ANALYSIS: ALIGNING INFORMATION SYSTEMS AND BUSINESS PROCESSES IN THE COFFEE INDUSTRY

The coffee industry, which encompasses more than 25 million smallholder farmers worldwide, requires efficient and sustainable supply chain management. Integrating Information Systems (IS) with business processes in the coffee sector can enhance operational performance. This study utilizes a combination of literature reviews and field surveys to examine the alignment of IS in the industry. The primary findings underscore the significance of web-based and desktop solutions in facilitating stakeholder access to accurate information. Entity Relationship Diagrams (ERD) and Data Flow Diagrams (DFD) ensure a comprehensive understanding of the system's structure. The integration of IS supports sustainable practices by monitoring environmental impact and promoting fair trade. The discussion highlights the benefits of digital transformation, including improved decision-making and operational efficiency. Adopting structured approaches such as ERD and DFD facilitates effective communication among stakeholders. The integration of IS with sustainable practices reflects a commitment to environmental stewardship and ethical business conduct. Recommendations include the continued development of IS customized to meet the needs of stakeholders, fostering collaboration for innovation, enhancing digital literacy, designing sustainable IS solutions, and advocating for the adoption of IS policies. The implementation of these recommendations has the potential to drive efficiency, sustainability, and stakeholder engagement in the coffee industry

Digital Signal Processing for The Development of Deep Learning-Based Speech Recognition Technology

This research discusses digital signal processing in the context of developing deep learning-based speech recognition technology. Given the increasing demand for accurate and efficient speech recognition systems, digital signal processing techniques are essential. The research method used is an experimental method with a quantitative approach. This research method consists of several stages: introduction, research design, data collection, data preprocessing, Deep Learning Model Development, performance training and evaluation, experiments and testing, and data analysis. These findings are expected to contribute to developing more sophisticated and applicable speech recognition systems in various fields. For example, in virtual assistants such as Siri and Google Assistant, improved speech recognition accuracy will allow for more natural interactions and faster responses, improving the user experience. This technology can be used in security systems for safer and more reliable voice authentication, replacing or supplementing passwords and fingerprints. Additionally, in accessibility technology, more accurate voice recognition will be particularly beneficial for individuals with visual impairments or mobility, allowing them to control devices and access information with just voice commands. Other benefits include improvements in automated phone apps, automatic transcription for meetings or conferences, and the development of smart home devices that can be fully voice-operated

Aggregates of diketopyrrolopyrrole dimers in solution

Dimers based on diketopyrrolopyrrole (DPP) chromophores have gained tremendous interest as an excellent material building block for organic solar cells and photodiodes. However, a counterintuitive blue-shift in their solution absorption spectra occurs with an increasing number of thiophene units bridging the DPP moieties. We allocate this to aggregates in solution, which might hinder adequate mixing in blends, leading to poor film forming quality and reduced charge generation in solution processed devices. Hence, identification of such aggregates is crucial in order to find measures for device optimisation. In this study, we present synthesis and characterisation of three pyridyl end-capped DPP dimers of different conjugation length using thiophene linkers and compare their parent monomer to evidence the behaviours of aggregates in solution. We employ conventional and temperature dependent UV–Vis spectroscopy, fluorescence and excitation-emission spectroscopy as well as TD-DFT calculations to show that such DPP dimers predominantly form aggregates in solution even at low concentrations. By disentangling the spectroscopic behaviour of both aggregated and non-aggregated species, we refute literature's explanation that the apparent blue shift in absorption arises from a reduced conjugation length due to more molecular flexibility. Instead, absorption and emission signals of non-aggregated dimers are mostly masked by their aggregated species. This work provides a tool set using common laboratory spectroscopic equipment to identify and characterise solution aggregates—information particularly important towards optimisation of organic electronics processed from solution

Nano-Alignment in Semiconducting Polymer Films: A Path to Achieve High Current Density and Brightness in Organic Light Emitting Transistors

