Miracle-dz/UniFAR-FTUs huggingface dataset:
Stakeholder influence and relationships inform engagement strategies in marine conservation

ABSTRACT Many current marine conservation approaches do not adequately consider the diverse social elements and human aspects necessary to achieve conservation outcomes. The results of conservation research are therefore not always useful for conservation managers to apply in practice. To address this gap, this study combines qualitative methods and quantitative social network analysis to help conservation managers gain in-depth insight into social elements of marine conservation and opportunities that can help achieve conservation outcomes. Specifically, using the North West Shelf Flatback Turtle Conservation Program as a case study, our analysis shows that 1) a stakeholder’s position in the turtle conservation network is not the sole or best indicator of their leadership potential to achieve conservation outcomes, 2) peripheral stakeholders are also important for trust, decision-making, and future success, 3) mixed-methods can identify additional opportunities to maintain and further build trust and influence between diverse stakeholders 4) building relationships to support conservation outcomes is accomplished by leveraging stakeholders’ roles in the conservation program. By identifying who has influence and who needs to be involved in marine conservation to achieve success, this study demonstrates the value of mixed-methods research approaches. Specifically, our findings show how social network approaches can help conservation managers and stakeholders strategically build communication and engagement strategies that can be used to achieve conservation outcomes.
Impact of Perceived Supervisor Support and Rewards and Recognition Toward Performance Through Work Satisfaction and Employee Engagement in Employee Marketing Banks

The company as an organization has a dependency on individuals within the company itself. Employees as individuals in the company are part of the organizational structure that has a major role in determining the achievement of company goals. In the perspective of employees, direct supervisors' attitudes and actions can increase employee engagement or even create an atmosphere where an employee becomes disengaged (feeling not part of the company/organization). In addition to marketing employees, rewards and recognition are generally used as the main motivation to improve their performance.For this reason, the purpose of this study is to determine the role of perceived supervisor support, rewards, and recognition, employee engagement on performance mediated by job satisfaction. The study was conducted with a survey using a questionnaire where respondents used 170 marketing employees in the banking industry in Tangerang. Data were analyzed using Structural Equation Model (SEM). The results showed there was a relationship between rewards and recognition of employee engagement, there was a relationship between rewards and recognition of performance, there was not a relationship between perceived supervisor support for performance. Employee engagement mediates the relationship between rewards and recognition of job satisfaction. Job satisfaction mediates the relationship between employee engagement on performance.
A Compact Low-Loss Broadband Polarization Independent Silicon 50/50 Splitter

We present a low-loss, compact, polarization insensitive 3-dB optical power-splitter for submicron silicon waveguides. The splitter is optimized over a broad wavelength span ranging from 1300 to 1700 nm through finite difference time domain simulations. The splitter junction footprint is only 1.8 × 1.3 µm, which is smaller than previously reported broadband multimode junction splitters. Furthermore, the demonstrated device overcomes the fabrication limitations of previous splitters while maintaining a compact size without any sharp angles. The device was fabricated using electron beam lithography. The experimental results show a small insertion loss of less than 0.3 dB for both the TE and TM polarizations.
Decision tree-based detection of blowing snow events 
in the European Alps

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow, and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and a constant threshold wind speed have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in-situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity). Twenty repetitions of random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subset were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if divergent distributions exist between stations. Although both the site-specific DTMs and site-independent DTM show strong performance for accurately detecting blowing snow, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.
Cross-Sectional and Longitudinal Tests of Social Cognitive Model of Well-Being in Korean College Students

This study examined the cross-cultural utility of a modified social cognitive model of academic and life satisfaction (Lent & Brown, 2008) by adding independent and interdependent self-construals with Korean college students in a cross-sectional (Study 1) and a longitudinal design (Study 2). In Study 1, 604 participants completed measures of academic self-efficacy, outcome expectations, goal progress, environmental support, positive affect, academic satisfaction, life satisfaction, and self-construals. In Study 2, 171 participants completed the same measures at two time points with a 15-week interval. Results of Study 1 indicated that the modified model provided a good fit to the data and that 21 out of 25 of the hypothesized paths were significant. In Study 2, the bidirectional model, which included three theorized sets of reciprocal relations (i.e., academic satisfaction to life satisfaction, positive affect to both environmental support and self-efficacy, and self-efficacy to both outcome expectations and goal progress) demonstrated an optimal fit to the data. Overall, the findings of the present study provide evidence for the validity of the modified social cognitive well-being model in Korean populations.
FFP3 respirators protect healthcare workers against infection  with SARS-CoV-2

