Periods Of High Demand Research Articles

Green energy systems for powering electric vehicles considering telecommunication system with case study of Pakistan

The objective is to analyze the sustainability and efficiency of Pakistan’s telecommunication sector by developing a framework for base transceiver stations integrating renewable energy and charging stations. Various renewable energy sources such as solar, wind, biomass and hydropower were considered as the object of research. The following methodological steps were implemented in this work: site analysis; determination of optimal sizing of plants, energy storage systems and electric vehicle charging stations; cost-benefit analysis methods; greenhouse gas emissions estimation; and system design methods for integrating selected renewable energy sources and energy storage solutions, taking into account the operational requirements of the base transceiver stations. It is found that switching to hybrid renewable energy systems can significantly reduce dependence on diesel generators. It is shown that operating costs can be reduced by more than 80% compared to conventional diesel-fueled systems. Also, the introduction of hybrid renewable energy sources can lead to significant reductions in CO2 emissions. The integration of battery storage systems has been shown to improve the reliability of energy supply by ensuring uninterrupted operation during periods of high demand and blackouts. The proposed structure scheme for base transceiver stations is designed to accommodate future growth in the share of electric vehicles and technological advancements in renewable energy and electric vehicle charging. By prioritizing the integration of renewable technologies along with charging station infrastructure, telecom service providers in Pakistan can reduce their carbon footprint and operational costs. This approach not only addresses the unpredictability of the electricity grid, especially in rural areas, but also positions the telecoms sector as an active participant in global efforts to combat climate change.

Hospitalization prediction from the emergency department using computer vision AI with short patient video clips

In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use “eye-balling” or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.

Analysis of Queueing Systems in Fast Food Restaurants Using the M/M/c Model: A Case Study during Peak Hours

This study evaluates the queueing system of a fast-food restaurant using the M/M/c model to optimize the number of service counters (servers) for reducing customer waiting times during peak hours. The analysis involved simulating different configurations with 1, 2, and 3 servers, considering a customer arrival rate of 20 customers per minute and a service rate of 25 customers per minute. Results demonstrate a clear relationship between the number of servers and system performance. A single-server system resulted in an average total time of 12 seconds per customer in the system, highlighting significant delays during peak times. Introducing a second server reduced the average waiting time in the system to 4.44 seconds, striking an effective balance between service efficiency and resource utilization. However, adding a third server showed minimal improvement, as the system's utility ratio declined significantly, suggesting underutilized resources. Based on these findings, a two-server configuration is identified as the optimal solution, efficiently managing the customer arrival rate while maintaining a balanced utility ratio. This study emphasizes the practical value of combining queueing models and simulations to improve operational efficiency in fast-food service systems. The insights can guide decision-making processes for restaurant managers aiming to enhance customer satisfaction and optimize resource allocation during high-demand periods.

Nuclear Cogeneration to Support a Net-Zero, High-Renewable Electricity Grid

UK Government projections anticipate increasing electricity use, provided by variable renewables (i.e., wind and solar PV). A side effect of increasing the proportion of variable renewable generation is increased support costs, including curtailment, energy storage, and (most significantly) the cost of supplying electricity for periods of high demand when variable renewable generation is low. As the proportion of variable renewable capacity increases, demand for supporting capacity increases but the capacity factor of the support generation decreases, raising the support costs. Using nuclear power for dedicated baseload supply makes the situation worse. This paper explores in the UK context an original low-cost solution using nuclear cogeneration with hydrogen production as the main application. Electricity is diverted at low cost to the grid at times of high demand when renewables are not available. This ensures nuclear maintains a high capacity factor. When higher temperature advanced systems become available, using thermal energy storage will increase the nuclear electrical capacity. This “Flexible Nuclear” scenario substantially reduces support costs for accommodating variable renewables, saving GBP 14 bn/yr and leading to an 80% reduction in CO2 equivalent emissions, compared to a recent UK Government scenario utilising a large capacity of hydrogen and unabated gas generation at very low capacity factors.

