Time Usage Research Articles

Electric vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization

The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need for more coordinated and grid aware EVs charging and discharging strategies. However, coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies as the complexity increases with more control variables and EVs, necessitating large optimization and decision search spaces. In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit. The optimization problem is addressed using Harris Hawks Optimization (HHO) considering criteria related to energy grid balancing, time usage preference, and the location of EV drivers. The EVs schedules, associated with the position of individuals from the population, are adjusted through exploration and exploitation operations, and their technical and operational feasibility is ensured, while the rabbit individual is updated with a non-dominated EV schedule selected per iteration using a roulette wheel algorithm. The solution is evaluated within the framework of an e-mobility service in Terni city. The results indicate that coordinated charging and discharging of EVs not only meet balancing service requirements but also align with user preferences with minimal deviations. The assessment of the determined solutions’ quality and efficacy shows promising outcomes, with convergence after 100 iterations reflected in a generational distance of 0.35 and a Pareto front error of 1.01, while the distribution of solutions exhibits strong hypervolume thus covering a significant portion of the objective space.

LANDSCAPE ATTRACTIVENESS AND PLACE IDENTITY: CONSIDERING THE ROLE OF URBAN PARKS

Urban parks, vital public spaces for community health and well-being, also face challenges related to safety and security. Balancing the need for recreational spaces with ensuring safety is paramount for urban stability. However, existing research on the nexus between landscape attractiveness and perceived safety often overlooks the influence of factors such as park usage patterns, place identity, and park typologies. To address this research gap, we conducted a survey among 411 urban park users and employed SmartPLS and SPSS for data analysis. Our findings reveal a robust correlation between landscape attractiveness and perceived safety. Furthermore, we uncover that the combined influence of place identity and time spent in the park can serve as positive mediators in this relationship. Surprisingly, our analysis indicates that time spent in the park alone does not exert a significant mediating effect. Notably, our results highlight nuanced variations: the association between landscape attractiveness and place identity is particularly pronounced in parks with lower landscape attractiveness, while the mediating role of place identity on the relationship between park usage time and perceived safety is more pronounced in parks with higher landscape attractiveness. These findings contribute to our understanding of how urban park landscapes relate to perceived safety and provide new insights for improving the safety of urban parks, offering valuable insights for urban planners and public health policymakers in designing healthier urban environments.

The effects of goal setting, notification alerts, and sharing results with peers on smartphone usage

AbstractSmartphones are highly complex machines serving multiple purposes. Although access to different applications may be a source of inspiration and may lead to increased well‐being, excessive screen time usage can lead to increased stress, reduced physical activity, and neck and shoulder discomfort. The purpose of the present study was to evaluate the effects of (a) goal setting, (b) turning off notifications, and (c) sharing information about screen time with peers on smartphone usage in a sample of young adults who reported interest in reducing their smartphone usage. Goal setting and turning off notifications for social media applications effectively reduced smartphone usage for five out of seven participants. Sharing information with peers did not lead to decreased smartphone usage. Follow‐up measures showed an increase in smartphone usage compared to the intervention phases. With only a few studies on strategies to reduce smartphone usage within behavior analysis, the current study expands the literature and provides directions for future studies.

Effectiveness of Insecticide Treated Mosquito Nets on the Death of Anopheles subpictus Mosquitoes

The prevalence of malaria in East Nusa Tenggara based on positive diagnosis results containing malaria parasites and symptoms in the 2013 - 2015 period decreased but has not yet reached the National Strategic Plan target, namely in 2013 it was 21 per 1,000 population (o/oo), in 2014 it was 14 o/oo, and in 2015 it became 8 o/oo (NTT Health Office, 2016). In 2017 there was a spike in malaria cases to 211,409 cases (API = 44.96 o/oo). Kupang Regency with 1,108 cases (API = 24.87 o/oo). Malaria cases in the Tarus Community Health Center work area are the eighth highest, namely 7 cases. This study aims to analyze the effectiveness of insecticide-treated bed nets on the death of Anopheles subpictus mosquitoes. The variables observed were the length of time the mosquito net was used, the frequency of washing the mosquito net, the method of washing the mosquito net, and the percentage of Anopheles subpictus mosquito deaths. The effectiveness of insecticide-treated bed nets on the death of Anophles subpictus mosquitoes with usage time < 3 years (100%), usage time 3 – 5 years (70%) and usage time > 5 years (40%). The effectiveness of insecticide-treated bed nets that have never been washed reaches 100% mortality within 24 hours of observation. Meanwhile, mosquito nets with a washing frequency of 1 – 10 times (70%) kill mosquitoes, and a washing frequency of > 10 times (50%). The method of washing mosquito nets carried out by the community in the Puskesmas Working Area is 100% using detergent. In the control group that used mosquito nets that did not contain insecticide, no Anopheles subpictus mosquito deaths occurred (0%). Suggestions for health workers to increase education regarding the correct use of mosquito nets, so that a higher level of effectiveness of mosquito nets is obtained as a means of personal protection in avoiding mosquito bites.