IntroductionConsistent with World Health Organization (WHO) advice [1], Public Health England (PHE) recommends that healthcare workers (HCWs) caring for patients with coronavirus disease 2019 (COVID-19) should use fluid resistant surgical masks type IIR (FRSMs) as respiratory protective equipment (RPE), unless aerosol generating procedures (AGPs) are being undertaken or are likely, when a filtering face piece 3 (FFP3) respirator should be used [2]. In recent guidance, an FFP3 respirator is recommended if “an unacceptable risk of transmission remains following rigorous application of the hierarchy of control” [3]. Conversely, guidance from the Centers for Disease Control and Prevention (CDC) recommends that HCWs caring for patients with COVID-19 should use an N95 or higher level respirator [4]. WHO guidance suggests that a respirator, such as FFP3, may be used for HCWs in the absence of AGPs if availability or cost is not an issue [1]. A recent systematic review undertaken for PHE concluded that: “patients with SARS-CoV-2 infection who are breathing, talking or coughing generate both respiratory droplets and aerosols, but FRSM (and where required, eye protection) are considered to provide adequate staff protection” [5]. Nevertheless, FFP3 respirators are more effective in preventing aerosol transmission than FRSMs, and observational data suggests that they may improve protection for HCWs [6]. It has therefore been suggested that respirators should be considered as a means of affording the best available protection [7], and some organisations have decided to provide FFP3 (or equivalent) respirators to HCWs caring for COVID-19 patients, despite a lack of mandate from local or national guidelines [8]. Data from the HCW testing programme at Cambridge University Hospitals NHS Foundation Trust (CUHNFT) during the first wave of the UK severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic indicated a higher incidence of infection amongst HCWs caring for patients with COVID-19, compared with those who did not [9]. Subsequent studies have confirmed this observation [10, 11]. This disparity persisted at CUHNFT in December 2020, despite control measures consistent with PHE guidance and audits indicating good compliance. The CUHNFT infection control committee therefore implemented a change of RPE for staff on “red” (COVID-19) wards from FRSMs to FFP3 respirators. In this study, we analyse the incidence of SARS-CoV-2 infection in HCWs before and after this transition.
Management and climate variability effects on understory productivity of forest and savanna ecosystems in Oklahoma, USA

The productivity of herbaceous and understory woody vegetation is critical for wildlife habitat, livestock forage, and biodiversity, and is in ﬂ uenced by both annual weather patterns and tree dominance. With the goals to inform management and understand climate change implications, we determined the effects of tree harvest, prescribed ﬁ re, and 31 yr of climate variability on understory aboveground net primary productivity (ANPP) for ecosystems ranging from mature forest to grassland in a long-term study in southeastern Oklahoma, USA. In 1984, starting with a mature forest dominated by Pinus echinata and Quercus stellata , replicated experimental units were created by various combinations of pine harvest, hardwood thinning, and subsequent ﬁ re return intervals (1 – 4 yr and none). Understory ANPP (forbs, grasses, and woody plants) was measured by clip plots at the end of each growing season. Stepwise regression models were developed between understory ANPP and tree basal area, litter accumulation, ﬁ re return interval, and monthly, seasonal, and annual weather variables. Understory ANPP was dominated by grasses and ranged from 27 g (cid:1) m (cid:3) 2 (cid:1) yr (cid:3) 1 for the mature forest to 374 g (cid:1) m (cid:3) 2 (cid:1) yr (cid:3) 1 for an annually burned grassland/savanna. In general, herbaceous ANPP was inversely related to tree dominance (basal area), litter accumulation, and early and late growing season temperatures, and positively related to June precipitation. Understory woody ANPP was in ﬂ uenced by tree basal area, positively in ﬂ uenced by precipitation, and negatively in ﬂ uenced by summer soil moisture de ﬁ cits. Our results indicate that prescribed ﬁ re, through its negative in ﬂ uence on tree basal area and litter accumulation, is critical to maintaining highly productive understories and that a three-year return interval is a threshold to stall redevelopment of forest. For herbaceous ANPP, timing of precipitation, especially mid-growing season, appears more important than total precipitation, and higher temperatures within the range our site experienced did not have a large negative effect. In contrast, understory woody ANPP was negatively in ﬂ uenced by drought indicating climate change may have variable effects on different functional groups.
Production and quality evaluation of soy cheese (tofu) using various coagulants

