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Ortho-Unit Polygons can be Guarded with at most n - 4 8 Guards
No full textAbstract An orthogonal polygon is called an ortho-unit polygon if its vertices have integer coordinates, and all of its edges have length one. In this paper we prove that any ortho-unit polygon with n ≥ 12 vertices can be guarded with at most ⌊ n - 4 8 ⌋ guards, which is a tight bound
Brewing Resilience: A Case Study in Adapting Small Business Strategy with Systems Thinking
No full textThis thesis explores how systems thinking—a methodology often reserved for large organizations—can be effectively applied to small businesses facing complex challenges. Using Lamplighter Brewing Co., an independent microbrewery in Cambridge, Massachusetts, as a case study, the research examines how the brewery adapted to the disruptions of the COVID-19 pandemic and the evolving economic landscape that followed. It documents the iterative application of systems thinking principles to identify root causes, leverage points, and actionable solutions to address issues such as declining revenue, rising costs, and misaligned organizational structures.
Lamplighter's interventions ranged from restructuring its management and marketing teams to pivoting its sales and production strategies. By leveraging tools such as causal loop diagrams and stock-and-flow models, the brewery uncovered systemic dynamics driving its performance. The research highlights the importance of iterative learning, targeted interventions, and holistic analysis in fostering resilience and sustainability in resource-constrained environments.
While focused on the craft brewing industry, the findings offer transferable insights for small businesses in similarly dynamic sectors, demonstrating that systems thinking can empower smaller organizations to navigate complexity, adapt strategically, and thrive amidst uncertainty.S.M
Impact of Introducing Technical Design Elements in Makerspace Trainings
No full textMakerspaces are used as a tool in higher education to support curricular, hands-on projects and encourage student extracurricular and personal projects. Because access to making is more self-driven, there is a gap between what makerspace trainings teach students and what students are expected to know by the time they reach capstone courses in engineering. To test the effects of introducing a technical makerspace training to students, several steps were taken. First, known barriers to making were explored and organized into categories. Second, Design Expertise was defined as a means to combat these barriers: it is a combination of (1) knowledge, (2) skill, (3) perspective, and (4) motivation. Third, a rigorous framework, the Design-Fabrication-Performance (DFP) matrix was created to break down design expertise into manageable chunks. Next, existing makerspace trainings at MIT were characterized using the DFP matrix. Afterwards, the DFP matrix was used to design a new, experimental training which would incorporate engineering design thinking and expertise with the typical makerspace machine training structure. Finally, 23 student participants were recruited, surveyed using a Likert scale (1 = strongly disagree, 5 = strongly agree), and interviewed to understand the impact of the training on participant perspectives, engineering identity, and maker motivation. Initial results suggest that student self-efficacy increases as a result of the training, This outcome is shown by the highest average differential of all survey responses (M = 0.78, SD = 0.85) for question 15: “I am confident in my ability to use GIR level knowledge to design and make things that perform as intended”. The maker training reinforced the motivation to make things for a majority of students, with the average score for the associated question being 4.48 (SD: 0.85). The training also positively impacted some traditionally marginalized groups in STEM. For the statement "I feel comfortable in engineering at MIT", women averaged 3.27 and men 3.90 before the training. The average differentials in the post- and pretraining scores to this question for these groups were 0.4 and 0.91 respectively. The training also appears to level playing field for students with less advanced backgrounds in engineering and science. For the question “I am confident in my ability to solve GIR level problems on my own”, students with parents with graduate degrees or higher averaged 4.44 before the training, while those with parents with undergraduate degrees or lower averaged 3.57. The average differentials are 0.22 and 0.64 respectively. Although students saw the value in modeling systems before design and fabrication, several questions demonstrated that students found modeling to be tedious and preferred to test and iterate on their designs in the makerspace; further work is needed to eliminate barriers to sustain student interest and participation in the long term. A longitudinal study following these students would also be needed to reveal long term outcomes such as STEM retention and long-term makerspace usage.S.M
Toward the Understanding of Brain’s Molecular Language
No full textWhat underlies the extraordinary capacity of neurons to process information, form memories, and orchestrate complex behaviors? Over a century of research has established that proteins are the central functional molecules of the cell, yet translating this knowledge into an understanding of emergent neural phenomena and effective treatments for neurological disorders remains elusive. We argue that this paradox stems from studying proteins in isolation, overlooking how their function is fundamentally shaped by spatial context and interactions with DNA, RNA, other proteins, lipids, carbohydrates, and metabolites. This coordinated
molecular interplay, we posit, ultimately gives rise to the complex neural circuits and behaviors observed in higher organisms. Intriguingly, Alfred Binet foreshadowed this perspective as early
as 1889 when he suggested that even simple, single-celled organisms—lacking anatomically defined nervous systems—might harbor a "diffuse nervous system" of molecular interactions