Enhancing Cloud Computing Environments with AI-Driven Resource Allocation Models

Cloud computing has changed the way businesses work by letting them handle resources in ways that were never possible before. But managing these resources well is still a big problem that affects speed, cost-effectiveness, and user happiness. Many old ways of doing things depend on static provisioning or heuristic-based methods, which can cause resources to be underused or over-provisioned. AI-driven resource distribution models, on the other hand, use machine learning algorithms to move resources around based on real-time data and predictive analytics. This method makes things more flexible and quick to respond by making sure that resources are distributed in a way that matches changing needs and work habits. Key parts of AI-driven models include using data on past usage to figure out what resources will be needed in the future. By looking at patterns, these models make sure that there aren't any resource shortages during times of high usage and that resources aren't provisioned when they aren't needed, which saves money. AI also makes it possible for resources to be scaled up automatically when the task changes. The system changes how resources are used in real time by keeping an eye on performance metrics and workload patterns all the time. This makes sure that performance and user experience are always at their best. AI-driven models also help optimize resources by making the best use of them while still following service level agreements (SLAs). These models make the whole system more efficient and lower operational costs by moving resources around between applications and services on the fly.

DGIDS: Dynamic graph-based intrusion detection system for CAN

The Controller Area Network (CAN) is widely used in automobiles to interconnect safety-critical electronic control units (ECUs). Unfortunately, CAN does not have inherent security mechanisms as originally designed, which has drawn significant attention from the research community. Currently, the mainstream CAN protection strategy is the Intrusion Detection System (IDS). However, many statistics-based IDSs are unable to identify the identifier (ID) of the attacked message; they can only identify anomalies within a specific time window. Moreover, these systems are often tested solely on public datasets, lacking theoretical validation of their effectiveness. To address these shortcomings, we propose a real-time intrusion detection system based on a dynamic graph. The graph is dynamically constructed based on the arrival of messages, and features are extracted concurrently. By utilizing the distribution of features extracted during the offline phase, our system achieves real-time detection of incoming messages and identifies the ID of the attacked message. Additionally, we introduce a method to theoretically validate the detection system through permutation and probabilistic statistical analysis. Experiments and theoretical analysis demonstrate that our proposed IDS can effectively detect a wide range of attacks with reduced detection time and memory usage.

The usage time of social media as a predictor of customer loyalty

The usage time of social media as a predictor of customer loyalty

Clinical outcomes for minimally invasive sacroiliac joint fusion with allograft using a posterior approach.

Sacroiliac joint (SIJ) dysfunction can occur as a result of injury, degeneration, or inflammation. This dysfunction presents symptoms of pain at various locations, including the low back, hips, buttocks, and legs. The diagnosis of SIJ dysfunction is challenging and cannot be achieved solely with imaging studies such as X-rays, MRI, or CT. The current gold standard diagnostic modality is intra-articular SIJ blocks using two differing local anesthetics. Current treatments for SIJ dysfunction may be beneficial for short-term relief but lack long-term efficacy. The purpose of our study was to examine the outcomes of patients who underwent minimally invasive, posterior SIJ fusion using allograft at a single center. This was a retrospective study which received exemption from the WCG IRB. Data regarding preoperative and postoperative pain levels, surgical time, complications, and medication usage were obtained retrospectively from patient electronic medical records and prescription drug monitoring program reports. No mapping was completed prior to the procedure. Pain was assessed with the 11-point (0-10) Visual Analogue Scale (VAS) and medication usage was assessed using Morphine Milligram Equivalents (MME). Patients were included if they had been diagnosed with SIJ dysfunction using two intra-articular diagnostic blocks that resulted in at least an 80% decrease in pain and had failed conservative management. Patients with sacral insufficiency fractures were excluded. VAS scores reduced from 8.26 (SD = 1.09) at baseline to 2.59 (SD = 2.57), 2.55 (SD = 2.56), 2.71 (SD = 2.88), and 2.71 (SD = 2.88) at 3, 6, 9, and 12 months, respectively. MME reduced from 78.21 mg (SD = 51.33) to 58.95 mg (SD = 48.64), 57.61 mg (SD = 47.92), 61.71 mg (SD = 45.64), and 66.29 mg (SD = 51.65) at 3, 6, 9, and 12 months, respectively. All reductions in VAS scores and MME were statistically significant. No adverse events occurred, and the average operating room time was 40.16 min (SD = 6.27). Minimally invasive, posterior SIJ fusion using allograft is a safe and efficacious method for managing SIJ dysfunction.

