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Sustainable management of water resources in large river basins in a climate change scenario – A case study of Po River Basin
No full textLe risorse di acqua dolce a livello globale sono sotto pressione a causa dell'aumento delle emissioni di gas serra, dell'aumento delle temperature e del forzamento antropogenico. Il settore agricolo, in quanto principale consumatore di acqua dolce, è particolarmente vulnerabile a questi cambiamenti. Il bacino del fiume Po, il più grande bacino fluviale e centro agricolo d'Italia, negli ultimi anni ha subito frequenti siccità. Questo studio intende fornire alcune informazioni sulla gestione sostenibile delle risorse idriche nei grandi bacini fluviali colpiti dal cambiamento climatico. Gli obiettivi specifici comprendevano l'analisi delle tendenze climatiche storiche, la valutazione dei modelli di evapotraspirazione e la valutazione delle minacce di aridità nel bacino del Po su scala stagionale e spaziale. I test di Mann-Kendall e stagionali di Mann-Kendall, combinati con la pendenza di Theil-Sen, sono stati applicati per rilevare le tendenze passate e i tassi di cambiamento dal 1960 al 2020. La classificazione pluviometrica di Bandini per l'Italia è stata aggiornata utilizzando i dati attuali per meglio caratterizzare i regimi di precipitazione. I dati relativi alla temperatura e alle precipitazioni sono stati estrapolati altitudinalmente per eliminare gli effetti della quota, e sono state elaborate mappe climatologiche mensili aggiornate per il bacino. Il fabbisogno idrico del bacino è stato valutato utilizzando l'evapotraspirazione potenziale di Thornthwaite (indicativa della domanda d'acqua dell'ecosistema come caratteristica climatica) e l'evapotraspirazione delle colture ottenuta dall'evapotraspirazione di riferimento di Hargreaves e Samani corretta con i coefficienti di coltura (secondo le classi di copertura del suolo accoppiate con il metodo FAO). Inoltre, la domanda di evapotraspirazione delle colture è stata determinata per quattro scenari colturali: miglio e lenticchie, orzo e lenticchie, frumento invernale e fagiolo verde, riso e lenticchie. Le tendenze spaziali e temporali dell'aridità sono state valutate utilizzando quattro indici di aridità basati su due diversi aspetti. Essi son oindici basati su temperatura e precipitazioni, cio gli indici di aridit' di e indici di De Martonne e De Martonne Pinna sul l'evapotraspirazione, cioè indici di aridità di Melisenda e Budyko. I risultati hanno mostrato tendenze significative verso l'alto in tutte le metriche di temperatura, soprattutto durante le stagioni estive con tassi da 0,5 \degree C a 0,7 \degree C per decennio. Oltre l'80\% del bacino presentava una domanda evapotraspirativa delle colture superiore a quella potenziale, indicando esigenze di irrigazione. Mentre il bacino è classificato come umido su base annua, circa il 40\% sperimenta condizioni mediterranee o semi-aride durante l'estate, che coincide con la stagione di crescita massima. Tra quelli considerati, lo scenario di coltivazione del riso e delle lenticchie ha mostrato il deficit idrico più elevato, raggiungendo i 500 mm annui. Questi risultati evidenziano la necessità di un'azione immediata attraverso strategie sostenibili che includano selezione delle colture, efficienza dell'irrigazione e gestione integrata delle risorse idriche per garantire la sostenibilità idrologica e agricola del bacino.Freshwater resources globally are under stress due to increasing greenhouse gas emissions, rising temperatures, and anthropogenic forcing. The agricultural sector, as the largest consumer of freshwater, is particularly vulnerable to these changes. The Po river basin, Italy's largest river basin and agrarian hub, has been experiencing frequent droughts in recent years. This study set out to provide some insights regarding the sustainable management of water resources in large river basins suffering from climate change.
