Innovating in an Uncertain World: Understanding the Social, Technical and Systemic Barriers to Farmers Adopting New Technologies

Modeling the cognitive antecedents and consequences of emotion

Agriculture has been identified as a stressful industry and there is evidence that chronic stress may contribute to the development or progression of mental health disorders, specifically anxiety and depression. Young adult farmers and ranchers may be at increased risk of mental health disorders when compared to more experienced counterparts due to additional stressors. The objectives of this study were to identify the occupational stressors of young adult farmers and ranchers in the Midwest and estimate the prevalence of anxiety and depression among this population. An online survey consisting of reliable and valid instruments was used to identify the sources of stress and prevalence of anxiety and depression among the young farming and ranching population. The survey included items related to stress, anxiety, depression, general health, and farm/personal demographics. A total of 170 young farmers and ranchers responded to the survey. The mean age of the sample was 28.9 (SD 4.4) years. Approximately 71% of respondents met the criteria for Generalized Anxiety Disorder (GAD-7 score ≥ 5) and 53% met the criteria for Major Depressive Disorder (PHQ-9 score ≥ 5). Of seven presented stress domains, personal finances and time pressures were the sources of greatest concern. Personal finances, time pressures, economic conditions, and employee relations were associated with anxiety and depression. The burden of depression and anxiety is high among young adult farmers and ranchers. Stressors commonly affiliated with farming and ranching are associated with anxiety and depression. Additional research should further explore the burden of mental health disorders among the population and examine protective factors for mental illness and opportunities for interventions.

Indonesia has a strong commitment on increasing renewable energy resource utilization to produce electricity. One of the potential renewable energy resources is hydropower. In Indonesia, the hydropower is divided into small scale hydropower (less than or equal to 10 MW) and hydropower (more than 10 MW). In order to enhance the utilization of small scale hydropower several financial and regulation supports have been released. In this study the growth of small scale independent power producer has been investigated. The study focused in Sumatera Utara province which is known as the province with big potency of small scale hydropower and the electricity infrastructure is quite developed. The results reveal that there are 115 locations of small scale hydropower projects had been developing in the province. The total capacity is 930.3 MW. However, only seven of the projects (6%) have been in operation stage and produce electricity to the grid. Almost all of the projects have experienced on over design budget and time. There are several technical and social barriers that affect the growth. Technical barriers include low quality of pre and full feasibility studies, low quality of detailed engineering design (DED), limited access to relevant expertise, tend to underestimate the technical complexity of developing a small-scale hydropower. Social barriers include the environmental issue, land acquisition, and electricity needs of local people.

Bioprocess engineering: now and beyond 2000

Bioprocess engineering: now and beyond 2000

Removing barriers to a sharing economy helps attain sustainable development goals in ASEAN countries

The Investigating of Barriers of Development of E-banking in Iran

BackgroundSince the 2000s, local governments have contracted out more and more social services to social work organizations in China. Social workers are thus experiencing the inconsistency between local governments' and clients' demands and the deviation from the professional duty of helping clients, which may result in conflicting and unclear roles in their jobs and further lead to burnout. Based on the Role Stress-burnout Model and the previous theoretical and field-work investigations, this study examined the effects of the government-client work interaction frequency gap on social workers' burnout as well as the mediating effects of role ambiguity and conflict and the moderating effects of the non-front-line work.MethodsA cross-sectional study of 2,643 front-line social workers and 2,599 supervisors or managers from 56 major cities all over China was conducted. Work burnout was measured by the 22-item three-dimension Maslach's Burnout Inventory Scale. Rizzo et al.'s 14-item scale measured role conflict and ambiguity. The government-client working interaction frequency gap was measured by the difference between the five-point level of work interaction frequency with governments minus the one with clients. Structural equation modeling was adopted to test the mediation and moderation models.ResultsResults showed that for the front-line social workers, besides directly reducing personal accomplishment, the government-client work interaction frequency gap could indirectly neutralize its alleviating effects on emotional exhaustion (Mediating effect ratio = −63.64 %), make its total effects on depersonalization significant (37.03%), and reduce personal accomplishment further (23.08%) through increasing social workers' feeling of role conflict. However, the above mediating effects of role conflict were not significant for social workers with extra management or supervision workload, revealing the moderating effects of non-front-line work.ConclusionsThis study revealed that front-line social workers in China who had more work interaction with governments and less with clients could have higher role conflict, increasing their burnout further. Therefore, social work educational programs should include adequate mental adjustment courses and practical emplacement to prepare students for the potential role conflict. Furthermore, higher-level governments should issue relevant regulations to form a collaborative rather than an employment relationship between local governments and social worker organizations.

ABSTRACTGiven the competitiveness of twenty-first-century airport landscape, catalyzed by airports’ evolution toward multi-service, and market-driven firms, a thorough investigation into employees’ creativity and its antecedents at the airport environment is warranted. Adopting the two-dimensional job demand stressors – outcome relationships framework and the cognitive-relational theory of stress, the current study interrogated the challenge (i.e. workload and time pressure)/ hindrance (i.e. role conflict and role ambiguity) stressors – creativity curvilinear relationships, and the buffering effects of within-person resources – dispositional mindfulness, and core self-evaluation. Using multi-sourced, cross-sectional data from employees in three airports in Ghana, the research findings showed creativity to have a U-shaped relationship with role ambiguity and role conflict, but with time pressure the relationship was an inverted U-shape. Employees’ workload showed a near-linear relationship with creativity, flattening at high levels of workload. Core self-evaluation displayed itself as an effective buffering component on role ambiguity – and time pressure – creativity relations but not role conflict and workload. Dispositional mindfulness interacted with role ambiguity, role conflict and time pressure – creativity relations, but not workload. To optimize employees’ creative performance, the study findings make a strong case for attending to individual-level factors necessary for stressors management. Further implications and recommendations are discussed.

The objective of this research paper is the exploration of the crucial factors in adoption of electrical vehicles in India using Fuzzy Analytic Hierarchy Process methodology. A group dialogue with several electrical vehicle users and experts, academicians in this domain has been performed to identify factors in adoption of electrical vehicles in India. In total, nine factors in adoption of electrical vehicles were recognized and the comparative weight of each factors in adoption of electrical vehicles is calculated with triangular-styled fuzzy numerals in paired comparative study. Later, the critical factors in adoption of electrical vehicles are categorized in 3 categories as ‘Technical Barriers’, ‘Infrastructure Barriers’, ‘Economical Barriers’. These 3 categories of factors in adoption of electrical vehicles are evaluated and as compared the use of fuzzy analytical hierarchical technique method to get ranking in phrases of priority. The ranking of these factors may be instrumental for policy makers to plan future strategies in Indian context.

The COVID-19 pandemic disrupted teaching and learning in a variety of institutions. During the pandemic, e-learning replaced traditional classes to prevent transmission among lecturers/teachers and university students. The study’s main objective was to identify the attitude and barriers to e-learning perceptions among university students and lecturers during the COVID-19 pandemic in Malaysia in 2021. The specific objectives were to identify the infrastructure, attitudinal, technical expertise, and human and educational skill barriers to e-learning implementation during the COVID-19 pandemic and to identify significant differences in e-learning perceptions among university students and lecturers. A cross-sectional study collected closed-ended questionnaires from university students and lecturers shared via electronic platforms. The minimum sample size required in this study was 462; 847 respondents (217 were lecturers/teachers and 630 university students) voluntarily participated. Cronbach’s alpha coefficient of the questionnaire of attitudes and barriers and its values was 0.851 and 0.96, respectively. Findings were tabulated with mean scores for the attitudes towards the use of e-learning, and barriers to e-learning included five categories: infrastructure barriers, attitudinal barriers, technical expertise barriers, human barriers, and educational skill barriers. University students and teachers/lecturers did not prefer replacing traditional teaching methods with e-learning as it affects their interaction. Administration, faculty admins, and the department of education should take note of the improvement and the necessary measures that can be applied for effective and better teaching and learning during the lockdown due to COVID-19.

uropean Union (EU) has set the target of achieving carbon neutral society by 2050. The main way to ensure low carbon energy transition is to accelerate the penetration of renewables in households. However, there are many economic, social, technical, and behavioural barriers to this. In addition, the technologies for renewable energy generation are not affordable for low income households which are experiencing energy poverty and energy affordability problems. The paper critically analyses the barriers and state policies and measures for the support of renewable energy micro generation technologies in households. Based on the conducted analysis, the paper discusses the best ways to combat energy poverty and transition to low carbon energy in the EU. The main findings of the paper indicate that developing well-targeted policy measures for support of renewable energy technologies and energy renovation would be more beneficial than paying energy bills of low-income vulnerable population. Such policies would also help to solve the problem of just low carbon energy transition, as currently the vulnerable population are facing greater economic, social, behavioural, infrastructure and other barriers to using renewable energy in their homes.

