Resource Assessment Research Articles

Cardio Vascular Disease Risk Assessment by Medical Officers According to the Guidelines at Healthy Lifestyle Centers in Western and North Western Provinces and the Factors Affecting Implementation of Guidelines

Introduction: Healthy Lifestyle Centers (HLC) were established in 2011 to carry out Cardiovascular Disease (CVD) Risk Assessments and follow up with patients. The capacity of primary care institutions (PCI) for CVD risk assessment has not been studied in the recent past.Objective: To evaluate the existing CVD risk assessment guideline and assess its application process by Medical Officers (MOs) and facilities available at HLCs for CVD risk assessment.Methodology: A descriptive study with three components was carried out. Component one – CVD risk assessment Guideline evaluated through Key informant interviews (KII); component two- assessment of the Knowledge and practices on CVD risk assessment of all MOs working in HLCs using a self-administered questionnaire and an observational checklist; component three- assessment of available facilities and resources to carry out CVD risk assessment using a checklist.Results and Discussion: The study was conducted in 141 HLCs accessible during the Covid period. Assessment of CVD risk assessment guidelines showed Problems in methodological standards and in the identification and summary of evidence. MOs need to gain Knowledge in screening those under 35 years, in reading the WHO/ISH Risk Prediction Chart and in advising on further management and follow-up. All HLCs have at least one medical officer, but there is a lack of nursing officers and health care assistants.Conclusions and recommendations: There was a gap in Knowledge in screening criteria and management of those under 35 years of age. It is recommended to carry out training at national level every six months so all staff newly appointed to primary care could be trained. It is also recommended to appoint at least one nursing officer or a Public Health Nursing Officer (PHNO) to an HLC.

Hybrid optimization for DC resistivity imaging via intelligible-in-time logic and the interior point method.

Geophysical DC resistivity imaging is crucial in subsurface exploration, environmental studies, and resource assessment. However, traditional inversion techniques face challenges in accurately resolving complex subsurface features because of the inherent nonlinearities in geophysical data. To overcome these challenges, we propose a hybrid optimization approach that combines incomprehensible but intelligible-in-time (IbI) logic with the interior point method (IPM). The IbI logic framework leverages complexity and temporal intelligibility, allowing for a dynamic interpretation of subsurface phenomena. By integrating IbI with IPM, our approach benefits from global exploration and local refinement, leading to improved convergence speed and solution quality. The objective function formulated for the inversion process includes data misfit and model regularization terms, which promote accurate and smooth solutions. Our methodology involves the use of the IbI logic algorithm (ILA) for initial global search, which identifies promising regions in the search space. This is followed by the application of IPM for local optimization. This synergy between the two algorithms ensures robustness and efficiency in handling large datasets and complex geological models. We conducted tests using synthetic and real DC resistivity data to validate our approach. The synthetic test demonstrated the accurate reconstruction of subsurface anomalies, whereas the real data test successfully identified fault zones, which is consistent with previous studies. The hybrid optimization algorithm significantly improves the resolution of subsurface structures and enhances geophysical data inversion practices. It balances the exploration and refinement phases effectively, optimizing the computation time and ensuring precise model delineation.

Estimating Cooling Energy Demand from Building Attributes and Environmental Parameters using 3D City Models

Abstract. Most of the global population have shifted to urbanization with advancements in technology. With this transition comes the responsibility of applying these technologies to promote sustainable development practice. Since energy demand is highest in the urbanized areas, it is important that proper assessment and management of energy resource is considered in policy-making and urban planning. This study investigated the estimation of cooling energy demand in Iloilo City Proper, Philippines using a 3D city model integrated with building attributes including building functions and year of construction, while also taking into consideration meteorological factors in the area. The proposed method used the application SimStadt2.0, following the German computation standard DINV18599. Building functions and year of construction were extracted from available building attributes data and satellite images respectively, using the free and open-source software QGIS. A CityGML Level of Detail 1 was generated from 3Dfier using building footprints and LiDAR point cloud data, along with the extracted building attributes of the 5,426 buildings. Meteorological data from INSEL 8 were also considered in the estimation of cooling energy demand in SimStadt2.0. Results showed a monthly energy demand of 12.33 kWh to 313,530.08 kWh in the study area. The estimated energy demand values were higher than the standard mean for different building functions in the country, but within the expected range of values for each season. Urban Heat Islands (UHIs), analyzed using Land Surface Temperature (LST) values, also have significant correlation to areas with higher cooling energy demands. However, inconsistencies can imply the need for further investigation.