Organic light emitting field effect transistors (LEFETs) integrate light emission of a diode with logic functions of a transistor into a single device architecture. This integration has the potential to provide simplified displays at low costs and access to injection lasing. However, the charge carrier mobility in LEFETs is a limiting factor in realizing high current densities along with a trade-off between brightness and efficiency. Herein, we present a technique controlling the nanoscale morphology of semiconducting polymers using nanoscale grooved substrates and dip-coating deposition to achieve high current density. We then applied this approach to heterostructure LEFETs and demonstrated brightness exceeding 29000 cd m–2 at an EQE of 0.4% for a yellow emitter and 9600 cd m–2 at an EQE of 0.7% for a blue emitter. These results represent a significant advancement in organic optoelectronics and are an important milestone toward the realization of new applications in displays and electrically pumped lasing

Anomaly Detection Algorithms for Low-Dimensional and High-Dimensional Data: A Critical Study

Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection

Post Thrombolytic St-Segment Resolution Outcome in Acute Myocardial Infarction Patients

OBJECTIVES: The main objective of this study was to assess post thrombolytic resolution of ST-segment and its outcome in patients with acute myocardial infarction. METHODOLOGY: This Prospective Comparative Study was carried out at the Cardiology Unit of Ayub Teaching Hospital, Abbottabad. All patients irrespective of gender and age with ST-Segment elevation myocardial infarction (STEMI), having no immediate access to angioplasty and thrombolysed with streptokinase, were included in this study. ECG was taken at the beginning and 90 minutes after the administration of streptokinase. Based on ST-segment resolution on ECG taken at 90 minutes these patients were classified into group A and B. Group A included patients with ST-segment resolution while group B showed no resolution of ST-segment after streptokinase administration. These patients were followed during their hospital stay for complications such as arrhythmias, cardiogenic shock, acquired ventricular septal defects (VSD) aneurysm and death. RESULTS: Among 115 patients, 94 were male and 21 female. Group A included 102 (89%) patients and group B included 13 (11%). In group A, only 1 (0.98 %) patient developed complications and in group B, 13 patients (100%) developed complications. Arrhythmias were the most common complication among MI patients in group A while cardiogenic shock was the commonest complication in group B. CONCLUSION: ST-segment resolution is a practical and applicable indicator of successful thrombolysis and has a significant correlation with clinical outcome in acute myocardial patients after thrombolysis with streptokinase

Efficient and Stable Solution-Processed Organic Light Emitting Transistors using a High-k Dielectric

We report the development of highly efficient and stable solution-processed organic light emitting transistors (OLETs) that combine a polymer heterostructure with the transparent high-k dielectric poly(vinylidenefluoride0.62-trifluoroethylene0.31-chlorotrifluoroethylene0.7) (P(VDF-TrFE-CTFE)). The polymer heterostructure comprises of the poly[4-(4,4- dihexadecyl-4H-cyclopenta[1,2-b:5,4-b’]dithiophen-2-yl)-alt-[1,2,5]thiadiazolo[3,4- c]pyridine] (PCDTPT) and Super Yellow as charge transporting and light emitting layers, respectively. Device characterization shows that the use of P(VDF-TrFE-CTFE) leads to larger channel currents (≈2 mA) and lower operating voltages (-35 V) than for previously reported polymer based OLETs. Furthermore, the combined transparency of the dielectric and gate electrode, results in efficient bottom emission with external quantum efficiency of ≈0.88 % at a luminance L ≥ 2000 cd m−2. Importantly, the resulting OLETs exhibit excellent shelf life and operational stability. The present work represents a significant step forward in the pursuit of all-solution-processed OLET technology for lighting and display applications

High mobility solution-processed hybrid light emitting transistors

We report the design, fabrication, and characterization of high-performance, solution-processed hybrid (inorganic-organic) light emitting transistors (HLETs). The devices employ a high-mobility, solution-processed cadmium sulfide layer as the switching and transport layer, with a conjugated polymer Super Yellow as an emissive material in non-planar source/drain transistor geometry. We demonstrate HLETs with electron mobilities of up to 19.5 cm2/V s, current on/off ratios of >107, and external quantum efficiency of 10-2% at 2100 cd/m2. These combined optical and electrical performance exceed those reported to date for HLETs. Furthermore, we provide full analysis of charge injection, charge transport, and recombination mechanism of the HLETs. The high brightness coupled with a high on/off ratio and low-cost solution processing makes this type of hybrid device attractive from a manufacturing perspective.open0