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The purpose of this study is to know and analyze the effect of wage variables on regional investment policy, the influence of inflation variables on regional investment policy, and the influence of exchange rate variables on investment policy of the Region on Labor. In this study, the population taken is the entire workforce whose data comes from the Central Bureau of Statistics in Sidoarjo which amounted to 64,792 workers. Data analysis using multiple linear regressions with the help of SPSS program version 20 showed that there is an influence of wages, inflation, and exchange rate on local investment policy. Based on the results of calculations and test results conducted, it can be explained that there is an effect of wages on regional investment of labor followed by the characteristics of inflation on labor that affects the exchange rate of investment. This illustrates for policymakers which empirical evidence exists in a series of time to test the theoretical basis while establishing fiscal, monetary, or exchange rate policies to stabilize output and employment by using interest rates, money supply, and exchange rates as instruments for achieving goals.

Subthreshold depression and major depressive symptoms are prevalent mental health conditions that significantly impact quality of life and contribute to South Korea's high suicide rate. Despite their importance, few studies have examined temporal changes in the occurrence of these disorders in the Korean population. This study aimed to estimate the prevalence of subthreshold depression and MDD using a large, representative sample of the South Korean population and analyze trends over time. Data were obtained from 10,848 participants aged 19 and above in the Korean National Health and Nutrition Examination Survey (KNHNES) in 2014 and 2018. Depression severity was categorized using cutoff scores of 5-14 for subthreshold symptoms and ≥ 15 for severe symptoms. The prevalence of subthreshold depression increased from 12.90% in 2014 to 15.20% in 2018, while MDD rose from 4.7% to 7.0% (p < 0.001). Logistic regression analysis revealed that MDD (OR = 8.1, 95% CI = 2.34-11.23), college education (OR = 7.9, 95% CI = 4.23-10.00), and age above 65 years (OR = 8.1, 95% CI = 2.58-12.58) exhibited similar risks for suicide attempts. Since 2014, there has been a sharp and sustained increase in both subthreshold and severe depressive symptoms among the Korean population. This alarming trend underscores the critical need for targeted prevention and intervention strategies.

The objective of this study was to analyze how small and medium enterprises (SMEs) cope with environmental changes due to the COVID-19 pandemic by pursuing the business model transformation with the support of digital technologies. To achieve the objective, this study used a multiple case study design with qualitative analysis to examine the data obtained from interviews, observation, and field visits. Seven manufacturing SMEs from Indonesia were selected using a theoretical sampling technique, with the purpose of achieving some degree of variation to allow us to undertake replication logic. Our analysis demonstrates that SMEs adopt a different degree of digital transformations, which can be summarized into three paths, depending on the firms' contextual factors. First, SMEs with a high level of digital maturity who respond to the challenges by accelerating the transition toward digitalized firms; second, SMEs experiencing liquidity issues but a low level of digital maturity who decide to digitalize the sales function only; and, third, the SMEs that have very limited digital literacy but are supported by a high level of social capital. This last group of firms solves the challenges by finding partners who possess excellent digital capabilities. The qualitative case study method allows us to conduct in-depth and detailed analysis, but has thin generalizability. To address this limitation, future research can use a survey covering various industries to test the proposed theory that has resulted from this study, so that the generalizability can be assured.

A correlation between health-related quality of life (HRQoL) and physical activity has been identified. Many studies have discussed whether this correlation is significantly associated with depression in the general and diabetic populations. However, the role of depression in this relationship, especially in individuals with diabetes, remains incompletely understood. This study investigated the relationship between PA and HRQoL, with depression as a potential mediator, in individuals with diabetes. This cross-sectional study involved 1,472 individuals with diabetes who participated in the Korea Health Panel Survey (KHPS) from 2019 to 2020. Their sociodemographic characteristics, PA, depressive symptoms, and HRQoL based on EuroQol-five-dimension (EQ-5D) scores were extracted from the KHPS. The mediating effect of depression on PA and HRQoL was investigated using multiple regression and a mediation effect test. HRQoL was positively associated with PA, regular exercise, and varying degrees of walking activity. Conversely, depression was substantially negatively associated with HRQoL. Mediation analysis confirmed that depression partially mediated the relationship between PA and HRQoL. Specifically, for PA and regular exercise, the indirect effect of depression accounted for 46.61% (B = 0.002, p < 0.05) and 33.82% (B = 0.003, p < 0.001). In individuals with diabetes, depression was found to mediate the effect of PA on HRQoL. Therefore, conducting depression screening and managing depressive symptoms in this population is crucial to enhancing HRQoL through PA interventions. Consequently, strategies to enhance HRQoL can be effectively implemented and customized in response to particular depression screening outcomes.

Radar is a technology that is always used by the military to detect an object because it can determine the shape, size, position, distance, and speed of an object. This enables the national defence system to have anti-radar technology to protect defence equipment or other important defence objects. One way that can be applied is using radar-absorbing material to coat the surface of the object. A good radar-absorbing material is made of a combination of dielectric materials with magnetic materials. MWCNT/ZnO composites were produced by a high energy milling method. The various milling times (0, 1, 3 and 5 h) using HEM on the microwave absorbing properties of the composites has an effect, which was studied. The experimental results show that the optimum microwave absorption ability is reached when the HEM process is carried out for 5 h with a thickness of 2.0 mm. The optimum return loss is −26.4 dB at a frequency of 11.2 GHz and the bandwidth correlative to the return loss is below −10 dB with a frequency greater than 1.5 GHz. When comparing MWCNT/ZnO without HEM treatment, the results show that HEM treatment can also increase microwave absorption properties.

