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Le stockage alimentaire à large échelle est considéré comme une condition nécessaire, bien que non suffisante, à l’émergence des inégalités sociales au sein des sociétés humaines. Dans le cadre de cet article, nous nous interrogeons sur l’émergence de telles pratiques en Europe occidentale au Paléolithique récent. Le registre ethnoarchéologique et ethnohistorique indique que la consommation différée des ressources alimentaires est primordiale dans des environnements à fluctuations saisonnières importantes mais le stockage à large échelle aboutissant à une réduction drastique du degré de mobilité ne peut se développer que dans des environnements très particuliers où les ressources fournissant la base de l’alimentation sont surabondantes saisonnièrement. La déshydratation, la fermentation, la congélation et la conservation dans de la graisse sont autant de techniques traditionnelles permettant une préservation des aliments sur le long terme. Les indices archéologiques signant un stockage des ressources sont particulièrement ténus et nécessitent la prise en compte d’un faisceau d’arguments relevant du registre archéozoologique et d’autres données plus contextuelles. L’enquête transchronologique qui est ensuite menée montre que les techniques de conservation des aliments étaient déjà maîtrisées par les Néandertaliens avant l’arrivée des humains anatomiquement modernes en Europe. Une intensification de cette pratique au début du Paléolithique récent n’est pas attestée. Au Magdalénien, le stockage des ressources alimentaires est avéré et semble prendre de l’importance. Pour autant, les indices d’un stockage en masse sont absents et les prérequis écologiques exposés par A. Testart (1982a) comme la condition sine qua non à l’émergence de sociétés hiérarchiques ne sont pas réunis. Large-scale food storage is considered a necessary, though not sufficient, condition for the emergence of social inequalities in human societies. In this paper, we examine the emergence of such practices in Western Europe during the Late Palaeolithic. The ethnoarchaeological and ethnohistorical record indicates that delayed consumption of food is primordial in environments with significant seasonal fluctuations, but large-scale storage leading to a drastic reduction in the degree of mobility can only develop in very particular environments where the resources providing the basis of food are seasonally overabundant. Dehydration, fermentation, freezing and preservation in fat are all traditional techniques for the long-term preservation of food. Nevertheless, the archaeological evidence for resource storage is particularly tenuous and requires consideration of a range of arguments from the archaeozoological record and other more contextual data. The subsequent transchronological survey shows that food preservation techniques were already mastered by the Neanderthals before the arrival of Homo sapiens sapiens in Europe. There is no evidence of an intensification of this practice at the beginning of the Late Paleolithic. In the Magdalenian, the storage of food resources is proven and seems to be gaining importance. However, there is no evidence of mass storage and the ecological prerequisites outlined by A. Testart (1982a) as the sine qua non for the emergence of hierarchical societies are not present.

In the fast-growing Integrated Circuits (IC) industry, memory is one of the few keys to have systems with improved and fast performance. Only one transistor and a capacitor are required for Dynamic Random-Access Memory (DRAM) bit. It is widely used for mass storage. Although the high-efficiency tests are performed to provide the reliability of the memories, maintaining acceptable yield and quality is still the most critical task. To perform a high-speed effective test of DRAM memories, a built-in self-test (BIST) mechanism is proposed.

Sized satellite images (pyramids) are not widely used in classification due to their dissimilarity with respect to the original image. But varied size images can reduce the cost, the time and mass storage of the classification system and can give us an initial impression of the result. This research work is an investigation to study the dependency upon varied size images in classification instead of using the original satellite image. The reference map is prepared to study the performance of the proposed system. Then, the varied size image is constructed for each band of the satellite image. Then, the classification is carried out using competitive learning neural networks (CLNN) method for all digital image pyramids, either the original satellite image or its sized images. The last step is the evaluation of the studied elements such as accuracy, classification time and storage volume. Some resized images are useful in such application, so the study suggests which size suits such applications.

Universal Serial Bus (USB) has become the dominant Plug & Play interface for personal computers and continues to grow. Any digital communication source emits secondary or unwanted emissions, called compromising emanations, as they can be received and used to reconstruct the original transmitted information, thereby compromising the transmitted messages. This paper presents a number of experimental results regarding the USB communication between a personal computer and a USB memory device (USB bulk transfer) that has been performed in a specialized laboratory, and illustrates the capability of restoring information transmitted at bit level only from receiving the compromising radiation emitted by this communications bus. Comparative results for a shielded and unshielded device will also be illustrated. Finally, some TEMPEST protection methods are identified and presented against leakage of information through the compromising radiation of USB communication.

