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1. Different places, different problems: profiles of crime and disorder at residential parcels

   https://doi.org/10.1186/s40163-022-00165-0  

   O’Brien, Daniel T.; Ristea, Alina; Hangen, Forrest; Tucker, Riley (June 2022, Crime Science)

   Abstract Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone. 

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2. Comparing machine and deep learning‐based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging

   https://doi.org/10.1002/hbm.25286  

   Smucny, Jason; Davidson, Ian; Carter, Cameron_S (November 2020, Human Brain Mapping)

   Abstract Previous work using logistic regression suggests that cognitive control‐related frontoparietal activation in early psychosis can predict symptomatic improvement after 1 year of coordinated specialty care with 66% accuracy. Here, we evaluated the ability of six machine learning (ML) algorithms and deep learning (DL) to predict “Improver” status (>20% improvement on Brief Psychiatric Rating Scale [BPRS] total score at 1‐year follow‐up vs. baseline) and continuous change in BPRS score using the same functional magnetic resonance imaging‐based features (frontoparietal activations during the AX‐continuous performance task) in the same sample (individuals with either schizophrenia (n =65, 49M/16F, mean age 20.8 years) or Type I bipolar disorder (n= 17, 9M/8F, mean age 21.6 years)). 138 healthy controls were included as a reference group. “Shallow” ML methods included Naive Bayes, support vector machine, K Star, AdaBoost, J48 decision tree, and random forest. DL included an explainable artificial intelligence (XAI) procedure for understanding results. The best overall performances (70% accuracy for the binary outcome and root mean square error = 9.47 for the continuous outcome) were achieved using DL. XAI revealed left DLPFC activation was the strongest feature used to make binary classification decisions, with a classification activation threshold (adjusted beta = .017) intermediate to the healthy control mean (adjusted beta = .15, 95% CI = −0.02 to 0.31) and patient mean (adjusted beta = −.13, 95% CI = −0.37 to 0.11). Our results suggest DL is more powerful than shallow ML methods for predicting symptomatic improvement. The left DLPFC may be a functional target for future biomarker development as its activation was particularly important for predicting improvement. 

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3. Understanding disorder in monolayer graphene devices with gate-defined superlattices

   https://doi.org/10.1088/1361-6528/ad7853  

   Kammarchedu, Vinay; Butler, Derrick; Rashid, Asmaul_Smitha; Ebrahimi, Aida; Kayyalha, Morteza (September 2024, Nanotechnology)

   Abstract Engineering superlattices (SLs)—which are spatially periodic potential landscapes for electrons—is an emerging approach for the realization of exotic properties, including superconductivity and correlated insulators, in two-dimensional materials. While moiré SL engineering has been a popular approach, nanopatterning is an attractive alternative offering control over the pattern and wavelength of the SL. However, the disorder arising in the system due to imperfect nanopatterning is seldom studied. Here, by creating a square lattice of nanoholes in the SiO₂dielectric layer using nanolithography, we study the SL potential and the disorder formed in hBN-graphene-hBN heterostructures. Specifically, we observe that while electrical transport shows distinct SL satellite peaks, the disorder of the device is significantly higher than graphene devices without any SL. We use finite-element simulations combined with a resistor network model to calculate the effects of this disorder on the transport properties of graphene. We consider three types of disorder: nanohole size variations, adjacent nanohole mergers, and nanohole vacancies. Comparing our experimental results with the model, we find that the disorder primarily originates from nanohole size variations rather than nanohole mergers in square SLs. We further confirm the validity of our model by comparing the results with quantum transport simulations. Our findings highlight the applicability of our simple framework to predict and engineer disorder in patterned SLs, specifically correlating variations in the resultant SL patterns to the observed disorder. Our combined experimental and theoretical results could serve as a valuable guide for optimizing nanofabrication processes to engineer disorder in nanopatterned SLs. 

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4. Critical behavior in the artificial axon

   https://doi.org/10.1088/2399-6528/ac43d0  

   Pi, Ziqi; Zocchi, Giovanni (December 2021, Journal of Physics Communications)

   Abstract The Artificial Axon is a unique synthetic system, based on biomolecular components, which supports action potentials. Here we examine, experimentally and theoretically, the properties of the threshold for firing in this system. As in real neurons, this threshold corresponds to the critical point of a saddle-node bifurcation. We measure the delay time for firing as a function of the distance to threshold, recovering the expected scaling exponent of −1/2. We introduce a minimal model of the Morris-Lecar type, validate it on the experiments, and use it to extend analytical results obtained in the limit of ‘fast’ ion channel dynamics. In particular, we discuss the dependence of the firing threshold on the number of channels. The Artificial Axon is a simplified system, an Ur-neuron, relying on only one ion channel species for functioning. Nonetheless, universal properties such as the action potential behavior near threshold are the same as in real neurons. Thus we may think of the Artificial Axon as a cell-free breadboard for electrophysiology research. 

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5. The impact of site selectivity and disorder on the thermoelectric properties of Yb ₂₁ Mn ₄ Sb ₁₈ solid solutions: Yb ₂₁ Mn _4−x Cd _x Sb ₁₈ and Yb _21−y Ca _y Mn ₄ Sb ₁₈

   https://doi.org/10.1039/d1ma00497b  

   He, Allan; Cerretti, Giacomo; Kauzlarich, Susan M. (August 2021, Materials Advances)

   Thermoelectric materials can convert heat into electricity. They are used to generate electricity when other power sources are not available or to increase energy efficiency by recycling waste heat. The Yb 21 Mn 4 Sb 18 phase was previously shown to have good thermoelectric performance due to its large Seebeck coefficient (∼290 μV K −1 ) and low thermal conductivity (0.4 W m −1 K −1 ). These characteristics stem respectively from the unique [Mn 4 Sb 10 ] 22− subunit and the large unit cell/site disorder inherent in this phase. The solid solutions, Yb 21 Mn 4− x Cd x Sb 18 ( x = 0, 0.5, 1.0, 1.5) and Yb 21− y Ca y Mn 4 Sb 18 ( y = 3, 6, 9, 10.5) have been prepared, their structures characterized and thermoelectric properties from room temperature to 800 K measured. A detailed look into the structural disorder for the Cd and Ca solid solutions was performed using synchrotron powder X-ray diffraction and pair distribution function methods and shows that these are highly disordered structures. The substitution of Cd gives rise to more metallic behavior whereas Ca substitution results in high resistivity. As both Cd and Ca are isoelectronic substitutions, the changes in properties are attributed to changes in the electronic structure. Both solid solutions show that the thermal conductivities remain extremely low (∼0.4 W m −1 K −1 ) and that the Seebeck coefficients remain high (>200 μV K −1 ). The temperature dependence of the carrier mobility with increased Ca substitution, changing from approximately T −1 to T −0.5 , suggests that another scattering mechanism is being introduced. As the bonding changes from polar covalent with Yb to ionic for Ca, polar optical phonon scattering becomes the dominant mechanism. Experimental studies of the Cd solid solutions result in a max zT of ∼1 at 800 K and, more importantly for application purposes, a ZT avg ∼ 0.6 from 300 K to 800 K. 

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