An Optimization of Nanostructure Aluminum on Porous Silicon at Different Aluminum Thickness

The growth of aluminum nanostructure was conducted on porous silicon substrate by depositing a layer of aluminum via thermal evaporation method. The deposition process of the aluminum nanostructure was under the annealing temperature at 350°C for 1 hour. The weight of aluminum was varied for each sample in order to obtain different thickness of aluminum deposited on the sample. The weight of aluminum used in this experiment were 12mg ,18mg ,50mg and 74mg with the corresponding aluminum thickness deposited of 112nm, 163nm, 205nm and 332nm. Characterization on the morphology of the sample are conducted by using Atomic force microscopy (AFM), Raman spectroscopy and IV measurements. Based on the result obtained, the optimum weight of aluminum was 50mg of aluminum since it is provide the higher conductivity value on the sample

Common variants in SOX-2 and congenital cataract genes contribute to age-related nuclear cataract

Nuclear cataract is the most common type of age-related cataract and a leading cause of blindness worldwide. Age-related nuclear cataract is heritable (h2 = 0.48), but little is known about specific genetic factors underlying this condition. Here we report findings from the largest to date multi-ethnic meta-analysis of genome-wide association studies (discovery cohort N = 14,151 and replication N = 5299) of the International Cataract Genetics Consortium. We confirmed the known genetic association of CRYAA (rs7278468, P = 2.8 × 10−16) with nuclear cataract and identified five new loci associated with this disease: SOX2-OT (rs9842371, P = 1.7 × 1

Preventing Shoulder-Surfing Attacks using Digraph Substitution Rules and Pass-Image Output Feedback

In this paper, we focus on methods to prevent shoulder-surfing attacks. We initially adopted digraph substitution rules from PlayFair cipher as our proposed method. PlayFair cipher is a modern cryptography method, which exists at the intersection of the disciplines of mathematics and computer science. However, according to our preliminary study it was insufficient to prevent shoulder-surfing attacks. Thus, a new method had to be proposed. In this new proposed method, we improvised the digraph substitution rules and used these rules together with an output feedback method to determine a pass-image. Our proposed method was evaluated with a user study. The results showed our proposed method was robust against both direct observation and video-recorded shoulder-surfing attacks. © 2019 by the authors

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people’s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people’s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected

A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates

TiO2 Based Dye-Sensitized Solar Cell Prepare by Using Spray Pyrolysis Deposition (SPD)

Titanium dioxide (TiO2) thin films have been produced on the Fluorine-doped Tin Oxide (FTO) glass substrates by using the techniques of spray pyrolysis deposition and annealed at different of temperature. The film was annealed within 3 hours for each thin film. The different temperature that used for annealed is 300℃, 400℃, and 500℃. In this study is shown that, when the temperature is increased, the efficiency of the thin film also increases. On the surface, morphology and electrical properties of TiO2 DSSC thin film were examined by using FESEM and electrical measurement was calculated by I-V analysis. FESEM procedure characterizes, is for surface morphology and grain size. Raman spectroscopy analysis used to provide a fingerprint by which molecules can be identified and information about molecular vibrations that can be used for sample identification and quantitation. on electrical properties, the measurement of resistance and resistivity used common methods 2 point-prob and the efficiency of DSSC was measured by Solar Simulator. At the end, the objectives accomplish to produce TiO2 thin film with high efficiency to be applied in the DSSC

Systemic Literature Review of Recognition-Based Authentication Method Resistivity to Shoulder-Surfing Attacks

The rapid advancement of information technology (IT) has given rise to a new era of efficient and fast communication and transactions. However, the increasing adoption of and reliance on IT has led to the exposure of personal and sensitive information online. Safeguarding this information against unauthorized access remains a persistent challenge, necessitating the implementation of improved computer security measures. The core objective of computer security is to ensure the confidentiality, availability, and integrity of data and services. Among the mechanisms developed to counter security threats, authentication stands out as a pivotal defense strategy. Graphical passwords have emerged as a popular authentication approach, yet they face vulnerability to shoulder-surfing attacks, wherein an attacker can clandestinely observe a victim’s actions. Shoulder-surfing attacks present a significant security challenge within the realm of graphical password authentication. These attacks occur when an unauthorized individual covertly observes the authentication process of a legitimate user by shoulder surfing the user or capturing the interaction through a video recording. In response to this challenge, various methods have been proposed to thwart shoulder-surfing attacks, each with distinct advantages and limitations. This study thus centers on reviewing the resilience of existing recognition-based graphical password techniques against shoulder-surfing attacks by conducting a comprehensive examination and evaluation of their benefits, strengths, and weaknesses. The evaluation process entailed accessing pertinent academic resources through renowned search engines, including Web of Science, Science Direct, IEEE Xplore, ProQuest, Scopus, Springer, Wiley Online Library, and EBSCO. The selection criteria were carefully designed to prioritize studies that focused on recognition-based graphical password methods. Through this rigorous approach, 28 studies were identified and subjected to a thorough review. The results show that fourteen of them adopted registered objects as pass-objects, bolstering security through object recognition. Additionally, two methods employed decoy objects as pass-objects, enhancing obfuscation. Notably, one technique harnessed both registered and decoy objects, amplifying the security paradigm. The results also showed that recognition-based graphical password techniques varied in their resistance to different types of shoulder-surfing attacks. Some methods were effective in preventing direct observation attacks, while others were vulnerable to video-recorded and multiple-observation attacks. This vulnerability emerged due to attackers potentially extracting key information by analyzing user interaction patterns in each challenge set. Notably, one method stood out as an exception, demonstrating resilience against all three types of shoulder-surfing attacks. In conclusion, this study contributes to a comprehensive understanding of the efficacy of recognition-based graphical password methods in countering shoulder-surfing attacks by analyzing the diverse strategies employed by these methods and revealing their strengths and weaknesses

Lazertinib Versus Gefitinib as First-Line Treatment in Patients With <i>EGFR</i>-Mutated Advanced Non-Small-Cell Lung Cancer: Results From LASER301

PURPOSE Lazertinib is a potent, CNS-penetrant, third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. This global, phase III study (LASER301) compared lazertinib versus gefitinib in treatment-naive patients with EGFR-mutated (exon 19 deletion [ex19del]/L858R) locally advanced or metastatic non-small-cell lung cancer (NSCLC).PATIENTS AND METHODS Patients were 18 years and older with no previous systemic anticancer therapy. Neurologically stable patients with CNS metastases were allowed. Patients were randomly assigned 1:1 to lazertinib 240 mg once daily orally or gefitinib 250 mg once daily orally, stratified by mutation status and race. The primary end point was investigator-assessed progression-free survival (PFS) by RECIST v1.1.RESULTS Overall, 393 patients received double-blind study treatment across 96 sites in 13 countries. Median PFS was significantly longer with lazertinib than with gefitinib (20.6 v 9.7 months; hazard ratio [HR], 0.45; 95% CI, 0.34 to 0.58; P < .001). The PFS benefit of lazertinib over gefitinib was consistent across all predefined subgroups. The objective response rate was 76% in both groups (odds ratio, 0.99; 95% CI, 0.62 to 1.59). Median duration of response was 19.4 months (95% CI, 16.6 to 24.9) with lazertinib versus 8.3 months (95% CI, 6.9 to 10.9) with gefitinib. Overall survival data were immature at the interim analysis (29% maturity). The 18-month survival rate was 80% with lazertinib and 72% with gefitinib (HR, 0.74; 95% CI, 0.51 to 1.08; P = .116). Observed safety of both treatments was consistent with their previously reported safety profiles.CONCLUSION Lazertinib demonstrated significant efficacy improvement compared with gefitinib in the first-line treatment of EGFR-mutated advanced NSCLC, with a manageable safety profile.N