Returning to work after childbirth and women’s mental health: the place of work-family conflict and of social supports in a population of working mothers in Paris area

Abstract Objective Analyzing in a cross-sectional study the factors associated with mental health in 1511 recently delivered women returning to work, the mediating role of work-family interface and the moderating role of social supports. Methods Data were collected at 3 months after returning to work to assess work characteristics (COPSOQ), work-family conflict (SWING), anxiodepressive symptoms (HADS), commuting, childcare, household demands, social supports. Results Anxiety and to lesser extent depressive symptoms were associated with work factors, the opposite for the non work demands. The mediation by work-family conflict and the moderation of social supports were present especially for anxiety symptoms. Taking into account the parental constraints by the hierarchy, family/friends support and paid personnel reduced anxiodepressive symptoms. Conclusions Promoting mental health at returning to work of new mothers needs the assessment of total workload and resources.

Exploring geothermal energy prospects through radioelement enrichment zones in Gabal Abu Hashim area in southeastern Aswan by geophysical and remote sensing data integration

Alteration zones in the Eastern Desert are promising for minerals and geological resources exploration. Remote sensing and geophysical techniques offer cost-effective tools for identifying new exploration sites. Additionally, their use in mapping potential alteration zones is crucial for enhancing exploration. Geophysical and remote sensing data are integrated to perform a comprehensive study of minerals alteration associated with radioactive materials under controlling geological structures. This study aims to explore the associated radiogenic heat and geothermal energy to expand the geothermal resources assessments. The study utilizes Spectral Angle Mapper classification, band algebra, principal component analysis (PCA), surface lineament density, and decorrelation stretch techniques and Multiple Criteria Decision Analysis to enhance the mapping of mineralized alterations in the study area. It uses VNIR-SWIR ASTER data to identify hydrothermal alteration minerals and dominant alteration zones, also Landsat 8 Thermal Infrared Sensor (TIRS) offers two adjacent thermal bands, benefiting land surface temperature (LST) radiation from space in the Gabal Abu Hashim area. The area contains minerals alteration indicators like chlorite, alunite, illite, kaolinite, sericite, with less abundant ferrous minerals and epidote. Airborne gamma-ray spectrometry (AGRS) was used to identify naturally occurring radioactive anomalous zones, including potassium (K), equivalent uranium (eU), and equivalent thorium (eTh), to estimate the radiogenic heat production (RHP) in selected areas of the study area. The examination of AGRS data indicated that the studied region has radioelement concentrations ranging from 2.8 to 148 ppm, 18 to 144 ppm, and 0.004 to 9% of (eU), (eTh), and (K), respectively, indicating the existence of various rock types. The Radioelements Composite Image (RCI) successfully highlighted the radioelements enriched zones associated with younger granite, older granite, and metamorphic rocks, particularly those with extensive hydrothermal alteration. The results successfully discriminated alteration zones associated with radioelements K, U, and Th potential parts in the regional shear oblique zone. The weighted overlay GIS model was used to produce the alteration zones potentiality map, and to identify five zones of significant variations in heat production across different geological formations. The northern and southeastern regions demonstrate high alterations and land surface temperature corresponding to areas of high fault density and shear zones. The results of this study reveal that the proposed methods of remote sensing and AGRS data are effective in detecting areas rich in K, eU, and eTh in alteration zones associated with high radiogenic heat production in younger granite, older granite, and metamorphic rocks.

