Tanzania like any other developing countries is depending on uncertain rainfall for their subsistence and commercial agriculture. In this paper the start and end of rains for Mbeya Meteorological Station in Tanzania Mainland was analyzed and critically examined. Data were kindly given by Tanzania Meteorological Agency (TMA) during face to face Statistics in Applied Climatology (fSIAC) workshop which was held at Sokoine University of Agriculture (SUA) the year 2013 prior to online Course on Statistics in Applied climatology (eSIAC). In this study data were analyzed using Instat (for windows version 3.3.7) package developed by the Statistical Services Centre of the University of Reading. The analysis showed that much of rains start early December all the way to May. There is also 50 percent chance of having below and above the mean for both total annual rainfall and number of rains, in other word one in two years the total rains are below means likewise the number of rain days.

Efficient commencement of clinical trials is pivotal for their successful execution. The purpose of this study was to determine and examine the factors that contribute to trial start-up delays. The study assessed 20 clinical trials' start-up stages in order to identify relevant variables. Results showed that trials with an emphasis on devices, trials that did not outsource, trials that utilized less auxiliary services, and trials that employed interventional designs had quicker start-up times than observational studies. Furthermore, approval times were substantially shortened by using a centralized Institutional Review Board (IRB) rather than a local IRB. Furthermore, compared to studies that successfully enrolled subjects, those who failed to do so required more time to complete contracts and budgets and secure IRB clearance. This study underscores the diverse impact of clinical trial features on start-up timelines. Understanding these factors is crucial for optimizing the initiation process of clinical trials.

The aim of this paper is to attempt to understand why the popular academic and policy field of promoting, studying and evangelising “entrepreneurship” should have been associated with great success but, in the past twenty years or more in many advanced economies, so much failure. From the US to lesser and developing countries, emerging economies and the European Union, entrepreneurship, especially in regard to start-ups and particularly high-tech start-ups have been in constant more or less recent decline. This is seldom registered in the mainstream literature where a positive and benign profile is generally presented. The paper examines this phenomenon, ties it partly with the “productivity paradox” and seeks tentative hypotheses in relation to the apparent illusions if not delusions regarding “entrepreneurship”.

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the related items. Example of questions may include "do you like this book?" and the users answer,"yes", "no", "I have not read it (unknown)", will reflect the degree of interest for the item by the users. As a consequence, the system can learn the users' preferences from these answers. The goal of active learning is to correctly choose the questions (items) for users. Thus it is necessary to personalize the questionnaires to retrieve the maximum information by avoiding "unknown" answers. In this paper, we propose an active learning technique that exploits past users' interests and past users' predictions in order to identify the best questions to ask.

Many pigments and dyes are not only valuable molecules in manufacturing, but also pollutants. Science has certainly been producing sustainable alternatives to counter the issue of dyes, but this is not enough. There is also a need for change in the business attitude and leadership approach of the organizations that operate in the industry. Only through the successful combination of new technologies and forward-looking decision making, it will be possible to alter the status quo and deal with the multiple environmental and supply chain challenges that these businesses are and will be facing. With a focus on colorants, this critical review article will reflect on the importance of both a biotechnological approaches and sustainable leadership to achieve a sustainable, forward-looking fashion industry.

Recently, the application of deep reinforcement learning in recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns and sparse data issues in recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This paper proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in recommender system. Our experiment is carried out on MovieLens dataset. The result shows that, DRL based recommender system is superior than traditional algorithms in minimum error and the application of tags has little effect on accuracy when making up for interpretability. In addition, DRL based recommender system has excellent performance on user cold start problems.

The art work of this present paper is to show properly conditions and aspects to reach stability in the process control, as also to avoid instability, by using digital PID controllers, with the intention of help the industrial community. The discussed control strategy used to reach stability is to manipulate variables that are directly proportional to their controlled variables. To validate and to put the exposed principles into practice, it was done two simulations of a continuous fermentation tank process start-up with the yeast strain S. Cerevisiae NRRL-Y-132, in which the substrate feed stream contains different types of sugar derived from cane bagasse hydrolysate. These tests were carried out through the resolution of conservation laws, more specifically mass and energy balances, in which the fluid temperature and level inside the tank were the controlled variables. The results showed the facility in stabilize the system by following the exposed proceedings.