within their cytoplasm enabling complex behaviors. However, the historical progression of neuroscience, largely dictated by available methodologies and oscillating between siloed reductionist molecular approaches and systems-level analyses, has not yet been able to fully capture this intricate molecular choreography underlying neural function. In this review, we examine how studying molecular species in isolation, while yielding important insights, has ultimately proven insufficient for understanding emergent neural functions. We propose that recent technological advances in expansion microscopy, molecular anchoring, machine learning-enabled
protein detection, and cryo-fixation now make it possible to map molecular networks in their native context. This integrative approach promises to illuminate the molecular "language" of the brain, shedding light on how collective interactions among biomolecules
give rise to neuronal emergent abilities—and guide future therapeutic innovations.S.M
Accelerating the Discovery of Novel Metal Organic Chalcogenolates: A Computational and Machine Learning-Driven Approach
No full textMetal Organic Chalcogenolates (MOChas) are a class of robust, self-assembling, and hybrid materials featuring inorganic metalo-chalcogen frameworks that are scaffolded by organic ligands. These low-dimensional structures exhibit tunable optoelectronic properties, making them promising candidates for various applications, including optical sensors and nanotechnology. This tunable relationship between MOCha structural arrangements and targeted properties opens up a vast yet challenging search space for novel MOCha structures. Density Functional Theory (DFT) can predict properties of materials with good accuracy, making it a powerful choice for even hypothetical materials. However, the discovery of novel MOChas structures is constrained by poor scalability of DFT relaxation times for large systems and a lack of high-throughput design methods that can capture the complex geometries of MOChas. In this work, we employ DFT calculations to investigate the energetic and electronic properties of various MOChas, and provide insight into the optical behavior and kinetic favorability of such structures. To address the computational bottlenecks of high-throughput design and DFT workloads, we discuss the use of machine-learned interatomic potentials and various generative models that can enable rapid prototyping of novel MOCha structures.S.M
Leveraging Structure for Efficient and Dexterous Contact-Rich Manipulation
No full textContact-rich manipulation has proved challenging due to the need to consider multiple combinatoric possibilities of making or breaking contact with the surrounding environment. As a result, existing methods have often resorted to combinatorial optimization that utilizes dynamics structure but considers all possibilities exhaustively, or compute-heavy and inefficient sampling methods that utilize blackbox optimization such as Reinforcement Learning (RL). In this thesis, I aim to show that by combining structured contact smoothing in conjunction with local gradient-based control and sampling-based motion planning, we can bypass the combinatorial explosion of contact modes while still leveraging structure and achieve highly efficient contact-rich manipulation. To achieve this capability, I first shed light on how RL abstracts contact modes and optimizes difficult landscapes by combining stochastic smoothing and zeroth-order optimization; yet, I show how following a similar stochastic strategy while using gradients suffers from several drawbacks such as empirical bias and high variance. To leverage structure in a more helpful manner, I propose a method for smoothing contact dynamics without relying on stochastic smoothing, bypassing these drawbacks. Using this smoothing scheme, I present a highly efficient and performant local control based on gradient-based trajectory optimization and model predictive control. Finally, I connect these local control capabilities with global sampling-based motion planners to achieve long-horizon global plans. The proposed method achieves contact-rich plans such as dexterous in-hand reorientation and whole-body manipulation much more efficiently than RL while being highly scalable compared to methods that explicitly reason about contact modes. These results achieve a reduction of contact-rich manipulation to kinodynamic motion planning, and exposes the true difficulty of contact-rich manipulation from combinatorial explosion in contact modes to combinatorial and highly non-local decisions over motion planning behaviors.Ph.D
Modeling, Design, and Assembly of Spring Tires
No full textWith a renewed interest in the Moon and the need for autonomous lunar rovers that drive longer distances and operate over extended durations, designing efficient and robust mobility systems is paramount. Created by NASA Glenn Research Center, the spring tire is a compliant airless tire engineered for planetary rover missions in lunar and Martian environments. It consists of hundreds of coiled springs woven together to create a toroidal-shaped mesh wheel that can deform to uneven terrain, providing additional durability and traction. This work aims to apply this technology to two robotic testbeds: ERNEST, an autonomous lunar traversal rover built at NASA Jet Propulsion Laboratory, and IPEx, a lunar regolith mining robot built at Kennedy Space Center. This thesis discusses the modeling of these spring tires with numerical methods along with the design of two spring tire prototypes for use on the aforementioned rover platforms. A streamlined assembly process for these compliant wheels is also outlined as well as the results of compression testing, rough terrain driving, and drawbar pull testing to assess their performance.M.Eng
Encourage circular practices in the supply chain
No full textEvery year 300 million tons of plastic waste are produced, and the amount of plastic produced increases with the world population. The more people there are on the planet, the more waste is produced. The concepts of circular economy are gaining popularity. Companies are looking to implement circular strategies to maximize the use of materials, reduce waste and help the environment while improving their corporate image.
