Risk Avoidance Research Articles

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Integrating renewable wind power (WP) into the grid exacerbates variability and challenges reliability. Establishing an effective forecasting system is crucial for risk avoidance, but achieving effective, interpretable, and accurate predictions remains an obstacle due to the high volatility and uncertainty of WP. This study proposes an innovative and trustworthy wind power forecasting system combined with point and interval predictions. Specifically, the adaptive multi-scale convolutional receptive field (AMSCF) combined with gated recurrent unit (GRU) for WP point prediction is proposed to capture the spatiotemporal dependencies of WP generation. This AMSCF method uses different size receptive fields, fuses them using channel dimension concatenation, operates pooling layer and adaptive fusion through field attention, which extracts hierarchical features for WP forecasting. Then conditional mixture Copula (Con-mCopula) function integrated with multiobjective optimizer is established for WP interval prediction, which reduces the WP interval prediction accuracy error as some information neglected. The wind power cluster dataset from Germany was selected for experiments. Based on the results, for both datasets, the designed prediction system can achieve better point and interval prediction performance resulting in a reduction of mean absolute error (MAE) by approximately 40 % compared to other existing components in the market. This study deconstructs the “Black Box” in deep network for energy forecasting through interpretability analysis, which accelerates the development of AI technology in WP forecasting system and provides decision-makers with trustworthy WP forecasting assistance.

Elephant pathway use in a human‐dominated landscape

Habitat loss and fragmentation are two of the biggest threats facing wildlife today. Understanding the role of wildlife pathways in connecting resource areas is key for maintaining landscape connectivity, reducing the impacts of habitat loss and helping address human–wildlife conflict. In this study, we used sign surveys and camera trapping to understand the fine scale movement of elephants moving between a protected area and agricultural zone in the Masai Mara, Kenya. We used generalised linear models to determine factors driving high frequency of pathway use by elephants. Our results showed strong seasonal trends in pathway use, with peaks coinciding with the dry season. However, no correlations between rainfall and pathway use were found. Temporal patterns of pathway use indicate that elephants use risk avoidance strategies by moving between the two areas at times of low human disturbance. Spatial analysis revealed that the most frequently used pathways were closer to farms, saltlicks and forest and those that had a higher percentage of forest cover. Our models also showed a positive relationship between pathway use and the number of elephant crop raiding incidents, highlighting that pathways can play a role in human–elephant conflict. As habitat loss continues, pathways may become more important for linking resources. However, they are also likely to facilitate movement into farmland. The results from this study provide an opportunity for planned management activities to ensure connectivity and to mitigated conflict.

Application of drone technology in power grid engineering

Abstract With the dynamic adjustments in eco-control zones, spatial layouts can be further optimized, but this has brought some challenges to the planning and construction of power grid projects. Utilizing drones and related technologies, it is possible to conduct surveys of eco-control zones with clear boundaries and unclear boundaries, which is beneficial for early risk avoidance in power grid projects. The results indicate that the use of orthophoto and oblique photography can effectively measure the boundaries of control areas and classify the ecological types within the research area. Maximizing the use of drone surveying technology can effectively identify control area boundaries, but attention must be paid to the factors affecting drone operations to prevent excessive measurement errors and reduce the accuracy of control area identification.

Research on the effectiveness of hedging on SSE 50 stock index futures

Stock index futures is an important investment product, which gives financial market participants a new way to manage risks, especially in the case of investors holding more spot positions, the risk avoidance function of stock index futures is more prominent. Therefore, this paper to the optimal hedging ratio of stock index futures as the research focus, the above card 50 stock index futures main contract (code: IHL 8) as the research object, on this basis, using OLS and VAR model, for the SSE 50 index (code: 000016), and use the effectiveness of hedging index He to evaluate the effectiveness of the hedge, so as to get the most effective hedging model. Through the empirical research in this paper, it is found that the hedge effect obtained by using the VAR model is better than that of the traditional OLS model. By using the stock index futures, the market participants can effectively avoid the risk of the spot market.