This article reviews the resistive random-access memory (ReRAM) technology initialization back in the 1960s and its heavily focused research and development from the early 2000s. This review goes through various oxygen/oxygen vacancy and metal-ion-based ReRAM devices and their operation mechanisms. This review also benchmarks the performance of various oxygen/oxygen vacancy and metal-ion-based ReRAM devices with general trend drawn. Being a semiconductor memory and storage technology, the commercialization attempts for both stand-alone mass storage/storage-class memory and embedded nonvolatile memory are also reviewed. Looking toward the coming era, the potential of using ReRAM technology to improve machine learning efficiency is discussed.

Purpose The performance of the conventional 6T SRAM cell can be improved by using GNRFET devices with multi-threshold technology. The proposed cell shows the strong capability to operate at the minimum supply voltage of 325 mV, whereas the conventional Si-CMOS 6 T SRAM unable to operate below 725 mV, which result in an acceptable failure rate.The advance of Si-CMOS (complementary metal-oxide-semiconductor) based 6 T SRAM cell faces inherent limitation with aggressive downscaling. Hence, there is a need to propose alternatives for the conventional cells. Design/methodology/approach This study aims to improve the performance of the conventional 6T SRAM cell using dual threshold technology, device sizing, optimization of supply voltage under process variation with GNRFET technology. Further performance can be enhanced by resolving half-select issue. Findings The GNRFET-based 6T SRAM cell demonstrates that it is capable of continued improve the performance under the process, voltage, and temperature (PVT) variations significantly better than its CMOS counterpart. Research limitations/implications Nano-material fabrication technology of GNRFETs is in the early stage; hence, the different transistor models can be used to evaluate the parameters of future GNRFETs circuit. Practical implications GNRFET devices are suitable for implementing low power and high density SRAM cell. Social implications The conventional Si-CMOS 6 T SRAM cell is a core component and used as the mass storage element in cache memory in computer system organization, mobile phone and other data storage devices. Originality/value This paper presents a new approach to implement an alternative design of GNRFET -based 6T SRAM cell with doped reservoirs that also supports process variation. In addition, multi-threshold technology optimizes the performance of the proposed cell. The proposed design provides a means to analyze delay and power of GNRFET-based SRAM under process variation with considering edge roughness, and offers design and fabrication insights for cell in the future.

Composting offers sustainable management of organic wastes by converting them into nutrient-rich bio-manure and thereby reduces other environmental impacts. Conventional methods of monitoring and process control involve multiple hurdles to effectively utilize the available resources in producing good quality compost. Therefore, it is necessary to introduce smart technologies in composting to make it convenient for small-scale units in the urban areas and for large-scale operations in the peripheral localities. Present study investigates the scope of digitalization in bringing user-friendly solutions such as an Internet of things (IoT)-based composter by reviewing the recent trends in digital-based design and implementation aspects. Various available hardware and software components were identified and the critical issues in selecting the data transmission protocols were reviewed. The main aspects of hardware components rely on the accuracy and efficiency in the acquisition, conversion and storage of data which are to be embedded with suitable microprocessor units. The selection of software mainly depends on the available gateways, data transfer protocols and mass storage facilities. As far as the gathered data are concerned, the quality (accuracy, reliability, free from noises), safety (free from hacks, low power loss) and availability for post-processing (data analytics and data mining) proclaim huge scope in reframing the design-thinking approach. Further, it is noted that human perceptions and expectations tend to make a compromise in the design approach which need to be addressed through informed interactions. It is envisaged that a low cost, smart and rapid composter can be emerged as a sustainable solution.

Cloud computing technology and Artificial Intelligence (AI) techniques are becoming significant components in many applications across multiple domains, including agriculture. Cloud computing allows the mass storage of various types of data readily available for descriptive, predictive, and prescriptive analytics. AI techniques, such as deep neural networks, have achieved promising disease detection and yield prediction outcomes. Not many agricultural systems have applied cloud computing and AI and have been made available for farmers to use practically. This study discusses the design and development of a digital agriculture platform using AI and cloud technology. The proposed platform provides an end-to-end digital agriculture system to farmers. This study discusses the benefits of applying cloud computing and AI according to the proposed system and existing studies. The achieved outcomes delivered a significant edge over the conventional platform for digital agriculture. We developed a plant disease diagnosis model to enable farmers to identify diseases through a mobile application on the proposed platform. Our proposed model showed significant results for identifying and diagnosing plant diseases with high accuracy.