Come Rain and Shine? Exploring the Effects of Mobile Weather Applications on Users’ Movements

All Weather conditions affect human behaviors and the growing number of Mobile Weather Applications (MWAs) has amplified this effect. Yet, little is known about how human seek to actively control their behavior by appropriating mobile technology to anticipate changing weather conditions. Guided by Anticipatory Behavioral Control Theory (ABCT), this study endeavors to bride the abovementioned knowledge gap by investigating how the interface design and usage of MWAs would impact the relationship between abnormal weather conditions and users’ movement patterns. From analyzing panel data collected on the hourly movement trajectories of over 1.95 million anonymous mobile phone users over a 2-month period, we strive to shed light on the moderating influence of content representation and usage intensity of MWAs on the relationship between weather conditions and human behaviors

Pengaruh Cuaca Terhadap Perilaku Belanja Konsumen Minimarket: Studi Pada Minimarket Indomaret

Prediksi perilaku konsumen pada perusahaan ritel bertujuan untuk menghadapi tantangan perubahan perilaku belanja, sehingga industri ritel perlu mengetahui faktor-faktor yang mempengaruhi perilaku konsumen. Perusahaan ritel menghadapi banyak faktor yang tidak pasti, salah satunya adalah cuaca. Tujuan penelitian ini adalah: untuk menguji pengaruh cuaca cerah, hujan, berawan, suhu, kualitas udara terhadap keputusan pembelian. Penelitian ini dilakukan di Toko Minimarket Indomaret yang berlokasi di Kota Jakarta. Cuaca cerah, hujan, berawan, suhu dan kualitas udara sebagai variabel bebas dan keputusan pembelian sebagai variabel terikat. Penelitian ini merupakan penelitian kuantitatif menggunakan jenis data primer. Kuesioner berskala likert digunakan untuk mengumpulkan data. Teknik pengambilan sampel adalah convenience sampling. Sampel penelitian ini berupa konsumen minimarket Indomaret berjumlah 150 orang.   Uji regresi linier berganda digunakan untuk menguji hipotesis. Hasil penelitian menunjukkan bahwa cuaca cerah berpengaruh positif dan signifikan terhadap keputusan pembelian. Cuaca hujan, berawan, suhu dan kualitas udara berpengaruh negatif dan signifikan terhadap keputusan pembelian

In search of time to bring the message on social media: Effects of temporal targeting and weather on digital consumers

Marketers always incline to deliver advertising messages to the right consumer at the right time. Yet, the question of when exactly should such a persuasive message be sent to a consumer remains elusive in the existing literature. The current study aims to address this research question within the theoretical framework of contextual marketing. The authors argue that contextual information such as time and weather can be used to design more effective mobile advertising campaigns on social media. The results of a field experiment in cooperation with a local restaurant suggest that ads delivered at consumers’ pre-decision stage (i.e., non-meal time) are more effective than those delivered at the decision stage (i.e., meal time) to increase consumer spending on the dining-in service. Furthermore, unpleasant weather conditions (i.e., less sunlight) are found to improve the effectiveness of advertising on consumer spending on mobile app food delivery orders. Overall, the authors open future research avenues by demonstrating how and why the two contextual factors, time and weather, influence digital consumer behavior

Experimental Study on the Impact of Weather Conditions on Wide Code Division Multiple Access Signals in Nigeria

In cellular network activities, before a site is integrated it is expected that each cell of the site meets the Nigerian Communication Commission (NCC) standard of ≥98% for both service accessibility and call completion rate which in turn depicts a ≤2% in both blocked call rate (BCR) and dropped call rate (DCR). It is suggested that weather conditions have a very strong negative effect on the performance of wideband code division multiple access (WCDMA) network as it could lead to signal attenuation or change the polarization. In this paper, we study the impact of weather conditions on WCDMA network in Nigeria. To achieve this, network samples (log-files) were collected weekly during a driving test in Enugu State Nigeria for a period of five years for both rainy and dry seasons, in which blocked and dropped calls were extracted. Results show that during adverse weather conditions, BCR and DCR rise greater than 8% and 4% respectively. Although with a slight relationship between the weather conditions, the weather condition during the dry season has a better-blocked call rate of 8.76% in comparison with the rainy season with 12.89%. Calls tend to drop more during the dry season. From the outcome of the experiment, a model was developed for predicting an unknown network call statistics variables

