Changing Weather Conditions Research Articles

Housing conditions and the health and wellbeing impacts of climate change: a scoping review.

Housing conditions are emerging as an important consideration in climate change adaptation. Housing modifications have the potential to improve health outcomes by reducing exposure to changing weather conditions and extreme events. This scoping review aimed to explore the existing evidence examining the contribution of housing conditions to the impacts of climate change on health and identify any research gaps. Literature searches were conducted in Scopus and PubMed from January 2013 to September 2023 and data were analysed using thematic analysis. The review included 38 articles consisting of original studies, reviews, and reports, with broad geographical coverage. The most common focus among included articles was on heat-health impacts; housing conditions found to improve heat-health health outcomes included air conditioning, ventilation, and window shading, and there was support for multifaceted housing adaptations rather than single fixes. Ventilation was found to be a priority for improving indoor air quality, while inappropriate insulation and excessive air tightness were found to increase indoor heat and reduce indoor air quality. The scoping review reveals a need for more empirical and qualitative research into indoor heat in homes, climate change hazards other than heat, and intervention studies to inform climate change adaptation policies around housing and improve public health outcomes.

SOLAR TRACKING SYSTEM USING ARDUINO

This research focuses on the development of a solar tracking system using an Arduino microcontroller to optimize the energy efficiency of photovoltaic solar panels. The conventional fixed solar panel setup often leads to suboptimal energy capture as the panel’s orientation remains static, resulting in reduced efficiency due to the sun’s changing position throughout the day. To address this, the proposed system employs a dual-axis solar tracker mechanism that automatically adjusts the solar panel's orientation to align with the sun's trajectory, maximizing solar energy absorption. The system utilizes Light Dependent Resistors (LDRs) placed on either side of the solar panel to detect variations in light intensity. Based on the sensor readings, the Arduino microcontroller processes the information and drives servo motors to move the panel in real time, ensuring it faces the sun. The servo motors allow for precise angular adjustments, enabling both horizontal and vertical movement to track the sun across the sky. The Arduino-based control system is simple, cost-effective, and easily programmable, offering an accessible solution for enhancing solar power efficiency. A comparative analysis between the solar tracker and a fixed-panel setup shows that the tracking system significantly improves energy capture by maintaining optimal sunlight exposure throughout the day. The paper also discusses the challenges involved in system calibration, the reliability of sensor data, and the mechanical aspects of the tracking mechanism. Results demonstrate that the solar tracking system can increase energy output by up to 30% compared to static panels. The findings indicate that the proposed low-cost solar tracking system holds significant potential for large-scale solar applications, contributing to the increased adoption of renewable energy solutions and enhancing the overall performance of solar power systems. Experimental evaluations were conducted to compare the energy output of the solar tracker against a fixed-panel setup. The results indicate a significant improvement in energy generation with the tracking system. The solar tracker’s ability to follow the sun maximizes light absorption and increases the efficiency of the photovoltaic panel by up to 30% compared to a fixed panel. The system demonstrates notable advantages, including higher energy yields, better adaptability to changing weather conditions, and the ability to capture sunlight at different times of the day. Key Words: Solar Tracker, Light Detecting Resistor (LDR), Arduino, Servo Motor

Evaluation of Semantic Segmentation Performance for a Multimodal Roadside Vehicle Detection System on the Edge.

Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks. Different vision sensors offer varying advantages for detecting targets in changing environments and weather conditions. To analyze the benefits of both, corresponding LiDAR and camera data were collected at multiple roadside sites and then trained on separate semantic segmentation networks for object detection. A custom CNN architecture was built to handle highly asymmetric LiDAR data, and a network inspired by DeepLabV3+ was used for camera data. The performance of both networks was evaluated, and showed comparable accuracy. Inferences run on embedded platforms showed real-time execution matching the performance on the training hardware for edge deployments anywhere. Both camera and LiDAR semantic segmentation networks were successful in identifying vehicles of interest from the proposed viewpoint. These highly accurate vehicle detection networks can pair with a tracking mechanism to establish a non-intrusive roadside detection system.

Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation

Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), R2 increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.