The specific objectives included the analysis of historical climatic trends, assessment of evapotranspiration patterns and, evaluation of aridity threats across the Po river basin on seasonal and spatial scales. Mann-Kendall and seasonal Mann-Kendall tests, combined with Theil-Sen's slope, were applied to detect past trends and rates of change from 1960 to 2020. The classical Bandini Pluviometric classification for Italy was updated using current data to better characterize precipitation regimes. Temperature and precipitation data were altitudinally detrended to eliminate elevation influences, and updated monthly climatological maps were developed for the basin. The basin’s water demand was assessed using Thornthwaite potential evapotranspiration (indicative of ecosystem water demand as a climatic characteristic) and crop evapotranspiration obtained from the Hargreaves and Samani's reference evapotranspiration adjusted with crop coefficients (as per Corine land cover classes). Additionally, crop evapotranspiration demand was determined for four cropping scenarios which were millet and lentils, barley and lentils, winter wheat and green beans, and rice and lentils. The spatial and temporal trends in aridity were assessed using four aridity indices based on two different aspects. Temperature and precipitation-based i.e., De Martonne and De Martonne Pinna combinative indices, and evapotranspiration-based i.e., Melisenda and Budyko aridity indices.
Results showed significant increasing trends in all temperature metrics, most prominently during summer seasons with rates of 0.5 degree C to 0.7 degree C per decade. Over 80% of the basin showed higher crop evapotranspiration than potential evapotranspiration, indicating irrigation needs. While the basin is classified as humid on an annual basis, about 40% experiences Mediterranean to semi-arid conditions during summer, coinciding with peak growing season. The rice-lentils cropping system showed the highest water deficit, reaching 500 mm annually. These findings highlight the need for immediate action through sustainable strategies including crop selection, irrigation efficiency, and integrated water resources management to ensure the basin's hydrological and agricultural sustainability
Predictive topology refinements in distributed stream processing system
No full textCloud computing has evolved the big data technologies to a consolidated paradigm with SPaaS (Streaming processing-as-a-service). With a number of enterprises offering cloud-based solutions to end-users and other small enterprises, there has been a boom in the volume of data, creating interest of both industry and academia in big data analytics, streaming applications, and social networking applications. With the companies shifting to cloud-based solutions as a service paradigm, the competition grows in the market. Good quality of service (QoS) is a must for the enterprises, as they strive to survive in a competitive environment. However, achieving reasonable QoS goals to meet SLA agreement cost-effectively is challenging due to variation in workload over time. This problem can be solved if the system has the ability to predict the workload for the near future. In this paper, we present a novel topology-refining scheme based on a workload prediction mechanism. Predictions are made through a model based on a combination of SVR, autoregressive, and moving average model with a feedback mechanism. Our streaming system is designed to increase the overall performance by making the topology refining robust to the incoming workload on the fly, while still being able to achieve QoS goals of SLA constraints. Apache Flink distributed processing engine is used as a testbed in the paper. The result shows that the prediction scheme works well for both workloads, i.e., synthetic as well as real traces of data
PruNet: Class-Blind Pruning Method For Deep Neural Networks
Full text linkDNNs are highly memory and computationally
intensive, due to which they are unfeasible to deploy in real time
or mobile applications, where power and memory resources are
scarce. Introducing sparsity in the network is a way to reduce
those requirements. However, systematically employing pruning
under given accuracy requirements is a challenging problem.