Design and research of enterprise risk management avoidance system based on KGN-LLM algorithm

With the increasing complexity of enterprise asset management, traditional risk management methods have become inadequate to meet the demands of real-time monitoring and efficient decision-making. In order to improve the efficiency and security of enterprise asset management, this paper proposes a method for reasoning the avoidance of enterprise asset flow management risks based on the KGN-LLM algorithm. It primarily involves the field of financial risk avoidance, including preprocessing and analysis of enterprise asset flow data, constructing a knowledge graph of enterprise asset flow data using the KGN-LLM model, monitoring real-time asset management data, identifying and analyzing risks, evaluating identified risks, and implementing asset management risk avoidance decisions. It is demonstrated that this method of reasoning the avoidance of enterprise asset flow management risks based on the KGN-LLM algorithm can effectively improve the efficiency and security of enterprise asset management.

Effectiveness Analysis of Entrepreneurial Legal Risk Prevention Based on Multi-Modal Deep Learning Model

With the emergence of the upsurge of entrepreneurship, entrepreneurs are increasingly concerned about legal risks. In the process of entrepreneurship, legal risk is the biggest hidden danger of entrepreneurial enterprises. The prevention and avoidance of legal risks is a long and arduous process, and not all risks can be identified and avoided in time. The deep learning method has brought great changes to the fields of speech recognition, image recognition and natural language processing. The tasks in these fields only involve single-mode input, but more recent applications need to involve multi-mode intelligence. Multimodal deep learning mainly includes three aspects: multimodal learning representation, multimodal signal fusion and multimodal application. Through the research on the legal risks in recent years, this paper believed that there are many legal problems in the development of entrepreneurial enterprises, including contract disputes, trade secret disputes, trademark infringement disputes, copyright infringement disputes, and so on. This paper aimed to study the problem of Legal Risks of Entrepreneurship (LRE) that entrepreneurs are concerned about, and proposed a new solution from the perspective of image recognition technology. 486 entrepreneurs were investigated by questionnaire. In the process of entrepreneurship, 87.04% of entrepreneurs encountered legal risks, and they would turn to lawyers for help, which is a better way. However, in most cases, entrepreneurs are exposed to LRE because of their insufficient understanding of economic law, so they solve it through lawyers. However, if a lawyer is hired, the cost would be very high, which would bring great economic pressure to the enterprise. Only 17.90% of entrepreneurs would safeguard their legitimate rights and interests through their own knowledge and legal weapons without resorting to lawyers. It can be seen that entrepreneurs have relatively low practical ability in the use of LRE, and their legal practical ability is obviously insufficient.

Business model innovation for reducing uncertainty in sustainability transitions: A case study of the wood construction industry

Sustainability transitions are a significant challenge that requires established industries to adopt innovative ways of doing business. Research suggests that while this is possible through business model innovation (BMI), risk avoidance by regime actors and high levels of future uncertainty act as barriers to successful transitions. Specifically, we lack knowledge about how established companies innovate their business model (BM) to reduce uncertainty related to sustainability transitions. We explore the case of a large forest‐based manufacturing company in the construction industry, Stora Enso. We find that, by pursuing transformative BMI and combining multiple value creation logics, a company can reduce different types of uncertainty while shaping its business ecosystem towards more sustainable opportunities. We show that the BM can serve as an organizational tool for collectively exploring new knowledge, reducing uncertainty and driving change in a business ecosystem.

Reasons for difficulties in the interpretation of thyroid scintigraphy results with 99mTc-pertechnetate in functional autonomy of thyroid nodules and Graves’ disease: a clinical case

Thyrotoxicosis is a condition caused by an excess of thyroid hormones, such as thyroxine (T4) and triiodothyronine (T3), in the body, leading to corresponding clinical manifestations. One of the main challenges in the differential diagnosis of various forms of thyrotoxicosis is the similarity in the clinical presentation of diseases. Therefore, the main tools for accurate diagnosis verification in the wide spectrum of thyrotoxicosis nosologies are the correct sequence of laboratory and instrumental investigations and subsequent interpretation of the obtained results. Only accurately established disease etiology will allow for the selection of the most appropriate and effective treatment for a particular patient, as well as the avoidance of unjustified risks of possible complications. In this article, we will discuss the main difficulties that may arise during the differential diagnosis of various forms of thyrotoxicosis, as well as address some key aspects of proper diagnosis.