Innovative Photovoltaic-Aeration Integration: Enhancing Energy Efficiency and Grid Stability in Wastewater Treatment

Abstract This paper presents a detailed investigation into enhancing the energy efficiency of wastewater treatment plants (WWTPs) by integrating photovoltaic (PV) systems, emphasizing power flow analysis and experimental validation. Recognizing the substantial energy demands of aeration processes in WWTPs, this study proposes an innovative integration of PV panels with aeration tanks. This approach generates renewable energy and optimizes energy use through the thermal interaction between the PV panels and the aeration tanks. Key findings demonstrate a 15% overall increase in energy efficiency and a 5% improvement in PV efficiency due to aeration-induced cooling, along with a reduction in voltage fluctuations by up to 30% during high-demand periods. Additionally, the integration offsets approximately 20% of the WWTP's total energy consumption. The research is structured into two main components: a comprehensive power flow study using DIgSILENT PowerFactory and a laboratory experiment to validate the integration's effectiveness. The power flow analysis evaluates the electrical impact of PV integration on the WWTP's power grid, focusing on scenarios such as load fluctuations, grid disturbances, and the synchronization of PV generation with plant energy needs. The simulation results indicate that the integration significantly enhances the stability and efficiency of the plant's electrical system, reducing reliance on traditional energy sources. Concurrently, a laboratory experiment explored the practical effects of integrating PV systems with aeration tanks. The experiment demonstrated that the cooling effect provided by the aeration tanks leads to increased PV efficiency and notable energy savings. These experimental results align with the simulation findings, confirming the efficacy of this integrated approach. This study introduces a novel methodology for integrating renewable energy technologies into industrial processes, showcasing the potential for significant energy savings and improved operational efficiency in WWTPs. Future research will focus on scaling this integration strategy and assessing its long-term impacts on energy efficiency and wastewater treatment effectiveness.

School Start Times for Solar Alignment: Evaluating the Benefits of Schedule Optimisation for Peak and Cost Reduction

The global push towards sustainable energy usage and the increasing adoption of renewable energy sources, such as solar power, requires innovative approaches to energy management, particularly in energy-intensive sectors such as education. This study proposes a change in school start time from 7 a.m. to 9 a.m. to align operational hours with periods of off-peak electricity demand and maximum solar availability. Four scenarios are compared: baseline (current schedule without solar), shifted schedule without solar, baseline with solar, and shifted schedule with solar integration. The analysis reveals that shifting the school’s operational hours alone leads to a peak demand reduction of 40%, mitigating strain on the grid during high-demand periods. Solar integration without schedule has a less pronounced effect on peak demand (26%). The combination of schedule shifting and solar integration delivers the most significant benefits, with the highest cost reductions (28%) and peak demand reductions (60%). This study demonstrates that synchronised solar energy generation and optimised scheduling can enhance energy efficiency and long-term financial savings, offering a practical solution for reducing operational costs and improving sustainability in schools. This study demonstrates how public institutions can contribute to the energy transition by adapting their operational schedules to align with renewable energy availability, rather than relying on conventional fixed schedules.

Impact of stormwater on biofilm density and microbial community composition in water distribution networks.

Harvesting of stormwater and injecting it into aquifers for storage and recovery during high water demand periods is a promising technology for augmenting conventional water reserves. However, little has been known on how stormwater impacts the biofouling of water distribution infrastructure. This study evaluated the effect on harvested and limestone aquifer treated stormwater on biofilm formation in a pilot distribution pipe network compared to an identical drinking water pipe rig. Coupons made of cement, copper and polyvinyl chloride (PVC) pipe materials were installed to each pipe rig and exposed to stormwater or drinking water. The total cell counts determined by flow cytometry on the pilot rig coupons were in the order of 105 to 107 cells/cm2 for both source waters and showed some variation over the duration of the study. The culturable cell counts were somewhat higher for stormwater exposed coupons than for coupons in mains water rig. The total number of thermotolerant coliforms was notably higher on coupons exposed to stormwater than on those exposed to mains water. Considerable differences were observed in the bacterial and eukaryotic communities on coupons made of various materials and exposed to mains water and stormwater using pyrosequencing. Moreover, seasonal variations were observed in community composition and diversity. A number of bacterial and eukaryotic families and genera harbouring potential human pathogens were detected in both mains water and stormwater systems, with larger numbers of genera observed in the latter indicating a potentially increased risk of exposure to pathogens with stormwater. The stormwater system also harboured sulfur reducers, and a greater diversity of iron oxidisers. A number of bacterial genera that contribute to nitrogen cycling were observed in both mains water and stormwater systems. A number of bacteria grazing eukaryotes were detected, indicating that the biofilm communities are quite dynamic and the abundance of bacteria is able to support higher level eukaryotes.