Food formulations that incorporate soy ingredients are being exploited as veritable alternatives. Cheese analogues are healthy alternatives to obtain the benefits of consuming functional foods. There is a need to enhance the production and quality of soy cheese. In this study, soy cheese (tofu) samples were prepared from soybean using four selected coagulants - Moringa oleifera seed extract (MC), tamarind seed extract (TC), lime juice (LC) and alum (AC). The effects of the coagulants on the physicochemical, microbial and sensory qualities of the soy cheese samples were evaluated. The results of the physical properties of the samples showed that MC had higher yield (12.84%) and acidity (0.37%), while AC had the highest values in hardness (84.55%) and dry matter (36.72%) content. The result of the proximate composition revealed that MC had the lowest moisture content (60.52 %) and the highest protein content (22.51%). The ash content of the soy cheese samples from plant coagulants MC (3.00%), TC (2.80%) and LC (3.5%) were lower than that of the alum coagulant AC (3.89%). The cheese from alum coagulant (AC) had more minerals than those from plant coagulants. TC had the highest vitamin C (2.69 mg/100 g), while MC had the highest vitamin B1 (6.07 mg/100 g) and B2 (29.21 mg/100 g) contents. There was no detection of coliform in the soy cheese samples. There were no significant differences (p>0.05) in the appearance and overall acceptability of MC sample and LC sample MC sample from Moringa oleifera seed extract coagulant had the highest sensory scores for flavour (8.35), texture (8.40) and overall acceptability (8.63). The results of quality properties of the cheese samples differed with the coagulants used. Moringa Oleifera coagulated cheese (MC) had superiority in nutrients and sensory qualities Hence, it is recommended for tofu production.
Efficient Fault-Tolerant Information Barrier Coverage in Internet of Things

Information barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in Internet of Things. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, there exist multiple types of fault that sensors may have during operation. It is, thereby, difficult to recognize faulty sensors as well as their invalid output and attain accurate intrusion detection. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine-grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of information barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC-SR501 infrared sensors, and design hardware and software to build a practical detection system. Extensive experimental results and simulation results validate the information coverage model and demonstrate that TrusDet has a very low false alarm rate.
Advanced extreme learning machine‐based ensemble classification scheme with enhanced data perturbation for human DNA
 sequences

The dramatic growth in machine learning has brought in significant features and quantified non‐linear associations in the data derived from sensitive medical datasets. The data should be preserved without influencing the associated classifications by applying a robust, effective and reliable data perturbation technique before enforcing ensemble classification. In this paper, an Integrated Condensation Scheme imposed Privacy Preserving Rotation‐based Data Perturbation and Ensemble Classification (ICS‐PPR‐DPEC) is proposed for ensuring privacy of such sensitive data. Condensation Algorithm‐based Data Perturbation is used for constructing homogenous groups determined from the distance between tuples. It also generates a rotation matrix for conducting perturbation that ensures higher data sensitivity protection before it is sent for classification. Advanced Extreme Learning Machine‐based Ensemble Classification Scheme includes kernel, norm‐optimized and regularized Extreme Learning Machine (ELM)‐based classifiers for attaining predominant classification accuracy in identifying human DNA sequences. This approach facilitates classification by constructing ensembles which are trained through randomly resampled ELM classifiers. It includes an objective function that systematically improves the accuracy and diversity among resulting ensembles. The experimental results of the proposed ICS‐PPR‐DPEC are found to be excellent in terms of classification Accuracy, Precision, Recall, and Kappa statistic when compared to the benchmarked techniques.
Exploration of Antimicrobial Potency of Mangrove Symbiont Against Multi-Drug Resistant Bacteria