The formation of the required AC voltage level is currently one of the most important problems. An unstable voltage level can lead to the failure of domestic and industrial equipment, also to the appearance of a lighting equipment flicker effect, which can adversely affect the operating mode. In this regard, the development of AC voltage regulators that perform the function of stabilizers has become relevant. In turn, new topologies should be energy efficient and reliable, this can be achieved by reducing the number of semiconductor elements, which in turn will reduce losses and increase efficiency. In addition to voltage stabilization, AC voltage regulators have found application as soft-start devices for asynchronous motors, and have become competitors to frequency converters. The power level can vary from units to tens of kilowatts where such devices can be used. This paper provides a review of AC voltage regulators, which can be used both as soft starters and AC voltage stabilizers. The analysis of the circuits was carried out and the efficiency was evaluated through their total harmonic distortions (THD), power factor and efficiency coefficient. Experiments were also performed for a number of circuits, where their operating modes were evaluated. Eventually, all circuits were divided into full number of switches with two or more AC switches per phase, with a reduced number of switches, where one AC switch per phase and with low number of switches – with one complementary pair of AC switches for all three phases.

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI. Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories. A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.

Efficient use of the operating room (OR) is crucial for any hospital. One of the major inefficiencies in the OR is surgical cases not starting or finishing on time as scheduled. When a case is delayed, it affects all subsequent cases in that OR. This study uses discrete choice analysis to determine the significant factors, including team familiarity, that influence OR case on-time start and finish. A case is considered on-time if the documented procedure start and finish times are no more than 10 minutes after the scheduled start and finish times. The analysis uses surgical case data from a large tertiary referral hospital and academic center in Greenville, South Carolina. The case data includes all surgical cases (15,091) performed during regular workdays in 2013. Two binary logit models are developed: one for case on-time start and one for case on-time finish. Results indicate that higher team familiarity between surgeon and anesthesiologist, surgeon and circulating nurse, surgeon and scrub nurse, and surgeon and CRNA improve the likelihood of an OR case on-time start and on-time finish. This finding indicates that the OR scheduling staff in the study hospital make a concerted effort to schedule the surgical teams with members who have worked well together in the past.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

Reservoir in the Central Structural Zone of the Xihu Sag is the Huagang Formation, dominated by natural gas reservoirs, and the reservoir in the Western Slope Zone is the Pinghu Formation, dominated by light oil and wet gas. In this paper, the Gas Drive Water Displacement- Magnetic Resonance Imaging (GWD-MRI) experiments are used to simulate the charging characteristics of the sandstone migration layer. The centrifugal- Magnetic Resonance (Cen-NMR) experiments simulate the short-term fast trap charging process, and the Semi-Permeable Baffle (SPB) charging experiment simulates the slow trap accumulation process. Studies have shown that there is a start-up pressure for the migration layer charging, and the start-up pressure of the core with a permeability of 0.3mD is about 0.6MPa. Experimental simulations have confirmed that there is a front zone with changed water saturation, with a width of about 1-1.5cm. The migration layer charging mainly has two actions, drive effect and carry effect. The drive effect reduces the water saturation to 70%-80%, and the carry effect can still reduce the water saturation by 5%-10%. The water saturation of rapid charging is mainly affected by the petro-physical. The porosity is high, the water saturation is low. The water saturation decreased significantly with the increase of centrifugal force when the centrifugal force is smaller. If the centrifugal force is greater than 0.8 MPa, the water saturation decreases slowly. The water saturation of the trap slowly charging is basically maintained at 40%-50%, which matches the water saturation of the airtight coring from 40%-55%, the petro-physical do not affect the final water saturation.