Since the coronavirus pandemic, digital transformation has progressed faster and faster, which has boosted digital communication. Social networks began to play a fundamental role in communication since they are an efficient means of interacting with people worldwide in real-time. Due to social networks' social impact, they can be used to influence people's decision-making.
This study aims to develop a model that encourages people to adopt recycling habits for polyethylene terephthalate (PET) bottles through social networks focused on the population of the United States. This study will use analytics tools such as the Bass Diffusion Model, and an economic analysis of the viability will be carried out to implement the proposed strategies
Deterministic near-optimal distributed listing of cliques
No full textThis article is part of a collection for a Special Issue of Distributed Computing: by invitation only, this special issue highlights the best papers from the ACM Symposium on Principles of Distributed Computing (PODC 2022) held in Salerno, Italy, on July 25-29 2022.The importance of classifying connections in large graphs has been the motivation for a rich line of work on distributed subgraph finding that has led to exciting recent breakthroughs. A crucial aspect that remained open was whether deterministic algorithms can be as efficient as their randomized counterparts, where the latter are known to be tight up to polylogarithmic factors. We give deterministic distributed algorithms for listing cliques of size p in n 1 - 2 / p + o ( 1 ) rounds in the Congest model. For triangles, our n 1 / 3 + o ( 1 ) round complexity improves upon the previous state of the art of n 2 / 3 + o ( 1 ) rounds (Chang and Saranurak, in: 2020 IEEE 61st annual symposium on foundations of computer science (FOCS), pp 377–388. IEEE Computer Society, Los Alamito, 2020. https://doi.org/10.1109/FOCS46700.2020.00043 ). For cliques of size p ≥ 4 , ours are the first non-trivial deterministic distributed algorithms. Given known lower bounds, for all values p ≥ 3 our algorithms are tight up to an n o ( 1 ) subpolynomial factor, which comes from the deterministic routing procedure we use
A new look at the environmental conditions favorable to secondary ice production
No full textThis study attempts a new identification of mechanisms of secondary ice production (SIP) based on the observation of small faceted ice crystals (hexagonal plates or columns) with typical sizes smaller than 100 µm. Due to their young age, such small ice crystals can be used as tracers for identifying the conditions for SIP. Observations reported here were conducted in oceanic tropical mesoscale convective systems (MCSs) and midlatitude frontal clouds in the temperature range from 0 to −15 ∘C and heavily seeded by aged ice particles. It was found that in both MCSs and frontal clouds, SIP was observed right above the melting layer and extended to higher altitudes with colder temperatures. The roles of six possible mechanisms to generate the SIP particles are assessed using additional observations. In most observed SIP cases, small secondary ice particles spatially correlated with liquid-phase, vertical updrafts and aged rimed ice particles. However, in many cases, neither graupel nor liquid drops were observed in the SIP regions, and therefore, the conditions for an active Hallett–Mossop process were not met. In many cases, large concentrations of small pristine ice particles were observed right above the melting layer, starting at temperatures as warm as −0.5 ∘C. It is proposed that the initiation of SIP above the melting layer is stimulated by the recirculation of large liquid drops through the melting layer with convective turbulent updrafts. After re-entering a supercooled environment above the melting layer, they impact with aged ice, freeze, and shatter. The size of the splinters generated during SIP was estimated as 10 µm or less. A principal conclusion of this work is that only the freezing-drop-shattering mechanism could be clearly supported by the airborne in situ observations