Doctor of Philosophy

dissertationWith the health care focus shifting from chronic disease management to efforts around preventative care, worksites may be a key population for interventions to improve health. Because walking is commonly utilized in worksite wellness programs (WWP) and self-efficacy is a strong predictor of exercise adherence, the purpose of this study is to determine the value of incorporating the self-efficacy theory with technology to increase and sustain walking for exercise behavior in a healthcare worksite population. This study, consisting of two parts, seeks to answer the following research questions: Will messages based upon the self-efficacy theory delivered during a 1-mile walk significantly increase beliefs around walking for exercise? Will a smartphone application plus self-efficacy messages delivered via text message increase self-efficacy beliefs as well as sustain walking behavior? Do self-efficacy beliefs associated with walking transfer to other forms of physical activity? A pilot study consisting of a one-group, mixed methods, pre-post test nonexperimental design (N=16) tested the delivery of self-efficacy messages as well as a tool to measure walking self-efficacy beliefs. These beliefs were measured before and after a 1-mile walking session during which verbal self-efficacy messages were delivered. Paired t-test analysis confirmed that self-efficacy beliefs significantly improved. The sustainability study, a two-group randomized control true experimental design, incorporated smartphone technology for tracking walking behavior over 6 weeks and delivery of text messages (N=73). Both groups used a smartphone application to track their walks and the intervention group received weekly text messages based upon the self-efficacy theory. Self-efficacy beliefs increased significantly within each group, but there was not a significant difference between groups at posttest, which means that the smartphone application and monitoring of behavior may have increased beliefs, but the text messages did not have a significant effect. There was value in the text messages for behavior change as the intervention group sustained the walking behavior one week longer than the control group. While this study design is a novel approach to improving the walking for exercise behavior of worksite population, it should not be used as a sole intervention and instead be combined with other modalities to create a multifaceted WWP

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Discount, coupon, or both? An empirical data-based analysis for online garment retailers\u27 optimal promotion strategies

We investigate an online retailer’s optimal strategies for her promotion of the garments that are classified based on their price levels and life cycle stages. Accordingly we consider nine scenarios. For each scenario, the retailer implements a promotion strategy that involves only a discount, only a coupon, or both of them. We develop a three-stage approach, in which we first perform regression analysis to identify the significant variables, then obtain the optimal decisions, and .find the best scenario for the retailer. In general, the promotion with a discount depth is optimal for the garments at the introduction and decline stages, whereas that with a coupon is optimal for the garments at the maturity stage. Sales promotions for new garments cannot help to arouse potential customers’ interests, whereas those for mature garments can significantly improve the reading rate. The most profitable garments are the high-priced garments at the introduction stage; but, the best sales garments are the low-priced garments at the maturity stage. The spring season is the best one for the retailer to promote the high-priced garments and the garments at the decline stage, the summer and autumn seasons are the best for a few scenarios, and the winter is a slack season for any promotion. The garments at the introduction and maturity stages may have a higher conversion rate on holidays. Any time limit in sales promotions influences customers’ interests but may not affect their purchase intentions

A Deep Learning approach for monitoring severe rainfall in urban catchments using consumer cameras. Models development and deployment on a case study in Matera (Italy) Un approccio basato sul Deep Learning per monitorare le piogge intense nei bacini urbani utilizzando fotocamere generiche. Sviluppo e implementazione di modelli su un caso di studio a Matera (Italia)