IntroductionThe COVID-19 pandemic has impacted multiple facets of daily living: personal finances, physical activity, and mental and physical health. These changes can result in additional stress and negatively affect sleep. It is important for sleep medicine providers to understand how their patients are impacted by these changes to optimize their care. In this study, we evaluated the association of poor sleep with stress, anxiety, emotional support, social isolation, and depression among sleep medicine clinic patients during the COVID-19 pandemic.MethodsSleep medicine clinic patients were distributed an online survey at baseline followed by a 6-month follow-up survey (December 2020 - May 2021). Participants answered questions regarding Insomnia Severity Index (ISI), Patient-Reported Outcomes Measurement Information System (PROMIS) measures (sleep disturbance and sleep-related impairments), and COVID-19 testing. Stepwise linear regression was performed using SAS to determine if self-reported poor sleep predicted stress, anxiety, emotional support, social isolation, and depression. This study was approved by Baylor College of Medicine IRB. Informed consent was obtained from all subjects involved in the study.ResultsEighty-one adults completed baseline survey, and 54 adults (mean age 55.2±18.4 years, 61% female, 70% Caucasian) completed 6-month follow-up survey. At baseline, anxiety had a significant effect on sleep disturbance (0.43±0.11, p=0.0001), sleep-related impairments (0.53±0.12, p=0.0001) and ISI (0.28±0.08, p=0.0004). Upon follow-up, an increase in ISI predicted higher perceived stress (0.18±0.07, p=0.013) and worse anxiety (0.61±0.16, p=0.0003). An increase in sleep disturbance predicted a decrease in emotional support (0.25±0.12, p=0.038). Additionally, an increase in sleep-related impairments predicted an increase in social isolation (0.39±0.11, p=0.0002) and depression (0.57±0.07, p<0.0001). Interestingly, only 3 participants tested positive for COVID-19.ConclusionIn this study of sleep medicine clinic patients during the COVID-19 pandemic, we observed that poor sleep predicted greater stress, anxiety, social isolation, and depression along with less emotional support. This study illustrates the importance of addressing stress management, mental health (anxiety, depression), and emotional support when treating sleep medicine clinic patients during the COVID-19 pandemic.Support (If Any)National Institutes of Health (NIH) Grant #R01NR018342 (PI: Nowakowski); Department of Veteran Affairs, Veterans Health Administration, Office of Research and Development; Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413)

Collaborative Signal Processing multiagent -based on a for target tracking in wireless sensor network is proposed in this paper. Dynamic coalition is used to perform distributed tracking. When a target occurs in the sensing area, sensor nodes try to form dynamic coalition in each field for fulfilling tasks. Each sensor node, which has incomplete information about its dynamic and uncertain world, must respond to sensed events within time constraints. The aim of Collaborative Signal Processing in wireless sensor network is to insure that all necessary parts of the tasks being dealt with are involved in the operations of at least one sensor node. All sensor nodes must act in a purposeful and consistent manner within the available computational and resource limitations Utilizing multiagent technology can make good use of each sensor nodes' limited energy and perform tasks coordinately.

Today’s warfighters operate in a highly dynamic and uncertain world, and face many competing demands. Asymmetric warfare and the new focus on small, agile forces has altered the framework by which time critical information is digested and acted upon by decision makers. Finding and integrating decision-relevant information is increasingly difficult in data-dense environments. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. Yet these advanced technologies must be balanced with awareness of the underlying context to accurately interpret machine-processed indicators and warnings and recommendations. One promising approach to this challenge brings together information retrieval strategies from text, video, and imagery. In this paper, we describe a technology demonstration that represents two years of tri-service research seeking to meld text and video for enhanced content awareness. The demonstration used multisource data to find an intelligence solution to a problem using a common dataset. Three technology highlights from this effort include 1) Incorporation of external sources of context into imagery normalcy modeling and anomaly detection capabilities, 2) Automated discovery and monitoring of targeted users from social media text, regardless of language, and 3) The concurrent use of text and imagery to characterize behaviour using the concept of kinematic and text motifs to detect novel and anomalous patterns. Our demonstration provided a technology baseline for exploiting heterogeneous data sources to deliver timely and accurate synopses of data that contribute to a dynamic and comprehensive worldview.