We propose a novel methodology that iteratively applies a
magnitude-based Class-Blind pruning to compress a DNN for
obtaining a sparse model. It is a generic methodology and can
be applied to different types of DNNs. We demonstrate that
retraining after pruning is essential to restore the accuracy of
the network. Experimental results show that our methodology
is able to reduce the model size by around two orders of
magnitude, without noticeably affecting the accuracy. It requires
several iterations of pruning and retraining, but can achieve up
to 190x Memory Saving Ratio (for the LeNet on the MNIST
dataset) when compared to the baseline model. Similar results
are also obtained for more complex networks like 91x for
VGG-16 on the CIFAR100 dataset. If we combine this work
with an efficient coding for sparse networks, like Compressed
Sparse Column (CSC) or Compressed Sparse Row (CSR), we
can obtain a reduced memory footprint. Our methodology can
be complemented by other compression techniques, like weight
sharing, quantization or fixed-point conversion, that allows to
further reduce memory and computations
GIS--based application of Benfratello's method to estimate the irrigation deficit and its variability in the Capitanata plain under climate change
No full text
NILAI-NILAI PENDIDIKAN KONTRA RADIKALISME DALAM KITAB I’TIQĀD AL-BUKHĀRĪ
Full text linkHanif Muhammad Kamil. Nilai-nilai Pendidikan Kontra Radikalisme dalam Kitab I’tiqād Al-Bukhārī. Skripsi. Yogyakarta: Program Studi Pendidikan Agama Islam Fakultas Ilmu Tarbiyah dan Keguruan UIN Sunan Kalijaga, 2018. Latar belakang dari penelitian ini adalah maraknya aksi terorisme yang terjadi di Indonesia disebabkan oleh masyarakat yang tidak dapat menerima perubahan sosial dengan baik sehingga terjadi banyak kesenjangan dalam lingkungan kehidupan. Aksi terorisme yang memunculkan segala bentuk tindakan radikal sangat membahayakan umat manusia. Banyak masyarakat terjerumus tindakan radikalisme karena jauh dari pemahaman agama yang benar. Maka dibutuhkan pemahaman agama yang benar melalui proses pendidikan untuk dapat menangkal segala bentuk radikalisme yang muncul di masyarakat. Imam al- Bukhari seorang ulama Ahlussunnah di dalam kitab I’tiqād al-Bukhārī memberikan acuan bagaimana seorang Muslim dapat beragama dengan benar agar tidak mengikuti langkah orang kafir dalam melakukan aksi radikal, karena Islam merupakan agama yang penuh kedamaian. Adapun penelitian ini bertujuan untuk mengetahui nilai-nilai pendidikan kontra radikalisme dalam kitab I’tiqād al- Bukhārī dan relevansinya terhadap pendidikan Islam di Indonesia Penelitian ini merupakan jenis penelitian kepustakaan (library research), dalam artian bahwa data-data dalam penelitian ini yang bersumber dari kajian pustaka, baik ensiklopedia, jurnal, dan sebagainya. Dalam menghimpun data, penulis mendapatkannya dari dua sumber, yaitu sumber primer dan sekunder. Penelitian ini bersifat deskriptif analitis, yaitu penelitian yang menggambarkan apa yang menjadi gagasan dalam kitab I’tiqād Al-Bukhārī karya Imam al-Bukhari tentang nilai-nilai kontra radikalisme. Sedangkan metode analisis dalam penelitian ini ialah analisis konten, yakni penelitian berupa pembahasan mendalam terhadap isi suatu informasi tertulis dengan memaparkannya. Hasil penelitian ini mengungkapkan bahwa terdapat empat nilai kontra radikalisme dalam kitab tersebut, yaitu 1) Damai dengan masyarakat umum; 2) Damai dengan pemerintah; 3) Damai dengan sesama umat Islam; 4) dan Damai dengan penganut agama lain. Relevansi nilai-nilai kontra radikalisme terhadap pendidikan Islam di Indonesia terdapat pada lima bidang inti, yakni relevansi terhadap tujuan pendidikan Islam di Indonesia, relevansi terhadap kurikulum pendidikan Islam di Indonesia, relevansi terhadap pendidik dan peserta didik serta metode pendidikan Islam di Indonesia
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks
Full text linkIn today's era of smart cyber-physical systems, Deep Neural Networks (DNNs) have become ubiquitous due to their state-of-the-art performance in complex real-world applications. The high computational complexity of these networks, which translates to increased energy consumption, is the foremost obstacle towards deploying large DNNs in resource-constrained systems. Fixed-Point (FP) implementations achieved through post-training quantization are commonly used to curtail the energy consumption of these networks. However, the uniform quantization intervals in FP restrict the bit-width of data structures to large values due to the need to represent most of the numbers with sufficient resolution and avoid high quantization errors. In this paper, we leverage the key insight that (in most of the scenarios) DNN weights and activations are mostly concentrated near zero and only a few of them have large magnitudes. We propose CoNLoCNN, a framework to enable energy-efficient low-precision deep convolutional neural network inference by exploiting: (1) non-uniform quantization of weights enabling simplification of complex multiplication operations; and (2) correlation between activation values enabling partial compensation of quantization errors at low cost without any run-time overheads. To significantly benefit from non-uniform quantization, we also propose a novel data representation format, Encoded Low-Precision Binary Signed Digit, to compress the bit-width of weights while ensuring direct use of the encoded weight for processing using a novel multiply-and-accumulate (MAC) unit design
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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