreaction. The results of this research are as follows: First, markets overreaction anomaly event has occured in Indonesian stock market in periods of Covid-19 pandemic. The reversal of return occurs for most shares those have the potential to be profitable or detrimental, or have been proven those it profits rates has increased or decreased in the periods of the pandemic. Second, the contrarian strategy is relevant to be implemented in the short term in Indonesian stock market, which is in this pandemic. By implementing the contrarian strategy, a profitable return is obtained from the difference between the returns of the losers and the winners through their each of observation period. Third, market risk factors based on the CAPM have a significant effect only for the losers stocks.
Subject-orientation as design language for integration across organisational control layers

Recent research and development in production industry reveals a movement to cyber-physical production systems and Internet of things enabled manufacturing. In this context, ways in which enterprise processes are conceptualised and executed are changing. Decentralising production by applying interlinked cyber-physical production resources breaks up the traditional boundaries between different process abstraction layers. Heterogeneous smart devices and processes at all levels in the industrial control need to interact in such systems. In this paper, requirements from the field of Business Process Management towards the vertical integration of business and production processes are derived. With respect to the identified requirements the eligibility of the Subject-oriented Process Management (S-BPM) approach for vertical process integration is depicted. At the core, the paper investigates the applicability of S-BPM to achieve vertical process integration and reports lessons learnt from two industrial application scenarios. The first scenario aims to encode human properties to adapt workplaces to human needs. The second scenario aims towards supporting the execution and tracking of dynamic production processes.
Categories, Standards and Recent Trends in Wireless Power Transfer: A Survey

Background/Objectives: The main objective of this survey is to identify the innovative ideas of wireless charging, its history, types and also the various standards adopted along with the challenges, scope and its salient features. Methods/ Statistical Analysis: This wireless charging is an ingenious approach that transfers the electrical power via the atmospheric medium. In general, it uses the magnetic field or a microwave instead of the traditional copper cables in order to transfer the power. Findings: WPT is the acronym of Wireless Power Transfer. Wireless Power Transfer is an emerging trend that revolutionizes the way of transferring power without cables and makes our life wireless. Now a days, there is a great development in the cruising range of electronic devices, the main issue in these gadgets is frequently charging the rechargeable batteries and connected to the power supply via a chord. One of the most significant failures of the electronic devices is surmounted by using the concept of wireless power transfer. WPT is a convenient, flexible and safe technology available to energize electronic gadgets. Applications/Improvements: Wireless charging can be applicable for automatic wireless energy transfer, consumer electronics, industrial devices, transportation and medical devices like pacemakers, defibrillator and ventricular gadgets.
A deep learning approach for port congestion estimation and prediction

ABSTRACT This study proposes high-frequency container port congestion measures based on Automatic Identification System (AIS) data. Vessel movement information of 3,957 container ships from March 2017 to April 2017 is included. The world top 20 container ports’ berth and anchorage areas are identified through Density Based Spatial Clustering of Applications with Noise (DBSCAN) and convex hull methods, and their hourly port congestion statuses are depicted in terms of the traffic volume and turnaround time. The constructed congestion measures overcome the disadvantages of the traditionally used port or industry data, which is heterogenous, behind the time and not easy to obtain publicly. The higher frequency (hourly) of the proposed measures can effectively monitor any slight change in port performance. A Long Short-Term Memory (LSTM) neural network model is then proposed for congestion prediction using constructed congestion measures. Point prediction and sequence prediction are both performed. We innovatively introduce congestion propagation effects into the prediction model as input features. Using Shanghai, Singapore and Ningbo ports as case studies, results show that the inclusion of congestion propagation effect can improve the prediction performance especially for sequence prediction. This study provides significant implications and decision support for container shipping market participants.
CEO satisfaction and SME innovation: unveiling the role of family involvement

ABSTRACT Family involvement in SMEs can affect strategic decisions about innovation. The differences in the innovative behaviours between family and nonfamily SMEs are mostly attributed to socioemotional wealth, which emphasizes the noneconomic goals of family firms. The literature on how family involvement affects firm innovation remains inconclusive. In this study, we distinguish the different steps of innovation and use CEO satisfaction with business performance to show that greater family involvement is ultimately good for innovation performance. We examine how CEO satisfaction with business performance affects firm innovation and, in particular, how family involvement affects this relationship. Using a sample of 308 Czech SMEs and multiple mediation and moderated mediation analyses, we find that high levels of family involvement enhanced SMEs innovation performance by strengthening the influence of CEO satisfaction on product innovation.
Developing a Biosensor-Based Immunoassay to Detect HPV E6 Oncoprotein in the Saliva Rinse Fluid of Oral Cancer Patients

