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This paper addresses the critical security challenges in the internet of things (IoT) landscape by implementing an innovative solution that combines convolutional neural networks (CNNs) for feature extraction and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. By applying the introduced algorithm to hyperparameter optimization, better-performing models are constructed capable of efficiently handling intrusion detection. Two experiments are carried out to evaluate the introduced technique. The first experiment tackles detection through binary classification. The second experiment handles the task by specifically identifying the type of intrusion through multi-class classification. A publicly accessible real-world dataset has been utilized for experimentation and several contemporary algorithms have been subjected to a comparative analysis. The introduced algorithm constructed models with the best performance in both cases. The outcomes have been meticulously statistically evaluated and the best-performing model has been analyzed using Shapley additive explanations to determine feature importance for model decisions.

With the global population continuing to increase, the demand for food and energy has escalated, resulting in severe environmental pressures. Traditional methods of food and energy production have left a significant footprint on the environment, primarily due to the emission of greenhouse gases and a notable surge in waste production. Nevertheless, scientists have recently focused on developing sustainable solutions by managing biomass waste and converting it into useful products. Various biomass conversion technologies, including pyrolysis, gasification, and fermentation, have emerged to transform waste materials into valuable commodities like biofuels, fertilizers, and chemicals. These technologies present an alternative to conventional energy production methods and decrease reliance on non-renewable resources. Furthermore, the by-products generated through biomass conversion, such as biochar, possess utility as valuable soil amendments. This review emphasizes the potential of biomass conversion technologies in providing sustainable solutions for waste management, food and energy production, and reducing negative environmental impacts while providing valuable by-products for agricultural use. The focus is on Lebanon, which is facing a waste and energy crisis, with an aim to encourage and promote sustainable practices by highlighting different green waste management technologies. Focusing on the application of biochar in soil, our goal is to provide cost-effective and eco-friendly solutions to various agricultural and environmental challenges in Lebanon. This includes using biochar from biomass waste as a soil amendment to boost crop yields, remediate soil pollution, reduce soil drought stress, and address other related issues.

Wild medicinal mushrooms are known to contain significant amounts of essential biochemical compounds with potential health benefits. Therefore, this study aimed to investigate the metal elements and biochemical constituents of wild turkey tail (Trametes versicolor) mushrooms collected from the Shivalik foothills of the Himalayas, India. Mushroom samples were purposefully collected from eleven (11) sampling sites located in three (3) districts of North Indian states (Uttar Pradesh and Uttarakhand). The results of this study indicated that wild T. versicolor showed the presence of eight metal elements (Cd: 0.011–0.139, Cr: 0.225–0.680, Cu: 1.073–3.108, Fe: 4.273–8.467, Mn: 2.157–3.892, Zn: 3.069–4.478, Ni: 0.065–0.186, and Co: 0.035–0.120 mg/kg). The samples also showed a significant presence of total phenolics (51.81–70.13 mg GAE/g), flavonoids (9.02–14.01 mg QE/mg), lycopene (0.02–0.08 mg/g), and β-carotene (0.31–0.72 mg/g). The proximate analysis also showed that T. versicolor is a good source of carbohydrate (38.33%–41.94%), protein (8.12%–11.06%), fat (0.93%–1.26%), moisture (63.80%–70.64%), dietary fiber (9.59%–14.30%), and total ash (2.42%–3.48%). In addition, gas chromatography (GC-FID) analysis revealed the presence of the five most dominant fatty acids, including linoleic acid (18:2n6c), palmitic acid (C16:0), oleic acid (18:1n9c), linolenic acid (18:3n3), and stearic acid (C18:0). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were helpful in identifying variations and similarities among different constituents of T. versicolor at selected sampling sites. Due to its low metal element content and rich biochemical profile, T. versicolor was highlighted in this research for its significant potential as a functional food or nutraceutical ingredient. This work promotes its sustainable use in the healthcare and food industries and lays the groundwork for further research into its therapeutic applications.

AbstractAgriculture provides a living for a huge proportion of Pakistan’s people, making it one of the country’s most vital sectors. In this paper, we investigated the impact of irrigation sources (IS), agricultural machinery (AM), total food grains (TFG), and total cropped area (TCA) on the agriculture sector of Pakistan by using the annual data from 1991 to 2020. Using the symmetric (ARDL) approach, short-run and long-run estimations were employed to illustrate the connection between variables. A unidirectional linkage for the variables was checked through the VECM (Vector Error Correction Model) based Granger causality that is extracted. Further, FMOLS (Fully Modified Least Squares) and DOLS (Dyngamic Least Squares) techniques were also employed to encounter the robustness of the analysis. Results during the short-run and long-run show that the variables total irrigation sources (IS), agriculture machinery (AM), and total food grains (TFG) show the constructive impact on the agriculture sector of Pakistan, while the variable total cropped area (TCA) demonstrate the negative impact on the agriculture sector. Similarly, the consequences of VECM-based Granger causality show that all variables have unidirectional linkages. Furthermore, the findings of the FMOLS and DOLS explore that the variables irrigation sources (IS), agricultural machinery (AM), and total food grains (TFG) show the productive impact on the agriculture sector of Pakistan. But, unfortunately, the variable total cropped area (TCA) demonstrates the negative impact on the agriculture sector. No doubt, the agricultural sector significantly contributes to the growth of any economy. The government of Pakistan has to adopt new policies and plans that place a greater emphasis on the country’s irrigation network and arable land in order to increase agricultural output.