The Short-Term Assessment of Risk and Treatability (START) is deemed the most appropriate instrument for assessing violence risks and management because of its balanced approach between dynamic risk and protective factors. Although several facets of reliability and predictive validity of this tool were strong, its inter-rater reliability, construct validity, and implementation in Asian population were under-investigated. The objective of this research was to examine the inter-rater reliability and construct validity of the START: Thai version within forensic psychiatric inpatients. The participants consisted of 118 forensic psychiatric inpatients hospitalized at Galya Rajanagarindra Institute in Thailand. Trained mental health professionals (i.e., psychiatrists, forensic nurses, clinical psychologists, social workers, and occupational therapists) assessed each participant across twenty domains of the Thai START. The inter-rater reliability was examined using the intraclass correlation coefficient and a confirmatory factor analysis for ordinal data was used to test the construct validity of the scale. The main finding showed a good-to-excellent inter-rater reliability and supported two relational constructs (i.e., strength vs vulnerability subscales) of the Thai START. The Thai START is a promising tool for using in Thai forensic psychiatric setting but some items were not significant in contributing to the scale. This study also provides the guideline for implementing the tool in non-Western forensic psychiatric populations.

Instead of simplified steady-state models, with modern computers, one can solve the complete aero-thermodynamics happening in gas turbine engines. In the present article, we describe a mathematical model and numerical procedure to represent the transient response of a PT6A gas turbine engine operating at off-design conditions. The aero-thermal model consists of a set of algebraic and ordinary differential equations that arise from the application of the mass, linear momentum, angular momentum, and energy balances in each engine's component. The solution code has been developed in Matlab-Simulink using a block-oriented approach. Transient simulations of the PT6A engine start-up have been carried out by changing the original Jet-A1 fuel with biodiesel blends. Time plots of the main thermodynamic variables are shown, especially those regarding the structural integrity of the burner. Numerical results have been validated against reported experimental measurements and GasTurb simulations. The computer model has been capable to predict acceptable fuel blends, such that the real PT6A engine can be substituted to avoid the risk of damaging it.

The food system represents a key industry for Europe and particularly Germany. However, it is also the single most significant contributor to climate and environmental change. A food system transformation is necessary to overcome the system's major and constantly increasing challenges in the upcoming decades. One possible facilitator for this transformation are radical and disrup-tive innovations that start-ups develop. There are many challenges for start-ups in general and food start-ups in particular. Various support opportunities and resources are crucial to ensure the success of food start-ups. One aim of this study is to identify how the success of start-ups in the food system can be supported and further strengthened by players in the innovation ecosystem in Germany. There is still room for improvement and collaboration toward a thriving innovation ecosystem. A successful innovation ecosystem is characterised by a well-organised, collaborative, and supportive environment with a vivid exchange between the members in the ecosystem. The interviewees confirmed this, and although the different actors are already cooperating, there is still room for improvement. The most common recommendation for improving cooperation is learning from other countries and bringing the best to Germany.

The increasing social pressure for decarbonization has placed businesses under considerable scrutiny to actively reduce carbon emissions. A critical step towards achieving this objective is to shift conventional production and consumption systems to more sustainable alternatives. Thus, there is the emergent need to understand the patterns and drivers of the transformative business models (BMs) that underpin that shift. This study adopts a mixed-methods approach that integrates different literature streams--including Sustainability Transitions Theory (STT), Strategic Niche Management (SNM), and the Business Models approach--and stakeholders’ interviews to investigate the key elements of business models that lead towards sustainable practices. This research examines the organizational arrangements of European start-ups operating between 2014 and 2020. The transformation towards decarbonized production and consumption is characterized by efficient combination of business strategies that incorporate advanced technologies (ATs), such as artificial intelligence (AI), machine learning (ML) and its algorithms, along with sustainable elements, resulting in transformative business models. By exploring the driving elements behind the transition to low-carbon approaches, this study fills a significant gap in the existing literature on business models. The findings from this research also hold relevance for policymakers to promote decarbonization.