Transmission of Human papillomavirus (HPVs) is faithfully associated with carcinogenesis of oral cavity and oropharyngeal cancers. Therefore, clinical researchers may need to generate customized antibodies for the upcoming ELISA-based analysis to discover rare but valuable biomarkers. The aim of study was to develop and generate a biosensor-based immunoassay for early screening HPV-related oral cancer via saliva rinse fluid analysis. A peptide fragment of high-risk HPV subtype 16/18 protein, E6 protein (HP-1 protein sequence 48–66), was designed and synthesized, followed by the generation of polyclonal antibodies (anti-HP1 IgY) in our university-based laboratories. The titer and specificity of antibodies were determined by enzyme-linked immunosorbent assay (ELISA), and the Surface Plasmon Resonance (SPR) biosensor-based method was developed. Kinetic analyses by SPR confirmed that this designed peptide showed a high affinity with its generated polyclonal antibodies. Saliva fluid samples of thirty oral cancer patients and 13 healthy subjects were analyzed. SPR indicated that 26.8% of oral cancer patients had higher resonance unit (ΔRU) values than normal subjects. In conclusion, we developed a biosensor-based immunoassay to detect HPV E6 oncoprotein in the saliva rinse fluid for early screening and discrimination of HPV-related oral cancer patients.
Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model

This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm. ” The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the “Option strategy of volatility prediction with dynamic thresholds. ” With the new algorithm above, the strategy improves the return-risk ratio in test set (measured by Sharpe ratio: 1.99–2.07).
Islamic Knowledge: Factors Effective Islamic Knowledge Transfer For Visually Impaired Users Via Malaysia E-Learning Websites

It is necessary for visually impaired users (VIUs) to be able to make use of information communication technologies (ICTs), especially e-Learning websites to acquire knowledge through them. This paper explores the factors that influence effective Islamic knowledge transfer (EiKT) via e-Learning websites for VIUs in Malaysia according to the knowledge providers (KPs), service providers (SPs) and VIUs. The paper is exploratory and applies thematic analysis to analyze the qualitative data that were collected through the use of 41 interviews with the three groups of participants. Four factors were identified which influence Islamic knowledge transfer (KT) through the use of this websites. The paper has identified the factors and suggests the considerations of these factors in making decisions on the transfer of Islamic knowledge to VIUs through the use of them.
A Quick Assessment Method for the Mud Eruption Hazard Risks of the Lusi Surrounding Area with a Special Reference to Ground Deformation Behavior

The Lusi mud eruption in Sidoarjo of East Java has provided huge impact to the people living in the surrounding area. The eruption has been followed by ground subsidence that has changed the ground structure. An assessment method has been introduced for analyzing the hazard. The method employs a rating system to quantify the hazard parameters, in which ground deformation parameters are significant. A hazard risk index is therefore obtained by multiplying hazard potential and vulnerability parameters.
Content of sterols in in vitro propagated Chamerion angustifolium (L.) Holub plants

Summary Introduction: Chamerion angustifolium (L.) Holub (syn. Epilobium angustifolium L.) plants have been used in the treatment and alleviating symptoms of mild Benign Prostatic Hyperplasia (BPH). Plants are abundant in polyphenols, particularly ellagitannins, phenolic acids and flavonoids. Apart from polyphenols, herb of this species also contains steroids, triterpenes and fatty acids. Objective: The aim of this study was to determine the content of campesterol, β-sitosterol and stigmasterol in C. angustifolium genotypes cultivated in vitro. Methods: Plants grown in vitro and the plants harvested from field were subjected to the HPLC-DAD analysis. Results: The investigated genotypes differed in sterol content. Stigmasterol (375.64–577.77 mg/100 g of dry weight – DW) was a predominant compound among the tested sterols. In contrast to in vitro cultures, plants harvested from field synthesized mainly β-sitosterol (103.05 mg/100 g DW), whereas campesterol and stigmasterol were less abundant. Conclusions: C. angustifolium in vitro cultures are rich source of phytosterols, particularly stigmasterol. Genotype had a significant effect on phytosterol accumulation under in vitro conditions.
KLASIFIKASI EMOSI PADA CUITAN DI TWITTER DENGAN PRINCIPAL COMPONENT ANALYSIS DAN SUPPORT VECTOR MACHINE