The purpose of this study is to analyze how dynamic capabilities are integrated into open innovation to support firms pursuing innovation. Dynamic capabilities enable firms to adjust to emerging changes through redesigning resource configuration. Nevertheless, how dynamic capabilities are integrated into open innovation for obtaining external knowledge and resources has not been observed in previous studies. Utilizing seven small and medium enterprises (SMEs) with various degrees of knowledge and technology intensity as subjects, this qualitative study identified a typology of pathways for integrating dynamic capabilities into open innovation. This study found that firms’ internal resources coupled with complementary assets obtained from open innovation determine what strategies to deploy and what capabilities are needed to execute the strategies. The fit among firms’ resources, strategies, capabilities and emerging business environment is not serendipitous, but rather it must be designed and supported by collective efforts from participants across organizations. In other words, the nature of knowledge and degree of technology adoption determine how sensing, seizing and performing are applied in each phase of open innovation. The main drawback of qualitative study is that it cannot cover a large number of subjects, although it can scrutinize an abundant number of detailed data. Future research can analyze the findings of this study using a survey method covering a large number of firms from various industries so that generalizability can be assured.

ZnO is a popular photocatalyst that is often used for the degradation of dyes and bacteria. However, the catalytic performance of ZnO is only optimal under UV light exposure. This study aims to determine the degradation performance of rhodamine b, methylene blue, and Escherichia coli using 0, 5, 10, 15, and 20% Fe-doped ZnO (ZnO:Fe). Deposition of thin film was carried out using the sol-gel method with a spray-coating technique, while the degradation was carried out under halogen light exposure for 3 h. The optical characterization results show that 20% Fe-doped ZnO has the highest transmittance and the lowest energy band gap of 3.21 eV based on Tauc’s plot method. All thin films are hydrophilic with the largest contact angle of 68.54° by 20% Fe-doped ZnO and the lowest contact angle of 52.96° by 5% Fe-doped ZnO. The surface morphology of the thin film resembles a creeping root that is cracked and agglomerated. XRD test results show that the thin film is dominated by ZnO peaks with a wurtzite structure with a hexagonal plane phase and a crystal size of 115.5 A°. The 20% Fe-doped ZnO thin film had the most efficient degradation performance of 70.79% for rhodamine b, 65.31% for blue, and 67% for E. coli bacteria. Therefore, Fe-doped ZnO is a brilliant photocatalyst material that can degrade various pollutants even under visible light.

The purpose of this study is to know and test does wages affect the Regional Investment, does inflation affect the Regional Investment, Does Exchange Rate Effect on Regional Investment, and which among wages, Inflation, and Exchange Rate is very dominant influence on investment area. This research uses quantitative approach, while the object of research is all labor available in Malang Region. And researchers take data from the Central Bureau of Statistics in the city of Malang, amounting to 1.273.579 workers. The results of data analysis using multiple linear regressions with the help of SPSS program version 20 and the discussion that has been done, all variables affect the local investment.

Learning a language is essentially learning vocabulary, and it is the lexical competence that enables the learners to use the language with ease. It will be argued that such an ability includes, among the important ones, the knowledge of semantic properties and syntactic behavior of the lexical item as well as its collocation. The acquisition of the semantic properties of a lexical item is ncccssaary to support the learner's ability to distinguish different senses encoded in the lexical item, and the knowledge of syntactic behavior reflects the learner's ability to recognize and produce the syntactic variants into which a lexical item can enter. The collocational competence is the knowledge of the lexical behavior in particular that enables the learner to envisage the possible cooccurrence of other words with the given lexical item. Thus, the acquisition of lexical competence would cover a large part of syntax. This understanding of the nature and characteristics of lexicon would raise some questions on the relevance of putting great emphasis on the teaching of grammar only.

Fault delineation is a crucial process for exploring stratigraphic structure and reservoir properties from seismic data. It plays a vital role as faults often offer valuable insights into the accumulation and migration paths of geological resources. As the size of seismic data increases, fault picking becomes a laborious task, demanding high accuracy from interpreters. Automation is essential to expedite this process and minimize human subjectivity in fault picking. To automate this task, we employ deep learning algorithms, particularly convolutional neural networks (CNNs). In this research, we utilize a 3D U-Net architecture known as FaultSeg3D, with a focal loss function for the training process. The dataset comprises 220 pairs of synthetic data, including 200 pairs for train/test and 20 pairs for validation. Results from the training process indicate a converging loss function curve, with values of 0.0154 for training and 0.0308 for testing. This convergence signifies the success of the training process. Quantitatively, fault delineation estimates from the CNN model demonstrate favorable values based on performance metrics, including precision, recall, and F-1 score, on validation data—approximately 0.6, 0.9, and 0.75, respectively. Each of these value was obtained from testing on validation data. Qualitatively or visually, the fault delineation estimates on validation data using the CNN model outperform the variance attribute. The resulting fault delineation estimate from the CNN model appears more continuous, with fewer inaccuracies compared to the variance attribute. Considering the excellent performance metrics when applied to synthetic data, this potential could be promising to continue with its application to field data which can be carried out for further research.