Expert Personnel Research Articles

Multiple drivers of invasive lionfish culling efficiency in marine protected areas

AbstractDesigning effective local management for invasive species poses a major challenge for conservation, yet factors affecting intervention success and efficiency are rarely evaluated and incorporated into practice. We coordinated regional efforts by divers to cull invasive lionfish (Pterois spp.) on 33 U.S. Atlantic, Gulf of Mexico, and Caribbean protected coral reefs from 2013 to 2019 and estimated removal efficiency and efficacy as a function of environmental and habitat conditions, invasion status, and personnel expertise. Highly experienced individuals culling during crepuscular periods (<2 hr from sunrise/sunset) are three times more efficient (in terms of minutes) than novice divers during midday, suggesting: (a) retention of experienced individuals is key for efficient programs, and (b) planning culls with personnel and time of day in mind increases the number of sites covered with the same effort. Lionfish behavior and habitat characteristics had little effect on removal efficiency and efficacy, but divers had higher capture success at reefs with higher lionfish densities. We suggest reefs with persistently <20 fish ha−1 as low priority, given that impacts to native fauna are unlikely and culling effectiveness declines to <50% below this level. Incorporating efficiency factors in spatial management planning along with density estimates derived from remotely sensed data can ensure limited resources for control are extended across a greater range of invaded habitats.

Overcoming Trusting Barriers in Inter-organizational Identity Theft Prevention in Knowledge Sharing: A Case of UK Retailing Industry

Inter-organizational knowledge sharing basically connects two or more firms with one another in exchange relationship for skills, expertise and knowledgeable personnel. Knowledge plays a key role in organizational performance but it is given a very little attention in terms of information security and requires addressing security concerns due to following reasons: First, because retailing is the most victimized channel due to online trade. Second, if organizational criminal wings can compromise the information system of one firm, they can attack other firms too. Hence, identity theft is a collective problem. A security breach is among the top three business stories and identity theft is the key reason why it occurs in online retailing. Identity theft is a serious issue of society at a large and its prevention requires knowledge sharing by several actors. Through different case studies, interviews and theoretical and empirical analysis, this study highlights some unknown side of prevention collaboration in the retail industry in the United Kingdom (UK) which is of theoretical and practical importance. This paper theoretically furthers our understanding of how communication of organizations with comparable goals is handled differently in information security management. Practically, it evaluates relationships of retailing firms, online fraud forums and law enforcement departments in the fraud prevention process. Our research outlines how retailers' partnerships with different stakeholders will help them solve and reduce identity theft by presenting a structure for inter-organizational fraud prevention in retailing organizations.

NagareDB: A Resource-Efficient Document-Oriented Time-Series Database

The recent great technological advance has led to a broad proliferation of Monitoring Infrastructures, which typically keep track of specific assets along time, ranging from factory machinery, device location, or even people. Gathering this data has become crucial for a wide number of applications, like exploration dashboards or Machine Learning techniques, such as Anomaly Detection. Time-Series Databases, designed to handle these data, grew in popularity, becoming the fastest-growing database type from 2019. In consequence, keeping track and mastering those rapidly evolving technologies became increasingly difficult. This paper introduces the holistic design approach followed for building NagareDB, a Time-Series database built on top of MongoDB—the most popular NoSQL Database, typically discouraged in the Time-Series scenario. The goal of NagareDB is to ease the access to three of the essential resources needed to building time-dependent systems: Hardware, since it is able to work in commodity machines; Software, as it is built on top of an open-source solution; and Expert Personnel, as its foundation database is considered the most popular NoSQL DB, lowering its learning curve. Concretely, NagareDB is able to outperform MongoDB recommended implementation up to 4.7 times, when retrieving data, while also offering a stream-ingestion up to 35% faster than InfluxDB, the most popular Time-Series database. Moreover, by relaxing some requirements, NagareDB is able to reduce the disk space usage up to 40%.

Clinical efficacy of drug-eluting coronary stents based on real-world data.