Salah satu platform media sosial dengan total pengguna aktif harian terbesar adalah Twitter. Melalui Twitter, orang-orang bisa membagikan suatu pesan yang disebut dengan tweet. Ungkapan yang diekspresikan pada tweet dapat merefleksikan bagaimana emosi atau perasaan yang dimiliki seseorang. Emosi yang terkandung dalam sebuah tweet bisa dikenali lewat proses analisis sentimen. Namun, data teks Twitter tidak terstruktur, mengingat saat ini penggunaan singkatan kata, emoji, atau bahkan frasa khusus banyak dijumpai pada tweet, termasuk tweet yang diunggah oleh masyarakat Indonesia. Sehingga, untuk mengidentifikasi emosi dari data teks Twitter melalui proses analisis sentimen dibutuhkan penerapan metode yang tepat. Di sisi lain, Machine Learning telah banyak diaplikasikan dalam melakukan tugas analisis sentimen. Kerangka kerja yang disajikan pada penelitian ini melibatkan penggunaan dari algoritma Machine Learning untuk dapat menganalisis emosi yang dimuat tweet berbahasa indonesia. Selebihnya, implementasi metode FastText dan teknik ekstraksi fitur PCA juga diterapkan agar output yang diberikan maksimal. Secara keseluruhan hasil penelitian menunjukkan bahwa classifier Support Vector Machine (SVM) dengan fungsi kernel RBF yang dikombinasikan menggunakan PCA memiliki kinerja yang unggul dalam mengklasifikasikan emosi pada tweet berbahasa indonesia, dimana berturut-turut Accuracy, Precision, Recall, serta F1 Score yang dicapai sebesar 70,52%, 74,60%, 69,80%, dan juga 71,20%.
Use of Nest Web Cameras and Citizen Science to Quantify Osprey Prey Delivery Rate and Nest Success

Abstract. Ospreys (Pandion haliaetus) are obligate piscivores and their nesting success depends on sufficient amounts of fish delivered to the nests during the breeding season. Nests are considered successful when pairs raise a minimum of one young to fledging or near-fledging age. Through web cameras and online broadcasts of Osprey nests, citizen scientists quantified daily number of fish deliveries, nest survival, and nest success. We received and analyzed curated data (one to seven seasons, 2014–2020) from citizen scientist groups representing 19 Osprey web cameras from four countries in North America and Europe. We compared the average and the coefficient of variation of the number of fish delivered per day within the early breeding season between the failed and successful nests using a Wilcoxon rank-sum test. We also analyzed the effects of the average and the coefficient of variation of the number of fish delivered per day on the number of days of nest survival and whether a nest was successful or not using generalized linear mixed models. Successful and failed nests had significant differences in the average number of fish delivered per day and the failed nests had a higher variation in the number of fish deliveries. Moreover, the variation and average number of fish delivered per day had strong associations with whether a nest would fail or succeed. The global effort and manner in which these data were collected are novel and can further our understanding of this charismatic species. The combination of citizen science and technology is a powerful modern tool that can provide insights and has the potential to advance raptor research worldwide.
Fast Finite-Time Path-Following Control for Autonomous Vehicle via Complete Model-Free Approach

Without any knowledge of the vehicle model and its parameters, a novel complete model-free path-following control strategy is developed for autonomous vehicles based on the time-delay estimation (TDE) technique. Different from the existing time-delay control (TDC) approaches, an adaptive nonsingular terminal sliding mode (ANTSM) control law is designed to stabilize the path-following errors without any information of the suitable control gain, which is significant in the conventional TDC scheme, and the boundary of the TDE error, which is necessary for the sliding-mode-based control scheme. The proposed model-free control structure can dynamically update the gain of the designed controller and the boundaries of the TDE error, and the practical finite-time convergence of the preview error can be achieved. In the HIL tests, the comparative results demonstrate that the proposed ANTSM model-free control strategy can provide superior comprehensive tracking performance over both the model-based sliding mode controller and the conventional TDC controller, while the autonomous vehicle follows desired paths.