Pengaruh Perubahan Iklim Terhadap Pertumbuhan Berbagai Jenis Tumbuhan; Bunga Kertas (Bougenvillea Glabra), Tasbih (Canna Indica), Glodokan Tiang (P. Longifolia), Pucuk Merah (S. Myrtifolium) Dan Lamtoro (Leulamtoro Leucocephala). Di Wilayah Kota Medan Sum

Climate change refers to changes in the pattern and intensity of climate elements over a specific period, typically compared to a 30-year average. These changes can include shifts in average weather conditions or changes in the distribution of weather events. This research was conducted around Jl. Pancing, Medan, North Sumatra, on December 6, 2024, with the aim of analyzing the impact of climate change on the growth of various plant species in urban areas. The research method used is quantitative descriptive. The results show that some plants in the area demonstrate a good ability to adapt to fluctuating temperature, rainfall, and light intensity. Plants such as leucaena, Canna indica, Monoon longifolium, and Bougainvillea glabra exhibit adaptation mechanisms in terms of morphology, physiology, and ecology, enabling them to survive and grow under changing weather conditions due to climate change.

Does The Greenhouse Adoption Improve Farmers’ Welfare? Evidence from Melon Farmers in Indonesia

Greenhouse technology presents a promising solution to the challenges of agricultural productivity and sustainability, particularly for small-scale farmers facing climate change, resource constraints, and unpredictable weather conditions. This study was the first attempt to investigate the impact of greenhouse adoption on farmers’ welfare which focused on household income per capita and food security status. The samples of this study were conducted 201 melon farmers in East Java, Indonesia. This study analyzed data to discern the effects of greenhouse adoption on farmers’ welfare by employing endogenous switching regression to address potential endogeneity issues. The analysis revealed that greenhouse adoption significantly and positively affected household income per capita and food security status. Through rigorous data analysis, this research had contributed valuable insights into the socio-economic implications of adopting greenhouses in agricultural practices. The results highlighted the role of greenhouses in boosting farmers’ resilience, improving livelihoods, and enhancing food security, providing a strong case for promoting greenhouse technology in rural agricultural practices.

Smart Materials in Green Architecture: The Role of ETFE and Phase Change Materials in Sustainable Building Design

Given the importance of energy in achieving environmental sustainability in green buildings, one of the most significant solutions is to reduce physical building consumption and instead use renewable energy to prevent the depletion of natural resources and environmental pollution. Utilizing advanced technologies and intelligent systems in architecture is one of the methods that can achieve energy management in buildings and ensure comfortable conditions for the occupants of green buildings. In the field of future-oriented architecture, it is expected that in the future, materials and technologies developed over the past century will define the different challenges ahead to elevate sustainable development in the building industry. Using solar energy, one of the latest innovations in buildings, and other measures such as ETFE panels (a green roof idea with an intelligent system) and thermochromic materials, which change color to minimize heat exchange between interior and exterior spaces of the building, show that today's world faces energy crises. The use of smart materials tailored to environmental conditions can significantly impact building design and have beneficial effects in terms of compatibility with the environment, increased lifespan of materials, and the adaptability of materials to changing weather conditions, thus aiding in achieving sustainable architecture. Therefore, this article aims to introduce intelligent materials and their applications and benefits in green building architecture based on library studies. The primary goal is to identify and highlight the most crucial aspects of using intelligent materials and their performance in buildings and how these materials behave and perform in architectural designs. The best way to leverage the benefits of these smart energy-saving materials and management for achieving sustainable architecture will be discussed.

Cost-Effectiveness Analysis of a System of Distinguishable Subsystems with Repair Preference and Weather Conditions

Cost-effectiveness analysis of a reliability model of two distinguishable electricity resources is done in this paper. Subsystem-A is taken as the primary, whereas subsystem-B is taken as the secondary source of electricity. Subsystem-A has three modes – operation, repair, and activation, and subsystem-B has four modes – operation, inspection, minor repair, and major repair. Availability of a full-time technician is considered to perform all repair and activation activities. The technician initiates the repair of subsystem-A immediately whenever required, whereas inspection is carried out for subsystem-B to identify the type of repair required. Normal and abnormal weather conditions are considered to study the impact of weather conditions on repair and activation activities. Only subsystem-A needs activation after repair, and no repair/activation is carried out in abnormal weather, while weather conditions do not affect inspection or repair activities of subsystem-B. Failure and repair rates of both the subsystems are exponentially distributed, whereas a general distribution is taken for the operation rate of subsystem-A. Various reliability components like Mean Time to System Failure (MTSF), steady-state availability, busy period of the server, and profit of the system model are evaluated using the semi-Markov process. Random values are taken to show the impact of increasing failure rate of subsystem-A and rate of change of weather condition from normal to abnormal on MTSF and the cost-benefit of the system model. Graphs are drawn for MTSF and profit of the system model, which clearly indicates that MTSF and profit of the system model are higher for a lesser rate of change in weather conditions.