The resistance change and stress response mechanisms of chlorine-resistant bacteria under microplastic stress in drinking water distribution system

The presence of both chlorine-resistant bacteria (CRB) and microplastics (MPs) in drinking water distribution systems (DWDS) poses a threat to water quality and human health. However, the risk of CRB bio evolution under the stress of MPs remains unclear. In this study, polypropylene (PP) and polyethylene (PE) were selected to study the adsorption and desorption behavior of sulfamethoxazole (SMX), and it was clear that MPs had the risk of carrying pollutants into DWDS and releasing them. The results of the antibiotic susceptibility test and disinfection experiment confirmed that MPs could enhance the resistance of CRB to antibiotics and disinfectants. Bacteria epigenetic resistance mechanisms were approached from multiple perspectives, including physiological and biochemical characteristics, as well as molecular regulatory networks. When MPs enter DWDS, CRB could attach to the surface of MPs and directly interact with both MPs and the antibiotics they release. This attachment process promoted changes in the composition and content of extracellular polymers (EPS) within cells, enhanced surface hydrophobicity, stimulated oxidative stress function, and notably elevated the relative abundance of certain antibiotic resistance genes (ARGs). This study elucidates the mechanism by which MPs alter the intrinsic properties of CRB, providing valuable insights into the effective avoidance of biological risks to water quality during CRB evolution.

Review of Data Security and Privacy in Cloud Computing

Cloud computing, thus, ensure more healthy demand for cloud services, certain measures against threats in order to protect information must be taken. In essence, the current paper seeks to extend knowledge regarding the improvement of paradigms of cloud computing with particular focus on the aspect of certification of authentic users and protection of the contents that are hosted in these environments. It mentions a new approach in the context of the authentication method that was deployed for the purpose of the AAA certification is presented there along with the watermarking and this RSA algorithms to enhance the security of the cloud file-sharing. This concept is far more effective than the conventional access policies that are top down and cal beforehand for systematic risk avoidance; hence, it helps to minimize the chances of an organization being vulnerable to various risks by right management. Iturbide’s data loss prevention technique eliminates the probability of losing sensitive information to the rest of the world as well as other unauthorized persons or organizations regaining access to privileged keys while Shamir’s secret sharing algorithm- polynomial interpolation ensures that the generation of keys is not a time-consuming exercise. In order to address the issues of persistent security threats in cloud computing paradigm, the proposed model includes computation, encryption and access to improve the overall security of cloud environment. However, these measures have been implemented, some of them can offer the best security when it comes to the ever-evolving threat. To maintain the privacy of clients’ data in the CL reinforcement learning, the paper suggests the use of homomorphic encryption for privacy-preserving RL inference. Therefore – as a result of the work – the necessity of implementing the suggested security model for the cloud computing environments as well as protecting the data and enhancing the level of trust in cloud solutions is stated. Keywords - QoS (Quality of Service), Energy Efficiency, Ontology, AAA (Authentication, Authorization, and Accounting), RSA Algorithm, RSA Algorithm, ReDCIM (Reconfigurable Digital Computing-In-Memory) Processor, tableopencache, LSTM (Long Short-Term Memory)

Tropical Shrimp Biofloc Aquaculture within Greenhouses in the Mediterranean: Preconditions, Perspectives, and a Prototype Description