Enhancing Oil and Gas Exploration and Development: The Role of Remote Sensing

The oil and gas industry are getting weaker in terms of exploration and development; it demands a new generation of game-changing solutions as far as resource extraction is concerned with environmental footprints. Remote sensing technology has become an essential facilitator in this mission by giving detailed spatial data and information to support decisions. This article looks at the diverse ways in which remote sensing is used to contribute to oil and gas exploration, from geological mapping and resource assessment through environmental monitoring to regulatory compliance. Using satellite, airborne and geophysical data to identify the best drilling locations, conduct hydrocarbon potential assessment and monitor any environmental consequences is possible with modern technology its need for ground surveys and exploratory drilling. Remote sensing is also playing a key role in exploration activity and EOR by monitoring surface deformation. In the end, use of remote sensing does more than just make operations easier and lowers operational costs in the oil and gas industry and supports environmentally and risk mitigation. Keywords: Oil and Gas Exploration, Geological Mapping, Environmental Monitoring, Drilling Optimization, Remote sensing

Measuring and modelling directional effects in the frame of TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope)

Abstract. TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.

Influence of Model Resolution on Wind Energy Simulations over Tibetan Plateau Using CMIP6 HighResMIP

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain.

Applying triple collocation for verifying wind resource measurements and reanalysis data

Abstract Dual comparisons or dual collocation, respectively, is a standard approach in wind data analysis, and particularly in the wind energy resource assessment context, for comparing and relating two time series from different sources describing the same quantity as e.g. the wind speed at a specific height over a certain period of time. This comparison can be used to validate a data source, or to derive a correction for one of the two datasets. Though being widely used, dual collocation comes with inherent flaws which are partly unknown or ignored. A major problem is that dual collocation requires the definition of a reference in advance. Triple collocation may instead allow for a more objective and comprehensive evaluation, where (three) suitable data sources are available, and possibly help to identify which datasets are most suited to represent the relevant wind resource. In this study, we apply the triple collocation methodology to two typical wind resource assessment test cases. Results are promising in providing more information on the analysed datasets and improving the use of different types of data for wind resource assessment in the future.

Assessment on major browse feed resources and determine their chemical composition in Korhaye zone, Somali Region, Ethiopia

Assessment of major browse feed resources and determine the chemical composition of selected browse feed resource in Korahay Zone, Somali Regional State, was the aim of this study. Kebeles and the household heads were selected purposively in study area. The samples of fresh leaves (800 g) of selected browse species were collected to determine chemical compositions. SPSS version 20 was used for data analysis. The feed related constraints were ranked and analyzed by the ranking index. The current result indicated that, total of 22 trees, 6 shrub, 4 bush and 6 herbaceous species used as feed for livestock in dry and/or wet season were identified in the study area. DM, OM, CP, NDF, ADF, ADL and Ash of Dobera glabra, Grewia bicolor, Boscia minimfolia, Prosopis juliflora, Acacia Senegal, Unidentified (Alankhayir), Unidentified (Geed chic) were determined in current laboratory analysis. DM, OM, CP, NDF, ADF, ADL and Ash of Dobera glabra was 92.8 %, 94.28 %, 14.4 %, 42.5 %, 26.9 %, 5.22 % and 5.73 % respectively. Finally, feed quality and quantity, drought, invasive species and ecological deterioration were identified as major constraints of feed resources in study area. Due to this, intervention and further study should be conducted to tackle existing feed and feed related issues.

The benefits and burdens of wind power systems in reaching China’s renewable energy goals: Implications from resource and environment assessment

Recognizing the economy’s growing reliance on global energy landscape transformation on wind power deployment, as well as the general reality that renewable facilities require lower operational but higher up-front inputs than fossil-based power systems, this paper focuses on the life-cycle burdens of wind power systems and their substitution benefits compared with coal-fired power systems. The estimation considers the consumption of nonrenewable sources of energy, emissions of greenhouse gases, and other environmental resource factors that have received significantly less research attention, such as industrial land use, water use, PM2.5, SO2, NOX, and Hg emissions. The scope of this study differs from earlier ones in that it includes a comprehensive description of all the equipment, materials, and services used, as opposed to earlier studies that either omit crucial supporting infrastructure or just focus on the plant’s physical structure as its primary materials. Results based on a typical plant in China show that the state-of-the-art onshore wind power systems can provide significant reductions in nonrenewable energy use (9.2 MJ/kWh) and GHG emissions (782.8 GtCO2/kWh). To produce an equivalent amount of electricity, wind power systems require more than three times industrial land use and almost one-third of industrial water use of that for traditional coal-fired power systems. The avoided SO2, Hg, PM2.5, and NOX emissions per unit of electricity generation account for 60.6%-89.3% of the total air pollution emissions induced by supercritical coal-fired power systems. By integrating 3797 operating plant-specific data and fixed-point wind energy information, this study scales up results of the single plant to a country level. The macro picture implies different opportunities born by wind power systems in easing multiple resource and environmental pressures, highlighting the significance of designing hierarchical strategies to improve penetration levels of wind power. By 2050, cumulative climate benefits obtained from onshore wind power technology are predicted to reach 74.2 Gt CO2, achieving around 17.2%-45.5% of the national carbon-neutral goal.