Bi-level programming model of location and charging electric vehicle stations in distribution networks

Electric vehicles (EVs) are currently a focal point in distribution network research due to their potential benefits. Integrating EVs into distribution networks can enhance voltage profile stability. However, unplanned EV connection and charging can lead to increased peak load power consumption, voltage profile irregularities, and heightened network losses. Addressing the placement of charging stations in distribution networks is crucial to prevent voltage drop crises and mitigate other issues. This study proposes a Bi-level particle programming optimization programming approach, utilizing the Particle Swarm Optimization (PSO) algorithm to minimize losses and determine optimal station locations. The algorithm iteratively minimizes losses while considering location and operating costs until a stopping condition is met. Additionally, an EV charging planning algorithm is introduced to manage EV connections without exacerbating peak load or causing severe voltage drops. Simulations conducted on an IEEE 33-bus system using MATLAB analyze the proposed design's impact on power consumption, system losses, and network voltage. Simulations in an IEEE 33-bus system show that the proposed model shifts peak power consumption by about 4.6% to about 11.6% under different EV penetration scenarios. It achieves a reduction in the network losses by up to 0.39%, while the voltage deviations in critical buses are minimized to enhance stability during periods of high demand for EVs. Results demonstrate that the programmed charging algorithm effectively reduces power consumption at peak load, minimizes network losses, and smooths the daily voltage profile.

Optimizing bifacial PV performance: The impact of reflectors and free space luminescent solar concentrators on winter yield

In this study, we present a novel solar energy harvesting system incorporating free-space luminescent solar concentrators (FSLSCs) integrated with bifacial photovoltaic (PV) modules. The FSLSC design features a luminophore-doped waveguide, an angle- and wavelength-selective notch filter, and a Lambertian reflector, enabling efficient photon recycling. Unlike traditional luminescent solar concentrators, the FSLSC aims to emit photons into free space within a defined emission cone, enhancing light redirection towards PV modules. We developed a three-dimensional ray tracing model to analyze system performance, including different reflector configurations and emission cones. The study focuses on optimizing energy yield in urban settings, particularly during winter months, by examining the effects of diffuse and specular reflectors, and various FSLSC configurations. Our results demonstrate that FSLSCs can enhance winter energy production in the Netherlands by up to 60 %, compared to a conventional optimal tilt monofacial system. The findings highlight the potential of FSLSCs and specialized reflectors to increase PV system efficiency and offer flexible solutions for improving energy yield throughout the year, particularly during periods of high demand.

Bioeconomic modeling of on-growing Caribbean spiny lobster in Florida

The Caribbean spiny lobster fishery in Florida has recently shifted from supplying frozen tails in the domestic market to a higher value live export market, primarily in China. The fishery's seasonal landing pattern limits supply during periods of high demand and high price, resulting in lost revenue. In addition, about 25 % of lobster landings are unsuitable for the live export market. Of these lobsters, approximately 50 % are molting, and short-term live storage could make them viable for live export after molting is complete. We developed a bioeconomic model to examine the economic feasibility of on-growing wild-caught lobsters for sale later in the season when demand and price are higher. We also used the model to examine the feasibility and profitability of short-term aquaculture-based storage of molting lobsters for later sale in the live export market. The results suggest evidence of investment feasibility. Annual net returns and net present value to perpetuity were positive in all scenarios, but net returns were 359–1055 % higher in short-term storage of molting lobsters. Net present values to perpetuity ranged from $898,744 to $5,106,181. Cost of production ($/kg) ranged from $21.29 to $29.07, with the lowest cost for short-term storage of molting lobsters; however, this was also the most capital-intensive scenario, with costs more than five times that of the other scenarios. Risk analysis showed variations in stocking density, sale price, and survival of spiny lobsters were the major contributors to economic risk in longer-term on-growing scenarios, whereas sale price and input price were the major contributors in short-term storage of lobsters.

Optimizing traffic management for public services during high-demand periods using cloud load balancers

In times of high demand, effective traffic management is essential for public service platforms to maintain reliable, continuous access for users. This paper explores the role of cloud load balancers as a solution for optimizing traffic distribution in public services during peak periods. By dynamically distributing network traffic across multiple servers, cloud load balancers enhance system scalability, flexibility, and response time, which are critical for minimizing service disruptions. The paper further discusses best practices for configuring load balancers, including geographic balancing and redundancy, and highlights the importance of integrating dynamic scaling and monitoring tools to adapt to sudden traffic surges. Additionally, the potential of artificial intelligence (AI) and predictive analytics in advancing cloud load balancing is considered, emphasizing their value for predictive scaling and automated traffic management. Key challenges, including data privacy and dependency on third-party infrastructure, are also addressed, with recommendations for enhancing load balancer deployment in public service applications. This paper concludes with strategic recommendations for public service providers to leverage cloud technology effectively, ensuring improved reliability, security, and cost-efficiency in service delivery. Keywords: Cloud Load Balancers, Traffic Management, Public Service Platforms, Dynamic Scaling, Predictive Analytics, Service Reliability.

Design of a Reverse Osmosis Desalination Plant Powered by Renewables for a Small Mediterranean Island

Abstract Water shortage is one of the main problems for small, isolated islands, especially in the Mediterranean Sea. Water supply in those areas relies on maritime transport through tanker ships, especially during periods of high demand. However, this solution is unsuitable for isolated islands due to the high costs and environmental impact. This study aims to assess the feasibility of powering a desalination facility in a remote location with renewable energy sources to assess the potential cost savings and reduction of greenhouse gas emissions compared to the current supply mechanisms. To this end, the Greek island of Tilos is selected as a case study due to its high unexploited renewable energy production during winter months. The study hypothesizes using a seawater reverse osmosis (SWRO) unit to increase the sustainability of the water supply and promote the island’s self-sufficiency. Two control strategies have been adopted, simulating a demand-driven and a renewable production-driven scenarios. Results show a levelized cost of water that ranges between 1 and 2 €/m3, which is consistent with the average cost for the existing desalination plants in Grece. The adoption of a SWRO facility coupled with water storage systems results always in a more cost-effective solution than maritime transport, leading also to a relevant reduction of the environmental impact.

The Effect of Supplier Collaboration, Transport Efficiency, Technology Adoption, and Market Demand on Distribution Efficiency and Profitability of Pumpkin Seed Supply Chain in Indonesia

This study examines the effects of supplier collaboration, transportation efficiency, technology adoption, and market demand on the distribution efficiency and profitability of the pumpkin seed supply chain in Indonesia. Using a quantitative approach, data were collected from 150 respondents involved in the supply chain, including suppliers, distributors, and logistics providers. The data were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS 3). The findings reveal that supplier collaboration, transportation efficiency, and technology adoption significantly enhance distribution efficiency, which in turn positively impacts profitability. Market demand was also found to moderate the relationship between distribution efficiency and profitability, particularly during periods of high demand. These findings underscore the importance of fostering collaboration, optimizing transportation, and adopting technology to improve the supply chain’s operational performance and financial outcomes. The study provides valuable insights for practitioners and policymakers aiming to enhance the agricultural supply chain's competitiveness and sustainability in Indonesia.

Determining Appropriate Number of Labors in Yellow Noodle SMEs Using K-Means Clustering Method: Meeting Demand While Minimizing Company Costs

Yellow noodle SMEs face the challenge of aligning their workforce with the fluctuating customer demand. Addressing this issue involves scaling up production staffing during peak yellow noodle demand. This study aimed to determine when and how much production labor should be added. Workforce expansion occurred through contractual arrangements during demand surges. This study used the K-Means clustering method to determine demand clusters. Each cluster underwent detailed analysis to calculate the precise number of required production workers. The study used the noodle sales in 2021–2022. Results showed that yellow noodle SMEs had eight permanent workers. Demand clusters were categorized into three classes. High-demand periods were observed in May and December, while moderate demand occurred in January, February, March, April, June, July, and August. September, October, and November constituted the low-demand cluster. Based on these findings, the study recommended adding one worker during high-demand and moderate-demand periods to maintain operational efficiency. Remarkably, no workforce additions were advised during the low-demand phase to optimize resource allocation and cost-effectiveness. In summary, this research addresses labor management challenges for yellow noodle SMEs. Leveraging data-driven approaches and cluster analysis offers SMEs a strategic framework to enhance workforce planning thereby improving operational efficiency and cost-effectiveness. Keywords: yellow noodle SMEs, workforce demand clustering, operational efficiency

Electrical Conductivity of Binary, Ternary, Quaternary and Quinary Molten Salt Mixtures Based on NaCl-CaCl2

As intermittent energy sources like solar energy and wind power emerge, the need for energy storage becomes important, energy availability needs to be ensured also when the Sun is not shining, and the wind is not blowing. Energy storage can also be used for peak shaving purposes during periods of high demand. Energy storage solutions need to be inexpensive and reliable. Novel all-liquid batteries are considered one option for stationary energy storage and the Na-Zn battery is currently being investigated. During charging Na metal is formed on the negative electrode from a NaCl containing electrolyte and ZnCl2 is formed from a Zn pool on the positive electrode. The electrical conductivity of the molten salt is an important factor in the ohmic loss through the electrolyte. The composition of the electrolyte decides the electrical conductivity, and this conductivity also changes during the charge/discharge cycles of the battery as the electrolyte composition changes accordingly. Electrical conductivity has been measured on different compositions of NaCl-CaCl2, NaCl-CaCl2-LiCl, NaCl-CaCl2-BaCl2, NaCl-CaCl2-ZnCl2, NaCl-CaCl2-BaCl2-SrCl2, NaCl-CaCl2-BaCl2-ZnCl2 and NaCl-CaCl2-BaCl2-SrCl2-ZnCl2 molten salts in an in-house built apparatus. The smaller ions (Li and Na) give higher electrical conductivity, while the larger ions (Ba, Sr, and Zn) reduce the electrical conductivity.

DESENVOLVIMENTO DE PROTÓTIPO UTILIZANDO MICROCONTROLADORES PARA CONTROLE DE EFICIÊNIA ENERGÉTICA EM RESIDÊNCIAS UNIFAMILIARES

The search for solutions that aim to improve the way we use electrical energy in our daily lives is of great importance when it comes to sustainability. Energy efficiency plays a fundamental role in the development of sustainable technologies. In a period of high demand for electricity and concerns about the environment, energy efficiency has become increasingly important, both in industrial and domestic applications. In this scenario, microcontrollers play a fundamental role in optimizing and controlling electrical energy. Among its various applications is monitoring and managing energy consumption in real time, making intelligent decisions to minimize waste. These systems, when integrated with sensors and other devices, allow the development of solutions that not only save energy, but also make environments smarter and more sustainable. Observing this context, this study aims to develop a device capable of providing accurate information about the electrical energy consumption of equipment. The developed prototype aims to provide detailed information about the energy consumption of monitored equipment, contributing to better energy management. The developed energy meter prototype demonstrated the potential to contribute significantly to energy efficiency, by providing accurate and real-time data, thus resulting in a more intelligent use of the energy consumed, as, with knowledge of real-time data, it is possible to monitor and work on energy optimization.

Electric load prediction using SARIMAX

ABSTRACT Electricity demand variation is one of the prevalent phenomena in Nepal. This variation can cause excess electricity during low-demand periods and a shortage during high-demand periods. An early prediction of electricity demand allows for levelling such fluctuations by introducing dynamic tariffs based on varying demands. This study aims to use Python’s time series models to make predictions about electrical demands and propose a dynamic tariff system. The dataset of Panama City’s hourly electrical load is brought into use. Two models, namely the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Factors (SARIMAX) and the BATS model, are deemed to be the two best time series models. The selection of optimal seasonal and non-seasonal orders in SARIMAX is made by comparing Bayes Information Criterion (BIC). For the SARIMAX model, a daily dataset has been taken by summing the hourly data for each day, whereas for the BATS model, hourly data for the year 2018 has been used. The SARIMAX model resulted in the best fit with the evaluation metrics values: Root Mean Square Error (RMSE) of 975,336.88, Mean Average error (MAE) of 739.09, and R2 score of 0.75. However, SARIMAX’s limitation is that the use of daily data does not allow dynamic tariff prediction, which is possible with the BATS model.

Numerical study of a hybrid system of solar panels, a diesel generator, and a wind turbine using a battery system

This paper gives an overall quantitative analysis of a hybrid renewable energy system (HRES) incorporating solar photovoltaic system, wind system, diesel generator and battery storage system. The system is aimed at coordination the management of the energy resources for providing the off-grid or remote facilities. The best intervention is thus to increase the consumption of renewable energy sources to minimize the reliance on diesel generators. The findings show that the hybrid system is capable of supplying different levels of power, which is pumped from the solar PV panels at maximum of 30.38 kW for the photovoltaic system at maximum solar intensity, and the wind power system to a maximum of 3213 kW of power generation under standard wind potential. The diesel generator serves a backup power source, which ranges from 91.24 kW and 864 at a rate of 250 kW during high activity and 5 kW during high-demand periods. Battery storage integration has a crucial part in matching the short-term supply-demand imbalance problem. The study suggests that the hybrid system reduces diesel use, emissions, and improves energy sustainability making the system a worthy proposition for remote areas. The findings therefore affirm the use of hybrid systems in off grid electrification since they operate economically and are eco-friendly.

Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty

The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained.

Patterns and characteristics of visits to psychiatric emergency departments: a three-year data study in China.

The composition and characteristics of emergency patients in the Affiliated Brain Hospital, Guangzhou Medical University during 2020-2022 were retrospectively analyzed to provide data support for the optimization of the process of psychiatric emergency and the elastic allocation of emergency medical staff. This study collected data from patients who sought medical attention at the emergency department of the Affiliated Brain Hospital, Guangzhou Medical University between January 1, 2020, and December 31, 2022. The fundamental information of these patients was statistically analyzed using descriptive analytic methods. In addition, a comprehensive statistical analysis was performed on the data of patient visits, which included precise triage time points, months, and seasons, in order to evaluate the temporal distribution of patient visits. The patient population had an average age of 36.4 years and was slightly more female (54.08%). The mean age of the male and female patients was 36.4 ± 18.91 and 36.4 ± 16.80 years, respectively. There was no statistically significant age difference between the male and female patients (p > 0.05). The top five diseases were mental disorder (6,483 cases), bipolar disorder (3,017 cases), depressive episode (2522 cases), schizophrenia (1778 cases) and anxiety state (1097 cases), accounting for 35.63%, 16.58%, 13.86%, 9.77% and 6.03% of the total, respectively. Additionally, a notable record of psychiatric drug intoxication was noted. Significant comorbidity with physical disorders, such as hypertension (9.36%), hypokalemia (3.41%), diabetes (2.83%), and cerebral infarction (2.79%), was also seen. The results of seasonal and monthly analysis indicated that emergency attendance patterns fluctuated, peaking in the spring and fall. The patterns of daily visits also revealed two peak times. The first peak occurs from 8:00 to 10:00, and the second peak occurs from 14:00 to 16:00. This study emphasizes the increasing occurrence of mental problems in psychiatric crises, particularly among younger populations, underscoring the necessity for comprehensive care methods. Specialized treatment methods and collaborative networks are required to address the substantial prevalence of psychiatric medication poisoning. Efficient allocation of resources and heightened security protocols are vital in emergency departments, particularly during periods of high demand and in handling instances of patient hostility.