Biofloc technology (BFT) offers an innovative eco-friendly approach that is particularly applicable in shrimp farming. Penaeus vannamei is the most important seafood species in terms of global economic value. Nevertheless, its increasing global demand highlights the necessity for sustainable production of P. vannamei shrimps outside their native range, assuring the avoidance of genetic pollution risk. Towards this direction, the present study focuses on the feasibility of tropical shrimp species aquaculture in indoor systems evaluating BFT application in temperate zones. The achievability of P. vannamei cultivation inside greenhouses in temperate latitudes is thoroughly examined and a representative experimental biofloc setup for P. vannamei within a greenhouse in Northern Greece is demonstrated. Nevertheless, there are two major limitations, related to economy and ecology, namely the energy demand for high seawater temperature and the fact that most reared shrimps are non-indigenous species setting risk for genetic pollution, respectively. To overcome the former, energy-saving measures such as tank and greenhouse insulation in combination with a microclimate chamber construction were implemented to optimize water temperature at minimal cost. Concerning the latter, there is clear evidence that P. vannamei populations cannot be established in the Mediterranean, setting aside any environmental risk. Overall, based on the developed and tested pilot prototype, employment of optimal management practices, innovative manufacturing and clean energy alternatives, and the utilization of ecosystem services could reduce the environmental impact and maximize the profitability of biofloc operations. These actions could probably permit sustainable and economically viable farming of P. vannamei employing BFT within greenhouses in the Mediterranean.

Proactive cursorial and ambush predation risk avoidance in four African herbivore species.

Most herbivores must balance demands to meet nutritional requirements, maintain stable thermoregulation and avoid predation. Species-specific predator and prey characteristics determine the ability of prey to avoid predation and the ability of predators to maximize hunting success. Using GPS collar data from African wild dogs, lions, impala, tsessebes, wildebeest and zebra in the Okavango Delta, Botswana, we studied proactive predation risk avoidance by herbivores. We considered predator activity level in relation to prey movement, predator and prey habitat selection, and preferential use of areas by prey. We compared herbivore behaviour to lion and wild dog activity patterns and determined the effect of seasonal resource availability and prey body mass on anti-predator behaviour. Herbivore movement patterns were more strongly correlated with lion than wild dog activity. Habitat selection by predators was not activity level dependent and, while prey and predators differed to some extent in their habitat selection, there were also overlaps, probably caused by predators seeking habitats with high prey abundance. Areas favoured by lions were used by herbivores more when lions were less active, whereas wild dog activity level was not correlated with prey use. Prey body mass was not a strong predictor of the strength of proactive predation avoidance behaviour. Herbivores showed stronger anti-predator behaviours during the rainy season when resources were abundant. Reducing movement when top predators are most active and avoiding areas with a high likelihood of predator use during the same periods appear to be common strategies to minimize predation risk. Such valuable insights into predator-prey dynamics are only possible when using similar data from multiple sympatric species of predator and prey, an approach that should become more prevalent given the ongoing integration of technological methods into ecological studies.

Population and community consequences of perceived risk from humans in wildlife

AbstractHuman activities catalyse risk avoidance behaviours in wildlife across taxa and systems. However, the broader ecological significance of human‐induced risk perception remains unclear, with a limited understanding of how phenotypic responses scale up to affect population or community dynamics. We present a framework informed by predator–prey ecology to predict the occurrence of non‐consumptive effects (NCE) and trait‐mediated indirect effects (TMIE) of anthropogenic disturbances. We report evidence from a comprehensive review of the different types of human‐induced behavioural and physiological phenotypic changes and their influence on vital rates and population parameters in wildlife. Evidence for human‐induced NCEs and TMIEs is mixed, with half of published studies finding a relationship between human activities, phenotypic change and population outcomes. The net effects of anthropogenic NCEs and TMIEs depend on the mismatch between the phenotypic response and the lethality of human activity. However, strong research biases in taxa, systems, human disturbance types and demographic measures prevent unified inference about the prevalence of population responses to human activities. Coexistence with and conservation of wildlife requires additional research linking human‐induced phenotypic change to population and community outcomes.

Towards Promoting the Efficiency of Enterprise Investment Management with Artificial Intelligence