Highlight ResearchAntimicrobial potential against the test microbesRhizhopora mucronata isolate showed 95% homology with Bacillus subtilis, and 97% homology with Bacillus oceanisediminis,Acanthus ilicifolius isolate showed 96% homology with Paracoccus caeni, and 89% homology with Bacillus circulans. The study found 4 isolates with antimicrobial potency against MDR pathogenic microbes.The symbiont microbes taken from Rhizophora mucronata and Acanthus ilicifolius were determined to be of the genus Bacillus and Paracoccus AbstractAntimicrobial property of mangrove symbiont have the ability to fight Multi Drug Resistant bacteria which were Staphylococcus aureus, Escherichia coli, and Vibrio haryeyi. This study aimed to determine the potential of symbiont microbes from the root of Rhizopora mucronata and Acanthus iilicifolius as antimicrobial agents against multi-drug resistant (MDR) pathogenic microbes. This research was conducted during July to November 2020. The MDR bacteria were S. aureus, E. coli, and V. harveyi MDR test microbes. The symbiont microbes were identified through molecular analyses (PCR 16S rDNA). Isolation of symbiont microbes from R. mucronata resulted in 16 isolates, while isolation from A. iilicifolius resulted in 14 isolates. Based on the antimicrobial qualitative test against S. aureus, 8 out of 16 microbial isolates from R. mucronata were found to show antimicrobial properties. The testing of A. ilicifolius symbiont microbes against S. aureus showed 8 out of 14 isolates with antimicrobial properties. The test against E. coli resulted in 2 out of 16 microbial isolates from R. mucronata and 5 out of 14 isolates from A. ilicifolius with antimicrobial properties. The test against V. harveyi resulted in two out of 16 microbial isolates from R.mucronata and 4 out of 14 isolates from A. ilicifolius with antimicrobial properties. The quantitative test found 2 isolates from R. mucronta, namely isolates RM10 and RM12, with antimicrobial properties against MDR strain E. coli, with the best isolate being RM10, which produced 11.22 mm of inhibition zone diameter. Furthermore, the selection of isolates was based on the size of the inhibition zone, the clearness of the inhibition zone and the potential for antibacterial activity. Based on their overall antimicrobial potential against the test microbes, four isolates were selected. Molecular analyses of RM12 isolate showed 95% homology with Bacillus subtilis, of RM 10 isolate showed 97% homology with Bacillus oceanisediminis, of AC isolate showed 96% homology with Paracoccus caeni, and of AC 5 isolate showed 89% homology with Bacillus circulans. The study found four isolates with antimicrobial potency against MDR pathogenic microbes. The symbiont microbes taken from R. mucronata and A. ilicifolius were determined to be of the genus Bacillus and Paracoccus.
Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs. We propose a simple stochastic time-series model to represent the export regime of a catchment beyond simple regression. We propose how to get the required data with the least effort when the use of high-frequency in situ analyzers is not feasible or restricted. Sampling strategies based on extreme events are essential for reducing the prediction uncertainty of the catchment behavior.
Practices and Perceptions in Manifestation Determination Reviews

Abstract Manifestation determination reviews (MDRs) are a federally mandated procedure designed to protect the rights of students with disabilities. This study sought to examine practices and perceptions of the MDR process as held by school psychologists. One hundred thirty-six practicing school psychologists responded to a nationwide survey posted on various social media sites. The results revealed that school psychologists regularly engage in the process and view it as valuable in protecting the rights of students with disabilities. Despite lacking formalized training, school psychologists are viewed as holding relative expertise in the MDR process. Practice and policy implications are discussed, and we urge future research in this neglected topic area. Impact Statement Manifestation determination reviews (MDRs) are a federally required component of special education practice of which little is known. School psychologists were surveyed about their practices and perceptions related to the MDR process. The results revealed that despite reporting minimal training in MDRs, school psychologists are viewed as trusted members of the MDR team.