In the last 50 years, flooding has figured as the most frequent and widespread natural disaster globally. Extreme precipitation events stemming from climate change could alter the hydro-geological regime resulting in increased flood risk. Near real-time precipitation monitoring at local scale is essential for flood risk mitigation in urban and suburban areas, due to their high vulnerability. Presently, most of the rainfall data is obtained from ground‐based measurements or remote sensing that provide limited information in terms of temporal or spatial resolution. Other problems may be due to the high costs. Furthermore, rain gauges are unevenly spread and usually placed away from urban centers. In this context, a big potential is represented by the use of innovative techniques to develop low-cost monitoring systems. Despite the diversity of purposes, methods and epistemological fields, the literature on the visual effects of the rain supports the idea of camera-based rain sensors but tends to be device-specific. The present thesis aims to investigate the use of easily available photographing devices as rain detectors-gauges to develop a dense network of low-cost rainfall sensors to support the traditional methods with an expeditious solution embeddable into smart devices. As opposed to existing works, the study focuses on maximizing the number of image sources (like smartphones, general-purpose surveillance cameras, dashboard cameras, webcams, digital cameras, etc.). This encompasses cases where it is not possible to adjust the camera parameters or obtain shots in timelines or videos. Using a Deep Learning approach, the rainfall characterization can be achieved through the analysis of the perceptual aspects that determine whether and how a photograph represents a rainy condition. The first scenario of interest for the supervised learning was a binary classification; the binary output (presence or absence of rain) allows the detection of the presence of precipitation: the cameras act as rain detectors. Similarly, the second scenario of interest was a multi-class classification; the multi-class output described a range of quasi-instantaneous rainfall intensity: the cameras act as rain estimators. Using Transfer Learning with Convolutional Neural Networks, the developed models were compiled, trained, validated, and tested. The preparation of the classifiers included the preparation of a suitable dataset encompassing unconstrained verisimilar settings: open data, several data owned by National Research Institute for Earth Science and Disaster Prevention - NIED (dashboard cameras in Japan coupled with high precision multi-parameter radar data), and experimental activities conducted in the NIED Large Scale Rainfall Simulator. The outcomes were applied to a real-world scenario, with the experimentation through a pre-existent surveillance camera using 5G connectivity provided by Telecom Italia S.p.A. in the city of Matera (Italy). Analysis unfolded on several levels providing an overview of generic issues relating to the urban flood risk paradigm and specific territorial questions inherent with the case study. These include the context aspects, the important role of rainfall from driving the millennial urban evolution to determining present criticality, and components of a Web prototype for flood risk communication at local scale. The results and the model deployment raise the possibility that low‐cost technologies and local capacities can help to retrieve rainfall information for flood early warning systems based on the identification of a significant meteorological state. The binary model reached accuracy and F1 score values of 85.28% and 0.86 for the test, and 83.35% and 0.82 for the deployment. The multi-class model reached test average accuracy and macro-averaged F1 score values of 77.71% and 0.73 for the 6-way classifier, and 78.05% and 0.81 for the 5-class. The best performances were obtained in heavy rainfall and no-rain conditions, whereas the mispredictions are related to less severe precipitation. The proposed method has limited operational requirements, can be easily and quickly implemented in real use cases, exploiting pre-existent devices with a parsimonious use of economic and computational resources. The classification can be performed on single photographs taken in disparate conditions by commonly used acquisition devices, i.e. by static or moving cameras without adjusted parameters. This approach is especially useful in urban areas where measurement methods such as rain gauges encounter installation difficulties or operational limitations or in contexts where there is no availability of remote sensing data. The system does not suit scenes that are also misleading for human visual perception. The approximations inherent in the output are acknowledged. Additional data may be gathered to address gaps that are apparent and improve the accuracy of the precipitation intensity prediction. Future research might explore the integration with further experiments and crowdsourced data, to promote communication, participation, and dialogue among stakeholders and to increase public awareness, emergency response, and civic engagement through the smart community idea.Negli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart