Field Expertise Research Articles

A decision support system based on Bayesian modelling for pest management: Application to wireworm risk assessment in maize fields

Protecting crops against pests is a major issue in the current agricultural production system. In particular, assessing the risk to crops can promote integrated pest management (IPM) strategies that encourage natural control mechanisms and advocate the use of pesticides as a last resort. In this study, we focused on wireworms, major soil-dwelling insect pests inflicting severe economic damage on various crops (including maize, potatoes and cereals) across Europe and North America. We have developed an original hierarchical Bayesian model that explicitly accounts for biological knowledge and uncertainty in field observations, rather than relying solely on statistical correlations, to predict the level of wireworm infestation. The model was calibrated and validated using a substantial dataset originating from an agro-environmental survey carried out over three consecutive years (2012–2014) in France, which provides the wireworm abundance in 419 maize fields, together with information on the landscape context, field history, weather conditions, soil characteristics and farming practices associated to each field. Model outcomes show good agreement with current knowledge from literature and field expertise in terms of the effects of variables on wireworm abundance, and provide fairly good predictive capacity. Subsequently, the model was encapsulated as a software (R shiny application) to predict the risk of wireworm infestation in any field of interest, and can be used by farmers or agricultural advisors as a decision support system for the implementation of IPM strategies. The conceptual framework that we implemented can be adapted to a wide range of similar situations involving other crops and pests.

22 Neonatal regional outreach education in Quebec – a formal needs assessment

Abstract Background In Montreal (Quebec, Canada), newborn care is provided in either highly specialized hospitals for complex/critically ill patients or community hospitals for healthier patients. Community health care practitioners (CHP) have varying levels of comfort and competency in newborn care. Also, because of the unpredictable nature of obstetrics, CHP will need to manage critically ill newborns throughout their careers. Such high acuity, low frequency events can be difficult to manage even in experienced hands. Therefore, there is an imperative to develop a neonatal training curriculum for CHP, with an emphasis on critical newborns. Objectives As a first step, the objective of this study was to perform a comprehensive needs assessment of neonatal outreach education for CHP working in the Greater Montreal Area (Quebec, Canada). Design/Methods Using a mixed methods design (figure), the needs assessment was divided in 3 sections. First, the felt educational needs of the target population (CHP) using an online questionnaire. Second, through focus group discussion, the normative needs of the medical personnel who transport critical newborns. Third, the expressed educational needs of CHP through analysis of neonatal transport data between 2017-2020. Quantitative data was analyzed using descriptive statistics, including means and medians. Qualitative data was coded using conventional content analysis, with the primary technique being descriptive and pattern coding. Results The questionnaire reached 158 participants across 13 community hospitals. CHP are seeking training with procedures, critical situations, and resuscitation. Also, resource sharing, more field expertise, and additional resources for training (time, credits) were identified. Three focus groups (15 participants) also revealed that procedural training, critical newborn management and ventilation were important, as well as Crisis Resource Management (CRM) training. Both the questionnaire and focus groups identified specific medical conditions of interest. The expressed needs (transport data, 947 transports) helped reinforce felt and normative needs. Simulation was identified as a key educational technique. Conclusion A neonatal educational outreach curriculum will need to include procedural training, CRM teaching and certain specific medical conditions including extreme prematurity, hypoxic-ischemic encephalopathy (HIE) and pneumothorax. Procedural training and the principles of ventilation are also critically important. Simulation should be integral, in situ and with varied content. The fear of being judged is omnipresent and should be recognized during outreach education. Educational resources should be shared on an easily accessible, central website.

Unvoicing a field's expertise: A two-pronged citation and language analysis

The field of Developmental Education (DE) draws from a distinct and multidisciplinary body of research and scholarship to facilitate students' transitions to college and to support their postsecondary academic success. However, highly cited scholars and policymakers external to the field perpetuate negative perceptions of DE, arguing for reform or elimination without inclusion of field experts. Through a combination of Citation Content Analysis and Transitivity Analysis, this study examined citation trends and verb transitivity to uncover the voices privileged as experts within an influential publication by the Community College Research Center and aimed to uncover how the authors (re)presented the DE field, literature, scholarship, its members, and its students. Findings revealed a single citation of an in-field DE scholar and repeated erasure of developmental educators and DE scholarship through passivation of DE professionals as social actors. Implications are discussed for the importance of including inside-field experts’ voices in discussions about their field as a necessary component of developing a critical praxis of integrating scholarship and practice in support of students.

Selection of Desalination System for Commercial Vessels by Implementing Improved Intuitionistic Fuzzy TOPSIS Method

The process of desalination of seawater on commercial vessels is a vital event in the daily life of the crew. As a result of the rapid developments in ballast water treatment operations, the question of whether ballast water treatment systems can be used as the main desalination system in the daily life of the crew on commercial vessels has been raised. This study introduces the current desalination systems on commercial vessels and their evaluation criteria. According to the literature and our professional industrial research, there are three most suitable alternatives such as “freshwater generator,” “reverse osmosis method,” and “attaching additional membrane filter to mechanical and ultraviolet (UV) ballast water treatment system.” Similarly, the evaluation criteria are determined as “installation cost of the system,” “operational cost of the system,” “risk of the system polluting the environment,” “risk of the system harming the human health,” “ease of repair and maintenance of the system,” “ease of use of the system,” and “ease of installing the system on the ship.” Due to the need for field expertise, we conducted several rounds of expert consultations. The novelty of this study is twofold. First, we improved the intuitionistic fuzzy TOPSIS (IF-TOPSIS) method by extending an expert prioritization technique. We also used an intuitionistic fuzzy scale for the expert weights. Second, as far as we know, this is the first study that employs an improved IF-TOPSIS method for desalination systems on commercial vessels. Moreover, we compared the proposed method and other methods to analyze the results. Results show that “freshwater generator” is more promising than “attaching additional membrane filter to mechanical and UV ballast water treatment system” that is prior to “reverse osmosis method.” This study helps the users and authorities understand the evaluation and select the desalination systems.

Coupling spatial modeling with expert opinion approaches to restore multispecies connectivity of major transportation infrastructure

It is widely recognized that major transportation infrastructures such as highways degrade habitats, increase the risk of collision mortality, and cause alterations in landscape connectivity by reducing connections between habitats due to the barrier effect. Wildlife crossings (underpasses and overpasses) have therefore become standard practice to reduce these effects on wildlife. Most studies aim to identify relevant locations for the creation of new wildlife crossings, but it is very rare for results based on modeling tools or animal movement records, for example, to be confronted with expert opinion and knowledge. Our paper provides a methodological framework combining modeling tools and field expertise from a multi-scale perspective. We used complementary connectivity metrics from graph theory to accurately quantify and spatialize the most relevant areas to improve habitat connectivity with multispecies conservation goals. The connectivity analysis was coupled with field expertise to take into account the local context, and technical and land feasibility. We prioritized existing road structures for improvement, found the best locations for new wildlife crossings, and made specific, costed technical recommendations. We empirically tested this approach on a large highway network in Northern France. This allowed us to define work principles to improve 109 existing road structures and to create underpasses or overpasses on 15 road sections. This innovative study for operational purposes has been a useful tool to assist the decision-making process of land-use planners who wish to improve wildlife crossings on major transportation infrastructures.

A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage

AbstractPhotographs of bridges can reveal considerable technical information such as the part of the structure that is damaged and the type of damage. Maintenance and inspection engineers can benefit greatly from a technology that can automatically extract and express such information in readable sentences. This is possibly the first study on developing a deep learning model that can generate sentences describing the damage condition of a bridge from images through an image captioning method. Our study shows that by introducing an attention mechanism into the deep learning model, highly accurate descriptive sentences can be generated. In addition, often multiple forms of damage can be observed in the images of bridges; hence, our algorithm is adapted to output multiple sentences to provide a comprehensive interpretation of complex images. In our dataset, the scores of Bilingual Evaluation Understudy (BLEU)‐1 to BLEU‐4 were 0.782, 0.749, 0.711, and 0.693, respectively, and the percentage of correctly output explanatory sentences is 69.3%. All of these results are better than the model without the attention mechanism. The developed method makes it possible to provide user‐friendly, text‐based explanations of bridge damage in images, making it easier for engineers with relatively little experience and even administrative staff without extensive technical expertise to understand images of bridge damage. Future research in this field is expected to lead to the unification of field expertise with artificial intelligence (AI), which will be the foundation of the evolutionary development of bridge inspection AI.

Influence of Spirituality in the Career and STEM-Based Research Approach of Scientists for Sustainable Development: A Study on the Perspective of Scientists from a Public Research University in Malaysia

This explanatory case study aims to formulate a framework of spiritual influence on Science, Technology, Engineering, and Mathematics (STEM)-based research for sustainable development from the perspective of university scientists. This single-site case qualitative study involves seven participants from a Malaysian public research university with a variety of specific research fields. Questions regarding the driving factors of their research strategy for sustainable technology developments were asked. Consequently, the main driving factors on using STEM approaches for sustainable developments are career competency enhancements. This theme can be proven with elements, such as STEM for enhancing the ability to expand a specific field contribution, improving interdisciplinary management, obtaining outsource support, and enhancing innovative capability. Meanwhile, spiritual influence on sustainable development is a part of the philosophy of participants and can be proven through the elements of moral and ethical beliefs with respect to religious values in their scientific career and STEM-based research practice. Therefore, the influence of spirituality is a crucial concept, which can ensure social, emotional, economic, and environmental sustainability development. This framework is built among limited participants. Thus, confirming this framework by using a wide range of samples is encouraged. Moreover, this study generally has implications for sustainable development using STEM-based practice for specific field expertise. The concept aims to provide remarkable contributions to the growth of comprehensive, holistic, and sustainable development and to extend the current literature on STEM research approaches by inculcating spiritual elements.

Assessing groundwater status and human perception in drought-prone areas: a case of Bankura-I and Bankura-II blocks, West Bengal (India).

Ground water is a precious natural resource in every aspect of human life from natural to artificial environment. Ground water is an indicator of regional development by improving the economic domain through intensive agricultural practices, and aesthetic value through sufficient ground water supply as drinking water, fertile soil, and healthy vegetation. However, ground water availability and associated human perceptions were the main themes of the present study. In this study, both primary and secondary data were incorporated to understand the human adaptation behaviour in drought-prone areas of the fringe of Chhotanagpur plateau region (Bankura-I and II blocks) to ground water storage. GWPZ mapping is a very important exposure to knowing the ground reality. So, the MCDM-AHP method has been developed based on the eight dominant conditioning factors viz. geomorphology, lithology, lineament density, soil, drainage density, LULC, average slope, and slope aspect using GIS analytics with field expertise. The output result was validated with comparing 105 inventory stations where 0.850 AUC value was good for accepting the GWPZ model. As a result, a major portion of the study area is dominated by poor to moderate possibilities of ground water level (ground water level drops 1–2 m during the pre-monsoon) due to Proterozoic granite gneiss formation over the study area. With comparing demographic status, it was not favourable for a healthy lifestyle. Agriculture dominated rural environment of Bankura-I and II blocks is far away from the modern digital environment that is why ground water has played a very crucial role in the field of development. Moreover, to build up a good level of ground water recharge zone through rainfall harvesting, and sustainable land use planning will be the best management practices. So, availability of ground water should be a positive sign of development in the field of the economic sector and sustainable human society.

Medical leadership in transformation: new ideas and practices at a crossroads in social sciences

Medical leadership in transformation: new ideas and practices at a crossroads in social sciences This BMJ Leader special section comprises four distinct contributions presented during a healthcare division panel symposium...

Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification

Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.

Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm

Sugarcane plays an important role in food and energy production in Brazil and worldwide. The large availability of satellite sensors and advanced techniques for processing data have improved the forecasting sugarcane yield on a local and global scale, but more work is needed on exploiting the synergy between remote sensing, meteorological and agronomic data. In this study, we combined such data sources to forecast sugarcane yield using a random forest (RF) algorithm on an extensive area of 50,000 ha, over four years. Images from Landsat satellites were processed to time series of surface reflectance and spectral indices. The approach focused on the development of predictive models which only used data acquired and accessible several months before the harvest. First, three RF models were calibrated with different predictors to forecast the sugarcane yield at harvest: using Landsat satellite images and meteorological data (RF1); agronomic and meteorological data (RF2); a combination of Landsat satellite images, agronomic and meteorological data (RF3). As a comparison, we also tested the influence of including knowledge on the future harvest date in the models RF2 and RF3 (RF4 and RF5). The average values of R2 for RF1, RF2, and RF3 were 0.66, 0.50 and 0.74, respectively. The model with the highest values of R2 (RF3) had a Root Mean Square Error (RMSE) of 9.9 ton ha−1 on yield forecast, approximately 15% of the yield average. Including the harvest date improved the RF2 and RF3 models to reach R2 = 0.69 and RMSE = 10.8 ton ha−1 for RF4, and R2 = 0.76 and RMSE of 9.4 ton ha−1 for RF5. A blind forecasting test for the 2016 yields showed similar prediction than the forecast made by in situ field expertise. This result has the potential to assist management of sugarcane production.

“We Take EVERYONE”: Perceptions of External Assessment and Accountability at the Community College

ABSTRACT Assessment, accountability, and transparency represent a greater focus than ever in higher education. Community colleges are not exempt from these requirements, though they are overlooked in the creation of state and national assessment instruments. Therefore, we designed and distributed the Perceptions of Community College Assessment (PCCA) survey, a national mixed methods survey of the perceptions of community college employees (N = 198) who administer or assist in the administration of state and federal community college assessments. Respondents reported low confidence in the accuracy of existing assessment measures used at the state (e.g., performance-based funding measures) and federal (e.g., the College Scorecard) levels. Respondents believed much of the work of community colleges is not captured in existing external assessments which can lead to inaccurate educational data overall. Based on our findings, we recommend that existing federal and state assessment measures be reevaluated, and the classifications of community colleges be expanded to reflect the realities and complexities of modern day community colleges. We further recommend that community college leaders be provided an opportunity to actively assist in the creation of new measures and classifications based on their in the field expertise. Through this research, we seek to educate state and federal decision makers and to provide a tool for community college leaders to advocate for policy and practical changes in the accountability systems used to assess community colleges.

The Gill-Oxygen Limitation Theory and size at maturity/maximum size relationships for salmonid populations occupying flowing waters.

The slowing of growth as fish age has long been believed to be related to energy expenditure for maturation, and this rationalization has been used to explain why, across nearly all fish species, the relationship between size at first maturity (Lm ) and maximum (Lmax ) or asymptotic length (L∞ ) is relatively constant. In contrast, the Gill-Oxygen Limitation Theory (GOLT) postulates that (a) fish growth slows because as they grow, their two-dimensional ability to extract oxygen from the water diminishes relative to their three-dimensional weight gain, and (b) they can only invest energy for maturation if oxygen supply at their size at first maturity (Qm ) exceeds that needed for maintenance metabolism (Q∞ ). It has been reported previously across dozens of marine fish species that the relationship between Qm and Q∞ is linear and, further, it can be mathematically converted to Lm vs. L∞ by raising both terms to the power of D (the gill surface factor), resulting in a slope of 1.36. If the GOLT is universal, a similar slope should exist for Lm D vs. L∞ D relationships for freshwater species across multiple individual populations that reside in disparate habitats, although to our knowledge this has never been evaluated. For analysis, we used existing data from previous studies conducted on 51 stream-dwelling populations of redband trout Oncorhynchus mykiss gairdneri, Yellowstone cutthroat trout O. clarkii bouvieri and mountain whitefish Prosopium williamsoni. The resulting Lm D vs. L∞ D slopes combining all data points (1.35) or for all species considered separately (range = 1.29-1.40) were indeed equivalent to the slope originally produced for the marine species from which the GOLT-derived relationship was first reported. We briefly discuss select papers both supporting and resisting various aspects of the GOLT, note that it could potentially explain shrinking sizes of marine fish, and call for more concerted research efforts combining laboratory and field expertise in fish growth research.

Spécificités épidémiologiques de la COVID-19 en Afrique : préoccupation de santé publique actuelle ou future ?

ObjectifsL’Afrique est le continent le moins armé pour lutter contre l’épidémie de COVID-19. Cependant, l’Afrique qui regroupe 17 % de la population mondiale, ne pèserait que 5 % des cas mondiaux (source : OMS à la date du 04/08/2020). Dans ce travail, les auteurs essaient d’identifier et de comprendre les raisons de ces données épidémiologiques.MéthodeDe nombreux suivis d’indicateurs ont été réalisés grâce principalement aux rapports de l’OMS. Ces données ont été confrontées à la littérature, ainsi qu’à l’expertise de terrain de l’association « biologie sans frontières » en Afrique.RésultatsLes points suivants marquent la singularité de la COVID-19 en Afrique : (1) une capacité diagnostique insuffisante liée au produit intérieur brut, (2) une population jeune réduisant la population à risque et le nombre de décès, (3) un climat favorable (chaud et humide) limitant la transmission virale, (4) de nombreux facteurs socioculturels pouvant limiter la déclaration des cas.ConclusionAujourd’hui, cette crise sanitaire est médiatiquement omniprésente alors que le nombre de décès reste limité en Afrique. En parallèle, des actions concernant les réelles priorités de santé publique africaines (paludisme, diarrhées, SIDA…) sont ralenties ou interrompues.

Joining forces to build up knowledge in health promotion: lessons from a French coalition initiative

Abstract Issue French public health authorities increasingly rationalize access to public funding, favoring evidence-based programs. Health Promotion (HP) interventions are especially urged to prove their efficiency in this context. To tackle this issue, HP practitioners hold experiential knowledge (EK) that proves useful to assess both the complexity and efficiency of HP interventions. United by this conviction in a multidisciplinary coalition, HP experts from various backgrounds came together five years ago to promote Experiential Knowledge in HP (EKHP) in France. A national committee to promote EKHP 670 HP practitioners were surveyed in 2016. Results reported vast amounts of under-documented and often untapped field expertise in HP and numerous obstacles regarding access to scientific literature or systematic reviews. Consequently, the coalition launched a National Committee for EKHP, meeting 5 times a year since 2016. Members of 6 national HP organizations, of 4 regional HP institutes, national and local public health administrators, researchers, consultants and field workers participate. Steered by the French Society for Public Health and the National Federation for Health Education and Promotion, the committee devised a threefold action plan: 1/ advocate EKHP in all relevant institutional spaces, 2/ develop tools for EKHP, 3/ mobilize for EKHP at the local level. Results The committee designed a method for capitalizing, collecting and circulating EK and published in 2020 a guidebook and a toolkit. French Public Health authorities agreed to share EK nationwide on their online portal. Dissemination within the French HP community has started, through the committee members' networks, and will be amplified with a training program launched in 2021 at the National School of Public Health. Lessons Attention must be brought to HP practitioners’ experiential knowledge, both to recognize HP practitioners' expertise and to help improve the understanding of how HP interventions work. Key messages Experiential knowledge in HP remains undervalued and untapped in France. Dedicated practitioners, policymakers and researchers formed a multidisciplinary committee to promote and disseminate EKHP. A multilevel strategy combining advocacy and tool building can be effective at promoting experiential knowledge. A multidisciplinary coalition provided the necessary context-specific levers in France.

Research paper recommender system based on public contextual metadata

Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

Greening and Browning Trends across Peru’s Diverse Environments

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

Arap Dilinin Yapısından Kaynaklanan Çeviri Zorlukları

Çeviri, insanlık tarihi boyunca toplumların bilimsel, kültürel, edebî, diplomatik vs. nedenlerle başvurduğu bir yoldur. Toplumlar ve diller arasındaki etkileşim, bir topluma veya dile ait zenginliğin diğerine aktarılması kanalıyla etkin bir şekilde sağlanmıştır. Çeviri eyleminin kendine has kural ve yöntemleri olmakla beraber birtakım zorlukları da vardır. Bu çalışmada daha çok çeviri deneyimi çerçevesinde karşılaşılan zorluklar ele alınmıştır. Çalışmada ele alınan yapısal zorlukların başında okuma zorluğu gelmektedir. Bilindiği gibi Arap Dilinde hareke kullanımı bazı metinler dışında hemen hemen yok gibidir. Bu durum sessiz harflerin okunmasını zorlaştırmakta, doğru anlam ve doğru çevirinin önünde bir engel oluşturmaktadır. Dilbilgisi kaynaklı zorluklar da çeviri zorluklarına sebebiyet vermektedir. İyi düzeyde gramer bilgisine sahip olanlar bile dilbilgisi kaynaklı zorluklarla karşılaşabilmektedir. Arapça i’râb kurallarının teorik düzeyden pratik düzeye aktarılması ve bol metin deneyimi zorlukların aşılmasına yardımcı olacaktır. Bu zorluklar örnek cümle ve metinlerle izah edilmiş, söz konusu cümlelerin tercümeleriyle ilgili öneriler sunulmuştur. Eşdeğer ifadeler bulunmaya çalışılmıştır. Kaynak dil ile hedef dil arasındaki dil yapısı, kültür, üslup vs. gibi farklılıklar eşdeğer çeviri imkânlarını kısıtlamaktadır. Diller ve kültürler arasındaki tüm bu farklılıklara rağmen eşdeğer çeviri imkânları gösterilmeye çalışılmıştır. Dillerin kendine özgü yapısından, çevirmenin bizzat kendisinden yahut iki dilin sahip olduğu kültürel farklılıklardan dolayı çeviri işleminde birtakım zorluklar yaşanabilir. Çevirmen her iki dilin de özelliklerine son derece hâkim olmalıdır. Böylece çeviri esnasında ortaya çıkan bu sorunların üstesinden gelecektir.

Penggunaan Algoritma Profile Matching Untuk Perekrutan Karyawan

The success of a company is located on how to manage the human resources it owns. The proper management of resources and really appropriate employee field expertise is mandatory. To get a truly qualified human resources then it is necessary to do the recruitment of employees who actually comply with the operational standards of procedures. Inappropriate employee recruitment process will result in employees with poor performance and not as expected by the company. Assessment of prospective employees test results sometimes with certain prospective employees to harm the company. To overcome the problem then this research using profile matching method with the result of the research in the form of each prospective employees. Leaders can see the value of each prospective employee and can make a decision

An integrated conceptual framework of decision-making in soccer refereeing

ABSTRACT Decision-making (DM) is a critical aspect of soccer refereeing. Referees make numerous repetitive decisions, many of which are dependent on appropriate field location and effective interaction with their assistants. Considering the complexity of the sequential DM process, it is not surprising that referees exhibit high decision error rates. Most extant research has focused on the underlying mechanism of the DM process as well as on various influential factors. Less attention has been given to models that consider the range of influential factors which affect the DM process. This article, therefore, presents a new conceptual framework of sequential DM for soccer referees that relies on Tenenbaum’s (2003. Expert athletes: An integrated approach to decision making. In J. Starkes & A. Ericsson (Eds.), Expert performance in sports: Advances in research on sport expertise (pp. 191–218). Human Kinetics) conceptual framework and the existing literature. We start by reviewing research on DM in soccer refereeing and then discuss conceptual considerations. We then present the new conceptual framework. This model incorporates a range of factors, including physical fitness and fatigue, field positioning, visual attention, contextual factors, game management, expertise, psychological factors, and team factors. Data obtained from a sample of 20 elite referees and assistants demonstrated the face validity of the new model. Finally, we introduce implications for training referees’ DM skills and highlight avenues for future research and conceptual developments.