Accuracy assessment of offshore wind measurements partially compensated by MCP method considering data availability

Abstract In Japan, most offshore promotion areas for bottom-fixed wind turbines are in near-shore areas, where offshore wind speeds are not uniform. The dual-scanning LiDAR system (DSL), which is a remote-sensing technology, is widely used to measure the mean wind speed, wind direction, and turbulence for resource and site assessments. However, the availability of DSL data tends to be lower than the key performance indicators proposed in the Carbon Trust in Offshore Wind Accelerator project owing to the characteristics of DSL. Missing observed data can be predicted using the measure–correlate–predict (MCP) method, and the uncertainty of the MCP method can be examined using a prediction function. However, a method to evaluate the accuracy of the final dataset comprising observed data and data predicted by the MCP method is yet to be devised. In this study, a set of formulas is proposed to evaluate the accuracy of indices, such as the coefficient of determination, slope, and offset of linear regression, for a partially complemented dataset using the MCP method. The proposed formulas are validated against on-site measurements.

Wind stress modification by offshore wind turbines: A numerical study of wave blocking impacts

Offshore wind energy, as a clean and renewable resource, offers numerous advantages over onshore wind energy due to higher wind speeds and greater turbine capacity. However, the inadequate representation of wave-atmosphere interaction within the marine-atmospheric boundary layer may constrain wind resource assessments and, consequently, the design and layout of offshore wind turbines. By using the third-generation spectral wave model WAVEWATCH III, high-resolution numerical experiments which are capable of resolving the wind turbine foundation have been conducted to explore the blocking impact of wind turbine foundation on downstream wind stress. The findings provided significant insights into the factors influencing this effect, including foundation diameter, sea state, water depth, and wave directional spreading. Specifically, higher model resolution enables a more detailed characterization of wind stress distribution behind wind turbines. Increasing the foundation diameter resulted in a reduction of downstream wind stress. Notably, changes in wind stress exhibit a strong dependence on the sea state. The use of double periodic boundary conditions for the simulation domain enables an approximate representation of the entire wind farm by using a single turbine. Model results indicate that after waves pass through 20 turbines, domain-averaged wind stress decreases by approximately 3%, a figure that increases to 6% after passing through 50 turbines. The findings suggest the need to explore to what extent the changes in wind stress can alter wind profiles and how to parameterize the blocking impact of turbine foundations in wave models. This could have significant implications for wind farm site selection and layout design.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