Abstract Determining evapotranspiration (ET) is essential for water accounting and for the management of irrigated agriculture from farm to region. We describe here a collaborative initiative aimed at establishing a prototype ET network in six countries of North Africa and the Near East (NENA Region). The network utilizes a low-cost and open-source system, termed the CORDOVA-ET, consisting of a base station and sensing nodes to collect the weather data needed to determine reference and actual ET (ETo and ETa). Here, we describe the network deployment processes, system architecture, data collection methodology, quality control procedures, and some of the ET results obtained so far during a four-year period, starting in 2018. The network has been developed as an iterative and collaborative process, where training and capacity building have been the main drivers. The feedback and experiences gathered from the users have helped improve the different versions of the prototypes and enhance their assembly, deployment, reliability, and ease of operation. At the same time, the involvement in the construction, maintenance, and data analysis has also provided valuable insight into calculating ET from energy balance methods. The network operated during six cropping seasons and the results were mixed, data integrity (hourly and daily) varied from 95 to 23%, depending on the country and season. Validation of ET estimates was performed using the ECMWF ERA5 dataset as an independent reference. The energy balance algorithm implemented in the system to determine ETa was validated using the OpenCropLib Python library. While the results of data validation demonstrated the reliability and accuracy of the CORDOVA-ET system, network operations required significant support and special motivation on the part of the users. It is concluded that collaboration among users, together with support services and participation of different stakeholders interested in agricultural water management would be essential elements to ensure the sustainability of the ET network.

Along with the concept of improving reforestation efforts in Lebanon, this study aimed to provide a land suitability analysis for forest species in Lebanon while considering the effect of climate change. Herein, the soil evaluation criteria developed by FAO (The Food and Agriculture Organization) for land suitability classification were implemented through the weighted overlay method to produce suitability maps based on natural variables (soil, climate, and topography) influencing the presence of the species on the land. Cedrus libani, Quercus calliprinos, Ceratonia siliqua, Eucalyptus globulus, and Pinus halepensis are the species considered in this study. The results of this study provide useful information to inform reforestation activities in Lebanon, considering the expected climate change projections for medium- (2050) and long-term (2070) periods, according to two different scenarios (RCP4.5 and RCP8.5) and three General Circulation Models: CCSM4, GFDL-CM3, and HadGEM2-ES. The suitability maps showed a generally critical situation for the spatial distribution of forest species under future climate change compared to the current situation (1970–2000). The distribution of thermophilic species, which tolerate high temperatures (over 20 °C), was projected to expand compared to the current situation. In contrast, the expansion of cold-adapted species may be limited by future climate change conditions. It is crucial to consider the expected effects of climate change to better select species for reforestation and, therefore, to maintain forest cover in Lebanon.

The Internet of Things (IoT) has succeeded to be one of the important future communication technologies. The evolution of IoT has accelerated along with the emergence of 5G considered as a leading IoT service provider. In this context, the low power wide area network (LPWAN) has recently attracted attention as it provides an impeccable infrastructure for massive Machine-Type Communications (mMTCs). Long range wide area network (LoRaWAN) is one of the most adopted LPWAN technologies in the world. However, an efficient integration of LoRaWAN technology into the 5G system (5GS) is required. In this article, we review briefly related work trying to integrate LoRaWAN into the 5GS. Then, we present our solution for the integration and we detail the adopted network architecture. We propose new authentication methods based on the extensible authentication protocol (EAP) providing secure access, and an adaptation function to attain seamless and efficient integration. In addition, we evaluate our solution in terms of performance and security. Moreover, a comparison of our solution with related work confirms the efficiency of our solution. International audience

The post-pandemic age has heightened the importance of digitalizing organizational practices and fostering innovation for SMEs to ensure resilient business operations in a volatile work environment. This research paper examines the impact of digital transformation leadership on the digitalization of business practices and the development of innovative business models while considering the roles of organizational agility and knowledge transfer. A survey of 270 employees from SMEs in Lebanon was conducted using the convenience sampling technique. Data analysis was performed quantitatively through Partial Least Squares—Structural Equation Modeling (PLS-SEM). The results reveal that adopting digital transformation leadership fosters organizational agility, enabling effective digital transformation and business model innovation with a robust knowledge transfer system. This study contributes to the fields of digital transformation and innovation, and offers practical insights for SME managers navigating uncertainties and market volatility in the post-pandemic period.