The Bright Side of MAUP: an Enquiry on the Determinants of Industrial Agglomeration in the United States

Using county employment data for US and two appositely developed zoning algorithms, I compare the industrial concentration of manufacturing sectors calculated following the standard metropolitan and micropolitan statistical areas definition with two other counterfactuals, obtained by “gerrymandering” the original sample of counties. The methodology allows i) to obtain an unbiased estimate of industrial agglomeration which significantly improves on existing indices, and ii) to provide a ranking of industries according to their responsiveness to labour market determinants of agglomeration. Results show that labour market determinants explain one quarter of the variation of spatial agglomeration across industries.Industrial Agglomerations, MAUP, Industrial Concentration

The Collective Household Enterprise Model: An Empirical Analysis

This paper estimates a household model where both the production and consumption sides are observed. The household activities produce both marketable and nonmarketable products. Family members consume market goods, domestically produced goods and leisure. This household equilibrium model is described within a collective framework. The data are from a nation-wide sample of Italian farm-households. The estimation is implemented using a generalized Heckman estimator to account for corner solutions generated by the fact that not all households are engaged in all enterpreneurial activities and do not consume some of all goods and leisure. The identification of the sharing rule stems from the assignability of clothing consumption and leisure.Household collective model, household and domestic productions, consumption and leisure, separability, Consumer/Household Economics,

The presence of larger inventive firms in cities helps smaller firms to be more inventive as well – eventually

While the positive effects of similar firms locating close to one another – so called agglomeration effects – are well known, do these positive spillovers also apply to large inventive firms? In new research, Carlo Menon examines the effect that companies which have a large number of patents have on patents granted by other companies in the same area. He finds that through knowledge spillover effects, a ten percent increase in the number of patents from Top Inventing Companies can lead to an increase in patents from smaller companies by about 2 percent over the following 4 to 8 years

The causal effect of credit guarantees for SMEs: evidence from Italy

We evaluate the effectiveness of a partial credit guarantee program implemented in a large Italian region using unique microdata from a broad set of firms. Our results show that the policy was effective to the extent that it resulted in an improved financial condition for the beneficiary firms. While the total amount of bank debt was unaffected, firms showed a significant increase in the long-term component. Furthermore, targeted firms benefited from a substantial decrease in interest rates. On the other hand, there is some evidence that the probability of default increases as a consequence of the treatment, although the effect is only marginally significant. There are, instead, no effects on the real outcomes

Towards The Development of A Wearable Feedback System for Monitoring the Activities of the Upper-Extremities

Background Body motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system which can predict different postures of the upper-extremities by classifying force myographic (FMG) signals of the forearm in real-time. Methods An easy to use force sensor resistor (FSR) strap to extract the upper-extremities FMG signals was prototyped. The FSR strap was designed to be placed on the proximal portion of the forearm and capture the activities of the main muscle groups with eight force input channels. The non-kernel based extreme learning machine (ELM) classifier with sigmoid based function was implemented for real-time classification due to its fast learning characteristics. A test protocol was designed to classify in real-time six upper-extremities postures that are needed to successfully complete a drinking task, which is a functional exercise often used in constraint-induced movement therapy. Six healthy volunteers participated in the test. Each participant repeated the drinking task three times. FMG data and classification results were recorded for analysis. Results The obtained results confirmed that the FMG data captured from the FSR strap produced distinct patterns for the selected upper-extremities postures of the drinking task. With the use of the non-kernel based ELM, the postures associated to the drinking task were predicted in real-time with an average overall accuracy of 92.33% and standard deviation of 3.19%. Conclusions This study showed that the proposed wearable FSR strap was able to detect eight FMG signals from the forearm. In addition, the implemented ELM algorithm was able to correctly classify in real-time six postures associated to the drinking task. The obtained results therefore point out that the proposed system has potential for providing instant feedback during functional rehabilitation exercises

An Active Compression Bandage Based On Shape Memory Alloys: A Preliminary Investigation

Background Disorders associated with excessive swelling of the lower extremities are common. They can be associated with pain, varicose veins, reduced blood pressure when standing and may cause syncope or fainting. The common physical remedy to these disorders is the use of compression stockings and pneumatic compression leg massagers, which both attempt to limit blood pooling and capillary filtration in the lower limbs. However, compression stockings provide a constant pressure, and their efficiency has been challenged according to some recent studies. Air compression leg massagers on the other hand, restricts patient mobility. In this work we therefore present an innovative active compression bandage based on the use of a smart materials technology that could produce intermittent active pressure to mitigate the symptoms of lower extremity disorders. Methods An active compression bandage (ACB), actuated by shape memory alloy (SMA) wires, was designed and prototyped. The ACB was wrapped around a calf model to apply an initial pressure comparable to the one exerted by commercial compression stockings. The ACB was controlled to apply different values of compression. A data acquisition board and a LabVIEW program were used to acquire both the pressure data exerted by the ACB and the electrical current required to actuate the SMA wires. An analytical model of the ACB based on a SMA constitutive model was developed. An optimizer was implemented to identify optimal parameters of the model to best estimate the performance of the ACB. Results The maximum increase in pressure due to the SMA wires activation was 40.8% higher than the initially applied pressure to the calf model. The analytical model of the ACB estimated the behaviour of the ACB with less than 0.32 mmHg difference with the experimental results. Conclusions The prototyped ACB was able to apply an initial compression comparable to the one applied by commercial compression stockings. Activation of the ACB resulted in an increase of compression up to 9.06 mmHg. Comparison between analytical and experimental results showed the analytical model was suitable to predict the behaviour of the ACB

Firm size and judicial efficiency in Italy: evidence from the neighbour's tribunal

We investigate the causal relationship between judicial efficiency and firm size across Italian municipalities, exploiting spatial discontinuities in tribunals' jurisdiction for identification. Results show that halving the length of civil proceedings, average firm size would increase by around 8-12%, everything else equal. Results are robust to a number of different specifications, based on two different databases

EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control