ENHANCING EFFICIENCY AND SUSTAINABILITY: GREEN ENERGY SOLUTIONS FOR WATER SUPPLY COMPANIES

Water supply enterprises significantly impact local communities by providing essential services. These companies face high electricity costs for water extraction, purification, and transportation, affecting efficiency and reliability. Therefore, implementing innovative technologies to enhance sustainability in the water supply sector is crucial. This article explores the opportunities and strategies for renewable energy transition at water supply companies. Using investment, sensitivity, and strategic analyses, as well as a case study approach, the research examines technologies, conceptual frameworks, mechanisms, and approaches to integrate green power into water supply operations, addressing high energy costs and promoting sustainability. The article identifies solar, wind, hydroelectric, biomass, and geothermal energy technologies as the most prominent renewable power solutions for water supply enterprises. The developed conceptual framework for implementing these technologies includes needs assessment and goal setting; resource assessment and technology selection; system design and integration; financing and investment; regulatory compliance and permitting; stakeholder engagement and capacity building; and monitoring, evaluation, and continuous improvement. The mechanisms and approaches to integrate green energy solutions within the developed framework can involve on-site renewable energy generation, power purchase agreements, energy storage and microgrid systems, energy efficiency and demand management, and collaborative and community-based models. As a case study, the article examines a 120-kW solar power plant project for a water supply enterprise, demonstrating profitability with a net present value of 60,370 USD and an internal rate of return exceeding 21%. The project's payback period is estimated at 8.38 years, acceptable within industry standards. Sensitivity analysis indicates the project's financial resilience. Increasing electricity prices will boost profitability, justifying the solar power plant investment amid inflation and economic instability. Additionally, the project ensures reliable water transport and environmental benefits by reducing CO2 emissions through solar energy use. Thus, transitioning to renewable energy at water supply enterprises is feasible and essential for long-term sustainability, transforming operations to be more resilient, efficient, and environmentally friendly.

Cost management analysis of Objective Structured Clinical Examination (OSCE): guide to the universities of medical sciences

BackgroundThe Objective Structured Clinical Examination (OSCE) is a crucial assessment tool for evaluating learners’ ability to apply theoretical knowledge in practical clinical situations. It is widely accepted by both students and educators, and the costs associated with conducting OSCE assessments vary depending on the field of study, how it is implemented, staffing needs, standardized patients, and duration of the examination. This study examines the expenses related to administering OSCEs in medical universities.MethodConducted from June to September 2023; this mixed-method study elucidated the cost intricacies of executing a two-day OSCE with 14 eight-minute stations for 100 nursing students. This process unfolds in two phases: a qualitative segment comprising text reviews and 45-minute in-depth interviews with faculty members and OSCE experts, leading to the development of a validated checklist, followed by a quantitative phase in which the tool was distributed to 25 faculty members and 5 specialists for completion.ResultThe examination costs were delineated into three primary components: time, human resources, equipment, consumables, and necessary supplies. In 2023, the total implementation cost of the OSCE for 100 students across 14 clinical stations was $1028.07, with an estimated per-learner cost of $37.50. Human resources incurred the highest expenditure ($1649.37); while supply costs were relatively lower ($1072.17). Educational infrastructure expenses were excluded because the study focused on the Nursing and Midwifery Faculty’s Clinical Skills Center.ConclusionVarious factors influence OSCE costs, including national production capabilities of medical supplies, institutional credibility, governance status, examination frequency, student demographics, assessor composition, station count, course content, and examinee volume. The insights derived from this comprehensive examination are significant as entry benchmarks for healthcare systems and higher-level academic evaluations. Understanding OSCE cost dynamics facilitates resource optimization and assessment strategy refinement, thereby improving medical education efficacy.

The Environmental Niche of the Light Purse Seine Fleet in the Northwest Pacific Ocean Based on Automatic Identification System Data

Ecosystem-based fisheries management requires high-precision fisheries information to provide relevant data for natural resource management, assessment, and marine spatial planning. This study utilizes Automatic Identification System (AIS) data from light purse seine vessels from the Chinese mainland that were collected from May to November between 2020 and 2022, along with the corresponding environmental data. By applying boosted regression trees (BRTs) and generalized additive models (GAMs), this study establishes nonlinear relationships between fishing intensity and predictor variables and explores the ecological and environmental drivers behind the spatial distribution of light purse seine vessels from the Chinese mainland in the Northwest Pacific. This research identifies the key influencing factors and reveals significant seasonal preferences for different marine environments in various months, with chlorophyll-a being the primary influencing factor. The predicted fishing effort closely resembles observed data, providing valuable information to support fisheries resource management and planning.

