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The susceptibility of West Java to landslides, coupled with its high population density, prompts research into landslide potential in the region. Notably, Gunung Batu and Graha Puspa in West Bandung Regency feature substantial andesite slopes situated along the Lembang fault and serve as tourist attractions. This study aims to assess the stability of these slopes using rock mass rating (RMR) and slope mass rating (SMR). The analysis integrates methods like rock mass classification (RMR), kinematics analysis, and empirical slope stability analysis (SMR), drawing on data obtained from uniaxial compressive strength (UCS) tests and scanline surveys. The andesite slopes exhibit RMR values ranging from 74 to 81, indicating good to very good rock mass quality. Design parameters and engineering properties are identified, including rock mass cohesion and internal friction angle, suggesting safe cut slope angles. Specific slopes are found susceptible to toppling or wedge failures. SMR values range from 72 to 96, categorizing the slopes as class I-II, indicating stability and a low probability of failure. Minor reinforcement options like scaling, toe ditch, fence, and spot bolting are proposed. Considering West Java’s seismic and rainfall risks, the study recommends further modelling to incorporate variations in seismic acceleration and water content. The method would yield safety factors and cut-off values under different slope conditions, enhancing the understanding and management of landslide risks in the region.

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

When a reinforced concrete beam does not have enough shear strength and / or little amount of secondary rebar are reinforced, the "shear failure" is possible. Such type of failure is not acceptable in civil engineering due it does not indicate any prior notice before the complete failure of the intended structural member. One of the most popular solutions to overcome such shortcoming is the addition of steel fibers due to the ability for enhancing mechanical properties and ductility. Wide beams are such beams that have high width if compared with its thickness, such structural members are frequent in many reinforced concrete building systems and may also face "shear failure" during its service life. The current study investigates the structural behavior of the Steel Fiber Reinforced Concrete Wide Beams by proposing an experimental program comprising casting and testing of twenty beams specimen. The effective shear spans to depth ratio ratios were 2.5 and 3.5, respectively. Two nominal strength levels 30 MPa "Normal strength concrete" and 60 MPa "High strength concrete are also included. The types of steel fibers used are the "End Hocked" as well as the "Staggered" in 0.5% and 1.5% volume fractions for each one. The results showed that the addition of steel fibers enhances the consequent mechanical properties and the relevant structural behavior of wide beams to a serious concern. Furthermore, the addition of steel fibers modified the shear failure mode to "flexural". The first cracking load increased in "Normal strength" from 21.95% to 73.73% by adding "End Hocked" and from 12.19% to 45.45% by adding "Staggered" while such range reported 15.95% to 45.76% for "End Hocked" and 7.25% to 28.81% for "Staggered" in "High strength". Additionally, the ultimate load increased in "Normal strength" from 19.75% to 65.98% by adding "End Hocked" and from 10.52% to 43.81% by adding "Staggered" while such range reported 13.5% to43.57 % for "End Hocked" and 7.25% 29.29to % for "Staggered" in "High strength"

The high bandwidth and data rate demands of future wireless communications such as 6G and beyond can no longer be met with existing technologies that are mainly based on naturally occurring three-dimensional materials. The terahertz (THz) band refers to the electromagnetic frequency range lying between the millimetre-wave band and the infrared band, and it has become an increasingly promising frequency band that has the potential to be exploited for high-speed wireless communication networks of the future. Lately, the realisation of THz devices has been expedited through the emergence of two-dimensional materials (2DMs), such as graphene, perovskites, and transition metal dichalcogenides that can potentially unlock some long-standing problems related to the development of efficient control of transmission and detection of THz waves. In this paper, we cover several essential electrical features of 2DM, namely graphene, MOs2, and perovskite enabled THz devices that are being used to design efficient systems for future wireless communication systems.

Abstract This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

In this article, a printed series fed tri-slot coplanar Vivaldi antenna is studied and investigated for RADAR application. The antenna consists of three exponentially tapered slots excited by a single microstrip line. The three slots are series fed simultaneously with a radial stub on the microstrip feed line for impedance matching. The proposed antenna covers a frequency of 7.8-11.8 GHz at −10 dB impedance bandwidth. The antenna gain varies from 7.5 dB at the lowest operating frequency and increases to around 9.5 dB at the center frequency of 10 GHz. A key versatility of the proposed design is the ability to modify the ground plane size without affecting the antenna impedance and gain. This makes the antenna suitable to incorporate RADAR transceiver components without modifying the existing antenna design. The proposed antenna is fabricated and measured results show good agreement with simulated results.

Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

In this paper, we use the surface equivalence theorem and boundary conditions to develop a set of integral equations to study the electromagnetic (EM) scattering from thin material sheets. The proposed scheme is well-suited for EM investigation of two-dimensional (2D) materials that are increasingly becoming key components in the development of terahertz (THz) frequency devices. We also discuss the electronic properties of 2D materials that control the EM scattering, out of which existence of plasma waves at THz frequencies is most significant. Finally, the far-field scattering response for infinitesimally thin 2D materials is presented.

This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food.

This manuscript presents a novel mechanism (at the physical layer) for authentication and transmitter identification in a body-centric nanoscale communication system operating in the terahertz (THz) band. The unique characteristics of the propagation medium in the THz band renders the existing techniques (say for impersonation detection in cellular networks) not applicable. In this work, we considered a body-centric network with multiple on-body nano-senor nodes (of which some nano-sensors have been compromised) who communicate their sensed data to a nearby gateway node. We proposed to protect the transmissions on the link between the legitimate nano-sensor nodes and the gateway by exploiting the path loss of the THz propagation medium as the fingerprint/feature of the sender node to carry out authentication at the gateway. Specifically, we proposed a two-step hypothesis testing mechanism at the gateway to counter the impersonation (false data injection) attacks by malicious nano-sensors. To this end, we computed the path loss of the THz link under consideration using the high-resolution transmission molecular absorption (HITRAN) database. Furthermore, to refine the outcome of the two-step hypothesis testing device, we modeled the impersonation attack detection problem as a hidden Markov model (HMM), which was then solved by the classical Viterbi algorithm. As a bye-product of the authentication problem, we performed transmitter identification (when the two-step hypothesis testing device decides no impersonation) using (i) the maximum likelihood (ML) method and (ii) the Gaussian mixture model (GMM), whose parameters are learned via the expectation–maximization algorithm. Our simulation results showed that the two error probabilities (missed detection and false alarm) were decreasing functions of the signal-to-noise ratio (SNR). Specifically, at an SNR of 10 dB with a pre-specified false alarm rate of 0.2, the probability of correct detection was almost one. We further noticed that the HMM method outperformed the two-step hypothesis testing method at low SNRs (e.g., a 10% increase in accuracy was recorded at SNR = −5 dB), as expected. Finally, it was observed that the GMM method was useful when the ground truths (the true path loss values for all the legitimate THz links) were noisy.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

Afyon Müzesi'ne bir "Hitit Kabartması"nın gelmesi ilginç olmayabilir. Ama bu kabartmanın Emirdağ'ın Daydalı Köyü'nde bulunmuş olması ilginç sayılır ve üzerinde durulmağa değer. İşte ben burada bu önemli buluntuyu tanıtmağa ve yorumlamağa çalışacağım. Afyon Müzesi’nin 7222 Envanter Numarasında kayıtlı olan söz konusu eser, 1967 yılında Hüseyin Uçak'tan 250 liraya satın alınmıştır. Hüseyin Uçak, Eskişehir'in Çifteler İlçesi'nde oturur ve hurda demir ticareti yapar. Hurda toplamak üzere bütün köyleri dolaştığı için, eski eserle de ilgilenmiş, isteğimiz üzerine, müzeye birçok eser getirmiş, birçok eserin de yerini bildirmiştir. Yine böyle bir gelişinde, Emirdağ'ın Daydalı Köyü'nde bir evin samanlığındaki direğin altında kara taştan insan vücutlu, hayvan başlı, kanatlı bir cin kabartması bulunduğunu haber verdi. Hemen getirmesi istendi. Kısa bir süre sonra kabartmamız geldi ve müzemizde layık olduğu yeri aldı. Kabartmamızın buluntu yeri olan Daydalı Köyü Emirdağ'ın 20 km. kuzey-doğusundadır. Bu köyün ayni zamanda ünlü Amorion' (Hisar Köy) a uzaklığı sadece 5 km. dir. Daydalı Köyü 60 haneli, 300 nüfuslu küçük bir çiftçi ve hayvancı köyüdür. Emirdağ-Hisar Köy-Davulga yolunun kuzeyinde kalır. Sapa bir yerdedir. Yabancı da, yerli de, hattâ görevli de pek uğramamış. Bakımsız, terkedilmiş bir görünüşü vardır. Ve biz bu eserin, 20 yıl süreyle burada, önce dışarıda kapı önünde, sonra samanlıkta direk altında durup kaybolmayışını, biraz da, buna borçluyoz.

Copyright of Anbar Journal of Agricultural Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)