Forecast assessment of water regime efficiency and changes in water consumption on drained lands of Western Polissia of Ukraine in a changing climate

The article presents forecast data on the impact of changing weather and climatic conditions on evaporation and water demand on the drained lands of Western Polissia of Ukraine in different periods of their development. These data are the basis for the development of appropriate adaptive measures aimed at reducing the negative impact of changing weather and climate conditions on drained lands and increasing the efficiency of their use. Computer simulation modeling of various climatic scenarios was planned to accomplish the objective. The forecast was made through a machining experiment based on the implementation of a corresponding complex of forecast-simulation models regarding the main regime-technological variable parameters of drainage systems, local climatic conditions, water regime, water management technologies, and productivity of drained lands for schematized natural, agrotechnical, and meliorative conditions. Vegetation values of total evaporation and water demand formation of drained lands were determined for the long-term forecast module of water supply regarding variable climatic and agro-meliorative conditions. The technological efficiency of different irrigation technologies (subsoil irrigation, sprinkler irrigation) of drained lands was evaluated. The research showed that in the Western Polissia of Ukraine, the water supply module for irrigating drained lands varies significantly depending on the type of crops, drained soil (mineral, peat), irrigation technologies, and heat and moisture supply conditions during the vegetation period. It has been experimentally determined that under current climatic conditions, the value of the water supply module is 2.9 times higher than the existing recommended design values, and under the predicted conditions of their change, this excess is 5.2 times. At the same time, the water demand of cultivated crops increases almost 2–3 times and determines the need to increase water consumption for moistening drained lands in conditions of sufficient water shortage. It has been established that the main content of the necessary adaptive measures is to increase the moisture availability of soils with regulated water regime by switching from periodic to regular moistening of them through the use of appropriate resource-saving technologies for regulating the water regime.

Field-based investigation of low adhesion leaf layers: Assessing layer initiation and environmental influences

Changing weather conditions are known to impact on adhesion at the railhead. However, the influence on actual leaf layer “seeding” (formation) and low adhesion has not been measured in the field. This paper presents findings from a field survey conducted in autumn 2023 on a narrow gauge railway in Wales, observing leaf layer initiation, development, and environmental influences on adhesion levels. Using friction testing, timelapse imagery, and chemical analysis, the study examines the impact of weather and layer characteristics on friction. Results show that moisture content in the layer has a significant impact on adhesion levels, with a linear relationship observed between moisture and friction. Leaf layer initiation and development were observed, with water playing a crucial role in layer formation. Chemical analysis revealed the presence of iron oxides and biopolymers in the leaf layers. The results give insight into the variation in adhesion levels in leaf layer that have been measured through indirect measures in the past.

A Method of Applying Mixtures to Optimize Grounding during Installation of Vertical Composite Grounding Devices

The article considers the method of using mixtures to optimize the electrophysical parameters of grounding devices in conjunction with vertical composite grounding conductors. It has been found that fluctuations in soil resistivity caused by changes in weather and climatic conditions can lead to instability of the ground loop resistance. The study shown that without appropriate measures, the resistance of the loop as a result of seasonal changes in soil properties may exceed acceptable values. This is fraught with deviations in the resistance to current spreading of grounding devices beyond the limits of acceptable parameters. To compensate for these fluctuations, a method is proposed to reduce the seasonality factor. Reducing seasonality plays an important role in ensuring the safety of service personnel and farm animals by maintaining the resistance of the grounding device within the limits of regulatory values. The authors discuss methods for artificially reducing the resistance of the ground loop, including increasing its size and using deep ground electrodes. The results of vertical electrode probing of the soil at the sites of grounding conductors are presented, the effect of humidity on the resistivity of the soil is shown, the influence of soil layering and the presence of moisture-saturated soil layers is considered. A method is proposed that allows the mixture to be introduced together with a vertical composite grounding device, the design of the coupling, tip and auxiliary device, experimental studies of the proposed designs are carried out and the results of measuring the current spreading resistance of such a grounding device with both standard and proposed couplings are presented. A comparison was made with a grounding device without the use of mixtures. The measurement results demonstrate that with an increase in the length of the grounding device, its diameter and the volume of the injected mixture, the resistance decreases. It is shown that the proposed solution makes it possible to reduce seasonality by 1.64–2.1 times, depending on the couplings used, and to obtain a grounding conductor with an equivalent diameter dozens of times larger than the diameter of a composite grounding conductor. The authors propose the use of soil-replacing mixtures to reduce soil resistivity and ensure the stability of the grounding loop throughout the entire service life. The proposed method of applying mixtures without pre-drilling makes it possible to reduce the cost of constructing grounding devices.