Assessment of Major Livestock Forage Resource and Forage Balance in Kembata Tembaro Zone of southern Ethiopia

The study was conducted in Kambata tambaro Zone, centeral Ethiopia Region; with the general objective of assessing the livestock forage balance using cross-sectional study design from November 2018 to May 2019. For this study Three districts, namely Doyogana, Tambaro and Hadaro tunto were purposively selected from the study area. A total of six kebeles and 361 respondents were selected by random sampling technique from the study districts. Semi-structured questioners, focusedgroup discussion, key informant interview and personal observation were used for data collection. The data were analyzed using statistical package for social science (SPSS, version 23). Analysis of variance with Tukey test was used for mean comparison of the quantitative variables while chi-square test was employed for significance level of qualitative variables. Accordingly, the overall average landholding per respondent in Kambata tambaro was 1.79ha while the holding in the Hadaro, Tambaro and Doyogana found to be 1.86, 1.67, 1.80ha, respectively. The average livestock holding of a household in the Hadaro, Tambaro and Doyogana was 3.90, 4.06 and 4.56 TLU, respectively, with an overall average of 4.13 TLU. Households in the Doyoganadistricts possessed significantly larger (P<0.05) number of TLU than the households in the other two distircts while no significant difference (P>0.05) was reported amongdistricts in landholding size. The available feed sources ranked by the respondents in decreasing order includs straw, grazing land, hay, green fodder, maize and sorghum stover, bush and forest and concentrate. The overall average utilizable feed DM supply in the study area was estimated to be 4.74 ton with significantly higher (P<0.001) DM supply in the Hadaro (5.75 tons) than in the Tambaro (4.02 tons) and Doyogana (4.34 tons per respondent) areas. However, for year round feeding, the average DM demand of livestock in the Hadaro, Tambaro and Doyogana was 8.89, 9.26 and 10.41 tons per respondent, ..

Psychological Capital Promotion in Universities

Abstract Background The rise in student depression and anxiety highlights a crucial intersection between education and public health. Academic pressures intensify mental health issues, necessitating comprehensive strategies to address them. Objectives This study assessed the effectiveness of a Psychological Capital (PsyCap) intervention in enhancing university students’ PsyCap and reducing anxiety, aligning with broader public health education goals. Methods A randomized controlled trial was conducted, where students either received a PsyCap intervention or were placed on a wait-list. The intervention, based on Luthans et al.’s guidelines, was delivered online and included self-reflection, goal-setting, and resource assessment. Results Post-intervention, the PsyCap of students in the intervention group significantly increased (M = 1.098, SD = 0.24, CI [0.98, 1.21], t(17) = 19.72, p < .001). There was also a significant reduction in GAD scores (M = -0.24, SD = 0.46, CI [-0.47, -0.01], t(17) = -2.19, p = .043). No significant changes were observed in the control group. Conclusions Short-term PsyCap interventions can significantly improve students’ mental resilience and decrease anxiety, suggesting their integration into educational curricula. This study underlines the need for educational and public health sectors to collaborate in mitigating mental health issues among students, particularly in response to crises like COVID-19. Addressing the rising anxiety among students through brief, online PsyCap interventions shows promise. This approach not only enhances PsyCap but also lowers anxiety, advocating for their regular inclusion in educational settings to support student mental health. Key messages • Results from a controlled trial revealed increases in PsyCap and decreases in anxiety.Findings advocate integrating such interventions into educational curricula promoting student mental health. • Recent trends indicate a rise in anxiety among students, challenging both educational and public health. Addressing this, the study evaluated the impact of a brief online (PsyCap) intervention.

Ocean Wave Energy Conversion: A Review

The globally increasing demand for energy has encouraged many countries to search for alternative renewable sources of energy. To this end, the use of energy from ocean waves is of great interest to coastal countries. Hence, an assessment of the available resources is required to determine the appropriate locations where the higher amount of wave energy can be generated. The current paper presents a review of the resource characterizations for wave energy deployment. The paper gives, at first, a brief introduction and background to wave energy. Afterward, a detailed description of formulations and metrics used for resource characterization is introduced. Then, a classification of WECs (wave energy converters) according to their working principle, as well as PTO (power take off) mechanisms used for these WECs are introduced. Moreover, different sources for the long-term characterization of wave climate conditions are reviewed, including in situ measurements, satellite altimeters, and data reanalysis on one hand, and numerical simulations based on spectral wave models on the other hand. Finally, the review concludes by illustrating the economic feasibility of wave farms based on the use of the levelized cost of the energy index.