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LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (Ttx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting Ttx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces Ttx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios.

Sclerotinia sclerotiorum is a newly emerging phytopathogen in Bangladesh causing white mold disease in many plants, including important horticultural and field crops. In this study, we isolated S. sclerotiorum strains from infected parts of various host crops and characterized them using morphophysiological and genetic approaches. White mold of mustard; pod rot or fruit rot of bush bean and garden pea; head rot of cauliflower; flower rot or blossom rot of rose and Salvia; fruit rot of squash; inflorescence rot and pod rot of country bean; and leaf drop or rot of coriander and lettuce were observed in several regions of Bangladesh. From the infected crops, a total of 36 fungal strains were isolated and identified as S. sclerotiorum using internal transcribed spacer (ITS) sequencing. The S. sclerotiorum isolates showed white to off-white mycelial growth with loose to dense velvety aerial mycelia. Round to irregular shaped sclerotia formation was observed, with 4-38 sclerotia present per Petri plate. Apothecia formation from sclerotia was also noted under both natural and artificial conditions. In the present study, as many as 13 crops: Cosmos bipinnatus, Amaranthus cruentus, Leucas aspera, Glycine max, Dahlia hortensis, Hibiscus rosa-sinensis, Rosa chinensis, Salvia officinalis, Cucurbita pepo, Lagenaria siceraria, Brassica oleracea var. italica, Coriandrum sativum , and Lactuca sativa were identified as new host crops of S. sclerotiorum in Bangladesh based on morphological characteristics and ITS sequences.

Weeds are one of the major restrictions to sustaining crop productivity. Weeds often outcompete crops for nutrients, soil moisture, solar radiation, space and provide platforms for breeding of pests and diseases. The ever-growing global food insecurity triggers the need for spatially explicit innovative geospatial technologies that can deliver timely detection of weeds within agro-ecological systems. This will help pinpoint maize fields to be prioritized for weed control. Satellite remote sensing offers incomparable opportunities for precision agriculture, ecological applications and vegetation characterisation, with vast socioeconomic benefits. This work compares and evaluates the strength of Sentinel-2 (S2) satellite with the constellation of Dove nanosatellites i.e. PlanetScope (PS) data in detecting and mapping Striga (Striga hermonthica) weed within intercropped maize fields in Rongo sub-county in western Kenya. We applied the S2 and PS derived spectral data and vegetation indices in mapping the Striga occurrence. Data analysis was implemented, using the Guided Regularised Random Forest (GRRF) classifier. Comparatively, Sentinel-2 demonstrated slightly lower Striga detection capacity than PlanetScope, with an overall accuracy of 88% and 92%, respectively. The results further showed that the VNIR (Blue, Green Red and NIR) and the Atmospheric resistance Vegetation Index (ARVI) were the most fundamental variables in detecting and mapping Striga presence in maize fields. Findings from this work demonstrate that Sentinel-2 data has the capability to provide spatial explicit near real-time field level Striga detection – a previously daunting task with broadband multispectral sensors.

IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.

Water, one of the most valuable resources, is underutilized in irrigated rice production. The yield of rice, a staple food across the world, is highly dependent on having proper irrigation systems. Alternate wetting and drying (AWD) is an effective irrigation method mainly used for irrigated rice production. However, unattended, manual, small-scale, and discrete implementations cannot achieve the maximum benefit of AWD. Automation of large-scale (over 1000 acres) implementation of AWD can be carried out using wide-area wireless sensor network (WSN). An automated AWD system requires three different WSNs: one for water level and environmental monitoring, one for monitoring of the irrigation system, and another for controlling the irrigation system. Integration of these three different WSNs requires proper dimensioning of the AWD edge elements (sensor and actuator nodes) to reduce the deployment cost and make it scalable. Besides field-level monitoring, the integration of external control parameters, such as real-time weather forecasts, plant physiological data, and input from farmers, can further enhance the performance of the automated AWD system. Internet of Things (IoT) can be used to interface the WSNs with external data sources. This research focuses on the dimensioning of the AWD system for the multilayer WSN integration and the required algorithms for the closed loop control of the irrigation system using IoT. Implementation of the AWD for 25,000 acres is shown as a possible use case. Plastic pipes are proposed as the means to transport and control proper distribution of water in the field, which significantly helps to reduce conveyance loss. This system utilizes 250 pumps, grouped into 10 clusters, to ensure equal water distribution amongst the users (field owners) in the wide area. The proposed automation algorithm handles the complexity of maintaining proper water pressure throughout the pipe network, scheduling the pump, and controlling the water outlets. Mathematical models are presented for proper dimensioning of the AWD. A low-power and long-range sensor node is developed due to the lack of cellular data coverage in rural areas, and its functionality is tested using an IoT platform for small-scale field trials.

Vibration isolation has been widely applied to filter the external excitation energy and impact forces in building structures and equipment. Wire rope isolator (WRI), a kind of isolator for vibration and shock isolation, shows a better performance in attenuating these forces. WRIs are able to deviate these external forces through their mechanical configuration and high-energy dissipative capabilities. The application of WRI demands knowledge of its behavior and the relation between various geometrical properties and input force. The present work investigates the influence of geometrical parameters, such as wire rope diameter, number of coils, and displacement amplitude on the hysteresis behavior of WRI under quasi-static loading in both vertical and horizontal directions. The hysteresis behavior of different WRIs was evaluated using the calculated parameters from hysteresis force-displacement curves: energy loss ratio (ELR), and effective stiffness. The study indicates that the geometric properties significantly influence the effective stiffness than the energy loss ratio. It is observed that, increased displacement amplitude results in decreased ELR and hence damping capabilities. The study also confirms that the wire rope isolator possesses a good ability in damping through its stiffness and high-energy dissipation capability.