Mapping Variable Wildfire Source Areas Through Inverse Modeling

Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models.

Business Plan for the Creation of an Eco-Recreational Park in Pachuca, Hidalgo, Mexico

Objective: Propose a business plan for the creation of an eco-recreational park in Pachuca, Hidalgo, Mexico, specifically located in Cerro del Lobo for the recovery of the forest, containing the deforestation process, as well as the creation of an ecological park. That provides education and awareness of ecological environmental values to the residents and visitors of the region. Theoretical Framework: As a theoretical reference from the literature, various models and methodologies for the creation of a business plan were consulted, as well as the opinion of experts, including that of a university professor specialized in the subject of business plans. Likewise, primary and secondary sources were consulted to analyze the general characteristics of the region and serve as a guide for the development of the proposed plan. Method: The study adopted for this research is a combination of methodologies proposed for the creation of a business plan and includes the complete process of a general structure, concisely writing each of its parts and proposing instruments that would be useful in the execution of the plan. As well as the different procedures for implementation, the data was collected through documentary research and in-depth interviews with experts in the field. Results and Discussion: The correct implementation of the proposed business plan based on the physical and biological characteristics of Cerro de Lobo and the species that inhabit it, such as its flora and fauna. They allowed the construction of an eco-recreational park that promotes an ecological culture and raises awareness among the inhabitants of the municipality of Pachuca and surrounding places about environmental and ecological values; In addition to this, the recovery of the forest is sought, investing in a reforestation process, which is why the park is currently operating. With this project, benefits are achieved for citizens and the environment. Research Implications: The findings of this research have significant implications for both the government in power, the residents of the area, as well as visitors to the park. It is important to adopt flexible implementation strategies and take advantage of advanced technologies inherent to improve processes and facilitate decision making by adapting to changing weather conditions and dynamic market conditions. Originality/Value: This study contributes to the existing body of knowledge by offering new insights into the proposal and implementation of a business plan for the creation of eco-tourism parks, through the tuning of methodologies. The practical implications derived from this study serve as a valuable resource for government institutions seeking to increase operational efficiency and strategic effectiveness in an increasingly complex panorama that cannot leave aside the care of the environment and the search for benefits for citizens.

Modelling the evolution of an ice sheet’s weathering crust

Abstract The weathering crust is a layer of porous ice that can form at the surface of an ice sheet. It grows and decays in response changing weather and climate conditions, affecting the albedo, the melt rate and the transport of meltwater across the surface. To understand this behaviour, we seek time-dependent solutions to a continuum, thermodynamic model for the porosity, temperature and thickness of the weathering crust, and the internal and surface melt rates. We find solutions using a numerical enthalpy method, presented in this study. We use idealized ‘switching’ and sinusoidal forcings to explore the different dynamics exhibited during growth and decay, the timescales involved, and the impact of diurnal vs. annual variations. The results demonstrate qualitative agreement with observations, and provide insight into the relative importance of different surface heat fluxes during the growth and decay of the crust. The model therefore provides a useful tool for exploring the response of the weathering crust to climate change.

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

Data-enhanced convolutional network based on air conditioning system start/stop time prediction

Most enterprise workshop operators frequently adjust the start/stop time of air conditioning systems based on indoor and outdoor temperatures and humidity to accommodate changing demand and weather conditions. However, relying on personal subjective experience for these adjustments often leads to operational delays or energy waste due to the lack of precision in determining optimal timing. Predicting air conditioning system start and stop times is crucial for energy consumption and savings in HVAC systems. Traditional data-driven methods have been insufficient in this regard, as they mainly focus on feature mapping and overlook the dynamic coupling relationships of process variables, resulting in subpar predictions. In response to this challenge, the paper introduces a novel approach known as the Periodicity and Long-Term Convolutional Neural Network (PLCNN). This method converts one-dimensional regression prediction data into two-dimensional data containing time series features to capture the dynamic coupling characteristics of the air conditioning system while maintaining the independent variation relationships of features. Experimental results using real factory floor data have demonstrated the superior performance of the PLCNN method. Specifically, this method achieved a 14.96% lower error rate compared to the traditional method and an 8.18% improvement compared to the deep learning method. Moreover, the implementation of the PLCNN method in the optimal control of air conditioning systems led to a significant 19.43% reduction in total monthly energy consumption. In conclusion, the proposed method offers a promising alternative to traditional approaches to forecasting and provides a solution to the common challenges encountered in traditional prediction tasks.