With the continuous development and optimization of artificial intelligence, enterprises have begun to pay attention to the use of artificial intelligence for the optimization of work and the improvement of efficiency, and in the management of enterprise investment, a number of companies have begun to use a large number of artificial intelligence technologies in the integration and analysis of data, information processing and analysis, risk management, efficiency improvement, and so on. Enterprises encounter problems in analyzing data and information in investment management, and problems in risk management, all of which affect the improvement of their investment returns. Enterprises face a huge amount of data in the process of investment management, it is difficult to calculate a large number of data models in a relatively short period of time, and it is impossible to analyze and extract more information. In the face of investment risk, the control of risk in the past investment process is more based on experience to judge, there is a great deal of subjective assumptions in risk avoidance. And in the investment process companies also want to increase returns, improve efficiency, avoid risk, and achieve a more automated and intelligent trading process. Enterprises need to use artificial intelligence to technically solve the problems of data aggregation, arithmetic, and analysis; use artificial intelligence to optimize the problem of processing large amounts of information; use artificial intelligence technology to enhance risk management and predictive analysis, strengthen intelligent decision-making and portfolio optimization, and provide insights into big data analysis and market trends. The wide application of artificial intelligence technology in enterprises also has a certain impact on the development of enterprises, and it has a certain impact on the planning, feasibility, whole-process control, investment evaluation, investment report, and investment efficiency of investment in the investment link, which makes it widely concerned in the society and in enterprises.

Behavioral profile alterations and predation susceptibility of Japanese medaka fish exposed to phenytoin, an antiepileptic drug

Antiepileptic drugs, such as phenytoin, are often leaked into aquatic systems through sewage facilities due to their low metabolic rate. Fish, such as the Japanese medaka (Oryzias latipes), demonstrate abnormal swimming behavior such as equilibrium abnormalities, rotational behavior, and vertical swimming, when exposed to phenytoin. Therefore, it is hypothesized that predator avoidance may be hindered. This study aimed to investigate the effects of phenytoin exposure-induced behavioral abnormalities in predator avoidance in Japanese medaka. The results showed that individuals with behavioral abnormalities had a reduced ability to avoid danger. Furthermore, the fish demonstrated a delayed recognition reaction to approaching predators. Additionally, predatory fish, such as silver pike characin (Ctenolucius hujeta), were more likely to prey upon abnormal individuals. In conclusion, the fish exposed to phenytoin demonstrated behavioral changes that increased its predation risk. This study is the first to determine the effects of behavioral abnormalities in Japanese medaka which was induced after phenytoin exposure on predator risk avoidance.

Critical incidents: what the psychologist sees

In the life span of a critical incident, every decision counts. The opportunity to prevent the event has passed, so the avoidance and mitigation of risk becomes the primary goal. The same is true of early intervention in trauma. This paper shares the experience of these events from the perspective of a visiting psychologist. It will include tips for Emergency Management Teams (EMTs) in setting the psychologist up for effective interventions. Also, a triage model that can be used to mitigate psychological harm and channel the most impacted crew members towards help is discussed. It will also suggest ways that operators can collaborate with Employee Assistance Providers to ensure that impacted crew members receive trauma informed care post crisis.

Interval prediction of crude oil spot price volatility: An improved hybrid model integrating decomposition strategy, IESN and ARIMA

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By “divide and conquer”, excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction.

“I go back to it every f**king time”: the normalization of problematic Trenbolone use in online anabolic-androgenic steroid communities

Introduction This study addresses the dearth of research on specific anabolic-androgenic steroids (AAS), focusing on the contentious substance Trenbolone. With increased potency and risks, Trenbolone’s effects, both in animal studies and among human users, raise concerns for neurodegeneration, genotoxicity, and significant social harms. Despite these risks, people persist using Trenbolone and other AAS, navigating the harm-to-benefit ratio. The study explores the representation of Trenbolone in online AAS forums, to gain nuanced insight into dynamics of identity and behavior specific to the Trenbolone context. Methods We employed a digital ethnographical approach to collect data from four Australian AAS forums. Grounded in a social constructivist framework and recognizing the influential role of identity and social categorization in shaping behaviors within these digital communities, we utilized inductive thematic analysis to identify patterns and themes from user discussions about Trenbolone. Results Three key themes were developed: shared cultural language and beliefs, experienced users’ perceptions of Trenbolone mastery and risk avoidance, and the normalization of problematic use of Trenbolone and signs of dependence within the community. The findings reveal a nuanced interplay of language, beliefs, and behaviors, providing insights into how the community navigates its cultural norms, showcases expertise, and collectively grapples with usage patterns. Conclusions We delineate the emergence of a 'Trenbolone prototype’ within online AAS-using communities. The findings point to a potential disconnect between community beliefs and empirical evidence on Trenbolone’s health effects. Addressing these disparities, public health campaigns should align with the community’s language and norms for effective harm reduction.