The vibrations caused by earthquake ground motions or the operations of heavy machineries can affect the functionality of equipment and cause damages to the hosting structures and surrounding equipment. A Wire rope isolator (WRI), which is a type of passive isolator known to be effective in isolating shocks and vibrations, can be used for vibration isolation of lightweight structures and equipment. The primary advantage of the WRI is that it can provide isolation in all three planes and in any orientation. The load-supporting capability of the WRI is identified from the static stiffness in the loading direction. Static stiffness mainly depends on the geometrical and material properties of the WRI. This study develops an analytical model for the static stiffness in the vertical direction by using Castigliano’s second theorem. The model is validated by using the experimental results obtained from a series of monotonic loading tests. The flexural rigidity of the wire ropes required in the model is obtained from the transverse bending test. Then, the analytical model is used to conduct a parametric analysis on the effects of wire rope diameter, width, height, and number of turns (loops) on vertical stiffness. The wire rope diameter influences stiffness more than the other geometric parameters. The developed model can be accurately used for the evaluation and design of WRIs.

Background: Ovarian cancer is the leading cause of death among cancers of the female genital tract, with poor outcomes despite chemotherapy. There was a persistent socioeconomic gradient in 1-year survival in England and Wales for more than 3 decades (1971–2001). Inequalities in 5-year survival persisted for more than 20 years but have been smaller for women diagnosed around 2000. We explored one possible explanation. Methods: We analysed data on 1406 women diagnosed with ovarian cancer during 1991–1998 and recruited to one of two randomised clinical trials. In the second International Collaborative Ovarian Neoplasm (ICON2) trial, women diagnosed between 1991 and 1996 were randomised to receive either the three-drug combination cyclophosphamide, doxorubicin and cisplatin (CAP) or single-agent carboplatin given at optimal dose. In the ICON3 trial, women diagnosed during 1995–1998 were randomised to receive either the same treatments as ICON2, or paclitaxel plus carboplatin. Relative survival at 1, 5 and 10 years was estimated for women in five categories of socioeconomic deprivation. The excess hazard of death over and above background mortality was estimated by fitting multivariable regression models with Poisson error structure and a dedicated link function in a generalised linear model framework, adjusting for the duration of follow-up and the confounding effects of age, Federation of Gynecology and Obstetrics (FIGO) stage and calendar period. Results: Unlike women with ovarian cancer in the general population, no statistically significant socioeconomic gradient was seen for women with ovarian cancer treated in the two randomised controlled trials. The deprivation gap in 1-year relative survival in the general population was statistically significant at −6.7% (95% CI (−8.1, −5.3)), compared with −3.6% (95% CI (−10.4, +3.2)) in the trial population. Conclusions: Although ovarian cancer survival is significantly lower among poor women than rich women in England and Wales, there was no evidence of an association between socioeconomic deprivation and survival among women with ovarian cancer who were treated and followed up consistently in two well-conducted randomised controlled trials. We conclude that the persistent socioeconomic gradient in survival among women with ovarian cancer, at least for 1-year survival, may be due to differences in access to treatment and standards of care.

This study was aimed to evaluate the effects of using different levels of olive (Olea europaea) leaf extract on fresh and preserved mutton meatballs. Meatballs were divided into four different groups and treated as T0 (0), T1 (0.1), T2 (0.2) and T3 (0.3%), respectively based on olive leaf extract supplementation. Days of intervals of experiment were 0, 5, 10 days. Samples were preserved at 4˚C for up to 10 days. Different types of analysis such as, sensory (color, flavor, juiciness and overall acceptability), proximate composition (CP %), physicochemical (pH), biochemical (POV, FFA and TBARS) and microbiological (TVC, TCC and TYMC) were determined. Color, flavor and acceptability reduced significantly (p < 0.05) with the increase of the storage periods. Values of the studied quality parameters in all the treatment groups differed significantly (p < 0.05). Based on our findings 0.3% olive leaf extract is found suitable to add in mutton meatballs as a source of natural antioxidant.

Poor crop yields remain one of the main causes of chronic food insecurity in Africa. This is largely caused by insect pests, weeds, unfavourable climatic conditions and degraded soils. Weed and pest control, based on the climate-adapted ‘push-pull’ system, has become an important target for sustainable intensification of food production adopted by many small-holder farmers. However, essential baseline information using remotely sensed data is missing, specifically for the ‘push-pull’ companion crops. In this study, we investigated the spectral uniqueness of two of the most commonly used ‘companion’ crops (i.e. greenleaf Desmodium (Desmodium intortum) and Brachiaria (Brachiaria cv Mulato) with co-occurring soil, green maize, and maize stover. We used FieldSpec® Handheld 2™ analytical spectral device to collect in situ hyperspectral data in the visible and near-infrared region of the electromagnetic spectrum. Random forest was then used to discriminate among the different companion crops, green maize, maize stover and the background soil.