El Niño southern oscillation, weather patterns, and bacillary dysentery in the Yangtze River Basin, China

BackgroundIncreasingly intense weather anomalies associated with interannual climate variability patterns, like El Niño-southern oscillation (ENSO), could exacerbate the occurrence and transmission of infectious diseases. However, research in China remains limited in understanding the impacts and intermediate weather changes of ENSO on bacillary dysentery (BD). This study aimed to reveal the relationship between ENSO, weather conditions, and the incidence of BD, and to identify the potential meteorological pathways moderated by ENSO in the ENSO-BD connections.MethodsBD disease data and meteorological data, as well as ENSO index, from 2005 to 2020 were obtained for 95 cities in the Yangtze River Basin. We first established the associations between ENSO events and BD, ENSO and weather, as well as weather and BDs using two-stage statistical models. Then, we applied a causal mediation analysis to identify the specific meteorological changes in the ENSO-BD relationship.ResultsIn the Yangtze River Basin, both El Niño (IRR: 1.06, 95%CI: 1.04 ~ 1.08) and La Niña (IRR: 1.03, 95%CI: 1.02 ~ 1.05) events were found to increase the risk of BD. Variations of ENSO index were associated with changes in local weather conditions. Both the increases in regional temperatures and rainfall were associated with a higher risk of BD. In the casual mediation analyses, we identified that higher temperatures and excessive rainfall associated with La Niña and El Niño events mediated the ENSO’s effect on BD, with mediation proportions of 38.58% and 34.97%, respectively.ConclusionsLong-term climate variability, like ENSO, can affect regional weather conditions and lead to an increased risk of BD. We identified the mediating weather patterns in the relationship between ENSO and BD, which could improve targeted health interventions and establish an advanced early warning system in response to the BD epidemic.

GPR Image Retrieval and Localization Based on Spatial Prior Constraints

Abstract Localizing Ground Penetrating Radar (LGPR) offers the distinct advantage of being unaffected by weather and light conditions changes. As a novel auxiliary driving localizing system, LGPR enhances the robustness of the auto-drive system. Traditional ground penetrating radar localizing technologies rely heavily on high-quality map data, exhibiting optimal performance in environments with complete, high-precision maps. However, deviations from the mapped trajectory can compromise localization accuracy. We introduce a novel approach: a ground penetrating radar image retrieval and localization method based on spatial prior constraints of array antennas to address this challenge. This method aims to mitigate mismatches in ground penetrating radar navigation and localization encountered under incomplete map coverage. We performed a comparative analysis with the LGPR baseline and then validated them experimentally on our dataset. The results show that the proposed method achieved significant enhancement in accuracy. In particular, the superiority of the proposed method is demonstrated in low overlap GPR image matching and localisation.

Development and evaluation of a COOI24m pelletizer

It is essential to formulate appropriate feeding pellets with the required nutrient compositions for poultry animals to meet the growing demand for protein through the provision of eggs and meat. Forming the required feed concentrates into pellet-mash requires a pelletizing machine, which is, in most cases, very expensive for the local farmers due to the high foreign exchange rate. Based on this, the study focuses on the development of a simple and affordable pelletizing machine from locally sourced materials. The machine consists of a frame, hopper, die roller, pelletizer, and internal combustion (5.5 hp), and it was run at 950, 600, and 450 revolutions per minute. The formation and discharge of pellets from the machine increased with the pelletizing efficiency (Pe) of 70.3% to 84.6% at a moisture content (MC) of 15.8% to 27.1%, and the Pe decreased at a moisture content above 29.0%. Also, the drying temperature (DT) has a significant effect on the pellet moisture content (MC) and bulk density at P > 0.05. The effects of weather conditions on pellets produced using the COOI24m pelletizer were assessed in this study. The analysis showed that changes in weather conditions had an insignificant effect on the quality and properties of the pellets. The machine is highly recommended for use by small-scale livestock farmers for the local production of pellet-mash of different compositions because of its affordability, ease of use, and high working efficiency.

Introducing CHiDO-A No Code Genomic Prediction software implementation for the characterization and integration of driven omics.

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties that can be more resilient to the increasingly variable climatic conditions. Leveraging large and diverse datasets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This paper introduces characterization and integration of driven omics (CHiDO), an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process datasets, CHiDO's intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platform's mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available.