As the main implant material, coronary stent is one of the main factors that affect clinical treatment outcome and patient cost. The choice for coronary stent in clinic depends on multiply factors including patient preference, medical personnel expertise and current technology, and no recognized method has been established for the evaluation and choice of coronary stent. In this study, we aim to evaluate the safety, effectiveness, and economy of a few drug-eluting coronary stents in clinic. A retrospective analysis was employed to study the cases treated by drug-eluting stent intervention in a third grade hospital from January 2018 to December 2019. Based on the specific products, these cases were assigned into a domestic stent A group, a domestic stent B group, a domestic stent C group, and an imported stent group. According to the severity of the disease, they were divided into an angina pectoris, an acute myocardial infarction, and an ischemic heart disease subgroups. Layer analysis was carried out for inter-subgroup comparisons in terms of safety (indicated by mortality rate, complication rate, and infection rate), efficacy (indicated by 31-day return rate, 365-day myocardial infarction rate, restenosis rate, secondary intervention rate, and thrombosis rate), and economy (indicated by averaged total cost and coronary stent cost). Among the 4 products, there was no difference in safety (P>0.05). Except for patients with acute myocardial infarction, there was no significant difference in the effectiveness of the 4 products for the other 2 diseases. The economical aspect of the domestic stent B and the domestic stent C was better than that of the domestic stent A and the imported stent (all P<0.05). With all the indicators of these 4 stents taken into consideration, we conclude that hospitals may appropriately increase the share of domestic coronary stent in product selection and in-hospital management. This measure helps increase product localization rate and ease the financial burden for patients by reducing coronary stent cost.

Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys.

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

Using a systems thinking approach to understand teachers perceptions and use of assistive technology in the republic of Ireland

Purpose Assistive technology (AT) enables and promotes inclusion and participation, especially for persons with a disability, ageing populations, and those with non-communicable diseases. AT is essential for generating collaborative learning environments, inclusive of students with a range of specific needs and required supports. However, many teachers lack the training, resources, and skills to use AT in the class. This study uses a systems thinking approach to consider teachers AT needs in the Irish classroom. Methods Three hundred and five teachers completed an online survey, and 10 took part in semi-structured focus groups. The survey examined teachers experiences with AT, their training needs, and the context within which they need support. Results Teachers reported low levels of AT knowledge, skills, and confidence. Many described limited access to training and support for AT use. Results also reveal non-systems thinking, whereby there is no meaningful linking across the main strategic drivers of AT - policy, products, provision, and personnel, to support the people at the centre of the AT system – between these and the contextual factors that influence their effectiveness. Conclusion An AT system which is easy to navigate, which has policies and provision that supports educators, and has expert personnel on hand to support teachers, is crucial if AT is to become embedded in Irish classrooms. This research shows that a comprehensive AT system in education should include policies, practices, personnel, and products that interact to support people to engage easily and successfully with AT in the classroom. IMPLICATIONS FOR REHABILITATION Describes current trends in how teachers are engaging with Assistive Technology. Describes the current trends in disabilities in Irish classroom and teachers requirements for support. Understand the system challenges facing teachers attempting to use Assistive Technology. Support the development of Assistive Technology training for teachers which are informed by teachers needs. Inform the development and improving policies and processes for common situations.

Critical analysis of factors affecting the efficiency of nutrition therapy teams.

Nutrition therapy (NT), defined as the provision of oral, enteral, or parenteral nutrition, should be considered a part of the holistic approach to patient care, while quality assessment of its practices is crucial. The present study aimed to identify variables capable of explaining the reason behind compliance of NTTs with the regulatory requirements. A cross-sectional study focused on assessing the quality of nutritional therapy practices among NTTs who previously stated to be able to comply with requirements of the Brazilian law versus those who were not able to meet these demands. Hospitals were classified as A (teams that had previously reported to be able to meet the requirements) and B (those unable to comply with them). The study was approved by the ethics committees of all participant institutions. Twenty six hospitals met the inclusion criteria. The main hospital features, such as size, classification, service type, and percentage of hospitals providing NT were similar. Hospitals differed on the median number of patients undergoing NT per week: in group B (41.0; IR=28.0-58.0) and group A (11.0; IR=5.0-48.0) (p<0.05), but there were no differences regarding personnel expertise. Both hospital groups had low quality control of the NT processes. Lack of quality assurance is responsible for the inadequate services in both type of institutions.

Identifying Spatial and Temporal Variations in Concrete Bridges with Ground Penetrating Radar Attributes

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.

Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine

Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are time-consuming and require expert personnel. This work investigates novel sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. In our methodology, machine vision is applied to assess downy mildew sporulation, while hyperspectral imaging is used to explore its potential capability towards early detection of this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew (Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R2) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation between downy mildew severity by computer vision and expert’s visual assessment. Furthermore, an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in grapevine.

The Civil Rights Revolution at Work: What Went Wrong

The civil rights and women's movements led to momentous changes in public policy and corporate practice that have made the United States the global paragon of equal opportunity. Yet diversity in the corporate hierarchy has increased incrementally. Lacking clear guidance from policymakers, personnel experts had devised their own arsenal of diversity programs. Firms implicated their own biased managers through diversity training and grievance systems and created a paper trail for personnel decisions, but they maintained the deeper structures that perpetuate inequality. Firms that changed systems for recruiting and developing workers, organizing work, and balancing work and life saw diversity increase up the hierarchy, but those firms are all too rare. The courts and federal agencies have found management processes that do not explicitly discriminate to be plausibly unbiased, and they rarely require systemic reforms. Our elaborate corporate diversity programs and public regulatory systems have largely failed to open opportunity, but social science research points to a path forward.

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.

Novel expert system to study human stress based on thermographic images

Human stress is a physical or emotional feeling. It can come from any situation or thought that makes one feel frustrated or nervous. Different biological manifestations take place in the presence of stress, such as tension, headache, and insomnia. Recent studies have reported that human stress can be related to facial expressions and fingertips due to temperature changes in the skin. Infrared thermography is a non-invasive technology that allows for the monitoring and analysis of human skin temperature. However, many works reported in the literature perform a manual analysis, depending on expert personnel, resulting in considerable human and economic efforts. In addition, the analyses reported to date with thermography are only based on the study of body parts. To reduce the limitations of these methodologies, expert systems have been proposed that simulate the thought process of a human expert to solve decision problems, which may be helpful for people focused on the health and psychology area who do not have expertise in the study of stress. Therefore, this paper proposes a novel expert system based on infrared thermography and thermal analysis of facial skin and fingertips, a rule-based method, and heuristic knowledge to classify and diagnose human stress in undergraduate university students. The system had the support of experts in the study of human stress and was validated in a stress study. The novel expert system was implemented in a local database that consisted of a group of 100 participants, undergraduate university students, of which 70 were stimulated by the Trier Social Stress Test (TSST) protocol and 30 were not induced to human stress, obtaining an accuracy in the expert system stress classification of 91.0%.

A cell-free bioassay for the detection of androgens.

Androgens remain abused performance-enhancing drugs in sports. Technologies based on mass spectrometry can detect all forms of androgens but fail if the androgen represents a novel structure. A bioassay detects androgens based on function rather than structure. To date, there has been limited adoption of cell-based in vitro bioassays as a screening tool for nontargeted androgen detection because they require expert personnel and specialized equipment to perform. We now describe the development of a cell-free version of an androgen in vitro bioassay. Stage 1 involved in vitro transcription/translation reactions (IVTT) using a DNA template encoding an enhancer/androgen response element (ARE) regulatory region upstream of a minimal promoter that drives expression of a reporter protein. The assay detected testosterone across the concentration range of 106.7 to 0.0144 ng/ml (3.7 × 10-7 to 5 × 10-11 M), with an EC50 of 6.63 ng/ml (23 nM). To reduce complexity, Stages 2-4 of development included just in vitro transcription (IVT) reactions, whereby the output was an RNA molecule. Stage 2 involved directly labelling the RNA molecule with fluorophore-labelled nucleotide triphosphates, Stage 3 involved reverse transcription-polymerase chain reaction (PCR) of the RNA molecule, and Stage 4 utilized an RNA aptamer, Mango II, as its RNA output. The Stage 4 product detected testosterone across the range of 106.7-0.0001 ng/ml (3.7 × 10-7 to 5 × 10-13 M), with an EC50 of 0.04 ng/ml (0.155 nM). Further to this, we show that the Stage 4 product can detect other androgenic molecules. Relative to cell-based bioassays, the Stage 4 product is easy to perform and could be developed into a routine, high-throughput, nontargeted androgen screen.

Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers

Frequency response analysis (FRA) is a method of monitoring a power transformer’s mechanical integrity. However, identifying the type of fault and its severity by comparing measured responses is still challenging and mostly relies on personnel expertise. This paper is taking one step forward to standardize the FRA interpretation process by proposing guidelines based on various international standards and FRA case studies. In this study, the FRA signature is divided into three regions: low-, mid- and high-frequency regions. The deviation from the fingerprint signature for various faults is classified into small, large, and no variations, based on the calculation of the correlation coefficient. The proposed guidelines are developed based on the frequency regions, and the level of variation is represented using a simple arrow method to simplify the interpretation process. A case study is conducted on a three-phase 11/0.433 kV, 500 kVA distribution transformer with a short circuit winding fault to validate the proposed guidelines.

Performance of the hematology analyzer XN-31 prototype in the detection of Plasmodium infections in an endemic region of Colombia

Early and accurate diagnosis is critical in reducing the morbidity and mortality associated with malaria. Microscopy (MI) is the current diagnostic gold standard in the field; however, it requires expert personnel, is time-consuming, and has limited sensitivity. Although rapid diagnostic tests for antigen detection (RDTs) are an alternative to diagnosis, they also have limited sensitivity and produce false positive results in detecting recent past infection. The automated hematology analyzer XN-31 prototype (XN-31p) (Sysmex Corporation, Kobe, Japan) is able to identify plasmodium-infected erythrocytes, count parasitemia and perform complete blood-cell counts within one minute. The performance of the XN-31p in diagnosing malaria was evaluated and compared with real-time polymerase chain reaction (qPCR), MI and RDT in an endemic area of Colombia where Plasmodium falciparum and Plasmodium vivax are present. Acute febrile patients were enrolled from July 2018 to April 2019 in Quibdó, Colombia. Malaria diagnoses were obtained from MI and RDT in the field and later confirmed by qPCR. Venous blood samples in EDTA were processed with an XN-31p in the field. Sensitivity, specificity, positive/negative predictive values, and the likelihood ratios of positive and negative tests were calculated with respect to the results from qPCR, MI and RDT. The intraclass correlation coefficient (ICC) and Bland–Altman plot were used to evaluate the concordance in the parasitemia with respect to MI. A total of 1,754 subjects were enrolled. The mean age was 27.0 years (IQR 14–44); 89.6% were Afro-Colombians, 94.3% lived in urban areas and 0.91% were pregnant. With respect to qPCR, the XN-31p showed a sensitivity of 90% (95% CI 87.24–92.34) and a specificity of 99.83% (95% CI 99.38–99.98) in detecting Plasmodium spp.; both parameters were equivalent to those for MI and RDT. Using MI as the reference, the XN-31p showed a sensitivity of 98.09% (95% CI 96.51–99.08), a specificity of 99.83% (95% CI 99.4–99.98), an ICC of 0.85 (95% CI 0.83–0.87) and an average difference of − 3096 parasites/µL when compared with thick-smear MI and an ICC of 0.98 (95% CI 0.97–0.98) and an average difference of − 0.0013% when compared with thin-smear MI. The XN-31p offers a rapid and accurate alternative method for diagnosing malaria in clinical laboratories in areas where P. falciparum and P. vivax cocirculate.

The impressions of school managers on psychological counseling and guidance services: A descriptional study

School guidance and counselling is a programme that is complementary to the education process and is seen as a support service for the pupil. The aim of this study is to examine the primary school administrators' views on psychological counseling and guidance services in their schools. The research was carried out with 20 school principals and vice principals who have been working in schools for at least one year with a psychological counselor. In the study, a semi-structured interview form developed by the researchers was used as a data collection tool. Considering the field studies, studies were carried out on three main principles: educational guidance, personal / social guidance, and vocational guidance, and three categories of studies, evaluations and suggestions for each theme. According to the findings, school guidance services were it is not considered sufficient in many respects and it is emphasized that the system has deficits. The main deficiencies mentioned can be expressed as the lack of expert personnel in the field, problems arising from infrastructure and central structure and equipment.