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Combined effects of organic solvent and ultrasonic pretreatments on cellulose fiber extraction from hemp

Extracting pure cellulose fibers from hemp bast for potential textile industry applications requires the removal of non-cellulosic gummy materials. However, traditional degumming methods are largely limited because of the long procedure time, high energy consumption, heavy environmental pollution and insufficient efficiency. Organic solvents have been proved to be an innovative method for lignocellulose extraction. Herein, ethylene glycol solvent combined ultrasonic pretreatment was explored to degum hemp fibers without any further treatment. The purified hemp fibers were systematically characterized using analytical test techniques. Results revealed that the combined degumming could effectively remove non-cellulosic components without damaging the cellulose, and reducing the reaction temperature by 10 ℃ for reaction process. Comparing to the traditional method, the fibers by ultrasonic pretreatment-ethylene glycol process had superior average yield of 68.32 %, higher thermal stability (maximum degradation temperature reached 395 ℃), higher breaking tenacity of 6.13 cN/dtex, higher crystallinity of 78 % and the cellulose content of over 91 %. Moreover, the degumming time and chemical usage were reduced to 40 % and 51.8 %, respectively. Besides, the changes of fibers chemical structure were further investigated and verified by FTIR, EDS, and 13C NMR analysis. Featured with less chemical and energy consumption, eco-friendly and short time-consuming, this treatment has a broad application scope for hemp cellulose fiber extraction.

A Novel Simulation Model and Training Program for Minimally Invasive Surgery of Hallux Valgus.

Minimally invasive surgery (MIS) for hallux valgus (HV) has gained popularity. However, adopting this technique faces the challenges of a pronounced learning curve. This study aimed to address these challenges by developing and validating an innovative simulation model and training program, targeting enhanced proficiency in HV MIS. A training program and a high-fidelity simulation model for HV MIS were designed based on experts' recommendations. Four foot and ankle surgeons without experience in MIS formed the novice group and took the program that encompassed six-session instructional lessons, hands-on practice on simulated models, and immediate feedback. The program concluded with a cadaveric surgery. Four foot and ankle experienced MIS surgeons formed the expert group and underwent the same procedure with one simulated model. Participants underwent blind assessment, including Objective Structured Assessment of Technical Skills (OSATS), surgical time, and radiograph usage. Expert evaluation of the simulation model indicated high satisfaction with anatomical representation, handling properties, and utility as a training tool. The expert group consistently outperformed novices at the initial assessment across all outcomes, demonstrating OSATS scores of 24 points (range, 23 to 25) versus 15.5 (range, 12 to 17), median surgical time of 22.75 minutes (range, 12 to 27) versus 48.75 minutes (range, 38 to 60), and median radiograph usage of 70 (range, 53 to 102) versus 232.5 (range, 112 to 280). Novices exhibited a significant improvement in OSATS scores from the fifth session onward (P = 0.01), reaching the desired performance of 20 points. Performance at the final training with the simulated model did not differ from cadaveric surgery outcomes for all parameters. This study validated a simulation model and training program, allowing nonexperienced HV MIS foot and ankle surgeons to enhance their surgical proficiency and effectively complete a substantial portion of the learning curve at the fifth session, and this performance was successfully transferred to a cadaver model. III.

Enhanced Computational Biased Proportional Navigation with Neural Networks for Impact Time Control

Advanced computational methods are being applied to address traditional guidance problems, yet research is still ongoing regarding how to utilize them effectively and scientifically. A numerical root-finding method was proposed to determine the bias in biased proportional navigation to achieve the impact time control without time-to-go estimation. However, the root-finding algorithm in the original method might experience efficiency and convergence issues. This paper introduces an enhanced method based on neural networks, where the bias is directly output by the neural networks, significantly improving computational efficiency and addressing convergence issues. The novelty of this method lies in the development of a reasonable structure that appropriately integrates off-the-shelf machine learning techniques to effectively enhance the original iteration-based methods. In addition to demonstrating its effectiveness and performance of its own, two comparative scenarios are presented: (a) Evaluate the time consumption when both the proposed and the original methods operate at the same update frequency. (b) Compare the achievable update frequencies of both methods under the condition of equal real-world time usage.

