Collective human attitudes influenced by macro-forces that impact environmental issues are partially correlated to our behaviors for the good and the harm of the planet. In this thesis, I will explore how collective human attitudes contribute to pro-environmental behaviors, common and pre-existing frames of mind on major conservation dilemmas, and finally suggest future directions on how humans could be inclined to take on more environmental responsibility through an increase in human-environmental connectivity. It is found that humans are largely driven by institution structures, education, and social influence. In conclusion, more efforts should be placed to further analyze these structural incentives for pro-environmental behaviors and use them to make environmental stewardship more accessible for all people and diverse circumstances. This can be done by evaluating the human dimensions of what influences human attitudes and behaviors, how to use these forces to systematically influence pro-environmental choices, applying these structural forces to main conservation issues, and further incorporating moral discourse into the environmental research in order to appeal correctly to all aspects and perspectives. Only when human connectivity is understood in relation to the natural sciences will we be able to make positive change in the direction of a healthier Earth.

Forensic entomology is an important field of forensic science that utilizes insect evidence in criminal investigations. Blow flies (Diptera: Calliphoridae) are among the first colonizers of remains and are therefore frequently used in determining the minimum postmortem interval (mPMI). Blow fly development, however, is influenced by a variety of factors including temperature and feeding substrate type. Unfortunately, dietary fat content remains an understudied factor on the development process, which is problematic given the relatively high rates of obesity in the United States. To study the effects of fat content on blow fly development we investigated the survivorship, adult weight and development of Lucilia sericata (Meigen; Diptera: Calliphoridae) and Phormia regina (Meigen; Diptera: Calliphoridae) on ground beef with a 10%, 20%, or 27% fat content. As fat content increased, survivorship decreased across both species with P. regina being significantly impacted. While P. regina adults were generally larger than L. sericata across all fat levels, only L. sericata demonstrated a significant (P < 0.05) difference in weight by sex. Average total development times for P. regina are comparable to averages published in other literature. Average total development times for L. sericata, however, were nearly 50 hours higher. These findings provide insight on the effect of fat content on blow fly development, a factor that should be considered when estimating a mPMI. By understanding how fat levels affect the survivorship and development of the species studied here, we can begin improving the practice of insect evidence analysis in casework.

Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased data is difficult to spot as the programming field is homogeneous and this issue reflects underlying societal biases. Facial recognition programs do not identify minorities at the same rate as their Caucasian counterparts leading to false positives in identifications and an increase of run-ins with the law. AI does not have the ability to role-reverse judge as a human does and therefore its use should be limited until a more equitable program is developed and thoroughly tested.

Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.

In the past decade, the use of mobile applications, specifically mobile applications focused on improving the health and fitness of users, has increased exponentially. As more consumers look towards mobile health applications to improve their health through dieting, exercise, and weight management, it is important to analyze how the concept of gamification can encourage sustained interaction and approval of these health-focused applications. This thesis aims to understand the prevalence of gamification amongst a large sample of health and fitness applications, identify and code the gamification features used in these apps, and finally, understand how different gamification features relate to the popularity and willingness to advocate using eWOM on behalf of a mobile app.

Managing a work-home balance is a daunting task for any parent. It is often difficult to take leave from work to care for one’s family due to financial barriers, which simultaneously poses a threat to family development. Although many countries have parental leave policies in place to account for this, effectiveness of these policies vary by country. This study aims to find to what extent parental leave has an impact on the quality of life. In this study, quality of life was investigated by the rank of the country on the Happiness Index and through the lens of achieving sustainable family development, which was subsequently described to be reflected by a country’s governmental resources provided during parental leave, as well as the country’s Gender Inequality Index. Through a cross-cultural review of literature, it was found that there seems to be an indirect, complex correlation of parental leave to the quality of life, and external factors such as sociocultural ideals, gender inequality, and varying workplace practices have greater significance on quality of life.

"Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved."

Glioblastoma (GB) is one of the deadliest cancers and the most common form of adult primary brain tumors. SGEF (ARHGEF26) has been previously shown to be overexpressed in GB tumors, play a role in cell invasion/migration, and increase temozolomide (TMZ) resistance.[3] It was hypothesized parental LN229 cell lines with SGEF knockdown (LN229-SGEFi) will show decreased metabolism in the MTS assay and decreased colony formation in a colony formation assay compared to parental LN229 cells after challenging the two cell lines with TMZ. For WB and co-immunoprecipitation (co-IP), parental LN229 cells with endogenous SGEF and BRCA were expected to interact and stain in the BRCA1:IP WB. LN229-SGEFi cells were expected to show very little SGEF precipitated due to shRNA targeted knockdown of SGEF. In conditions with mutations in the BRCA1 binding site (LN229-SGEFi + AdBRCAm/AdDM), SGEF expression was expected to decrease compared to parental LN229 or LN229-SGEFi cells reconstituted with WT SGEF (LN229-SGEFi + AdWT). LN229 infected with AdSGEF with a mutated nuclear localization signal (LN229-SGEFi + AdNLS12m) were expected to show BRCA and SGEF interaction since whole cell lysates were used for the co-IP. MTS data showed no significant differences in metabolism between the two cell lines at all three time points (3, 5, and 7 days). Western blot analysis was successful at imaging both SGEF and BRCA1 protein bands from whole cell lysate. The CFA showed no significant difference between cell lines after being challenged with 500uM TMZ. The co-IP immunoblot showed staining for BRCA1 and SGEF for all lysate samples, including unexpected lysates such as LN229-SGEFi, LN229-SGEFi + AdBRCAm, and LN229-SGEFi + AdDM. These results suggested either an indirect protein interaction between BRCA1 and SGEF, an additional BRCA binding site not included in the consensus, or possible detection of the translocated SGEF in knockdown cells lines since shRNA cannot enter the nucleus. Further optimization of CO-IP protocol, MTS assay, and CFA will be needed to characterize the SGEF/BRCA1 interaction and its role in cell survival.