Some Aspects Of Professional Training Of Forensic Experts For Internal Affairs Bodies

This article is devoted to the issues of introduction of new approaches to training, retraining and advanced training of personnel for the forensic expertise departments of the internal affairs bodies. The normative legal acts of the Republic of Uzbekistan in training, retraining and advanced training of forensic experts are considered. The author analyzes the foreign experience and formulates proposals for improving the training of expert personnel for internal affairs bodies.

Resilience Training Efficacy by Instructor Specialization: A Program Evaluation with Army Recruits

A growing recognition of the importance of resilience skills for psychological health and optimal performance has led to the development of a wide array of resilience trainings. Across the military, resilience training has been implemented by military personnel and training experts. To date, no direct assessment of the comparative efficacy of instructor type on training outcomes has been conducted. To that end, we conducted a program evaluation that examined differences in the efficacy of a two-hour resilience training course for new recruits in Basic Combat Training on resilience-focused knowledge and skills as a function of instructor specialization. Soldiers (n = 401) received training from either a training specialist or a drill sergeant. Findings revealed that soldiers’ knowledge and perceived resilience skills improved regardless of instructor type. Soldiers preferred training specialists to drill sergeants; however, soldiers who received training from drill sergeants had greater improvements in knowledge as compared to soldiers instructed by training specialists. Soldiers with high levels of pre-training resilience who received training from drill sergeants showed the greatest improvements in knowledge. We conclude that drill sergeants, when provided with train-the-trainer preparation by subject matter experts, can efficaciously provide resilience training to new Army recruits during Basic Combat Training.

Organization of antibiotic stewardship in Europe: the way to go.

SummaryIt is more than two decades ago that a European Union conference on “The Microbial Threat” hosted by the Danish Government in Copenhagen in September 1998 issued recommendations to encourage good practice in the use of antimicrobial agents and reduce inappropriate prescribing. Essential components of those recommendations were antimicrobial teams in hospitals and the use of feedback to prescribers as well as educational activities. Two decades later, important surveillance systems on both antimicrobial resistance as well as on antibiotic consumption are functioning at the European level and in most European countries; European Committee on Antimicrobial Susceptibility Testing (EUCAST) has thoroughly re-evaluated, standardized and harmonized antibiotic susceptibility testing and breakpoints; there have been educational activities in many countries; and stewardship teams are now included in many guidelines and policy papers and recommendations. Yet, antimicrobial resistance problems in Europe have shifted from methicillin-resistant Staphylococus aureus (MRSA) to vancomycin-resistent Enterococcus faecium (VRE) and to multidrug-resistant gramnegative bacteria, while antibiotic consumption volumes, trends and patterns across countries do not show major and highly significant improvements. The way to go further is to recognize that better prescribing comes at a cost and requires investment in expert personnel, practice guideline drafting, and implementation aids, and, secondly, the setting of clear goals and quantitative targets for prescribing quality.

May chronic cough in chronic obstructive pulmonary disease be a contraindication of Percutaneous Endoscopic Gastrostomy placement: a case report

BackgroundPercutaneous Endoscopic Gastrostomy (PEG) can involve some complications, despite the good safety of its track record. The Buried Bumper Syndrome (BBS) is a rare, late and dangerous complication that consists in the erosion of the internal bumper through the gastric wall.Case presentationWe report the development of BBS in a man with chronic obstructive pulmonary disease (COPD) who had a persistent chronic cough which was prevalently but not solely in the morning and required placement of a PEG tube for continuous infusion of Levodopa/carbidopa intestinal gel for advanced Parkinson's disease.ConclusionWe believe that COPD with chronic cough while not representing an absolute contraindication to PEG placement, may potentially cause BBS and therefore an appropriate regimen of tube care by expert personnel is mandatory in this setting.