Exploring different technology addiction types in young adults: a latent profile analysis

This study aimed to examine the different latent profiles of technology addiction in young adults, and the criteria for belonging to these profiles, in relation to sociodemographic factors (gender and SES) and technology use status (daily social network usage time, daily instant messaging usage time, daily online gaming usage time, daily website usage time, and connection location). Data were collected from a sample of 1,431 young adults [988 (69.1%) female, 443 (30.9%) male]. Latent Profile Analysis (LPA) was used to identify the latent profiles, and Multinomial Regression Analysis was employed to examine the relationships between the latent profiles and sociodemographic and technology use. The LPA revealed four latent profiles of technology addiction in young adults: “Non-Addicts (NA)”, “Moderate Technology Addicts-Gamers (MTAG)”, “High Technology Addicts-Non-Gamers (HTANG)”, and “High Technology Addicts-Gamers (HTAG)”. The criteria for belonging to these four profiles were statistically significantly related to sociodemographic and technology use factors, except for connection location, and age. These findings provided further support for our knowledge about planning programs or multi-layered intervention models to prevent technology addiction in young adults.

A python library for the fast and scalable computation of biologically meaningful individual specific networks

Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Public art’s counter-temporalities, the case of the Garden Library, south Tel Aviv

In research on public art, there is a tendency to link art’s transformative potentialities to its temporal dimensions, namely duration, rhythm and the type of social relations they produce. However, beyond acknowledgement of what it might do, temporality received little conceptual attention. Using The Garden Library as a case study, this article foregrounds temporality as a theoretical and methodological framework to examine public art’s performative and structural patterns. The Garden Library is a multi-lingual and cultural space for refugees and migrant workers founded by the art group Arteam in the southern neighbourhood Neve Sha’anan, Tel Aviv in 2009. This article traces the project’s changing temporalities and considers how they create an inclusive time-space that is largely unavailable for the migrant communities living in Israel. It largely draws from art history and human geography scholarship to conceptualise time and temporality in relation to space, practice, and displacement and to further deepen our understating of the unique modes of knowledge and socio-political critique public art enables. The article also brings forth the entanglement of multiple oppressed temporalities, including native Palestinians and non-Jewish refugees, and reads the Garden Library against the background of Israel’s settler-colonial and ethnocratic state. Centring temporality, therefore, has two goals. The first is to examine the usage of time, alongside space, as a control mechanism and to draw links between shared experiences of dislocation and dispossession that are often studied separately. The second is to amplify the occasional collisions and intersections of marginalised temporal landscapes, including that of the Mizrahi (Arab-Jews) working class residents of Neve Sha’anan, and the possibility of such moments to produce counter-temporalities that adjust and resist forms of spatio-temporal power.

Screen Time Usage and Mental well-being among adolescents: A Cross-Sectional Study

Modern kids are exposed to a wide variety of gadgets and are surrounded by technological advances, including television, cell phones, laptops, and other devices. Young people now spend more time than ever before using electronic gadgets in their daily lives and participating in screen based activities. Aim: This study aims to examine screen time usage, mental well-being and its effect among adolescents. Methods: A cross-sectional study was done among 100 adolescents. Responses were collected by self-structured screen time usage, Warwick-Edinburg Mental well-being scale and self-structured screen time effects scale. Findings: The collected data were analysed by using SPSS version 20.0 and the findings revealed that 42% and 45% of adolescents were using smartphone for 1-3 hours on weekends and weekdays respectively. Forty six percent of adolescents were had low mental well-being. The excessive screen time use reported often feeling moods, headache, frustrated, poor concentration and problem in their eyes. The screen time usage were significantly associated with effects of screen usage (r= 0.27, p=0.04). Conclusion: The study concluded that the adolescents had higher screen time usage on weekends. Increased screen time use predominantly was associated with higher negative effects.

Nomophobia, loneliness and depressive symptom levels of adults living in a district of Türkiye.

This study was conducted to evaluate whether smart cell phones detach people from social life, make them lonely, cause depressive symptoms and whether there is a relationship between them. This is a cross-sectional study using face-to-face survey method. The sample was determined as 376 participants, with 95% confidence interval, 50% probability depending on the probability of having a smartphone or not. Participants in the sample were determined by systematic random sampling from adults aged 20 and over in Çiftlik district of Niğde province. The NMP-Q, DASS-42 and UCLA-LS were used. The median age of the 376 participants (192 male, 184 female) was 30 and the mean age was 32.0 (±10.94) years. Participants that had social media accounts were 68.9% of the population. Over 70% of the participants had mild, moderate or severe nomophobia. Nomophobia status of the participants was affected by the mean duration of the first-time smartphone use (P = .017), the mean daily smartphone usage time of the participants (P < .001), the mean number of smartphone users in the participants' families (P = .003), the mean depression and stress scores of the participants (P < .001), having social media account (P = .001), and declaration of smart phone dependency (P = .005). Nomophobia status was not affected by participants' gender, mean age, educational level, employment status, economic status and marital status (P > .05). The duration of daily smartphone use, the duration of first-time smartphone use, and having multiple smartphone users in family increased nomophobia. Also, having active social media accounts and self-reported smartphone addiction found correlated to nomophobia. Smartphone addiction increases depression, anxiety, and stress scores of the participants.