Risk Management Techniques on Financial Performance of State-Owned Sugar Companies in Kenya

Financial performance of sugar companies in Kenya is generally poor and has been fluctuating over a long period. The state-owned sugar companies are worse compared to the privately owned companies. The sugar sector has been orbiting around sugar scarcity, inefficiencies, failure to pay farmers, failure to renovate and modernize production unit, failure to battle with imported sugar, recurrent losses and political interference. The main objective of this study was to evaluate the effect of risk management techniques on financial performance of state-owned sugar companies in Kenya. The specific objectives guiding the study were; to establish the effect of risk avoidance on financial performance of state-owned sugar companies in Kenya, and to assess the effect of risk acceptance on financial performance of state-owned sugar companies in Kenya. The study was anchored on the risk management theory, model risk management and the theory of financial control. The study adopted a descriptive research design with a target population of fifty four respondents being employees from the six state-owned sugar companies in Kenya. Questionnaires were the main data collection tool. Data was analyzed with the help of statistical package for social science version 28, Analysis of Variances, Correlation and regression analysis tools were used to establish the relationship between the variables. The findings of the study revealed a statistically significant regression effect and indicative of accomplished prediction of financial performance of state-owned sugar factories through the F-calculated value which showed that the model was significant. Risk avoidance explained 53.9% of financial performance and risk acceptance explained 61.9% of financial performance of state-owned sugar factories in western Kenya. The findings of the study will provide an insight to finance practitioners on the risk management techniques and their relationship with organizational financial performance as well as contributing to the scholarly literature on risk management techniques in other sectors of the economy. Keywords: Risk, Risk Avoidance, Risk Acceptance, Financial Performance, Risk management Techniques

A Concurrent Analysis of Drug Relapse Risk, Drug Avoidance Self-Efficacy, and Aftercare Experiences among Aftercare Program Clients

The cases of drug use and abuse have predominantly increased globally. This issue has become the most significant challenge among mental health professionals, the national government, and the general population. With the absence of substantial research about drug relapse and avoidance in the Philippines, this mixed-method research seeks to find out the relationship between drug relapse risk and drug avoidance self-efficacy among substance users who participated in the aftercare program. This study employed a concurrent mixed method design, combining both quantitative and qualitative approaches. In the quantitative phase, the Advance Warning of Relapse (AWARE) Questionnaire and Drug Avoidance Self-Efficacy Scale (DASES) were administered to 66 participants from the Aftercare Program (ACP) at the Department of Health Treatment and Rehabilitation Center in Eastern Visayas. The aim was to establish a correlation between the risk of drug relapse and drug avoidance self-efficacy. In the qualitative phase, interviews were conducted with five participants who met the study's inclusion criteria. Purposive sampling was used to select these participants. Quantitative findings revealed that ACP clients had low drug relapse risk and high drug avoidance self-efficacy. Moreover, Pearson product-moment correlation shows an inverse relationship between lower drug relapse risk and high drug avoidance self-efficacy, and vice-versa. However, the correlation between these variables is weak, indicating that drug avoidance self-efficacy is not a strong predictor of relapse risk in aftercare program clients. Similarly, qualitative results exposed the client’s experiences in the After Care Program. Six themes emerge from their experiences, such as their various reasons for taking the drugs, the effects of substance use, their effective coping mechanisms, the treatment of their communities, their accomplishment of recovery goals, and overcoming challenges in their recovery. The study suggests that individuals who participate in the aftercare programs are trying their best to recover and increase their self-efficacy to avoid using drugs. This strong self-efficacy, in combination with various factors such as family support, the acceptance of the community, and continued reminders of the consequences of substance abuse, decreased their vulnerability to use drugs in high-risk situations. The findings will increase the awareness of Filipinos in the prevention and treatment of substance use among their loved ones, relatives, and friends. Likewise, the findings recommend improving the aftercare program to increase its effectiveness and efficiency in preventing relapse among individuals in recovery.